-
Notifications
You must be signed in to change notification settings - Fork 926
/
vad.py
596 lines (500 loc) · 21.6 KB
/
vad.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
535
536
537
538
539
540
541
542
543
544
545
546
547
548
549
550
551
552
553
554
555
556
557
558
559
560
561
562
563
564
565
566
567
568
569
570
571
572
573
574
575
576
577
578
579
580
581
582
583
584
585
586
587
588
589
590
591
592
593
594
595
596
import bisect
import functools
import os
from abc import ABC
from collections.abc import Callable
from typing import List, NamedTuple, Optional, Union
import numpy as np
import torch
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 faster_whisper.utils import get_assets_path
# The code below is adapted from https://github.com/snakers4/silero-vad.
class VadOptions(NamedTuple):
"""VAD options.
Attributes:
threshold: Speech threshold. Silero VAD outputs speech probabilities for each audio chunk,
probabilities ABOVE this value are considered as SPEECH. It is better to tune this
parameter for each dataset separately, but "lazy" 0.5 is pretty good for most datasets.
min_speech_duration_ms: Final speech chunks shorter min_speech_duration_ms are thrown out.
max_speech_duration_s: Maximum duration of speech chunks in seconds. Chunks longer
than max_speech_duration_s will be split at the timestamp of the last silence that
lasts more than 100ms (if any), to prevent aggressive cutting. Otherwise, they will be
split aggressively just before max_speech_duration_s.
min_silence_duration_ms: In the end of each speech chunk wait for min_silence_duration_ms
before separating it
speech_pad_ms: Final speech chunks are padded by speech_pad_ms each side
"""
threshold: float = 0.5
min_speech_duration_ms: int = 250
max_speech_duration_s: float = float("inf")
min_silence_duration_ms: int = 2000
speech_pad_ms: int = 400
def get_speech_timestamps(
audio: torch.Tensor,
vad_options: Optional[VadOptions] = None,
**kwargs,
) -> List[dict]:
"""This method is used for splitting long audios into speech chunks using silero VAD.
Args:
audio: One dimensional float array.
vad_options: Options for VAD processing.
kwargs: VAD options passed as keyword arguments for backward compatibility.
Returns:
List of dicts containing begin and end samples of each speech chunk.
"""
if vad_options is None:
vad_options = VadOptions(**kwargs)
threshold = vad_options.threshold
min_speech_duration_ms = vad_options.min_speech_duration_ms
max_speech_duration_s = vad_options.max_speech_duration_s
min_silence_duration_ms = vad_options.min_silence_duration_ms
window_size_samples = 512
speech_pad_ms = vad_options.speech_pad_ms
sampling_rate = 16000
min_speech_samples = sampling_rate * min_speech_duration_ms / 1000
speech_pad_samples = sampling_rate * speech_pad_ms / 1000
max_speech_samples = (
sampling_rate * max_speech_duration_s
- window_size_samples
- 2 * speech_pad_samples
)
min_silence_samples = sampling_rate * min_silence_duration_ms / 1000
min_silence_samples_at_max_speech = sampling_rate * 98 / 1000
audio_length_samples = len(audio)
model = get_vad_model()
state, context = model.get_initial_states(batch_size=1)
speech_probs = []
for current_start_sample in range(0, audio_length_samples, window_size_samples):
chunk = audio[current_start_sample : current_start_sample + window_size_samples]
if len(chunk) < window_size_samples:
chunk = np.pad(chunk, (0, int(window_size_samples - len(chunk))))
speech_prob, state, context = model(chunk, state, context, sampling_rate)
speech_probs.append(speech_prob)
triggered = False
speeches = []
current_speech = {}
neg_threshold = threshold - 0.15
# to save potential segment end (and tolerate some silence)
temp_end = 0
# to save potential segment limits in case of maximum segment size reached
prev_end = next_start = 0
for i, speech_prob in enumerate(speech_probs):
if (speech_prob >= threshold) and temp_end:
temp_end = 0
if next_start < prev_end:
next_start = window_size_samples * i
if (speech_prob >= threshold) and not triggered:
triggered = True
current_speech["start"] = window_size_samples * i
continue
if (
triggered
and (window_size_samples * i) - current_speech["start"] > max_speech_samples
):
if prev_end:
current_speech["end"] = prev_end
speeches.append(current_speech)
current_speech = {}
