chore: bump version to 3.4.2

* fix: improve handling of speaker embeddings in transcribe_task

* chore: bump version to 3.4.1

pyproject.toml

@@ -2,7 +2,7 @@
 urls = { repository = "https://github.com/m-bain/whisperx" }
 authors = [{ name = "Max Bain" }]
 name = "whisperx"
-version = "3.3.4"
+version = "3.4.2"
 description = "Time-Accurate Automatic Speech Recognition using Whisper."
 readme = "README.md"
 requires-python = ">=3.9, <3.13"

whisperx/__main__.py

@@ -44,6 +44,7 @@ def cli():
     parser.add_argument("--min_speakers", default=None, type=int, help="Minimum number of speakers to in audio file")
     parser.add_argument("--max_speakers", default=None, type=int, help="Maximum number of speakers to in audio file")
     parser.add_argument("--diarize_model", default="pyannote/speaker-diarization-3.1", type=str, help="Name of the speaker diarization model to use")
+    parser.add_argument("--speaker_embeddings", action="store_true", help="Include speaker embeddings in JSON output (only works with --diarize)")
 
     parser.add_argument("--temperature", type=float, default=0, help="temperature to use for sampling")
     parser.add_argument("--best_of", type=optional_int, default=5, help="number of candidates when sampling with non-zero temperature")

whisperx/alignment.py

@@ -424,7 +424,7 @@ def get_wildcard_emission(frame_emission, tokens, blank_id):
     wildcard_mask = (tokens == -1)
 
     # Get scores for non-wildcard positions
-    regular_scores = frame_emission[tokens.clamp(min=0)]  # clamp to avoid -1 index
+    regular_scores = frame_emission[tokens.clamp(min=0).long()]  # clamp to avoid -1 index
 
     # Create a mask and compute the maximum value without modifying frame_emission
     max_valid_score = frame_emission.clone()   # Create a copy

whisperx/diarize.py

@@ -26,25 +26,81 @@ class DiarizationPipeline:
         num_speakers: Optional[int] = None,
         min_speakers: Optional[int] = None,
         max_speakers: Optional[int] = None,
-    ):
+        return_embeddings: bool = False,
+    ) -> Union[tuple[pd.DataFrame, Optional[dict[str, list[float]]]], pd.DataFrame]:
+        """
+        Perform speaker diarization on audio.
+
+        Args:
+            audio: Path to audio file or audio array
+            num_speakers: Exact number of speakers (if known)
+            min_speakers: Minimum number of speakers to detect
+            max_speakers: Maximum number of speakers to detect
+            return_embeddings: Whether to return speaker embeddings
+
+        Returns:
+            If return_embeddings is True:
+                Tuple of (diarization dataframe, speaker embeddings dictionary)
+            Otherwise:
+                Just the diarization dataframe
+        """
         if isinstance(audio, str):
             audio = load_audio(audio)
         audio_data = {
             'waveform': torch.from_numpy(audio[None, :]),
             'sample_rate': SAMPLE_RATE
         }
-        segments = self.model(audio_data, num_speakers = num_speakers, min_speakers=min_speakers, max_speakers=max_speakers)
-        diarize_df = pd.DataFrame(segments.itertracks(yield_label=True), columns=['segment', 'label', 'speaker'])
+
+        if return_embeddings:
+            diarization, embeddings = self.model(
+                audio_data,
+                num_speakers=num_speakers,
+                min_speakers=min_speakers,
+                max_speakers=max_speakers,
+                return_embeddings=True,
+            )
+        else:
+            diarization = self.model(
+                audio_data,
+                num_speakers=num_speakers,
+                min_speakers=min_speakers,
+                max_speakers=max_speakers,
+            )
+            embeddings = None
+
+        diarize_df = pd.DataFrame(diarization.itertracks(yield_label=True), columns=['segment', 'label', 'speaker'])
         diarize_df['start'] = diarize_df['segment'].apply(lambda x: x.start)
         diarize_df['end'] = diarize_df['segment'].apply(lambda x: x.end)
-        return diarize_df
+
+        if return_embeddings and embeddings is not None:
+            speaker_embeddings = {speaker: embeddings[s].tolist() for s, speaker in enumerate(diarization.labels())}
+            return diarize_df, speaker_embeddings
+        
+        # For backwards compatibility
+        if return_embeddings:
+            return diarize_df, None
+        else:
+            return diarize_df
 
 
 def assign_word_speakers(
     diarize_df: pd.DataFrame,
     transcript_result: Union[AlignedTranscriptionResult, TranscriptionResult],
-    fill_nearest=False,
-) -> dict:
+    speaker_embeddings: Optional[dict[str, list[float]]] = None,
+    fill_nearest: bool = False,
+) -> Union[AlignedTranscriptionResult, TranscriptionResult]:
+    """
+    Assign speakers to words and segments in the transcript.
+
+    Args:
+        diarize_df: Diarization dataframe from DiarizationPipeline
+        transcript_result: Transcription result to augment with speaker labels
+        speaker_embeddings: Optional dictionary mapping speaker IDs to embedding vectors
+        fill_nearest: If True, assign speakers even when there's no direct time overlap
+
+    Returns:
+        Updated transcript_result with speaker assignments and optionally embeddings
+    """
     transcript_segments = transcript_result["segments"]
     for seg in transcript_segments:
         # assign speaker to segment (if any)
@@ -75,8 +131,12 @@ def assign_word_speakers(
                         # sum over speakers
                         speaker = dia_tmp.groupby("speaker")["intersection"].sum().sort_values(ascending=False).index[0]
                         word["speaker"] = speaker
-        
-    return transcript_result            
+
+    # Add speaker embeddings to the result if provided
+    if speaker_embeddings is not None:
+        transcript_result["speaker_embeddings"] = speaker_embeddings
+
+    return transcript_result
 
 
 class Segment:

whisperx/transcribe.py

@@ -59,6 +59,10 @@ def transcribe_task(args: dict, parser: argparse.ArgumentParser):
     max_speakers: int = args.pop("max_speakers")
     diarize_model_name: str = args.pop("diarize_model")
     print_progress: bool = args.pop("print_progress")
+    return_speaker_embeddings: bool = args.pop("speaker_embeddings")
+
+    if return_speaker_embeddings and not diarize:
+        warnings.warn("--speaker_embeddings has no effect without --diarize")
 
     if args["language"] is not None:
         args["language"] = args["language"].lower()
@@ -209,10 +213,20 @@ def transcribe_task(args: dict, parser: argparse.ArgumentParser):
         results = []
         diarize_model = DiarizationPipeline(model_name=diarize_model_name, use_auth_token=hf_token, device=device)
         for result, input_audio_path in tmp_results:
-            diarize_segments = diarize_model(
-                input_audio_path, min_speakers=min_speakers, max_speakers=max_speakers
+            diarize_result = diarize_model(
+                input_audio_path, 
+                min_speakers=min_speakers, 
+                max_speakers=max_speakers, 
+                return_embeddings=return_speaker_embeddings
             )
-            result = assign_word_speakers(diarize_segments, result)
+
+            if return_speaker_embeddings:
+                diarize_segments, speaker_embeddings = diarize_result
+            else:
+                diarize_segments = diarize_result
+                speaker_embeddings = None
+
+            result = assign_word_speakers(diarize_segments, result, speaker_embeddings)
             results.append((result, input_audio_path))
     # >> Write
     for result, audio_path in results: