diff --git a/README.md b/README.md index 80e734a4..c8246c01 100644 --- a/README.md +++ b/README.md @@ -17,16 +17,16 @@

- - 🎙️ Stream audio - - | 💾 Installation | + + 🎙️ Stream audio + + | - 🧠 Available models + 🧠 Models
@@ -44,10 +44,6 @@ 🔬 Research - | - - 👨‍💻 Reproducibility -

@@ -66,8 +62,8 @@ create your own AI pipeline, benchmark it, tune its hyper-parameters, and even s - Speaker Diarization - Voice Activity Detection -- Transcription (coming soon) -- [Speaker-Aware Transcription](https://betterprogramming.pub/color-your-captions-streamlining-live-transcriptions-with-diart-and-openais-whisper-6203350234ef) (coming soon) +- Transcription ([coming soon](https://github.com/juanmc2005/diart/pull/144)) +- [Speaker-Aware Transcription](https://betterprogramming.pub/color-your-captions-streamlining-live-transcriptions-with-diart-and-openais-whisper-6203350234ef) ([coming soon](https://github.com/juanmc2005/diart/pull/147)) ## 💾 Installation @@ -234,7 +230,7 @@ optimizer(num_iter=100) This will write results to an sqlite database in `/output/dir`. -### Distributed optimization +### Distributed tuning For bigger datasets, it is sometimes more convenient to run multiple optimization processes in parallel. To do this, create a study on a [recommended DBMS](https://optuna.readthedocs.io/en/stable/tutorial/10_key_features/004_distributed.html#sphx-glr-tutorial-10-key-features-004-distributed-py) (e.g. MySQL or PostgreSQL) making sure that the study and database names match: @@ -278,8 +274,8 @@ import diart.operators as dops from diart.sources import MicrophoneAudioSource from diart.blocks import SpeakerSegmentation, OverlapAwareSpeakerEmbedding -segmentation = SpeakerSegmentation.from_pyannote("pyannote/segmentation") -embedding = OverlapAwareSpeakerEmbedding.from_pyannote("pyannote/embedding") +segmentation = SpeakerSegmentation.from_pretrained("pyannote/segmentation") +embedding = OverlapAwareSpeakerEmbedding.from_pretrained("pyannote/embedding") mic = MicrophoneAudioSource() stream = mic.stream.pipe( @@ -364,7 +360,7 @@ If you found diart useful, please make sure to cite our paper: } ``` -## 👨‍💻 Reproducibility +### Reproducibility ![Results table](https://github.com/juanmc2005/diart/blob/main/table1.png?raw=true)