File size: 21,412 Bytes
bbbf06e
48d8572
c0e541b
d906b98
 
f288ceb
 
33a2c1e
c0e541b
ea7f8cc
0cb931d
95261ed
0cb931d
ea7f8cc
 
 
 
 
 
 
f288ceb
 
 
 
 
c52f09b
48d8572
bc0cb58
d906b98
 
 
 
 
 
 
 
 
 
 
 
 
 
bc0cb58
f288ceb
 
89bd811
 
 
48d8572
 
 
 
b604ea1
 
5bbbb16
 
 
95261ed
5bbbb16
bbbf06e
 
 
084aa80
48d8572
95261ed
48d8572
5bbbb16
 
 
95261ed
 
5bbbb16
 
 
 
 
f288ceb
bbbf06e
 
f288ceb
530547e
 
 
 
 
 
bbbf06e
c0e541b
bbbf06e
 
f288ceb
bbbf06e
 
 
 
5bbbb16
 
f288ceb
bbbf06e
 
 
 
 
f288ceb
c0e541b
ff19da3
95261ed
c0e541b
ff19da3
 
 
 
95261ed
 
 
ff19da3
 
 
 
 
c0e541b
95261ed
f288ceb
5bbbb16
8781620
5bbbb16
48d8572
c0e541b
5bbbb16
d906b98
c0e541b
d906b98
5bbbb16
bc0cb58
 
 
95261ed
 
33a2c1e
 
95261ed
 
 
 
 
 
 
 
 
 
 
 
 
 
 
c0e541b
 
 
95261ed
c0e541b
95261ed
 
 
 
 
 
bc0cb58
f288ceb
 
 
 
 
 
f1fe464
f288ceb
95261ed
84fa1f8
f288ceb
 
84fa1f8
f288ceb
bc0cb58
8781620
31ba778
bc0cb58
 
f288ceb
89bd811
33a2c1e
89bd811
48d8572
ea7f8cc
48d8572
 
 
f1fe464
 
 
48d8572
f1fe464
33a2c1e
 
 
8781620
b604ea1
f288ceb
d906b98
 
 
 
 
 
 
 
 
 
 
f288ceb
 
 
 
 
f1fe464
 
f288ceb
48d8572
5bbbb16
d906b98
f1fe464
 
f288ceb
 
33a2c1e
5bbbb16
 
31ba778
 
 
 
 
d906b98
 
 
 
 
 
5bbbb16
d906b98
 
 
 
bc0cb58
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
8781620
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
74b1efd
 
 
8781620
 
 
 
 
 
 
 
 
 
 
 
48d8572
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
b604ea1
f288ceb
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
bbbf06e
f288ceb
 
 
 
 
 
 
bbbf06e
 
f288ceb
 
bbbf06e
95261ed
bbbf06e
c0e541b
5bbbb16
c0e541b
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
b604ea1
 
c0e541b
 
 
b604ea1
c0e541b
b604ea1
c0e541b
 
bbbf06e
b604ea1
 
 
 
 
 
 
02253c6
b604ea1
 
 
bbbf06e
c0e541b
 
 
bcee468
bbbf06e
c0e541b
 
 
 
 
 
 
 
 
 
 
bbbf06e
 
5bbbb16
 
bbbf06e
530547e
 
 
 
c0e541b
bbbf06e
 
 
c0e541b
bbbf06e
c0e541b
 
bbbf06e
 
 
 
 
 
 
 
 
 
b604ea1
 
 
bbbf06e
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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
from abc import ABC, abstractmethod
from collections import Counter, deque
import time

from typing import Any, Deque, Iterator, List, Dict

from pprint import pprint
from src.hooks.whisperProgressHook import ProgressListener, SubTaskProgressListener, create_progress_listener_handle
from src.modelCache import GLOBAL_MODEL_CACHE, ModelCache

from src.segments import merge_timestamps
from src.whisperContainer import WhisperCallback

# Workaround for https://github.com/tensorflow/tensorflow/issues/48797
try:
    import tensorflow as tf
except ModuleNotFoundError:
    # Error handling
    pass

import torch

import ffmpeg
import numpy as np

from src.utils import format_timestamp
from enum import Enum

class NonSpeechStrategy(Enum):
    """
    Ignore non-speech frames segments.
    """
    SKIP = 1
    """
    Just treat non-speech segments as speech.
    """
    CREATE_SEGMENT = 2
    """
    Expand speech segments into subsequent non-speech segments.
    """
    EXPAND_SEGMENT = 3

# Defaults for Silero
SPEECH_TRESHOLD = 0.3

# Minimum size of segments to process
MIN_SEGMENT_DURATION = 1

# The maximum time for texts from old segments to be used in the next segment 
MAX_PROMPT_WINDOW = 0 # seconds (0 = disabled)
PROMPT_NO_SPEECH_PROB = 0.1 # Do not pass the text from segments with a no speech probability higher than this

VAD_MAX_PROCESSING_CHUNK = 60 * 60 # 60 minutes of audio

class TranscriptionConfig(ABC):
    def __init__(self, non_speech_strategy: NonSpeechStrategy = NonSpeechStrategy.SKIP, 
                       segment_padding_left: float = None, segment_padding_right = None, max_silent_period: float = None, 
                       max_merge_size: float = None, max_prompt_window: float = None, initial_segment_index = -1):
        self.non_speech_strategy = non_speech_strategy
        self.segment_padding_left = segment_padding_left
        self.segment_padding_right = segment_padding_right
        self.max_silent_period = max_silent_period
        self.max_merge_size = max_merge_size
        self.max_prompt_window = max_prompt_window
        self.initial_segment_index = initial_segment_index

class PeriodicTranscriptionConfig(TranscriptionConfig):
    def __init__(self, periodic_duration: float, non_speech_strategy: NonSpeechStrategy = NonSpeechStrategy.SKIP, 
                       segment_padding_left: float = None, segment_padding_right = None, max_silent_period: float = None, 
                       max_merge_size: float = None, max_prompt_window: float = None, initial_segment_index = -1):
        super().__init__(non_speech_strategy, segment_padding_left, segment_padding_right, max_silent_period, max_merge_size, max_prompt_window, initial_segment_index)
        self.periodic_duration = periodic_duration

class AbstractTranscription(ABC):
    def __init__(self, sampling_rate: int = 16000):
        self.sampling_rate = sampling_rate

    def get_audio_segment(self, str, start_time: str = None, duration: str = None):
        return load_audio(str, self.sampling_rate, start_time, duration)

    def is_transcribe_timestamps_fast(self):
        """
        Determine if get_transcribe_timestamps is fast enough to not need parallelization.
        """
        return False

    @abstractmethod
    def get_transcribe_timestamps(self, audio: str, config: TranscriptionConfig, start_time: float, end_time: float):
        """
        Get the start and end timestamps of the sections that should be transcribed by this VAD method.

        Parameters
        ----------
        audio: str
            The audio file.
        config: TranscriptionConfig
            The transcription configuration.

        Returns
        -------
        A list of start and end timestamps, in fractional seconds.
        """
        return 

    def get_merged_timestamps(self, timestamps: List[Dict[str, Any]], config: TranscriptionConfig, total_duration: float):
        """
        Get the start and end timestamps of the sections that should be transcribed by this VAD method,
        after merging the given segments using the specified configuration.

        Parameters
        ----------
        audio: str
            The audio file. 
        config: TranscriptionConfig
            The transcription configuration.

        Returns
        -------
        A list of start and end timestamps, in fractional seconds.
        """
        merged = merge_timestamps(timestamps, config.max_silent_period, config.max_merge_size, 
                                  config.segment_padding_left, config.segment_padding_right)

        if config.non_speech_strategy != NonSpeechStrategy.SKIP:
            # Expand segments to include the gaps between them
            if (config.non_speech_strategy == NonSpeechStrategy.CREATE_SEGMENT):
                # When we have a prompt window, we create speech segments betwen each segment if we exceed the merge size
                merged = self.fill_gaps(merged, total_duration=total_duration, max_expand_size=config.max_merge_size)
            elif config.non_speech_strategy == NonSpeechStrategy.EXPAND_SEGMENT: 
                # With no prompt window, it is better to just expand the segments (this effectively passes the prompt to the next segment)
                merged = self.expand_gaps(merged, total_duration=total_duration)
            else:
                raise Exception("Unknown non-speech strategy: " + str(config.non_speech_strategy))

            print("Transcribing non-speech:")
            pprint(merged)
        return merged

    def transcribe(self, audio: str, whisperCallable: WhisperCallback, config: TranscriptionConfig, 
                   progressListener: ProgressListener = None):
        """
        Transcribe the given audo file.

        Parameters
        ----------
        audio: str
            The audio file.
        whisperCallable: WhisperCallback
            A callback object to call to transcribe each segment.

        Returns
        -------
        A list of start and end timestamps, in fractional seconds.
        """

        max_audio_duration = get_audio_duration(audio)
        timestamp_segments = self.get_transcribe_timestamps(audio, config, 0, max_audio_duration)

        # Get speech timestamps from full audio file
        merged = self.get_merged_timestamps(timestamp_segments, config, max_audio_duration)

        # A deque of transcribed segments that is passed to the next segment as a prompt
        prompt_window = deque()

        print("Processing timestamps:")
        pprint(merged)

        result = {
            'text': "",
            'segments': [],
            'language': ""
        }
        languageCounter = Counter()
        detected_language = None

        segment_index = config.initial_segment_index

        # For each time segment, run whisper
        for segment in merged:
            segment_index += 1
            segment_start = segment['start']
            segment_end = segment['end']
            segment_expand_amount = segment.get('expand_amount', 0)
            segment_gap = segment.get('gap', False)

            segment_duration = segment_end - segment_start

            if segment_duration < MIN_SEGMENT_DURATION:
                continue

            # Audio to run on Whisper
            segment_audio = self.get_audio_segment(audio, start_time = str(segment_start), duration = str(segment_duration))
            # Previous segments to use as a prompt
            segment_prompt = ' '.join([segment['text'] for segment in prompt_window]) if len(prompt_window) > 0 else None
    
            # Detected language
            detected_language = languageCounter.most_common(1)[0][0] if len(languageCounter) > 0 else None

            print("Running whisper from ", format_timestamp(segment_start), " to ", format_timestamp(segment_end), ", duration: ", 
                  segment_duration, "expanded: ", segment_expand_amount, "prompt: ", segment_prompt, "language: ", detected_language)
            
            scaled_progress_listener = SubTaskProgressListener(progressListener, base_task_total=max_audio_duration, sub_task_start=segment_start, sub_task_total=segment_duration) 
            segment_result = whisperCallable.invoke(segment_audio, segment_index, segment_prompt, detected_language, progress_listener=scaled_progress_listener)

            adjusted_segments = self.adjust_timestamp(segment_result["segments"], adjust_seconds=segment_start, max_source_time=segment_duration)

            # Propagate expand amount to the segments
            if (segment_expand_amount > 0):
                segment_without_expansion = segment_duration - segment_expand_amount

                for adjusted_segment in adjusted_segments:
                    adjusted_segment_end = adjusted_segment['end']

                    # Add expand amount if the segment got expanded
                    if (adjusted_segment_end > segment_without_expansion):
                        adjusted_segment["expand_amount"] = adjusted_segment_end - segment_without_expansion

            # Append to output
            result['text'] += segment_result['text']
            result['segments'].extend(adjusted_segments)

            # Increment detected language
            if not segment_gap:
                languageCounter[segment_result['language']] += 1

            # Update prompt window
            self.__update_prompt_window(prompt_window, adjusted_segments, segment_end, segment_gap, config)
            
        if detected_language is not None:
            result['language'] = detected_language

        return result
    
    def __update_prompt_window(self, prompt_window: Deque, adjusted_segments: List, segment_end: float, segment_gap: bool, config: TranscriptionConfig):
        if (config.max_prompt_window is not None and config.max_prompt_window > 0):
            # Add segments to the current prompt window (unless it is a speech gap)
            if not segment_gap:
                for segment in adjusted_segments:
                    if segment.get('no_speech_prob', 0) <= PROMPT_NO_SPEECH_PROB:
                        prompt_window.append(segment)

            while (len(prompt_window) > 0):
                first_end_time = prompt_window[0].get('end', 0)
                # Time expanded in the segments should be discounted from the prompt window
                first_expand_time = prompt_window[0].get('expand_amount', 0)

                if (first_end_time - first_expand_time < segment_end - config.max_prompt_window):
                    prompt_window.popleft()
                else:
                    break

    def include_gaps(self, segments: Iterator[dict], min_gap_length: float, total_duration: float):
        result = []
        last_end_time = 0

        for segment in segments:
            segment_start = float(segment['start'])
            segment_end = float(segment['end'])

            if (last_end_time != segment_start):
                delta = segment_start - last_end_time

                if (min_gap_length is None or delta >= min_gap_length):
                    result.append( { 'start': last_end_time, 'end': segment_start, 'gap': True } )
            
            last_end_time = segment_end
            result.append(segment)