# previously reached silence (< neg_thres) and is still not speech (< thres)
if next_start < prev_end:
triggered = False
else:
current_speech["start"] = next_start
prev_end = next_start = temp_end = 0
else:
current_speech["end"] = window_size_samples * i
speeches.append(current_speech)
current_speech = {}
prev_end = next_start = temp_end = 0
triggered = False
continue
if (speech_prob < neg_threshold) and triggered:
if not temp_end:
temp_end = window_size_samples * i
# condition to avoid cutting in very short silence
if (window_size_samples * i) - temp_end > min_silence_samples_at_max_speech:
prev_end = temp_end
if (window_size_samples * i) - temp_end < min_silence_samples:
continue
else:
current_speech["end"] = temp_end
if (
current_speech["end"] - current_speech["start"]
) > min_speech_samples:
speeches.append(current_speech)
current_speech = {}
prev_end = next_start = temp_end = 0
triggered = False
continue
if (
current_speech
and (audio_length_samples - current_speech["start"]) > min_speech_samples
):
current_speech["end"] = audio_length_samples
speeches.append(current_speech)
for i, speech in enumerate(speeches):
if i == 0:
speech["start"] = int(max(0, speech["start"] - speech_pad_samples))
if i != len(speeches) - 1:
silence_duration = speeches[i + 1]["start"] - speech["end"]
if silence_duration < 2 * speech_pad_samples:
speech["end"] += int(silence_duration // 2)
speeches[i + 1]["start"] = int(
max(0, speeches[i + 1]["start"] - silence_duration // 2)
)
else:
speech["end"] = int(
min(audio_length_samples, speech["end"] + speech_pad_samples)
)
speeches[i + 1]["start"] = int(
max(0, speeches[i + 1]["start"] - speech_pad_samples)
)
else:
speech["end"] = int(
min(audio_length_samples, speech["end"] + speech_pad_samples)
)
return speeches
def collect_chunks(audio: torch.Tensor, chunks: List[dict]) -> torch.Tensor:
"""Collects and concatenates audio chunks."""
if not chunks:
return torch.tensor([], dtype=torch.float32)
return torch.cat([audio[chunk["start"] : chunk["end"]] for chunk in chunks])
class SpeechTimestampsMap:
"""Helper class to restore original speech timestamps."""
def __init__(self, chunks: List[dict], sampling_rate: int, time_precision: int = 2):
self.sampling_rate = sampling_rate
self.time_precision = time_precision
self.chunk_end_sample = []
self.total_silence_before = []
previous_end = 0
silent_samples = 0
for chunk in chunks:
silent_samples += chunk["start"] - previous_end
previous_end = chunk["end"]
self.chunk_end_sample.append(chunk["end"] - silent_samples)
self.total_silence_before.append(silent_samples / sampling_rate)
def get_original_time(
self,
time: float,
chunk_index: Optional[int] = None,
) -> float:
if chunk_index is None:
chunk_index = self.get_chunk_index(time)
total_silence_before = self.total_silence_before[chunk_index]
return round(total_silence_before + time, self.time_precision)
def get_chunk_index(self, time: float) -> int:
sample = int(time * self.sampling_rate)
return min(
bisect.bisect(self.chunk_end_sample, sample),
len(self.chunk_end_sample) - 1,
)
@functools.lru_cache
def get_vad_model():
"""Returns the VAD model instance."""
path = os.path.join(get_assets_path(), "silero_vad.onnx")
return SileroVADModel(path)
class SileroVADModel:
def __init__(self, path):
try:
import onnxruntime
except ImportError as e:
raise RuntimeError(
"Applying the VAD filter requires the onnxruntime package"
) from e
opts = onnxruntime.SessionOptions()
opts.inter_op_num_threads = 1
opts.intra_op_num_threads = 1
opts.log_severity_level = 4
self.session = onnxruntime.InferenceSession(
path,
providers=["CPUExecutionProvider"],
sess_options=opts,
)
def get_initial_states(self, batch_size: int):
state = np.zeros((2, batch_size, 128), dtype=np.float32)
context = np.zeros((batch_size, 64), dtype=np.float32)
return state, context
def __call__(self, x, state, context, sr: int):
if len(x.shape) == 1:
x = np.expand_dims(x, 0)
if len(x.shape) > 2:
raise ValueError(
f"Too many dimensions for input audio chunk {len(x.shape)}"
)
if sr / x.shape[1] > 31.25:
raise ValueError("Input audio chunk is too short")
x = np.concatenate([context, x], axis=1)
ort_inputs = {
"input": x,
"state": state,
"sr": np.array(sr, dtype="int64"),
}
out, state = self.session.run(None, ort_inputs)
context = x[..., -64:]
