Merge c72c627d10 into d700b56c9c

pyproject.toml

@@ -2,7 +2,7 @@
 urls = { repository = "https://github.com/m-bain/whisperx" }
 authors = [{ name = "Max Bain" }]
 name = "whisperx"
-version = "3.4.0"
+version = "3.3.4"
 description = "Time-Accurate Automatic Speech Recognition using Whisper."
 readme = "README.md"
 requires-python = ">=3.9, <3.13"

whisperx/__main__.py

@@ -44,7 +44,6 @@ 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

@@ -5,7 +5,7 @@ C. Max Bain
 import math
 
 from dataclasses import dataclass
-from typing import Iterable, Optional, Union, List
+from typing import Iterable, Union, List, Callable, Optional
 
 import numpy as np
 import pandas as pd
@@ -120,6 +120,7 @@ def align(
     return_char_alignments: bool = False,
     print_progress: bool = False,
     combined_progress: bool = False,
+    on_progress: Callable[[int, int], None] = None
 ) -> AlignedTranscriptionResult:
     """
     Align phoneme recognition predictions to known transcription.
@@ -149,6 +150,9 @@ def align(
             percent_complete = (50 + base_progress / 2) if combined_progress else base_progress
             print(f"Progress: {percent_complete:.2f}%...")
 
+        if on_progress:
+            on_progress(sdx + 1, total_segments)
+            
         num_leading = len(segment["text"]) - len(segment["text"].lstrip())
         num_trailing = len(segment["text"]) - len(segment["text"].rstrip())
         text = segment["text"]

whisperx/asr.py

@@ -1,6 +1,8 @@
 import os
-from typing import List, Optional, Union
 from dataclasses import replace
+import warnings
+from typing import List, Union, Optional, NamedTuple, Callable
+from enum import Enum
 
 import ctranslate2
 import faster_whisper
@@ -103,6 +105,12 @@ class FasterWhisperPipeline(Pipeline):
     # - add support for timestamp mode
     # - add support for custom inference kwargs
 
+    class TranscriptionState(Enum):
+        LOADING_AUDIO = "loading_audio"
+        GENERATING_VAD_SEGMENTS = "generating_vad_segments"
+        TRANSCRIBING = "transcribing"
+        FINISHED = "finished"
+
     def __init__(
         self,
         model: WhisperModel,
@@ -197,8 +205,12 @@ class FasterWhisperPipeline(Pipeline):
         print_progress=False,
         combined_progress=False,
         verbose=False,
+            on_progress: Callable[[TranscriptionState, Optional[int], Optional[int]], None] = None,
     ) -> TranscriptionResult:
         if isinstance(audio, str):
+            if on_progress:
+                on_progress(self.__class__.TranscriptionState.LOADING_AUDIO)
+
             audio = load_audio(audio)
 
         def data(audio, segments):
@@ -216,6 +228,8 @@ class FasterWhisperPipeline(Pipeline):
         else:
             waveform = Pyannote.preprocess_audio(audio)
             merge_chunks = Pyannote.merge_chunks
+        if on_progress:
+            on_progress(self.__class__.TranscriptionState.GENERATING_VAD_SEGMENTS)
 
         vad_segments = self.vad_model({"waveform": waveform, "sample_rate": SAMPLE_RATE})
         vad_segments = merge_chunks(
@@ -255,16 +269,22 @@ class FasterWhisperPipeline(Pipeline):
         segments: List[SingleSegment] = []
         batch_size = batch_size or self._batch_size
         total_segments = len(vad_segments)
+
+        if on_progress:
+            on_progress(self.__class__.TranscriptionState.TRANSCRIBING, 0, total_segments)
+
         for idx, out in enumerate(self.__call__(data(audio, vad_segments), batch_size=batch_size, num_workers=num_workers)):
             if print_progress:
                 base_progress = ((idx + 1) / total_segments) * 100
                 percent_complete = base_progress / 2 if combined_progress else base_progress
                 print(f"Progress: {percent_complete:.2f}%...")
+
+            if on_progress:
+                on_progress(self.__class__.TranscriptionState.TRANSCRIBING, idx + 1, total_segments)
+
             text = out['text']
             if batch_size in [0, 1, None]:
                 text = text[0]
-            if verbose:
-                print(f"Transcript: [{round(vad_segments[idx]['start'], 3)} --> {round(vad_segments[idx]['end'], 3)}] {text}")
             segments.append(
                 {
                     "text": text,
@@ -273,6 +293,9 @@ class FasterWhisperPipeline(Pipeline):
                 }
             )
 
+        if on_progress:
+            on_progress(self.__class__.TranscriptionState.FINISHED)
+
         # revert the tokenizer if multilingual inference is enabled
         if self.preset_language is None:
             self.tokenizer = None

whisperx/diarize.py

@@ -26,81 +26,25 @@ 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)
-        if return_embeddings:
+        diarize_df = pd.DataFrame(segments.itertracks(yield_label=True), columns=['segment', 'label', 'speaker'])
-            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)
-
-        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],
-    speaker_embeddings: Optional[dict[str, list[float]]] = None,
+    fill_nearest=False,
-    fill_nearest: bool = False,
+) -> dict:
-) -> 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)
@@ -132,10 +76,6 @@ def assign_word_speakers(
                         speaker = dia_tmp.groupby("speaker")["intersection"].sum().sort_values(ascending=False).index[0]
                         word["speaker"] = speaker
         
-    # Add speaker embeddings to the result if provided
-    if speaker_embeddings is not None:
-        transcript_result["speaker_embeddings"] = speaker_embeddings
-
     return transcript_result            
 
 

whisperx/transcribe.py

@@ -59,10 +59,6 @@ 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()
@@ -213,13 +209,10 @@ 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, speaker_embeddings = diarize_model(
+            diarize_segments = diarize_model(
-                input_audio_path, 
+                input_audio_path, min_speakers=min_speakers, max_speakers=max_speakers
-                min_speakers=min_speakers, 
-                max_speakers=max_speakers, 
-                return_embeddings=return_speaker_embeddings
             )
-            result = assign_word_speakers(diarize_segments, result, speaker_embeddings)
+            result = assign_word_speakers(diarize_segments, result)
             results.append((result, input_audio_path))
     # >> Write
     for result, audio_path in results: