-
Notifications
You must be signed in to change notification settings - Fork 1.7k
Expand file tree
/
Copy pathASR.py
More file actions
1546 lines (1341 loc) · 56.2 KB
/
ASR.py
File metadata and controls
1546 lines (1341 loc) · 56.2 KB
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
597
598
599
600
601
602
603
604
605
606
607
608
609
610
611
612
613
614
615
616
617
618
619
620
621
622
623
624
625
626
627
628
629
630
631
632
633
634
635
636
637
638
639
640
641
642
643
644
645
646
647
648
649
650
651
652
653
654
655
656
657
658
659
660
661
662
663
664
665
666
667
668
669
670
671
672
673
674
675
676
677
678
679
680
681
682
683
684
685
686
687
688
689
690
691
692
693
694
695
696
697
698
699
700
701
702
703
704
705
706
707
708
709
710
711
712
713
714
715
716
717
718
719
720
721
722
723
724
725
726
727
728
729
730
731
732
733
734
735
736
737
738
739
740
741
742
743
744
745
746
747
748
749
750
751
752
753
754
755
756
757
758
759
760
761
762
763
764
765
766
767
768
769
770
771
772
773
774
775
776
777
778
779
780
781
782
783
784
785
786
787
788
789
790
791
792
793
794
795
796
797
798
799
800
801
802
803
804
805
806
807
808
809
810
811
812
813
814
815
816
817
818
819
820
821
822
823
824
825
826
827
828
829
830
831
832
833
834
835
836
837
838
839
840
841
842
843
844
845
846
847
848
849
850
851
852
853
854
855
856
857
858
859
860
861
862
863
864
865
866
867
868
869
870
871
872
873
874
875
876
877
878
879
880
881
882
883
884
885
886
887
888
889
890
891
892
893
894
895
896
897
898
899
900
901
902
903
904
905
906
907
908
909
910
911
912
913
914
915
916
917
918
919
920
921
922
923
924
925
926
927
928
929
930
931
932
933
934
935
936
937
938
939
940
941
942
943
944
945
946
947
948
949
950
951
952
953
954
955
956
957
958
959
960
961
962
963
964
965
966
967
968
969
970
971
972
973
974
975
976
977
978
979
980
981
982
983
984
985
986
987
988
989
990
991
992
993
994
995
996
997
998
999
1000
"""Specifies the inference interfaces for Automatic speech Recognition (ASR) modules.
Authors:
* Aku Rouhe 2021
* Peter Plantinga 2021
* Loren Lugosch 2020
* Mirco Ravanelli 2020
* Titouan Parcollet 2021
* Abdel Heba 2021
* Andreas Nautsch 2022, 2023
* Pooneh Mousavi 2023
* Sylvain de Langen 2023, 2024
* Adel Moumen 2023, 2024, 2025
* Pradnya Kandarkar 2023
"""
import functools
import itertools
from dataclasses import dataclass
from typing import Any, List, Optional, Tuple
import sentencepiece
import torch
import torchaudio
from tqdm import tqdm
import speechbrain
from speechbrain.inference.interfaces import Pretrained
from speechbrain.utils.data_utils import split_path
from speechbrain.utils.dynamic_chunk_training import DynChunkTrainConfig
from speechbrain.utils.fetching import fetch
from speechbrain.utils.streaming import split_fixed_chunks
class EncoderDecoderASR(Pretrained):
"""A ready-to-use Encoder-Decoder ASR model
The class can be used either to run only the encoder (encode()) to extract
features or to run the entire encoder-decoder model
(transcribe()) to transcribe speech. The given YAML must contain the fields
specified in the *_NEEDED[] lists.
Arguments
---------
*args : tuple
**kwargs : dict
Arguments are forwarded to ``Pretrained`` parent class.
