[pull] main from m-bain:main

README.md

@@ -113,10 +113,7 @@ You may also need to install ffmpeg, rust etc. Follow openAI instructions here h
 
 ### Speaker Diarization
 
-To **enable Speaker Diarization**, include your Hugging Face access token (read) that you can generate from [Here](https://huggingface.co/settings/tokens) after the `--hf_token` argument and accept the user agreement for the following models: [Segmentation](https://huggingface.co/pyannote/segmentation-3.0) and [Speaker-Diarization-3.1](https://huggingface.co/pyannote/speaker-diarization-3.1) (if you choose to use Speaker-Diarization 2.x, follow requirements [here](https://huggingface.co/pyannote/speaker-diarization) instead.)
-
-> **Note**<br>
-> As of Oct 11, 2023, there is a known issue regarding slow performance with pyannote/Speaker-Diarization-3.0 in whisperX. It is due to dependency conflicts between faster-whisper and pyannote-audio 3.0.0. Please see [this issue](https://github.com/m-bain/whisperX/issues/499) for more details and potential workarounds.
+To **enable Speaker Diarization**, include your Hugging Face access token (read) that you can generate from [Here](https://huggingface.co/settings/tokens) after the `--hf_token` argument and accept the user agreement for the [speaker-diarization-community-1](https://huggingface.co/pyannote/speaker-diarization-community-1) model.
 
 <h2 align="left" id="example">Usage 💬 (command line)</h2>
 
@@ -197,7 +194,7 @@ print(result["segments"]) # after alignment
 # import gc; import torch; gc.collect(); torch.cuda.empty_cache(); del model_a
 
 # 3. Assign speaker labels
-diarize_model = DiarizationPipeline(use_auth_token=YOUR_HF_TOKEN, device=device)
+diarize_model = DiarizationPipeline(token=YOUR_HF_TOKEN, device=device)
 
 # add min/max number of speakers if known
 diarize_segments = diarize_model(audio)
@@ -291,8 +288,8 @@ And uses the wonderful pyannote VAD / Diarization https://github.com/pyannote/py
 
 Valuable VAD & Diarization Models from:
 
-- [pyannote audio][https://github.com/pyannote/pyannote-audio]
-- [silero vad][https://github.com/snakers4/silero-vad]
+- [pyannote-audio](https://github.com/pyannote/pyannote-audio) — Speaker diarization powered by the [speaker-diarization-community-1](https://huggingface.co/pyannote/speaker-diarization-community-1) model, licensed under [CC-BY-4.0](https://creativecommons.org/licenses/by/4.0/) by [pyannoteAI](https://www.pyannote.ai)
+- [silero-vad](https://github.com/snakers4/silero-vad)
 
 Great backend from [faster-whisper](https://github.com/guillaumekln/faster-whisper) and [CTranslate2](https://github.com/OpenNMT/CTranslate2)
 

pyproject.toml

@@ -2,7 +2,7 @@
 urls = { repository = "https://github.com/m-bain/whisperx" }
 authors = [{ name = "Max Bain" }]
 name = "whisperx"
-version = "3.7.7"
+version = "3.8.0"
 description = "Time-Accurate Automatic Speech Recognition using Whisper."
 readme = "README.md"
 requires-python = ">=3.10, <3.14"
@@ -13,8 +13,9 @@ dependencies = [
     "faster-whisper>=1.1.1",
     "nltk>=3.9.1",
     "numpy>=2.1.0",
+    "omegaconf>=2.3.0",
     "pandas>=2.2.3",
-    "pyannote-audio>=3.3.2,<4.0.0",
+    "pyannote-audio>=4.0.0",
     "huggingface-hub<1.0.0",
     "torch~=2.8.0",
     "torchaudio~=2.8.0",
@@ -36,6 +37,12 @@ include-package-data = true
 where = ["."]
 include = ["whisperx*"]
 
+# torchcodec (transitive dep of pyannote-audio >=4) has no wheels for Linux aarch64
+[tool.uv]
+override-dependencies = [
+    "torchcodec>=0.6.0; (sys_platform == 'linux' and platform_machine == 'x86_64') or sys_platform == 'darwin' or sys_platform == 'win32'",
+]
+
 [tool.uv.sources]
 torch = [
   { index = "pytorch-cpu", marker = "sys_platform == 'darwin'" },

whisperx/__main__.py

@@ -45,7 +45,7 @@ def cli():
     parser.add_argument("--diarize", action="store_true", help="Apply diarization to assign speaker labels to each segment/word")
     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("--diarize_model", default="pyannote/speaker-diarization-community-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")