return out, state, context
# BSD 2-Clause License
# Copyright (c) 2024, Max Bain
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
# 1. Redistributions of source code must retain the above copyright notice, this
# list of conditions and the following disclaimer.
# 2. Redistributions in binary form must reproduce the above copyright notice,
# this list of conditions and the following disclaimer in the documentation
# and/or other materials provided with the distribution.
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
# FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
# SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
# The code below is copied from whisper-x (https://github.com/m-bain/whisperX)
# and adapted for faster_whisper.
class SegmentX:
def __init__(self, start, end, speaker=None):
self.start = start
self.end = end
self.speaker = speaker
class VoiceActivitySegmentation(VoiceActivityDetection, ABC):
"""Pipeline wrapper class for Voice Activity Segmentation based on VAD scores."""
def __init__(
self,
segmentation: PipelineModel = "pyannote/segmentation",
device: Optional[Union[str, torch.device]] = None,
fscore: bool = False,
use_auth_token: Optional[str] = None,
**inference_kwargs,
):
"""Initialize the pipeline with the model name and the optional device.
Args:
dict parameters of VoiceActivityDetection class from pyannote:
segmentation (PipelineModel): Loaded model name.
device (torch.device or None): Device to perform the segmentation.
fscore (bool): Flag indicating whether to compute F-score during inference.
use_auth_token (str or None): Optional authentication token for model access.
inference_kwargs (dict): Additional arguments from VoiceActivityDetection pipeline.
"""
super().__init__(
segmentation=segmentation,
device=device,
fscore=fscore,
use_auth_token=use_auth_token,
**inference_kwargs,
)
def apply(
self, file: AudioFile, hook: Optional[Callable] = None
) -> SlidingWindowFeature:
"""Apply voice activity detection on the audio file.
Args:
file (AudioFile): Processed file.
hook (callable): Hook called with signature: hook("step_name", step_artefact, file=file)
Returns:
segmentations (SlidingWindowFeature): Voice activity segmentation.
"""
# setup hook (e.g. for debugging purposes)
hook = self.setup_hook(file, hook=hook)
# apply segmentation model if needed
# output shape is (num_chunks, num_frames, 1)
if self.training:
if self.CACHED_SEGMENTATION in file:
segmentations = file[self.CACHED_SEGMENTATION]
else:
segmentations = self._segmentation(file)
file[self.CACHED_SEGMENTATION] = segmentations
else:
segmentations: SlidingWindowFeature = self._segmentation(file)
return segmentations
class BinarizeVadScores:
"""Binarize detection scores using hysteresis thresholding.
Reference:
Gregory Gelly and Jean-Luc Gauvain. "Minimum Word Error Training of
RNN-based Voice Activity Detection", InterSpeech 2015.
Modified by Max Bain to include WhisperX's min-cut operation
https://arxiv.org/abs/2303.00747
"""
def __init__(
self,
onset: float = 0.5,
offset: Optional[float] = None,
min_duration_on: float = 0.0,
min_duration_off: float = 0.0,
pad_onset: float = 0.0,
pad_offset: float = 0.0,
max_duration: float = float("inf"),
):
"""Initializes the parameters for Binarizing the VAD scores.
Args:
onset (float, optional):
Onset threshold. Defaults to 0.5.
offset (float, optional):
Offset threshold. Defaults to `onset`.
min_duration_on (float, optional):
Remove active regions shorter than that many seconds. Defaults to 0s.
min_duration_off (float, optional):
Fill inactive regions shorter than that many seconds. Defaults to 0s.
pad_onset (float, optional):
Extend active regions by moving their start time by that many seconds.
Defaults to 0s.
pad_offset (float, optional):
Extend active regions by moving their end time by that many seconds.
Defaults to 0s.
max_duration (float):
The maximum length of an active segment.
"""
super().__init__()
self.onset = onset
self.offset = offset or onset
self.pad_onset = pad_onset
self.pad_offset = pad_offset
self.min_duration_on = min_duration_on
self.min_duration_off = min_duration_off
self.max_duration = max_duration
def __get_active_regions(self, scores: SlidingWindowFeature) -> Annotation:
"""Extract active regions from VAD scores.
Args:
scores (SlidingWindowFeature): Detection scores.
Returns:
active (Annotation): Active regions.
"""
num_frames, num_classes = scores.data.shape
frames = scores.sliding_window
timestamps = [frames[i].middle for i in range(num_frames)]
# annotation meant to store 'active' regions
active = Annotation()
for k, k_scores in enumerate(scores.data.T):
label = k if scores.labels is None else scores.labels[k]
# initial state
start = timestamps[0]
is_active = k_scores[0] > self.onset
curr_scores = [k_scores[0]]
curr_timestamps = [start]
t = start
# optionally add `strict=False` for python 3.10 or later
for t, y in zip(timestamps[1:], k_scores[1:]):
# currently active
if is_active:
curr_duration = t - start
if curr_duration > self.max_duration:
search_after = len(curr_scores) // 2
# divide segment
min_score_div_idx = search_after + np.argmin(
curr_scores[search_after:]
)
min_score_t = curr_timestamps[min_score_div_idx]
region = Segment(
start - self.pad_onset, min_score_t + self.pad_offset
)
active[region, k] = label
start = curr_timestamps[min_score_div_idx]
curr_scores = curr_scores[min_score_div_idx + 1 :]
curr_timestamps = curr_timestamps[min_score_div_idx + 1 :]
# switching from active to inactive
elif y < self.offset:
region = Segment(start - self.pad_onset, t + self.pad_offset)
active[region, k] = label
start = t
is_active = False
curr_scores = []
curr_timestamps = []
curr_scores.append(y)
curr_timestamps.append(t)
# currently inactive
else:
# switching from inactive to active
if y > self.onset:
start = t
is_active = True
# if active at the end, add final region
if is_active:
region = Segment(start - self.pad_onset, t + self.pad_offset)
active[region, k] = label
return active
def __call__(self, scores: SlidingWindowFeature) -> Annotation:
"""Binarize detection scores.
Args:
scores (SlidingWindowFeature): Detection scores.
Returns:
active (Annotation): Binarized scores.
"""
active = self.__get_active_regions(scores)
# because of padding, some active regions might be overlapping: merge them.
# also: fill same speaker gaps shorter than min_duration_off
if self.pad_offset > 0.0 or self.pad_onset > 0.0 or self.min_duration_off > 0.0:
if self.max_duration < float("inf"):
raise NotImplementedError("This would break current max_duration param")
active = active.support(collar=self.min_duration_off)
# remove tracks shorter than min_duration_on
if self.min_duration_on > 0:
for segment, track in list(active.itertracks()):
if segment.duration < self.min_duration_on:
del active[segment, track]
return active
def merge_chunks(
segments,
chunk_length,
onset: float = 0.5,
offset: Optional[float] = None,
edge_padding: float = 0.1,
):
"""
Merge operation described in whisper-x paper
"""
curr_end = 0
merged_segments = []
seg_idxs = []
speaker_idxs = []
assert chunk_length > 0
binarize = BinarizeVadScores(max_duration=chunk_length, onset=onset, offset=offset)
segments = binarize(segments)
segments_list = []
for speech_turn in segments.get_timeline():
segments_list.append(
SegmentX(
max(0.0, speech_turn.start - edge_padding),
speech_turn.end + edge_padding,
"UNKNOWN",
)
) # 100ms edge padding to account for edge errors
if len(segments_list) == 0:
print("No active speech found in audio")
return []
# Make sur the starting point is the start of the segment.
curr_start = segments_list[0].start
for idx, seg in enumerate(segments_list):
# if any segment start timing is less than previous segment end timing,
# reset the edge padding. Similarly for end timing.
if idx > 0:
if seg.start < segments_list[idx - 1].end:
seg.start += edge_padding
if idx < len(segments_list) - 1:
if seg.end > segments_list[idx + 1].start:
seg.end -= edge_padding
if seg.end - curr_start > chunk_length 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