Merge branch 'main' into main

README.md

@@ -278,7 +278,7 @@ Bug finding and pull requests are also highly appreciated to keep this project g
 
 * [ ] Add benchmarking code (TEDLIUM for spd/WER & word segmentation)
 
-* [ ] Allow silero-vad as alternative VAD option
+* [x] Allow silero-vad as alternative VAD option
 
 * [ ] Improve diarization (word level). *Harder than first thought...*
 
@@ -300,7 +300,9 @@ Borrows important alignment code from [PyTorch tutorial on forced alignment](htt
 And uses the wonderful pyannote VAD / Diarization https://github.com/pyannote/pyannote-audio
 
 
-Valuable VAD & Diarization Models from [pyannote audio](https://github.com/pyannote/pyannote-audio)
+Valuable VAD & Diarization Models from:
+- [pyannote audio][https://github.com/pyannote/pyannote-audio]
+- [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)
 

whisperx/__init__.py

@@ -1,4 +1,4 @@
-from .transcribe import load_model
 from .alignment import load_align_model, align
 from .audio import load_audio
 from .diarize import assign_word_speakers, DiarizationPipeline
+from .asr import load_model

whisperx/alignment.py

@@ -1,4 +1,4 @@
-""""
+"""
 Forced Alignment with Whisper
 C. Max Bain
 """
@@ -15,8 +15,13 @@ from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
 
 from .audio import SAMPLE_RATE, load_audio
 from .utils import interpolate_nans
-from .types import AlignedTranscriptionResult, SingleSegment, SingleAlignedSegment, SingleWordSegment
+from .types import (
-import nltk
+    AlignedTranscriptionResult,
+    SingleSegment,
+    SingleAlignedSegment,
+    SingleWordSegment,
+    SegmentData,
+)
 from nltk.tokenize.punkt import PunktSentenceTokenizer, PunktParameters
 
 PUNKT_ABBREVIATIONS = ['dr', 'vs', 'mr', 'mrs', 'prof']
@@ -133,6 +138,8 @@ def align(
 
     # 1. Preprocess to keep only characters in dictionary
     total_segments = len(transcript)
+    # Store temporary processing values
+    segment_data: dict[int, SegmentData] = {}
     for sdx, segment in enumerate(transcript):
         # strip spaces at beginning / end, but keep track of the amount.
         if print_progress:
@@ -184,10 +191,12 @@ def align(
         sentence_splitter = PunktSentenceTokenizer(punkt_param)
         sentence_spans = list(sentence_splitter.span_tokenize(text))
 
-        segment["clean_char"] = clean_char
+        segment_data[sdx] = {
-        segment["clean_cdx"] = clean_cdx
+            "clean_char": clean_char,
-        segment["clean_wdx"] = clean_wdx
+            "clean_cdx": clean_cdx,
-        segment["sentence_spans"] = sentence_spans
+            "clean_wdx": clean_wdx,
+            "sentence_spans": sentence_spans
+        }
             
     aligned_segments: List[SingleAlignedSegment] = []
     
@@ -203,13 +212,14 @@ def align(
             "end": t2,
             "text": text,
             "words": [],
+            "chars": None,
         }
 
         if return_char_alignments:
             aligned_seg["chars"] = []
 
         # check we can align
-        if len(segment["clean_char"]) == 0:
+        if len(segment_data[sdx]["clean_char"]) == 0:
             print(f'Failed to align segment ("{segment["text"]}"): no characters in this segment found in model dictionary, resorting to original...')
             aligned_segments.append(aligned_seg)
             continue
@@ -219,8 +229,8 @@ def align(
             aligned_segments.append(aligned_seg)
             continue
 
-        text_clean = "".join(segment["clean_char"])
+        text_clean = "".join(segment_data[sdx]["clean_char"])
-        tokens = [model_dictionary.get(c, -1) for c in text_clean]
+        tokens = [model_dictionary[c] for c in text_clean]
 
         f1 = int(t1 * SAMPLE_RATE)
         f2 = int(t2 * SAMPLE_RATE)
@@ -271,8 +281,8 @@ def align(
         word_idx = 0
         for cdx, char in enumerate(text):
             start, end, score = None, None, None
-            if cdx in segment["clean_cdx"]:
+            if cdx in segment_data[sdx]["clean_cdx"]:
-                char_seg = char_segments[segment["clean_cdx"].index(cdx)]
+                char_seg = char_segments[segment_data[sdx]["clean_cdx"].index(cdx)]
                 start = round(char_seg.start * ratio + t1, 3)
                 end = round(char_seg.end * ratio + t1, 3)
                 score = round(char_seg.score, 3)
@@ -298,9 +308,9 @@ def align(
         aligned_subsegments = []
         # assign sentence_idx to each character index
         char_segments_arr["sentence-idx"] = None
-        for sdx, (sstart, send) in enumerate(segment["sentence_spans"]):
+        for sdx2, (sstart, send) in enumerate(segment_data[sdx]["sentence_spans"]):
             curr_chars = char_segments_arr.loc[(char_segments_arr.index >= sstart) & (char_segments_arr.index <= send)]
-            char_segments_arr.loc[(char_segments_arr.index >= sstart) & (char_segments_arr.index <= send), "sentence-idx"] = sdx
+            char_segments_arr.loc[(char_segments_arr.index >= sstart) & (char_segments_arr.index <= send), "sentence-idx"] = sdx2
 
             sentence_text = text[sstart:send]
             sentence_start = curr_chars["start"].min()

whisperx/asr.py

@@ -1,6 +1,5 @@
 import os
-import warnings
+from typing import List, Optional, Union
-from typing import List, NamedTuple, Optional, Union
 from dataclasses import replace
 
 import ctranslate2
@@ -14,8 +13,7 @@ from transformers.pipelines.pt_utils import PipelineIterator
 
 from .audio import N_SAMPLES, SAMPLE_RATE, load_audio, log_mel_spectrogram
 from .types import SingleSegment, TranscriptionResult
-from .vad import VoiceActivitySegmentation, load_vad_model, merge_chunks
+from .vads import Vad, Silero, Pyannote
-
 
 def find_numeral_symbol_tokens(tokenizer):
     numeral_symbol_tokens = []
@@ -106,7 +104,7 @@ class FasterWhisperPipeline(Pipeline):
     def __init__(
         self,
         model: WhisperModel,
-        vad: VoiceActivitySegmentation,
+        vad,
         vad_params: dict,
         options: TranscriptionOptions,
         tokenizer: Optional[Tokenizer] = None,
@@ -208,7 +206,16 @@ class FasterWhisperPipeline(Pipeline):
                 # print(f2-f1)
                 yield {'inputs': audio[f1:f2]}
 
-        vad_segments = self.vad_model({"waveform": torch.from_numpy(audio).unsqueeze(0), "sample_rate": SAMPLE_RATE})
+        # Pre-process audio and merge chunks as defined by the respective VAD child class 
+        # In case vad_model is manually assigned (see 'load_model') follow the functionality of pyannote toolkit
+        if issubclass(type(self.vad_model), Vad):
+            waveform = self.vad_model.preprocess_audio(audio)
+            merge_chunks =  self.vad_model.merge_chunks
+        else:
+            waveform = Pyannote.preprocess_audio(audio)
+            merge_chunks = Pyannote.merge_chunks
+
+        vad_segments = self.vad_model({"waveform": waveform, "sample_rate": SAMPLE_RATE})
         vad_segments = merge_chunks(
             vad_segments,
             chunk_size,
@@ -296,7 +303,8 @@ def load_model(
     compute_type="float16",
     asr_options: Optional[dict] = None,
     language: Optional[str] = None,
-    vad_model: Optional[VoiceActivitySegmentation] = None,
+    vad_model: Optional[Vad]= None,
+    vad_method: Optional[str] = "pyannote",
     vad_options: Optional[dict] = None,
     model: Optional[WhisperModel] = None,
     task="transcribe",
@@ -309,6 +317,7 @@ def load_model(
         whisper_arch - The name of the Whisper model to load.
         device - The device to load the model on.
         compute_type - The compute type to use for the model.
+        vad_method - The vad method to use. vad_model has higher priority if is not None.
         options - A dictionary of options to use for the model.
         language - The language of the model. (use English for now)
         model - The WhisperModel instance to use.
@@ -374,6 +383,7 @@ def load_model(
     default_asr_options = TranscriptionOptions(**default_asr_options)
 
     default_vad_options = {
+        "chunk_size": 30, # needed by silero since binarization happens before merge_chunks
         "vad_onset": 0.500,
         "vad_offset": 0.363
     }
@@ -381,10 +391,17 @@ def load_model(
     if vad_options is not None:
         default_vad_options.update(vad_options)
 
+    # Note: manually assigned vad_model has higher priority than vad_method!
     if vad_model is not None:
+        print("Use manually assigned vad_model. vad_method is ignored.")
         vad_model = vad_model
     else:
-        vad_model = load_vad_model(torch.device(device), use_auth_token=None, **default_vad_options)
+        if vad_method == "silero":
+            vad_model = Silero(**default_vad_options)
+        elif vad_method == "pyannote":
+            vad_model = Pyannote(torch.device(device), use_auth_token=None, **default_vad_options)
+        else:
+            raise ValueError(f"Invalid vad_method: {vad_method}")
 
     return FasterWhisperPipeline(
         model=model,

whisperx/diarize.py

@@ -79,7 +79,7 @@ def assign_word_speakers(
 
 
 class Segment:
-    def __init__(self, start, end, speaker=None):
+    def __init__(self, start:int, end:int, speaker:Optional[str]=None):
         self.start = start
         self.end = end
         self.speaker = speaker

whisperx/transcribe.py

@@ -46,6 +46,7 @@ def cli():
     parser.add_argument("--return_char_alignments", action='store_true', help="Return character-level alignments in the output json file")
 
     # vad params
+    parser.add_argument("--vad_method", type=str, default="pyannote", choices=["pyannote", "silero"], help="VAD method to be used")
     parser.add_argument("--vad_onset", type=float, default=0.500, help="Onset threshold for VAD (see pyannote.audio), reduce this if speech is not being detected")
     parser.add_argument("--vad_offset", type=float, default=0.363, help="Offset threshold for VAD (see pyannote.audio), reduce this if speech is not being detected.")
     parser.add_argument("--chunk_size", type=int, default=30, help="Chunk size for merging VAD segments. Default is 30, reduce this if the chunk is too long.")
@@ -110,6 +111,7 @@ def cli():
     return_char_alignments: bool = args.pop("return_char_alignments")
 
     hf_token: str = args.pop("hf_token")
+    vad_method: str = args.pop("vad_method")
     vad_onset: float = args.pop("vad_onset")
     vad_offset: float = args.pop("vad_offset")
 
@@ -175,7 +177,7 @@ def cli():
     results = []
     tmp_results = []
     # model = load_model(model_name, device=device, download_root=model_dir)
-    model = load_model(model_name, device=device, device_index=device_index, download_root=model_dir, compute_type=compute_type, language=args['language'], asr_options=asr_options, vad_options={"vad_onset": vad_onset, "vad_offset": vad_offset}, task=task, threads=faster_whisper_threads)
+    model = load_model(model_name, device=device, device_index=device_index, download_root=model_dir, compute_type=compute_type, language=args['language'], asr_options=asr_options, vad_method=vad_method, vad_options={"chunk_size":chunk_size, "vad_onset": vad_onset, "vad_offset": vad_offset}, task=task, threads=faster_whisper_threads)
 
     for audio_path in args.pop("audio"):
         audio = load_audio(audio_path)

whisperx/types.py

@@ -1,4 +1,4 @@
-from typing import TypedDict, Optional, List
+from typing import TypedDict, Optional, List, Tuple
 
 
 class SingleWordSegment(TypedDict):
@@ -30,6 +30,17 @@ class SingleSegment(TypedDict):
     text: str
 
