Blacken code with good version

src/diart/blocks/base.py

@@ -23,7 +23,7 @@ class HyperParameter:
     @staticmethod
     def from_name(name: Text) -> "HyperParameter":
         """Create a HyperParameter object given its name.
-        
+
         Parameters
         ----------
         name: str

src/diart/blocks/diarization.py

@@ -193,7 +193,9 @@ def __call__(
         for wav, seg, emb in zip(waveforms, segmentations, embeddings):
             # Add timestamps to segmentation
             sw = SlidingWindow(
-                start=wav.extent.start, duration=seg_resolution, step=seg_resolution,
+                start=wav.extent.start,
+                duration=seg_resolution,
+                step=seg_resolution,
             )
             seg = SlidingWindowFeature(seg.cpu().numpy(), sw)
 

src/diart/blocks/vad.py

@@ -125,7 +125,8 @@ def set_timestamp_shift(self, shift: float):
         self.timestamp_shift = shift
 
     def __call__(
-        self, waveforms: Sequence[SlidingWindowFeature],
+        self,
+        waveforms: Sequence[SlidingWindowFeature],
     ) -> Sequence[tuple[Annotation, SlidingWindowFeature]]:
         batch_size = len(waveforms)
         msg = "Pipeline expected at least 1 input"
@@ -152,7 +153,9 @@ def __call__(
         for wav, vad in zip(waveforms, voice_detection):
             # Add timestamps to segmentation
             sw = SlidingWindow(
-                start=wav.extent.start, duration=seg_resolution, step=seg_resolution,
+                start=wav.extent.start,
+                duration=seg_resolution,
+                step=seg_resolution,
             )
             vad = SlidingWindowFeature(vad.cpu().numpy(), sw)
 

src/diart/inference.py

@@ -123,7 +123,9 @@ def __init__(
             )
 
         # Form batches
-        self.stream = self.stream.pipe(ops.buffer_with_count(count=self.batch_size),)
+        self.stream = self.stream.pipe(
+            ops.buffer_with_count(count=self.batch_size),
+        )
 
         if self.do_profile:
             self.stream = self.stream.pipe(
@@ -221,7 +223,8 @@ def __call__(self) -> Annotation:
                 ops.do(StreamingPlot(config.duration, config.latency)),
             )
         observable.subscribe(
-            on_error=self._handle_error, on_completed=self._handle_completion,
+            on_error=self._handle_error,
+            on_completed=self._handle_completion,
         )
         # FIXME if read() isn't blocking, the prediction returned is empty
         self.source.read()
@@ -303,7 +306,10 @@ def get_file_paths(self) -> List[Path]:
         return list(self.speech_path.iterdir())
 
     def run_single(
-        self, pipeline: blocks.Pipeline, filepath: Path, progress_bar: ProgressBar,
+        self,
+        pipeline: blocks.Pipeline,
+        filepath: Path,
+        progress_bar: ProgressBar,
     ) -> Annotation:
         """Run a given pipeline on a given file.
         Note that this method does NOT reset the
@@ -325,7 +331,10 @@ def run_single(
         """
         padding = pipeline.config.get_file_padding(filepath)
         source = src.FileAudioSource(
-            filepath, pipeline.config.sample_rate, padding, pipeline.config.step,
+            filepath,
+            pipeline.config.sample_rate,
+            padding,
+            pipeline.config.step,
         )
         pipeline.set_timestamp_shift(-padding[0])
         inference = StreamingInference(
@@ -348,7 +357,9 @@ def run_single(
         return pred
 
     def evaluate(
-        self, predictions: List[Annotation], metric: BaseMetric,
+        self,
+        predictions: List[Annotation],
+        metric: BaseMetric,
     ) -> Union[pd.DataFrame, List[Annotation]]:
         """If a reference path was provided,
         compute the diarization error rate of a list of predictions.
@@ -435,7 +446,9 @@ class Parallelize:
     """
 
     def __init__(
-        self, benchmark: Benchmark, num_workers: int = 4,
+        self,
+        benchmark: Benchmark,
+        num_workers: int = 4,
     ):
         self.benchmark = benchmark
         self.num_workers = num_workers

src/diart/mapping.py

@@ -126,12 +126,14 @@ def hard_map(
 
     @staticmethod
     def correlation(scores1: np.ndarray, scores2: np.ndarray) -> SpeakerMap:
-        score_matrix_per_frame = np.stack(  # (local_speakers, num_frames, global_speakers)
-            [
-                scores1[:, speaker : speaker + 1] * scores2
-                for speaker in range(scores1.shape[1])
-            ],
-            axis=0,
+        score_matrix_per_frame = (
+            np.stack(  # (local_speakers, num_frames, global_speakers)
+                [
+                    scores1[:, speaker : speaker + 1] * scores2
+                    for speaker in range(scores1.shape[1])
+                ],
+                axis=0,
+            )
         )
         # Calculate total speech "activations" per local speaker
         local_speech_scores = np.sum(scores1, axis=0).reshape(-1, 1)
@@ -213,7 +215,9 @@ def _loose_check_valid(self, src: int, tgt: int) -> bool:
         return self.is_source_speaker_mapped(src)
 
     def valid_assignments(
-        self, strict: bool = False, as_array: bool = False,
+        self,
+        strict: bool = False,
+        as_array: bool = False,
     ) -> Union[Tuple[List[int], List[int]], Tuple[np.ndarray, np.ndarray]]:
         source, target = [], []
         val_type = "strict" if strict else "loose"

src/diart/models.py

@@ -93,7 +93,9 @@ def recreate_session(self):
             onnxruntime.GraphOptimizationLevel.ORT_ENABLE_ALL
         )
         self.session = onnxruntime.InferenceSession(
-            self.path, sess_options=options, providers=[self.execution_provider],
+            self.path,
+            sess_options=options,
+            providers=[self.execution_provider],
         )
 
     def to(self, device: torch.device) -> ONNXModel:

src/diart/operators.py

@@ -134,7 +134,8 @@ def initial() -> "OutputAccumulationState":
     @property
     def cropped_waveform(self) -> SlidingWindowFeature:
         return SlidingWindowFeature(
-            self.waveform[: self.next_sample], self.waveform.sliding_window,
+            self.waveform[: self.next_sample],
+            self.waveform.sliding_window,
         )
 
     def to_tuple(
@@ -144,7 +145,9 @@ def to_tuple(
 
 
 def accumulate_output(
-    duration: float, step: float, patch_collar: float = 0.05,
+    duration: float,
+    step: float,
+    patch_collar: float = 0.05,
 ) -> Operator:
     """Accumulate predictions and audio to infinity: O(N) space complexity.
     Uses a pre-allocated buffer that doubles its size once full: O(logN) concat operations.

src/diart/sources.py

@@ -238,7 +238,10 @@ def __init__(
         self.server.set_fn_message_received(self._on_message_received)
 
     def _on_message_received(
-        self, client: Dict[Text, Any], server: WebsocketServer, message: AnyStr,
+        self,
+        client: Dict[Text, Any],
+        server: WebsocketServer,
+        message: AnyStr,
     ):
         # Only one client at a time is allowed
         if self.client is None or self.client["id"] != client["id"]: