From 37a48cca7584ad1419ce1e9809eef0fcb1bc2899 Mon Sep 17 00:00:00 2001 From: fabiocat93 Date: Fri, 9 Aug 2024 11:42:30 -0400 Subject: [PATCH] adding tests, doc, tutorial, and fixing bugs for speech enhancement #2 --- .autorc | 7 + .github/ISSUE_TEMPLATE/bug_report.yaml | 47 ++ .github/ISSUE_TEMPLATE/development_task.yaml | 32 + .github/ISSUE_TEMPLATE/general.yaml | 17 + .github/ISSUE_TEMPLATE/support_request.yaml | 47 ++ .github/PULL_REQUEST_TEMPLATE/advanced.md | 26 + .github/PULL_REQUEST_TEMPLATE/basic.md | 4 + .github/dependabot.yaml | 27 + .github/workflows/docs.yaml | 95 +++ .github/workflows/publish.yaml | 43 ++ .github/workflows/release.yaml | 42 + .github/workflows/test.yaml | 111 +++ .gitignore | 175 +++++ .pre-commit-config.yaml | 85 +++ ACTION_PLAN.md | 80 ++ CHANGELOG.md | 299 ++++++++ CODE_OF_CONDUCT.md | 101 +++ CONTRIBUTING.md | 102 +++ LICENSE.txt | 68 ++ README.md | 56 ++ docs_style/pdoc-theme/syntax-highlighting.css | 610 +++++++++++++++ docs_style/pdoc-theme/theme.css | 44 ++ index.rst | 16 + pyproject.toml | 165 ++++ src/senselab/__init__.py | 7 + src/senselab/audio/__init__.py | 1 + .../audio/data_structures/__init__.py | 3 + src/senselab/audio/data_structures/audio.py | 173 +++++ src/senselab/audio/tasks/__init__.py | 1 + .../audio/tasks/classification/__init__.py | 1 + .../speech_emotion_recognition.py | 136 ++++ .../audio/tasks/data_augmentation/__init__.py | 1 + .../data_augmentation/data_augmentation.py | 69 ++ .../tasks/features_extraction/__init__.py | 1 + .../tasks/features_extraction/opensmile.py | 75 ++ .../features_extraction/praat_parselmouth.py | 151 ++++ .../tasks/features_extraction/torchaudio.py | 191 +++++ .../features_extraction/torchaudio_squim.py | 78 ++ .../audio/tasks/forced_alignment/__init__.py | 1 + .../audio/tasks/forced_alignment/constants.py | 47 ++ .../tasks/forced_alignment/data_structures.py | 103 +++ .../forced_alignment/forced_alignment.py | 715 ++++++++++++++++++ src/senselab/audio/tasks/plotting/plotting.py | 92 +++ .../audio/tasks/preprocessing/__init__.py | 1 + .../tasks/preprocessing/preprocessing.py | 253 +++++++ .../tasks/speaker_diarization/__init__.py | 1 + .../audio/tasks/speaker_diarization/api.py | 51 ++ .../tasks/speaker_diarization/pyannote.py | 109 +++ .../tasks/speaker_embeddings/__init__.py | 3 + .../audio/tasks/speaker_embeddings/api.py | 44 ++ .../audio/tasks/speaker_embeddings/doc.md | 29 + .../tasks/speaker_embeddings/speechbrain.py | 104 +++ .../tasks/speaker_verification/__init__.py | 1 + .../speaker_verification.py | 61 ++ .../tasks/speech_enhancement/__init__.py | 3 + .../audio/tasks/speech_enhancement/api.py | 30 + .../audio/tasks/speech_enhancement/doc.md | 26 + .../tasks/speech_enhancement/speechbrain.py | 110 +++ .../audio/tasks/speech_to_text/__init__.py | 3 + .../audio/tasks/speech_to_text/api.py | 48 ++ .../audio/tasks/speech_to_text/doc.md | 77 ++ .../audio/tasks/speech_to_text/huggingface.py | 174 +++++ .../speech_to_text_evaluation/__init__.py | 3 + .../tasks/speech_to_text_evaluation/doc.md | 107 +++ .../tasks/speech_to_text_evaluation/utils.py | 90 +++ .../audio/tasks/ssl_embeddings/__init__.py | 1 + .../audio/tasks/ssl_embeddings/api.py | 52 ++ .../self_supervised_features.py | 114 +++ .../audio/tasks/text_to_speech/__init__.py | 1 + .../audio/tasks/text_to_speech/api.py | 37 + .../audio/tasks/text_to_speech/huggingface.py | 78 ++ .../tasks/voice_activity_detection/api.py | 39 + .../audio/tasks/voice_cloning/__init__.py | 1 + src/senselab/audio/tasks/voice_cloning/api.py | 43 ++ .../audio/tasks/voice_cloning/knnvc.py | 84 ++ src/senselab/text/.gitkeep | 0 src/senselab/text/__init__.py | 1 + src/senselab/text/tasks/__init__.py | 1 + .../tasks/embeddings_extraction/__init__.py | 1 + .../text/tasks/embeddings_extraction/api.py | 30 + .../embeddings_extraction/huggingface.py | 99 +++ .../sentence_transformers.py | 67 ++ src/senselab/utils/__init__.py | 1 + src/senselab/utils/config.json | 16 + src/senselab/utils/config.py | 17 + src/senselab/utils/constants.py | 5 + src/senselab/utils/data_structures/dataset.py | 352 +++++++++ src/senselab/utils/data_structures/device.py | 79 ++ src/senselab/utils/data_structures/file.py | 68 ++ .../utils/data_structures/language.py | 38 + src/senselab/utils/data_structures/logging.py | 20 + src/senselab/utils/data_structures/model.py | 168 ++++ .../utils/data_structures/pydra_helpers.py | 49 ++ .../utils/data_structures/script_line.py | 116 +++ src/senselab/utils/tasks/cca_cka.py | 138 ++++ src/senselab/utils/tasks/cosine_similarity.py | 47 ++ src/senselab/utils/tasks/cross_correlation.py | 55 ++ .../utils/tasks/dimensionality_reduction.py | 161 ++++ .../tasks/dimensionality_reduction_table.md | 7 + src/senselab/utils/tasks/eer.py | 23 + src/senselab/utils/tasks/input_output.py | 197 +++++ src/senselab/utils/tasks/plotting.py | 54 ++ src/senselab/utils/tasks/pooling.py | 71 ++ src/senselab/video/.gitkeep | 0 src/senselab/video/__init__.py | 1 + src/senselab/video/data_structures/video.py | 126 +++ src/senselab/video/tasks/input_output.py | 56 ++ .../video/tasks/input_output_pydra.py | 7 + src/tests/__init__.py | 1 + src/tests/audio/__init__.py | 1 + src/tests/audio/conftest.py | 97 +++ src/tests/audio/data_structures/__init__.py | 1 + src/tests/audio/data_structures/audio_test.py | 77 ++ src/tests/audio/tasks/__init__.py | 1 + src/tests/audio/tasks/classification_test.py | 37 + .../audio/tasks/data_augmentation_test.py | 40 + .../audio/tasks/features_extraction_test.py | 207 +++++ .../audio/tasks/forced_alignment_test.py | 208 +++++ src/tests/audio/tasks/preprocessing_test.py | 137 ++++ .../audio/tasks/speaker_diarization_test.py | 60 ++ .../audio/tasks/speaker_embeddings_test.py | 145 ++++ .../audio/tasks/speaker_verification_test.py | 40 + .../audio/tasks/speech_enhancement_test.py | 101 +++ .../tasks/speech_to_text_evaluation_test.py | 64 ++ src/tests/audio/tasks/speech_to_text_test.py | 125 +++ src/tests/audio/tasks/text_to_speech_test.py | 42 + .../tasks/voice_activity_detection_test.py | 34 + src/tests/audio/tasks/voice_cloning_test.py | 120 +++ .../audio_48khz_mono_16bits.wav | Bin 0 -> 472488 bytes .../audio_48khz_stereo_16bits.wav | Bin 0 -> 991012 bytes .../output_dataset/data-00000-of-00001.arrow | Bin 0 -> 648 bytes .../output_dataset/dataset_info.json | 16 + .../output_dataset/state.json | 13 + .../video_48khz_stereo_16bits.mp4 | Bin 0 -> 10767094 bytes src/tests/text/conftest.py | 11 + .../text/tasks/embeddings_extraction_test.py | 40 + src/tests/utils/__init__.py | 1 + src/tests/utils/data_structures/__init__.py | 1 + .../utils/data_structures/dataset_test.py | 229 ++++++ .../utils/data_structures/language_test.py | 48 ++ src/tests/utils/data_structures/model_test.py | 34 + .../data_structures/pydra_helpers_test.py | 27 + src/tests/utils/tasks/__init__.py | 1 + src/tests/utils/tasks/cca_cka_test.py | 53 ++ .../utils/tasks/cosine_similarity_test.py | 47 ++ .../utils/tasks/cross_correlation_test.py | 53 ++ .../tasks/dimensionality_reduction_test.py | 64 ++ src/tests/utils/tasks/eer_test.py | 28 + src/tests/utils/tasks/pooling_test.py | 51 ++ .../dimensionality_reduction_tutorial.ipynb | 219 ++++++ tutorials/extract_speaker_embeddings.ipynb | 226 ++++++ tutorials/getting_started.ipynb | 525 +++++++++++++ tutorials/speech_enhancement.ipynb | 106 +++ tutorials/speech_to_text.ipynb | 242 ++++++ 154 files changed, 11567 insertions(+) create mode 100644 .autorc create mode 100644 .github/ISSUE_TEMPLATE/bug_report.yaml create mode 100644 .github/ISSUE_TEMPLATE/development_task.yaml create mode 100644 .github/ISSUE_TEMPLATE/general.yaml create mode 100644 .github/ISSUE_TEMPLATE/support_request.yaml create mode 100644 .github/PULL_REQUEST_TEMPLATE/advanced.md create mode 100644 .github/PULL_REQUEST_TEMPLATE/basic.md create mode 100644 .github/dependabot.yaml create mode 100644 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src/senselab/audio/tasks/classification/speech_emotion_recognition.py create mode 100644 src/senselab/audio/tasks/data_augmentation/__init__.py create mode 100644 src/senselab/audio/tasks/data_augmentation/data_augmentation.py create mode 100644 src/senselab/audio/tasks/features_extraction/__init__.py create mode 100644 src/senselab/audio/tasks/features_extraction/opensmile.py create mode 100644 src/senselab/audio/tasks/features_extraction/praat_parselmouth.py create mode 100644 src/senselab/audio/tasks/features_extraction/torchaudio.py create mode 100644 src/senselab/audio/tasks/features_extraction/torchaudio_squim.py create mode 100644 src/senselab/audio/tasks/forced_alignment/__init__.py create mode 100644 src/senselab/audio/tasks/forced_alignment/constants.py create mode 100644 src/senselab/audio/tasks/forced_alignment/data_structures.py create mode 100644 src/senselab/audio/tasks/forced_alignment/forced_alignment.py create mode 100644 src/senselab/audio/tasks/plotting/plotting.py 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src/tests/data_for_testing/output_dataset/dataset_info.json create mode 100644 src/tests/data_for_testing/output_dataset/state.json create mode 100644 src/tests/data_for_testing/video_48khz_stereo_16bits.mp4 create mode 100644 src/tests/text/conftest.py create mode 100644 src/tests/text/tasks/embeddings_extraction_test.py create mode 100644 src/tests/utils/__init__.py create mode 100644 src/tests/utils/data_structures/__init__.py create mode 100644 src/tests/utils/data_structures/dataset_test.py create mode 100644 src/tests/utils/data_structures/language_test.py create mode 100644 src/tests/utils/data_structures/model_test.py create mode 100644 src/tests/utils/data_structures/pydra_helpers_test.py create mode 100644 src/tests/utils/tasks/__init__.py create mode 100644 src/tests/utils/tasks/cca_cka_test.py create mode 100644 src/tests/utils/tasks/cosine_similarity_test.py create mode 100644 src/tests/utils/tasks/cross_correlation_test.py create mode 100644 src/tests/utils/tasks/dimensionality_reduction_test.py create mode 100644 src/tests/utils/tasks/eer_test.py create mode 100644 src/tests/utils/tasks/pooling_test.py create mode 100644 tutorials/dimensionality_reduction_tutorial.ipynb create mode 100644 tutorials/extract_speaker_embeddings.ipynb create mode 100644 tutorials/getting_started.ipynb create mode 100644 tutorials/speech_enhancement.ipynb create mode 100644 tutorials/speech_to_text.ipynb diff --git a/.autorc b/.autorc new file mode 100644 index 00000000..0d7abe50 --- /dev/null +++ b/.autorc @@ -0,0 +1,7 @@ +{ + "onlyPublishWithReleaseLabel": true, + "baseBranch": "main", + "author": "bot ", + "noVersionPrefix": true, + "plugins": ["git-tag"] +} diff --git a/.github/ISSUE_TEMPLATE/bug_report.yaml b/.github/ISSUE_TEMPLATE/bug_report.yaml new file mode 100644 index 00000000..369381ef --- /dev/null +++ b/.github/ISSUE_TEMPLATE/bug_report.yaml @@ -0,0 +1,47 @@ +name: Bug Report +description: This is a template for reporting a bug. +# If you want to enable automatic linking to projects, +# uncomment the following line and replace the project ID +# with the ID of your project. +# projects: ["senselab"] +title: 'Bug: ' +labels: [bug] +body: +- type: textarea + id: description + attributes: + label: Description + description: Please provide a clear and concise description of the bug. + placeholder: Describe the bug here + validations: + required: true +- type: textarea + id: steps-to-reproduce + attributes: + label: Steps to Reproduce + description: Please provide detailed steps to reproduce the bug. + placeholder: Describe the steps to reproduce the bug here. + validations: + required: true +- type: textarea + id: expected-results + attributes: + label: Expected Results + description: Please describe what you expected to happen. + placeholder: Describe the expected results here. + validations: + required: true +- type: textarea + id: actual-results + attributes: + label: Actual Results + description: Please describe what actually happened. + placeholder: Describe the actual results here. + validations: + required: true +- type: textarea + id: additional-notes + attributes: + label: Additional Notes + description: Add any additional notes or context here. + placeholder: Add additional notes here. diff --git a/.github/ISSUE_TEMPLATE/development_task.yaml b/.github/ISSUE_TEMPLATE/development_task.yaml new file mode 100644 index 00000000..b7518607 --- /dev/null +++ b/.github/ISSUE_TEMPLATE/development_task.yaml @@ -0,0 +1,32 @@ +name: Development Task +description: This is a template for a development task +# If you want to enable automatic linking to projects, +# uncomment the following line and replace the project ID +# with the ID of your project. +# projects: ["senselab"] +title: 'Task: ' +labels: [task] +body: +- type: textarea + id: description + attributes: + label: Description + description: What is the task about? Why is it needed? What is the current state, if any? + placeholder: Describe the task here + validations: + required: true +- type: textarea + id: tasks + attributes: + label: Tasks + description: What are the concrete (sub-)tasks that need to be performed? + placeholder: Describe the tasks here. + value: '- [ ] Task 1' + validations: + required: true +- type: textarea + id: freeform-notes + attributes: + label: Freeform Notes + description: Add any additional notes here. + placeholder: Add notes here. diff --git a/.github/ISSUE_TEMPLATE/general.yaml b/.github/ISSUE_TEMPLATE/general.yaml new file mode 100644 index 00000000..f5254c76 --- /dev/null +++ b/.github/ISSUE_TEMPLATE/general.yaml @@ -0,0 +1,17 @@ +name: Other +description: This is a template for reporting a general issue. +# If you want to enable automatic linking to projects, +# uncomment the following line and replace the project ID +# with the ID of your project. +# projects: ["senselab"] +title: '' +labels: [other] +body: +- type: textarea + id: description + attributes: + label: Text + description: Please, provide a clear and concise piece of information. + placeholder: Please, provide a clear and concise piece of information. + validations: + required: false diff --git a/.github/ISSUE_TEMPLATE/support_request.yaml b/.github/ISSUE_TEMPLATE/support_request.yaml new file mode 100644 index 00000000..18494367 --- /dev/null +++ b/.github/ISSUE_TEMPLATE/support_request.yaml @@ -0,0 +1,47 @@ +name: Support Request +description: This is a template for submitting a support request. +# If you want to enable automatic linking to projects, +# uncomment the following line and replace the project ID +# with the ID of your project. +# projects: ["senselab"] +title: 'Support: ' +labels: [question] +body: +- type: textarea + id: issue-description + attributes: + label: Issue Description + description: Please provide a clear and concise description of the issue you are facing. + placeholder: Describe the issue here + validations: + required: true +- type: textarea + id: environment-details + attributes: + label: Environment Details + description: Please provide details about your environment, including system specifications, software versions, etc. + placeholder: Describe your environment details here. + validations: + required: false +- type: textarea + id: attempted-solutions + attributes: + label: Attempted Solutions + description: Describe any steps you have already taken to resolve the issue. + placeholder: Describe any attempted solutions here. + validations: + required: false +- type: textarea + id: reproduction-steps + attributes: + label: Reproduction Steps + description: If applicable, provide steps to reproduce the issue. + placeholder: Describe the steps to reproduce the issue, if applicable. + validations: + required: false +- type: textarea + id: additional-notes + attributes: + label: Additional Notes + description: Add any other context or screenshots about the support request here. + placeholder: Add additional notes or screenshots here. diff --git a/.github/PULL_REQUEST_TEMPLATE/advanced.md b/.github/PULL_REQUEST_TEMPLATE/advanced.md new file mode 100644 index 00000000..44e7fdb8 --- /dev/null +++ b/.github/PULL_REQUEST_TEMPLATE/advanced.md @@ -0,0 +1,26 @@ +# Advanced Pull Request Template + +## Description +Briefly describe the changes you've made. + +## Related Issue(s) +Link any related issues here. + +## Motivation and Context +Explain why these changes are necessary and what problem they solve. + +## How Has This Been Tested? +Describe how you have tested these changes. + +## Screenshots (if appropriate): +Include any relevant screenshots. + +## Types of changes +- [ ] Bug fix (non-breaking change which fixes an issue) +- [ ] New feature (non-breaking change which adds functionality) +- [ ] Breaking change (fix or feature that would cause existing functionality to change) + +## Checklist: +- [ ] My code follows the code style of this project. +- [ ] I have added tests to cover my changes. +- [ ] All new and existing tests passed. diff --git a/.github/PULL_REQUEST_TEMPLATE/basic.md b/.github/PULL_REQUEST_TEMPLATE/basic.md new file mode 100644 index 00000000..6906a43a --- /dev/null +++ b/.github/PULL_REQUEST_TEMPLATE/basic.md @@ -0,0 +1,4 @@ +# Basic Pull Request Template + +## Description +Briefly describe the changes you've made. diff --git a/.github/dependabot.yaml b/.github/dependabot.yaml new file mode 100644 index 00000000..2e1c12cd --- /dev/null +++ b/.github/dependabot.yaml @@ -0,0 +1,27 @@ +# Please see the documentation for all configuration options: +# https://docs.github.com/github/administering-a-repository/configuration-options-for-dependency-updates + +# Dependabot configuration for updating dependencies and GitHub Actions +version: 2 +updates: + # Configuration for Python package updates using pip +- package-ecosystem: pip + directory: / + schedule: + interval: monthly + open-pull-requests-limit: 5 + groups: + production-dependencies: + dependency-type: production + development-dependencies: + dependency-type: development + + # Configuration for GitHub Actions updates +- package-ecosystem: github-actions + directory: / + schedule: + interval: monthly + open-pull-requests-limit: 5 + +# Enable automatic security updates +security_updates: true diff --git a/.github/workflows/docs.yaml b/.github/workflows/docs.yaml new file mode 100644 index 00000000..a3366973 --- /dev/null +++ b/.github/workflows/docs.yaml @@ -0,0 +1,95 @@ +name: Build and Publish Docs + +permissions: + contents: write + pages: write + id-token: write + +on: + release: + types: [published] + +jobs: + build-docs: + runs-on: ${{ matrix.os }} + strategy: + fail-fast: false + matrix: + os: [ubuntu-latest] + python-version: ['3.10'] + include: + - os: ubuntu-latest + python-version: '3.10' + deploy: true # Only deploy from this configuration + outputs: + deploy: ${{ steps.set-deploy-output.outputs.deploy }} + steps: + - uses: actions/checkout@v4 + with: # no need for the history + fetch-depth: 0 + - uses: actions/setup-python@v5 + with: + python-version: ${{ matrix.python-version }} + - name: Install ffmpeg (Ubuntu) + if: startsWith(matrix.os, 'ubuntu') + run: sudo apt-get update && sudo apt-get install -y ffmpeg + - name: Install ffmpeg (macOS) + if: startsWith(matrix.os, 'macos') + run: brew install ffmpeg + - name: Install ffmpeg (Windows) + if: startsWith(matrix.os, 'windows') + run: choco install ffmpeg + - name: Install pipx and ensure it's up to date + run: | + python -m pip install --upgrade pipx + pipx ensurepath + shell: bash + - name: Install poetry + run: pipx install poetry==1.7.1 + shell: bash + - name: Install dependencies with Poetry + run: | + poetry run pip install iso-639 + poetry install --with dev,docs + shell: bash + - name: Build docs + env: + HF_TOKEN: ${{ secrets.HF_TOKEN }} + run: | + APP_MODULE_NAME=$(ls -1 src | sort | head -1) + echo "APP_MODULE_NAME: $APP_MODULE_NAME" + poetry run pdoc src/"$APP_MODULE_NAME" -o docs -t docs_style/pdoc-theme --docformat google --favicon https://readthedocs.org/favicon.ico + touch docs/.nojekyll + shell: bash + - name: Determine if deployment is needed + id: set-deploy-output + run: echo "::set-output name=deploy::true" + shell: bash + - uses: actions/upload-artifact@v3 + with: + name: documentation + path: docs + + publish-docs: + needs: build-docs + if: ${{ needs.build-docs.outputs.deploy == 'true' }} + runs-on: ${{ matrix.os }} + strategy: + fail-fast: false + matrix: + os: [ubuntu-latest] # For demonstration, other OSes are commented out + python-version: ['3.10'] + include: + - os: ubuntu-latest + python-version: '3.10' + deploy: true # Only deploy from this configuration + steps: + - uses: actions/checkout@v4 + - uses: actions/download-artifact@v3 + with: + name: documentation + path: docs + - uses: JamesIves/github-pages-deploy-action@v4 + with: + folder: docs + branch: docs diff --git a/.github/workflows/publish.yaml b/.github/workflows/publish.yaml new file mode 100644 index 00000000..13871492 --- /dev/null +++ b/.github/workflows/publish.yaml @@ -0,0 +1,43 @@ +name: Publish to PyPI + +on: + release: + types: [published] + +jobs: + pypi-release: + runs-on: ${{ matrix.os }} + strategy: + matrix: + os: [ubuntu-latest] + python-version: ['3.10'] + steps: + - uses: actions/checkout@v4 + with: + fetch-depth: 0 # Includes getting tags + - name: Set up Python ${{ matrix.python-version }} + uses: actions/setup-python@v5 + with: + python-version: ${{ matrix.python-version }} + - name: Install pipx and ensure it's up to date + run: | + python -m pip install --upgrade pipx + pipx ensurepath + shell: bash + - name: Install poetry + run: pipx install poetry + shell: bash + - name: Install poetry dynamic versioning tool + run: pipx inject poetry "poetry-dynamic-versioning[plugin]" + shell: bash + - name: Build package + run: poetry build + shell: bash + - name: Configure Poetry PyPI token + run: poetry config pypi-token.pypi ${{ secrets.PYPI_TOKEN }} + shell: bash + - name: Publish to PyPI + env: + POETRY_REQUESTS_TIMEOUT: 120 + run: poetry publish + shell: bash diff --git a/.github/workflows/release.yaml b/.github/workflows/release.yaml new file mode 100644 index 00000000..47a9022a --- /dev/null +++ b/.github/workflows/release.yaml @@ -0,0 +1,42 @@ +name: Auto-release + +on: + push: + branches: + - main + +env: + AUTO_VERSION: v11.1.2 + +jobs: + auto-release: + runs-on: ubuntu-latest + + steps: + - uses: actions/checkout@v4 + + - name: Prepare repository + # Fetch full git history and tags + run: git fetch --unshallow --tags + + - name: Unset header + # checkout@v2 adds a header that makes branch protection report errors + # because the Github action bot is not a collaborator on the repo + run: git config --local --unset http.https://github.com/.extraheader + + - name: Set up Python + uses: actions/setup-python@v5 + with: + python-version: '3.10' + + - name: Download auto + run: | + auto_download_url="$(curl -fsSL https://api.github.com/repos/intuit/auto/releases/tags/$AUTO_VERSION | jq -r '.assets[] | select(.name == "auto-linux.gz") | .browser_download_url')" + wget -O- "$auto_download_url" | gunzip > ~/auto + chmod a+x ~/auto + + - name: Create release + run: | + ~/auto shipit -vv + env: + GH_TOKEN: ${{ secrets.AUTO_ORG_TOKEN }} diff --git a/.github/workflows/test.yaml b/.github/workflows/test.yaml new file mode 100644 index 00000000..ba93a87f --- /dev/null +++ b/.github/workflows/test.yaml @@ -0,0 +1,111 @@ +name: Python Tests + +on: + pull_request: + +jobs: + unit: + runs-on: ${{ matrix.os }} + strategy: + fail-fast: false + matrix: + include: + - {os: ubuntu-latest, architecture: x64, python-version: '3.10'} + - {os: ubuntu-latest, architecture: x64, python-version: '3.11'} + - {os: ubuntu-latest, architecture: x64, python-version: '3.12'} + - {os: macos-latest, architecture: x64, python-version: '3.10'} + - {os: macos-latest, architecture: arm64, python-version: '3.10'} + - {os: macos-latest, architecture: x64, python-version: '3.11'} + - {os: macos-latest, architecture: arm64, python-version: '3.11'} + - {os: macos-latest, architecture: x64, python-version: '3.12'} + - {os: macos-latest, architecture: arm64, python-version: '3.12'} + # - {os: windows-latest, architecture: x64, python-version: '3.10'} + # - {os: windows-latest, architecture: x64, python-version: '3.11'} + env: + GITHUB_ACTIONS: true + steps: + - uses: actions/checkout@v4 + with: # no need for the history + fetch-depth: 1 + - name: Set up Python ${{ matrix.python-version }} + uses: actions/setup-python@v5 + with: + python-version: ${{ matrix.python-version }} + + - name: Install ffmpeg (Ubuntu) + if: startsWith(matrix.os, 'ubuntu') + run: sudo apt-get update && sudo apt-get install -y ffmpeg + - name: Install ffmpeg (macOS) + if: startsWith(matrix.os, 'macos') + run: brew install ffmpeg + - name: Install ffmpeg (Windows) + if: startsWith(matrix.os, 'windows') + run: choco install ffmpeg + + - name: Install pipx and ensure it's up to date + run: | + python -m pip install --upgrade pipx + pipx ensurepath + shell: bash + - name: Install poetry + run: pipx install poetry==1.7.1 + shell: bash + - name: Install dependencies with Poetry + run: | + poetry run pip install iso-639 + poetry install --with dev + shell: bash + - name: Run unit tests + id: run-tests + env: + HF_TOKEN: ${{ secrets.HF_TOKEN }} + run: > + poetry run pytest \ + --junitxml=pytest.xml \ + --cov-report=term-missing:skip-covered \ + --cov-report=xml:coverage.xml \ + --cov=src src/tests \ + --log-level=DEBUG \ + --verbose + shell: bash + - name: Upload coverage to Codecov + uses: codecov/codecov-action@v4 + with: + token: ${{ secrets.CODECOV_TOKEN }} + + pre-commit: + runs-on: ${{ matrix.os }} + strategy: + matrix: + os: [ubuntu-latest] # For demonstration, other OSes are commented out: macos-latest, windows-latest + python-version: ['3.10'] # For speeding up the process we removed "3.11" for now + steps: + - uses: actions/checkout@v4 + with: # no need for the history + fetch-depth: 1 + - name: Set up Python + uses: actions/setup-python@v5 + with: + python-version: ${{ matrix.python-version }} + - name: Install pipx and ensure it's up to date + run: | + python -m pip install --upgrade pipx + pipx ensurepath + shell: bash + - name: Install poetry + run: pipx install poetry==1.7.1 + shell: bash + - name: Install dependencies with Poetry + run: | + poetry run pip install iso-639 + poetry install --with dev + shell: bash + - name: Install pre-commit + run: pipx install pre-commit + shell: bash + - name: Run pre-commit + env: + SKIP: pytest + run: | + poetry run pre-commit run --all-files + shell: bash diff --git a/.gitignore b/.gitignore new file mode 100644 index 00000000..4ddeae03 --- /dev/null +++ b/.gitignore @@ -0,0 +1,175 @@ +# Ruff +.ruff_cache + +# Mac +.DS_Store + +# Byte-compiled / optimized / DLL files +__pycache__/ +*.py[cod] +*$py.class + +# C extensions +*.so + +# Distribution / packaging +.Python +build/ +develop-eggs/ +dist/ +downloads/ +eggs/ +.eggs/ +lib/ +lib64/ +parts/ +sdist/ +var/ +wheels/ +share/python-wheels/ +*.egg-info/ +.installed.cfg +*.egg +MANIFEST + +# PyInstaller +# Usually these files are written by a python script from a template +# before PyInstaller builds the exe, so as to inject date/other infos into it. +*.manifest +*.spec + +# Installer logs +pip-log.txt +pip-delete-this-directory.txt + +# Unit test / coverage reports +htmlcov/ +.tox/ +.nox/ +.coverage +.coverage.* +.cache +scripts/cache/ +nosetests.xml +coverage.xml +*.cover +*.py,cover +.hypothesis/ +.pytest_cache/ +cover/ + +# Translations +*.mo +*.pot + +# Django stuff: +*.log +local_settings.py +db.sqlite3 +db.sqlite3-journal + +# Flask stuff: +instance/ +.webassets-cache + +# Scrapy stuff: +.scrapy + +# Sphinx documentation +# docs/_build/ + +# PyBuilder +.pybuilder/ +target/ + +# Jupyter Notebook +.ipynb_checkpoints + +# IPython +profile_default/ +ipython_config.py + +# pyenv +# For a library or package, you might want to ignore these files since the code is +# intended to run in multiple environments; otherwise, check them in: +# .python-version + +# pipenv +# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control. +# However, in case of collaboration, if having platform-specific dependencies or dependencies +# having no cross-platform support, pipenv may install dependencies that don't work, or not +# install all needed dependencies. +#Pipfile.lock + +# poetry +# Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control. +# This is especially recommended for binary packages to ensure reproducibility, and is more +# commonly ignored for libraries. +# https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control +poetry.lock + +# pdm +# Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control. +#pdm.lock +# pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it +# in version control. +# https://pdm.fming.dev/#use-with-ide +.pdm.toml + +# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm +__pypackages__/ + +# Celery stuff +celerybeat-schedule +celerybeat.pid + +# SageMath parsed files +*.sage.py + +# Environments +.env +.venv +env/ +venv/ +ENV/ +env.bak/ +venv.bak/ + +# Spyder project settings +.spyderproject +.spyproject + +# Rope project settings +.ropeproject + +# mkdocs documentation +/site + +# mypy +.mypy_cache/ +.dmypy.json +dmypy.json + +# Pyre type checker +.pyre/ + +# pytype static type analyzer +.pytype/ + +# Cython debug symbols +cython_debug/ + +# PyCharm +# JetBrains specific template is maintained in a separate JetBrains.gitignore that can +# be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore +# and can be added to the global gitignore or merged into this file. For a more nuclear +# option (not recommended) you can uncomment the following to ignore the entire idea folder. +.idea/ + +data/ +# pdoc documentation +docs/ + +# Speechbrain models +pretrained_models +src/tests/audio/tasks/pretrained_models/ diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml new file mode 100644 index 00000000..2847615a --- /dev/null +++ b/.pre-commit-config.yaml @@ -0,0 +1,85 @@ +fail_fast: false + +repos: +- repo: https://github.com/python-poetry/poetry + rev: 1.8.0 + hooks: + - id: poetry-check + +- repo: https://github.com/astral-sh/ruff-pre-commit + rev: v0.4.10 + hooks: + - id: ruff + - id: ruff-format + +- repo: https://github.com/pre-commit/mirrors-mypy + rev: v1.10.0 + hooks: + - id: mypy + args: [--ignore-missing-imports] + additional_dependencies: + - pydantic>=1.10.4 + - types-requests +- repo: https://github.com/macisamuele/language-formatters-pre-commit-hooks + rev: v2.13.0 + hooks: + - id: pretty-format-yaml + args: + - --autofix + - --indent=2 + - id: pretty-format-toml + exclude: ^poetry.lock$ + args: + - --autofix + - --indent=2 + - --no-sort + +- repo: https://github.com/pre-commit/pre-commit-hooks + rev: v4.6.0 + hooks: + - id: check-added-large-files + args: [--maxkb=15000] + - id: check-case-conflict + - id: end-of-file-fixer + - id: trailing-whitespace + - id: pretty-format-json + args: + - --autofix + - --indent=4 + - --no-sort-keys + - id: check-merge-conflict + - id: check-yaml + - id: check-json + - id: check-toml + - id: detect-private-key + - id: name-tests-test + +- repo: https://github.com/codespell-project/codespell + rev: v2.3.0 + hooks: + - id: codespell + args: [--skip=*.ipynb] + additional_dependencies: + - tomli +- repo: https://github.com/hija/clean-dotenv + rev: v0.0.7 + hooks: + - id: clean-dotenv + +- repo: local + hooks: + - id: yaml-file-extension + name: Prefer .yaml over .yml. + entry: YAML files must have .yaml extension. + language: fail + files: \.yml$ + +- repo: local + hooks: + - id: pytest + name: pytest + entry: poetry run pytest + language: system + types: [python] + pass_filenames: false + always_run: true diff --git a/ACTION_PLAN.md b/ACTION_PLAN.md new file mode 100644 index 00000000..5624e305 --- /dev/null +++ b/ACTION_PLAN.md @@ -0,0 +1,80 @@ +# Action Plan for Senselab + +Senselab is a Python package designed to streamline the processing and analysis of behavioral data, including voice and speech, text, and video. Our goal is to implement robust and reproducible methodologies. + +This action plan outlines our initial functionalities and integrations for Senselab. As we progress, we will continue to enhance and expand these capabilities to support more advanced and comprehensive behavioral data analysis. +Anyone should feel free to suggest more features and methods. + +For an updated project progress, please see the [Project Board](https://github.com/orgs/sensein/projects/45). + + +## AUDIO + +1. **Speech to Text API** + - Integrate models supported by the Huggingface “automatic-speech-recognition” pipeline. + +2. **Forced Alignment API** + - Integrate wav2vec2 similar to whisperX (but without using WhisperX). + +3. **Text to Speech API** + - Integrate models supported by the Huggingface “text-to-speech” pipeline. + - Suggested models (by Stan): + - [Parler-tts](https://github.com/huggingface/parler-tts) - Apache 2.0 License - Great generic voices. + - [Metavoice-src](https://github.com/metavoiceio/metavoice-src) - Apache 2.0 License - Supports voice cloning with good quality. + - [OpenVoice](https://github.com/myshell-ai/OpenVoice) - MIT License. + - [Bark](https://huggingface.co/suno/bark) - MIT License. + - [GPT-SoVITS](https://github.com/RVC-Boss/GPT-SoVITS) - MIT License - Supports voice cloning. + +4. **Speaker Diarization API** + - Integrate all models for speaker diarization by pyannote.audio accessible through the Huggingface hub. + +5. **Voice Activity Detection API** + - Integrate all models for speaker diarization by pyannote.audio accessible through the Huggingface hub. + - Include torchaudio methods for VAD. + +6. **Deep-learning Embeddings Extraction API** + - Integrate all audio models available in the Huggingface hub. + +7. **Handcrafted Features Extraction API** + - Integrate features from opensmile, praat, and torchaudio. + - Consider integrating features from [Nick’s table](https://docs.google.com/spreadsheets/d/18V_FrE3jYm1Msl4rg8xl8WVBDprbZrRFIVm3nEf2uTQ/edit?usp=sharing) in the future. + +8. **Data Augmentation API** + - Integrate all methods from torch-audiomentations. + +9. **Preprocessing API** + - Integrate methods for converting stereo to mono and for resampling. + +10. **Voice Cloning API** + - Integrate KNNVC. + +11. **Speech Emotion Recognition API** + - Integrate some existing and original models from Huggingface. + +## TEXT + +1. **Semantic Embeddings Extraction API** + - Integrate some existing models from Huggingface sentence-transformer. + +2. **Text Emotion Recognition API** + - Integrate some existing models from Huggingface. + +3. **Text Sentiment Analysis API** + - Integrate some existing models from Huggingface. + +## VIDEO + +1. **Preprocessing API** + - Integrate methods for extracting audio and changing the frame rate. + +2. **Landmark Extraction API** + - Integrate Mediapipe. + +3. **Facial Action Unit Extraction API** + - Integrate PyFeat. + +4. **Facial Emotion Recognition API** + - Integrate PyFeat. + +5. **Body Pose Estimation API** + - Integrate Mediapipe. diff --git a/CHANGELOG.md b/CHANGELOG.md new file mode 100644 index 00000000..8ae0c279 --- /dev/null +++ b/CHANGELOG.md @@ -0,0 +1,299 @@ +# 0.7.5 (Thu Aug 08 2024) + +#### 🐛 Bug Fix + +- fixing bug in script line [#131](https://github.com/sensein/senselab/pull/131) ([@fabiocat93](https://github.com/fabiocat93)) + +#### ⚠️ Pushed to `main` + +- Update README.md ([@fabiocat93](https://github.com/fabiocat93)) + +#### Authors: 1 + +- Fabio Catania ([@fabiocat93](https://github.com/fabiocat93)) + +--- + +# 0.7.4 (Tue Jul 30 2024) + +#### 🐛 Bug Fix + +- Fixing doc style (adding button style) [#125](https://github.com/sensein/senselab/pull/125) ([@fabiocat93](https://github.com/fabiocat93)) + +#### Authors: 1 + +- Fabio Catania ([@fabiocat93](https://github.com/fabiocat93)) + +--- + +# 0.7.3 (Tue Jul 30 2024) + +#### 🐛 Bug Fix + +- Changing the style of the doc (dark mode) [#123](https://github.com/sensein/senselab/pull/123) ([@fabiocat93](https://github.com/fabiocat93)) + +#### Authors: 1 + +- Fabio Catania ([@fabiocat93](https://github.com/fabiocat93)) + +--- + +# 0.7.2 (Tue Jul 30 2024) + +#### 🐛 Bug Fix + +- fixing docs [#122](https://github.com/sensein/senselab/pull/122) ([@fabiocat93](https://github.com/fabiocat93)) + +#### Authors: 1 + +- Fabio Catania ([@fabiocat93](https://github.com/fabiocat93)) + +--- + +# 0.7.1 (Mon Jul 29 2024) + +#### 🐛 Bug Fix + +- fixing link to tutorials and documentation style [#121](https://github.com/sensein/senselab/pull/121) ([@fabiocat93](https://github.com/fabiocat93)) + +#### Authors: 1 + +- Fabio Catania ([@fabiocat93](https://github.com/fabiocat93)) + +--- + +# 0.7.0 (Mon Jul 29 2024) + +#### 🚀 Enhancement + +- 14 add forced alignment [#89](https://github.com/sensein/senselab/pull/89) ([@ibevers](https://github.com/ibevers) [@fabiocat93](https://github.com/fabiocat93)) + +#### 🐛 Bug Fix + +- 109 review and test speech to text task [#119](https://github.com/sensein/senselab/pull/119) ([@fabiocat93](https://github.com/fabiocat93)) + +#### Authors: 2 + +- [@ibevers](https://github.com/ibevers) +- Fabio Catania ([@fabiocat93](https://github.com/fabiocat93)) + +--- + +# 0.6.4 (Sun Jul 28 2024) + +#### 🐛 Bug Fix + +- Add and improve documentation for dimensionality reduction [#94](https://github.com/sensein/senselab/pull/94) (mbsilva@node097.ib.cluster [@900miles](https://github.com/900miles) [@fabiocat93](https://github.com/fabiocat93)) + +#### Authors: 5 + +- [@900miles](https://github.com/900miles) +- Fabio Catania ([@fabiocat93](https://github.com/fabiocat93)) +- Miles B Silva (mbsilva@node063.ib.cluster) +- Miles B Silva (mbsilva@node088.ib.cluster) +- Miles B Silva (mbsilva@node097.ib.cluster) + +--- + +# 0.6.3 (Thu Jul 25 2024) + +#### 🐛 Bug Fix + +- Update pyproject.toml [#114](https://github.com/sensein/senselab/pull/114) ([@satra](https://github.com/satra)) + +#### Authors: 1 + +- Satrajit Ghosh ([@satra](https://github.com/satra)) + +--- + +# 0.6.2 (Wed Jul 24 2024) + +#### 🐛 Bug Fix + +- Pydra [#115](https://github.com/sensein/senselab/pull/115) ([@wilke0818](https://github.com/wilke0818)) + +#### Authors: 1 + +- [@wilke0818](https://github.com/wilke0818) + +--- + +# 0.6.1 (Tue Jul 23 2024) + +#### 🐛 Bug Fix + +- adjusting dependencies and github actions plus adding pooling methods as a task plus fixing speechbrain issues plus adding some documentation [#93](https://github.com/sensein/senselab/pull/93) ([@fabiocat93](https://github.com/fabiocat93) [@GasserElbanna](https://github.com/GasserElbanna) mbsilva@node087.ib.cluster [@900miles](https://github.com/900miles)) +- pre_commit_experiments [#113](https://github.com/sensein/senselab/pull/113) ([@fabiocat93](https://github.com/fabiocat93)) +- :sparkles: add pooling methods [#96](https://github.com/sensein/senselab/pull/96) ([@GasserElbanna](https://github.com/GasserElbanna)) + +#### 📝 Documentation + +- Add documentation for speaker embeddings task and fix audio length bug [#97](https://github.com/sensein/senselab/pull/97) (mbsilva@node087.ib.cluster [@900miles](https://github.com/900miles) [@fabiocat93](https://github.com/fabiocat93)) + +#### Authors: 6 + +- [@900miles](https://github.com/900miles) +- Fabio Catania ([@fabiocat93](https://github.com/fabiocat93)) +- Gasser Elbanna ([@GasserElbanna](https://github.com/GasserElbanna)) +- Miles B Silva (mbsilva@node063.ib.cluster) +- Miles B Silva (mbsilva@node087.ib.cluster) +- Miles B Silva (mbsilva@node088.ib.cluster) + +--- + +# 0.6.0 (Thu Jul 11 2024) + +#### 🚀 Enhancement + +- release 0.6.0 is for fixing bugs and adjusting docs [#92](https://github.com/sensein/senselab/pull/92) ([@ibevers](https://github.com/ibevers) [@fabiocat93](https://github.com/fabiocat93)) + +#### 🐛 Bug Fix + +- Add b2ai speaker verification functions [#87](https://github.com/sensein/senselab/pull/87) ([@ibevers](https://github.com/ibevers)) +- attempting to fix documentation [#84](https://github.com/sensein/senselab/pull/84) ([@fabiocat93](https://github.com/fabiocat93)) + +#### Authors: 2 + +- [@ibevers](https://github.com/ibevers) +- Fabio Catania ([@fabiocat93](https://github.com/fabiocat93)) + +--- + +# 0.5.1 (Fri Jun 28 2024) + +#### 🐛 Bug Fix + +- fixing documentation issue [#83](https://github.com/sensein/senselab/pull/83) ([@fabiocat93](https://github.com/fabiocat93)) + +#### Authors: 1 + +- Fabio Catania ([@fabiocat93](https://github.com/fabiocat93)) + +--- + +# 0.5.0 (Fri Jun 28 2024) + +#### 🚀 Enhancement + +- Release 050 [#82](https://github.com/sensein/senselab/pull/82) ([@fabiocat93](https://github.com/fabiocat93) mbsilva@node084.ib.cluster [@GasserElbanna](https://github.com/GasserElbanna)) + +#### 🐛 Bug Fix + +- adding text embeddings extraction with sentencetransformers [#79](https://github.com/sensein/senselab/pull/79) ([@fabiocat93](https://github.com/fabiocat93)) +- Add dimensionality reduction task [#81](https://github.com/sensein/senselab/pull/81) (mbsilva@node084.ib.cluster [@900miles](https://github.com/900miles)) +- adding the text to speech task [#80](https://github.com/sensein/senselab/pull/80) ([@fabiocat93](https://github.com/fabiocat93)) +- adding speech enhancement task [#78](https://github.com/sensein/senselab/pull/78) ([@fabiocat93](https://github.com/fabiocat93)) + +#### Authors: 6 + +- [@900miles](https://github.com/900miles) +- Fabio Catania ([@fabiocat93](https://github.com/fabiocat93)) +- Gasser Elbanna ([@GasserElbanna](https://github.com/GasserElbanna)) +- Miles B Silva (mbsilva@node076.ib.cluster) +- Miles B Silva (mbsilva@node084.ib.cluster) +- Miles B Silva (mbsilva@node085.ib.cluster) + +--- + +# 0.4.0 (Fri Jun 21 2024) + +#### 🚀 Enhancement + +- Release 040 [#77](https://github.com/sensein/senselab/pull/77) ([@wilke0818](https://github.com/wilke0818) [@fabiocat93](https://github.com/fabiocat93) mbsilva@OPENMIND7.MIT.EDU) + +#### 🐛 Bug Fix + +- VAD and speaker diarization [#74](https://github.com/sensein/senselab/pull/74) ([@fabiocat93](https://github.com/fabiocat93)) +- Audio classification functionality [#76](https://github.com/sensein/senselab/pull/76) ([@wilke0818](https://github.com/wilke0818)) +- Add code for speaker embeddings task [#75](https://github.com/sensein/senselab/pull/75) (mbsilva@OPENMIND7.MIT.EDU [@fabiocat93](https://github.com/fabiocat93) [@900miles](https://github.com/900miles)) +- Senselab io [#70](https://github.com/sensein/senselab/pull/70) ([@wilke0818](https://github.com/wilke0818) [@fabiocat93](https://github.com/fabiocat93)) + +#### Authors: 5 + +- [@900miles](https://github.com/900miles) +- [@wilke0818](https://github.com/wilke0818) +- Fabio Catania ([@fabiocat93](https://github.com/fabiocat93)) +- Miles B Silva (mbsilva@node059.ib.cluster) +- Miles B Silva (mbsilva@OPENMIND7.MIT.EDU) + +--- + +# 0.3.0 (Mon Jun 17 2024) + +#### 🚀 Enhancement + +- making release process lighter [#66](https://github.com/sensein/senselab/pull/66) ([@fabiocat93](https://github.com/fabiocat93)) +- Release 0.3.0 - let's go [#64](https://github.com/sensein/senselab/pull/64) ([@fabiocat93](https://github.com/fabiocat93)) +- Release 0.3.0 [#59](https://github.com/sensein/senselab/pull/59) ([@fabiocat93](https://github.com/fabiocat93) [@wilke0818](https://github.com/wilke0818)) + +#### 🐛 Bug Fix + +- Update test.yaml [#65](https://github.com/sensein/senselab/pull/65) ([@fabiocat93](https://github.com/fabiocat93)) +- Adding voice cloning API [#60](https://github.com/sensein/senselab/pull/60) ([@fabiocat93](https://github.com/fabiocat93)) +- adding torchaudio feats extraction [#61](https://github.com/sensein/senselab/pull/61) ([@fabiocat93](https://github.com/fabiocat93) [@wilke0818](https://github.com/wilke0818)) + +#### Authors: 2 + +- [@wilke0818](https://github.com/wilke0818) +- Fabio Catania ([@fabiocat93](https://github.com/fabiocat93)) + +--- + +# 0.2.1 (Thu Jun 13 2024) + +#### 🐛 Bug Fix + +- ref: adjust poetry to install opensmile dependency [#62](https://github.com/sensein/senselab/pull/62) ([@satra](https://github.com/satra)) + +#### Authors: 1 + +- Satrajit Ghosh ([@satra](https://github.com/satra)) + +--- + +# 0.2.0 (Mon Jun 10 2024) + +#### 🚀 Enhancement + +- Release 0.2.0 [#56](https://github.com/sensein/senselab/pull/56) ([@fabiocat93](https://github.com/fabiocat93)) +- Including the new custom interface, pre-processing functions, utility functions [#52](https://github.com/sensein/senselab/pull/52) ([@fabiocat93](https://github.com/fabiocat93)) +- Organizing classes and functions [#57](https://github.com/sensein/senselab/pull/57) ([@fabiocat93](https://github.com/fabiocat93)) + +#### Authors: 2 + +- [@fabiocat93](https://github.com/fabiocat93) +- [@wilke0818](https://github.com/wilke0818) + +--- + +# 0.1.1 (Mon May 6 2024) + +#### 🚀 Enhancement + +- Setting up the general architecture ([@fabiocat93](https://github.com/fabiocat93)) +- Setting up the pdoc documentation ([@fabiocat93](https://github.com/fabiocat93)) +- Update CONTRIBUTING.md ([@fabiocat93](https://github.com/fabiocat93)) +- Setting up ACTION_PLAN.md ([@fabiocat93](https://github.com/fabiocat93)) +- Fixing style issues and adding codespell ([@fabiocat93](https://github.com/fabiocat93)) + +#### Authors: 1 + +- [@fabiocat93](https://github.com/fabiocat93) + +--- + +# 0.1.0 (Mon May 6 2024) + +#### 🚀 Enhancement + +- This is a simple pull request to start the project and test CI [#1](https://github.com/sensein/senselab/pull/1) ([@fabiocat93](https://github.com/fabiocat93)) + +#### ⚠️ Pushed to `main` + +- customized from sensein python template ([@fabiocat93](https://github.com/fabiocat93)) +- Initial commit ([@fabiocat93](https://github.com/fabiocat93)) + +#### Authors: 1 + +- [@fabiocat93](https://github.com/fabiocat93) diff --git a/CODE_OF_CONDUCT.md b/CODE_OF_CONDUCT.md new file mode 100644 index 00000000..39fd3474 --- /dev/null +++ b/CODE_OF_CONDUCT.md @@ -0,0 +1,101 @@ +# Contributor Covenant Code of Conduct + +## Our Pledge + +We as members, contributors, and leaders pledge to make participation in our community a harassment-free experience for everyone, regardless of age, body size, visible or invisible disability, ethnicity, sex characteristics, gender identity and expression, level of experience, education, socioeconomic status, nationality, personal appearance, race, religion, or sexual identity and orientation. + +We pledge to act and interact in ways that contribute to an open, welcoming, diverse, inclusive, and healthy community. + +## Our Standards + +Examples of behavior that contributes to a positive environment for our +community include: + +- Demonstrating empathy and kindness toward other people +- Being respectful of differing opinions, viewpoints, and experiences +- Giving and gracefully accepting constructive feedback +- Accepting responsibility and apologizing to those affected by our mistakes, and learning from the experience +- Focusing on what is best not just for us as individuals, but for the + overall community + +Examples of unacceptable behavior include: + +- The use of sexualized language or imagery, and sexual attention or + advances of any kind - Trolling, insulting or derogatory comments, and personal or political attacks +- Public or private harassment +- Publishing others' private information, such as a physical or email + address, without their explicit permission +- Other conduct which could reasonably be considered inappropriate in a + professional setting + +## Enforcement Responsibilities + +Community leaders are responsible for clarifying and enforcing our standards of acceptable behavior and will take appropriate and fair corrective action in response to any behavior that they deem inappropriate, threatening, offensive, or harmful. + +Community leaders have the right and responsibility to remove, edit, or reject comments, commits, code, wiki edits, issues, and other contributions that are not aligned to this Code of Conduct, and will communicate reasons for moderation decisions when appropriate. + +## Scope + +This Code of Conduct applies within all community spaces, and also applies when an individual is officially representing the community in public spaces. +Examples of representing our community include using an official e-mail address, posting via an official social media account, or acting as an appointed representative at an online or offline event. + +## Enforcement + +Instances of abusive, harassing, or otherwise unacceptable behavior may be +reported to the community leaders responsible for enforcement at +fabiocat@mit.edu. + +All complaints will be reviewed and investigated promptly and fairly. + +All community leaders are obligated to respect the privacy and security of the reporter of any incident. + +## Enforcement Guidelines + +Community leaders will follow these Community Impact Guidelines in determining the consequences for any action they deem in violation of this Code of Conduct: + +### 1. Correction + +**Community Impact**: Use of inappropriate language or other behavior deemed unprofessional or unwelcome in the community. + +**Consequence**: A private, written warning from community leaders, providing clarity around the nature of the violation and an explanation of why the behavior was inappropriate. A public apology may be requested. + +### 2. Warning + +**Community Impact**: A violation through a single incident or series +of actions. + +**Consequence**: A warning with consequences for continued behavior. No +interaction with the people involved, including unsolicited interaction with those enforcing the Code of Conduct, for a specified period of time. This includes avoiding interactions in community spaces as well as external channels like social media. Violating these terms may lead to a temporary or permanent ban. + +### 3. Temporary Ban + +**Community Impact**: A serious violation of community standards, including +sustained inappropriate behavior. + +**Consequence**: A temporary ban from any sort of interaction or public +communication with the community for a specified period of time. No public or private interaction with the people involved, including unsolicited interaction with those enforcing the Code of Conduct, is allowed during this period. +Violating these terms may lead to a permanent ban. + +### 4. Permanent Ban + +**Community Impact**: Demonstrating a pattern of violation of community +standards, including sustained inappropriate behavior, harassment of an +individual, or aggression toward or disparagement of classes of individuals. + +**Consequence**: A permanent ban from any sort of public interaction within +the community. + +## Attribution + +This Code of Conduct is adapted from the [Contributor Covenant][homepage], +version 2.0, available at +https://www.contributor-covenant.org/version/2/0/code_of_conduct.html. + +Community Impact Guidelines were inspired by [Mozilla's code of conduct +enforcement ladder](https://github.com/mozilla/diversity). + +[homepage]: https://www.contributor-covenant.org + +For answers to common questions about this code of conduct, see the FAQ at +https://www.contributor-covenant.org/faq. Translations are available at +https://www.contributor-covenant.org/translations. diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md new file mode 100644 index 00000000..34713b35 --- /dev/null +++ b/CONTRIBUTING.md @@ -0,0 +1,102 @@ +# Contributing to ```senselab``` + +**Pull requests** are always welcome, and we appreciate any help you give. +Note that a code of conduct applies to all spaces managed by the `senselab` project, including issues and pull requests. Please see the [Code of Conduct](CODE_OF_CONDUCT.md) for details. + +## Workflow +Please use the following workflow when contributing: + +0. **Install poetry and poetry-dynamic-versioning plugin for dynamic versioning**: + - ```pipx install poetry==1.7.1``` + - ```pipx inject poetry "poetry-dynamic-versioning[plugin]"``` +1. **Create an issue**: Use GitHub to create an issuel, assign it to yourself (and any collaborators) and, if you have access, add it to the [Project board](https://github.com/orgs/sensein/projects/45). +2. **Create a branch**: Use GitHub's "Create a branch" button from the issue page to generate a branch associated with the issue. +3. **Clone the repo locally**: + ```git clone https://github.com/sensein/senselab.git``` +4. **Checkout locally**: + - ```git fetch origin``` + - ```git checkout ``` +5. **Install all required dependencies**: + - ```poetry run pip install iso-639``` + - ```poetry install --with dev,docs``` +6. **Install pre-commit hooks**: + ```poetry run pre-commit install``` +7. **Work locally on the issue branch.** + Note: The contributed code will be licensed under the same [license](LICENSE) as the rest of the repository. **If you did not write the code yourself, you must ensure the existing license is compatible and include the license information in the contributed files, or obtain permission from the original author to relicense the contributed code.** +8. **Commit and push regularly on your dev branch.** + - It is also OK to submit work in progress. + - Please, write unit tests for your code and test it locally: + ```poetry run pytest``` + - Please, document your code following [Google style guidelines](https://google.github.io/styleguide/) and the example at the end of this document. + You can manually check the documentation automatically generated from the docstrings: + ```poetry run pdoc src/senselab -t docs_style/pdoc-theme --docformat google```. + This command uses ```pdoc``` to generate the documentation for you and make it accessible through a web interface. + - If you installed the pre-commit hooks properly, some tests and checks will run, and the commit will succeed if all tests pass. If you prefer to run your tests manually, use the following commands: + - Static type checks: + ```poetry run mypy .``` + - Code style checks: + ```poetry run ruff check``` + - To automatically fix issues: + ```poetry run ruff check --fix``` + - Spell checking: + ```poetry run codespell``` +10. **Add repository secrets**: From your github web interface, add the following repository secrets: ```CODECOV_TOKEN``` (CodeCov), ```HF_TOKEN``` (HuggingFace), ```PYPI_TOKEN``` (Pypi). +11. **Submit a pull request**: Once you are done adding your new amazing functionality, submit a pull request to merge the upstream issue branch into the upstream main. +12. **Don’t worry much about point 9**: Just joking, there’s nothing there – just making sure you're paying attention! + +This approach ensures that tasks, issues, and branches all have names that correspond. +It also facilitates incremental neatly scoped changes since it tends to keep the scope of individual changes narrow. + +**If you would like to change this workflow, please use the current process to suggest a change to this document.** + +### The biometrics-book +If you feel that the functionality you have added to senselab requires some extra explanation, or you want to share some of the knowledge you obtained during the process (e.g., you implemented an API for speaker diarization and want to write a brief explanation about what speaker diarization means and how it's generally evaluated), you can contribute to the [Biometrics-book](https://github.com/sensein/biometrics-book)! + + +### An example of well documented function following Google-style + +```` +import statistics +from typing import Dict, List + +def calculate_statistics(data: List[float]) -> Dict[str, float]: + """ + Calculate statistics from a list of numbers. + + Args: + data (list of float): A list of floating-point numbers. + + Returns: + dict: A dictionary containing the mean, median, variance, and standard deviation of the input data. + + Raises: + ValueError: If the input data list is empty. + + Examples: + >>> calculate_statistics([1, 2, 3, 4, 5]) + {'mean': 3.0, 'median': 3.0, 'variance': 2.0, 'std_dev': 1.4142135623730951} + + >>> calculate_statistics([2.5, 3.5, 4.5, 5.5, 6.5]) + {'mean': 4.5, 'median': 4.5, 'variance': 2.5, 'std_dev': 1.5811388300841898} + + Note: + This function assumes the input data list is not empty. An empty list will raise a ValueError. + + Todo: + More statistics will be implemented in the future. + """ + if not data: + raise ValueError("The input data list is empty.") + + mean = statistics.mean(data) + median = statistics.median(data) + variance = statistics.variance(data) + std_dev = statistics.stdev(data) + + return { + 'mean': mean, + 'median': median, + 'variance': variance, + 'std_dev': std_dev + } +```` diff --git a/LICENSE.txt b/LICENSE.txt new file mode 100644 index 00000000..6b8b53b9 --- /dev/null +++ b/LICENSE.txt @@ -0,0 +1,68 @@ +Apache License +Version 2.0, January 2004 +http://www.apache.org/licenses/ + +TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION + +1. 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However, in accepting such obligations, You may act only on Your own behalf and on Your sole responsibility, not on behalf of any other Contributor, and only if You agree to indemnify, defend, and hold each Contributor harmless for any liability incurred by, or claims asserted against, such Contributor by reason of your accepting any such warranty or additional liability. + +END OF TERMS AND CONDITIONS diff --git a/README.md b/README.md new file mode 100644 index 00000000..13e5928a --- /dev/null +++ b/README.md @@ -0,0 +1,56 @@ +[![Build](https://github.com/sensein/senselab/actions/workflows/test.yaml/badge.svg?branch=main)](https://github.com/sensein/senselab/actions/workflows/test.yaml?query=branch%3Amain) +[![codecov](https://codecov.io/gh/sensein/senselab/graph/badge.svg?token=9S8WY128PO)](https://codecov.io/gh/sensein/senselab) +[![Ruff](https://img.shields.io/endpoint?url=https://raw.githubusercontent.com/astral-sh/ruff/main/assets/badge/v2.json)](https://github.com/astral-sh/ruff) + +[![PyPI](https://img.shields.io/pypi/v/senselab.svg)](https://pypi.org/project/senselab/) +[![Python Version](https://img.shields.io/pypi/pyversions/senselab)](https://pypi.org/project/senselab) +[![License](https://img.shields.io/pypi/l/senselab)](https://opensource.org/licenses/Apache-2.0) + +[![pages](https://img.shields.io/badge/api-docs-blue)](https://sensein.github.io/senselab) + +Welcome to ```senselab```! This is a Python package for streamlining the processing and analysis of behavioral data, such as voice and speech patterns, with robust and reproducible methodologies. + +**Caution:**: this package is still under development and may change rapidly over the next few weeks. + +## Installation +Install this package via: + +```sh +pip install senselab +``` + +Or get the newest development version via: + +```sh +pip install git+https://github.com/sensein/senselab.git +``` + +## Quick start +```Python +from senselab.audio.data_structures.audio import Audio +from senselab.audio.tasks.preprocessing.preprocessing import resample_audios + +audio1 = Audio.from_filepath('path_to_audio_file.wav') + +print("The original audio has a sampling rate of {} Hz.".format(audio1.sampling_rate)) +[audio1] = resample_audios([audio1], resample_rate=16000) +print("The resampled audio has a sampling rate of {} Hz.".format(audio1.sampling_rate)) +``` + +For more detailed information, check out our [**Getting Started Tutorial**](https://github.com/sensein/senselab/blob/main/tutorials/getting_started.ipynb). + + +## Why should I use senselab? +- **Modular Design**: Easily integrate or use standalone transformations for flexible data manipulation. +- **Pre-built Pipelines**: Access pre-configured pipelines to reduce setup time and effort. +- **Reproducibility**: Ensure consistent and verifiable results with fixed seeds and version-controlled steps. +- **Easy Integration**: Seamlessly fit into existing workflows with minimal configuration. +- **Extensible**: Modify and contribute custom transformations and pipelines to meet specific research needs. +- **Comprehensive Documentation**: Detailed guides, examples, and documentation for all features and modules. +- **Performance Optimized**: Efficiently process large datasets with optimized code and algorithms. +- **Interactive Examples**: Jupyter notebooks provide practical examples for deriving insights from real-world datasets. + +## Contributing +Please see [**CONTRIBUTING.md**](https://github.com/sensein/senselab/blob/main/CONTRIBUTING.md) before contributing. + +To find out what's currently in progress, please check the [**Project Board**](https://github.com/orgs/sensein/projects/45). diff --git a/docs_style/pdoc-theme/syntax-highlighting.css b/docs_style/pdoc-theme/syntax-highlighting.css new file mode 100644 index 00000000..20461aaf --- /dev/null +++ b/docs_style/pdoc-theme/syntax-highlighting.css @@ -0,0 +1,610 @@ +/* Import Roboto font */ +@import url('https://fonts.googleapis.com/css2?family=Roboto:wght@400;500;700&display=swap'); 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+} + +.pdoc { + --text: #000000; /* Black text for maximum contrast */ + --muted: #6d6d6d; /* Dark grey for muted text */ + --link: #0072b2; /* Blue for links, suitable for most types of color blindness */ + --link-hover: #004d80; /* Darker blue for link hover */ + --code: #f5f5f5; /* Very light grey background for code */ + --active: #ffd700; /* Gold for active elements */ + + --accent: #3989b529; /* Light grey for accents */ + --accent2: #b3b3b3; /* Medium grey for secondary accents */ + + --nav-hover: rgba(245, 245, 245, 0.5); /* Semi-transparent light grey for nav hover */ + --name: #d55e00; /* Orange for names, suitable for most types of color blindness */ + --def: #009e73; /* Green for definitions, suitable for most types of color blindness */ + --annotation: #cc79a7; /* Magenta for annotations, suitable for most types of color blindness */ +} + +@media (prefers-color-scheme: dark) { + :root { + --pdoc-background: #202020; /* Dark grey background */ + } + + .pdoc { + --text: #e0e0e0; /* Light grey text for good contrast */ + --muted: #a0a0a0; /* Medium grey for muted text */ + --link: #56b4e9; /* Light blue for links, suitable for most types of color blindness */ + --link-hover: #1f78b4; /* Darker blue for link hover */ + --code: #2a2a2a; /* Dark grey background for code */ + --active: #ffd700; /* Gold for active elements */ + + --accent: #3a3a3a; /* Dark grey for accents */ + --accent2: #555555; /* Medium dark grey for secondary accents */ + + --nav-hover: rgba(50, 50, 50, 0.5); /* Semi-transparent dark grey for nav hover */ + --name: #e69f00; /* Orange for names, suitable for most types of color blindness */ + --def: #009e73; /* Green for definitions, suitable for most types of color blindness */ + --annotation: #f0e442; /* Yellow for annotations, suitable for most types of color blindness */ + } +} diff --git a/index.rst b/index.rst new file mode 100644 index 00000000..868513d5 --- /dev/null +++ b/index.rst @@ -0,0 +1,16 @@ +.. senselab documentation master file, created by + sphinx-quickstart on Thu Jul 25 09:55:29 2024. + You can adapt this file completely to your liking, but it should at least + contain the root `toctree` directive. + +senselab documentation +====================== + +Add your content using ``reStructuredText`` syntax. See the +`reStructuredText `_ +documentation for details. + + +.. toctree:: + :maxdepth: 2 + :caption: Contents: diff --git a/pyproject.toml b/pyproject.toml new file mode 100644 index 00000000..37be721e --- /dev/null +++ b/pyproject.toml @@ -0,0 +1,165 @@ +[tool.poetry] +name = "senselab" +version = "0.0.1" +description = "Senselab is a Python package that simplifies building pipelines for speech and voice analysis." +authors = [ + "Fabio Catania ", + "Satra Ghosh " +] +maintainers = [ + "Fabio Catania ", + "Satra Ghosh " +] +license = "Apache-2.0" +readme = "README.md" +homepage = "https://github.com/sensein/senselab" +repository = "https://github.com/sensein/senselab" +documentation = "https://sensein.github.io/senselab" +keywords = ["voice", "speech", "audio"] +classifiers = [ + "Development Status :: 3 - Alpha", + "Programming Language :: Python :: 3.10", + "Programming Language :: Python :: 3.11", + "Programming Language :: Python :: 3.12", + "License :: OSI Approved :: Apache Software License", + "Operating System :: OS Independent" +] +packages = [{include = "senselab", from = "src"}] + +[tool.poetry.dependencies] +python = "~=3.10" +click = "~=8.1" +jsonschema = "~=4.21" +datasets = "~=2.19" +torch = "~=2.2" +torchvision = "~=0.17" +torchaudio = "~=2.2" +transformers = "~=4.39" +ffmpeg-python = "~=0.2" +ipykernel = "*" +pydra = "~=0.23" +pydantic = "~=2.7" +accelerate = "*" +huggingface-hub = "~=0.23" +praat-parselmouth = "~=0.4" +audiomentations = "~=0.35" +torch-audiomentations = "~=0.11" +sentence-transformers = "~=2.7" +jiwer = "~=3.0" +speechbrain = "~=1.0" +pyav = "~=12.1" +pyannote-audio = "~=3.2" +pycountry = "~=24.6" +types-requests = "~=2.32" +opensmile = "~=2.5" +numpy = "~=1.25" +umap-learn = "~=0.5" +scikit-learn = "~=1.5" +nltk = "~=3.8" + +[tool.poetry.group.dev] +optional = true + +[tool.poetry.group.dev.dependencies] +pytest = "~=8.2" +pytest-mock = "~=3.14" +mypy = "~=1.9" +pre-commit = "~=3.7" +pytest-cov = "~=5.0" +ruff = "~=0.3" +codespell = "~=2.3" +jupyter = "~=1.0" +ipywidgets = "~=8.1" + +[tool.poetry.group.docs] +optional = true + +[tool.poetry.group.docs.dependencies] +pdoc = "~=14.4" + +[tool.poetry.scripts] +senselab-cli = "senselab.cli:main" + +[tool.pytest.ini_options] +pythonpath = [ + "src" +] +testpaths = [ + "src/tests" +] + +[tool.mypy] +ignore_missing_imports = true +plugins = [ + "pydantic.mypy" +] + +[tool.ruff] +exclude = [ + ".bzr", + ".direnv", + ".eggs", + ".git", + ".git-rewrite", + ".hg", + ".mypy_cache", + ".nox", + ".pants.d", + ".pytype", + ".ruff_cache", + ".svn", + ".tox", + ".venv", + "__pypackages__", + "_build", + "buck-out", + "build", + "dist", + "node_modules", + "venv" +] +line-length = 120 +indent-width = 4 +src = ["src"] +target-version = "py310" + +[tool.ruff.lint] +select = ["ANN", "D", "E", "F", "I"] +ignore = [ + "ANN101", # self should not be annotated. + "ANN102" # cls should not be annotated. +] +fixable = ["ALL"] +unfixable = [] +dummy-variable-rgx = "^(_+|(_+[a-zA-Z0-9_]*[a-zA-Z0-9]+?))$" + +[tool.ruff.lint.pydocstyle] +convention = "google" + +[tool.ruff.lint.per-file-ignores] +"src/tests/**/*.py" = [] + +[tool.ruff.format] +quote-style = "double" +indent-style = "space" +skip-magic-trailing-comma = false +line-ending = "auto" + +[tool.poetry-dynamic-versioning] +enable = true +vcs = "git" +bump = true +style = "pep440" +pattern = "default-unprefixed" + +[tool.codespell] +skip = [ + "poetry.lock", + "docs_style/pdoc-theme/syntax-highlighting.css", + "*.ipynb" +] +ignore-words-list = ["senselab", "nd", "astroid", "wil", "SER", "te"] + +[build-system] +requires = ["poetry-core>=1.0.0", "poetry-dynamic-versioning>=1.0.0,<2.0.0"] +build-backend = "poetry_dynamic_versioning.backend" diff --git a/src/senselab/__init__.py b/src/senselab/__init__.py new file mode 100644 index 00000000..d2783b5c --- /dev/null +++ b/src/senselab/__init__.py @@ -0,0 +1,7 @@ +""".. include:: ../../README.md""" # noqa: D415 + +import nest_asyncio + +from senselab.utils.data_structures.pydra_helpers import * # NOQA + +nest_asyncio.apply() diff --git a/src/senselab/audio/__init__.py b/src/senselab/audio/__init__.py new file mode 100644 index 00000000..4987fdf8 --- /dev/null +++ b/src/senselab/audio/__init__.py @@ -0,0 +1 @@ +"""This module contains implementations related to audio processing.""" diff --git a/src/senselab/audio/data_structures/__init__.py b/src/senselab/audio/data_structures/__init__.py new file mode 100644 index 00000000..4d6cfa26 --- /dev/null +++ b/src/senselab/audio/data_structures/__init__.py @@ -0,0 +1,3 @@ +"""This module contains data structures for audio processing.""" + +from .audio import Audio, batch_audios, unbatch_audios # noqa: F401 diff --git a/src/senselab/audio/data_structures/audio.py b/src/senselab/audio/data_structures/audio.py new file mode 100644 index 00000000..d26e00f5 --- /dev/null +++ b/src/senselab/audio/data_structures/audio.py @@ -0,0 +1,173 @@ +"""Data structures relevant for handling audio files and metadata. + +The most basic unit is the Audio object which represents the necessary information of a loaded audio +file and its corresponding metadata. Other functionality and abstract data types are provided for +ease of maintaining the codebase and offering consistent public APIs. +""" + +import os +import uuid +from typing import Dict, List, Optional, Tuple, Union + +import numpy as np +import torch +import torchaudio +from pydantic import BaseModel, Field, ValidationInfo, field_validator + +from senselab.utils.constants import SENSELAB_NAMESPACE + + +class Audio(BaseModel): + """Pydantic model for audio and its corresponding metadata. + + Pydantic model for audio that holds the necessary attributes, the actual decoded audio data + and the sampling rate, to work with audio in python. Contains metadata information as needed + and has a unique identifier for every audio. + + Attributes: + waveform: The actual audio data read from an audio file, stored as a torch.Tensor + of shape (num_channels, num_samples) + sampling_rate: The sampling rate of the audio file + orig_path_or_id: Optional str for the original path or an ID to track file over time + metadata: Optional metadata dictionary of information associated with this Audio instance + (e.g. participant demographics, audio settings, location information) + """ + + waveform: torch.Tensor + sampling_rate: int + orig_path_or_id: Optional[str] = None + metadata: Dict = Field(default={}) + model_config = {"arbitrary_types_allowed": True} + + @field_validator("waveform", mode="before") + def convert_to_tensor( + cls, v: Union[List[float], List[List[float]], np.ndarray, torch.Tensor], _: ValidationInfo + ) -> torch.Tensor: + """Converts the audio data to torch.Tensor of shape (num_channels, num_samples).""" + temporary_tensor = None + if isinstance(v, list): + temporary_tensor = torch.tensor(v) + elif isinstance(v, np.ndarray): + temporary_tensor = torch.tensor(v) + elif isinstance(v, torch.Tensor): + temporary_tensor = v + else: + raise ValueError("Unsupported data type") + + if len(temporary_tensor.shape) == 1: + # make the audio data [channels=1, samples] + temporary_tensor = temporary_tensor.unsqueeze(0) + return temporary_tensor.to(torch.float32) + + @classmethod + def from_filepath(cls, filepath: str, metadata: Dict = {}) -> "Audio": + """Creates an Audio instance from an audio file. + + Args: + filepath: Filepath of the audio file to read from + metadata: Additional information associated with the audio file + """ + array, sampling_rate = torchaudio.load(filepath) + + return cls(waveform=array, sampling_rate=sampling_rate, orig_path_or_id=filepath, metadata=metadata) + + def generate_path(self) -> str: + """Generate a path like string for this Audio. + + Generates a path like string for the Audio by either utilizing the orig_path_or_id, checking + if it is a path (has an extension), otherwise using the id if orig_path_or_id is not an ID + and giving an extension and relative to the current directory. + """ + if self.orig_path_or_id: + if os.path.splitext(self.orig_path_or_id)[-1].lower(): + return self.orig_path_or_id + else: + return f"{self.orig_path_or_id}" + else: + return f"{self.id()}" + + def id(self) -> str: + """Generate a unique identifier for the Audio. + + Generate a unique identifier for the Audio where equivalent waveforms and sampling + rates generate the same IDs. + + Returns: String UUID of the Audio generated by an MD5 hash of the waveform and the sampling_rate + """ + return str(uuid.uuid3(uuid.uuid3(SENSELAB_NAMESPACE, str(self.waveform)), str(self.sampling_rate))) + + def __eq__(self, other: object) -> bool: + """Overloads the default BaseModel equality to correctly check equivalence, ignoring metadata.""" + if isinstance(other, Audio): + return self.id() == other.id() + return False + + +def batch_audios(audios: List[Audio]) -> Tuple[torch.Tensor, Union[int, List[int]], List[Dict]]: + """Batches the Audios together into a single Tensor, keeping individual Audio information separate. + + Batch all of the Audios into a single Tensor of shape (len(audios), num_channels, num_samples). + Keeps the Audio information related to each sampling rate and metadata separate for each Audio to + allow for unbatching after running relevant functionality. + + Args: + audios: List of audios to batch together. NOTE: Should all have the same number of channels + and is generally advised to have the same sampling rates if running functionality + that relies on the sampling rate. + + Returns: + Returns a tuple of a Tensor that will have the shape (len(audios), num_channels, num_samples), + the sampling rate (an integer if all have the same sampling rate), and a list of each individual + audio's metadata information. + + Raises: + RuntimeError: if all of the Audios do not have the same number of channels + """ + sampling_rates = [] + batched_audio = [] + metadatas = [] + for audio in audios: + sampling_rates.append(audio.sampling_rate) + batched_audio.append(audio.waveform) + metadatas.append(audio.metadata) + + return_sampling_rates: List[int] | int = int(sampling_rates[0]) if len(set(sampling_rates)) == 1 else sampling_rates + + return torch.stack(batched_audio), return_sampling_rates, metadatas + + +def unbatch_audios(batched_audio: torch.Tensor, sampling_rates: int | List[int], metadatas: List[Dict]) -> List[Audio]: + """Unbatches Audios into a List of Audio objects. + + Uses the batched Audios, their respective sampling rates, and their corresponding metadatas to create + a list of Audios. + + Args: + batched_audio: torch.Tensor of shape (batch_size, num_channels, num_samples) to unstack + sampling_rates: The sampling rate of each batched audio if they differ or a single sampling rate for all of them + metadatas: The respective metadata for each of the batched audios + + Returns: + List of Audio objects representing each of the Audios that were previously batched together + + Raises: + ValueError if the batched_audio is not in the correct shape or if the number of batched_audios does not + match the amount of metadata and sampling rates (if they were provided as a List) that were provided. + """ + if len(batched_audio.shape) != 3: + raise ValueError("Expected batched audios to be of shape (batch_size, num_channels, samples)") + elif batched_audio.shape[0] != len(metadatas) or ( + isinstance(sampling_rates, List) and batched_audio.shape[0] != len(sampling_rates) + ): + raise ValueError( + "Expected sizes of batched_audio, sampling_rates (if provided as a litst) \ + and metadata to be equal" + ) + + audios = [] + for i in range(len(metadatas)): + sampling_rate = sampling_rates[i] if isinstance(sampling_rates, List) else sampling_rates + metadata = metadatas[i] + audio = batched_audio[i] + audios.append(Audio(waveform=audio, sampling_rate=sampling_rate, metadata=metadata)) + return audios diff --git a/src/senselab/audio/tasks/__init__.py b/src/senselab/audio/tasks/__init__.py new file mode 100644 index 00000000..1acf8e89 --- /dev/null +++ b/src/senselab/audio/tasks/__init__.py @@ -0,0 +1 @@ +"""Tasks for audio processing.""" diff --git a/src/senselab/audio/tasks/classification/__init__.py b/src/senselab/audio/tasks/classification/__init__.py new file mode 100644 index 00000000..0e6207e9 --- /dev/null +++ b/src/senselab/audio/tasks/classification/__init__.py @@ -0,0 +1 @@ +"""This module defines the APIs for doing generalized audio classification tasks.""" diff --git a/src/senselab/audio/tasks/classification/speech_emotion_recognition.py b/src/senselab/audio/tasks/classification/speech_emotion_recognition.py new file mode 100644 index 00000000..a1895b1f --- /dev/null +++ b/src/senselab/audio/tasks/classification/speech_emotion_recognition.py @@ -0,0 +1,136 @@ +"""This module defines APIs for the user to run Speech Emotion Recognition (SER) on sets of audios.""" + +import warnings +from typing import Dict, List, Optional, Tuple + +from transformers import AutoConfig, pipeline + +from senselab.audio.data_structures.audio import Audio +from senselab.utils.data_structures.device import DeviceType, _select_device_and_dtype +from senselab.utils.data_structures.model import HFModel + + +def audio_classification_with_hf_models( + audios: List[Audio], model: HFModel, device: Optional[DeviceType] = None +) -> List[List[Dict]]: + """General audio classification functionality utilitzing HuggingFace pipelines. + + Classifies all audios, with no underlying assumptions on what the classification labels are, + and returns the output that the pipeline gives. + + Args: + audios: List of Audio objects that we want to run classification on + model: The HuggingFace model that will be used for running the inference + device: The device to run inference on + + Returns: + List of Lists of Dictionaries where each corresponds to the audio that it was ran on and the List of + Dictionaries are of the form [{'label': 'some_label', 'score': some_value},...] + + Raises: + ValueError if the given model does not have the audio-classification pipeline tag + UserWarning if the model tags don't include endpoints_compatible (seen on HuggingFace as Inference Endpoints) + as the behavior of the model might not output as expected as a result. + """ + model_info = model.get_model_info() + + if "audio-classification" not in model_info.pipeline_tag: + raise ValueError(f"The model '{model.path_or_uri}' is not suitable for audio classification. SORRY!") + if "endpoints_compatible" not in model_info.tags: + warnings.warn( + UserWarning(f"The model '{model.path_or_uri}' has not been tagged as an Inference Endpoint and \ + so we cannot guarantee its input and outputs are as expected") + ) + + device, _ = _select_device_and_dtype(user_preference=device, compatible_devices=[DeviceType.CUDA, DeviceType.CPU]) + + classification_pipeline = pipeline( + task="audio-classification", + model=model.path_or_uri, + revision=model.revision, + device=device.value, + ) + classification_outputs = [] + + # TODO: figure out adding batching and GPU support + for audio in audios: + classification_outputs.append(classification_pipeline(audio.waveform.numpy().squeeze())) + + return classification_outputs + + +def speech_emotion_recognition_with_hf_models( + audios: List[Audio], model: HFModel, device: Optional[DeviceType] = None +) -> List[Tuple[str, Dict]]: + """Function for running speech emotion recognition tasks using HuggingFace models. + + Uses an audio classification pipeline to run speech emotion recognition for every audio in audios using the + specified model. For each audio, we return the emotion that the model suggested has the highest probability + for single label classification as well as the full emotion output for multi-label classification. Function can + also be used for continuous speech emotion recognition, where the second element of each tuple will contain a + dictionary of the predicted continuous values. + + Args: + audios: List of Audio objects that we want to run speech emotion recognition on. If you wish to get the + emotion at different segments of the audio (e.g. when different speaker talk) please run the audios + through an appropriate segmentation task. + model: The HuggingFace model that will be used for running the inference + device: The device to run inference on + + Returns: + List of tuples where the first value is the single label classification of the audio (if appropriate) and the + second value is the full model output, which might be the probabilities for each evaluated emotion + (for discrete SER) or the continuous emotion predictions. + + Raises: + ValueError if the given model is not properly tagged with 'speech-emotion-recognition' or 'emotion-recognition' + or the model configuration does not contain an id2label property that predicts commonly used + emotions (happy, sad, neutral, positive, negative, etc.) + """ + model_info = model.get_model_info() + + tags = model_info.tags + + if not ("speech-emotion-recognition" in tags or "emotion-recognition" in tags) and not _are_emotions_in_config( + model + ): + raise ValueError( + f"The model '{model.path_or_uri}' is not suitable for speech emotion recognition. Please +" + "validate that it has the correct tags or use the more generic " + + "'audio_classification_with_hf_models' function." + ) + + audio_classifications = audio_classification_with_hf_models(audios, model, device) + # print(audio_classifications) + ser_output = [] + for classification in audio_classifications: + classification_output = {} + for label_score in classification: + classification_output[label_score["label"]] = label_score["score"] + single_classification = max(classification_output, key=lambda x: classification_output[x]) + ser_output.append((single_classification, classification_output)) + return ser_output + + +def _are_emotions_in_config(model: HFModel) -> bool: + config = AutoConfig.from_pretrained(model.path_or_uri) + id2label = config.id2label + # print(id2label) + if id2label: + # id2label = model.model_info.config['id2label'] + labels = list(id2label.values()) + if "positive" in labels and "negative" in labels and "neutral" in labels: # Simple, discrete + return True + if "valence" in labels or "arousal" in labels or "dominance" in labels: # conitnuous emotions + return True + if ( + "happy" in labels + or "happiness" in labels + or "sad" in labels + or "sadness" in labels + or "fear" in labels + or "anger" in labels + or "disgust" in labels + ): + return True + return False diff --git a/src/senselab/audio/tasks/data_augmentation/__init__.py b/src/senselab/audio/tasks/data_augmentation/__init__.py new file mode 100644 index 00000000..1d6a72b1 --- /dev/null +++ b/src/senselab/audio/tasks/data_augmentation/__init__.py @@ -0,0 +1 @@ +"""This module provides the API of the senselab audio data augmentation.""" diff --git a/src/senselab/audio/tasks/data_augmentation/data_augmentation.py b/src/senselab/audio/tasks/data_augmentation/data_augmentation.py new file mode 100644 index 00000000..784f9a84 --- /dev/null +++ b/src/senselab/audio/tasks/data_augmentation/data_augmentation.py @@ -0,0 +1,69 @@ +"""This module implements some utilities for audio data augmentation.""" + +from typing import List, Union + +import pydra +import torch +from torch_audiomentations import Compose + +from senselab.audio.data_structures.audio import ( + Audio, + batch_audios, + unbatch_audios, +) +from senselab.utils.data_structures.device import DeviceType, _select_device_and_dtype + + +def augment_audios( + audios: List[Audio], augmentation: Compose, device_options: Union[DeviceType, List[DeviceType]] = [DeviceType.CPU] +) -> List[Audio]: + """Augments all provided audios with a given augmentation, either individually or all batched together. + + Augment all audios with a user defined augmentation that can be a composition of multiple augmentations. This + augmentation is either performed on each audio individually or all of the audios provided are batched together + and run at once. NOTE: if batching, all audios must have the same sampling rate. + + Args: + audios: List of Audios whose data will be augmented with the given augmentations + augmentation: A Composition of augmentations to run on each audio (uses torch-audiomentations), should have its + output_type set to "dict" + device_options: The device, or a List of possible devices, to use for augmenting. If the chosen device + is MPS or CUDA then the audios are all batched together, so for optimal performance, batching should + be done by passing a batch_size worth of audios ar a time + + Returns: + List of audios that has passed the all of input audios through the provided augmentation. This does + not necessarily mean that the augmentation has been run on every audio. For more information, + see the torch-audiomentations documentation. + """ + augmentation.output_type = "dict" + new_audios = [] + device_type, dtype = _select_device_and_dtype( + compatible_devices=device_options if isinstance(device_options, List) else [device_options] + ) + if device_type == DeviceType.CPU: + for audio in audios: + audio_to_augment = audio.waveform.unsqueeze(0) + augmented_audio = augmentation(audio_to_augment, sample_rate=audio.sampling_rate).samples + new_audios.append( + Audio( + waveform=torch.squeeze(augmented_audio), + sampling_rate=audio.sampling_rate, + metadata=audio.metadata.copy(), + orig_path_or_id=audio.orig_path_or_id, + ) + ) + else: + batched_audios, sampling_rates, metadatas = batch_audios(audios) + + batched_audios = batched_audios.to(device=torch.device(str(device_type)), dtype=dtype) + sampling_rate = sampling_rates[0] if isinstance(sampling_rates, List) else sampling_rates + augmented_audio = augmentation(batched_audios, sample_rate=sampling_rate).samples + + augmented_audio = augmented_audio.detach().cpu() + return unbatch_audios(augmented_audio, sampling_rates, metadatas) + + return new_audios + + +augment_audios_pt = pydra.mark.task(augment_audios) diff --git a/src/senselab/audio/tasks/features_extraction/__init__.py b/src/senselab/audio/tasks/features_extraction/__init__.py new file mode 100644 index 00000000..58b26f20 --- /dev/null +++ b/src/senselab/audio/tasks/features_extraction/__init__.py @@ -0,0 +1 @@ +"""This module provides the API of the senselab audio features extraction.""" diff --git a/src/senselab/audio/tasks/features_extraction/opensmile.py b/src/senselab/audio/tasks/features_extraction/opensmile.py new file mode 100644 index 00000000..84b0bf23 --- /dev/null +++ b/src/senselab/audio/tasks/features_extraction/opensmile.py @@ -0,0 +1,75 @@ +"""This module contains functions for extracting openSMILE features.""" + +from typing import Any, Dict, List + +import opensmile +import pydra + +from senselab.audio.data_structures.audio import Audio + + +class OpenSmileFeatureExtractorFactory: + """A factory for managing openSMILE feature extractors.""" + + _extractors: Dict[str, opensmile.Smile] = {} + + @classmethod + def get_opensmile_extractor(cls, feature_set: str, feature_level: str) -> opensmile.Smile: + """Get or create an openSMILE feature extractor. + + Args: + feature_set (str): The openSMILE feature set. + feature_level (str): The openSMILE feature level. + + Returns: + opensmile.Smile: The openSMILE feature extractor. + """ + key = f"{feature_set}-{feature_level}" + if key not in cls._extractors: + cls._extractors[key] = opensmile.Smile( + feature_set=opensmile.FeatureSet[feature_set], + feature_level=opensmile.FeatureLevel[feature_level], + ) + return cls._extractors[key] + + +def extract_opensmile_features_from_audios( + audios: List[Audio], + feature_set: str = "eGeMAPSv02", + feature_level: str = "Functionals", +) -> List[Dict[str, Any]]: + """Apply feature extraction across a list of audio files. + + Args: + audios (List[Audio]): The list of audio objects to extract features from. + feature_set (str): The openSMILE feature set (default is "eGeMAPSv02"). + feature_level (str): The openSMILE feature level (default is "Functionals"). + + Returns: + List[Dict[str, Any]]: The list of feature dictionaries for each audio. + """ + + def _extract_feats_from_audio(sample: Audio, smile: opensmile.Smile) -> Dict[str, Any]: + """Extract features from a single audio sample using openSMILE. + + Args: + sample (Audio): The audio object. + smile (opensmile.Smile): The openSMILE feature extractor. + + Returns: + Dict[str, Any]: The extracted features as a dictionary. + """ + audio_array = sample.waveform.squeeze().numpy() + sampling_rate = sample.sampling_rate + sample_features = smile.process_signal(audio_array, sampling_rate) + # Convert to a dictionary with float values and return it + return { + k: v[0] if isinstance(v, list) and len(v) == 1 else v for k, v in sample_features.to_dict("list").items() + } + + smile = OpenSmileFeatureExtractorFactory.get_opensmile_extractor(feature_set, feature_level) + features = [_extract_feats_from_audio(audio, smile) for audio in audios] + return features + + +extract_opensmile_features_from_audios_pt = pydra.mark.task(extract_opensmile_features_from_audios) diff --git a/src/senselab/audio/tasks/features_extraction/praat_parselmouth.py b/src/senselab/audio/tasks/features_extraction/praat_parselmouth.py new file mode 100644 index 00000000..19704028 --- /dev/null +++ b/src/senselab/audio/tasks/features_extraction/praat_parselmouth.py @@ -0,0 +1,151 @@ +"""This module contains functions that extract features from audio files using the PRAAT library.""" + +from typing import Dict, List + +import parselmouth +import pydra +from parselmouth.praat import call + +from senselab.audio.data_structures.audio import Audio + + +def get_audios_durations(audios: List[Audio]) -> List[Dict[str, float]]: + """Returns the duration of the Audio objects.""" + + def get_audio_duration(audio: Audio) -> Dict[str, float]: + def _get_duration(sound: parselmouth.Sound) -> float: + return call(sound, "Get total duration") + + waveform = audio.waveform + sampling_rate = audio.sampling_rate + sound = parselmouth.Sound(waveform, sampling_frequency=sampling_rate) + duration = _get_duration(sound) + return {"duration": duration} + + durations = [] + for audio in audios: + durations.append(get_audio_duration(audio)) + return durations + + +def get_audios_f0_descriptors( + audios: List[Audio], + f0min: float, + f0max: float, + unit: str = "Hertz", +) -> List[Dict[str, float]]: + """Returns the fundamental frequency descriptors of the audios.""" + + def get_audio_f0(audio: Audio, f0min: float, f0max: float, unit: str) -> Dict[str, float]: + def _to_pitch(sound: parselmouth.Sound, f0min: float, f0max: float) -> parselmouth.Pitch: + return call(sound, "To Pitch", 0.0, f0min, f0max) + + def _get_mean_f0(pitch: parselmouth.Pitch, unit: str) -> float: + return call(pitch, "Get mean", 0, 0, unit) + + def _get_std_dev_f0(pitch: parselmouth.Pitch, unit: str) -> float: + return call(pitch, "Get standard deviation", 0, 0, unit) + + waveform = audio.waveform + sampling_rate = audio.sampling_rate + sound = parselmouth.Sound(waveform, sampling_frequency=sampling_rate) + + pitch = _to_pitch(sound, f0min, f0max) + return { + f"f0_mean_{unit}": _get_mean_f0(pitch, unit), + f"f0_std_dev_{unit}": _get_std_dev_f0(pitch, unit), + } + + return [get_audio_f0(audio, f0min, f0max, unit) for audio in audios] + + +def get_audios_harmonicity_descriptors(audios: List[Audio], f0min: float) -> List[Dict[str, float]]: + """Returns the harmonicity descriptors of the audios.""" + + def get_audio_harmonicity(audio: Audio, f0min: float) -> Dict[str, float]: + def _to_harmonicity(sound: parselmouth.Sound, f0min: float) -> parselmouth.Harmonicity: + return call(sound, "To Harmonicity (cc)", 0.01, f0min, 0.1, 1.0) + + def _get_mean_hnr(harmonicity: parselmouth.Harmonicity) -> float: + return call(harmonicity, "Get mean", 0, 0) + + def _get_std_dev_hnr(harmonicity: parselmouth.Harmonicity) -> float: + return call(harmonicity, "Get standard deviation", 0, 0) + + waveform = audio.waveform + sampling_rate = audio.sampling_rate + sound = parselmouth.Sound(waveform, sampling_frequency=sampling_rate) + + harmonicity = _to_harmonicity(sound, f0min) + return { + "harmonicity_mean": _get_mean_hnr(harmonicity), + "harmonicity_std_dev": _get_std_dev_hnr(harmonicity), + } + + return [get_audio_harmonicity(audio, f0min) for audio in audios] + + +def get_audios_jitter_descriptors( + audios: List[Audio], + f0min: float, + f0max: float, +) -> List[Dict[str, float]]: + """Returns the jitter descriptors of the audios.""" + + def get_audio_jitter(audio: Audio, f0min: float, f0max: float) -> Dict[str, float]: + def _to_point_process(sound: parselmouth.Sound, f0min: float, f0max: float) -> parselmouth.Data: + return call(sound, "To PointProcess (periodic, cc)", f0min, f0max) + + def _get_jitter(type: str, point_process: parselmouth.Data) -> float: + return call(point_process, f"Get jitter ({type})", 0, 0, 0.0001, 0.02, 1.3) + + waveform = audio.waveform + sampling_rate = audio.sampling_rate + sound = parselmouth.Sound(waveform, sampling_frequency=sampling_rate) + point_process = _to_point_process(sound, f0min, f0max) + return { + "local_jitter": _get_jitter("local", point_process), + "localabsolute_jitter": _get_jitter("local, absolute", point_process), + "rap_jitter": _get_jitter("rap", point_process), + "ppq5_jitter": _get_jitter("ppq5", point_process), + "ddp_jitter": _get_jitter("ddp", point_process), + } + + return [get_audio_jitter(audio, f0min, f0max) for audio in audios] + + +def get_audios_shimmer_descriptors( + audios: List[Audio], + f0min: float, + f0max: float, +) -> List[Dict[str, float]]: + """Returns the shimmer descriptors of the audios.""" + + def get_audio_shimmer(audio: Audio, f0min: float, f0max: float) -> Dict[str, float]: + def _to_point_process(sound: parselmouth.Sound, f0min: float, f0max: float) -> parselmouth.Data: + return call(sound, "To PointProcess (periodic, cc)", f0min, f0max) + + def _get_shimmer(type: str, sound: parselmouth.Sound, point_process: parselmouth.Data) -> float: + return call([sound, point_process], f"Get shimmer ({type})", 0, 0, 0.0001, 0.02, 1.3, 1.6) + + waveform = audio.waveform + sampling_rate = audio.sampling_rate + sound = parselmouth.Sound(waveform, sampling_frequency=sampling_rate) + point_process = _to_point_process(sound, f0min, f0max) + return { + "local_shimmer": _get_shimmer("local", sound, point_process), + "localDB_shimmer": _get_shimmer("local_dB", sound, point_process), + "apq3_shimmer": _get_shimmer("apq3", sound, point_process), + "apq5_shimmer": _get_shimmer("apq5", sound, point_process), + "apq11_shimmer": _get_shimmer("apq11", sound, point_process), + "dda_shimmer": _get_shimmer("dda", sound, point_process), + } + + return [get_audio_shimmer(audio, f0min, f0max) for audio in audios] + + +get_audios_durations_pt = pydra.mark.task(get_audios_durations) +get_audios_f0_descriptors_pt = pydra.mark.task(get_audios_f0_descriptors) +get_audios_harmonicity_descriptors_pt = pydra.mark.task(get_audios_harmonicity_descriptors) +get_audios_jitter_descriptors_pt = pydra.mark.task(get_audios_jitter_descriptors) +get_audios_shimmer_descriptors_pt = pydra.mark.task(get_audios_shimmer_descriptors) diff --git a/src/senselab/audio/tasks/features_extraction/torchaudio.py b/src/senselab/audio/tasks/features_extraction/torchaudio.py new file mode 100644 index 00000000..357f08bc --- /dev/null +++ b/src/senselab/audio/tasks/features_extraction/torchaudio.py @@ -0,0 +1,191 @@ +"""This module provides the implementation of torchaudio utilities for audio features extraction.""" + +from typing import Dict, List, Optional + +import pydra +import torch +import torchaudio + +from senselab.audio.data_structures.audio import Audio + + +def extract_spectrogram_from_audios( + audios: List[Audio], + n_fft: int = 400, + win_length: Optional[int] = None, + hop_length: Optional[int] = None, +) -> List[Dict[str, torch.Tensor]]: + """Extract spectrograms from a list of audio objects. + + Args: + audios (List[Audio]): List of Audio objects. + n_fft (int): Size of FFT, creates n_fft // 2 + 1 bins. Default is 400. + win_length (int): Window size. Default is None, using n_fft. + hop_length (int): Length of hop between STFT windows. Default is None, using win_length // 2. + + Returns: + List[Dict[str, torch.Tensor]]: List of Dict objects containing spectrograms. + """ + if win_length is None: + win_length = n_fft + if hop_length is None: + hop_length = win_length // 2 + spectrogram = torchaudio.transforms.Spectrogram( + n_fft=n_fft, + win_length=win_length, + hop_length=hop_length, + ) + spectrograms = [] + for audio in audios: + spectrograms.append({"spectrogram": spectrogram(audio.waveform).squeeze(0)}) + return spectrograms + + +def extract_mel_spectrogram_from_audios( + audios: List[Audio], + n_fft: Optional[int] = 400, + win_length: Optional[int] = None, + hop_length: Optional[int] = None, + n_mels: int = 128, +) -> List[Dict[str, torch.Tensor]]: + """Extract mel spectrograms from a list of audio objects. + + Args: + audios (List[Audio]): List of Audio objects. + n_fft (int): Size of FFT, creates n_fft // 2 + 1 bins. Default is 400. + win_length (int): Window size. Default is None, using n_fft. + hop_length (int): Length of hop between STFT windows. Default is None, using win_length // 2. + n_mels (int): Number of mel filter banks. Default is 128. + + Returns: + List[Dict[str, torch.Tensor]]: List of Dict objects containing mel spectrograms. + """ + if win_length is None: + win_length = n_fft + if hop_length is None: + if win_length is not None: + hop_length = win_length // 2 + else: + raise ValueError("win_length cannot be None") + mel_spectrograms = [] + for audio in audios: + mel_spectrogram = torchaudio.transforms.MelSpectrogram( + sample_rate=audio.sampling_rate, + n_fft=n_fft, + win_length=win_length, + hop_length=hop_length, + n_mels=n_mels, + )(audio.waveform) + mel_spectrograms.append({"mel_spectrogram": mel_spectrogram.squeeze(0)}) + return mel_spectrograms + + +def extract_mfcc_from_audios( + audios: List[Audio], + n_mfcc: int = 40, + n_ftt: Optional[int] = 400, + win_length: Optional[int] = None, + hop_length: Optional[int] = None, + n_mels: int = 128, +) -> List[Dict[str, torch.Tensor]]: + """Extract MFCCs from a list of audio objects. + + Args: + audios (List[Audio]): List of Audio objects. + n_mfcc (int): Number of MFCCs to return. Default is 40. + n_ftt (int): Size of FFT, creates n_ftt // 2 + 1 bins. Default is 400. + win_length (int): Window size. Default is None, using n_ftt. + hop_length (int): Length of hop between STFT windows. Default is None, using win_length // 2. + n_mels (int): Number of mel filter banks. Default is 128. + + Returns: + List[Dict[str, torch.Tensor]]: List of Dict objects containing MFCCs. + """ + if win_length is None: + win_length = n_ftt + if hop_length is None: + if win_length is not None: + hop_length = win_length // 2 + else: + raise ValueError("win_length cannot be None") + mfccs = [] + for audio in audios: + mfcc_transform = torchaudio.transforms.MFCC( + sample_rate=audio.sampling_rate, + n_mfcc=n_mfcc, + melkwargs={"n_fft": n_ftt, "win_length": win_length, "hop_length": hop_length, "n_mels": n_mels}, + ) + mfccs.append({"mfcc": mfcc_transform(audio.waveform).squeeze(0)}) + return mfccs + + +def extract_mel_filter_bank_from_audios( + audios: List[Audio], + n_mels: int = 128, + n_stft: int = 201, + n_fft: int = 400, + win_length: Optional[int] = None, + hop_length: Optional[int] = None, +) -> List[Dict[str, torch.Tensor]]: + """Extract mel filter bank from a list of audio objects. + + Args: + audios (List[Audio]): List of Audio objects. + n_mels (int): Number of mel filter banks. Default is 128. + n_stft (int): Number of bins in STFT. Default is 201. + n_fft (int): Size of FFT, creates n_fft // 2 + 1 bins. Default is 400. + win_length (int): Window size. Default is None, using n_fft. + hop_length (int): Length of hop between STFT windows. Default is None, using win_length // 2. + + Returns: + List[Dict[str, torch.Tensor]]: List of Dict objects containing mel filter banks. + """ + if win_length is None: + win_length = n_fft + if hop_length is None: + hop_length = win_length // 2 + + spectrograms = extract_spectrogram_from_audios(audios, n_fft, win_length, hop_length) + + mel_filter_banks = [] + for i, audio in enumerate(audios): + melscale_transform = torchaudio.transforms.MelScale( + sample_rate=audio.sampling_rate, n_mels=n_mels, n_stft=n_stft + ) + mel_filter_banks.append({"mel_filter_bank": melscale_transform(spectrograms[i]["spectrogram"]).squeeze(0)}) + return mel_filter_banks + + +def extract_pitch_from_audios( + audios: List[Audio], freq_low: int = 85, freq_high: int = 3400 +) -> List[Dict[str, torch.Tensor]]: + """Extract pitch from a list of audio objects. + + Pitch is detected using the detect_pitch_frequency function from torchaudio. + It is implemented using normalized cross-correlation function and median smoothing. + + Args: + audios (List[Audio]): List of Audio objects. + freq_low (int): Lowest frequency that can be detected (Hz). Default is 85. + freq_high (int): Highest frequency that can be detected (Hz). Default is 3400. + + Returns: + List[Dict[str, torch.Tensor]]: List of Dict objects containing pitches. + """ + pitches = [] + for audio in audios: + pitches.append( + { + "pitch": torchaudio.functional.detect_pitch_frequency( + audio.waveform, sample_rate=audio.sampling_rate, freq_low=freq_low, freq_high=freq_high + ).squeeze(0) + } + ) + return pitches + + +extract_spectrogram_from_audios_pt = pydra.mark.task(extract_spectrogram_from_audios) +extract_mel_spectrogram_from_audios_pt = pydra.mark.task(extract_mel_spectrogram_from_audios) +extract_mfcc_from_audios_pt = pydra.mark.task(extract_mfcc_from_audios) +extract_mel_filter_bank_from_audios_pt = pydra.mark.task(extract_mel_filter_bank_from_audios) +extract_pitch_from_audios_pt = pydra.mark.task(extract_pitch_from_audios) diff --git a/src/senselab/audio/tasks/features_extraction/torchaudio_squim.py b/src/senselab/audio/tasks/features_extraction/torchaudio_squim.py new file mode 100644 index 00000000..86569b11 --- /dev/null +++ b/src/senselab/audio/tasks/features_extraction/torchaudio_squim.py @@ -0,0 +1,78 @@ +"""This module provides the implementation of torchaudio squim utilities for audio features extraction.""" + +from typing import Any, Dict, List + +import pydra +from torchaudio.pipelines import SQUIM_OBJECTIVE, SQUIM_SUBJECTIVE + +from senselab.audio.data_structures.audio import Audio + +objective_model = SQUIM_OBJECTIVE.get_model() +subjective_model = SQUIM_SUBJECTIVE.get_model() + + +def extract_objective_quality_features_from_audios(audio_list: List[Audio]) -> Dict[str, Any]: + """Extracts objective audio features from a list of Audio objects. + + Currently, Torchaudio-Squim model only supports mono audio at 16000 Hz sampling rate. + + Args: + audio_list (List[Audio]): List of Audio objects. + + Returns: + Dict[str, Any]: Dictionary containing extracted features. + """ + if any(audio.waveform.shape[0] != 1 for audio in audio_list): + raise ValueError("Only mono audio is supported by Torchaudio-Squim model.") + + if any(audio.sampling_rate != 16000 for audio in audio_list): + raise ValueError("Only 16000 Hz sampling rate is supported by Torchaudio-Squim model.") + + features: Dict[str, Any] = {"stoi": [], "pesq": [], "si_sdr": []} + + for audio in audio_list: + stoi, pesq, si_sdr = objective_model(audio.waveform) + features["stoi"].append(stoi.item()) + features["pesq"].append(pesq.item()) + features["si_sdr"].append(si_sdr.item()) + + return features + + +def extract_subjective_quality_features_from_audios( + audio_list: List[Audio], non_matching_references: List[Audio] +) -> Dict[str, Any]: + """Extracts subjective audio features from a list of Audio objects. + + Currently, Torchaudio-Squim model only supports mono audio at 16000 Hz sampling rate. + + Args: + audio_list (List[Audio]): List of Audio objects. + non_matching_references (List[Audio]): Reference Audio objects for the subjective model. + + Returns: + Dict[str, Any]: Dictionary containing extracted features. + """ + # Check if any audio is not mono + if any(audio.waveform.shape[0] != 1 for audio in audio_list) or any( + ref.waveform.shape[0] != 1 for ref in non_matching_references + ): + raise ValueError("Only mono audio is supported by Torchaudio-Squim model.") + + # Check if any audio has a sampling rate other than 16000 Hz + if any(audio.sampling_rate != 16000 for audio in audio_list) or any( + ref.sampling_rate != 16000 for ref in non_matching_references + ): + raise ValueError("Only 16000 Hz sampling rate is supported by Torchaudio-Squim model.") + + features: Dict[str, Any] = {"mos": []} + + for i, audio in enumerate(audio_list): + mos = subjective_model(audio.waveform, non_matching_references[i].waveform) + features["mos"].append(mos.item()) + + return features + + +extract_objective_quality_features_from_audios_pt = pydra.mark.task(extract_objective_quality_features_from_audios) +extract_subjective_quality_features_from_audios_pt = pydra.mark.task(extract_subjective_quality_features_from_audios) diff --git a/src/senselab/audio/tasks/forced_alignment/__init__.py b/src/senselab/audio/tasks/forced_alignment/__init__.py new file mode 100644 index 00000000..4f73c5ea --- /dev/null +++ b/src/senselab/audio/tasks/forced_alignment/__init__.py @@ -0,0 +1 @@ +"""This module provides the API for forced alignment.""" diff --git a/src/senselab/audio/tasks/forced_alignment/constants.py b/src/senselab/audio/tasks/forced_alignment/constants.py new file mode 100644 index 00000000..5ddcf69d --- /dev/null +++ b/src/senselab/audio/tasks/forced_alignment/constants.py @@ -0,0 +1,47 @@ +"""Provides constants for align functionality.""" + +from senselab.utils.data_structures.model import HFModel, TorchAudioModel + +SAMPLE_RATE = 16000 + +MINIMUM_SEGMENT_SIZE = 400 + +PUNKT_ABBREVIATIONS = ["dr", "vs", "mr", "mrs", "prof"] + +LANGUAGES_WITHOUT_SPACES = ["ja", "zh"] + +DEFAULT_ALIGN_MODELS_TORCH = { + "fr": TorchAudioModel(path_or_uri="VOXPOPULI_ASR_BASE_10K_FR", revision="main"), + "de": TorchAudioModel(path_or_uri="VOXPOPULI_ASR_BASE_10K_DE", revision="main"), + "es": TorchAudioModel(path_or_uri="VOXPOPULI_ASR_BASE_10K_ES", revision="main"), + "it": TorchAudioModel(path_or_uri="VOXPOPULI_ASR_BASE_10K_IT", revision="main"), +} + +DEFAULT_ALIGN_MODELS_HF = { + "en": HFModel(path_or_uri="facebook/wav2vec2-base-960h", revision="main"), + "ja": HFModel(path_or_uri="jonatasgrosman/wav2vec2-large-xlsr-53-japanese", revision="main"), + "zh": HFModel(path_or_uri="jonatasgrosman/wav2vec2-large-xlsr-53-chinese-zh-cn", revision="main"), + "nl": HFModel(path_or_uri="jonatasgrosman/wav2vec2-large-xlsr-53-dutch", revision="main"), + "uk": HFModel(path_or_uri="Yehor/wav2vec2-xls-r-300m-uk-with-small-lm", revision="main"), + "pt": HFModel(path_or_uri="jonatasgrosman/wav2vec2-large-xlsr-53-portuguese", revision="main"), + "ar": HFModel(path_or_uri="jonatasgrosman/wav2vec2-large-xlsr-53-arabic", revision="main"), + "cs": HFModel(path_or_uri="comodoro/wav2vec2-xls-r-300m-cs-250", revision="main"), + "ru": HFModel(path_or_uri="jonatasgrosman/wav2vec2-large-xlsr-53-russian", revision="main"), + "pl": HFModel(path_or_uri="jonatasgrosman/wav2vec2-large-xlsr-53-polish", revision="main"), + "hu": HFModel(path_or_uri="jonatasgrosman/wav2vec2-large-xlsr-53-hungarian", revision="main"), + "fi": HFModel(path_or_uri="jonatasgrosman/wav2vec2-large-xlsr-53-finnish", revision="main"), + "fa": HFModel(path_or_uri="jonatasgrosman/wav2vec2-large-xlsr-53-persian", revision="main"), + "el": HFModel(path_or_uri="jonatasgrosman/wav2vec2-large-xlsr-53-greek", revision="main"), + "tr": HFModel(path_or_uri="mpoyraz/wav2vec2-xls-r-300m-cv7-turkish", revision="main"), + "da": HFModel(path_or_uri="saattrupdan/wav2vec2-xls-r-300m-ftspeech", revision="main"), + "he": HFModel(path_or_uri="imvladikon/wav2vec2-xls-r-300m-hebrew", revision="main"), + "vi": HFModel(path_or_uri="nguyenvulebinh/wav2vec2-base-vi", revision="main"), + "ko": HFModel(path_or_uri="kresnik/wav2vec2-large-xlsr-korean", revision="main"), + "ur": HFModel(path_or_uri="kingabzpro/wav2vec2-large-xls-r-300m-Urdu", revision="main"), + "te": HFModel(path_or_uri="anuragshas/wav2vec2-large-xlsr-53-telugu", revision="main"), + "hi": HFModel(path_or_uri="theainerd/Wav2Vec2-large-xlsr-hindi", revision="main"), + "ca": HFModel(path_or_uri="softcatala/wav2vec2-large-xlsr-catala", revision="main"), + "ml": HFModel(path_or_uri="gvs/wav2vec2-large-xlsr-malayalam", revision="main"), + "no": HFModel(path_or_uri="NbAiLab/nb-wav2vec2-1b-bokmaal", revision="main"), + "nn": HFModel(path_or_uri="NbAiLab/nb-wav2vec2-300m-nynorsk", revision="main"), +} diff --git a/src/senselab/audio/tasks/forced_alignment/data_structures.py b/src/senselab/audio/tasks/forced_alignment/data_structures.py new file mode 100644 index 00000000..6d77b271 --- /dev/null +++ b/src/senselab/audio/tasks/forced_alignment/data_structures.py @@ -0,0 +1,103 @@ +"""Provides data structures for align functionality.""" + +from dataclasses import dataclass +from typing import List, Optional, Tuple, TypedDict + + +class SingleWordSegment(TypedDict): + """A single word of a speech.""" + + word: str + start: Optional[float] + end: Optional[float] + score: float + + +class SingleCharSegment(TypedDict): + """A single char of a speech.""" + + char: str + start: Optional[float] + end: Optional[float] + score: float + + +class SingleSegment(TypedDict, total=False): + """A single segment (up to multiple sentences) of a speech.""" + + start: float + end: float + text: str + clean_char: Optional[List[str]] + clean_cdx: Optional[List[int]] + clean_wdx: Optional[List[int]] + sentence_spans: Optional[List[Tuple[int, int]]] + + +class SingleAlignedSegment(TypedDict): + """A single segment (up to multiple sentences) of a speech with word alignment.""" + + start: float + end: float + text: str + words: List[SingleWordSegment] + chars: Optional[List[SingleCharSegment]] + + +class TranscriptionResult(TypedDict): + """A list of segments and word segments of a speech.""" + + segments: List[SingleSegment] + language: str + + +class AlignedTranscriptionResult(TypedDict): + """A list of segments and word segments of a speech.""" + + segments: List[SingleAlignedSegment] + word_segments: List[SingleWordSegment] + + +@dataclass +class Point: + """Represents a point in the alignment path. + + Attributes: + token_index (int): The index of the token in the transcript. + time_index (int): The index of the time frame in the audio. + score (float): The alignment score for this point. + """ + + token_index: int + time_index: int + score: float + + +@dataclass +class Segment: + """Represents a segment of aligned text. + + Attributes: + label (str): The text label of the segment. + start (int): The start time index of the segment. + end (int): The end time index of the segment. + score (float): The alignment score for the segment. + """ + + label: str + start: int + end: int + score: float + + def __repr__(self) -> str: + """Provides a string representation of the segment. + + Returns: + str: A string representation of the segment. + """ + return f"{self.label}\t({self.score:4.2f}): [{self.start:5d}, {self.end:5d})" + + @property + def length(self) -> int: + """Calculates the length of the segment.""" + return self.end - self.start diff --git a/src/senselab/audio/tasks/forced_alignment/forced_alignment.py b/src/senselab/audio/tasks/forced_alignment/forced_alignment.py new file mode 100644 index 00000000..5a0545ee --- /dev/null +++ b/src/senselab/audio/tasks/forced_alignment/forced_alignment.py @@ -0,0 +1,715 @@ +"""Align function based on WhisperX implementation.""" + +import math +from typing import Any, Dict, List, Optional, Tuple + +import pandas as pd +import torch +from nltk.tokenize.punkt import PunktParameters, PunktSentenceTokenizer +from threadpoolctl import threadpool_limits +from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor + +from senselab.audio.data_structures.audio import Audio +from senselab.audio.tasks.forced_alignment.constants import ( + DEFAULT_ALIGN_MODELS_HF, + LANGUAGES_WITHOUT_SPACES, + MINIMUM_SEGMENT_SIZE, + PUNKT_ABBREVIATIONS, + SAMPLE_RATE, +) +from senselab.audio.tasks.forced_alignment.data_structures import ( + AlignedTranscriptionResult, + Point, + Segment, + SingleAlignedSegment, + SingleSegment, + SingleWordSegment, +) +from senselab.audio.tasks.preprocessing.preprocessing import extract_segments, pad_audios +from senselab.utils.data_structures.device import DeviceType, _select_device_and_dtype +from senselab.utils.data_structures.language import Language +from senselab.utils.data_structures.model import HFModel +from senselab.utils.data_structures.script_line import ScriptLine + + +def _preprocess_segments( + transcript: List[SingleSegment], + model_dictionary: Dict[str, int], + model_lang: Language, + print_progress: bool, + combined_progress: bool, +) -> List[SingleSegment]: + """Preprocess transcription segments by filtering characters, handling spaces, and preparing text. + + Args: + transcript (List[SingleSegment]): The list of transcription segments. + model_dictionary (Dict[str, int]): Dictionary for the alignment model. + model_lang (Language): Language of the model. + print_progress (bool): Whether to print progress. + combined_progress (bool): Whether to combine progress percentage. + + Returns: + List[SingleSegment]: The preprocessed transcription segments. + """ + total_segments = len(transcript) + + for sdx, segment in enumerate(transcript): + if print_progress: + base_progress = ((sdx + 1) / total_segments) * 100 + percent_complete = (50 + base_progress / 2) if combined_progress else base_progress + print(f"Progress: {percent_complete:.2f}%...") + + num_leading = len(segment["text"]) - len(segment["text"].lstrip()) + num_trailing = len(segment["text"]) - len(segment["text"].rstrip()) + text = segment["text"] + + # Split into words + if model_lang.alpha_2 not in LANGUAGES_WITHOUT_SPACES: + per_word = text.split(" ") + else: + per_word = [text] + + clean_char, clean_cdx = [], [] + for cdx, char in enumerate(text): + char_ = char.lower() + if model_lang.alpha_2 not in LANGUAGES_WITHOUT_SPACES: + char_ = char_.replace(" ", "|") + + if cdx < num_leading or cdx > len(text) - num_trailing - 1: + continue + elif char_ in model_dictionary.keys(): + clean_char.append(char_) + clean_cdx.append(cdx) + + clean_wdx = [] + for wdx, wrd in enumerate(per_word): + if any(c in model_dictionary.keys() for c in wrd): + clean_wdx.append(wdx) + + punkt_param = PunktParameters() + punkt_param.abbrev_types = set(PUNKT_ABBREVIATIONS) + sentence_splitter = PunktSentenceTokenizer(punkt_param) + sentence_spans = list(sentence_splitter.span_tokenize(text)) + + segment["clean_char"] = clean_char + segment["clean_cdx"] = clean_cdx + segment["clean_wdx"] = clean_wdx + segment["sentence_spans"] = sentence_spans + + return transcript + + +def _can_align_segment( + segment: SingleSegment, model_dictionary: Dict[str, int], t1: float, max_duration: float +) -> bool: + """Checks if a segment can be aligned. + + Args: + segment (SingleSegment): The segment to check. + model_dictionary (Dict[str, int]): Dictionary for character indices. + t1 (float): Start time of the segment. + max_duration (float): Maximum duration of the audio. + + Returns: + bool: True if the segment can be aligned, False otherwise. + """ + if segment["clean_char"] is None or len(segment["clean_char"]) == 0: + return False + if t1 >= max_duration: + return False + return True + + +def _get_prediction_matrix( + model: torch.nn.Module, + waveform_segment: torch.Tensor, + lengths: Optional[torch.Tensor], + model_type: str, + device: DeviceType, +) -> torch.Tensor: + """Generate prediction matrix from the alignment model. + + Args: + model (torch.nn.Module): The alignment model. + waveform_segment (torch.Tensor): The audio segment to be processed. + lengths (Optional[torch.Tensor]): Lengths of the audio segments. + model_type (str): The type of the model ('torchaudio' or 'huggingface'). + device (DeviceType): The device to run the model on. + + Returns: + torch.Tensor: The prediction matrix. + """ + with torch.inference_mode(): + if model_type == "torchaudio": + emissions, _ = model(waveform_segment.to(device.value), lengths=lengths) + elif model_type == "huggingface": + emissions = model(waveform_segment.to(device.value)).logits + else: + raise NotImplementedError(f"Align model of type {model_type} not supported.") + + emissions = torch.log_softmax(emissions, dim=-1) + + return emissions + + +def _assign_timestamps_to_characters( + text: str, segment: SingleSegment, char_segments: list, ratio: float, t1: float, model_lang: Language +) -> pd.DataFrame: + """Assigns timestamps to aligned characters and organizes them into a DataFrame. + + Args: + text (str): The text to align with the segment. + segment (SingleSegment): The segment containing character indices. + char_segments (list): List of character segments with alignment information. + ratio (float): The ratio of duration to waveform segment size. + t1 (float): Start time of the segment. + model_lang (Language): Language of the model. + + Returns: + pd.DataFrame: DataFrame containing character alignments with timestamps and word indices. + """ + char_segments_arr = [] + word_idx = 0 + for cdx, char in enumerate(text): + start, end, score = None, None, None + if segment["clean_cdx"] is not None and cdx in segment["clean_cdx"]: + char_seg = char_segments[segment["clean_cdx"].index(cdx)] + start = round(char_seg.start * ratio + t1, 3) + end = round(char_seg.end * ratio + t1, 3) + score = round(char_seg.score, 3) + + char_segments_arr.append( + { + "char": char, + "start": start, + "end": end, + "score": score, + "word-idx": word_idx, + } + ) + + if model_lang.alpha_2 in LANGUAGES_WITHOUT_SPACES: + word_idx += 1 + elif cdx == len(text) - 1 or text[cdx + 1] == " ": + word_idx += 1 + + return pd.DataFrame(char_segments_arr) + + +def _align_subsegments( + segment: SingleSegment, + char_segments_df: pd.DataFrame, + text: str, + word_segments: List[SingleWordSegment], + aligned_subsegments: List[SingleAlignedSegment], + return_char_alignments: bool, +) -> None: + """Aligns sentence spans to create subsegments and update word segments. + + Args: + segment (SingleSegment): The segment containing sentence spans. + char_segments_df (pd.DataFrame): DataFrame with character alignments. + text (str): The text to align with the segment. + word_segments (List[SingleWordSegment]): List to store word segments. + aligned_subsegments (List[SingleAlignedSegment]): List to store aligned subsegments. + return_char_alignments (bool): Flag to return character alignments. + + Returns: + None: The function modifies the word_segments and aligned_subsegments lists in place. + """ + for sdx, (sstart, send) in enumerate(segment["sentence_spans"] or []): + curr_chars = char_segments_df.loc[(char_segments_df.index >= sstart) & (char_segments_df.index <= send)] + char_segments_df.loc[(char_segments_df.index >= sstart) & (char_segments_df.index <= send), "sentence-idx"] = ( + sdx + ) + + sentence_text = text[sstart:send] + sentence_start = curr_chars["start"].min() + end_chars = curr_chars[curr_chars["char"] != " "] + sentence_end = end_chars["end"].max() + sentence_words = [] + + for word_idx in curr_chars["word-idx"].unique(): + word_chars = curr_chars.loc[curr_chars["word-idx"] == word_idx] + word_text = "".join(word_chars["char"].tolist()).strip() + if len(word_text) == 0: + continue + + word_chars = word_chars[word_chars["char"] != " "] + + word_start = word_chars["start"].min() + word_end = word_chars["end"].max() + word_score = round(word_chars["score"].mean(), 3) + + word_segment = SingleWordSegment(word=word_text, start=word_start, end=word_end, score=word_score) + + sentence_words.append(word_segment) + word_segments.append(word_segment) + + aligned_subsegment = SingleAlignedSegment( + text=sentence_text, start=sentence_start, end=sentence_end, words=sentence_words, chars=word_chars + ) + aligned_subsegments.append(aligned_subsegment) + + if return_char_alignments: + curr_chars = curr_chars[["char", "start", "end", "score"]] + curr_chars.fillna(-1, inplace=True) + curr_chars = curr_chars.to_dict("records") + curr_chars = [{key: val for key, val in char.items() if val != -1} for char in curr_chars] + aligned_subsegments[-1]["chars"] = curr_chars + + +def _align_single_segment( + segment: SingleSegment, + model: torch.nn.Module, + model_dictionary: Dict[str, int], + model_lang: Language, + model_type: str, + audio: Audio, + device: DeviceType, + t1: float, + t2: float, + return_char_alignments: bool, + interpolate_method: str, + aligned_segment: SingleAlignedSegment, + aligned_segments: List[SingleAlignedSegment], + word_segments: List[SingleWordSegment], +) -> None: + """Processes and aligns a single segment. + + Args: + segment (SingleSegment): The segment to align. + model (torch.nn.Module): The alignment model. + model_dictionary (Dict[str, int]): Dictionary for character indices. + model_lang (Language): Language of the model. + model_type (str): Either 'huggingface' or 'torchaudio'. + audio (Audio): The audio data. + device (DeviceType): The device to run the model on. + t1 (float): Start time of the segment. + t2 (float): End time of the segment. + return_char_alignments (bool): Flag to return character alignments. + interpolate_method (str): Method for interpolating NaNs. + aligned_segment (SingleAlignedSegment ): The aligned segment data. + aligned_segments (List[SingleAlignedSegment]): List to store aligned segments. + word_segments (List[SingleWordSegment]): List to store word segments. + + Returns: + None: The function modifies the aligned_segments and word_segments lists in place. + """ + text_clean = "".join(segment["clean_char"] or []) + tokens = [model_dictionary[c] for c in text_clean] + + extracted_segment = extract_segments([(audio, [(t1, t2)])])[0][0] + lengths = extracted_segment.waveform.shape[-1] + waveform_segment = pad_audios([extracted_segment], MINIMUM_SEGMENT_SIZE)[0].waveform + + emissions = _get_prediction_matrix(model, waveform_segment, lengths, model_type, device) + emission = emissions[0].cpu().detach() + + blank_id = 0 + for char, code in model_dictionary.items(): + if char == "[pad]" or char == "": + blank_id = code + + trellis = _get_trellis(emission, tokens, blank_id) + path = _backtrack(trellis, emission, tokens, blank_id) + + if path is None: + print(f'Failed to align segment ("{segment["text"]}"): backtrack failed, resorting to original...') + aligned_segments.append(aligned_segment) + return + + char_segments = _merge_repeats(path, text_clean) + + duration = t2 - t1 + ratio = duration * waveform_segment.size(0) / (trellis.size(0) - 1) + + char_segments_df = _assign_timestamps_to_characters(segment["text"], segment, char_segments, ratio, t1, model_lang) + + aligned_subsegments: List[SingleAlignedSegment] = [] + if isinstance(char_segments_df, pd.DataFrame): + char_segments_df["sentence-idx"] = None + else: + raise TypeError("char_segments_df must be a pandas DataFrame.") + + if segment["sentence_spans"] is not None: + _align_subsegments( + segment=segment, + char_segments_df=char_segments_df, + text=segment["text"], + word_segments=word_segments, + aligned_subsegments=aligned_subsegments, + return_char_alignments=True, + ) + + if aligned_subsegments: + aligned_subsegments_df = pd.DataFrame(aligned_subsegments) + + aligned_subsegments_df["start"] = _interpolate_nans( + aligned_subsegments_df["start"], method=interpolate_method + ) + aligned_subsegments_df["end"] = _interpolate_nans(aligned_subsegments_df["end"], method=interpolate_method) + agg_dict = {"text": " ".join, "words": "sum"} + if model_lang.alpha_2 in LANGUAGES_WITHOUT_SPACES: + agg_dict["text"] = "".join + if return_char_alignments: + agg_dict["chars"] = "sum" + aligned_subsegments_df.groupby(["start", "end"], as_index=False).agg(agg_dict) + aligned_subsegments = aligned_subsegments_df.to_dict("records") + + aligned_segments.extend(aligned_subsegments) + + +def _get_trellis(emission: torch.Tensor, tokens: List[int], blank_id: int = 0) -> torch.Tensor: + """Gets the trellis for token alignment. + + Args: + emission (torch.Tensor): The emission matrix from the model. + tokens (List[int]): The token IDs. + blank_id (int): The ID for the blank token. + + Returns: + torch.Tensor: The trellis matrix. + """ + num_frame = emission.size(0) + num_tokens = len(tokens) + + trellis = torch.empty((num_frame + 1, num_tokens + 1)) + trellis[0, 0] = 0 + trellis[1:, 0] = torch.cumsum(emission[:, 0], 0) + trellis[0, -num_tokens:] = -float("inf") + trellis[-num_tokens:, 0] = float("inf") + + for t in range(num_frame): + trellis[t + 1, 1:] = torch.maximum( + trellis[t, 1:] + emission[t, blank_id], + trellis[t, :-1] + emission[t, tokens], + ) + return trellis + + +def _backtrack( + trellis: torch.Tensor, emission: torch.Tensor, tokens: List[int], blank_id: int = 0 +) -> Optional[List[Point]]: + """Backtracks to find the best path through the trellis. + + Args: + trellis (torch.Tensor): The trellis matrix. + emission (torch.Tensor): The emission matrix from the model. + tokens (List[int]): The token IDs. + blank_id (int): The ID for the blank token. + + Returns: + Optional[List[Point]]: The best path as a list of Points. + """ + j = trellis.size(1) - 1 + t_start = int(torch.argmax(trellis[:, j]).item()) + + path = [] + for t in range(t_start, 0, -1): + stayed = trellis[t - 1, j] + emission[t - 1, blank_id] + changed = trellis[t - 1, j - 1] + emission[t - 1, tokens[j - 1]] + prob = emission[t - 1, tokens[j - 1] if changed > stayed else 0].exp().item() + path.append(Point(j - 1, t - 1, prob)) + + if changed > stayed: + j -= 1 + if j == 0: + break + else: + return None + return path[::-1] + + +def _merge_repeats(path: List[Point], transcript: str) -> List[Segment]: + """Merges repeated tokens in the alignment path. + + Args: + path (List[Point]): The alignment path. + transcript (str): The transcript text. + + Returns: + List[Segment]: The merged segments. + """ + i1, i2 = 0, 0 + segments = [] + while i1 < len(path): + while i2 < len(path) and path[i1].token_index == path[i2].token_index: + i2 += 1 + score = sum(path[k].score for k in range(i1, i2)) / (i2 - i1) + segments.append( + Segment( + transcript[path[i1].token_index], + path[i1].time_index, + path[i2 - 1].time_index + 1, + score, + ) + ) + i1 = i2 + return segments + + +def _interpolate_nans(x: pd.Series, method: str = "nearest") -> pd.Series: + """Interpolates NaN values in a pandas Series. + + Args: + x (pd.Series): The pandas Series. + method (str): The interpolation method (default: "nearest"). + + Returns: + pd.Series: The Series with interpolated NaNs. + """ + if x.notnull().sum() > 1: + return x.interpolate(method=method).ffill().bfill() + else: + return x.ffill().bfill() + + +def _align_segments( + transcript: List[SingleSegment], + model: torch.nn.Module, + model_dictionary: Dict[str, int], + model_lang: Language, + model_type: str, + audio: Audio, + device: DeviceType, + max_duration: float, + return_char_alignments: bool, + interpolate_method: str, +) -> Tuple[List[SingleAlignedSegment], List[SingleWordSegment]]: + """Align segments based on the predictions. + + Args: + transcript (List[SingleSegment]): The list of transcription segments. + model (torch.nn.Module): The alignment model. + model_dictionary (Dict[str, int]): Dictionary for character indices. + model_lang (str): Language of the model. + model_type (str): The type of the model ('torchaudio' or 'huggingface'). + audio (Audio): The audio data. + device (DeviceType): The device to run the model on. + max_duration (float): Maximum duration of the audio. + return_char_alignments (bool): Flag to return character alignments. + interpolate_method (str): Method for interpolating NaNs. + + Returns: + Tuple[List[SingleAlignedSegment], List[SingleWordSegment]]: The aligned segments and word segments. + """ + aligned_segments: List[SingleAlignedSegment] = [] + word_segments: List[SingleWordSegment] = [] + + for sdx, segment in enumerate(transcript): + t1 = segment["start"] + t2 = segment["end"] + text = segment["text"] + + aligned_segment: SingleAlignedSegment = {"start": t1, "end": t2, "text": text, "words": [], "chars": None} + + if return_char_alignments: + aligned_segment["chars"] = [] + + if _can_align_segment(segment, model_dictionary, t1, max_duration): + _align_single_segment( + segment, + model, + model_dictionary, + model_lang, + model_type, + audio, + device, + t1, + t2, + return_char_alignments, + interpolate_method, + aligned_segment, + aligned_segments, + word_segments, + ) + else: + print(f'Failed to align segment ("{segment["text"]}"), skipping...') + aligned_segments.append(aligned_segment) + + return (aligned_segments, word_segments) + + +def _convert_to_scriptline(data: AlignedTranscriptionResult) -> List[ScriptLine]: + """Convert a dictionary of segments and word segments to a list of ScriptLine objects. + + Args: + data (AlignedTranscriptionResult): The input dictionary with segments and word segments. + + Returns: + List[ScriptLine]: The list of ScriptLine objects. + """ + segments = data["segments"] + script_lines = [] + + for segment in segments: + words = segment["words"] + word_chunks = [ScriptLine(text=word["word"]) for word in words] + + # Handle 'nan' end values by setting them to None + start = segment["start"] + end: Optional[float] = segment["end"] + if end is not None and (isinstance(end, float) and math.isnan(end)): + end = None + + script_line = ScriptLine(text=segment["text"], start=start, end=end, chunks=word_chunks) + script_lines.append(script_line) + + return script_lines + + +def _align_transcription( + transcript: List[SingleSegment], + model: torch.nn.Module, + align_model_metadata: Dict[str, Any], + audio: Audio, + device: DeviceType, + interpolate_method: str = "nearest", + return_char_alignments: bool = False, + print_progress: bool = False, + combined_progress: bool = False, +) -> AlignedTranscriptionResult: + """Aligns phoneme recognition predictions to known transcription. + + Args: + transcript (List[SingleSegment]): The list of transcription segments. + model (torch.nn.Module): The alignment model. + align_model_metadata (Dict[str, Any]): Metadata for the alignment model. + audio (Audio): The audio data. + device (DeviceType): The device to run the model on. + interpolate_method (str): The method for interpolating NaNs (default: "nearest"). + return_char_alignments (bool): Whether to return character alignments (default: False). + print_progress (bool): Whether to print progress (default: False). + combined_progress (bool): Whether to combine progress (default: False). + + Returns: + AlignedTranscriptionResult: The aligned transcription result. + """ + max_duration = audio.waveform.shape[1] / audio.sampling_rate + + model_dictionary = align_model_metadata["dictionary"] + model_lang = align_model_metadata["language"] + model_type = align_model_metadata["type"] + + transcript = _preprocess_segments( + transcript, + align_model_metadata["dictionary"], + align_model_metadata["language"], + print_progress, + combined_progress, + ) + + aligned_segments, word_segments = _align_segments( + transcript=transcript, + model=model, + model_dictionary=model_dictionary, + model_lang=model_lang, + model_type=model_type, + audio=audio, + device=device, + max_duration=max_duration, + return_char_alignments=return_char_alignments, + interpolate_method=interpolate_method, + ) + return {"segments": aligned_segments, "word_segments": word_segments} + + +def align_transcriptions( + audios_and_transcriptions_and_language: List[Tuple[Audio, ScriptLine, Language]], +) -> List[List[ScriptLine]]: + """Aligns transcriptions with the given audio using a wav2vec2.0 model. + + Args: + audios_and_transcriptions_and_language (List[tuple[Audio, ScriptLine, Language]): + A list of tuples audios, corresponding transcriptions, and optionally a language. + The default language is English. + + Returns: + List[List[ScriptLine]]: A list of lists, where each inner list contains the aligned script lines for each audio. + """ + aligned_script_lines = [] + loaded_processors_and_models = {} + + for item in audios_and_transcriptions_and_language: + audio, transcription, language = (*item, None)[:3] + + # Set default language to English if not provided + if language is None: + language = Language(language_code="en") + + # Define the language code and load model + device = _select_device_and_dtype()[0] # DeviceType object + model_variant = DEFAULT_ALIGN_MODELS_HF.get(language.language_code, DEFAULT_ALIGN_MODELS_HF["en"]) + + if model_variant.path_or_uri not in loaded_processors_and_models: # Load model + processor = Wav2Vec2Processor.from_pretrained(model_variant.path_or_uri) + model = Wav2Vec2ForCTC.from_pretrained(model_variant.path_or_uri).to(device.value) + loaded_processors_and_models[model_variant.path_or_uri] = (processor, model) + + processor, model = loaded_processors_and_models[model_variant.path_or_uri] + + if audio.sampling_rate != SAMPLE_RATE: + raise ValueError(f"{audio.sampling_rate} rate is not equal to the training rate of {SAMPLE_RATE}.") + + # Ensure start and end are not None + start = transcription.start if transcription.start is not None else 0.0 + end = transcription.end if transcription.end is not None else audio.waveform.shape[1] / audio.sampling_rate + + # Ensure text is not None + text = transcription.text if transcription.text is not None else "" + + # Align each segment of the transcription + segments = [ + SingleSegment( + start=start, end=end, text=text, clean_char=None, clean_cdx=None, clean_wdx=None, sentence_spans=None + ) + ] + + with threadpool_limits(limits=1, user_api="blas"): + alignment = _align_transcription( + transcript=segments, + model=model, + align_model_metadata={ + "dictionary": processor.tokenizer.get_vocab(), + "language": language, + "type": "huggingface" if isinstance(model_variant, HFModel) else "torchaudio", + }, + audio=audio, + device=device, + return_char_alignments=True, + ) + aligned_script_lines.append(_convert_to_scriptline(alignment)) + + return aligned_script_lines + + +# Note: this code is derived from: https://github.com/m-bain/whisperX/tree/main + +# Copyright (c) 2022, Max Bain +# All rights reserved. + +# Redistribution and use in source and binary forms, with or without +# modification, are permitted provided that the following conditions are met: +# 1. Redistributions of source code must retain the above copyright +# notice, this list of conditions and the following disclaimer. +# 2. Redistributions in binary form must reproduce the above copyright +# notice, this list of conditions and the following disclaimer in the +# documentation and/or other materials provided with the distribution. +# 3. All advertising materials mentioning features or use of this software +# must display the following acknowledgement: +# This product includes software