        # Also include total duration if specified
        if (total_duration is not None and last_end_time < total_duration):
            delta = total_duration - segment_start

            if (min_gap_length is None or delta >= min_gap_length):
                result.append( { 'start': last_end_time, 'end': total_duration, 'gap': True } )

        return result

    # Expand the end time of each segment to the start of the next segment
    def expand_gaps(self, segments: List[Dict[str, Any]], total_duration: float):
        result = []

        if len(segments) == 0:
            return result

        # Add gap at the beginning if needed
        if (segments[0]['start'] > 0):
            result.append({ 'start': 0, 'end': segments[0]['start'], 'gap': True } )

        for i in range(len(segments) - 1):
            current_segment = segments[i]
            next_segment = segments[i + 1]

            delta = next_segment['start'] - current_segment['end']

            # Expand if the gap actually exists
            if (delta >= 0):
                current_segment = current_segment.copy()
                current_segment['expand_amount'] = delta
                current_segment['end'] = next_segment['start']
            
            result.append(current_segment)

        # Add last segment
        last_segment = segments[-1]
        result.append(last_segment)

        # Also include total duration if specified
        if (total_duration is not None):
            last_segment = result[-1]

            if (last_segment['end'] < total_duration):
                last_segment = last_segment.copy()
                last_segment['end'] = total_duration
                result[-1] = last_segment

        return result

    def fill_gaps(self, segments: List[Dict[str, Any]], total_duration: float, max_expand_size: float = None):
        result = []

        if len(segments) == 0:
            return result

        # Add gap at the beginning if needed
        if (segments[0]['start'] > 0):
            result.append({ 'start': 0, 'end': segments[0]['start'], 'gap': True } )

        for i in range(len(segments) - 1):
            expanded = False
            current_segment = segments[i]
            next_segment = segments[i + 1]

            delta = next_segment['start'] - current_segment['end']

            if (max_expand_size is not None and delta <= max_expand_size):
                # Just expand the current segment
                current_segment = current_segment.copy()
                current_segment['expand_amount'] = delta
                current_segment['end'] = next_segment['start']
                expanded = True

            result.append(current_segment)

            # Add a gap to the next segment if needed
            if (delta >= 0 and not expanded):
                result.append({ 'start': current_segment['end'], 'end': next_segment['start'], 'gap': True } )
            
        # Add last segment
        last_segment = segments[-1]
        result.append(last_segment)

        # Also include total duration if specified
        if (total_duration is not None):
            last_segment = result[-1]

            delta = total_duration - last_segment['end']

            if (delta > 0):
                if (max_expand_size is not None and delta <= max_expand_size):
                    # Expand the last segment
                    last_segment = last_segment.copy()
                    last_segment['expand_amount'] = delta
                    last_segment['end'] = total_duration
                    result[-1] = last_segment
                else:
                    result.append({ 'start': last_segment['end'], 'end': total_duration, 'gap': True } )

        return result

    def adjust_timestamp(self, segments: Iterator[dict], adjust_seconds: float, max_source_time: float = None):
        result = []

        for segment in segments:
            segment_start = float(segment['start'])
            segment_end = float(segment['end'])

            # Filter segments?
            if (max_source_time is not None):
                if (segment_start > max_source_time):
                    continue
                segment_end = min(max_source_time, segment_end)

                new_segment = segment.copy()

            # Add to start and end
            new_segment['start'] = segment_start + adjust_seconds
            new_segment['end'] = segment_end + adjust_seconds
            result.append(new_segment)
        return result

    def multiply_timestamps(self, timestamps: List[Dict[str, Any]], factor: float):
        result = []

        for entry in timestamps:
            start = entry['start']
            end = entry['end']

            result.append({
                'start': start * factor,
                'end': end * factor
            })
        return result


class VadSileroTranscription(AbstractTranscription):
    def __init__(self, sampling_rate: int = 16000, cache: ModelCache = None):
        super().__init__(sampling_rate=sampling_rate)
        self.model = None
        self.cache = cache
        self._initialize_model()

    def _initialize_model(self):
        if (self.cache is not None):
            model_key = "VadSileroTranscription"
            self.model, self.get_speech_timestamps = self.cache.get(model_key, self._create_model)
            print("Loaded Silerio model from cache.")
        else:
            self.model, self.get_speech_timestamps = self._create_model()
            print("Created Silerio model")

    def _create_model(self):
        model, utils = torch.hub.load(repo_or_dir='snakers4/silero-vad', model='silero_vad')
        
        # Silero does not benefit from multi-threading
        torch.set_num_threads(1) # JIT
        (get_speech_timestamps, _, _, _, _) = utils

        return model, get_speech_timestamps

    def get_transcribe_timestamps(self, audio: str, config: TranscriptionConfig, start_time: float, end_time: float):
        result = []

        print("Getting timestamps from audio file: {}, start: {}, duration: {}".format(audio, start_time, end_time))
        perf_start_time = time.perf_counter()

        # Divide procesisng of audio into chunks
        chunk_start = start_time

        while (chunk_start < end_time):
            chunk_duration = min(end_time - chunk_start, VAD_MAX_PROCESSING_CHUNK)

            print("Processing VAD in chunk from {} to {}".format(format_timestamp(chunk_start), format_timestamp(chunk_start + chunk_duration)))
            wav = self.get_audio_segment(audio, str(chunk_start), str(chunk_duration))

            sample_timestamps = self.get_speech_timestamps(wav, self.model, sampling_rate=self.sampling_rate, threshold=SPEECH_TRESHOLD)
            seconds_timestamps = self.multiply_timestamps(sample_timestamps, factor=1 / self.sampling_rate) 
            adjusted = self.adjust_timestamp(seconds_timestamps, adjust_seconds=chunk_start, max_source_time=chunk_start + chunk_duration)

            #pprint(adjusted)

            result.extend(adjusted)
            chunk_start += chunk_duration

        perf_end_time = time.perf_counter()
        print("VAD processing took {} seconds".format(perf_end_time - perf_start_time))

        return result

    def __getstate__(self):
        # We only need the sampling rate
        return { 'sampling_rate': self.sampling_rate }

    def __setstate__(self, state):
        self.sampling_rate = state['sampling_rate']
        self.model = None
        # Use the global cache
        self.cache = GLOBAL_MODEL_CACHE
        self._initialize_model()

# A very simple VAD that just marks every N seconds as speech
class VadPeriodicTranscription(AbstractTranscription):
    def __init__(self, sampling_rate: int = 16000):
        super().__init__(sampling_rate=sampling_rate)

    def is_transcribe_timestamps_fast(self):
        # This is a very fast VAD - no need to parallelize it
        return True

    def get_transcribe_timestamps(self, audio: str, config: PeriodicTranscriptionConfig, start_time: float, end_time: float):
        result = []

        # Generate a timestamp every N seconds
        start_timestamp = start_time

        while (start_timestamp < end_time):
            end_timestamp = min(start_timestamp + config.periodic_duration, end_time)
            segment_duration = end_timestamp - start_timestamp

            # Minimum duration is 1 second
            if (segment_duration >= 1):
                result.append( {  'start': start_timestamp, 'end': end_timestamp } )

            start_timestamp = end_timestamp

        return result

def get_audio_duration(file: str):
    return float(ffmpeg.probe(file)["format"]["duration"])

def load_audio(file: str, sample_rate: int = 16000, 
               start_time: str = None, duration: str = None):
    """
    Open an audio file and read as mono waveform, resampling as necessary

    Parameters
    ----------
    file: str
        The audio file to open

    sr: int
        The sample rate to resample the audio if necessary

    start_time: str
        The start time, using the standard FFMPEG time duration syntax, or None to disable.
    
    duration: str
        The duration, using the standard FFMPEG time duration syntax, or None to disable.

    Returns
    -------
    A NumPy array containing the audio waveform, in float32 dtype.
    """
    try:
        inputArgs = {'threads': 0}

        if (start_time is not None):
            inputArgs['ss'] = start_time
        if (duration is not None):
            inputArgs['t'] = duration

        # This launches a subprocess to decode audio while down-mixing and resampling as necessary.
        # Requires the ffmpeg CLI and `ffmpeg-python` package to be installed.
        out, _ = (
            ffmpeg.input(file, **inputArgs)
            .output("-", format="s16le", acodec="pcm_s16le", ac=1, ar=sample_rate)
            .run(cmd="ffmpeg", capture_stdout=True, capture_stderr=True)
        )
    except ffmpeg.Error as e:
        raise RuntimeError(f"Failed to load audio: {e.stderr.decode()}")

    return np.frombuffer(out, np.int16).flatten().astype(np.float32) / 32768.0