whisperx/asr.py

@@ -407,7 +407,7 @@ def load_model(
                 device_vad = f'cuda:{device_index}'
             else:
                 device_vad = device
-            vad_model = Pyannote(torch.device(device_vad), use_auth_token=None, **default_vad_options)
+            vad_model = Pyannote(torch.device(device_vad), token=None, **default_vad_options)
         else:
             raise ValueError(f"Invalid vad_method: {vad_method}")
 

whisperx/diarize.py

@@ -92,14 +92,14 @@ class DiarizationPipeline:
     def __init__(
         self,
         model_name=None,
-        use_auth_token=None,
+        token=None,
         device: Optional[Union[str, torch.device]] = "cpu",
     ):
         if isinstance(device, str):
             device = torch.device(device)
-        model_config = model_name or "pyannote/speaker-diarization-3.1"
+        model_config = model_name or "pyannote/speaker-diarization-community-1"
         logger.info(f"Loading diarization model: {model_config}")
-        self.model = Pipeline.from_pretrained(model_config, use_auth_token=use_auth_token).to(device)
+        self.model = Pipeline.from_pretrained(model_config, token=token).to(device)
 
     def __call__(
         self,
@@ -132,22 +132,15 @@ def __call__(
             'sample_rate': SAMPLE_RATE
         }
 
-        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
+        output = self.model(
+            audio_data,
+            num_speakers=num_speakers,
+            min_speakers=min_speakers,
+            max_speakers=max_speakers,
+        )
+
+        diarization = output.speaker_diarization
+        embeddings = output.speaker_embeddings if return_embeddings else 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)

whisperx/transcribe.py

@@ -214,7 +214,7 @@ def transcribe_task(args: dict, parser: argparse.ArgumentParser):
         logger.info("Performing diarization...")
         logger.info(f"Using model: {diarize_model_name}")
         results = []
-        diarize_model = DiarizationPipeline(model_name=diarize_model_name, use_auth_token=hf_token, device=device)
+        diarize_model = DiarizationPipeline(model_name=diarize_model_name, token=hf_token, device=device)
         for result, input_audio_path in tmp_results:
             diarize_result = diarize_model(
                 input_audio_path, 

whisperx/vads/pyannote.py

@@ -18,7 +18,7 @@
 logger = get_logger(__name__)
 
 
-def load_vad_model(device, vad_onset=0.500, vad_offset=0.363, use_auth_token=None, model_fp=None):
+def load_vad_model(device, vad_onset=0.500, vad_offset=0.363, token=None, model_fp=None):
     model_dir = torch.hub._get_torch_home()
 
     main_dir = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
@@ -40,7 +40,7 @@ def load_vad_model(device, vad_onset=0.500, vad_offset=0.363, use_auth_token=Non
 
     model_bytes = open(model_fp, "rb").read()
 
-    vad_model = Model.from_pretrained(model_fp, use_auth_token=use_auth_token)
+    vad_model = Model.from_pretrained(model_fp, token=token)
     hyperparameters = {"onset": vad_onset,
                     "offset": vad_offset,
                     "min_duration_on": 0.1,
@@ -192,11 +192,11 @@ def __init__(
             self,
             segmentation: PipelineModel = "pyannote/segmentation",
             fscore: bool = False,
-            use_auth_token: Union[Text, None] = None,
+            token: Union[Text, None] = None,
             **inference_kwargs,
     ):
 
-        super().__init__(segmentation=segmentation, fscore=fscore, use_auth_token=use_auth_token, **inference_kwargs)
+        super().__init__(segmentation=segmentation, fscore=fscore, token=token, **inference_kwargs)
 
     def apply(self, file: AudioFile, hook: Optional[Callable] = None) -> Annotation:
         """Apply voice activity detection
@@ -234,10 +234,10 @@ def apply(self, file: AudioFile, hook: Optional[Callable] = None) -> Annotation:
 
 class Pyannote(Vad):
 
-    def __init__(self, device, use_auth_token=None, model_fp=None, **kwargs):
+    def __init__(self, device, token=None, model_fp=None, **kwargs):
         logger.info("Performing voice activity detection using Pyannote...")
         super().__init__(kwargs['vad_onset'])
-        self.vad_pipeline = load_vad_model(device, use_auth_token=use_auth_token, model_fp=model_fp)
+        self.vad_pipeline = load_vad_model(device, token=token, model_fp=model_fp)
 
     def __call__(self, audio: AudioFile, **kwargs):
         return self.vad_pipeline(audio)