developed by Max Bain. +# 4. Neither the name of Max Bain nor the +# names of its contributors may be used to endorse or promote products +# derived from this software without specific prior written permission. + +# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDER ''AS IS'' AND ANY +# EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED +# WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE +# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE +# FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL +# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR +# SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER +# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, +# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE +# USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. diff --git a/src/senselab/audio/tasks/plotting/plotting.py b/src/senselab/audio/tasks/plotting/plotting.py new file mode 100644 index 00000000..cec622a0 --- /dev/null +++ b/src/senselab/audio/tasks/plotting/plotting.py @@ -0,0 +1,92 @@ +"""This module contains functions for plotting audio-related data.""" + +import matplotlib.pyplot as plt +import torch +from IPython.display import Audio, display + +from senselab.audio.data_structures.audio import Audio as AudioData + + +def plot_waveform(audio: AudioData, title: str = "Waveform", fast: bool = False) -> None: + """Plots the waveform of an Audio object. + + Args: + audio (AudioData): An instance of Audio containing waveform data and sampling rate. + title (str): Title of the plot. + fast (bool): If True, plots a downsampled version for a faster but less detailed view. + + Todo: + - Add option to save the plot + - Add option to choose the size of the Figure + """ + waveform = audio.waveform + sample_rate = audio.sampling_rate + + if fast: + # Downsampling the waveform and sample rate for a quicker plot + waveform = waveform[::10] + + num_channels, num_frames = waveform.shape + time_axis = torch.linspace(0, num_frames / sample_rate, num_frames) + + figure, axes = plt.subplots(num_channels, 1, figsize=(12, num_channels * 2)) + if num_channels == 1: + axes = [axes] # Ensure axes is iterable + for c, ax in enumerate(axes): + ax.plot(time_axis, waveform[c].numpy(), linewidth=1) + ax.set_ylabel(f"Channel {c + 1}") + ax.grid(True) + + figure.suptitle(title) + plt.xlabel("Time [s]") + plt.show(block=False) + + +def plot_specgram(spectrogram: torch.Tensor, sample_rate: int, title: str = "Spectrogram") -> None: + """Plots the spectrogram of an Audio object. + + Args: + spectrogram (torch.Tensor): A tensor representing the spectrogram. + sample_rate (int): The sampling rate of the audio data. + title (str): Title of the spectrogram plot. + + Todo: + - Add option to save the plot + - Add option to choose the size of the Figure + """ + if spectrogram.dim() != 2: + raise ValueError("Spectrogram must be a 2D tensor.") + plt.figure(figsize=(10, 4)) + plt.imshow( + spectrogram.numpy(), + aspect="auto", + origin="lower", + extent=(0, spectrogram.size(1) / sample_rate, 0, sample_rate / 2), + cmap="viridis", + ) + plt.colorbar(label="Magnitude (dB)") + plt.title(title) + plt.ylabel("Frequency [Hz]") + plt.xlabel("Time [Sec]") + plt.show(block=False) + + +def play_audio(audio: AudioData) -> None: + """Plays an audio file. + + Args: + audio (AudioData): An instance of Audio containing waveform data and sampling rate. + + Raises: + ValueError: If the number of channels is more than 2. + """ + waveform = audio.waveform.numpy() + sample_rate = audio.sampling_rate + + num_channels = waveform.shape[0] + if num_channels == 1: + display(Audio(waveform[0], rate=sample_rate)) + elif num_channels == 2: + display(Audio((waveform[0], waveform[1]), rate=sample_rate)) + else: + raise ValueError("Waveform with more than 2 channels are not supported.") diff --git a/src/senselab/audio/tasks/preprocessing/__init__.py b/src/senselab/audio/tasks/preprocessing/__init__.py new file mode 100644 index 00000000..54eac8fe --- /dev/null +++ b/src/senselab/audio/tasks/preprocessing/__init__.py @@ -0,0 +1 @@ +"""This module provides the API for the senselab audio preprocessing.""" diff --git a/src/senselab/audio/tasks/preprocessing/preprocessing.py b/src/senselab/audio/tasks/preprocessing/preprocessing.py new file mode 100644 index 00000000..c76ced3e --- /dev/null +++ b/src/senselab/audio/tasks/preprocessing/preprocessing.py @@ -0,0 +1,253 @@ +"""This module implements some utilities for the preprocessing task.""" + +from typing import List, Optional, Tuple + +import torch +from scipy import signal +from speechbrain.augment.time_domain import Resample + +from senselab.audio.data_structures.audio import Audio + + +def resample_audios( + audios: List[Audio], + resample_rate: int, + lowcut: Optional[float] = None, + order: int = 4, +) -> List[Audio]: + """Resamples a list of audio signals to a given sampling rate. + + Args: + audios (List[Audio]): List of audio objects to resample. + resample_rate (int): Target sampling rate. + lowcut (float, optional): Low cut frequency for IIR filter. + order (int, optional): Order of the IIR filter. Defaults to 4. + + Returns: + List[Audio]: Resampled audio objects. + """ + resampled_audios = [] + for audio in audios: + if lowcut is None: + lowcut = resample_rate / 2 - 100 + sos = signal.butter(order, lowcut, btype="low", output="sos", fs=resample_rate) + + channels = [] + for channel in audio.waveform: + filtered_channel = torch.from_numpy(signal.sosfiltfilt(sos, channel.numpy()).copy()).float() + resampler = Resample(orig_freq=audio.sampling_rate, new_freq=resample_rate) + resampled_channel = resampler(filtered_channel.unsqueeze(0)).squeeze(0) + channels.append(resampled_channel) + + resampled_waveform = torch.stack(channels) + resampled_audios.append( + Audio( + waveform=resampled_waveform, + sampling_rate=resample_rate, + metadata=audio.metadata.copy(), + orig_path_or_id=audio.orig_path_or_id, + ) + ) + return resampled_audios + + +def downmix_audios_to_mono(audios: List[Audio]) -> List[Audio]: + """Downmixes a list of Audio objects to mono by averaging all channels. + + Args: + audios (List[Audio]): A list of Audio objects with a tensor representing the audio waveform. + Shape: (num_channels, num_samples). + + Returns: + List[Audio]: The list of audio objects with a mono waveform averaged from all channels. Shape: (num_samples). + """ + down_mixed_audios = [] + for audio in audios: + down_mixed_audios.append( + Audio( + waveform=audio.waveform.mean(dim=0, keepdim=True), + sampling_rate=audio.sampling_rate, + metadata=audio.metadata.copy(), + orig_path_or_id=audio.orig_path_or_id, + ) + ) + + return down_mixed_audios + + +def select_channel_from_audios(audios: List[Audio], channel_index: int) -> List[Audio]: + """Selects a specific channel from a list of Audio objects. + + Args: + audios (List[Audio]): A list of Audio objects with a tensor representing the audio waveform. + Shape: (num_channels, num_samples). + channel_index (int): The index of the channel to select. + + Returns: + List[Audio]: The list of audio objects with the selected channel. Shape: (1, num_samples). + """ + mono_channel_audios = [] + for audio in audios: + if audio.waveform.size(0) <= channel_index: # should consider how much sense negative values make + raise ValueError("channel_index should be valid") + + mono_channel_audios.append( + Audio( + waveform=audio.waveform[channel_index, :], + sampling_rate=audio.sampling_rate, + metadata=audio.metadata.copy(), + orig_path_or_id=audio.orig_path_or_id, + ) + ) + return mono_channel_audios + + +def chunk_audios(data: List[Tuple[Audio, Tuple[float, float]]]) -> List[Audio]: + """Chunks the input audios based on the start and end timestamp. + + Args: + data: List of tuples containing an Audio object and a tuple with start and end (in seconds) for chunking. + + Returns: + List of Audios that have been chunked based on the provided timestamps + """ + chunked_audios = [] + + for audio, timestamps in data: + start, end = timestamps + if start < 0: + raise ValueError("Start time must be greater than or equal to 0.") + duration = audio.waveform.shape[1] / audio.sampling_rate + if end > duration: + raise ValueError(f"End time must be less than the duration of the audio file ({duration} seconds).") + start_sample = int(start * audio.sampling_rate) + end_sample = int(end * audio.sampling_rate) + chunked_waveform = audio.waveform[:, start_sample:end_sample] + + chunked_audios.append( + Audio( + waveform=chunked_waveform, + sampling_rate=audio.sampling_rate, + metadata=audio.metadata.copy(), + orig_path_or_id=audio.orig_path_or_id, + ) + ) + return chunked_audios + + +def extract_segments(data: List[Tuple[Audio, List[Tuple[float, float]]]]) -> List[List[Audio]]: + """Extracts segments from an audio file. + + Args: + data: List of tuples containing an Audio object and a list of tuples with start + and end (in seconds) for chunking. + + Returns: + List of lists of Audios that have been chunked based on the provided timestamps. + """ + extracted_segments = [] + for audio, timestamps in data: + segments_data = [(audio, ts) for ts in timestamps] + single_audio_segments = chunk_audios(segments_data) + extracted_segments.append(single_audio_segments) + return extracted_segments + + +def pad_audios(audios: List[Audio], desired_samples: int) -> List[Audio]: + """Pads the audio segment to the desired length. + + Args: + audios: The list of audio objects to be padded. + desired_samples: The desired length (in samples) for the padded audio. + + Returns: + A new Audio object with the waveform padded to the desired length. + """ + padded_audios = [] + for audio in audios: + current_samples = audio.waveform.shape[1] + + if current_samples >= desired_samples: + return [audio] + + padding_needed = desired_samples - current_samples + padded_waveform = torch.nn.functional.pad(audio.waveform, (0, padding_needed)) + padded_audio = Audio( + waveform=padded_waveform, + sampling_rate=audio.sampling_rate, + metadata=audio.metadata.copy(), + orig_path_or_id=audio.orig_path_or_id, + ) + padded_audios.append(padded_audio) + return padded_audios + + +def evenly_segment_audios( + audios: List[Audio], segment_length: float, pad_last_segment: bool = True +) -> List[List[Audio]]: + """Segments multiple audio files into evenly sized segments with optional padding for the last segment. + + Args: + audios: The list of Audio objects to be segmented. + segment_length: The desired length of each segment in seconds. + pad_last_segment: Whether to pad the last segment to the full segment length (default is False). + + Returns: + List of Audio objects that have been segmented. + """ + audios_and_segment_timestamps = [] + for i, audio in enumerate(audios): + total_duration = audio.waveform.shape[1] / audio.sampling_rate + segment_samples = int(segment_length * audio.sampling_rate) + + # Create a list of tuples with start and end times for each segment + timestamps = [ + (i * segment_length, (i + 1) * segment_length) for i in range(int(total_duration // segment_length)) + ] + if total_duration % segment_length != 0: + timestamps.append((total_duration - (total_duration % segment_length), total_duration)) + audios_and_segment_timestamps.append((audio, timestamps)) + + audio_segment_lists = extract_segments([(audio, timestamps)]) + + for i, audio_segment_list in enumerate(audio_segment_lists): + if pad_last_segment and len(audio_segment_list) > 0: + last_segment = audio_segment_list[-1] + if last_segment.waveform.shape[1] < segment_samples: + audio_segment_lists[i][-1] = pad_audios([last_segment], segment_samples)[0] + + return audio_segment_lists + + +def concatenate_audios(audios: List[Audio]) -> Audio: + """Concatenates all audios in the list, ensuring they have the same sampling rate and shape. + + Args: + audios: List of Audio objects to concatenate. + + Returns: + A single Audio object that is the concatenation of all input audios. + + Raises: + ValueError: If the audios do not all have the same sampling rate or shape. + """ + if not audios: + raise ValueError("The input list is empty. Please provide a list with at least one Audio object.") + + sampling_rate = audios[0].sampling_rate + num_channels = audios[0].waveform.shape[0] + + for audio in audios: + if audio.sampling_rate != sampling_rate: + raise ValueError("All audios must have the same sampling rate to concatenate.") + if audio.waveform.shape[0] != num_channels: + raise ValueError("All audios must have the same number of channels (mono or stereo) to concatenate.") + + concatenated_waveform = torch.cat([audio.waveform for audio in audios], dim=1) + + # TODO: do we want to concatenate metadata? TBD + + return Audio( + waveform=concatenated_waveform, + sampling_rate=sampling_rate, + ) diff --git a/src/senselab/audio/tasks/speaker_diarization/__init__.py b/src/senselab/audio/tasks/speaker_diarization/__init__.py new file mode 100644 index 00000000..5dc59962 --- /dev/null +++ b/src/senselab/audio/tasks/speaker_diarization/__init__.py @@ -0,0 +1 @@ +"""This module contains the speaker diarization API for senselab.""" diff --git a/src/senselab/audio/tasks/speaker_diarization/api.py b/src/senselab/audio/tasks/speaker_diarization/api.py new file mode 100644 index 00000000..125b78f8 --- /dev/null +++ b/src/senselab/audio/tasks/speaker_diarization/api.py @@ -0,0 +1,51 @@ +"""This module implements some utilities for the speaker diarization task. + +# TODO: add computing DER and more evaluation metrics +""" + +from typing import List, Optional + +import pydra + +from senselab.audio.data_structures.audio import Audio +from senselab.audio.tasks.speaker_diarization.pyannote import diarize_audios_with_pyannote +from senselab.utils.data_structures.device import DeviceType +from senselab.utils.data_structures.model import PyannoteAudioModel, SenselabModel +from senselab.utils.data_structures.script_line import ScriptLine + + +def diarize_audios( + audios: List[Audio], + model: SenselabModel, + num_speakers: Optional[int] = None, + min_speakers: Optional[int] = None, + max_speakers: Optional[int] = None, + device: Optional[DeviceType] = None, +) -> List[List[ScriptLine]]: + """Diarizes all audios using the given model. + + Args: + audios (List[Audio]): The list of audio objects to be diarized. + model (SenselabModel): The model used for diarization. + device (Optional[DeviceType]): The device to run the model on (default is None). + num_speakers (Optional[int]): The number of speakers (default is None). + min_speakers (Optional[int]): The minimum number of speakers (default is None). + max_speakers (Optional[int]): The maximum number of speakers (default is None). + + Returns: + List[List[ScriptLine]]: The list of script lines with speaker labels. + """ + if isinstance(model, PyannoteAudioModel): + return diarize_audios_with_pyannote( + audios=audios, + model=model, + device=device, + num_speakers=num_speakers, + min_speakers=min_speakers, + max_speakers=max_speakers, + ) + else: + raise NotImplementedError("Only Pyannote models are supported for now.") + + +diarize_audios_pt = pydra.mark.task(diarize_audios) diff --git a/src/senselab/audio/tasks/speaker_diarization/pyannote.py b/src/senselab/audio/tasks/speaker_diarization/pyannote.py new file mode 100644 index 00000000..7fdba011 --- /dev/null +++ b/src/senselab/audio/tasks/speaker_diarization/pyannote.py @@ -0,0 +1,109 @@ +"""This module implements the Pyannote Diarization task.""" + +from typing import Dict, List, Optional, Union + +import torch +from pyannote.audio import Pipeline +from pyannote.core import Annotation + +from senselab.audio.data_structures.audio import Audio +from senselab.utils.data_structures.device import DeviceType, _select_device_and_dtype +from senselab.utils.data_structures.model import PyannoteAudioModel +from senselab.utils.data_structures.script_line import ScriptLine + + +class PyannoteDiarization: + """A factory for managing Pyannote Diarization pipelines.""" + + _pipelines: Dict[str, Pipeline] = {} + + @classmethod + def _get_pyannote_diarization_pipeline( + cls, + model: PyannoteAudioModel, + device: Union[DeviceType, None], + ) -> Pipeline: + """Get or create a Pyannote Diarization pipeline. + + Args: + model (PyannoteAudioModel): The Pyannote model. + device (DeviceType): The device to run the model on. + + Returns: + Pipeline: The diarization pipeline. + """ + device, _ = _select_device_and_dtype( + user_preference=device, compatible_devices=[DeviceType.CUDA, DeviceType.CPU] + ) + key = f"{model.path_or_uri}-{model.revision}-{device}" + if key not in cls._pipelines: + pipeline = Pipeline.from_pretrained(checkpoint_path=f"{model.path_or_uri}@{model.revision}").to( + torch.device(device.value) + ) + cls._pipelines[key] = pipeline + return cls._pipelines[key] + + +def diarize_audios_with_pyannote( + audios: List[Audio], + model: PyannoteAudioModel = PyannoteAudioModel(path_or_uri="pyannote/speaker-diarization-3.1", revision="main"), + device: Optional[DeviceType] = None, + num_speakers: Optional[int] = None, + min_speakers: Optional[int] = None, + max_speakers: Optional[int] = None, +) -> List[List[ScriptLine]]: + """Diarizes a list of audio files using the Pyannote speaker diarization model. + + Args: + audios (List[Audio]): A list of audio files. + model (PyannoteAudioModel): The model to use for diarization. + device (Optional[DeviceType]): The device to use for diarization. + num_speakers (Optional[int]): Number of speakers, when known. + min_speakers (Optional[int]): Minimum number of speakers. Has no effect when `num_speakers` is provided. + max_speakers (Optional[int]): Maximum number of speakers. Has no effect when `num_speakers` is provided. + + Returns: + List[ScriptLine]: A list of ScriptLine objects containing the diarization results. + """ + + def _annotation_to_script_lines(annotation: Annotation) -> List[ScriptLine]: + """Convert a Pyannote annotation to a list of script lines. + + Args: + annotation (Annotation): The Pyannote annotation object. + + Returns: + List[ScriptLine]: A list of script lines. + """ + diarization_list: List[ScriptLine] = [] + for segment, _, label in annotation.itertracks(yield_label=True): + diarization_list.append(ScriptLine(speaker=label, start=segment.start, end=segment.end)) + return diarization_list + + # 16khz comes from the model cards of pyannote/speaker-diarization-3.1 + expected_sample_rate = 16000 + + # Check that all audio objects have the correct sampling rate + for audio in audios: + if audio.waveform.shape[0] != 1: + raise ValueError(f"Audio waveform must be mono (1 channel), but got {audio.waveform.shape[0]} channels") + if audio.sampling_rate != expected_sample_rate: + raise ValueError( + "Audio sampling rate " + + str(audio.sampling_rate) + + " does not match expected " + + str(expected_sample_rate) + ) + + pipeline = PyannoteDiarization._get_pyannote_diarization_pipeline(model=model, device=device) + results: List[List[ScriptLine]] = [] + for audio in audios: + diarization = pipeline( + {"waveform": audio.waveform, "sample_rate": audio.sampling_rate}, + num_speakers=num_speakers, + min_speakers=min_speakers, + max_speakers=max_speakers, + ) + results.append(_annotation_to_script_lines(diarization)) + + return results diff --git a/src/senselab/audio/tasks/speaker_embeddings/__init__.py b/src/senselab/audio/tasks/speaker_embeddings/__init__.py new file mode 100644 index 00000000..868263a0 --- /dev/null +++ b/src/senselab/audio/tasks/speaker_embeddings/__init__.py @@ -0,0 +1,3 @@ +""".. include:: ./doc.md""" # noqa: D415 + +from .api import extract_speaker_embeddings_from_audios # noqa: F401 diff --git a/src/senselab/audio/tasks/speaker_embeddings/api.py b/src/senselab/audio/tasks/speaker_embeddings/api.py new file mode 100644 index 00000000..d5f25d18 --- /dev/null +++ b/src/senselab/audio/tasks/speaker_embeddings/api.py @@ -0,0 +1,44 @@ +"""This module implements some utilities to extract speaker embeddings from an audio clip.""" + +from typing import List, Optional + +import torch + +from senselab.audio.data_structures.audio import Audio +from senselab.audio.tasks.speaker_embeddings.speechbrain import SpeechBrainEmbeddings +from senselab.utils.data_structures.device import DeviceType +from senselab.utils.data_structures.model import SenselabModel, SpeechBrainModel + + +def extract_speaker_embeddings_from_audios( + audios: List[Audio], + model: SenselabModel = SpeechBrainModel(path_or_uri="speechbrain/spkrec-ecapa-voxceleb", revision="main"), + device: Optional[DeviceType] = None, +) -> List[torch.Tensor]: + """Compute the speaker embedding of audio signals. + + Args: + audios (List[Audio]): A list of Audio objects containing the audio signals and their properties. + model (SpeechBrainModel): The model used to compute the embeddings + (default is "speechbrain/spkrec-ecapa-voxceleb"). + device (Optional[DeviceType]): The device to run the model on (default is None). + + Returns: + List[torch.Tensor]: A list of 1d tensors containing the speaker embeddings for each audio file. + + Raises: + NotImplementedError: If the model is not a Hugging Face model. + + Examples: + >>> audios = [Audio.from_filepath("sample.wav")] + >>> model = SpeechBrainModel(path_or_uri="speechbrain/spkrec-ecapa-voxceleb", revision="main") + >>> embeddings = extract_speaker_embeddings_from_audios(audios, model, device=DeviceType.CUDA) + >>> print(embeddings[0].shape) + torch.Size([192]) + """ + if isinstance(model, SpeechBrainModel): + return SpeechBrainEmbeddings.extract_speechbrain_speaker_embeddings_from_audios( + audios=audios, model=model, device=device + ) + else: + raise NotImplementedError("The specified model is not supported for now.") diff --git a/src/senselab/audio/tasks/speaker_embeddings/doc.md b/src/senselab/audio/tasks/speaker_embeddings/doc.md new file mode 100644 index 00000000..27728548 --- /dev/null +++ b/src/senselab/audio/tasks/speaker_embeddings/doc.md @@ -0,0 +1,29 @@ +# Speech to text evaluation + + + + + +## Overview + +Speaker embeddings are fixed-dimensional vector representations that capture the unique characteristics of a speaker's +voice, allowing for tasks such as speaker identification, verification, and diarization. + +Speaker embedding extraction is a crucial task in speaker recognition systems. It involves transforming variable-length +audio signals into fixed-size vector representations that encapsulate speaker-specific information while being robust +to variations in speech content, background noise, and recording conditions. + +## Model Architecture: +The default model used in this module (speechbrain/spkrec-ecapa-voxceleb) is based on the ECAPA-TDNN architecture, +which has shown strong performance across various speaker recognition tasks. +Other supported models include ResNet TDNN (speechbrain/spkrec-resnet-voxceleb) and +xvector (speechbrain/spkrec-xvect-voxceleb). + +**Note**: Performance can vary significantly depending on the specific dataset, task, and evaluation protocol used. +Always refer to the most recent literature for up-to-date benchmarks. + +## Learn more: +- [SpeechBrain](https://speechbrain.github.io/) +- [ECAPA-TDNN](https://arxiv.org/abs/2005.07143) +- [ResNet TDNN](https://doi.org/10.1016/j.csl.2019.101026) +- [xvector](https://doi.org/10.21437/Odyssey.2018-15) diff --git a/src/senselab/audio/tasks/speaker_embeddings/speechbrain.py b/src/senselab/audio/tasks/speaker_embeddings/speechbrain.py new file mode 100644 index 00000000..cb3b4bc5 --- /dev/null +++ b/src/senselab/audio/tasks/speaker_embeddings/speechbrain.py @@ -0,0 +1,104 @@ +"""Provides a factory for extracting speaker embeddings from a list of audios using speechbrain.""" + +from typing import Dict, List, Optional + +import torch +import torch.nn.functional as F +from speechbrain.inference.speaker import EncoderClassifier + +from senselab.audio.data_structures.audio import Audio +from senselab.utils.data_structures.device import DeviceType, _select_device_and_dtype +from senselab.utils.data_structures.model import SpeechBrainModel + + +class SpeechBrainEmbeddings: + """A factory for extracting speaker embeddings using speechbrain models.""" + + _models: Dict[str, EncoderClassifier] = {} + + @classmethod + def _get_speechbrain_model( + cls, + model: SpeechBrainModel, + device: Optional[DeviceType] = None, + ) -> EncoderClassifier: + """Get or create a SpeechBrain model. + + Args: + model (SpeechBrainModel): The SpeechBrain model. + device (Optional[DeviceType]): The device to run the model on. + Only CPU and CUDA are supported. + + Returns: + EncoderClassifier: The SpeechBrain model. + + Todo: + - Adding savedir for storing models + """ + device, _ = _select_device_and_dtype( + user_preference=device, compatible_devices=[DeviceType.CUDA, DeviceType.CPU] + ) + key = f"{model.path_or_uri}-{model.revision}-{device.value}" + if key not in cls._models: + cls._models[key] = EncoderClassifier.from_hparams( + source=model.path_or_uri, run_opts={"device": device.value} + ) + return cls._models[key] + + @classmethod + def extract_speechbrain_speaker_embeddings_from_audios( + cls, + audios: List[Audio], + model: SpeechBrainModel = SpeechBrainModel(path_or_uri="speechbrain/spkrec-ecapa-voxceleb", revision="main"), + device: Optional[DeviceType] = None, + ) -> List[torch.Tensor]: + """Compute the speaker embeddings of audio signals. + + Args: + audios (List[Audio]): A list of Audio objects containing the audio signals and their properties. + model (SpeechBrainModel): The model used to compute the embeddings + (default is "speechbrain/spkrec-ecapa-voxceleb"). + device (Optional[DeviceType]): The device to run the model on (default is None). + Only CPU and CUDA are supported. + + Returns: + List[torch.Tensor]: A list of tensors containing the speaker embeddings for each audio file. + + Todo: + - Optimizing the computation by working in batches + - Double-checking the input size of classifier.encode_batch + """ + classifier = cls._get_speechbrain_model(model=model, device=device) + # 16khz comes from the model cards of ecapa-tdnn, resnet, and xvector + expected_sample_rate = 16000 + + # Check that all audio objects have the correct sampling rate + for audio in audios: + if audio.waveform.shape[0] != 1: + raise ValueError(f"Audio waveform must be mono (1 channel), but got {audio.waveform.shape[0]} channels") + if audio.sampling_rate != expected_sample_rate: + raise ValueError( + "Audio sampling rate " + + str(audio.sampling_rate) + + " does not match expected " + + str(expected_sample_rate) + ) + + # Calculate relative lengths + lengths = torch.tensor([audio.waveform.shape[1] for audio in audios]) + max_len = torch.max(lengths) + + wav_lens = lengths.float() / max_len + + # Stack audio waveforms for batch processing, zero-padding to ensure equal length + waveforms = torch.stack( + [F.pad(audio.waveform, (0, max_len - audio.waveform.shape[1])) for audio in audios] + ).squeeze() + + # Compute embeddings in a batch + embeddings_batch = classifier.encode_batch(waveforms, wav_lens) + + # Split the batch embeddings into a list of individual embeddings + embeddings = [embedding.squeeze() for embedding in embeddings_batch] + + return embeddings diff --git a/src/senselab/audio/tasks/speaker_verification/__init__.py b/src/senselab/audio/tasks/speaker_verification/__init__.py new file mode 100644 index 00000000..941cfc0b --- /dev/null +++ b/src/senselab/audio/tasks/speaker_verification/__init__.py @@ -0,0 +1 @@ +"""Verifies whether two audio segments belong to the same speaker.""" diff --git a/src/senselab/audio/tasks/speaker_verification/speaker_verification.py b/src/senselab/audio/tasks/speaker_verification/speaker_verification.py new file mode 100644 index 00000000..5f739ff8 --- /dev/null +++ b/src/senselab/audio/tasks/speaker_verification/speaker_verification.py @@ -0,0 +1,61 @@ +"""Audio Processing and Speaker Verification Module. + +This module provides functions for resampling audio using an IIR filter and +verifying if two audio samples or files are from the same speaker using a +specified model. +""" + +from typing import List, Optional, Tuple + +from torch.nn.functional import cosine_similarity + +from senselab.audio.data_structures.audio import Audio +from senselab.audio.tasks.speaker_embeddings.speechbrain import SpeechBrainEmbeddings +from senselab.utils.data_structures.device import DeviceType, _select_device_and_dtype +from senselab.utils.data_structures.model import SpeechBrainModel + +TRAINING_SAMPLE_RATE = 16000 # spkrec-ecapa-voxceleb trained on 16kHz audio + + +def verify_speaker( + audios: List[Tuple[Audio, Audio]], + model: SpeechBrainModel = SpeechBrainModel(path_or_uri="speechbrain/spkrec-ecapa-voxceleb", revision="main"), + device: Optional[DeviceType] = None, + threshold: float = 0.25, +) -> List[Tuple[float, bool]]: + """Verifies if two audio samples are from the same speaker. + + Args: + audios (List[Tuple[Audio, Audio]]): A list of tuples, where each tuple contains + two audio samples to be compared. + model (SpeechBrainModel, optional): The model for speaker verification. + device (DeviceType, optional): The device to run the model on. Defaults to CPU. + threshold (float, optional): The threshold to determine same speaker. + + Returns: + List[Tuple[float, bool]]: A list of tuples containing the verification score and + the prediction for each pair of audio samples. The + verification score is a float indicating the similarity + between the two samples, and the prediction is a boolean + indicating if the two samples are from the same speaker. + """ + device = _select_device_and_dtype(compatible_devices=[DeviceType.CPU, DeviceType.CUDA])[0] + + scores_and_predictions = [] + for audio1, audio2 in audios: + if audio1.sampling_rate != TRAINING_SAMPLE_RATE: + raise ValueError(f"{model.path_or_uri} trained on {TRAINING_SAMPLE_RATE} \ + sample audio, but audio1 has sample rate {audio1.sampling_rate}.") + if audio2.sampling_rate != TRAINING_SAMPLE_RATE: + raise ValueError(f"{model.path_or_uri} trained on {TRAINING_SAMPLE_RATE} \ + sample audio, but audio2 has sample rate {audio2.sampling_rate}.") + + embeddings = SpeechBrainEmbeddings.extract_speechbrain_speaker_embeddings_from_audios( + audios=[audio1, audio2], model=model, device=device + ) + embedding1, embedding2 = embeddings + similarity = cosine_similarity(embedding1.unsqueeze(0), embedding2.unsqueeze(0)) + score = similarity.mean().item() + prediction = score > threshold + scores_and_predictions.append((score, prediction)) + return scores_and_predictions diff --git a/src/senselab/audio/tasks/speech_enhancement/__init__.py b/src/senselab/audio/tasks/speech_enhancement/__init__.py new file mode 100644 index 00000000..b03217f8 --- /dev/null +++ b/src/senselab/audio/tasks/speech_enhancement/__init__.py @@ -0,0 +1,3 @@ +""".. include:: ./doc.md""" # noqa: D415 + +from .api import enhance_audios # noqa: F401 diff --git a/src/senselab/audio/tasks/speech_enhancement/api.py b/src/senselab/audio/tasks/speech_enhancement/api.py new file mode 100644 index 00000000..1d956572 --- /dev/null +++ b/src/senselab/audio/tasks/speech_enhancement/api.py @@ -0,0 +1,30 @@ +"""This module provides the API for the senselab speech enhancement task.""" + +from typing import List, Optional + +from senselab.audio.data_structures.audio import Audio +from senselab.audio.tasks.speech_enhancement.speechbrain import SpeechBrainEnhancer +from senselab.utils.data_structures.device import DeviceType +from senselab.utils.data_structures.model import SenselabModel, SpeechBrainModel + + +def enhance_audios( + audios: List[Audio], + model: SenselabModel = SpeechBrainModel(path_or_uri="speechbrain/sepformer-wham16k-enhancement", revision="main"), + device: Optional[DeviceType] = None, +) -> List[Audio]: + """Enhances all audios using the given model. + + Args: + audios (List[Audio]): The list of audio objects to be enhanced. + model (SenselabModel): The model used for enhancement + (default is "speechbrain/sepformer-wham16k-enhancement"). + device (Optional[DeviceType]): The device to run the model on (default is None). + + Returns: + List[Audio]: The list of enhanced audio objects. + """ + if isinstance(model, SpeechBrainModel): + return SpeechBrainEnhancer.enhance_audios_with_speechbrain(audios=audios, model=model, device=device) + else: + raise NotImplementedError("Only SpeechBrain models are supported for now.") diff --git a/src/senselab/audio/tasks/speech_enhancement/doc.md b/src/senselab/audio/tasks/speech_enhancement/doc.md new file mode 100644 index 00000000..3478d882 --- /dev/null +++ b/src/senselab/audio/tasks/speech_enhancement/doc.md @@ -0,0 +1,26 @@ +# Speech enhancement + + + + + +## Task Overview +Speech Enhancement is a signal processing task that involves improving the quality of speech signals captured under noisy or degraded conditions. The goal of speech enhancement is to make speech signals clearer, more intelligible, and more pleasant to listen to, which can be used for various applications such as voice recognition, teleconferencing, and hearing aids. + + +## Models +By now, ```senselab``` supports all ```speechbrain``` models for speech enhancement. These include: +- [SepFormer](https://huggingface.co/speechbrain/sepformer-wham16k-enhancement) for audio clips sampled at 16KHz +- [SepFormer](https://huggingface.co/speechbrain/.sepformer-wham-enhancement) for audio clips sampled at 8KHz. +In the future, more models will be integrated. + + +## Evaluation +### Metrics + +Objective evaluation involves comparing speech enhanced outputs across different downstream tasks: + +- Using an automatic speaker verification tool to determine if the original speaker and the enhanced speaker can be distinguished from each other. +- Ensuring the intelligibility of speech content using an automatic speech recognition system to verify that the content remains unchanged. +- Assessing the preservation of the original speech's emotion after speech enhancement. +- ...more... diff --git a/src/senselab/audio/tasks/speech_enhancement/speechbrain.py b/src/senselab/audio/tasks/speech_enhancement/speechbrain.py new file mode 100644 index 00000000..6ee30ab5 --- /dev/null +++ b/src/senselab/audio/tasks/speech_enhancement/speechbrain.py @@ -0,0 +1,110 @@ +"""This module provides the Speechbrain interface for speech enhancement.""" + +import time +from typing import Dict, List, Optional, Tuple + +import torch +from speechbrain.inference.separation import SepformerSeparation as separator + +from senselab.audio.data_structures.audio import Audio +from senselab.utils.data_structures.device import DeviceType, _select_device_and_dtype +from senselab.utils.data_structures.logging import logger +from senselab.utils.data_structures.model import SpeechBrainModel + + +class SpeechBrainEnhancer: + """A factory for managing SpeechBrain enhancement pipelines.""" + + _models: Dict[str, separator] = {} + + @classmethod + def _get_speechbrain_model( + cls, + model: SpeechBrainModel, + device: Optional[DeviceType] = None, + ) -> Tuple[separator, DeviceType, torch.dtype]: + """Get or create a SpeechBrain enhancement model. + + Args: + model (SpeechBrainModel): The SpeechBrain model. + device (Optional[DeviceType]): The device to run the model on. + Only CPU and CUDA are supported. + + Returns: + separator: The SpeechBrain enhancement model. + device: The device used for the model. + dtype: The dtype used for the model. + """ + device, dtype = _select_device_and_dtype( + user_preference=device, compatible_devices=[DeviceType.CUDA, DeviceType.CPU] + ) + key = f"{model.path_or_uri}-{model.revision}-{device.value}" + if key not in cls._models: + cls._models[key] = separator.from_hparams(source=model.path_or_uri, run_opts={"device": device.value}) + return cls._models[key], device, dtype + + @classmethod + def enhance_audios_with_speechbrain( + cls, + audios: List[Audio], + model: SpeechBrainModel = SpeechBrainModel( + path_or_uri="speechbrain/sepformer-wham16k-enhancement", revision="main" + ), + device: Optional[DeviceType] = None, + batch_size: int = 16, + ) -> List[Audio]: + """Enhances all audio samples using the given speechbrain model. + + Args: + audios (List[Audio]): The list of audio objects to be enhanced. + model (SpeechBrainModel): The SpeechBrain model used for enhancement. + device (Optional[DeviceType]): The device to run the model on (default is None). + batch_size (int): The size of batches to use when processing on a GPU. + + Returns: + List[Audio]: The list of enhanced audio objects. + """ + # Take the start time of the model initialization + start_time_model = time.time() + enhancer, device, dtype = cls._get_speechbrain_model(model=model, device=device) + end_time_model = time.time() + elapsed_time_model = end_time_model - start_time_model + logger.info(f"Time taken to initialize the speechbrain model: {elapsed_time_model:.2f} seconds") + + expected_sample_rate = enhancer.hparams.sample_rate + + for audio in audios: + if audio.waveform.shape[0] != 1: + raise ValueError(f"Audio waveform must be mono (1 channel), but got {audio.waveform.shape[0]} channels") + if audio.sampling_rate != expected_sample_rate: + raise ValueError( + "Audio sampling rate " + + str(audio.sampling_rate) + + " does not match expected " + + str(expected_sample_rate) + ) + + # Take the start time of the enhancement + start_time_enhancement = time.time() + # Check that all audio objects have the correct sampling rate + for audio in audios: + if audio.waveform.shape[0] != 1: + raise ValueError(f"Audio waveform must be mono (1 channel), but got {audio.waveform.shape[0]} channels") + if audio.sampling_rate != expected_sample_rate: + raise ValueError( + "Audio sampling rate " + + str(audio.sampling_rate) + + " does not match expected " + + str(expected_sample_rate) + ) + + # Enhance waveforms in a batch + enhanced_waveform = enhancer.separate_batch(audio.waveform) + + audio.waveform = enhanced_waveform.reshape(1, -1) + + end_time_enhancement = time.time() + elapsed_time_enhancement = end_time_enhancement - start_time_enhancement + logger.info(f"Time taken for enhancing the audios: {elapsed_time_enhancement:.2f} seconds") + + return audios diff --git a/src/senselab/audio/tasks/speech_to_text/__init__.py b/src/senselab/audio/tasks/speech_to_text/__init__.py new file mode 100644 index 00000000..dc9a118b --- /dev/null +++ b/src/senselab/audio/tasks/speech_to_text/__init__.py @@ -0,0 +1,3 @@ +""".. include:: ./doc.md""" # noqa: D415 + +from .api import transcribe_audios # noqa: F401 diff --git a/src/senselab/audio/tasks/speech_to_text/api.py b/src/senselab/audio/tasks/speech_to_text/api.py new file mode 100644 index 00000000..2ddfb1b7 --- /dev/null +++ b/src/senselab/audio/tasks/speech_to_text/api.py @@ -0,0 +1,48 @@ +"""This module represents the API for the speech-to-text task within the senselab package. + +Currently, it supports only Hugging Face models, with plans to include more in the future. +Users can specify the audio clips to transcribe, the ASR model, the language, +the preferred device, and the model-specific parameters, and senselab handles the rest. +""" + +from typing import Any, List, Optional + +from senselab.audio.data_structures.audio import Audio +from senselab.audio.tasks.speech_to_text.huggingface import HuggingFaceASR +from senselab.utils.data_structures.device import DeviceType +from senselab.utils.data_structures.language import Language +from senselab.utils.data_structures.model import HFModel, SenselabModel +from senselab.utils.data_structures.script_line import ScriptLine + + +def transcribe_audios( + audios: List[Audio], + model: SenselabModel, + language: Optional[Language] = None, + device: Optional[DeviceType] = None, + **kwargs: Any, # noqa: ANN401 +) -> List[ScriptLine]: + """Transcribes all audios using the given model. + + Args: + audios (List[Audio]): The list of audio objects to be transcribed. + model (SenselabModel): The model used for transcription. + language (Optional[Language]): The language of the audio (default is None). + device (Optional[DeviceType]): The device to run the model on (default is None). + **kwargs: Additional keyword arguments to pass to the transcription function. + + Returns: + List[ScriptLine]: The list of script lines. + + Todo: + - Include more models (e.g., speechbrain, nvidia) + """ + try: + if isinstance(model, HFModel): + return HuggingFaceASR.transcribe_audios_with_transformers( + audios=audios, model=model, language=language, device=device, **kwargs + ) + else: + raise NotImplementedError("Only Hugging Face models are supported for now.") + except TypeError as e: + raise TypeError(e) # noqa: W0707 diff --git a/src/senselab/audio/tasks/speech_to_text/doc.md b/src/senselab/audio/tasks/speech_to_text/doc.md new file mode 100644 index 00000000..0b794aee --- /dev/null +++ b/src/senselab/audio/tasks/speech_to_text/doc.md @@ -0,0 +1,77 @@ +# Speech to text + + + + + +## Task Overview +Speech-to-Text (STT), also known as Automatic Speech Recognition (ASR), is the process of converting spoken language into written text. This technology has a wide range of applications, including transcription services and voice user interfaces. + +Notably, certain models can provide word- or sentence-level timestamps along with the transcribed text, making them ideal for generating subtitles. Additionally, some models are multilingual, and some of them leverage language identification blocks to enhance performance. + + +## Models +A variety of models are supported by ```senselab``` for ASR tasks. They can be explored on the [Hugging Face Hub](https://huggingface.co/models?library=transformers&pipeline_tag=automatic-speech-recognition&sort=downloads). Each model varies in performance, size, license, language support, and more. Performance may also vary depending on who is the speaker in the processed audio clips (there may be differences in terms of age, dialects, disfluencies). It is recommended to review the model card for each model before use. Also, always refer to the most recent literature for an informed decision. + +Popular models include: +- **Whisper** + - [tiny](https://huggingface.co/openai/whisper-tiny) + - [small](https://huggingface.co/openai/whisper-small) + - [medium](https://huggingface.co/openai/whisper-medium) + - [large](https://huggingface.co/openai/whisper-large-v3) + +- **Massively Multilingual Speech** + - [MMS 1b](https://huggingface.co/facebook/mms-1b-all) + +- **Massively Multilingual and Multimodal Machine Translation** + - [Seamless small](https://huggingface.co/facebook/seamless-m4t-unity-small-s2t) + - [Seamless medium](https://huggingface.co/facebook/hf-seamless-m4t-medium) + - [Seamless large](https://huggingface.co/facebook/seamless-m4t-v2-large) + +## Evaluation +### Metrics +The primary evaluation metric for ASR systems is the Word Error Rate (WER). WER is calculated as: + + WER = (Substitutions + Insertions + Deletions) / Number of words in the reference + +where: +- **Substitutions**: Incorrect words. +- **Insertions**: Extra words added. +- **Deletions**: Words omitted. + +Other important metrics include: +- **Character Error Rate (CER)** +- **Match Error Rate (MER)** +- **Word Information Lost (WIL)** +- **Word Information Preserved (WIP)** + +For detailed information on these metrics, refer to the [speech to text evaluation module](speech_to_text_evaluation). + +### Datasets (English Speech Benchmark - ESB) +The following table lists the datasets included in the [English Speech Benchmark (ESB)](https://arxiv.org/abs/2210.13352), which are generally used for evaluating ASR models in English: + +| Dataset | Domain | Speaking Style | Train (h) | Dev (h) | Test (h) | Transcriptions | License | +|----------------|-------------------------|---------------------|-----------|---------|----------|------------------------|--------------------| +| LibriSpeech | Audiobook | Narrated | 960 | 11 | 11 | Normalized | CC-BY-4.0 | +| Common Voice 9 | Wikipedia | Narrated | 1409 | 27 | 27 | Punctuated & Cased | CC0-1.0 | +| VoxPopuli | European Parliament | Oratory | 523 | 5 | 5 | Punctuated | CC0 | +| TED-LIUM | TED talks | Oratory | 454 | 2 | 3 | Normalized | CC-BY-NC-ND 3.0 | +| GigaSpeech | Audiobook, podcast, YouTube | Narrated, spontaneous | 2500 | 12 | 40 | Punctuated | Apache-2.0 | +| SPGISpeech | Financial meetings | Oratory, spontaneous| 4900 | 100 | 100 | Punctuated & Cased | User Agreement | +| Earnings-22 | Financial meetings | Oratory, spontaneous| 105 | 5 | 5 | Punctuated & Cased | CC-BY-SA-4.0 | +| AMI | Meetings | Spontaneous | 78 | 9 | 9 | Punctuated & Cased | CC-BY-4.0 | + +For more details on these datasets and how models are evaluated to obtain the ESB score, refer to the ESB paper. +Note that this list of datasets is not exhaustive. If you are interested in benchmarking models in different languages or under specific conditions, consult the relevant literature. + +### Benchmark +The [🤗 Open ASR Leaderboard](https://huggingface.co/spaces/hf-audio/open_asr_leaderboard) ranks and evaluates speech recognition models available on the Hugging Face Hub. The leaderboard uses the datasets included in the ESB paper to obtain robust evaluation scores for each model. The ESB score is a macro-average of the WER scores across the ESB datasets, providing a comprehensive indication of a model's performance across various domains and conditions. + +## Notes +- It is possible to fine-tune foundational speech models on a specific language without requiring large amounts of data. A detailed blog post on how to fine-tune a pre-trained Whisper checkpoint on labeled data for ASR can be found [here](https://huggingface.co/blog/fine-tune-whisper). + +Learn more: +- [Whisper](https://arxiv.org/abs/2212.04356) +- [MMS](https://arxiv.org/abs/2305.13516) +- [Seamless MMS](https://arxiv.org/abs/2308.11596) +- [Wav2vec 2.0](https://arxiv.org/abs/2006.11477) diff --git a/src/senselab/audio/tasks/speech_to_text/huggingface.py b/src/senselab/audio/tasks/speech_to_text/huggingface.py new file mode 100644 index 00000000..5d77d04a --- /dev/null +++ b/src/senselab/audio/tasks/speech_to_text/huggingface.py @@ -0,0 +1,174 @@ +"""This module provides a factory for managing Hugging Face ASR pipelines. + +To ensure correct functionality, call `transcribe_audios_with_transformers` serially or, +if you need to process multiple audios in parallel, pass the entire list of audios to the +function at once, rather than calling the function with one audio at a time. +""" + +import time +from typing import Any, Dict, List, Optional + +from transformers import pipeline + +from senselab.audio.data_structures.audio import Audio +from senselab.utils.data_structures.device import DeviceType, _select_device_and_dtype +from senselab.utils.data_structures.language import Language +from senselab.utils.data_structures.logging import logger +from senselab.utils.data_structures.model import HFModel +from senselab.utils.data_structures.script_line import ScriptLine + + +class HuggingFaceASR: + """A factory for managing Hugging Face ASR pipelines.""" + + _pipelines: Dict[str, pipeline] = {} + + @classmethod + def _get_hf_asr_pipeline( + cls, + model: HFModel, + return_timestamps: Optional[str], + max_new_tokens: int, + chunk_length_s: int, + batch_size: int, + device: Optional[DeviceType] = None, + ) -> pipeline: + """Get or create a Hugging Face ASR pipeline. + + Args: + model (HFModel): The Hugging Face model. + return_timestamps (Optional[str]): The level of timestamp details. + max_new_tokens (int): The maximum number of new tokens. + chunk_length_s (int): The length of audio chunks in seconds. + batch_size (int): The batch size for processing. + device (Optional[DeviceType]): The device to run the model on. + + Returns: + pipeline: The ASR pipeline. + """ + device, torch_dtype = _select_device_and_dtype( + user_preference=device, compatible_devices=[DeviceType.CUDA, DeviceType.CPU] + ) + key = ( + f"{model.path_or_uri}-{model.revision}-{return_timestamps}-" + f"{max_new_tokens}-{chunk_length_s}-{batch_size}-{device.value}" + ) + if key not in cls._pipelines: + cls._pipelines[key] = pipeline( + "automatic-speech-recognition", + model=model.path_or_uri, + revision=model.revision, + return_timestamps=return_timestamps, + max_new_tokens=max_new_tokens, + chunk_length_s=chunk_length_s, + batch_size=batch_size, + device=device.value, + torch_dtype=torch_dtype, + ) + return cls._pipelines[key] + + @classmethod + def transcribe_audios_with_transformers( + cls, + audios: List[Audio], + model: HFModel = HFModel(path_or_uri="openai/whisper-tiny"), + language: Optional[Language] = None, + return_timestamps: Optional[str] = "word", + max_new_tokens: int = 128, + chunk_length_s: int = 30, + batch_size: int = 16, + device: Optional[DeviceType] = None, + ) -> List[ScriptLine]: + """Transcribes all audio samples in the dataset. + + Args: + audios (List[Audio]): The list of audio objects to be transcribed. + model (HFModel): The Hugging Face model used for transcription. + language (Optional[Language]): The language of the audio (default is None). + return_timestamps (Optional[str]): The level of timestamp details (default is "word"). + max_new_tokens (int): The maximum number of new tokens (default is 128). + chunk_length_s (int): The length of audio chunks in seconds (default is 30). + batch_size (int): The batch size for processing (default is 16). + device (Optional[DeviceType]): The device to run the model on (default is None). + + Returns: + List[ScritpLine]: The list of script lines. + """ + + def _audio_to_huggingface_dict(audio: Audio) -> Dict: + """Convert an Audio object to a dictionary that can be used by the transformers pipeline. + + Args: + audio (Audio): The audio object. + + Returns: + Dict: The dictionary representation of the audio object. + """ + return { + "array": audio.waveform.squeeze().numpy(), + "sampling_rate": audio.sampling_rate, + } + + def _rename_key_recursive(obj: Dict[str, Any], old_key: str, new_key: str) -> Dict[str, Any]: + """Recursively rename keys in a dictionary.""" + if isinstance(obj, dict): + for key in list(obj.keys()): + if key == old_key: + obj[new_key] = obj.pop(old_key) + elif isinstance(obj[key], (dict, list)): + obj[key] = _rename_key_recursive(obj[key], old_key, new_key) + elif isinstance(obj, list): + obj = [_rename_key_recursive(item, old_key, new_key) for item in obj] + return obj + + # Take the start time of the pipeline initialization + start_time_pipeline = time.time() + # Get the Hugging Face pipeline + pipe = HuggingFaceASR._get_hf_asr_pipeline( + model=model, + return_timestamps=return_timestamps, + max_new_tokens=max_new_tokens, + chunk_length_s=chunk_length_s, + batch_size=batch_size, + device=device, + ) + + # Take the end time of the pipeline initialization + end_time_pipeline = time.time() + # Print the time taken for initialize the hugging face ASR pipeline + elapsed_time_pipeline = end_time_pipeline - start_time_pipeline + logger.info(f"Time taken to initialize the hugging face ASR pipeline: {elapsed_time_pipeline:.2f} seconds") + + # Retrieve the expected sampling rate from the Hugging Face model + expected_sampling_rate = pipe.feature_extractor.sampling_rate + + # Check that all audio objects are mono + for audio in audios: + if audio.waveform.shape[0] != 1: + raise ValueError(f"Stereo audio is not supported. Got {audio.waveform.shape[0]} channels") + if audio.sampling_rate != expected_sampling_rate: + raise ValueError( + f"Incorrect sampling rate. Expected {expected_sampling_rate}" f", got {audio.sampling_rate}" + ) + + # Convert the audio objects to dictionaries that can be used by the pipeline + formatted_audios = [_audio_to_huggingface_dict(audio) for audio in audios] + + # Take the start time of the transcription + start_time_transcription = time.time() + # Run the pipeline + transcriptions = pipe( + formatted_audios, generate_kwargs={"language": f"{language.name.lower()}"} if language else {} + ) + + # Take the end time of the transcription + end_time_transcription = time.time() + # Print the time taken for transcribing the audios + elapsed_time_transcription = end_time_transcription - start_time_transcription + logger.info(f"Time taken for transcribing the audios: {elapsed_time_transcription:.2f} seconds") + + # Rename the "timestamp" key to "timestamps" + transcriptions = _rename_key_recursive(transcriptions, "timestamp", "timestamps") + + # Convert the pipeline output to ScriptLine objects + return [ScriptLine.from_dict(t) for t in transcriptions] diff --git a/src/senselab/audio/tasks/speech_to_text_evaluation/__init__.py b/src/senselab/audio/tasks/speech_to_text_evaluation/__init__.py new file mode 100644 index 00000000..c5915d82 --- /dev/null +++ b/src/senselab/audio/tasks/speech_to_text_evaluation/__init__.py @@ -0,0 +1,3 @@ +""".. include:: ./doc.md""" # noqa: D415 + +from .utils import calculate_cer, calculate_mer, calculate_wer, calculate_wil, calculate_wip # noqa: F401 diff --git a/src/senselab/audio/tasks/speech_to_text_evaluation/doc.md b/src/senselab/audio/tasks/speech_to_text_evaluation/doc.md new file mode 100644 index 00000000..bb98d6ed --- /dev/null +++ b/src/senselab/audio/tasks/speech_to_text_evaluation/doc.md @@ -0,0 +1,107 @@ +# Speech to text evaluation + + + + + +# Overview + +Evaluating speech transcripts involves comparing a predicted transcript (hypothesis) generated by an automated speech recognition (ASR) system against a ground truth transcript (reference). This evaluation helps to determine the accuracy and performance of the ASR system. Various metrics can be used for this purpose, each capturing different aspects of the errors in the transcription process. + +## Key metrics + +### 1. Word Error Rate (WER) + +Word Error Rate (WER) is the most common metric used to evaluate the accuracy of ASR systems. It measures the number of word-level errors (insertions, deletions, and substitutions) divided by the total number of words in the reference. The lower the value, the better the performance of the ASR system, with an error rate of 0 indicating a perfect score. Notably, this is not a percentage as can go higher than 1. + +**Formula:** +```text +WER = (S + D + I) / N +``` + +Where: +- \( S \) = Number of substitutions +- \( D \) = Number of deletions +- \( I \) = Number of insertions +- \( N \) = Total number of words in the reference + +### 2. Character Error Rate (CER) + +Character Error Rate (CER) is similar to WER but operates at the character level rather than the word level. It measures the number of character-level errors divided by the total number of characters in the reference. The lower the value, the better the performance of the ASR system, with an error rate of 0 indicating a perfect score. +As for WER, CER is not a percentage as can go higher than 1. + +**Formula:** +```text +CER = (S + D + I) / N +``` + +Where: +- \( S \) = Number of substitutions +- \( D \) = Number of deletions +- \( I \) = Number of insertions +- \( N \) = Total number of characters in the reference + +### 3. Match Error Rate (MER) + +Match Error Rate (MER) calculates the proportion of word-level errors relative to the total number of correct matches plus the number of errors. The lower the value, the better the performance of the ASR system, with an error rate of 0 indicating a perfect score. + +**Formula:** +```text +MER = (S + D + I) / (S + D + I + C) +``` + +Where: +- \( S \) = Number of substitutions +- \( D \) = Number of deletions +- \( I \) = Number of insertions +- \( C \) = Number of correct words + +### 4. Word Information Lost (WIL) + +Word Information Lost (WIL) is computed as follows: + +**Formula:** +```text +WIL = 1 - (C / N) + (C / P) +``` + +Where: +- \( C \) = Number of correct words +- \( N \) = Number of words in the reference +- \( P \) = Number of words in the prediction + +The lower the WIL, the better the performance of the ASR system. + +### 5. Word Information Preserved (WIP) + +Word Information Preserved (WIP) is computed as follows: +**Formula:** +```text +WIP = (C / N) * (C / P) +``` + +Where: +- \( C \) = Number of correct words +- \( N \) = Number of words in the reference +- \( P \) = Number of words in the prediction + +The higher the value, the better the performance of the ASR system, with 1 being a perfect score. + +## Examples + +For a given reference "hello world" and hypothesis "hello duck": + +- **WER (Word Error Rate):** + ```math + WER = (0 + 1 + 0)/(2) = 0.5 + ``` + +- **CER (Character Error Rate):** + ```math + CER = (0 + 1 + 0)/(11) = 0.0909 + ``` + +- **MER (Match Error Rate):** + ```math + MER = (1)/(2) = 0.5 + ``` diff --git a/src/senselab/audio/tasks/speech_to_text_evaluation/utils.py b/src/senselab/audio/tasks/speech_to_text_evaluation/utils.py new file mode 100644 index 00000000..4dcc67dc --- /dev/null +++ b/src/senselab/audio/tasks/speech_to_text_evaluation/utils.py @@ -0,0 +1,90 @@ +"""This module implements some utilities for evaluating a transcription.""" + +import jiwer + +# TODO: add more metrics which take into account the meaning/intention + + +def calculate_wer(reference: str, hypothesis: str) -> float: + """Calculate the Word Error Rate (WER) between the reference and hypothesis. + + Args: + reference (str): The ground truth text. + hypothesis (str): The predicted text. + + Returns: + float: The WER score. + + Examples: + >>> calculate_wer("hello world", "hello duck") + 0.5 + """ + return jiwer.wer(reference, hypothesis) + + +def calculate_mer(reference: str, hypothesis: str) -> float: + """Calculate the Match Error Rate (MER) between the reference and hypothesis. + + Args: + reference (str): The ground truth text. + hypothesis (str): The predicted text. + + Returns: + float: The MER score. + + Examples: + >>> calculate_mer("hello world", "hello duck") + 0.5 + """ + return jiwer.mer(reference, hypothesis) + + +def calculate_wil(reference: str, hypothesis: str) -> float: + """Calculate the Word Information Lost (WIL) between the reference and hypothesis. + + Args: + reference (str): The ground truth text. + hypothesis (str): The predicted text. + + Returns: + float: The WIL score. + + Examples: + >>> calculate_wil("hello world", "hello duck") + 0.75 + """ + return jiwer.wil(reference, hypothesis) + + +def calculate_wip(reference: str, hypothesis: str) -> float: + """Calculate the Word Information Preserved (WIP) between the reference and hypothesis. + + Args: + reference (str): The ground truth text. + hypothesis (str): The predicted text. + + Returns: + float: The WIP score. + + Examples: + >>> calculate_wip("hello world", "hello duck") + 0.25 + """ + return jiwer.wip(reference, hypothesis) + + +def calculate_cer(reference: str, hypothesis: str) -> float: + """Calculate the Character Error Rate (CER) between the reference and hypothesis. + + Args: + reference (str): The ground truth text. + hypothesis (str): The predicted text. + + Returns: + float: The CER score. + + Examples: + >>> calculate_cer("hello world", "hello duck") + 0.45454545454545453 + """ + return jiwer.cer(reference, hypothesis) diff --git a/src/senselab/audio/tasks/ssl_embeddings/__init__.py b/src/senselab/audio/tasks/ssl_embeddings/__init__.py new file mode 100644 index 00000000..2667be85 --- /dev/null +++ b/src/senselab/audio/tasks/ssl_embeddings/__init__.py @@ -0,0 +1 @@ +"""This module provides the API for the senselab extract SSL embeddings task.""" diff --git a/src/senselab/audio/tasks/ssl_embeddings/api.py b/src/senselab/audio/tasks/ssl_embeddings/api.py new file mode 100644 index 00000000..14de2248 --- /dev/null +++ b/src/senselab/audio/tasks/ssl_embeddings/api.py @@ -0,0 +1,52 @@ +"""This module implements some utilities to extract embeddings from self-supervised models.""" + +from typing import List, Optional + +import pydra +import torch + +from senselab.audio.data_structures.audio import Audio +from senselab.audio.tasks.ssl_embeddings.self_supervised_features import SSLEmbeddingsFactory +from senselab.utils.data_structures.device import DeviceType +from senselab.utils.data_structures.model import HFModel, SenselabModel + + +def extract_ssl_embeddings_from_audios( + audios: List[Audio], + model: SenselabModel, + cache_dir: str = "~/", + device: Optional[DeviceType] = None, +) -> List[torch.Tensor]: + """Extract embedding of audio signals from pre-trained SSL models. + + Args: + audios (List[Audio]): A list of Audio objects containing the audio signals and their properties. + model (SenselabModel): The model used to extract their embeddings. + cache_dir (str): The path to where the model's weights will be saved. + device (Optional[DeviceType]): The device to run the model on (default is None). + + Returns: + List[torch.Tensor]: A list of 1d tensors containing the ssl embeddings for each audio file. + + Raises: + NotImplementedError: If the model is not a Hugging Face model. + + Examples: + >>> audios = [Audio.from_filepath("sample.wav")] + >>> model = HFModel(path_or_uri="facebook/wav2vec2-base", revision="main") + >>> embeddings = extract_ssl_embeddings_from_audios(audios, model, cache_dir="./", device=DeviceType.CUDA) + >>> print(embeddings[0].shape) + [13, 209, 768] ([# of Layers, Time Frames, Embedding Size]) + + Todo: + - Make the API compatible with other models than Hugging Face. + """ + if isinstance(model, HFModel): + return SSLEmbeddingsFactory.extract_ssl_embeddings( + audios=audios, model=model, cache_dir=cache_dir, device=device + ) + else: + raise NotImplementedError("The specified model is not supported for now.") + + +extract_ssl_embeddings_from_audios_pt = pydra.mark.task(extract_ssl_embeddings_from_audios) diff --git a/src/senselab/audio/tasks/ssl_embeddings/self_supervised_features.py b/src/senselab/audio/tasks/ssl_embeddings/self_supervised_features.py new file mode 100644 index 00000000..aade6cce --- /dev/null +++ b/src/senselab/audio/tasks/ssl_embeddings/self_supervised_features.py @@ -0,0 +1,114 @@ +"""This module contains functions for extracting features from pre-trained self-supervised models.""" + +from typing import Dict, List, Optional + +import torch +from transformers import AutoFeatureExtractor, AutoModel + +from senselab.audio.data_structures.audio import Audio +from senselab.utils.data_structures.device import DeviceType, _select_device_and_dtype +from senselab.utils.data_structures.model import HFModel + + +class SSLEmbeddingsFactory: + """A factory for managing self-supervised models.""" + + _feat_extractor: Dict[str, AutoFeatureExtractor] = {} + _model: Dict[str, AutoModel] = {} + + @classmethod + def _get_feature_extractor( + cls, + model: HFModel, + cache_dir: str = "~/", + ) -> AutoFeatureExtractor: + """Get or create a feature extractor for SSL model. + + Args: + model (HFModel): The HuggingFace model. + cache_dir (str): The path to where the model's weights will be saved. + + Returns: + AutoFeatureExtractor: The feature extractor for the model. + """ + key = f"{model.path_or_uri}-{model.revision}" + if key not in cls._feat_extractor: + cls._feat_extractor[key] = AutoFeatureExtractor.from_pretrained( + model.path_or_uri, revision=model.revision, cache_dir=cache_dir + ) + return cls._feat_extractor[key] + + @classmethod + def _load_model( + cls, + model: HFModel, + device: DeviceType, + cache_dir: str = "~/", + ) -> AutoModel: + """Load weights of SSL model. + + Args: + model (HFModel): The Hugging Face model. + cache_dir (str): The path to where the model's weights will be saved. + device (DeviceType): The device to run the model on. + + Returns: + AutoModel: The SSL model. + """ + key = f"{model.path_or_uri}-{model.revision}-{device.value}" + if key not in cls._model: + cls._model[key] = AutoModel.from_pretrained( + model.path_or_uri, revision=model.revision, cache_dir=cache_dir + ).to(device.value) + return cls._model[key] + + @classmethod + def extract_ssl_embeddings( + cls, + audios: List[Audio], + model: HFModel, + cache_dir: str = "~/", + device: Optional[DeviceType] = None, + ) -> List[torch.Tensor]: + """Compute the ssl embeddings of audio signals. + + Args: + audios (List[Audio]): A list of Audio objects containing the audio signals and their properties. + model (HFModel): The model used to compute the embeddings + (default is "facebook/wav2vec2-base"). + cache_dir (str): The path to where the model's weights will be saved. + device (Optional[DeviceType]): The device to run the model on (default is None). + Only CPU and CUDA are supported. + + Returns: + List[torch.Tensor]: A list of tensors containing the ssl embeddings for each audio file. + """ + device, _ = _select_device_and_dtype( + user_preference=device, compatible_devices=[DeviceType.CUDA, DeviceType.CPU] + ) + # Load feature extractor and model + feat_extractor = cls._get_feature_extractor(model=model, cache_dir=cache_dir) + sampling_rate = feat_extractor.sampling_rate + ssl_model = cls._load_model(model=model, cache_dir=cache_dir, device=device) + + # Check that all audio objects have the correct sampling rate + for audio in audios: + if audio.waveform.shape[0] != 1: + raise ValueError(f"Audio waveform must be mono (1 channel), but got {audio.waveform.shape[0]} channels") + if audio.sampling_rate != sampling_rate: + raise ValueError( + "Audio sampling rate " + str(audio.sampling_rate) + " does not match expected " + str(sampling_rate) + ) + # Pre-process audio using the SSL mode feature extractor + preprocessed_audios = [ + feat_extractor(audio.waveform, sampling_rate=sampling_rate, return_tensors="pt") for audio in audios + ] + + # Extract embeddings (hidden states from all layers) from pre-trained model + embeddings = [ + ssl_model(audio.input_values.squeeze(0).to(device.value), output_hidden_states=True) + for audio in preprocessed_audios + ] + embeddings = [torch.cat(embedding.hidden_states) for embedding in embeddings] + + return embeddings diff --git a/src/senselab/audio/tasks/text_to_speech/__init__.py b/src/senselab/audio/tasks/text_to_speech/__init__.py new file mode 100644 index 00000000..dc03dee3 --- /dev/null +++ b/src/senselab/audio/tasks/text_to_speech/__init__.py @@ -0,0 +1 @@ +"""This module provides the API for the senselab text to speech task.""" diff --git a/src/senselab/audio/tasks/text_to_speech/api.py b/src/senselab/audio/tasks/text_to_speech/api.py new file mode 100644 index 00000000..bcc6b866 --- /dev/null +++ b/src/senselab/audio/tasks/text_to_speech/api.py @@ -0,0 +1,37 @@ +"""This module implements some utilities for the text-to-speech task.""" + +from typing import List, Optional + +from senselab.audio.data_structures.audio import Audio +from senselab.audio.tasks.text_to_speech.huggingface import HuggingFaceTTS +from senselab.utils.data_structures.device import DeviceType +from senselab.utils.data_structures.language import Language +from senselab.utils.data_structures.model import HFModel, SenselabModel + + +def synthesize_texts( + texts: List[str], + model: SenselabModel = HFModel(path_or_uri="suno/bark", revision="main"), + language: Optional[Language] = None, + device: Optional[DeviceType] = None, +) -> List[Audio]: + """Synthesizes speech from all texts using the given model. + + Args: + texts (List[str]): The list of text strings to be synthesized. + model (SenselabModel): The model used for synthesis + (Default is "suno/bark"). + language (Optional[Language]): The language of the text (default is None). + device (Optional[DeviceType]): The device to run the model on (default is None). + + Returns: + List[Audio]: The list of synthesized audio objects. + + Todo: + - Include more models + - Include voice cloning for TTS + """ + if isinstance(model, HFModel): + return HuggingFaceTTS.synthesize_texts_with_transformers(texts=texts, model=model, device=device) + else: + raise NotImplementedError("Only Hugging Face models are supported for now.") diff --git a/src/senselab/audio/tasks/text_to_speech/huggingface.py b/src/senselab/audio/tasks/text_to_speech/huggingface.py new file mode 100644 index 00000000..b8469a09 --- /dev/null +++ b/src/senselab/audio/tasks/text_to_speech/huggingface.py @@ -0,0 +1,78 @@ +"""This module implements some utilities for the text-to-speech task.""" + +from typing import Any, Dict, List, Optional + +from transformers import pipeline + +from senselab.audio.data_structures.audio import Audio +from senselab.utils.data_structures.device import DeviceType, _select_device_and_dtype +from senselab.utils.data_structures.model import HFModel + + +class HuggingFaceTTS: + """A factory for managing Hugging Face TTS pipelines.""" + + _pipelines: Dict[str, pipeline] = {} + + @classmethod + def _get_hf_tts_pipeline( + cls, + model: HFModel, + device: Optional[DeviceType] = None, + ) -> pipeline: + """Get or create a Hugging Face TTS pipeline. + + Args: + model (HFModel): The Hugging Face model. + device (Optional[DeviceType]): The device to run the model on. + + Returns: + pipeline: The TTS pipeline. + """ + device, torch_dtype = _select_device_and_dtype( + user_preference=device, compatible_devices=[DeviceType.CUDA, DeviceType.CPU] + ) + key = f"{model.path_or_uri}-{model.revision}-{device.value}" + if key not in cls._pipelines: + cls._pipelines[key] = pipeline( + "text-to-speech", + model=model.path_or_uri, + revision=model.revision, + device=device.value, + torch_dtype=torch_dtype, + ) + return cls._pipelines[key] + + @classmethod + def synthesize_texts_with_transformers( + cls, + texts: List[str], + model: HFModel = HFModel(path_or_uri="suno/bark", revision="main"), + device: Optional[DeviceType] = None, + ) -> List[Audio]: + """Synthesizes speech from all the provided text samples. + + Several text-to-speech models are currently available in Transformers, + such as Bark, MMS, VITS and SpeechT5. + + Args: + texts (List[str]): The list of text strings to be synthesized. + model (HFModel): The Hugging Face model used for synthesis. + device (Optional[DeviceType]): The device to run the model on (default is None). + + Returns: + List[Audio]: The list of synthesized audio objects. + + Todo: + - Add speaker embeddings as they do in here: + https://huggingface.co/docs/transformers/tasks/text-to-speech + """ + pipe = HuggingFaceTTS._get_hf_tts_pipeline(model=model, device=device) + + synthesis_kwargs: Dict[str, Any] = {} + synthesized_audios = pipe(texts, **synthesis_kwargs) + + audios = [] + for synth in synthesized_audios: + audios.append(Audio(waveform=synth["audio"], sampling_rate=synth["sampling_rate"])) + return audios diff --git a/src/senselab/audio/tasks/voice_activity_detection/api.py b/src/senselab/audio/tasks/voice_activity_detection/api.py new file mode 100644 index 00000000..4640b3ec --- /dev/null +++ b/src/senselab/audio/tasks/voice_activity_detection/api.py @@ -0,0 +1,39 @@ +"""This module contains functions for computing Voice Activity Detection (VAD).""" + +from typing import List, Optional + +import pydra + +from senselab.audio.data_structures.audio import Audio +from senselab.audio.tasks.speaker_diarization.pyannote import diarize_audios_with_pyannote +from senselab.utils.data_structures.device import DeviceType +from senselab.utils.data_structures.model import PyannoteAudioModel, SenselabModel +from senselab.utils.data_structures.script_line import ScriptLine + + +def detect_human_voice_activity_in_audios( + audios: List[Audio], + model: SenselabModel = PyannoteAudioModel(path_or_uri="pyannote/speaker-diarization-3.1", revision="main"), + device: Optional[DeviceType] = None, +) -> List[List[ScriptLine]]: + """Diarizes all audios using the given model. + + Args: + audios (List[Audio]): The list of audio objects to be processed. + model (SenselabModel): The model used for voice activity detection. + device (Optional[DeviceType]): The device to run the model on (default is None). + + Returns: + List[List[ScriptLine]]: The list of script lines with voice label. + """ + if isinstance(model, PyannoteAudioModel): + results = diarize_audios_with_pyannote(audios=audios, model=model, device=device) + for sample in results: + for chunk in sample: + chunk.speaker = "VOICE" + return results + else: + raise NotImplementedError("Only Pyannote models are supported for now.") + + +detect_human_voice_activity_in_audios_pt = pydra.mark.task(detect_human_voice_activity_in_audios) diff --git a/src/senselab/audio/tasks/voice_cloning/__init__.py b/src/senselab/audio/tasks/voice_cloning/__init__.py new file mode 100644 index 00000000..f07ecd56 --- /dev/null +++ b/src/senselab/audio/tasks/voice_cloning/__init__.py @@ -0,0 +1 @@ +"""This module provides the API for the senselab voice cloning task.""" diff --git a/src/senselab/audio/tasks/voice_cloning/api.py b/src/senselab/audio/tasks/voice_cloning/api.py new file mode 100644 index 00000000..f05f77cc --- /dev/null +++ b/src/senselab/audio/tasks/voice_cloning/api.py @@ -0,0 +1,43 @@ +"""This module implements some utilities for the voice cloning task.""" + +from typing import Any, List, Optional + +import pydra + +from senselab.audio.data_structures.audio import Audio +from senselab.audio.tasks.voice_cloning.knnvc import KNNVC +from senselab.utils.data_structures.device import DeviceType +from senselab.utils.data_structures.model import SenselabModel, TorchModel + + +def clone_voices( + source_audios: List[Audio], + target_audios: List[Audio], + model: SenselabModel = TorchModel(path_or_uri="bshall/knn-vc", revision="master"), + device: Optional[DeviceType] = None, + **kwargs: Any, # noqa:ANN401 +) -> List[Audio]: + """Clones voices from source audios to target audios using the given model.""" + if len(source_audios) != len(target_audios): + raise ValueError("Source and target audios must have the same length.") + + if isinstance(model, TorchModel) and model.path_or_uri == "bshall/knn-vc": + topk = kwargs.get("topk", 4) + if not isinstance(topk, int): + raise ValueError("topk must be an integer.") + prematched_vocoder = kwargs.get("prematched_vocoder", True) + if not isinstance(prematched_vocoder, bool): + raise ValueError("prematched_vocoder must be a boolean.") + return KNNVC.clone_voices_with_knn_vc( + source_audios=source_audios, + target_audios=target_audios, + model=model, + prematched_vocoder=prematched_vocoder, + topk=topk, + device=device, + ) + else: + raise NotImplementedError("Only KNNVC is supported for now.") + + +clone_voices_pt = pydra.mark.task(clone_voices) diff --git a/src/senselab/audio/tasks/voice_cloning/knnvc.py b/src/senselab/audio/tasks/voice_cloning/knnvc.py new file mode 100644 index 00000000..13192079 --- /dev/null +++ b/src/senselab/audio/tasks/voice_cloning/knnvc.py @@ -0,0 +1,84 @@ +"""This module contains functions for voice cloning using KNNVC.""" + +from typing import Any, Dict, List, Optional + +import torch + +from senselab.audio.data_structures.audio import Audio +from senselab.utils.data_structures.device import DeviceType, _select_device_and_dtype +from senselab.utils.data_structures.model import TorchModel + + +class KNNVC: + """A factory for managing KNNVC pipelines.""" + + _pipelines: Dict[str, Any] = {} + + @classmethod + def _get_knnvc_pipeline( + cls, + model: TorchModel, + prematched_vocoder: bool, + topk: int, + device: Optional[DeviceType] = None, + ) -> Any: # noqa: ANN401 + """Get or create a KNNVC pipeline.""" + key = f"{model.path_or_uri}-{model.revision}-{prematched_vocoder}-{topk}-{device}" + if key not in cls._pipelines: + device, _ = _select_device_and_dtype( + user_preference=device, compatible_devices=[DeviceType.CUDA, DeviceType.CPU] + ) + knn_vc = torch.hub.load( + model="knn_vc", + repo_or_dir=model.path_or_uri, + prematched=prematched_vocoder, + trust_repo=True, + pretrained=True, + device=device.value, + ) + cls._pipelines[key] = knn_vc + return cls._pipelines[key] + + @classmethod + def clone_voices_with_knn_vc( + cls, + source_audios: List[Audio], + target_audios: List[Audio], + model: TorchModel = TorchModel(path_or_uri="bshall/knn-vc", revision="master"), + prematched_vocoder: bool = True, + topk: int = 4, + device: Optional[DeviceType] = None, + ) -> List[Audio]: + """Clones voices from source audios to target audios using KNNVC.""" + knn_vc = cls._get_knnvc_pipeline(model=model, prematched_vocoder=prematched_vocoder, topk=topk, device=device) + + cloned_audios = [] + for source_audio, target_audio in zip(source_audios, target_audios): + if source_audio.waveform.shape[0] > 1 or target_audio.waveform.shape[0] > 1: + raise ValueError("Only mono audio files are supported.") + source_sampling_rate = source_audio.sampling_rate + target_sampling_rate = target_audio.sampling_rate + # 16kHz is the only supported sampling rate for KNNVC + supported_sampling_rate = 16000 + if source_sampling_rate != supported_sampling_rate or target_sampling_rate != supported_sampling_rate: + raise ValueError( + f"Sampling rates {source_sampling_rate} and/or {target_sampling_rate} are not supported." + f"Only {supported_sampling_rate} sampling rate is supported." + ) + + source_waveform = source_audio.waveform + target_waveform = target_audio.waveform + + query_seq = knn_vc.get_features(source_waveform) + matching_set = knn_vc.get_matching_set([target_waveform]) + out_wav = knn_vc.match(query_seq, matching_set, topk=topk) + + cloned_audios.append( + Audio( + waveform=out_wav, + sampling_rate=source_sampling_rate, + orig_path_or_id=source_audio.orig_path_or_id, # TODO: this should be customized + ) + ) + + return cloned_audios diff --git a/src/senselab/text/.gitkeep b/src/senselab/text/.gitkeep new file mode 100644 index 00000000..e69de29b diff --git a/src/senselab/text/__init__.py b/src/senselab/text/__init__.py new file mode 100644 index 00000000..ee06887c --- /dev/null +++ b/src/senselab/text/__init__.py @@ -0,0 +1 @@ +"""This module contains implementations related to text processing.""" diff --git a/src/senselab/text/tasks/__init__.py b/src/senselab/text/tasks/__init__.py new file mode 100644 index 00000000..e524dc3c --- /dev/null +++ b/src/senselab/text/tasks/__init__.py @@ -0,0 +1 @@ +"""This module if for processing text.""" diff --git a/src/senselab/text/tasks/embeddings_extraction/__init__.py b/src/senselab/text/tasks/embeddings_extraction/__init__.py new file mode 100644 index 00000000..752bbfca --- /dev/null +++ b/src/senselab/text/tasks/embeddings_extraction/__init__.py @@ -0,0 +1 @@ +"""This module provides the API for the senselab text embeddings extraction.""" diff --git a/src/senselab/text/tasks/embeddings_extraction/api.py b/src/senselab/text/tasks/embeddings_extraction/api.py new file mode 100644 index 00000000..c4be2f05 --- /dev/null +++ b/src/senselab/text/tasks/embeddings_extraction/api.py @@ -0,0 +1,30 @@ +"""This module provides a wrapper method for extracting embeddings from text using different models.""" + +from typing import List, Optional + +import torch + +from senselab.text.tasks.embeddings_extraction.huggingface import HFFactory +from senselab.text.tasks.embeddings_extraction.sentence_transformers import SentenceTransformerFactory +from senselab.utils.data_structures.device import DeviceType +from senselab.utils.data_structures.model import HFModel, SenselabModel, SentenceTransformersModel + + +def extract_embeddings_from_text( + pieces_of_text: List[str], model: SenselabModel, device: Optional[DeviceType] = None +) -> List[torch.Tensor]: + """Extracts embeddings from a list of strings using the specified model. + + Args: + pieces_of_text (List[str]): A list of strings to extract embeddings from. + model (SenselabModel): The model used for extracting embeddings. + device (Optional[DeviceType]): The device to run the model on. Defaults to None. + + Returns: + List[torch.Tensor]: A list of embeddings for the input strings. + """ + if isinstance(model, SentenceTransformersModel): + return SentenceTransformerFactory.extract_text_embeddings(pieces_of_text, model, device) + elif isinstance(model, HFModel): + return HFFactory.extract_text_embeddings(pieces_of_text, model, device) + raise ValueError("Unsupported model type for text embedding extraction.") diff --git a/src/senselab/text/tasks/embeddings_extraction/huggingface.py b/src/senselab/text/tasks/embeddings_extraction/huggingface.py new file mode 100644 index 00000000..0c365eb0 --- /dev/null +++ b/src/senselab/text/tasks/embeddings_extraction/huggingface.py @@ -0,0 +1,99 @@ +"""This module contains functions for extracting features from pre-trained self-supervised models.""" + +from typing import Dict, List, Optional + +import torch +from transformers import AutoModel, AutoTokenizer + +from senselab.utils.data_structures.device import DeviceType, _select_device_and_dtype +from senselab.utils.data_structures.model import HFModel + + +class HFFactory: + """A factory for managing self-supervised models from Hugging Face.""" + + _tokenizers: Dict[str, AutoTokenizer] = {} + _models: Dict[str, AutoModel] = {} + + @classmethod + def _get_tokenizer( + cls, + model: HFModel, + ) -> AutoTokenizer: + """Get or create a tokenizer for SSL model. + + Args: + model (HFModel): The HuggingFace model. + + Returns: + AutoTokenizer: The tokenizer for the model. + """ + key = f"{model.path_or_uri}-{model.revision}" + if key not in cls._tokenizers: + cls._tokenizers[key] = AutoTokenizer.from_pretrained( + pretrained_model_name_or_path=model.path_or_uri, + revision=model.revision, + ) + return cls._tokenizers[key] + + @classmethod + def _load_model( + cls, + model: HFModel, + device: DeviceType, + ) -> AutoModel: + """Load weights of SSL model. + + Args: + model (HFModel): The Hugging Face model. + device (DeviceType): The device to run the model on. + + Returns: + AutoModel: The SSL model. + """ + key = f"{model.path_or_uri}-{model.revision}-{device.value}" + if key not in cls._models: + cls._models[key] = AutoModel.from_pretrained(model.path_or_uri, revision=model.revision).to(device.value) + return cls._models[key] + + @classmethod + def extract_text_embeddings( + cls, + pieces_of_text: List[str], + model: HFModel = HFModel(path_or_uri="sentence-transformers/all-MiniLM-L6-v2"), + device: Optional[DeviceType] = None, + ) -> List[torch.Tensor]: + """Extracts embeddings from a list of strings using a Hugging Face model. + + Args: + pieces_of_text (List[str]): A list of strings to extract embeddings from. + model (HFModel, optional): A Hugging Face model configuration. + Defaults to HFModel(path_or_uri="sentence-transformers/all-MiniLM-L6-v2"). + device (Optional[DeviceType], optional): The device to run the model on. + Defaults to None. + + Returns: + List[torch.Tensor]: A list of embeddings for the input strings. + """ + device, _ = _select_device_and_dtype( + user_preference=device, compatible_devices=[DeviceType.CUDA, DeviceType.CPU] + ) + # Load tokenizer and model + tokenizer = cls._get_tokenizer(model=model) + ssl_model = cls._load_model(model=model, device=device) + + embeddings = [] + + # Process each piece of text individually + for text in pieces_of_text: + # Tokenize sentence + encoded_input = tokenizer(text, return_tensors="pt").to(device) + + # Compute token embeddings + with torch.no_grad(): + model_output = ssl_model(**encoded_input, output_hidden_states=True) + hidden_states = model_output.hidden_states + concatenated_hidden_states = torch.cat([state.unsqueeze(0) for state in hidden_states], dim=0) + embeddings.append(concatenated_hidden_states.squeeze()) + + return embeddings diff --git a/src/senselab/text/tasks/embeddings_extraction/sentence_transformers.py b/src/senselab/text/tasks/embeddings_extraction/sentence_transformers.py new file mode 100644 index 00000000..9b9d27e8 --- /dev/null +++ b/src/senselab/text/tasks/embeddings_extraction/sentence_transformers.py @@ -0,0 +1,67 @@ +"""This module implements some utilities for using Sentence Transformers for embeddings extraction.""" + +from typing import Dict, List, Optional + +import torch +from sentence_transformers import SentenceTransformer + +from senselab.utils.data_structures.device import DeviceType, _select_device_and_dtype +from senselab.utils.data_structures.model import SentenceTransformersModel + + +class SentenceTransformerFactory: + """A factory for managing SentenceTransformer pipelines for text embeddings.""" + + _pipelines: Dict[str, SentenceTransformer] = {} + + @classmethod + def _get_sentencetransformer_pipeline( + cls, + model: SentenceTransformersModel, + device: Optional[DeviceType] = None, + ) -> SentenceTransformer: + """Get or create a SentenceTransformer pipeline. + + Args: + model (SentenceTransformersModel): The Hugging Face model configuration. + device (Optional[DeviceType]): The device to run the model on. + + Returns: + SentenceTransformer: The SentenceTransformer pipeline. + """ + device, _ = _select_device_and_dtype( + user_preference=device, compatible_devices=[DeviceType.CUDA, DeviceType.CPU] + ) + key = f"{model.path_or_uri}-{model.revision}-{device.value}" + if key not in cls._pipelines: + cls._pipelines[key] = SentenceTransformer( + model_name_or_path=model.path_or_uri, + revision=model.revision, + device=device.value, + ) + return cls._pipelines[key] + + @classmethod + def extract_text_embeddings( + cls, + pieces_of_text: List[str], + model: SentenceTransformersModel = SentenceTransformersModel( + path_or_uri="sentence-transformers/all-MiniLM-L6-v2", revision="main" + ), + device: Optional[DeviceType] = None, + ) -> List[torch.Tensor]: + """Extracts embeddings from a list of strings using a SentenceTransformer model. + + Args: + pieces_of_text (List[str]): A list of strings to extract embeddings from. + model (SentenceTransformersModel, optional): A Hugging Face model configuration. + Defaults to SentenceTransformersModel(path_or_uri="sentence-transformers/all-MiniLM-L6-v2"). + device (Optional[DeviceType], optional): The device to run the model on. + Defaults to None. + + Returns: + List[torch.Tensor]: A list of embeddings for the input strings. + """ + pipeline = cls._get_sentencetransformer_pipeline(model, device) + embeddings = pipeline.encode(pieces_of_text, convert_to_tensor=True) + return [embedding for embedding in embeddings] diff --git a/src/senselab/utils/__init__.py b/src/senselab/utils/__init__.py new file mode 100644 index 00000000..850d6089 --- /dev/null +++ b/src/senselab/utils/__init__.py @@ -0,0 +1 @@ +"""This module contains the implementation of utility functions.""" diff --git a/src/senselab/utils/config.json b/src/senselab/utils/config.json new file mode 100644 index 00000000..816836da --- /dev/null +++ b/src/senselab/utils/config.json @@ -0,0 +1,16 @@ +{ + "files": { + "audio": { + "extensions": [ + ".wav", + ".mp3" + ] + }, + "video": { + "extensions": [ + ".mp4", + ".avi" + ] + } + } +} diff --git a/src/senselab/utils/config.py b/src/senselab/utils/config.py new file mode 100644 index 00000000..d8641a6a --- /dev/null +++ b/src/senselab/utils/config.py @@ -0,0 +1,17 @@ +"""This module provides the implementation of config utilities.""" + +import json +import os +from typing import Any, Dict + + +def get_config() -> Dict[str, Any]: + """Reads and returns the contents of the config file.""" + # Get the directory of the current script + script_dir = os.path.dirname(__file__) + # Build the path to the config file + config_file_path = os.path.join(script_dir, "config.json") + # Load the config file + with open(config_file_path, "r", encoding="utf-8") as file: + config = json.load(file) + return config diff --git a/src/senselab/utils/constants.py b/src/senselab/utils/constants.py new file mode 100644 index 00000000..cf3d616e --- /dev/null +++ b/src/senselab/utils/constants.py @@ -0,0 +1,5 @@ +"""Constants used by Senselab.""" + +import uuid + +SENSELAB_NAMESPACE = uuid.uuid3(uuid.NAMESPACE_URL, "https://github.com/sensein/senselab") diff --git a/src/senselab/utils/data_structures/dataset.py b/src/senselab/utils/data_structures/dataset.py new file mode 100644 index 00000000..1b200de7 --- /dev/null +++ b/src/senselab/utils/data_structures/dataset.py @@ -0,0 +1,352 @@ +"""Data structures relevant for managing datasets.""" + +import math +import uuid +from typing import Dict, List, Union, no_type_check + +import torch +from datasets import Audio as HFAudio +from datasets import Dataset, Features, Image, Sequence, Value +from pydantic import BaseModel, Field, ValidationInfo, field_validator +from transformers.image_transforms import to_pil_image + +from senselab.audio.data_structures.audio import Audio +from senselab.video.data_structures.video import Video + + +class Participant(BaseModel): + """Data structure for a participant in a dataset.""" + + id: str = Field(default_factory=lambda: str(uuid.uuid4())) + metadata: Dict = Field(default={}) + + @field_validator("id", mode="before") + def set_id(cls, v: str) -> str: + """Set the unique id of the participant.""" + return v or str(uuid.uuid4()) + + def __eq__(self, other: object) -> bool: + """Overloads the default BaseModel equality to correctly check that ids are equivalent.""" + if isinstance(other, Participant): + return self.id == other.id + return False + + +class Session(BaseModel): + """Data structure for a session in a dataset.""" + + id: str = Field(default_factory=lambda: str(uuid.uuid4())) + metadata: Dict = Field(default={}) + + @field_validator("id", mode="before") + def set_id(cls, v: str) -> str: + """Set the unique id of the session.""" + return v or str(uuid.uuid4()) + + def __eq__(self, other: object) -> bool: + """Overloads the default BaseModel equality to correctly check that ids are equivalent.""" + if isinstance(other, Session): + return self.id == other.id + return False + + +class SenselabDataset(BaseModel): + """Class for