Example
-------
>>> from speechbrain.inference.ASR import EncoderDecoderASR
>>> tmpdir = getfixture("tmpdir")
>>> asr_model = EncoderDecoderASR.from_hparams(
... source="speechbrain/asr-crdnn-rnnlm-librispeech",
... savedir=tmpdir,
... ) # doctest: +SKIP
>>> asr_model.transcribe_file(
... "tests/samples/single-mic/example2.flac"
... ) # doctest: +SKIP
"MY FATHER HAS REVEALED THE CULPRIT'S NAME"
"""
HPARAMS_NEEDED = ["tokenizer"]
MODULES_NEEDED = ["encoder", "decoder"]
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
self.tokenizer = self.hparams.tokenizer
self.transducer_beam_search = False
self.transformer_beam_search = False
if hasattr(self.hparams, "transducer_beam_search"):
self.transducer_beam_search = self.hparams.transducer_beam_search
if hasattr(self.hparams, "transformer_beam_search"):
self.transformer_beam_search = self.hparams.transformer_beam_search
def transcribe_file(self, path, **kwargs):
"""Transcribes the given audiofile into a sequence of words.
Arguments
---------
path : str
Path to audio file which to transcribe.
**kwargs : dict
Arguments forwarded to ``load_audio``.
Returns
-------
str
The audiofile transcription produced by this ASR system.
"""
waveform = self.load_audio(path, **kwargs)
# Fake a batch:
batch = waveform.unsqueeze(0)
rel_length = torch.tensor([1.0])
predicted_words, predicted_tokens = self.transcribe_batch(
batch, rel_length
)
return predicted_words[0]
def encode_batch(self, wavs, wav_lens):
"""Encodes the input audio into a sequence of hidden states
The waveforms should already be in the model's desired format.
You can call:
``normalized = EncoderDecoderASR.normalizer(signal, sample_rate)``
to get a correctly converted signal in most cases.
Arguments
---------
wavs : torch.Tensor
Batch of waveforms [batch, time, channels] or [batch, time]
depending on the model.
wav_lens : torch.Tensor
Lengths of the waveforms relative to the longest one in the
batch, tensor of shape [batch]. The longest one should have
relative length 1.0 and others len(waveform) / max_length.
Used for ignoring padding.
Returns
-------
torch.Tensor
The encoded batch
"""
wavs = wavs.float()
wavs, wav_lens = wavs.to(self.device), wav_lens.to(self.device)
encoder_out = self.mods.encoder(wavs, wav_lens)
if self.transformer_beam_search:
encoder_out = self.mods.transformer.encode(encoder_out, wav_lens)
return encoder_out
def transcribe_batch(self, wavs, wav_lens):
"""Transcribes the input audio into a sequence of words
The waveforms should already be in the model's desired format.
You can call:
``normalized = EncoderDecoderASR.normalizer(signal, sample_rate)``
to get a correctly converted signal in most cases.
Arguments
---------
wavs : torch.Tensor
Batch of waveforms [batch, time, channels] or [batch, time]
depending on the model.
wav_lens : torch.Tensor
Lengths of the waveforms relative to the longest one in the
batch, tensor of shape [batch]. The longest one should have
relative length 1.0 and others len(waveform) / max_length.
Used for ignoring padding.
Returns
-------
list
Each waveform in the batch transcribed.
tensor
Each predicted token id.
"""
with torch.no_grad():
wav_lens = wav_lens.to(self.device)
encoder_out = self.encode_batch(wavs, wav_lens)
if self.transducer_beam_search:
inputs = [encoder_out]
else:
inputs = [encoder_out, wav_lens]
predicted_tokens, _, _, _ = self.mods.decoder(*inputs)
predicted_words = [
self.tokenizer.decode_ids(token_seq)
for token_seq in predicted_tokens
]
return predicted_words, predicted_tokens
def forward(self, wavs, wav_lens):
"""Runs full transcription - note: no gradients through decoding"""
return self.transcribe_batch(wavs, wav_lens)
class EncoderASR(Pretrained):
"""A ready-to-use Encoder ASR model
The class can be used either to run only the encoder (encode()) to extract
features or to run the entire encoder + decoder function model
(transcribe()) to transcribe speech. The given YAML must contain the fields
specified in the *_NEEDED[] lists.