 
+class SegmentData(TypedDict):
+    """
+    Temporary processing data used during alignment.
+    Contains cleaned and preprocessed data for each segment.
+    """
+    clean_char: List[str]  # Cleaned characters that exist in model dictionary
+    clean_cdx: List[int]   # Original indices of cleaned characters
+    clean_wdx: List[int]   # Indices of words containing valid characters
+    sentence_spans: List[Tuple[int, int]]  # Start and end indices of sentences
+
+
 class SingleAlignedSegment(TypedDict):
     """
     A single segment (up to multiple sentences) of a speech with word alignment.

whisperx/utils.py

@@ -241,7 +241,7 @@ class SubtitlesWriter(ResultWriter):
             line_count = 1
             # the next subtitle to yield (a list of word timings with whitespace)
             subtitle: list[dict] = []
-            times = []
+            times: list[tuple] = []
             last = result["segments"][0]["start"]
             for segment in result["segments"]:
                 for i, original_timing in enumerate(segment["words"]):

whisperx/vads/__init__.py

@@ -0,0 +1,3 @@
+from whisperx.vads.pyannote import Pyannote
+from whisperx.vads.silero import Silero
+from whisperx.vads.vad import Vad

whisperx/vads/pyannote.py

@@ -1,19 +1,21 @@
 import hashlib
 import os
 import urllib
-from typing import Callable, Optional, Text, Union
+from typing import Callable, Text, Union
+from typing import Optional
 
 import numpy as np
-import pandas as pd
 import torch
 from pyannote.audio import Model
 from pyannote.audio.core.io import AudioFile
 from pyannote.audio.pipelines import VoiceActivityDetection
 from pyannote.audio.pipelines.utils import PipelineModel
-from pyannote.core import Annotation, Segment, SlidingWindowFeature
+from pyannote.core import Annotation, SlidingWindowFeature
+from pyannote.core import Segment
 from tqdm import tqdm
 
-from .diarize import Segment as SegmentX
+from whisperx.diarize import Segment as SegmentX
+from whisperx.vads.vad import Vad
 
 # deprecated
 VAD_SEGMENTATION_URL = "https://whisperx.s3.eu-west-2.amazonaws.com/model_weights/segmentation/0b5b3216d60a2d32fc086b47ea8c67589aaeb26b7e07fcbe620d6d0b83e209ea/pytorch_model.bin"
@@ -21,12 +23,12 @@ VAD_SEGMENTATION_URL = "https://whisperx.s3.eu-west-2.amazonaws.com/model_weight
 def load_vad_model(device, vad_onset=0.500, vad_offset=0.363, use_auth_token=None, model_fp=None):
     model_dir = torch.hub._get_torch_home()
 
-    vad_dir = os.path.dirname(os.path.abspath(__file__))
+    main_dir = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
 
     os.makedirs(model_dir, exist_ok = True)
     if model_fp is None:
         # Dynamically resolve the path to the model file
-        model_fp = os.path.join(vad_dir, "assets", "pytorch_model.bin")
+        model_fp = os.path.join(main_dir, "assets", "pytorch_model.bin")
         model_fp = os.path.abspath(model_fp)  # Ensure the path is absolute
     else:
         model_fp = os.path.abspath(model_fp)  # Ensure any provided path is absolute
@@ -236,41 +238,26 @@ class VoiceActivitySegmentation(VoiceActivityDetection):
         return segmentations
 
 
-def merge_vad(vad_arr, pad_onset=0.0, pad_offset=0.0, min_duration_off=0.0, min_duration_on=0.0):
+class Pyannote(Vad):
 