maintaining SenseLab datasets and functionalities. + + Maintains collections of Audios, Videos, and metadata for use of the Senselab tools + and pipelines. Includes the ability to manage Sessions and Participants. + + Attributes: + audios: List of Audios that are generated based on list of audio filepaths + videos: List of Videos generated from a list of video filepaths + metadata: Metadata related to the dataset overall but not necessarily the metadata of + indivudal audios in the dataset + sessions: Session ID mapping to Session instance + participants: Mapping of participant ID to a Participant instance + """ + + participants: Dict[str, Participant] = Field(default_factory=dict) + sessions: Dict[str, Session] = Field(default_factory=dict) + audios: List[Audio] = [] + videos: List[Video] = [] + metadata: Dict = Field(default={}) + + @field_validator("audios", mode="before") + @classmethod + def generate_audios_from_filepaths(cls, v: Union[List[str], List[Audio]], _: ValidationInfo) -> List[Audio]: + """Generate the audios in the dataset from a list of audio filepaths. + + Generates the audios in the dataset by taking in a list of audio filepaths + or a list of Audios + + Args: + v: Input for audios attribute that we're validating by generating the Audios if filepaths + are provided or just the list of Audios if pre-generated and passed in + + Returns: + List of Audios that instantiates the audios attribute in the dataset + """ + audio_list = [] + if len(v) == 0: + return [] + else: + for audio in v: + if isinstance(audio, Audio): + audio_list.append(audio) + else: + audio_list.append(Audio.from_filepath(audio)) + return audio_list + + @field_validator("videos", mode="before") + @classmethod + def generate_videos_from_filepaths(cls, v: Union[List[str], List[Video]], _: ValidationInfo) -> List[Video]: + """Generate the videos in the dataset from a list of video filepaths. + + Generates the videos in the dataset by taking in a list of video filepaths + or a list of Videos + + Args: + v: Input for videos attribute that we're validating by generating the Videos if filepaths + are provided or just the list of Videos if pre-generated and passed in + + Returns: + List of Videos that instantiates the videos attribute in the dataset + """ + video_list = [] + if len(v) == 0: + return [] + else: + for video in v: + if isinstance(video, Video): + video_list.append(video) + elif isinstance(video, str): + video_list.append(Video.from_filepath(video)) + + else: + raise ValueError("Unsupported video list") + return video_list + + @classmethod + @no_type_check + def create_bids_dataset(cls, bids_root_filepath: str) -> "SenselabDataset": + """Create a dataset from a BIDS organized directory. + + Creates a new dataset based off of a BIDS directory structure as defined at + https://sensein.group/biometrics-book/updated_bids.html + """ + pass + + def create_audio_split_for_pydra_task(self, batch_size: int = 1) -> List[List[Audio]]: + """Splits the audio data for Pydra tasks. + + Creates a split of the audio data that can be used for creating individual Pydra tasks using + the .split functionality. Splits the data such that the inputs for a Pydra workflow are either + optimized for the GPU's batch size or a single Audio per CPU thread. + + Args: + batch_size: How to batch Audios for a Pydra task; defaults to 1 since CPU won't batch + + Returns: + List of Lists of Audio where each List of Audios will be an input to a Pydra task. + Each of the sublists are either of size 1 for CPUs or at most batch_size for GPU optimization. + + Raises: + ValueError if the batch size is invalid (less than 1) + """ + if batch_size > 1: + # Creates batches of at most size batch_size except the last which contains the remainder of audios + return [ + self.audios[batch_size * i : min(batch_size * (i + 1), len(self.audios))] + for i in range(math.ceil(len(self.audios) / batch_size)) + ] + elif batch_size < 1: + raise ValueError("Batch size must be greater than or equal to 1") + else: + return [[audio] for audio in self.audios] + + def audio_merge_from_pydra_task(self, audios_to_merge: List[List[Audio]]) -> None: + """Write later. + + Logic Pydra: + audios: List of audios that want to give to task + split: List[List[Audios]] -> task List[Audio] + pydra task(List[Audio]) -> List[Audio] + merge(List[List[Audio]]) <- might be a wrapped instead of List of lists + TODO: Figure out what a merge behavior looks like from Pydra + """ + self.audios = [] + for audio_task_input in audios_to_merge: + for audio_output in audio_task_input: + self.audios.append(audio_output) + + def add_participant(self, participant: Participant) -> None: + """Add a participant to the dataset.""" + if participant.id in self.participants: + raise ValueError(f"Participant with ID {participant.id} already exists.") + self.participants[participant.id] = participant + + def add_session(self, session: Session) -> None: + """Add a session to the dataset.""" + if session.id in self.sessions: + raise ValueError(f"Session with ID {session.id} already exists.") + self.sessions[session.id] = session + + def get_participants(self) -> List[Participant]: + """Get the list of participants in the dataset.""" + return list(self.participants.values()) + + def get_sessions(self) -> List[Session]: + """Get the list of sessions in the dataset.""" + return list(self.sessions.values()) + + def _get_dict_representation(self) -> Dict: + audio_data: Dict[str, List] = {} + video_data: Dict[str, List] = {} + senselab_dict: Dict[str, Union[Dict[str, List], List]] = { + "participants": [], + "sessions": [], + "audios": audio_data, + "videos": video_data, + "metadata": self.metadata.copy(), + } + participants_data = [] + sessions_data = [] + + video_frames_data = [] + video_fps_data = [] + video_path_data = [] + video_metadata = [] + video_audio_data = [] + video_audio_metadata = [] + + audio_waveform_data = [] + audio_metadata = [] + + for participant in self.get_participants(): + participants_data.append({"id": participant.id, "metadata": participant.metadata.copy()}) + senselab_dict["participants"] = participants_data + + for session in self.get_sessions(): + sessions_data.append({"id": session.id, "metadata": session.metadata.copy()}) + senselab_dict["sessions"] = sessions_data + + for audio in self.audios: + audio_waveform_data.append( + { + "array": audio.waveform.T, + "sampling_rate": audio.sampling_rate, + "path": audio.generate_path(), + } + ) + audio_metadata.append(audio.metadata.copy()) + audio_data["audio"] = audio_waveform_data + audio_data["metadata"] = audio_metadata + + for video in self.videos: + video_frames_data.append({"image": [to_pil_image(frame.numpy()) for frame in list(video.frames)]}) + video_fps_data.append(video.frame_rate) + video_path_data.append(video.generate_path()) + video_metadata.append(video.metadata.copy()) + video_audio_data.append( + None + if not video.audio + else { + "array": video.audio.waveform.T.to(torch.float32).numpy(), + "sampling_rate": video.audio.sampling_rate, + "path": video.audio.generate_path(), + } + ) + video_audio_metadata.append(None if not video.audio else video.audio.metadata.copy()) + + video_data["frames"] = video_frames_data + video_data["frame_rate"] = video_fps_data + video_data["path"] = video_path_data + video_data["metadata"] = video_metadata + video_data["audio"] = video_audio_data + video_data["audio_metadata"] = video_audio_metadata + # raise ValueError('fuck') + return senselab_dict + + def convert_senselab_dataset_to_hf_datasets(self) -> Dict[str, Dataset]: + """Converts Senselab datasets into HuggingFace datasets.""" + senselab_dict = self._get_dict_representation() + + # print(senselab_dict['videos']['audio'][0]) + + features = Features( + { + "frames": {"image": Sequence(feature=Image())}, + "frame_rate": Value("float32"), + "path": Value("string"), + "metadata": {}, + "audio": HFAudio(mono=False, sampling_rate=48000), + "audio_metadata": Value("string"), + } + ) + + audio_dataset = Dataset.from_dict(senselab_dict["audios"]).cast_column("audio", HFAudio(mono=False)) + + video_dataset = Dataset.from_dict(senselab_dict["videos"], features=features) + # print(video_dataset[0]) + + hf_datasets = {} + hf_datasets["audios"] = audio_dataset + hf_datasets["videos"] = video_dataset + + # TODO: Create datasets for participants and sessions + return hf_datasets + + @classmethod + def convert_hf_dataset_to_senselab_dataset(cls, hf_datasets: Dict[str, Dataset]) -> "SenselabDataset": + """Converts HuggingFace dataset to a Senselab dataset.""" + audios = [] + videos = [] + sessions: Dict[str, Session] = {} + participants: Dict[str, Participant] = {} + if "audios" in hf_datasets: + audio_dataset = hf_datasets["audios"] + for audio in audio_dataset: + audios.append( + Audio( + waveform=audio["audio"]["array"], + sampling_rate=audio["audio"]["sampling_rate"], + orig_path_or_id=audio["audio"]["path"], + metadata=audio["metadata"] if "metadata" in audio else {}, + ) + ) + + if "videos" in hf_datasets: + video_dataset = hf_datasets["videos"] + for video in video_dataset: + videos.append( + Video( + frames=video["frames"]["image"], + frame_rate=video["frame_rate"], + metadata=video["metadata"] if "metadata" in video else {}, + orig_path_or_id=video["path"], + audio=Audio( + waveform=video["audio"]["array"], + sampling_rate=video["audio"]["sampling_rate"], + orig_path_or_id=video["audio"]["path"], + ) + if video["audio"] + else None, + ) + ) + if "sessions" in hf_datasets: + pass + if "participants" in hf_datasets: + pass + + return SenselabDataset(participants=participants, sessions=sessions, audios=audios, videos=videos) + + def __eq__(self, other: object) -> bool: + """Overloads the default BaseModel equality to correctly check that datasets are equivalent.""" + if isinstance(other, SenselabDataset): + return ( + self.audios == other.audios + and self.videos == other.videos + and self.participants == other.participants + and self.sessions == other.sessions + ) + return False diff --git a/src/senselab/utils/data_structures/device.py b/src/senselab/utils/data_structures/device.py new file mode 100644 index 00000000..71fc2619 --- /dev/null +++ b/src/senselab/utils/data_structures/device.py @@ -0,0 +1,79 @@ +"""Utility functions for utilizing different devices in Senselab.""" + +from enum import Enum +from typing import Optional + +import torch + + +class DeviceType(Enum): + """Device types for PyTorch operations.""" + + CPU: str = "cpu" + CUDA: str = "cuda" + MPS: str = "mps" + + +DTYPE_MAP = {DeviceType.CPU: torch.float32, DeviceType.CUDA: torch.float16, DeviceType.MPS: torch.float32} + + +def _select_device_and_dtype( + user_preference: Optional[DeviceType] = None, + compatible_devices: list[DeviceType] = [ + DeviceType.CPU, + DeviceType.CUDA, + DeviceType.MPS, + ], +) -> tuple[DeviceType, torch.dtype]: + """Determines the device and data type for PyTorch operations. + + Allows users to give preferences for DeviceType, but determines based + on compatible and available devices. Chooses the fastest option if no + user preference is given. + + Args: + user_preference: Optional DeviceType that the user wants to use + compatible_devices: DeviceTypes that work with the functionality of the method calling this + Returns: + Tuple of (DeviceType, torch.dtype) where the device is both available and compatible and the + dtype is the best performing dtype for that DeviceType + Raises: + ValueError: if the user specifies a preference that is not available or compatible and a safety + call if no devices are available or compatible (we believe this to be impossible to trigger). + """ + if user_preference: + if not isinstance(user_preference, DeviceType): + raise ValueError(f"user_preference should be of type DeviceType, not {type(user_preference)}") + available_devices = [DeviceType.CPU] + if torch.cuda.is_available(): + available_devices.append(DeviceType.CUDA) + + if torch.backends.mps.is_available(): + available_devices.append(DeviceType.MPS) + + # Check compatible and available + useable_devices = [] + for device in available_devices: + if device in compatible_devices: + useable_devices.append(device) + + # User preference or fastest option + + if user_preference: + # user_preference = DeviceType(user_preference) if isinstance(user_preference,str) else user_preference + if user_preference not in useable_devices: + raise ValueError( + "The requested DeviceType is either not available or\ + compatible with this functionality." + ) + else: + return user_preference, DTYPE_MAP[user_preference] + else: + if DeviceType.CUDA in useable_devices: + return DeviceType.CUDA, DTYPE_MAP[DeviceType.CUDA] + elif DeviceType.MPS in useable_devices: + return DeviceType.MPS, DTYPE_MAP[DeviceType.MPS] + elif DeviceType.CPU in useable_devices: + return DeviceType.CPU, DTYPE_MAP[DeviceType.CPU] + else: + raise ValueError("Something went wrong and no devices were available or compatible.") diff --git a/src/senselab/utils/data_structures/file.py b/src/senselab/utils/data_structures/file.py new file mode 100644 index 00000000..76ca9207 --- /dev/null +++ b/src/senselab/utils/data_structures/file.py @@ -0,0 +1,68 @@ +"""This module provides the implementation of file utilities.""" + +import os +from pathlib import Path +from typing import List + +from pydantic import BaseModel, FilePath, field_validator + +from senselab.utils.config import get_config + + +class File(BaseModel): + """This class represents a file.""" + + filepath: FilePath + + @property + def type(self) -> str: + """Determine the type of the file based on its extension.""" + extension = os.path.splitext(self.filepath)[1].lower() + for type_name, type_info in get_config()["files"].items(): + if extension in type_info["extensions"]: + return type_name + raise ValueError("File type could not be determined from extension.") + + @field_validator("filepath") + def validate_filepath(cls, v: FilePath) -> FilePath: + """Validate the filepath.""" + if not os.path.exists(v): + raise ValueError(f"File {v} does not exist") + + file_extension = os.path.splitext(v)[1].lower() + valid_extensions = [ext for types in get_config()["files"].values() for ext in types["extensions"]] + + if file_extension not in valid_extensions: + raise ValueError( + f"Unsupported file extension: {file_extension}." f"Supported extensions: {valid_extensions}" + ) + + return v + + +def from_strings_to_files(list_of_files: List[str]) -> List[File]: + """Create a list of `File` objects from a list of strings.""" + return [File(filepath=Path(file)) for file in list_of_files] + + +def get_common_directory(files: List[str]) -> str: + """A function to get the common directory from a list of file paths. + + Parameters: + - files: a list of file paths + + Returns: + - the common directory among the file paths + """ + if len(files) == 1: + # Ensure the single path's directory ends with a separator + common_path = os.path.dirname(files[0]) + else: + # Use commonpath to find the common directory for multiple files + common_path = os.path.commonpath(files) + + # Check if the path ends with the os separator, add if not + if not common_path.endswith(os.sep): + common_path += os.sep + + return common_path diff --git a/src/senselab/utils/data_structures/language.py b/src/senselab/utils/data_structures/language.py new file mode 100644 index 00000000..126f8f25 --- /dev/null +++ b/src/senselab/utils/data_structures/language.py @@ -0,0 +1,38 @@ +"""This module provides the implementation of language data structures.""" + +import pycountry +import pycountry.db +from pydantic import BaseModel, field_validator + + +class Language(BaseModel): + """Data structure for a language.""" + + language_code: str + + @field_validator("language_code", mode="before") + def validate_language(cls, v: str) -> str: + """Validate that the language code is valid.""" + lang = ( + pycountry.languages.get(alpha_2=v) or pycountry.languages.get(alpha_3=v) or pycountry.languages.get(name=v) + ) + + if not lang: + raise ValueError(f"{v} is not a valid ISO language code or name") + + return lang.alpha_3 # ISO 639-3 + + @property + def alpha_2(self) -> str: + """Get the ISO 639-2 code for the language.""" + return pycountry.languages.get(alpha_3=self.language_code).alpha_2 + + @property + def alpha_3(self) -> str: + """Get the ISO 639-3 code for the language.""" + return self.language_code + + @property + def name(self) -> str: + """Get the name of the language.""" + return pycountry.languages.get(alpha_3=self.language_code).name diff --git a/src/senselab/utils/data_structures/logging.py b/src/senselab/utils/data_structures/logging.py new file mode 100644 index 00000000..73ccb20c --- /dev/null +++ b/src/senselab/utils/data_structures/logging.py @@ -0,0 +1,20 @@ +"""This module provides utilities for logging.""" + +import logging + +# Configure the logger +logger = logging.getLogger("senselab") +logger.setLevel(logging.DEBUG) + +# Create a console handler and set the level to debug +ch = logging.StreamHandler() +ch.setLevel(logging.DEBUG) + +# Create a formatter +formatter = logging.Formatter("%(asctime)s - %(name)s - %(levelname)s - %(message)s") + +# Add the formatter to the handler +ch.setFormatter(formatter) + +# Add the handler to the logger +logger.addHandler(ch) diff --git a/src/senselab/utils/data_structures/model.py b/src/senselab/utils/data_structures/model.py new file mode 100644 index 00000000..770e6c9b --- /dev/null +++ b/src/senselab/utils/data_structures/model.py @@ -0,0 +1,168 @@ +"""This module implements some utilities for the model class.""" + +import os +from pathlib import Path +from typing import Optional, Union + +import requests +import torch +import torchaudio +from huggingface_hub import HfApi +from huggingface_hub.hf_api import ModelInfo +from pydantic import BaseModel, Field, ValidationInfo, field_validator +from typing_extensions import Annotated + + +class SenselabModel(BaseModel): + """Base configuration for SenselabModel class.""" + + path_or_uri: Union[str, Path] + revision: Optional[str] = None + + @field_validator("path_or_uri", mode="before") + def validate_path_or_uri(cls, value: Union[str, Path]) -> Union[str, Path]: + """Validate the path_or_uri. + + This check is only for files and not for remote resources. + It does check if the path_or_uri is not empty and if it is an existing file. + """ + if not value: + raise ValueError("path_or_uri cannot be empty") + + if isinstance(value, Path) and not os.path.isfile(value): + raise ValueError("path_or_uri is not an existing file") + + # If the value is a string and looks like an existing file path, convert it to a Path object + if isinstance(value, str) and os.path.isfile(value): + value = Path(value) + if not is_torch_model(value): + raise ValueError("path_or_uri does not point to a valid torch model") + + return value + + +class HFModel(SenselabModel): + """HuggingFace model.""" + + revision: Annotated[str, Field(validate_default=True)] = "main" + info: Optional[ModelInfo] = None + + @field_validator("revision", mode="before") + def validate_hf_model_id(cls, value: str, info: ValidationInfo) -> Union[str, Path]: + """Validate the path_or_uri. + + This check is only for remote resources and not for files. + It checks if the specified Hugging Face model ID and revision exist in the remote Hub. + """ + path_or_uri = info.data["path_or_uri"] + if not isinstance(path_or_uri, Path): + if not check_hf_repo_exists(repo_id=str(path_or_uri), revision=value, repo_type="model"): + raise ValueError( + f"path_or_uri ({path_or_uri}) or specified revision ({value})" " is not a valid Hugging Face model" + ) + return value + + def get_model_info(self) -> ModelInfo: + """Gets the model info using the HuggingFace API and saves it as a property.""" + if not self.info: + api = HfApi() + self.info = api.model_info(repo_id=self.path_or_uri, revision=self.revision) + return self.info + + +class SpeechBrainModel(HFModel): + """SpeechBrain model.""" + + pass + + +class PyannoteAudioModel(HFModel): + """PyannoteAudioModel model.""" + + pass + + +class SentenceTransformersModel(HFModel): + """SentenceTransformersModel model.""" + + pass + + +class TorchModel(SenselabModel): + """Generic torch model.""" + + revision: Annotated[str, Field(validate_default=True)] = "main" + + @field_validator("revision", mode="before") + def validate_torch_model_id(cls, value: str, info: ValidationInfo) -> Union[str, Path]: + """Validate the path_or_uri. + + This check is only for remote resources and not for files. + It checks if the specified torch model ID and revision exist in the remote Hub. + """ + path_or_uri = info.data["path_or_uri"] + if not isinstance(path_or_uri, Path): + if not check_github_repo_exists(repo_id=str(path_or_uri), branch=value): + raise ValueError("path_or_uri or specified revision is not a valid github repo") + return value + + +class TorchAudioModel(SenselabModel): + """TorchAudio model.""" + + revision: Annotated[str, Field(validate_default=True)] = "main" + + @field_validator("revision", mode="before") + def validate_torchaudio_model_id(cls, value: str, info: ValidationInfo) -> Union[str, Path]: + """Validate the path_or_uri for torchaudio models. + + This check is only for remote resources and not for files. + It checks if the specified torchaudio model ID exists. + """ + path_or_uri = info.data["path_or_uri"] + if not isinstance(path_or_uri, Path): + if not check_torchaudio_model_exists(model_id=str(path_or_uri)): + raise ValueError("path_or_uri is not a valid torchaudio model") + return value + + +def check_torchaudio_model_exists(model_id: str) -> bool: + """Private function to check if a torchaudio model exists.""" + try: + _ = getattr(torchaudio.pipelines, model_id) + return True + except AttributeError: + return False + + +def is_torch_model(file_path: Path) -> bool: + """Check if a file is a torch model.""" + try: + _ = torch.load(file_path) + return True + except Exception: + return False + + +def check_hf_repo_exists(repo_id: str, revision: str = "main", repo_type: str = "model") -> bool: + """Private function to check if a Hugging Face repository exists.""" + api = HfApi() + try: + api.list_repo_commits(repo_id=repo_id, revision=revision, repo_type=repo_type) + return True + except Exception: + # raise RuntimeError(f"An error occurred: {e}") + return False + + +def check_github_repo_exists(repo_id: str, branch: str = "main") -> bool: + """Private function to check if a GitHub repository exists.""" + url = f"https://api.github.com/repos/{repo_id}/branches/{branch}" + response = requests.get(url, timeout=10) + if response.status_code == 200: + return True + elif response.status_code == 404: + return False + else: + response.raise_for_status() + return False diff --git a/src/senselab/utils/data_structures/pydra_helpers.py b/src/senselab/utils/data_structures/pydra_helpers.py new file mode 100644 index 00000000..82a9f6ad --- /dev/null +++ b/src/senselab/utils/data_structures/pydra_helpers.py @@ -0,0 +1,49 @@ +"""Functionality for Pydra.""" + +from typing import Iterator + +import numpy as np +import torch +from pydra.utils.hash import Cache, bytes_repr_sequence_contents, register_serializer + + +@register_serializer(torch.Tensor) +def bytes_repr_arraylike(obj: torch.Tensor, cache: Cache) -> Iterator[bytes]: + """Register a serializer for Torch tensors that allows Pydra to properly use them.""" + yield f"{obj.__class__.__module__}{obj.__class__.__name__}:".encode() + array = np.asanyarray(obj) + yield f"{array.size}:".encode() + if array.dtype == "object": + yield from bytes_repr_sequence_contents(iter(array.ravel()), cache) + else: + yield array.tobytes(order="C") + + +# TODO: Ignore this for now but need to decide how to incorporate Pydra into the package +# Pydra runner +# need function that allows for marking a task (could be obfuscated internally) +# need function that runs the actual code/does the parallelization +# getting results is a bit clunky right now so could clean that up +# internal Pydra wouldn't need obfuscations for add/split but external API might be useful +# perhaps dictionary structure could be intuitive? linked list? + +# what if we had a simple split/run structure: +# user: run X task with Y audios (Satra example); perhaps extraneous information of DeviceType, Model to use +# - kinda easy example of just create the pydra setup ourselves, no user worries +# Senselab "pipelines"/Tasks: + +# example: +# RAVDESS audios: +# - need to resample +# - perhaps run loudness normalization +# - run SER +# - run evaluation + +# get RAVDESS audios +# split into batches +# - run batches through resmapling +# - pass to normalization +# - run SER +# - get partial evaluation +# - combine evaluation +# - give average diff --git a/src/senselab/utils/data_structures/script_line.py b/src/senselab/utils/data_structures/script_line.py new file mode 100644 index 00000000..79ad1b72 --- /dev/null +++ b/src/senselab/utils/data_structures/script_line.py @@ -0,0 +1,116 @@ +"""This module contains the definition of the ScriptLine class.""" + +from typing import Any, Dict, List, Optional, Tuple, Union + +from pydantic import BaseModel, ValidationInfo, field_validator, model_validator + + +class ScriptLine(BaseModel): + """A class to represent a line in a script. + + Attributes: + text (Optional[str]): The full text of the line (if provided). + speaker (Optional[str]): The speaker of the line (if provided). + chunks (Optional[List['ScriptLine']]): A list of script lines (if provided). + """ + + text: Optional[str] = None + speaker: Optional[str] = None + start: Optional[float] = None + end: Optional[float] = None + chunks: Optional[List["ScriptLine"]] = None + + @model_validator(mode="before") + def validate_text_and_speaker(cls, values: Dict[str, Any], _: ValidationInfo) -> Dict[str, Any]: + """Validate that at least one of text or speaker is provided.""" + if not values.get("text") and not values.get("speaker"): + raise ValueError("At least text or speaker must be provided.") + return values + + @field_validator("text", "speaker") + def strings_must_be_stripped(cls, v: str, _: ValidationInfo) -> str: + """Strip the string of leading and trailing whitespace. + + Args: + v (str): The text or speaker of the script line. + + Returns: + str: The validated text or speaker of the script line. + """ + if v is not None: + v = v.strip() + return v + + @field_validator("start", "end") + def timestamps_must_be_positive(cls, v: float, _: ValidationInfo) -> float: + """Validate that the start and end timestamps are positive. + + Args: + v (float): The timestamp of the script line. + + Returns: + float: The validated timestamp. + """ + if v is not None: + if v < 0: + raise ValueError("Timestamps must be non-negative") + return v + + def get_text(self) -> Union[str, None]: + """Get the full text of the script line. + + Returns: + Optional[str, None]: The full text of the script line, or None if not provided. + """ + return self.text + + def get_speaker(self) -> Optional[str]: + """Get the speaker of the script line. + + Returns: + Optional[str]: The speaker of the script line. + """ + return self.speaker + + def get_timestamps(self) -> Tuple[Optional[float], Optional[float]]: + """Get the start and end timestamps of the script line. + + Returns: + Tuple[Optional[float], Optional[float]]: The start and end timestamps of the script line. + """ + return self.start, self.end + + def get_chunks(self) -> Optional[List["ScriptLine"]]: + """Get the list of chunks in the script line. + + Returns: + Optional[List['ScriptLine']]: The list of chunks. + """ + return self.chunks + + @classmethod + def from_dict(cls, d: Dict[str, Any]) -> "ScriptLine": + """Create a ScriptLine instance from a dictionary. + + Args: + d (Dict[str, Any]): The dictionary containing the script line data. + + Returns: + ScriptLine: An instance of ScriptLine. + """ + if "timestamps" in d: + start = d["timestamps"][0] + end = d["timestamps"][1] + elif "chunks" in d and "timestamps" in d["chunks"][0] and "timestamps" in d["chunks"][-1]: + start = d["chunks"][0]["timestamps"][0] + end = d["chunks"][-1]["timestamps"][1] + else: + start = None + end = None + return cls( + text=d["text"] if "text" in d else None, + speaker=d["speaker"] if "speaker" in d else None, + start=start, + end=end, + chunks=[cls.from_dict(c) for c in d["chunks"]] if "chunks" in d else None, + ) diff --git a/src/senselab/utils/tasks/cca_cka.py b/src/senselab/utils/tasks/cca_cka.py new file mode 100644 index 00000000..1ed1911c --- /dev/null +++ b/src/senselab/utils/tasks/cca_cka.py @@ -0,0 +1,138 @@ +"""This module is for computing CCA and CKA.""" + +from enum import Enum + +import pydra +import torch + + +def compute_cca(features_x: torch.Tensor, features_y: torch.Tensor) -> float: + """Compute the mean squared CCA correlation (R^2_{CCA}). + + Args: + features_x (torch.Tensor): A num_examples x num_features matrix of features. + features_y (torch.Tensor): A num_examples x num_features matrix of features. + + Returns: + float: The mean squared CCA correlations between X and Y. + """ + qx, _ = torch.linalg.qr(features_x) + qy, _ = torch.linalg.qr(features_y) + result = torch.norm(qx.t() @ qy) ** 2 / min(features_x.shape[1], features_y.shape[1]) + return result.item() if isinstance(result, torch.Tensor) else float(result) + + +class CKAKernelType(Enum): + """CKA kernel types.""" + + LINEAR = "linear" + RBF = "rbf" + + +def compute_cka( + features_x: torch.Tensor, + features_y: torch.Tensor, + kernel: CKAKernelType = CKAKernelType.LINEAR, + threshold: float = 1.0, +) -> float: + """Compute CKA between feature matrices. + + Args: + features_x (torch.Tensor): A num_examples x num_features matrix of features. + features_y (torch.Tensor): A num_examples x num_features matrix of features. + kernel (CKAKernelType): Type of kernel to use (CKAKernelType.LINEAR or CKAKernelType.RBF). + Default is CKAKernelType.LINEAR. + threshold (float): Fraction of median Euclidean distance to use as RBF kernel bandwidth + (used only if kernel is CKAKernelType.RBF). + + Returns: + float: The value of CKA between X and Y. + """ + + def _gram_linear(x: torch.Tensor) -> torch.Tensor: + """Compute Gram (kernel) matrix for a linear kernel. + + Args: + x (torch.Tensor): A num_examples x num_features matrix of features. + + Returns: + torch.Tensor: A num_examples x num_examples Gram matrix of examples. + """ + return x @ x.t() + + def _gram_rbf(x: torch.Tensor, threshold: float = 1.0) -> torch.Tensor: + """Compute Gram (kernel) matrix for an RBF kernel. + + Args: + x (torch.Tensor): A num_examples x num_features matrix of features. + threshold (float): Fraction of median Euclidean distance to use as RBF kernel bandwidth. + + Returns: + torch.Tensor: A num_examples x num_examples Gram matrix of examples. + """ + dot_products = x @ x.t() + sq_norms = torch.diag(dot_products) + sq_distances = -2 * dot_products + sq_norms[:, None] + sq_norms[None, :] + sq_median_distance = torch.median(sq_distances) + return torch.exp(-sq_distances / (2 * threshold**2 * sq_median_distance)) + + def _center_gram(gram: torch.Tensor) -> torch.Tensor: + """Center a symmetric Gram matrix. + + This is equivalent to centering the (possibly infinite-dimensional) features + induced by the kernel before computing the Gram matrix. + + Args: + gram (torch.Tensor): A num_examples x num_examples symmetric matrix. + + Returns: + torch.Tensor: A symmetric matrix with centered columns and rows. + + Raises: + ValueError: If the input is not a symmetric matrix. + """ + if not torch.allclose(gram, gram.t()): + raise ValueError("Input must be a symmetric matrix.") + + n = gram.size(0) + unit = torch.ones(n, n, device=gram.device) + eye = torch.eye(n, device=gram.device) + unit = unit / n + haitch = eye - unit + centered_gram = haitch.mm(gram).mm(haitch) + return centered_gram + + def _cka(gram_x: torch.Tensor, gram_y: torch.Tensor) -> torch.Tensor: + """Compute CKA. + + Args: + gram_x (torch.Tensor): A num_examples x num_examples Gram matrix. + gram_y (torch.Tensor): A num_examples x num_examples Gram matrix. + + Returns: + float: The value of CKA between X and Y. + """ + gram_x = _center_gram(gram_x) + gram_y = _center_gram(gram_y) + + scaled_hsic = torch.sum(gram_x * gram_y) + + normalization_x = torch.norm(gram_x) + normalization_y = torch.norm(gram_y) + return scaled_hsic / (normalization_x * normalization_y) + + if kernel == CKAKernelType.LINEAR: + gram_x = _gram_linear(features_x) + gram_y = _gram_linear(features_y) + elif kernel == CKAKernelType.RBF: + gram_x = _gram_rbf(features_x, threshold) + gram_y = _gram_rbf(features_y, threshold) + else: + raise ValueError("Unsupported kernel type. Use CKAKernelType.LINEAR or CKAKernelType.RBF.") + + result = _cka(gram_x, gram_y) + return result.item() if isinstance(result, torch.Tensor) else float(result) + + +compute_cca_pt = pydra.mark.task(compute_cca) +compute_cka_pt = pydra.mark.task(compute_cka) diff --git a/src/senselab/utils/tasks/cosine_similarity.py b/src/senselab/utils/tasks/cosine_similarity.py new file mode 100644 index 00000000..3cfac99a --- /dev/null +++ b/src/senselab/utils/tasks/cosine_similarity.py @@ -0,0 +1,47 @@ +"""This module provides the implementation of cosine similarity.""" + +import pydra +import torch + + +def compute_cosine_similarity(tensor1: torch.Tensor, tensor2: torch.Tensor) -> float: + """Compute the cosine similarity between two torch tensors. + + Args: + tensor1 (Tensor): The first input tensor. + tensor2 (Tensor): The second input tensor. + + Returns: + float: The cosine similarity between the two input tensors. + + Raises: + ValueError: If the input tensors are not of the same shape. + + Examples: + >>> tensor1 = torch.tensor([1.0, 2.0, 3.0]) + >>> tensor2 = torch.tensor([4.0, 5.0, 6.0]) + >>> cosine_similarity(tensor1, tensor2) + 0.9746318461970762 + + >>> tensor1 = torch.tensor([1.0, 0.0, -1.0]) + >>> tensor2 = torch.tensor([-1.0, 0.0, 1.0]) + >>> cosine_similarity(tensor1, tensor2) + -1.0 + + Note: + This function assumes the input tensors are 1-dimensional and have the same shape. + """ + if tensor1.dim() != 1 or tensor2.dim() != 1: + raise ValueError("Input tensors must be 1-dimensional") + if tensor1.shape != tensor2.shape: + raise ValueError("Input tensors must have the same shape") + + dot_product = torch.dot(tensor1, tensor2) + norm_tensor1 = torch.norm(tensor1) + norm_tensor2 = torch.norm(tensor2) + + cosine_sim = dot_product / (norm_tensor1 * norm_tensor2) + return cosine_sim.item() + + +cosine_similarity_pt = pydra.mark.task(compute_cosine_similarity) diff --git a/src/senselab/utils/tasks/cross_correlation.py b/src/senselab/utils/tasks/cross_correlation.py new file mode 100644 index 00000000..8411183f --- /dev/null +++ b/src/senselab/utils/tasks/cross_correlation.py @@ -0,0 +1,55 @@ +"""This module contains functions for computing the normalized cross-correlation between two signals.""" + +import numpy as np +import pydra +import torch +from scipy.signal import correlate + + +def compute_normalized_cross_correlation(signal1: torch.Tensor, signal2: torch.Tensor) -> torch.Tensor: + """Calculate the normalized cross-correlation between two signals. + + Args: + signal1 (torch.Tensor): The first input signal as a PyTorch tensor. + signal2 (torch.Tensor): The second input signal as a PyTorch tensor. + + Returns: + torch.Tensor: The normalized cross-correlation value between the two input signals. + + Examples: + >>> signal1 = torch.tensor([1.0, 2.0, 3.0, 4.0, 5.0]) + >>> signal2 = torch.tensor([2.0, 3.0, 4.0]) + >>> normalized_cross_correlation(signal1, signal2) + Tensor([0.30151134, 0.51298918, 0.77459667, 0.9486833 , 0.90453403, 0.70710678, 0.43643578]) + + Note: + This function assumes the input signals are one-dimensional + and contain sufficient elements for meaningful cross-correlation. + """ + # Ensure the inputs are 1D tensors + if signal1.ndim != 1 or signal2.ndim != 1: + raise ValueError("Input signals must be one-dimensional") + + # Convert PyTorch tensors to NumPy arrays + signal1 = signal1.numpy() + signal2 = signal2.numpy() + + # Calculate the energy of each signal + energy_signal1 = np.sum(signal1**2) + energy_signal2 = np.sum(signal2**2) + + # Check for zero energy to avoid division by zero + if energy_signal1 == 0 or energy_signal2 == 0: + raise ZeroDivisionError("One of the input signals has zero energy, causing division by zero in normalization") + + # Compute the cross-correlation + cross_correlation = correlate(signal1, signal2) + + # Calculate the normalized cross-correlation + normalized_cross_correlation = cross_correlation / np.sqrt(energy_signal1 * energy_signal2) + + print(normalized_cross_correlation) + return torch.Tensor(normalized_cross_correlation) + + +compute_normalized_cross_correlation_pt = pydra.mark.task(compute_normalized_cross_correlation) diff --git a/src/senselab/utils/tasks/dimensionality_reduction.py b/src/senselab/utils/tasks/dimensionality_reduction.py new file mode 100644 index 00000000..d9bc1fe7 --- /dev/null +++ b/src/senselab/utils/tasks/dimensionality_reduction.py @@ -0,0 +1,161 @@ +"""This module provides the implementation of dimensionality reduction. + +Dimensionality reduction is a crucial preprocessing step in many machine learning and data analysis pipelines. +It aims to project a high-dimensional dataset into a lower-dimensional subspace while preserving as much of the +significant structure and information as possible. This process is particularly useful for visualization, +noise reduction, and improving computational efficiency in downstream tasks. + +This module specifically implements three popular dimensionality reduction techniques: Principal Component Analysis +(PCA), t-Distributed Stochastic Neighbor Embedding (t-SNE), and Uniform Manifold Approximation and Projection (UMAP). + +### Task Overview +- Input: High-dimensional data (typically represented as a matrix or tensor), i.e. A tensor of shape (100, 10) +- Output: Lower-dimensional representation of the input data, i.e. A tensor of shape (100, 2) +- Goal: Preserve important structures or relationships in the data while reducing its dimensionality + +### Evaluation Metrics +The effectiveness of dimensionality reduction techniques can be evaluated using several metrics, including but not +limited to: +- **Reconstruction Error**: Measures the difference between the original data and its reduced representation when + projected back to the original space. Commonly used for techniques like PCA. +- **Trustworthiness**: Assesses how much the model preserves neighbor relationships between the high-dimensional + space and the low-dimensional space. +- **Continuity**: Evaluates how well the local structure of the data is preserved in the reduced space. + +### Supported models +- **Principal Component Analysis (PCA)**: PCA is a widely recognized technique in the field of data science + and machine learning. It excels in linear dimensionality reduction, making it an invaluable tool for + simplifying complex datasets. PCA's primary strength lies in its ability to capture the most significant + variance within the data. By identifying the principal components that account for the majority of the variation, + PCA allows researchers and analysts to reduce the dimensionality of their data while retaining its most important + features. This makes PCA particularly useful for tasks like data visualization and feature extraction. + +- **t-Distributed Stochastic Neighbor Embedding (t-SNE)**: t-SNE is primarily utilized as a visualization technique, + setting it apart from more general dimensionality reduction methods. It is able to preserve local structures + within data when projecting it into lower-dimensional spaces, typically two or three dimensions. This property + makes t-SNE exceptionally good at creating visually interpretable embeddings that can reveal clusters and patterns + in complex datasets. However, t-SNE is not suitable for tasks that require the preservation of global structures. + +- **Uniform Manifold Approximation and Projection (UMAP)**: UMAP is a relatively newer technique that has gained + significant attention due to its versatility and performance. UMAP is able to balance the preservation of both + global and local structures within data. Like t-SNE, UMAP can be used visualizing high-dimensional data in two + or three dimensions. However, UMAP is also effective as a general-purpose non-linear dimensionality reduction + technique, making it suitable for a wide range of applications where preserving the overall structure of the + data in lower dimensions is crucial. + +#### Summary: +.. include:: ./dimensionality_reduction_table.md + +References: +- PCA: https://www.nature.com/articles/s43586-022-00184-w +- t-SNE: https://jmlr.org/papers/v9/vandermaaten08a.html +- UAMP: https://arxiv.org/abs/1802.03426 + +### Notes: +This module leverages the implementations from scikit-learn for PCA and t-SNE, and the umap-learn library for UMAP. +The reduced data is returned as a PyTorch tensor, allowing seamless integration with PyTorch-based workflows. + +Learn more: +- PCA: https://scikit-learn.org/stable/modules/generated/sklearn.decomposition.PCA.html +- t-SNE: https://scikit-learn.org/stable/modules/generated/sklearn.manifold.TSNE.html +- UMAP: https://umap-learn.readthedocs.io/ +""" + +from typing import Literal + +import pydra +import torch +import umap +from sklearn.decomposition import PCA as sklearn_PCA +from sklearn.manifold import TSNE as sklearn_TSNE + + +def compute_dimensionality_reduction( + data: torch.Tensor, model: Literal["pca", "tsne", "umap"] = "pca", n_components: int = 2, **kwargs: object +) -> torch.Tensor: + """Reduce the dimensionality of the given data using the specified model. + + This function provides three methods: PCA, t-SNE, or UMAP. The reduced data is returned as a PyTorch tensor. + + Args: + data (torch.Tensor): Input data tensor of shape (n_samples, n_features). + model ({"pca", "tsne", "umap"}, optional): The dimensionality reduction model to use. + Choices are: + "pca" for Principal Component Analysis (default), + "tsne" for t-Distributed Stochastic Neighbor Embedding, or + "umap" for Uniform Manifold Approximation and Projection + n_components (int, optional): Number of dimensions in the output. + Must be less than or equal to the number of features in the input data. + Defaults to 2. + **kwargs: Additional keyword arguments to pass to the chosen model. + (See below for links to each model's documentation for available parameters.) + + Returns: + torch.Tensor: The reduced data tensor of shape (n_samples, n_components). + + Raises: + ValueError: If an invalid model choice is provided or if n_components is + greater than the number of features in the input data or less than 0. + + Examples: + >>> data = torch.randn(100, 10) # 100 samples, 10 features + >>> data.shape + torch.Size([100, 10]) + + >>> reduced_data = compute_dimensionality_reduction(data) + >>> print(reduced_data.shape) + torch.Size([100, 2]) + + >>> reduced_data = compute_dimensionality_reduction(data, model="pca", n_components=2, svd_solver="full") + >>> print(reduced_data.shape) + torch.Size([100, 2]) + + >>> reduced_data = compute_dimensionality_reduction(data, model="tsne", n_components=3, perplexity=30) + >>> print(reduced_data.shape) + torch.Size([100, 3]) + + >>> reduced_data = compute_dimensionality_reduction(data, model="umap", n_components=4, + force_approximation_algorithm=False, init='spectral', learning_rate=1.0, n_neighbors=5) + >>> print(reduced_data.shape) + torch.Size([100, 4]) + + Notes: + This function uses implementations from scikit-learn for PCA and t-SNE, and the umap-learn + library for UMAP. For detailed information about each method and its parameters, please + refer to the following documentation: + + - PCA: https://scikit-learn.org/stable/modules/generated/sklearn.decomposition.PCA.html + - t-SNE: https://scikit-learn.org/stable/modules/generated/sklearn.manifold.TSNE.html + - UMAP: https://umap-learn.readthedocs.io/en/latest/api.html + """ + if n_components > data.shape[1]: + raise ValueError("n_components must be less than or equal to the number of features in the input data") + + if n_components < 1: + raise ValueError("n_components must be greater than 0") + + if data.numel() == 0: + raise ValueError("Input data must not be empty") + + if model == "pca": + # Perform PCA + reducer = sklearn_PCA(n_components=n_components, **kwargs) + reduced_data = reducer.fit_transform(data) + + elif model == "tsne": + # Perform t-SNE + reducer = sklearn_TSNE(n_components=n_components, **kwargs) + reduced_data = reducer.fit_transform(data) + + elif model == "umap": + # Perform UMAP + reducer = umap.UMAP(n_components=n_components, **kwargs) + reduced_data = reducer.fit_transform(data) + + else: + raise ValueError(f"Invalid model choice: {model}") + + return torch.from_numpy(reduced_data).float() + + +compute_dimensionality_reduction_pt = pydra.mark.task(compute_dimensionality_reduction) diff --git a/src/senselab/utils/tasks/dimensionality_reduction_table.md b/src/senselab/utils/tasks/dimensionality_reduction_table.md new file mode 100644 index 00000000..1257ab4b --- /dev/null +++ b/src/senselab/utils/tasks/dimensionality_reduction_table.md @@ -0,0 +1,7 @@ +| Feature | PCA | t-SNE | UMAP | +|---------------------|-------------------------------|--------------------------------------|---------------------------------------------| +| Type | Linear | Non-linear | Non-linear | +| Preserves | Global structure (variance) | Local structure, cluster patterns | Local and global structure to some extent | +| Filters out | Noise, less important variance | Global structure, distances between clusters | Less important variance, noise | +| Best for | - Understanding main sources of variation
- Data compression
- Preprocessing
- Linear relationships | - Visualizing high-dimensional data
- Discovering clusters or patterns
- Exploratory data analysis | - General-purpose dimensionality reduction
- Balancing local and global structure preservation | +| Limitations | - Assumes linear relationships
- May miss complex patterns | - Can distort global structure
- Non-deterministic | - Sensitive to parameters
- Does not completely preserve density | diff --git a/src/senselab/utils/tasks/eer.py b/src/senselab/utils/tasks/eer.py new file mode 100644 index 00000000..c07c205e --- /dev/null +++ b/src/senselab/utils/tasks/eer.py @@ -0,0 +1,23 @@ +"""This module implements some utilities for computing the Equal Error Rate (EER).""" + +from typing import Tuple + +import pydra +import torch +from speechbrain.utils.metric_stats import EER + + +def compute_eer(predictions: torch.Tensor, targets: torch.Tensor) -> Tuple[float, float]: + """Compute the Equal Error Rate (EER). + + Args: + predictions (torch.Tensor): A 1D tensor of predictions. + targets (torch.Tensor): A 1D tensor of targets. + + Returns: + Tuple[float, float]: The EER and the threshold for the EER. + """ + return EER(predictions, targets) + + +compute_eer_pt = pydra.mark.task(compute_eer) diff --git a/src/senselab/utils/tasks/input_output.py b/src/senselab/utils/tasks/input_output.py new file mode 100644 index 00000000..4271e48b --- /dev/null +++ b/src/senselab/utils/tasks/input_output.py @@ -0,0 +1,197 @@ +"""This module implements some IOTask utilities. + +This modules is deprecated and will be removed soon. +""" + +import os +from typing import Any, Dict, List, Union + +import pydra +from datasets import Audio, Dataset, Image, load_dataset + +from senselab.utils.data_structures.file import File, from_strings_to_files +from senselab.utils.data_structures.model import check_hf_repo_exists + + +def read_files_from_disk(files: Union[str, List[str]]) -> Dict[str, Any]: + """Read files from disk and create a Hugging Face `Dataset` object.""" + if isinstance(files, str): + files = [files] + formatted_files = from_strings_to_files(files) + + def _from_files_to_dataset(files: List[File]) -> Dataset: + """Reading files from disk and create a HuggingFace `Dataset` object.""" + # Checking if all files are of the same type + if not all(file.type == files[0].type for file in files): + raise ValueError("All files must be of the same type.") + + # Loading file paths + file_data = [str(file.filepath) for file in files] + + # Creating the Dataset object + dict_obj = {files[0].type: file_data} + # Using the type of the first file as the key + + return _from_dict_to_hf_dataset(dict_obj) + + dataset = _from_files_to_dataset(formatted_files) + return _from_hf_dataset_to_dict(dataset) + + +def read_dataset_from_disk(input_path: str, split: str, streaming: bool = False) -> Dict[str, Any]: + """Loads a Hugging Face `Dataset` object from disk. + + It determines the format based on the file extension or directory. + """ + # Determine the input format. + if os.path.isdir(input_path): + input_format = "arrow" + else: + input_format = os.path.splitext(input_path)[1].strip(".") + + # Load the dataset + try: + dataset = load_dataset( + input_format, + split=split, + data_files=input_path, + streaming=streaming, + ) + return _from_hf_dataset_to_dict(dataset) + except Exception as e: + # Generic error handling, e.g., network issues, data loading issues + raise RuntimeError(f"An error occurred while loading the dataset: {str(e)}") + + +def read_dataset_from_hub( + remote_repository: str, + revision: str = "main", + split: str = "all", +) -> Dict[str, Any]: + """Loads a Hugging Face `Dataset` object from the Hugging Face Hub. + + It includes support for private repositories. + """ + if not check_hf_repo_exists(remote_repository, "main", "dataset"): + raise RuntimeError( + f"The repository {remote_repository} - {revision} - {split}" " does not exist or could not be accessed." + ) + + # Load the dataset + try: + dataset = load_dataset(path=remote_repository, revision=revision, split=split) + except Exception as e: + # Generic error handling, e.g., network issues, data loading issues + raise RuntimeError(f"An error occurred while loading the dataset: {str(e)}") + + return _from_hf_dataset_to_dict(dataset) + + +def push_dataset_to_hub( + dataset: Dict[str, Any], remote_repository: str, revision: str = "main", split: str = "all" +) -> None: + """Uploads a Hugging Face `Dataset` object to the Hugging Face Hub.""" + hf_dataset = _from_dict_to_hf_dataset(dataset) + + try: + # Upload the dataset to the Hugging Face Hub + hf_dataset.push_to_hub(repo_id=remote_repository, revision=revision, split=split) + except Exception as e: + raise RuntimeError(f"Failed to push dataset to the hub: {str(e)}") + return + + +def save_dataset_to_disk( + dataset: Dict[str, Any], + output_directory: str, + output_name: str, + output_format: str = "parquet", +) -> None: + """Saves a Hugging Face `Dataset` object to disk.""" + # TODO: optimize saving process (playing with batch size and num of cpus) + hf_dataset = _from_dict_to_hf_dataset(dataset) + + # Prepare the output path differently if the format is Arrow + if output_format == "arrow": + output_path = os.path.join(output_directory, output_name) + # No extension for Arrow, it's a directory + else: + output_path = os.path.join(output_directory, f"{output_name}.{output_format}") + + # Create the output directory, ignore error if it already exists + os.makedirs(output_directory, exist_ok=True) + + if output_format == "parquet": + + def _save_hf_dataset_as_parquet(dataset: Dataset, output_path: str) -> None: + """Saves a Hugging Face `Dataset` object to parquet format.""" + dataset.to_parquet(output_path) + + _save_hf_dataset_as_parquet(hf_dataset, output_path) + elif output_format == "json": + + def _save_hf_dataset_as_json(dataset: Dataset, output_path: str) -> None: + """Saves a Hugging Face `Dataset` object to json format.""" + dataset.to_json(output_path) + + _save_hf_dataset_as_json(hf_dataset, output_path) + elif output_format == "csv": + + def _save_hf_dataset_as_csv(dataset: Dataset, output_path: str) -> None: + """Saves a Hugging Face `Dataset` object to csv format.""" + dataset.to_csv(output_path) + + _save_hf_dataset_as_csv(hf_dataset, output_path) + elif output_format == "sql": + + def _save_hf_dataset_as_sql(dataset: Dataset, output_path: str) -> None: + """Saves a Hugging Face `Dataset` object to sql format.""" + dataset.to_sql(output_path) + + _save_hf_dataset_as_sql(hf_dataset, output_path) + elif output_format == "arrow": + + def _save_hf_dataset_as_arrow(dataset: Dataset, output_path: str) -> None: + """Saves a Hugging Face `Dataset` object in Apache Arrow format.""" + dataset.save_to_disk(output_path) + + _save_hf_dataset_as_arrow(hf_dataset, output_path) + else: + raise ValueError(f"Unsupported output format: {output_format}") + return + + +def _from_hf_dataset_to_dict(dataset: Dataset) -> Dict[str, Any]: + """Converts a Hugging Face `Dataset` object to a dictionary.""" + return dataset.to_dict() + + +def _from_dict_to_hf_dataset( + data: Dict[str, Any], + image_columns: list[str] = ["image"], + audio_columns: list[str] = ["audio"], +) -> Dataset: + """Converts a dictionary to a Hugging Face `Dataset` object. + + It casts image and audio columns. + """ + dataset = Dataset.from_dict(data) + + # Cast image columns + for column in image_columns: + if column in dataset.column_names: + dataset = dataset.cast_column(column, Image()) + + # Cast audio columns + for column in audio_columns: + if column in dataset.column_names: + dataset = dataset.cast_column(column, Audio(mono=False)) + + return dataset + + +read_files_from_disk_pt = pydra.mark.task(read_files_from_disk) +read_dataset_from_disk_pt = pydra.mark.task(read_dataset_from_disk) +read_dataset_from_hub_pt = pydra.mark.task(read_dataset_from_hub) +push_dataset_to_hub_pt = pydra.mark.task(push_dataset_to_hub) +save_dataset_to_disk_pt = pydra.mark.task(save_dataset_to_disk) diff --git a/src/senselab/utils/tasks/plotting.py b/src/senselab/utils/tasks/plotting.py new file mode 100644 index 00000000..e391f40e --- /dev/null +++ b/src/senselab/utils/tasks/plotting.py @@ -0,0 +1,54 @@ +"""This module implements plotting methods for utilities.""" + +import matplotlib.pyplot as plt + +from senselab.utils.data_structures.script_line import ScriptLine + + +def plot_transcript(transcript: ScriptLine) -> None: + """Plots the transcript visualization over time. + + Args: + transcript (ScriptLine): The transcript object containing chunks of text with start and end times. + + Returns: + None + + Todo: + - Add option to save the plot + - Add option to choose the size of the Figure + - Add check if transcript contains chunks with time information + """ + if transcript.chunks is None: + raise ValueError("The transcript does not contain any chunks.") + + chunks = transcript.chunks + + texts = [chunk.text for chunk in chunks] + start_times = [] + end_times = [] + for chunk in chunks: + # Ensure that chunks have start and end times + if chunk.start is None or chunk.end is None: + raise ValueError("Each chunk must have both start and end times.") + else: + start_times.append(chunk.start) + end_times.append(chunk.end) + + # Create a figure and axis + _, ax = plt.subplots(figsize=(12, 6)) + + # Plot each text segment and add text label + for i, text in enumerate(texts): + if start_times[i] is not None and end_times[i] is not None: + ax.plot([start_times[i], end_times[i]], [i, i], marker="o") + ax.text((start_times[i] + end_times[i]) / 2, i, text, ha="center", va="bottom") + + # Setting labels and title + ax.set_yticks(range(len(texts))) + ax.set_yticklabels([]) + ax.set_xlabel("Time (seconds)") + ax.set_title("Transcript Visualization Over Time") + + # Show the plot + plt.show() diff --git a/src/senselab/utils/tasks/pooling.py b/src/senselab/utils/tasks/pooling.py new file mode 100644 index 00000000..b0f00283 --- /dev/null +++ b/src/senselab/utils/tasks/pooling.py @@ -0,0 +1,71 @@ +"""This module implements pooling methods for torch tensors.""" + +from typing import Literal + +import pydra +import torch + + +def pooling( + data: torch.Tensor, + pool_type: Literal["mean", "min", "max"] = "mean", + dimension: int = 0, + keep_dimension: bool = False, +) -> torch.Tensor: + """Apply pooling to the input tensor along the specified dimension. + + This function provides three pooling methods: mean, min, or max. The pooled tensor is returned as a PyTorch tensor. + + Args: + data (torch.Tensor): Input tensor of shape (n_samples, n_features, n_channels). + pool_type (str, optional): The pooling method to use. + Choices are: + * "mean" for mean pooling + * "min" for min pooling + * "max" for max pooling + Defaults to "mean". + dimension (int, optional): The dimension along which to apply the pooling. + Defaults to 0. + keep_dimension (bool, optional): Whether to keep the original tensor dimension after pooling. + + Returns: + torch.Tensor: The pooled tensor of shape (n_samples, n_features, n_channels). + + Raises: + ValueError: If an invalid pooling method choice is provided. + + Examples: + >>> data = torch.randn(10, 20, 30) # 10 samples, 20 features, 30 channels + >>> data.shape + torch.Size([10, 20, 30]) + + >>> pooled_data = pooling(data, pool_type="mean", dimension=0, keep_dimension=True) + >>> print(pooled_data.shape) + torch.Size([1, 20, 30]) + + >>> pooled_data = pooling(data, pool_type="max", dimension=1, keep_dimension=False) + >>> print(pooled_data.shape) + torch.Size([10, 30]) + + >>> pooled_data = pooling(data, pool_type="min", dimension=2) + >>> print(pooled_data.shape) + torch.Size([10, 20]) + """ + # Ensure the dimension is valid + assert dimension < len(data.shape), "Invalid dimension provided." + + if data.numel() == 0: + raise ValueError("Input data must not be empty") + + # Applying the appropriate pooling based on the pool type + if pool_type == "max": + return torch.max(data, dim=dimension, keepdim=keep_dimension).values + elif pool_type == "mean": + return torch.mean(data, dim=dimension, keepdim=keep_dimension) + elif pool_type == "min": + return torch.min(data, dim=dimension, keepdim=keep_dimension).values + else: + raise ValueError("Unsupported pooling type. Choose 'max', 'avg', or 'min'.") + + +pooling_pt = pydra.mark.task(pooling) diff --git a/src/senselab/video/.gitkeep b/src/senselab/video/.gitkeep new file mode 100644 index 00000000..e69de29b diff --git a/src/senselab/video/__init__.py b/src/senselab/video/__init__.py new file mode 100644 index 00000000..d9a07e66 --- /dev/null +++ b/src/senselab/video/__init__.py @@ -0,0 +1 @@ +"""This module contains implementation for video processing.""" diff --git a/src/senselab/video/data_structures/video.py b/src/senselab/video/data_structures/video.py new file mode 100644 index 00000000..c0a0cb8b --- /dev/null +++ b/src/senselab/video/data_structures/video.py @@ -0,0 +1,126 @@ +"""Data structures relevant for video tasks and pipelines.""" + +import os +import uuid +from typing import Dict, List, Optional, Union + +import numpy as np +import PIL +import torch +from pydantic import BaseModel, Field, ValidationInfo, field_validator +from torchvision.io import read_video + +from senselab.audio.data_structures.audio import Audio +from senselab.utils.constants import SENSELAB_NAMESPACE + + +class Video(BaseModel): + """Pydantic model for video and its corresponding metadata. + + Pydantic model for video that holds the necessary attributes, the actual decoded video data + and the frame rate, to work with videos in python. Contains metadata information as needed + and has a unique identifier for every video. + + Attributes: + frames: Represent the video as a Tensor of all of its frames, each of which is an image + that we represent through a Tensor of (C, H, W) + frame_rate: Also known as the frames per second (fps), defines the time component + of a video (often an integer but some use cases of float approximations) + audio: the audio associated with the Video (optional) + orig_path_or_id: Optional str for the original path or an ID to track file over time + metadata: Optional metadata dictionary of information associated with this Video instance + (e.g. participant demographics, video settings, location information) + """ + + frames: torch.Tensor + frame_rate: float + audio: Optional[Audio] + orig_path_or_id: Optional[str] = Field(default_factory=lambda: str(uuid.uuid4())) + metadata: Dict = Field(default={}) + model_config = {"arbitrary_types_allowed": True} + + @field_validator("frames", mode="before") + def check_frames( + cls, v: Union[torch.Tensor, List[Union[torch.Tensor, np.ndarray, PIL.Image.Image]]], _: ValidationInfo + ) -> torch.Tensor: + # print(v) + """Check that the frames are the correct Tensor shape of (T,H,W,C).""" + if isinstance(v, torch.Tensor): + if len(v.shape) != 4: + raise ValueError( + "Expected frames to be of shape (T, H, W, C) where T is the number of frames, \ + C is the channels, and H and W are the height and width" + ) + else: + return v + elif isinstance(v, List): + transformed_frames = [] + # print('should get here, is list') + for frame in v: + if isinstance(frame, (torch.Tensor, np.ndarray)): + if len(frame.shape) != 3: + raise ValueError( + "Expected frame to be of shape (H, W, C) where \ + C is the channels, and H and W are the height and width" + ) + + transformed_frames.append(torch.Tensor(frame)) + elif isinstance(frame, PIL.Image.Image): + # print('should get here, is image') + transformed_frames.append(torch.Tensor(np.array(frame))) + else: + raise ValueError( + "Expected each frame in the video to be either a Tensor, numpy array, or PIL.Image" + ) + # print(transformed_frames) + return torch.stack(transformed_frames) + else: + raise ValueError("Expected sequence of frames to be either a List of frames or 4d Tensor") + + @classmethod + def from_filepath(cls, filepath: str, metadata: Dict = {}) -> "Video": + """Creates a Video instance from a video file. + + Args: + filepath: Filepath of the video file to read from + metadata: Additional information associated with the video file + """ + v_frames, a_frames, v_metadata = read_video(filename=filepath, pts_unit="sec") + v_fps = v_metadata["video_fps"] + a_fps = v_metadata["audio_fps"] + no_ext_filepath = "random_id" # os.path.splitext(filepath)[0] + v_audio = Audio(waveform=a_frames, sampling_rate=a_fps, orig_path_or_id=f"{no_ext_filepath}.wav") + + return cls(frames=v_frames, frame_rate=v_fps, audio=v_audio, orig_path_or_id=filepath, metadata=metadata) + + def generate_path(self) -> str: + """Generate a path like string for this Video. + + Generates a path like string for the Video by either utilizing the orig_path_or_id, checking + if it is a path (has an extension), otherwise using the id if orig_path_or_id is not an ID + and giving an extension and relative to the current directory. + """ + if self.orig_path_or_id: + if os.path.splitext(self.orig_path_or_id)[-1].lower(): + return self.orig_path_or_id + else: + return f"{self.orig_path_or_id}.mp4" + else: + return f"{self.id()}.mp4" + + def id(self) -> str: + """Generate a unique identifier for the Video. + + Generate a unique identifier for the Video where equivalent video frames and frame rate + and audio generate the same IDs. + + Returns: String UUID of the Video generated by an MD5 hash of the frames and the frame rate and audio + """ + temp_hash = uuid.uuid3(uuid.uuid3(SENSELAB_NAMESPACE, str(self.frames)), str(self.frame_rate)) + return str(temp_hash) if not self.audio else str(uuid.uuid3(temp_hash, self.audio.id())) + + def __eq__(self, other: object) -> bool: + """Overloads the default BaseModel equality to correctly check equivalence, ignoring metadata.""" + if isinstance(other, Audio): + return self.id() == other.id() + return False diff --git a/src/senselab/video/tasks/input_output.py b/src/senselab/video/tasks/input_output.py new file mode 100644 index 00000000..85ac11b8 --- /dev/null +++ b/src/senselab/video/tasks/input_output.py @@ -0,0 +1,56 @@ +"""This module implements the video IOTask.""" + +import os +import tempfile +from pathlib import Path +from typing import Any, Dict, List, Union + +import ffmpeg + +# import shutil +from senselab.utils.data_structures.file import from_strings_to_files, get_common_directory +from senselab.utils.tasks.input_output import read_files_from_disk + + +def extract_audios_from_local_videos( + files: Union[str, List[str]], + audio_format: str = "wav", + acodec: str = "pcm_s16le", +) -> Dict[str, Any]: + """Read files from disk and create a Hugging Face `Dataset` object.""" + + def _extract_audio_from_local_video(video_path: Path, output_audio_path: str, format: str, acodec: str) -> None: + """Extract audio from a video file.""" + try: + # Input stream configuration + input_stream = ffmpeg.input(video_path) + + # Audio extraction configuration + audio_stream = input_stream.audio.output(output_audio_path, format=format, acodec=acodec) + + # Execute ffmpeg command + ffmpeg.run(audio_stream, overwrite_output=True) + + except ffmpeg.Error as e: + print("An error occurred while extracting audio:", str(e)) + + if isinstance(files, str): + files = [files] + formatted_files = from_strings_to_files(files) + common_path = get_common_directory(files) + + # Create a temporary directory to hold the audio files + temp_dir = tempfile.mkdtemp() + + audio_files_paths = [] + for file in formatted_files: + base_file_name = os.path.splitext(str(file.filepath).replace(common_path, ""))[0] + output_audio_path = os.path.join(temp_dir, f"{base_file_name}.{audio_format}") + _extract_audio_from_local_video(file.filepath, output_audio_path, format=audio_format, acodec=acodec) + audio_files_paths.append(output_audio_path) + + audio_dataset = read_files_from_disk(audio_files_paths) + + # Clean up the temporary non empty directory + # shutil.rmtree(temp_dir) + return audio_dataset diff --git a/src/senselab/video/tasks/input_output_pydra.py b/src/senselab/video/tasks/input_output_pydra.py new file mode 100644 index 00000000..9a5a64ca --- /dev/null +++ b/src/senselab/video/tasks/input_output_pydra.py @@ -0,0 +1,7 @@ +"""This module defines a pydra API for the video input and output task.""" + +import pydra + +from senselab.video.tasks.input_output import extract_audios_from_local_videos + +extract_audios_from_local_videos_pt = pydra.mark.task(extract_audios_from_local_videos) diff --git a/src/tests/__init__.py b/src/tests/__init__.py new file mode 100644 index 00000000..129621ec --- /dev/null +++ b/src/tests/__init__.py @@ -0,0 +1 @@ +"""This module is for testing the senselab package.""" diff --git a/src/tests/audio/__init__.py b/src/tests/audio/__init__.py new file mode 100644 index 00000000..40d47feb --- /dev/null +++ b/src/tests/audio/__init__.py @@ -0,0 +1 @@ +"""This module contains the tests of the audio utilities.""" diff --git a/src/tests/audio/conftest.py b/src/tests/audio/conftest.py new file mode 100644 index 00000000..d2a7e029 --- /dev/null +++ b/src/tests/audio/conftest.py @@ -0,0 +1,97 @@ +"""This script includes some fixtures for pytest unit testing.""" + +from typing import Callable, List + +import pytest +import torch + +from senselab.audio.data_structures.audio import Audio +from senselab.audio.tasks.preprocessing.preprocessing import concatenate_audios, resample_audios +from senselab.utils.data_structures.device import DeviceType + +# Global variables for file paths +MONO_AUDIO_PATH = "src/tests/data_for_testing/audio_48khz_mono_16bits.wav" +STEREO_AUDIO_PATH = "src/tests/data_for_testing/audio_48khz_stereo_16bits.wav" + + +@pytest.fixture +def mono_audio_sample() -> Audio: + """Fixture for sample mono audio.""" + return Audio.from_filepath(MONO_AUDIO_PATH) + + +@pytest.fixture +def stereo_audio_sample() -> Audio: + """Fixture for sample stereo audio.""" + return Audio.from_filepath(STEREO_AUDIO_PATH) + + +@pytest.fixture +def resampled_mono_audio_sample(mono_audio_sample: Audio, resampling_rate: int = 16000) -> Audio: + """Fixture for resampled mono audio sample.""" + return resample_audios([mono_audio_sample], resampling_rate)[0] + + +@pytest.fixture +def resampled_stereo_audio_sample(stereo_audio_sample: Audio, resampling_rate: int = 16000) -> Audio: + """Fixture for resampled stereo audio sample.""" + return resample_audios([stereo_audio_sample], resampling_rate)[0] + + +@pytest.fixture +def audio_with_metadata() -> Audio: + """Fixture for generating an audio object with metadata. + + Returns: + Audio: An audio object with random noise and metadata. + """ + waveform = torch.randn(1, 16000) # 1 second of random noise + metadata = {"source": "test"} + return Audio(waveform=waveform, sampling_rate=16000, metadata=metadata) + + +@pytest.fixture +def audio_with_different_bit_depths() -> List[Audio]: + """Fixture for generating a list of audio objects with different bit depths. + + Returns: + List[Audio]: A list containing 16-bit and 24-bit audio objects. + """ + waveform_16bit = torch.randn(1, 16000).short() # Simulate 16-bit audio + waveform_24bit = torch.randn(1, 16000).int() # Simulate 24-bit audio + return [ + Audio(waveform=waveform_16bit.float(), sampling_rate=16000), + Audio(waveform=waveform_24bit.float(), sampling_rate=16000), + ] + + +@pytest.fixture +def audio_with_extreme_amplitude() -> Audio: + """Fixture for generating an audio object with extreme amplitude values. + + Returns: + Audio: An audio object with random noise of extreme amplitude. + """ + waveform = torch.randn(1, 16000) * 1e6 # 1 second of extreme amplitude noise + return Audio(waveform=waveform, sampling_rate=16000) + + +@pytest.fixture +def resampled_mono_audio_sample_x2(resampled_mono_audio_sample: Audio) -> Audio: + """Fixture for sample mono audio.""" + return concatenate_audios([resampled_mono_audio_sample, resampled_mono_audio_sample]) + + +@pytest.fixture(scope="session") +def is_device_available() -> Callable[[DeviceType], bool]: + """Check if a device is available.""" + + def _is_device_available(device: DeviceType) -> bool: + """Check if a device is available.""" + if device == DeviceType.CUDA: + return torch.cuda.is_available() + elif device == DeviceType.MPS: + return torch.backends.mps.is_available() + return True # CPU is always available + + return _is_device_available diff --git a/src/tests/audio/data_structures/__init__.py b/src/tests/audio/data_structures/__init__.py new file mode 100644 index 00000000..a977f463 --- /dev/null +++ b/src/tests/audio/data_structures/__init__.py @@ -0,0 +1 @@ +"""This module contains the tests of the audio data structures.""" diff --git a/src/tests/audio/data_structures/audio_test.py b/src/tests/audio/data_structures/audio_test.py new file mode 100644 index 00000000..b136470c --- /dev/null +++ b/src/tests/audio/data_structures/audio_test.py @@ -0,0 +1,77 @@ +"""Module for testing Audio data structures.""" + +import torch +import torchaudio + +from senselab.audio.data_structures.audio import Audio +from tests.audio.conftest import MONO_AUDIO_PATH, STEREO_AUDIO_PATH + + +def load_audio(file_path: str) -> tuple[torch.Tensor, int]: + """Loads audio data from the given file path.""" + return torchaudio.load(file_path) + + +def test_mono_audio_creation(mono_audio_sample: Audio) -> None: + """Tests mono audio creation.""" + mono_audio_data, mono_sr = load_audio(MONO_AUDIO_PATH) + mono_audio = Audio( + waveform=mono_audio_data, + sampling_rate=mono_sr, + orig_path_or_id=MONO_AUDIO_PATH, + ) + assert mono_audio == mono_audio_sample, "Mono audios are not exactly equivalent" + + +def test_stereo_audio_creation(stereo_audio_sample: Audio) -> None: + """Tests stereo audio creation.""" + stereo_audio_data, stereo_sr = load_audio(STEREO_AUDIO_PATH) + stereo_audio = Audio( + waveform=stereo_audio_data, + sampling_rate=stereo_sr, + orig_path_or_id=STEREO_AUDIO_PATH, + ) + assert stereo_audio == stereo_audio_sample, "Stereo audios are not exactly equivalent" + + +def test_stereo_audio_uuid_creation(stereo_audio_sample: Audio) -> None: + """Tests stereo audio creation with different UUID.""" + stereo_audio_data, stereo_sr = load_audio(STEREO_AUDIO_PATH) + stereo_audio_uuid = Audio(waveform=stereo_audio_data, sampling_rate=stereo_sr) + assert stereo_audio_sample == stereo_audio_uuid, "Stereo audio with different IDs should still be equivalent" + + +def test_audio_single_tensor(mono_audio_sample: Audio) -> None: + """Tests mono audio creation with single tensor.""" + mono_audio_data, mono_sr = load_audio(MONO_AUDIO_PATH) + audio_single_tensor = Audio(waveform=mono_audio_data[0], sampling_rate=mono_sr) + assert torch.equal( + mono_audio_sample.waveform, audio_single_tensor.waveform + ), "Mono audios of tensor shape (num_samples,) should be reshaped to (1, num_samples)" + + +def test_audio_from_list(mono_audio_sample: Audio) -> None: + """Tests mono audio creation from list.""" + mono_audio_data, mono_sr = load_audio(MONO_AUDIO_PATH) + audio_from_list = Audio(waveform=list(mono_audio_data[0]), sampling_rate=mono_sr) + assert torch.equal( + mono_audio_sample.waveform, audio_from_list.waveform + ), "List audio should've been converted to Tensor" + + +def test_audio_from_list_of_lists(mono_audio_sample: Audio) -> None: + """Tests mono audio creation from list of lists.""" + mono_audio_data, mono_sr = load_audio(MONO_AUDIO_PATH) + audio_from_list_of_lists = Audio(waveform=[list(mono_audio_data[0])], sampling_rate=mono_sr) + assert torch.equal( + mono_audio_sample.waveform, audio_from_list_of_lists.waveform + ), "List of lists audio should've been converted to Tensor" + + +def test_audio_from_numpy(mono_audio_sample: Audio) -> None: + """Tests mono audio creation from numpy array.""" + mono_audio_data, mono_sr = load_audio(MONO_AUDIO_PATH) + audio_from_numpy = Audio(waveform=mono_audio_data.numpy(), sampling_rate=mono_sr) + assert torch.equal( + mono_audio_sample.waveform, audio_from_numpy.waveform + ), "NumPy audio should've been converted to Tensor" diff --git a/src/tests/audio/tasks/__init__.py b/src/tests/audio/tasks/__init__.py new file mode 100644 index 00000000..5cf7f181 --- /dev/null +++ b/src/tests/audio/tasks/__init__.py @@ -0,0 +1 @@ +"""This module contains the tests of the audio tasks.""" diff --git a/src/tests/audio/tasks/classification_test.py b/src/tests/audio/tasks/classification_test.py new file mode 100644 index 00000000..ffedd5d1 --- /dev/null +++ b/src/tests/audio/tasks/classification_test.py @@ -0,0 +1,37 @@ +"""Test audio classification APIs.""" + +import os + +from senselab.audio.data_structures.audio import Audio +from senselab.audio.tasks.classification.speech_emotion_recognition import speech_emotion_recognition_with_hf_models +from senselab.audio.tasks.preprocessing.preprocessing import resample_audios +from senselab.utils.data_structures.model import HFModel +from tests.audio.conftest import MONO_AUDIO_PATH + +if os.getenv("GITHUB_ACTIONS") != "true": + + def test_speech_emotion_recognition() -> None: + """Tests speech emotion recognition.""" + audio_dataset = [Audio.from_filepath(MONO_AUDIO_PATH)] + + resampled_audios = resample_audios(audio_dataset, 16000) # some pipelines resample for us but can't guarantee + + # Discrete test + result = speech_emotion_recognition_with_hf_models( + resampled_audios, HFModel(path_or_uri="ehcalabres/wav2vec2-lg-xlsr-en-speech-emotion-recognition") + ) + top_emotion, emotion_probs = result[0] + rav_emotions = ["angry", "calm", "disgust", "fearful", "happy", "neutral", "sad", "surprised"] + assert top_emotion in rav_emotions, "Top emotion should be in RAVDESS Dataset" + + for emotion in emotion_probs: + assert emotion in rav_emotions + + # Continuous test + result = speech_emotion_recognition_with_hf_models( + resampled_audios, HFModel(path_or_uri="audeering/wav2vec2-large-robust-12-ft-emotion-msp-dim") + ) + emotion, continuous_values = result[0] + assert emotion in ["arousal", "valence", "dominance"], "No emotion here but rather is one of \ + arousal, valence, or dominance" + assert set(continuous_values.keys()) == set(["arousal", "valence", "dominance"]) diff --git a/src/tests/audio/tasks/data_augmentation_test.py b/src/tests/audio/tasks/data_augmentation_test.py new file mode 100644 index 00000000..415ce4d0 --- /dev/null +++ b/src/tests/audio/tasks/data_augmentation_test.py @@ -0,0 +1,40 @@ +"""Module for testing data augmentation on audios.""" + +import torch +from torch_audiomentations import Compose, PolarityInversion + +from senselab.audio.data_structures.audio import Audio +from senselab.audio.tasks.data_augmentation.data_augmentation import augment_audios +from senselab.utils.data_structures.dataset import SenselabDataset +from tests.audio.conftest import MONO_AUDIO_PATH, STEREO_AUDIO_PATH + + +def test_audio_data_augmentation() -> None: + """Test data augmentations using the new Audio data types.""" + apply_augmentation = Compose(transforms=[PolarityInversion(p=1, output_type="dict")], output_type="dict") + + audio_paths = [ + MONO_AUDIO_PATH, + STEREO_AUDIO_PATH, + ] + audio_dataset_from_paths = SenselabDataset(audios=audio_paths) + mono_audio, stereo_audio = audio_dataset_from_paths.create_audio_split_for_pydra_task() + mono_inverted = augment_audios(mono_audio, apply_augmentation) + stereo_inverted = augment_audios(stereo_audio, apply_augmentation) + assert torch.equal( + mono_audio[0].waveform, -1 * mono_inverted[0].waveform + ), "Audio should have been inverted by the augmentation" + assert torch.equal( + stereo_audio[0].waveform, -1 * stereo_inverted[0].waveform + ), "Audio should have been inverted by the augmentation and not affected by stereo audio" + + batched_audio = SenselabDataset( + audios=[ + Audio(waveform=stereo_audio[0].waveform[0], sampling_rate=stereo_audio[0].sampling_rate), + Audio(waveform=stereo_audio[0].waveform[1], sampling_rate=stereo_audio[0].sampling_rate), + ] + ).create_audio_split_for_pydra_task(2) + batch_inverted = augment_audios(batched_audio[0], apply_augmentation) + assert torch.equal(batched_audio[0][0].waveform, -1 * batch_inverted[0].waveform) and torch.equal( + batched_audio[0][1].waveform, -1 * batch_inverted[1].waveform + ) diff --git a/src/tests/audio/tasks/features_extraction_test.py b/src/tests/audio/tasks/features_extraction_test.py new file mode 100644 index 00000000..d613d0bc --- /dev/null +++ b/src/tests/audio/tasks/features_extraction_test.py @@ -0,0 +1,207 @@ +"""This script contains unit tests for the features extraction tasks.""" + +import os + +import pytest +import torch + +from senselab.audio.data_structures.audio import Audio +from senselab.audio.tasks.features_extraction.opensmile import extract_opensmile_features_from_audios +from senselab.audio.tasks.features_extraction.praat_parselmouth import ( + get_audios_durations, + get_audios_f0_descriptors, + get_audios_harmonicity_descriptors, + get_audios_jitter_descriptors, + get_audios_shimmer_descriptors, +) +from senselab.audio.tasks.features_extraction.torchaudio import ( + extract_mel_filter_bank_from_audios, + extract_mel_spectrogram_from_audios, + extract_mfcc_from_audios, + extract_pitch_from_audios, + extract_spectrogram_from_audios, +) + + +def test_extract_spectrogram_from_audios(resampled_mono_audio_sample: Audio) -> None: + """Test extraction of spectrogram from audio.""" + result = extract_spectrogram_from_audios([resampled_mono_audio_sample]) + assert isinstance(result, list) + assert all(isinstance(spec, dict) for spec in result) + assert all("spectrogram" in spec for spec in result) + assert all(isinstance(spec["spectrogram"], torch.Tensor) for spec in result) + # Spectrogram shape is (freq, time) + assert all(spec["spectrogram"].dim() == 2 for spec in result) + assert all(spec["spectrogram"].shape[0] == 201 for spec in result) + + +def test_extract_mel_spectrogram_from_audios(resampled_mono_audio_sample: Audio) -> None: + """Test extraction of mel spectrogram from audio.""" + result = extract_mel_spectrogram_from_audios([resampled_mono_audio_sample]) + assert isinstance(result, list) + assert all(isinstance(spec, dict) for spec in result) + assert all("mel_spectrogram" in spec for spec in result) + assert all(isinstance(spec["mel_spectrogram"], torch.Tensor) for spec in result) + # Mel spectrogram shape is (n_mels, time) + assert all(spec["mel_spectrogram"].dim() == 2 for spec in result) + assert all(spec["mel_spectrogram"].shape[0] == 128 for spec in result) + + +def test_extract_mfcc_from_audios(resampled_mono_audio_sample: Audio) -> None: + """Test extraction of MFCC from audio.""" + result = extract_mfcc_from_audios([resampled_mono_audio_sample]) + assert isinstance(result, list) + assert all(isinstance(mfcc, dict) for mfcc in result) + assert all("mfcc" in mfcc for mfcc in result) + assert all(isinstance(mfcc["mfcc"], torch.Tensor) for mfcc in result) + # MFCC shape is (n_mfcc, time) + assert all(mfcc["mfcc"].dim() == 2 for mfcc in result) + assert all(mfcc["mfcc"].shape[0] == 40 for mfcc in result) + + +def test_extract_mel_filter_bank(resampled_mono_audio_sample: Audio) -> None: + """Test extraction of mel filter bank from audio.""" + result = extract_mel_filter_bank_from_audios([resampled_mono_audio_sample]) + assert isinstance(result, list) + assert all(isinstance(mel_fb, dict) for mel_fb in result) + assert all("mel_filter_bank" in mel_fb for mel_fb in result) + assert all(isinstance(mel_fb["mel_filter_bank"], torch.Tensor) for mel_fb in result) + # Mel filter bank shape is (n_mels, time) + assert all(mel_fb["mel_filter_bank"].dim() == 2 for mel_fb in result) + assert all(mel_fb["mel_filter_bank"].shape[0] == 128 for mel_fb in result) + + +def test_extract_pitch_from_audios(resampled_mono_audio_sample: Audio) -> None: + """Test extraction of pitch from audio.""" + result = extract_pitch_from_audios([resampled_mono_audio_sample]) + assert isinstance(result, list) + assert all(isinstance(pitch, dict) for pitch in result) + assert all("pitch" in pitch for pitch in result) + assert all(isinstance(pitch["pitch"], torch.Tensor) for pitch in result) + # Pitch shape is (time) + assert all(pitch["pitch"].dim() == 1 for pitch in result) + + +def test_get_audios_durations(resampled_mono_audio_sample: Audio) -> None: + """Test extraction of audio durations.""" + result = get_audios_durations([resampled_mono_audio_sample]) + assert isinstance(result, list) + assert all(isinstance(duration, dict) for duration in result) + assert all("duration" in duration for duration in result) + assert all(isinstance(duration["duration"], float) for duration in result) + + +def test_get_audios_f0_descriptors(resampled_mono_audio_sample: Audio) -> None: + """Test extraction of fundamental frequency descriptors from audio.""" + result = get_audios_f0_descriptors([resampled_mono_audio_sample], f0min=75.0, f0max=500.0) + assert isinstance(result, list) + assert all(isinstance(f0, dict) for f0 in result) + assert all("f0_mean_Hertz" in f0 for f0 in result) + assert all("f0_std_dev_Hertz" in f0 for f0 in result) + assert all(isinstance(f0["f0_mean_Hertz"], float) for f0 in result) + assert all(isinstance(f0["f0_std_dev_Hertz"], float) for f0 in result) + + +def test_get_audios_harmonicity_descriptors(resampled_mono_audio_sample: Audio) -> None: + """Test extraction of harmonicity descriptors from audio.""" + result = get_audios_harmonicity_descriptors([resampled_mono_audio_sample], f0min=75.0) + assert isinstance(result, list) + assert all(isinstance(harmonicity, dict) for harmonicity in result) + assert all("harmonicity_mean" in harmonicity for harmonicity in result) + assert all("harmonicity_std_dev" in harmonicity for harmonicity in result) + assert all(isinstance(harmonicity["harmonicity_mean"], float) for harmonicity in result) + assert all(isinstance(harmonicity["harmonicity_std_dev"], float) for harmonicity in result) + + +def test_get_audios_jitter_descriptors(resampled_mono_audio_sample: Audio) -> None: + """Test extraction of jitter descriptors from audio.""" + result = get_audios_jitter_descriptors([resampled_mono_audio_sample], f0min=75.0, f0max=500.0) + assert isinstance(result, list) + assert all(isinstance(jitter, dict) for jitter in result) + assert all("local_jitter" in jitter for jitter in result) + assert all("localabsolute_jitter" in jitter for jitter in result) + assert all("rap_jitter" in jitter for jitter in result) + assert all("ppq5_jitter" in jitter for jitter in result) + assert all("ddp_jitter" in jitter for jitter in result) + assert all(isinstance(jitter["local_jitter"], float) for jitter in result) + assert all(isinstance(jitter["localabsolute_jitter"], float) for jitter in result) + assert all(isinstance(jitter["rap_jitter"], float) for jitter in result) + assert all(isinstance(jitter["ppq5_jitter"], float) for jitter in result) + assert all(isinstance(jitter["ddp_jitter"], float) for jitter in result) + + +def test_get_audios_shimmer_descriptors(resampled_mono_audio_sample: Audio) -> None: + """Test extraction of shimmer descriptors from audio.""" + result = get_audios_shimmer_descriptors([resampled_mono_audio_sample], f0min=75.0, f0max=500.0) + assert isinstance(result, list) + assert all(isinstance(shimmer, dict) for shimmer in result) + assert all("local_shimmer" in shimmer for shimmer in result) + assert all("localDB_shimmer" in shimmer for shimmer in result) + assert all("apq3_shimmer" in shimmer for shimmer in result) + assert all("apq5_shimmer" in shimmer for shimmer in result) + assert all("apq11_shimmer" in shimmer for shimmer in result) + assert all("dda_shimmer" in shimmer for shimmer in result) + assert all(isinstance(shimmer["local_shimmer"], float) for shimmer in result) + assert all(isinstance(shimmer["localDB_shimmer"], float) for shimmer in result) + assert all(isinstance(shimmer["apq3_shimmer"], float) for shimmer in result) + assert all(isinstance(shimmer["apq5_shimmer"], float) for shimmer in result) + assert all(isinstance(shimmer["apq11_shimmer"], float) for shimmer in result) + assert all(isinstance(shimmer["dda_shimmer"], float) for shimmer in result) + + +def test_extract_opensmile_features_from_audios(resampled_mono_audio_sample: Audio) -> None: + """Test extraction of openSMILE features from audio.""" + # Perform eGeMAPSv02 and Functionals features extraction + result = extract_opensmile_features_from_audios([resampled_mono_audio_sample]) + + # Assert the result is a list of dictionaries, and check each dictionary + assert isinstance(result, list) + assert all(isinstance(features, dict) for features in result) + + # Ensure that each dictionary contains the expected keys (e.g., certain features from eGeMAPS) + expected_keys = {"F0semitoneFrom27.5Hz_sma3nz_amean", "jitterLocal_sma3nz_amean", "shimmerLocaldB_sma3nz_amean"} + for features in result: + assert set(features.keys()).issuperset(expected_keys) + + # Check the types of the values to ensure they are either floats or integers + for features in result: + assert all(isinstance(value, (float, int)) for value in features.values()) + + +if os.getenv("GITHUB_ACTIONS") != "true": + from senselab.audio.tasks.features_extraction.torchaudio_squim import ( + extract_objective_quality_features_from_audios, + extract_subjective_quality_features_from_audios, + ) + + def test_extract_objective_quality_features_from_audios(resampled_mono_audio_sample: Audio) -> None: + """Test extraction of objective quality features from audio.""" + result = extract_objective_quality_features_from_audios([resampled_mono_audio_sample]) + assert isinstance(result, dict) + assert "stoi" in result + assert "pesq" in result + assert "si_sdr" in result + assert all(isinstance(feature, float) for feature in result["stoi"]) + assert all(isinstance(feature, float) for feature in result["pesq"]) + assert all(isinstance(feature, float) for feature in result["si_sdr"]) + + def test_extract_objective_quality_features_from_audios_invalid_audio(mono_audio_sample: Audio) -> None: + """Test extraction of objective quality features from invalid audio.""" + with pytest.raises(ValueError, match="Only 16000 Hz sampling rate is supported by Torchaudio-Squim model."): + extract_objective_quality_features_from_audios([mono_audio_sample]) + + def test_extract_subjective_quality_features_from_audios(resampled_mono_audio_sample: Audio) -> None: + """Test extraction of subjective quality features from audio.""" + result = extract_subjective_quality_features_from_audios( + audio_list=[resampled_mono_audio_sample], non_matching_references=[resampled_mono_audio_sample] + ) + assert isinstance(result, dict) + assert "mos" in result + assert all(isinstance(feature, float) for feature in result["mos"]) + + def test_extract_subjective_quality_features_invalid_audio(mono_audio_sample: Audio) -> None: + """Test extraction of subjective quality features from invalid audio.""" + with pytest.raises(ValueError, match="Only 16000 Hz sampling rate is supported by Torchaudio-Squim model."): + extract_subjective_quality_features_from_audios( + audio_list=[mono_audio_sample], non_matching_references=[mono_audio_sample] + ) diff --git a/src/tests/audio/tasks/forced_alignment_test.py b/src/tests/audio/tasks/forced_alignment_test.py new file mode 100644 index 00000000..53f8c9b3 --- /dev/null +++ b/src/tests/audio/tasks/forced_alignment_test.py @@ -0,0 +1,208 @@ +"""Tests for forced alignment functions.""" + +import os + +import numpy as np +import pandas as pd +import pytest +import torch +from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor + +from senselab.audio.data_structures.audio import Audio +from senselab.audio.tasks.forced_alignment.data_structures import ( + Point, + SingleSegment, +) +from senselab.audio.tasks.forced_alignment.forced_alignment import ( + _align_segments, + _align_transcription, + _can_align_segment, + _get_prediction_matrix, + _interpolate_nans, + _merge_repeats, + _preprocess_segments, + align_transcriptions, +) +from senselab.utils.data_structures.device import DeviceType +from senselab.utils.data_structures.language import Language +from senselab.utils.data_structures.script_line import ScriptLine + + +@pytest.fixture +def dummy_segment() -> SingleSegment: + """Fixture for a dummy segment.""" + return SingleSegment( + start=0.0, + end=1.0, + text="test", + clean_char=["t", "e", "s", "t"], + clean_cdx=[0, 1, 2, 3], + clean_wdx=[0], + sentence_spans=None, + ) + + +@pytest.fixture +def dummy_model() -> tuple: + """Fixture for a dummy model and processor.""" + model_name = "facebook/wav2vec2-base-960h" + processor = Wav2Vec2Processor.from_pretrained(model_name) + model = Wav2Vec2ForCTC.from_pretrained(model_name) + return model, processor + + +@pytest.fixture +def script_line_fixture() -> ScriptLine: + """Pytest fixture to create a ScriptLine object. + + Returns: + ScriptLine: An instance of ScriptLine. + """ + data = { + "text": "test", + "speaker": "Speaker Name", + "start": 0.0, + "end": 10.0, + "chunks": [ + {"text": "Chunk 1 text", "speaker": "Chunk 1 speaker", "start": 0.0, "end": 5.0}, + {"text": "Chunk 2 text", "speaker": "Chunk 2 speaker", "start": 5.0, "end": 10.0}, + ], + } + return ScriptLine.from_dict(data) + + +def test_preprocess_segments() -> None: + """Test preprocessing of segments.""" + transcript = [SingleSegment(start=0.0, end=1.0, text="test")] + model_dictionary = {"t": 0, "e": 1, "s": 2} + preprocessed_segments = _preprocess_segments( + transcript, model_dictionary, Language(language_code="en"), False, False + ) + assert preprocessed_segments[0]["clean_char"] == ["t", "e", "s", "t"] + + +def test_can_align_segment(dummy_segment: SingleSegment) -> None: + """Test if a segment can be aligned.""" + model_dictionary = {"t": 0, "e": 1, "s": 2} + assert _can_align_segment(dummy_segment, model_dictionary, 0.0, 10.0) + + +def test_merge_repeats() -> None: + """Test merging of repeated tokens.""" + path = [Point(0, 0, 1.0), Point(0, 1, 1.0), Point(1, 2, 1.0)] + transcript = "test" + segments = _merge_repeats(path, transcript) + assert len(segments) == 2 + + +def test_interpolate_nans() -> None: + """Test interpolation of NaN values.""" + series = pd.Series([0.0, np.nan, 2.0]) + interpolated_series = _interpolate_nans(series) + assert interpolated_series.isnull().sum() == 0 + + +if os.getenv("GITHUB_ACTIONS") != "true": + + def test_get_prediction_matrix(dummy_model: tuple) -> None: + """Test generation of prediction matrix.""" + model, _ = dummy_model + waveform_segment = torch.randn(1, 16000) + prediction_matrix = _get_prediction_matrix(model, waveform_segment, None, "huggingface", DeviceType.CPU) + assert prediction_matrix.shape[0] > 0 + + def test_align_segments(mono_audio_sample: Audio, dummy_model: tuple) -> None: + """Test alignment of segments.""" + model, processor = dummy_model + model_dictionary = processor.tokenizer.get_vocab() + + # Create a sample transcript + transcript = [SingleSegment(start=0.0, end=1.0, text="test")] + + # Preprocess the transcript segments + preprocessed_transcript = _preprocess_segments( + transcript, + model_dictionary, + model_lang=Language(language_code="en"), + print_progress=False, + combined_progress=False, + ) + + # Ensure the model dictionary has the necessary keys + for char in "test": + if char not in model_dictionary: + model_dictionary[char] = len(model_dictionary) + + aligned_segments, word_segments = _align_segments( + transcript=preprocessed_transcript, + model=model, + model_dictionary=model_dictionary, + model_lang=Language(language_code="en"), + model_type="huggingface", + audio=mono_audio_sample, + device=DeviceType.CPU, + max_duration=10.0, + return_char_alignments=False, + interpolate_method="nearest", + ) + assert isinstance(aligned_segments, list) + assert isinstance(word_segments, list) + + def test_align_transcription_faked(resampled_mono_audio_sample: Audio, dummy_model: tuple) -> None: + """Test alignment of transcription.""" + model, processor = dummy_model + transcript = [ + SingleSegment( + start=0.0, + end=1.0, + text="test", + clean_char=["t", "e", "s", "t"], + clean_cdx=[0, 1, 2, 3], + clean_wdx=[0], + sentence_spans=None, + ) + ] + aligned_result = _align_transcription( + transcript=transcript, + model=model, + align_model_metadata={ + "dictionary": processor.tokenizer.get_vocab(), + "language": Language(language_code="en"), + "type": "huggingface", + }, + audio=resampled_mono_audio_sample, + device=DeviceType.CPU, + ) + assert "segments" in aligned_result + assert "word_segments" in aligned_result + + def test_align_transcriptions_fixture(resampled_mono_audio_sample: Audio, script_line_fixture: ScriptLine) -> None: + """Test alignment of transcriptions.""" + audios_and_transcriptions_and_language = [ + (resampled_mono_audio_sample, script_line_fixture, Language(language_code="en")), + (resampled_mono_audio_sample, script_line_fixture, Language(language_code="fr")), + ] + aligned_transcriptions = align_transcriptions(audios_and_transcriptions_and_language) + assert len(aligned_transcriptions) == 2 + assert len(aligned_transcriptions[0]) == 1 + assert aligned_transcriptions[0][0].text == "test" + + def test_align_transcriptions_multilingual( + resampled_mono_audio_sample: Audio, script_line_fixture: ScriptLine + ) -> None: + """Test alignment of transcriptions.""" + languages = ["de", "es"] + expected_text = "test" # Replace with the appropriate expected text for your fixtures + + for lang in languages: + audios_and_transcriptions_and_language = [ + (resampled_mono_audio_sample, script_line_fixture, Language(language_code=lang)) + ] + aligned_transcriptions = align_transcriptions(audios_and_transcriptions_and_language) + assert len(aligned_transcriptions) == 1, f"Failed for language: {lang}" + assert len(aligned_transcriptions[0]) == 1, f"Failed for language: {lang}" + assert aligned_transcriptions[0][0].text == expected_text, f"Failed for language: {lang}" + + +if __name__ == "__main__": + pytest.main() diff --git a/src/tests/audio/tasks/preprocessing_test.py b/src/tests/audio/tasks/preprocessing_test.py new file mode 100644 index 00000000..e11e684a --- /dev/null +++ b/src/tests/audio/tasks/preprocessing_test.py @@ -0,0 +1,137 @@ +"""Module for testing the preprocessing functionality of Audios.""" + +import math + +import pytest +import torch +from pytest import FixtureRequest + +from senselab.audio.data_structures.audio import Audio +from senselab.audio.tasks.preprocessing.preprocessing import ( + chunk_audios, + downmix_audios_to_mono, + evenly_segment_audios, + extract_segments, + pad_audios, + select_channel_from_audios, +) + + +def test_resample_audios( + mono_audio_sample: Audio, + stereo_audio_sample: Audio, + resampled_mono_audio_sample: Audio, + resampled_stereo_audio_sample: Audio, +) -> None: + """Tests functionality for resampling Audio objects.""" + resample_rate = 16000 + + for sample, resampled_sample in zip( + [mono_audio_sample, stereo_audio_sample], [resampled_mono_audio_sample, resampled_stereo_audio_sample] + ): + expected_size = sample.waveform.shape[1] / sample.sampling_rate * resample_rate + assert ( + math.ceil(expected_size) == resampled_sample.waveform.shape[1] + ), f"Expected size {math.ceil(expected_size)}, but got {resampled_sample.waveform.shape[1]}" + + +def test_downmix_audios(mono_audio_sample: Audio, stereo_audio_sample: Audio) -> None: + """Tests functionality for downmixing Audio objects.""" + for sample in [mono_audio_sample, stereo_audio_sample]: + down_mixed_audio = downmix_audios_to_mono([sample])[0] + assert down_mixed_audio.waveform.dim() == 2, "Audio should maintain (num_channels, num_samples) shape" + assert down_mixed_audio.waveform.shape[0] == 1, "Audio should be mono after downmixing" + assert down_mixed_audio.waveform.size(1) == sample.waveform.size( + 1 + ), "Downmixed audio should have correct number of samples" + if sample.waveform.shape[0] == 2: + assert torch.isclose( + down_mixed_audio.waveform, sample.waveform.mean(dim=0, keepdim=True) + ).all(), "Downmixed audio should be the mean of the stereo channels" + + +def test_select_channel_from_audios(mono_audio_sample: Audio, stereo_audio_sample: Audio) -> None: + """Tests functionality for selecting a specific channel from Audio objects.""" + for sample in [mono_audio_sample, stereo_audio_sample]: + for channel in range(sample.waveform.shape[0]): + selected_channel_audio = select_channel_from_audios([sample], channel)[0] + assert selected_channel_audio.waveform.shape[0] == 1, "Selected channel audio should be mono" + assert ( + selected_channel_audio.waveform.shape[1] == sample.waveform.shape[1] + ), "Selected channel audio should have correct number of samples" + assert torch.equal( + selected_channel_audio.waveform[0, :], sample.waveform[channel, :] + ), "Selected channel audio should match the original selected channel" + + +def test_extract_segments(resampled_mono_audio_sample: Audio) -> None: + """Test segment extraction.""" + segments = [(0.0, 2.0), (2.0, 4.0)] + extracted = extract_segments([(resampled_mono_audio_sample, segments)]) + for i, segment in enumerate(extracted[0]): + expected_length = int((segments[i][1] - segments[i][0]) * resampled_mono_audio_sample.sampling_rate) + assert segment.waveform.shape[1] == expected_length + print(f"Extracted segment {i+1} has correct length: {segment.waveform.shape[1]} samples") + + +def test_pad_audios(resampled_mono_audio_sample: Audio, resampled_stereo_audio_sample: Audio) -> None: + """Test audio padding.""" + desired_samples = 1000000 + padded_mono_audio = pad_audios([resampled_mono_audio_sample], desired_samples)[0] + padded_stereo_audio = pad_audios([resampled_stereo_audio_sample], desired_samples)[0] + assert padded_mono_audio.waveform.shape[1] == desired_samples + assert padded_stereo_audio.waveform.shape[1] == desired_samples + assert len(padded_stereo_audio.waveform[0]) == desired_samples + assert len(padded_stereo_audio.waveform[1]) == desired_samples + + +@pytest.mark.parametrize( + "audio_sample_fixture, segment_length", + [ + pytest.param("resampled_mono_audio_sample", 1, id="mono"), + pytest.param("resampled_stereo_audio_sample", 1, id="stereo"), + ], +) +def test_evenly_segment_audios(audio_sample_fixture: str, segment_length: int, request: FixtureRequest) -> None: + """Test even audio segmentation.""" + audio_sample = request.getfixturevalue(audio_sample_fixture) + + segments = evenly_segment_audios([audio_sample], segment_length, pad_last_segment=True) + for i, segment in enumerate(segments[0]): + expected_length = int(segment_length * audio_sample.sampling_rate) + for channel in segment.waveform: + assert channel.shape[0] == expected_length + + +def test_chunk_audios(mono_audio_sample: Audio) -> None: + """Tests functionality for chunking Audio objects.""" + audio_duration = mono_audio_sample.waveform.shape[1] / mono_audio_sample.sampling_rate + test_data = [ + (mono_audio_sample, (0.0, 1.0)), # Normal case within bounds + (mono_audio_sample, (1.0, 2.0)), # Normal case within bounds + ] + + chunked_audios = chunk_audios(test_data) + for i, (original_audio, (start, end)) in enumerate(test_data): + start_sample = int(start * original_audio.sampling_rate) + end_sample = int(end * original_audio.sampling_rate) + expected_length = end_sample - start_sample + assert chunked_audios[i].waveform.shape[1] == expected_length + + with pytest.raises(ValueError, match="Start time must be greater than or equal to 0."): + chunk_audios([(mono_audio_sample, (-1.0, 1.0))]) + + with pytest.raises(ValueError) as e: + chunk_audios([(mono_audio_sample, (0.0, audio_duration + 1.0))]) + assert str(e.value) == f"End time must be less than the duration of the audio file ({audio_duration} seconds)." + + chunked_audio = chunk_audios([(mono_audio_sample, (0.0, audio_duration))])[0] + assert chunked_audio.waveform.shape[1] == mono_audio_sample.waveform.shape[1] + + +def test_concatenate_audios(resampled_mono_audio_sample: Audio, resampled_mono_audio_sample_x2: Audio) -> None: + """Tests functionality for concatenating Audio objects.""" + assert torch.equal( + resampled_mono_audio_sample_x2.waveform, + torch.cat([resampled_mono_audio_sample.waveform, resampled_mono_audio_sample.waveform], dim=1), + ) diff --git a/src/tests/audio/tasks/speaker_diarization_test.py b/src/tests/audio/tasks/speaker_diarization_test.py new file mode 100644 index 00000000..48d5b07e --- /dev/null +++ b/src/tests/audio/tasks/speaker_diarization_test.py @@ -0,0 +1,60 @@ +"""Tests for speaker diarization.""" + +import os + +if os.getenv("GITHUB_ACTIONS") != "true": + import pytest + + from senselab.audio.data_structures.audio import Audio + from senselab.audio.tasks.speaker_diarization.api import diarize_audios + from senselab.audio.tasks.speaker_diarization.pyannote import PyannoteDiarization, diarize_audios_with_pyannote + from senselab.utils.data_structures.device import DeviceType + from senselab.utils.data_structures.model import PyannoteAudioModel + from senselab.utils.data_structures.script_line import ScriptLine + + @pytest.fixture + def pyannote_model() -> PyannoteAudioModel: + """Fixture for Pyannote model.""" + return PyannoteAudioModel(path_or_uri="pyannote/speaker-diarization-3.1") + + def test_diarize_audios(resampled_mono_audio_sample: Audio, pyannote_model: PyannoteAudioModel) -> None: + """Test diarizing audios.""" + results = diarize_audios(audios=[resampled_mono_audio_sample], model=pyannote_model) + assert len(results) == 1 + assert isinstance(results[0][0], ScriptLine) + + def test_diarize_audios_with_pyannote( + resampled_mono_audio_sample: Audio, pyannote_model: PyannoteAudioModel + ) -> None: + """Test diarizing audios with Pyannote.""" + results = diarize_audios_with_pyannote( + audios=[resampled_mono_audio_sample], model=pyannote_model, device=DeviceType.CPU, num_speakers=2 + ) + assert len(results) == 1 + assert isinstance(results[0][0], ScriptLine) + + def test_pyannote_pipeline_factory(pyannote_model: PyannoteAudioModel) -> None: + """Test Pyannote pipeline factory.""" + pipeline1 = PyannoteDiarization._get_pyannote_diarization_pipeline( + model=pyannote_model, + device=DeviceType.CPU, + ) + pipeline2 = PyannoteDiarization._get_pyannote_diarization_pipeline( + model=pyannote_model, + device=DeviceType.CPU, + ) + assert pipeline1 is pipeline2 # Check if the same instance is returned + + def test_diarize_audios_with_pyannote_invalid_sampling_rate( + mono_audio_sample: Audio, pyannote_model: PyannoteAudioModel + ) -> None: + """Test diarizing audios with unsupported sampling_rate.""" + with pytest.raises(ValueError): + diarize_audios(audios=[mono_audio_sample], model=pyannote_model) + + def test_diarize_stereo_audios_with_pyannote_invalid( + resampled_stereo_audio_sample: Audio, pyannote_model: PyannoteAudioModel + ) -> None: + """Test diarizing audios with unsupported number of channels.""" + with pytest.raises(ValueError): + diarize_audios(audios=[resampled_stereo_audio_sample], model=pyannote_model) diff --git a/src/tests/audio/tasks/speaker_embeddings_test.py b/src/tests/audio/tasks/speaker_embeddings_test.py new file mode 100644 index 00000000..52258549 --- /dev/null +++ b/src/tests/audio/tasks/speaker_embeddings_test.py @@ -0,0 +1,145 @@ +"""Tests for speaker_embeddings.py.""" + +import os + +if os.getenv("GITHUB_ACTIONS") != "true": + import pytest + from torch import Tensor + + from senselab.audio.data_structures.audio import Audio + from senselab.audio.tasks.speaker_embeddings.api import extract_speaker_embeddings_from_audios + from senselab.utils.data_structures.model import SenselabModel, SpeechBrainModel + + @pytest.fixture + def ecapa_model() -> SpeechBrainModel: + """Fixture for the ECAPA-TDNN model.""" + return SpeechBrainModel(path_or_uri="speechbrain/spkrec-ecapa-voxceleb", revision="main") + + @pytest.fixture + def xvector_model() -> SpeechBrainModel: + """Fixture for the xvector model.""" + return SpeechBrainModel(path_or_uri="speechbrain/spkrec-xvect-voxceleb", revision="main") + + @pytest.fixture + def resnet_model() -> SpeechBrainModel: + """Fixture for the ResNet model.""" + return SpeechBrainModel(path_or_uri="speechbrain/spkrec-resnet-voxceleb", revision="main") + + def test_extract_speaker_embeddings_from_audio( + resampled_mono_audio_sample: Audio, + ecapa_model: SpeechBrainModel, + xvector_model: SpeechBrainModel, + resnet_model: SpeechBrainModel, + ) -> None: + """Test extracting speaker embeddings from audio.""" + embeddings = extract_speaker_embeddings_from_audios(audios=[resampled_mono_audio_sample], model=ecapa_model) + assert isinstance(embeddings, list) and all(isinstance(embedding, Tensor) for embedding in embeddings) + assert all(embedding.size(0) == 192 for embedding in embeddings) + + embeddings = extract_speaker_embeddings_from_audios(audios=[resampled_mono_audio_sample], model=xvector_model) + assert isinstance(embeddings, list) and all(isinstance(embedding, Tensor) for embedding in embeddings) + assert all(embedding.size(0) == 512 for embedding in embeddings) + + embeddings = extract_speaker_embeddings_from_audios(audios=[resampled_mono_audio_sample], model=resnet_model) + assert isinstance(embeddings, list) and all(isinstance(embedding, Tensor) for embedding in embeddings) + assert all(embedding.size(0) == 256 for embedding in embeddings) + + def test_extract_speaker_embeddings_from_multiple_audios( + resampled_mono_audio_sample: Audio, + ecapa_model: SpeechBrainModel, + xvector_model: SpeechBrainModel, + resnet_model: SpeechBrainModel, + ) -> None: + """Test extracting speaker embeddings from multiple audios.""" + embeddings = extract_speaker_embeddings_from_audios( + audios=[resampled_mono_audio_sample, resampled_mono_audio_sample], model=ecapa_model + ) + assert isinstance(embeddings, list) and all(isinstance(embedding, Tensor) for embedding in embeddings) + assert all(embedding.size(0) == 192 for embedding in embeddings) + + embeddings = extract_speaker_embeddings_from_audios( + audios=[resampled_mono_audio_sample, resampled_mono_audio_sample], model=xvector_model + ) + assert isinstance(embeddings, list) and all(isinstance(embedding, Tensor) for embedding in embeddings) + assert all(embedding.size(0) == 512 for embedding in embeddings) + + embeddings = extract_speaker_embeddings_from_audios( + audios=[resampled_mono_audio_sample, resampled_mono_audio_sample], model=resnet_model + ) + assert isinstance(embeddings, list) and all(isinstance(embedding, Tensor) for embedding in embeddings) + assert all(embedding.size(0) == 256 for embedding in embeddings) + + def test_extract_speaker_embeddings_from_multiple_audios_different_sizes( + resampled_mono_audio_sample: Audio, + resampled_mono_audio_sample_x2: Audio, + ecapa_model: SpeechBrainModel, + xvector_model: SpeechBrainModel, + resnet_model: SpeechBrainModel, + ) -> None: + """Test extracting speaker embeddings from multiple audios of differing lengths.""" + embeddings = extract_speaker_embeddings_from_audios( + audios=[resampled_mono_audio_sample, resampled_mono_audio_sample_x2], model=ecapa_model + ) + assert isinstance(embeddings, list) and all(isinstance(embedding, Tensor) for embedding in embeddings) + assert all(embedding.size(0) == 192 for embedding in embeddings) + + embeddings = extract_speaker_embeddings_from_audios( + audios=[resampled_mono_audio_sample, resampled_mono_audio_sample_x2], model=xvector_model + ) + assert isinstance(embeddings, list) and all(isinstance(embedding, Tensor) for embedding in embeddings) + assert all(embedding.size(0) == 512 for embedding in embeddings) + + embeddings = extract_speaker_embeddings_from_audios( + audios=[resampled_mono_audio_sample, resampled_mono_audio_sample_x2], model=resnet_model + ) + assert isinstance(embeddings, list) and all(isinstance(embedding, Tensor) for embedding in embeddings) + assert all(embedding.size(0) == 256 for embedding in embeddings) + + def test_error_wrong_model(resampled_mono_audio_sample: Audio) -> None: + """Test raising error when using a non-existent model.""" + with pytest.raises(ValueError): + extract_speaker_embeddings_from_audios( + audios=[resampled_mono_audio_sample], model=SpeechBrainModel(path_or_uri="nonexistent---") + ) + with pytest.raises(NotImplementedError): + extract_speaker_embeddings_from_audios( + audios=[resampled_mono_audio_sample], model=SenselabModel(path_or_uri="nonexistent---") + ) + + def test_extract_speechbrain_speaker_embeddings_from_audio_resampled( + mono_audio_sample: Audio, + ecapa_model: SpeechBrainModel, + xvector_model: SpeechBrainModel, + resnet_model: SpeechBrainModel, + ) -> None: + """Test extracting speaker embeddings from audio.""" + # Testing with the ecapa model + with pytest.raises(ValueError): + extract_speaker_embeddings_from_audios(audios=[mono_audio_sample], model=ecapa_model) + + # Testing with the xvector model + with pytest.raises(ValueError): + extract_speaker_embeddings_from_audios(audios=[mono_audio_sample], model=xvector_model) + + # Testing with the resnet model + with pytest.raises(ValueError): + extract_speaker_embeddings_from_audios(audios=[mono_audio_sample], model=resnet_model) + + def test_extract_speechbrain_speaker_embeddings_from_stereo_audio( + stereo_audio_sample: Audio, + ecapa_model: SpeechBrainModel, + xvector_model: SpeechBrainModel, + resnet_model: SpeechBrainModel, + ) -> None: + """Test extracting speaker embeddings from audio.""" + # Testing with the ecapa model + with pytest.raises(ValueError): + extract_speaker_embeddings_from_audios(audios=[stereo_audio_sample], model=ecapa_model) + + # Testing with the xvector model + with pytest.raises(ValueError): + extract_speaker_embeddings_from_audios(audios=[stereo_audio_sample], model=xvector_model) + + # Testing with the resnet model + with pytest.raises(ValueError): + extract_speaker_embeddings_from_audios(audios=[stereo_audio_sample], model=resnet_model) diff --git a/src/tests/audio/tasks/speaker_verification_test.py b/src/tests/audio/tasks/speaker_verification_test.py new file mode 100644 index 00000000..f42ccd38 --- /dev/null +++ b/src/tests/audio/tasks/speaker_verification_test.py @@ -0,0 +1,40 @@ +"""Test Module for Audio Processing and Speaker Verification. + +This module contains minimal tests to ensure the audio processing and speaker verification functions do not fail. + +Tests: + - test_resample_iir: Tests the resample_iir function. + - test_verify_speaker: Tests the verify_speaker function. + - test_verify_speaker_from_files: Tests the verify_speaker_from_files function. +""" + +import os + +import pytest + +from senselab.audio.data_structures.audio import Audio +from senselab.audio.tasks.preprocessing.preprocessing import resample_audios +from senselab.audio.tasks.speaker_verification.speaker_verification import ( + verify_speaker, +) + +if os.getenv("GITHUB_ACTIONS") != "true": + + @pytest.mark.large_model + def test_verify_speaker(mono_audio_sample: Audio) -> None: + """Tests the verify_speaker function to ensure it does not fail. + + Args: + mono_audio_sample (Audio): The mono audio sample to use for testing. + + Returns: + None + """ + mono_audio_sample = resample_audios([mono_audio_sample], 16000)[0] + assert mono_audio_sample.sampling_rate == 16000 + mono_audio_samples = [(mono_audio_sample, mono_audio_sample)] * 3 + scores_and_predictions = verify_speaker(mono_audio_samples) + assert scores_and_predictions + assert len(scores_and_predictions[0]) == 2 + assert isinstance(scores_and_predictions[0][0], float) + assert isinstance(scores_and_predictions[0][1], bool) diff --git a/src/tests/audio/tasks/speech_enhancement_test.py b/src/tests/audio/tasks/speech_enhancement_test.py new file mode 100644 index 00000000..38e9cf07 --- /dev/null +++ b/src/tests/audio/tasks/speech_enhancement_test.py @@ -0,0 +1,101 @@ +"""Tests for the speech enhancement task.""" + +import os +from typing import List + +if os.getenv("GITHUB_ACTIONS") != "true": + import pytest + from speechbrain.inference.separation import SepformerSeparation as separator + + from senselab.audio.data_structures.audio import Audio + from senselab.audio.tasks.speech_enhancement.api import enhance_audios + from senselab.audio.tasks.speech_enhancement.speechbrain import SpeechBrainEnhancer + from senselab.utils.data_structures.device import DeviceType + from senselab.utils.data_structures.model import SpeechBrainModel + + @pytest.fixture + def speechbrain_model() -> SpeechBrainModel: + """Fixture for Hugging Face model.""" + return SpeechBrainModel(path_or_uri="speechbrain/sepformer-wham16k-enhancement") + + def test_enhance_audios_stereo_audio( + resampled_stereo_audio_sample: Audio, speechbrain_model: SpeechBrainModel + ) -> None: + """Test that enhancing stereo audios raises a ValueError.""" + with pytest.raises(ValueError, match="Audio waveform must be mono"): + SpeechBrainEnhancer.enhance_audios_with_speechbrain( + audios=[resampled_stereo_audio_sample], model=speechbrain_model + ) + + def test_enhance_audios( + resampled_mono_audio_sample: Audio, resampled_mono_audio_sample_x2: Audio, speechbrain_model: SpeechBrainModel + ) -> None: + """Test enhancing audios.""" + enhanced_audios = enhance_audios( + audios=[resampled_mono_audio_sample, resampled_mono_audio_sample_x2], model=speechbrain_model + ) + assert len(enhanced_audios) == 2 + assert isinstance(enhanced_audios[0], Audio) + assert enhanced_audios[0].waveform.shape == resampled_mono_audio_sample.waveform.shape + + def test_speechbrain_enhancer_get_model(speechbrain_model: SpeechBrainModel) -> None: + """Test getting SpeechBrain model.""" + # TODO: add tests like these but with multithreading + model, _, _ = SpeechBrainEnhancer._get_speechbrain_model(model=speechbrain_model, device=DeviceType.CPU) + assert model is not None + assert isinstance(model, separator) + assert ( + model + == SpeechBrainEnhancer._models[ + f"{speechbrain_model.path_or_uri}-{speechbrain_model.revision}-{DeviceType.CPU.value}" + ] + ) + + def test_enhance_audios_with_speechbrain( + resampled_mono_audio_sample: Audio, resampled_mono_audio_sample_x2: Audio, speechbrain_model: SpeechBrainModel + ) -> None: + """Test enhancing audios with SpeechBrain.""" + enhanced_audios = SpeechBrainEnhancer.enhance_audios_with_speechbrain( + audios=[resampled_mono_audio_sample, resampled_mono_audio_sample_x2], model=speechbrain_model + ) + assert len(enhanced_audios) == 2 + assert isinstance(enhanced_audios[0], Audio) + assert enhanced_audios[0].waveform.shape == resampled_mono_audio_sample.waveform.shape + assert enhanced_audios[1].waveform.shape == resampled_mono_audio_sample_x2.waveform.shape + + def test_enhance_audios_incorrect_sampling_rate( + mono_audio_sample: Audio, speechbrain_model: SpeechBrainModel + ) -> None: + """Test enhancing audios with incorrect sampling rate.""" + mono_audio_sample.sampling_rate = 8000 # Incorrect sample rate for this model + with pytest.raises(ValueError, match="Audio sampling rate 8000 does not match expected 16000"): + SpeechBrainEnhancer.enhance_audios_with_speechbrain(audios=[mono_audio_sample], model=speechbrain_model) + + def test_enhance_audios_with_different_bit_depths(audio_with_different_bit_depths: List[Audio]) -> None: + """Test enhancing audios with different bit depths.""" + enhanced_audios = SpeechBrainEnhancer.enhance_audios_with_speechbrain(audios=audio_with_different_bit_depths) + assert len(enhanced_audios) == 2 + for audio in enhanced_audios: + assert isinstance(audio, Audio) + assert audio.waveform.shape == audio_with_different_bit_depths[0].waveform.shape + + def test_enhance_audios_with_metadata(audio_with_metadata: Audio) -> None: + """Test enhancing audios with metadata.""" + enhanced_audios = SpeechBrainEnhancer.enhance_audios_with_speechbrain(audios=[audio_with_metadata]) + assert len(enhanced_audios) == 1 + assert isinstance(enhanced_audios[0], Audio) + assert enhanced_audios[0].metadata == audio_with_metadata.metadata + + def test_enhance_audios_with_extreme_amplitude(audio_with_extreme_amplitude: Audio) -> None: + """Test enhancing audios with extreme amplitude values.""" + enhanced_audios = SpeechBrainEnhancer.enhance_audios_with_speechbrain(audios=[audio_with_extreme_amplitude]) + assert len(enhanced_audios) == 1 + assert isinstance(enhanced_audios[0], Audio) + assert enhanced_audios[0].waveform.shape == audio_with_extreme_amplitude.waveform.shape + + def test_model_caching(resampled_mono_audio_sample: Audio) -> None: + """Test model caching by enhancing audios with the same model multiple times.""" + SpeechBrainEnhancer.enhance_audios_with_speechbrain(audios=[resampled_mono_audio_sample]) + assert len(SpeechBrainEnhancer._models) == 1 + SpeechBrainEnhancer.enhance_audios_with_speechbrain(audios=[resampled_mono_audio_sample]) + assert len(SpeechBrainEnhancer._models) == 1 diff --git a/src/tests/audio/tasks/speech_to_text_evaluation_test.py b/src/tests/audio/tasks/speech_to_text_evaluation_test.py new file mode 100644 index 00000000..f58027ad --- /dev/null +++ b/src/tests/audio/tasks/speech_to_text_evaluation_test.py @@ -0,0 +1,64 @@ +"""Module for testing speech-to-text evaluation.""" + +from senselab.audio.tasks.speech_to_text_evaluation import ( + calculate_cer, + calculate_mer, + calculate_wer, + calculate_wil, + calculate_wip, +) + + +def test_calculate_wer() -> None: + """Tests the calculation of Word Error Rate (WER).""" + reference = "hello world" + hypothesis = "hello duck" + expected_wer = 0.5 + + wer = calculate_wer(reference, hypothesis) + + assert wer == expected_wer, f"Expected WER: {expected_wer}, but got: {wer}" + + +def test_calculate_mer() -> None: + """Tests the calculation of Match Error Rate (MER).""" + reference = "hello world" + hypothesis = "hello duck" + expected_mer = 0.5 + + mer = calculate_mer(reference, hypothesis) + + assert mer == expected_mer, f"Expected MER: {expected_mer}, but got: {mer}" + + +def test_calculate_wil() -> None: + """Tests the calculation of Word Information Lost (WIL).""" + reference = "hello world" + hypothesis = "hello duck" + expected_wil = 0.75 + + wil = calculate_wil(reference, hypothesis) + + assert wil == expected_wil, f"Expected WIL: {expected_wil}, but got: {wil}" + + +def test_calculate_wip() -> None: + """Tests the calculation of Word Information Preserved (WIP).""" + reference = "hello world" + hypothesis = "hello duck" + expected_wip = 0.25 + + wip = calculate_wip(reference, hypothesis) + + assert wip == expected_wip, f"Expected WIP: {expected_wip}, but got: {wip}" + + +def test_calculate_cer() -> None: + """Tests the calculation of Character Error Rate (CER).""" + reference = "hello world" + hypothesis = "hello duck" + expected_cer = 0.45454545454545453 + + cer = calculate_cer(reference, hypothesis) + + assert cer == expected_cer, f"Expected CER: {expected_cer}, but got: {cer}" diff --git a/src/tests/audio/tasks/speech_to_text_test.py b/src/tests/audio/tasks/speech_to_text_test.py new file mode 100644 index 00000000..cb0c26ad --- /dev/null +++ b/src/tests/audio/tasks/speech_to_text_test.py @@ -0,0 +1,125 @@ +"""Tests for the speech to text task.""" + +import os +from typing import Callable + +import pytest + +from senselab.utils.data_structures.script_line import ScriptLine + + +def test_scriptline_from_dict() -> None: + """Test creating ScriptLine from dict.""" + data = { + "text": "Hello world", + "chunks": [{"text": "Hello", "timestamps": [0.0, 1.0]}, {"text": "world", "timestamps": [1.0, 2.0]}], + } + scriptline = ScriptLine.from_dict(data) + + # Ensure chunks is not None before using it + assert scriptline.chunks is not None + assert len(scriptline.chunks) == 2 + assert scriptline.chunks[0].text == "Hello" + assert scriptline.chunks[0].get_timestamps()[0] == 0.0 + assert scriptline.chunks[0].get_timestamps()[1] == 1.0 + + assert scriptline.chunks[1].text == "world" + assert scriptline.chunks[1].get_timestamps()[0] == 1.0 + assert scriptline.chunks[1].get_timestamps()[1] == 2.0 + + +if os.getenv("GITHUB_ACTIONS") != "true": + from senselab.audio.data_structures.audio import Audio + from senselab.audio.tasks.speech_to_text import transcribe_audios + from senselab.audio.tasks.speech_to_text.huggingface import HuggingFaceASR + from senselab.utils.data_structures.device import DeviceType + from senselab.utils.data_structures.language import Language + from senselab.utils.data_structures.model import HFModel + + @pytest.fixture + def hf_model() -> HFModel: + """Fixture for Hugging Face model.""" + return HFModel(path_or_uri="openai/whisper-tiny") + + @pytest.fixture + def hf_model2() -> HFModel: + """Fixture for Hugging Face model.""" + return HFModel(path_or_uri="facebook/seamless-m4t-unity-small") + + @pytest.mark.parametrize("device", [DeviceType.CPU, DeviceType.CUDA]) # MPS is not available for now + def test_hf_asr_pipeline_factory(hf_model: HFModel, device: DeviceType, is_device_available: Callable) -> None: + """Test ASR pipeline factory.""" + if not is_device_available(device): + pytest.skip(f"{device} is not available") + + pipeline1 = HuggingFaceASR._get_hf_asr_pipeline( + model=hf_model, + return_timestamps="word", + max_new_tokens=128, + chunk_length_s=30, + batch_size=1, + device=device, + ) + pipeline2 = HuggingFaceASR._get_hf_asr_pipeline( + model=hf_model, + return_timestamps="word", + max_new_tokens=128, + chunk_length_s=30, + batch_size=1, + device=device, + ) + assert pipeline1 is pipeline2 # Check if the same instance is returned (this is the case for serial execution) + + @pytest.mark.parametrize("hf_model", ["hf_model", "hf_model2"], indirect=True) + def test_transcribe_audios( + resampled_mono_audio_sample: Audio, resampled_mono_audio_sample_x2: Audio, hf_model: HFModel + ) -> None: + """Test transcribing audios.""" + transcripts = transcribe_audios( + audios=[resampled_mono_audio_sample, resampled_mono_audio_sample_x2], model=hf_model + ) + assert len(transcripts) == 2 + assert isinstance(transcripts[0], ScriptLine) + assert ( + transcripts[0].text + == "This is Peter. This is Johnny. Kenny. And Joe. We just wanted to take a minute to thank you." + ) + + @pytest.mark.parametrize("hf_model", ["hf_model", "hf_model2"], indirect=True) + def test_transcribe_audios_with_params( + resampled_mono_audio_sample: Audio, resampled_mono_audio_sample_x2: Audio, hf_model: HFModel + ) -> None: + """Test transcribing audios.""" + transcripts = transcribe_audios( + audios=[resampled_mono_audio_sample, resampled_mono_audio_sample_x2], + model=hf_model, + language=Language(language_code="English"), + return_timestamps=False, + ) + assert len(transcripts) == 2 + assert isinstance(transcripts[0], ScriptLine) + # Note: we don't check the transcript because we have noticed that by specifying the language, + # the transcript is not correct with our sample audio + + def test_transcribe_audios_with_unsupported_params( + resampled_mono_audio_sample: Audio, resampled_mono_audio_sample_x2: Audio, hf_model: HFModel + ) -> None: + """Test transcribing audios with an unsupported param.""" + with pytest.raises(TypeError, match="got an unexpected keyword argument"): + transcribe_audios( + audios=[resampled_mono_audio_sample, resampled_mono_audio_sample_x2], + model=hf_model, + unsupported_param="unsupported_param", + ) + + def test_transcribe_stereo_audio(resampled_stereo_audio_sample: Audio, hf_model: HFModel) -> None: + """Test transcribing stereo audio.""" + # Create a mock stereo audio sample + with pytest.raises(ValueError, match="Stereo audio is not supported"): + transcribe_audios(audios=[resampled_stereo_audio_sample], model=hf_model) + + def test_transcribe_audio_with_wrong_sampling_rate(mono_audio_sample: Audio, hf_model: HFModel) -> None: + """Test transcribing stereo audio.""" + # Create a mock stereo audio sample + with pytest.raises(ValueError, match="Incorrect sampling rate."): + transcribe_audios(audios=[mono_audio_sample], model=hf_model) diff --git a/src/tests/audio/tasks/text_to_speech_test.py b/src/tests/audio/tasks/text_to_speech_test.py new file mode 100644 index 00000000..2832f43f --- /dev/null +++ b/src/tests/audio/tasks/text_to_speech_test.py @@ -0,0 +1,42 @@ +"""Tests for the text to speech task.""" + +import os + +if os.getenv("GITHUB_ACTIONS") != "true": + import pytest + + from senselab.audio.data_structures.audio import Audio + from senselab.audio.tasks.text_to_speech.api import HuggingFaceTTS, synthesize_texts + from senselab.utils.data_structures.device import DeviceType + from senselab.utils.data_structures.model import HFModel, SenselabModel + + @pytest.fixture + def hf_model() -> HFModel: + """Fixture for HF model.""" + return HFModel(path_or_uri="suno/bark-small", revision="main") + + def test_synthesize_texts(hf_model: HFModel) -> None: + """Test synthesizing texts.""" + texts = ["Hello world"] + audios = synthesize_texts(texts=texts, model=hf_model) + + assert len(audios) == 1 + assert isinstance(audios[0], Audio) + assert audios[0].waveform is not None + assert audios[0].sampling_rate > 0 + + def test_huggingface_tts_pipeline_factory(hf_model: HFModel) -> None: + """Test Hugging Face TTS pipeline factory.""" + device = DeviceType.CPU + pipeline1 = HuggingFaceTTS._get_hf_tts_pipeline(model=hf_model, device=device) + pipeline2 = HuggingFaceTTS._get_hf_tts_pipeline(model=hf_model, device=device) + + assert pipeline1 is pipeline2 # Check if the same instance is returned + + def test_invalid_model() -> None: + """Test synthesize_texts with invalid model.""" + texts = ["Hello world"] + model = SenselabModel(path_or_uri="-----", revision="main") + + with pytest.raises(NotImplementedError, match="Only Hugging Face models are supported for now."): + synthesize_texts(texts=texts, model=model) diff --git a/src/tests/audio/tasks/voice_activity_detection_test.py b/src/tests/audio/tasks/voice_activity_detection_test.py new file mode 100644 index 00000000..2b9c1ced --- /dev/null +++ b/src/tests/audio/tasks/voice_activity_detection_test.py @@ -0,0 +1,34 @@ +"""Tests for voice activity detection.""" + +import os + +import pytest + +from senselab.audio.data_structures.audio import Audio +from senselab.audio.tasks.voice_activity_detection.api import detect_human_voice_activity_in_audios +from senselab.utils.data_structures.model import SenselabModel + + +def test_detect_human_voice_activity_in_audios_with_invalid_model(mono_audio_sample: Audio) -> None: + """Test detecting human voice activity with an invalid model.""" + with pytest.raises(NotImplementedError): + detect_human_voice_activity_in_audios( + audios=[mono_audio_sample], model=SenselabModel(path_or_uri="some/invalid-model") + ) + + +if os.getenv("GITHUB_ACTIONS") != "true": + from senselab.utils.data_structures.model import PyannoteAudioModel + + @pytest.fixture + def pyannote_model() -> PyannoteAudioModel: + """Fixture for Pyannote model.""" + return PyannoteAudioModel(path_or_uri="pyannote/speaker-diarization-3.1") + + def test_detect_human_voice_activity_in_audios( + resampled_mono_audio_sample: Audio, pyannote_model: PyannoteAudioModel + ) -> None: + """Test detecting human voice activity in audios.""" + results = detect_human_voice_activity_in_audios(audios=[resampled_mono_audio_sample], model=pyannote_model) + assert len(results) == 1 + assert all(chunk.speaker == "VOICE" for chunk in results[0]) diff --git a/src/tests/audio/tasks/voice_cloning_test.py b/src/tests/audio/tasks/voice_cloning_test.py new file mode 100644 index 00000000..749e4bce --- /dev/null +++ b/src/tests/audio/tasks/voice_cloning_test.py @@ -0,0 +1,120 @@ +"""This script is for testing the voice cloning API.""" + +import os + +if os.getenv("GITHUB_ACTIONS") != "true": + import pytest + + from senselab.audio.data_structures.audio import Audio + from senselab.audio.tasks.voice_cloning.api import clone_voices + from senselab.utils.data_structures.device import DeviceType + from senselab.utils.data_structures.model import TorchModel + + @pytest.fixture + def torch_model() -> TorchModel: + """Fixture for torch model.""" + return TorchModel(path_or_uri="bshall/knn-vc", revision="master") + + def test_clone_voices_length_mismatch(resampled_mono_audio_sample: Audio, torch_model: TorchModel) -> None: + """Test length mismatch in source and target audios.""" + source_audios = [resampled_mono_audio_sample] + target_audios = [resampled_mono_audio_sample, resampled_mono_audio_sample] + + with pytest.raises(ValueError, match="Source and target audios must have the same length."): + clone_voices( + source_audios=source_audios, target_audios=target_audios, model=torch_model, device=DeviceType.CPU + ) + + def test_clone_voices_invalid_topk(resampled_mono_audio_sample: Audio, torch_model: TorchModel) -> None: + """Test invalid topk value.""" + source_audios = [resampled_mono_audio_sample] + target_audios = [resampled_mono_audio_sample] + + with pytest.raises(ValueError, match="topk must be an integer."): + clone_voices( + source_audios=source_audios, + target_audios=target_audios, + model=torch_model, + device=DeviceType.CPU, + topk="invalid", # type: ignore[arg-type] + ) + + def test_clone_voices_invalid_prematched_vocoder( + resampled_mono_audio_sample: Audio, torch_model: TorchModel + ) -> None: + """Test invalid prematched_vocoder value.""" + source_audios = [resampled_mono_audio_sample] + target_audios = [resampled_mono_audio_sample] + + with pytest.raises(ValueError, match="prematched_vocoder must be a boolean."): + clone_voices( + source_audios=source_audios, + target_audios=target_audios, + model=torch_model, + device=DeviceType.CPU, + prematched_vocoder="invalid", # type: ignore[arg-type] + ) + + def test_clone_voices_valid_input(resampled_mono_audio_sample: Audio, torch_model: TorchModel) -> None: + """Test cloning voices with valid input.""" + source_audios = [resampled_mono_audio_sample] + target_audios = [resampled_mono_audio_sample] + + try: + cloned_output = clone_voices( + source_audios=source_audios, + target_audios=target_audios, + model=torch_model, + device=DeviceType.CPU, + topk=5, + prematched_vocoder=False, + ) + assert isinstance(cloned_output, list), "Output must be a list." + assert len(cloned_output) == 1, "Output list should contain exactly one audio sample." + assert isinstance(cloned_output[0], Audio), "Each item in the output list should be an instance of Audio." + source_duration = source_audios[0].waveform.shape[1] + cloned_duration = cloned_output[0].waveform.shape[1] + + # Set tolerance to 1% of source duration + tolerance = 0.01 * source_duration + + # Check if the absolute difference is within the tolerance + assert abs(source_duration - cloned_duration) <= tolerance, ( + f"Cloned audio duration is not within acceptable range. Source: {source_duration}, " + f"Cloned: {cloned_duration}" + ) + + except Exception as e: + pytest.fail(f"An unexpected exception occurred: {e}") + + def test_clone_voices_unsupported_model(resampled_mono_audio_sample: Audio) -> None: + """Test unsupported model.""" + source_audios = [resampled_mono_audio_sample] + target_audios = [resampled_mono_audio_sample] + # this uri doesn't exist + unsupported_model = TorchModel(path_or_uri="sensein/senselab", revision="main") + + with pytest.raises(NotImplementedError, match="Only KNNVC is supported for now."): + clone_voices( + source_audios=source_audios, target_audios=target_audios, model=unsupported_model, device=DeviceType.CPU + ) + + def test_clone_voices_stereo_audio(resampled_stereo_audio_sample: Audio, torch_model: TorchModel) -> None: + """Test unsupported stereo audio.""" + source_audios = [resampled_stereo_audio_sample] + target_audios = [resampled_stereo_audio_sample] + + with pytest.raises(ValueError, match="Only mono audio files are supported."): + clone_voices( + source_audios=source_audios, target_audios=target_audios, model=torch_model, device=DeviceType.CPU + ) + + def test_clone_voices_invalid_sampling_rate(mono_audio_sample: Audio, torch_model: TorchModel) -> None: + """Test unsupported sampling rate.""" + source_audios = [mono_audio_sample] + target_audios = [mono_audio_sample] + + with pytest.raises(ValueError, match="Only 16000 sampling rate is supported."): + clone_voices( + source_audios=source_audios, target_audios=target_audios, model=torch_model, device=DeviceType.CPU + ) diff --git a/src/tests/data_for_testing/audio_48khz_mono_16bits.wav b/src/tests/data_for_testing/audio_48khz_mono_16bits.wav new file mode 100644 index 0000000000000000000000000000000000000000..3d3f3f70cdb16fed9eac67854328897e791136b5 GIT binary patch literal 472488 zcmY(r1)LN|v^`u^En};=ySo!0xLa@u?(P!YU4sXQ;O-hUI3W-$!3plW%i7Fzcgg?E z^76j-(Z8LY?dj>Nx^?B;b8pkUew{iS%eqLL>aA<{8aN_H6d{D-n0yLvMp42@6zS1* 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