-    active = Annotation()
+    def __init__(self, device, use_auth_token=None, model_fp=None, **kwargs):
-    for k, vad_t in enumerate(vad_arr):
+        print(">>Performing voice activity detection using Pyannote...")
-        region = Segment(vad_t[0] - pad_onset, vad_t[1] + pad_offset)
+        super().__init__(kwargs['vad_onset'])
-        active[region, k] = 1
+        self.vad_pipeline = load_vad_model(device, use_auth_token=use_auth_token, model_fp=model_fp)
 
+    def __call__(self, audio: AudioFile, **kwargs):
+        return self.vad_pipeline(audio)
 
-    if pad_offset > 0.0 or pad_onset > 0.0 or min_duration_off > 0.0:
+    @staticmethod
-        active = active.support(collar=min_duration_off)
+    def preprocess_audio(audio):
+        return torch.from_numpy(audio).unsqueeze(0)
 
-    # remove tracks shorter than min_duration_on
+    @staticmethod
-    if min_duration_on > 0:
+    def merge_chunks(segments,
-        for segment, track in list(active.itertracks()):
-            if segment.duration < min_duration_on:
-                    del active[segment, track]
-    
-    active = active.for_json()
-    active_segs = pd.DataFrame([x['segment'] for x in active['content']])
-    return active_segs
-
-def merge_chunks(
-    segments,
                      chunk_size,
                      onset: float = 0.5,
                      offset: Optional[float] = None,
                      ):
-    """
-    Merge operation described in paper
-    """
-    curr_end = 0
-    merged_segments = []
-    seg_idxs = []
-    speaker_idxs = []
-
         assert chunk_size > 0
         binarize = Binarize(max_duration=chunk_size, onset=onset, offset=offset)
         segments = binarize(segments)
@@ -281,27 +268,5 @@ def merge_chunks(
         if len(segments_list) == 0:
             print("No active speech found in audio")
             return []
-    # assert segments_list, "segments_list is empty."
+        assert segments_list, "segments_list is empty."
-    # Make sur the starting point is the start of the segment.
+        return Vad.merge_chunks(segments_list, chunk_size, onset, offset)
-    curr_start = segments_list[0].start
-
-    for seg in segments_list:
-        if seg.end - curr_start > chunk_size and curr_end-curr_start > 0:
-            merged_segments.append({
-                "start": curr_start,
-                "end": curr_end,
-                "segments": seg_idxs,
-            })
-            curr_start = seg.start
-            seg_idxs = []
-            speaker_idxs = []
-        curr_end = seg.end
-        seg_idxs.append((seg.start, seg.end))
-        speaker_idxs.append(seg.speaker)
-    # add final
-    merged_segments.append({ 
-                "start": curr_start,
-                "end": curr_end,
-                "segments": seg_idxs,
-            })    
-    return merged_segments

whisperx/vads/silero.py

@@ -0,0 +1,62 @@
+from io import IOBase
+from pathlib import Path
+from typing import Mapping, Text
+from typing import Optional
+from typing import Union
+
+import torch
+
+from whisperx.diarize import Segment as SegmentX
+from whisperx.vads.vad import Vad
+
+AudioFile = Union[Text, Path, IOBase, Mapping]
+
+
+class Silero(Vad):
+    # check again default values
+    def __init__(self, **kwargs):
+        print(">>Performing voice activity detection using Silero...")
+        super().__init__(kwargs['vad_onset'])
+
+        self.vad_onset = kwargs['vad_onset']
+        self.chunk_size = kwargs['chunk_size']
+        self.vad_pipeline, vad_utils = torch.hub.load(repo_or_dir='snakers4/silero-vad',
+                                                      model='silero_vad',
+                                                      force_reload=False,
+                                                      onnx=False,
+                                                      trust_repo=True)
+        (self.get_speech_timestamps, _, self.read_audio, _, _) = vad_utils
+
+    def __call__(self, audio: AudioFile, **kwargs):
+        """use silero to get segments of speech"""
+        # Only accept 16000 Hz for now.
+        # Note: Silero models support both 8000 and 16000 Hz. Although other values are not directly supported,
+        # multiples of 16000 (e.g. 32000 or 48000) are cast to 16000 inside of the JIT model!
+        sample_rate = audio["sample_rate"]
+        if sample_rate != 16000:
+            raise ValueError("Only 16000Hz sample rate is allowed")
+
+        timestamps = self.get_speech_timestamps(audio["waveform"],
+                                                model=self.vad_pipeline,
+                                                sampling_rate=sample_rate,
+                                                max_speech_duration_s=self.chunk_size,
+                                                threshold=self.vad_onset
+                                                # min_silence_duration_ms = self.min_duration_off/1000
+                                                # min_speech_duration_ms = self.min_duration_on/1000
+                                                # ...
+                                                # See silero documentation for full option list
+                                                )
+        return [SegmentX(i['start'] / sample_rate, i['end'] / sample_rate, "UNKNOWN") for i in timestamps]
+
+    @staticmethod
+    def preprocess_audio(audio):
+        return audio
+
+    @staticmethod
+    def merge_chunks(segments,
+                     chunk_size,
+                     onset: float = 0.5,
+                     offset: Optional[float] = None,
+                     ):
+        assert chunk_size > 0
+        return Vad.merge_chunks(segments, chunk_size, onset, offset)

whisperx/vads/vad.py

@@ -0,0 +1,74 @@
+from typing import Optional
+
+import pandas as pd
+from pyannote.core import Annotation, Segment
+
+
+class Vad:
+    def __init__(self, vad_onset):
+        if not (0 < vad_onset < 1):
+            raise ValueError(
+                "vad_onset is a decimal value between 0 and 1."
+            )
+
+    @staticmethod
+    def preprocess_audio(audio):
+        pass
+
+    # keep merge_chunks as static so it can be also used by manually assigned vad_model (see 'load_model')
+    @staticmethod
+    def merge_chunks(segments,
+                     chunk_size,
+                     onset: float,
+                     offset: Optional[float]):
+        """
+         Merge operation described in paper
+         """
+        curr_end = 0
+        merged_segments = []
+        seg_idxs: list[tuple]= []
+        speaker_idxs: list[Optional[str]] = []
+
+        curr_start = segments[0].start
+        for seg in segments:
+            if seg.end - curr_start > chunk_size and curr_end - curr_start > 0:
+                merged_segments.append({
+                    "start": curr_start,
+                    "end": curr_end,
+                    "segments": seg_idxs,
+                })
+                curr_start = seg.start
+                seg_idxs = []
+                speaker_idxs = []
+            curr_end = seg.end
+            seg_idxs.append((seg.start, seg.end))
+            speaker_idxs.append(seg.speaker)
+        # add final
+        merged_segments.append({
+            "start": curr_start,
+            "end": curr_end,
+            "segments": seg_idxs,
+        })
+
+        return merged_segments
+
+    # Unused function
+    @staticmethod
+    def merge_vad(vad_arr, pad_onset=0.0, pad_offset=0.0, min_duration_off=0.0, min_duration_on=0.0):
+        active = Annotation()
+        for k, vad_t in enumerate(vad_arr):
+            region = Segment(vad_t[0] - pad_onset, vad_t[1] + pad_offset)
+            active[region, k] = 1
+
+        if pad_offset > 0.0 or pad_onset > 0.0 or min_duration_off > 0.0:
+            active = active.support(collar=min_duration_off)
+
+        # remove tracks shorter than min_duration_on
+        if min_duration_on > 0:
+            for segment, track in list(active.itertracks()):
+                if segment.duration < min_duration_on:
+                    del active[segment, track]
+
+        active = active.for_json()
+        active_segs = pd.DataFrame([x['segment'] for x in active['content']])
+        return active_segs