Arguments
---------
*args : tuple
**kwargs : dict
Arguments are forwarded to ``Pretrained`` parent class.
Example
-------
>>> from speechbrain.inference.ASR import EncoderASR
>>> tmpdir = getfixture("tmpdir")
>>> asr_model = EncoderASR.from_hparams(
... source="speechbrain/asr-wav2vec2-commonvoice-fr",
... savedir=tmpdir,
... ) # doctest: +SKIP
>>> asr_model.transcribe_file(
... "samples/audio_samples/example_fr.wav"
... ) # doctest: +SKIP
"""
HPARAMS_NEEDED = ["tokenizer", "decoding_function"]
MODULES_NEEDED = ["encoder"]
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
self.tokenizer = self.hparams.tokenizer
self.set_decoding_function()
def set_decoding_function(self):
"""Set the decoding function based on the parameters defined in the hyperparameter file.
The decoding function is determined by the `decoding_function` specified in the hyperparameter file.
It can be either a functools.partial object representing a decoding function or an instance of
`speechbrain.decoders.ctc.CTCBaseSearcher` for beam search decoding.
Raises:
ValueError: If the decoding function is neither a functools.partial nor an instance of
speechbrain.decoders.ctc.CTCBaseSearcher.
Note:
- For greedy decoding (functools.partial), the provided `decoding_function` is assigned directly.
- For CTCBeamSearcher decoding, an instance of the specified `decoding_function` is created, and
additional parameters are added based on the tokenizer type.
"""
# Greedy Decoding case
if isinstance(self.hparams.decoding_function, functools.partial):
self.decoding_function = self.hparams.decoding_function
# CTCBeamSearcher case
else:
# 1. check if the decoding function is an instance of speechbrain.decoders.CTCBaseSearcher
if issubclass(
self.hparams.decoding_function,
speechbrain.decoders.ctc.CTCBaseSearcher,
):
# If so, we need to retrieve the vocab list from the tokenizer.
# We also need to check if the tokenizer is a sentencepiece or a CTCTextEncoder.
if isinstance(
self.tokenizer, speechbrain.dataio.encoder.CTCTextEncoder
):
ind2lab = self.tokenizer.ind2lab
vocab_list = [ind2lab[x] for x in range(len(ind2lab))]
elif isinstance(
self.tokenizer, sentencepiece.SentencePieceProcessor
):
vocab_list = [
self.tokenizer.id_to_piece(i)
for i in range(self.tokenizer.vocab_size())
]
else:
raise ValueError(
"The tokenizer must be sentencepiece or CTCTextEncoder"
)
# We can now instantiate the decoding class and add all the parameters
if hasattr(self.hparams, "test_beam_search"):
opt_beam_search_params = self.hparams.test_beam_search
# check if the kenlm_model_path is provided and fetch it if necessary
if "kenlm_model_path" in opt_beam_search_params:
source, fl = split_path(
opt_beam_search_params["kenlm_model_path"]
)
kenlm_model_path = str(
fetch(
fl, source=source, savedir=self.hparams.savedir
)
)
# we need to update the kenlm_model_path in the opt_beam_search_params
opt_beam_search_params["kenlm_model_path"] = (
kenlm_model_path
)
else:
opt_beam_search_params = {}
self.decoding_function = self.hparams.decoding_function(
**opt_beam_search_params, vocab_list=vocab_list
)
else:
raise ValueError(
"The decoding function must be an instance of speechbrain.decoders.CTCBaseSearcher"
)
def transcribe_file(self, path, **kwargs):
"""Transcribes the given audiofile into a sequence of words.
Arguments
---------
path : str
Path to audio file which to transcribe.
**kwargs : dict
Arguments forwarded to ``load_audio``.
Returns
-------
str
The audiofile transcription produced by this ASR system.
"""
waveform = self.load_audio(path, **kwargs)
# Fake a batch:
batch = waveform.unsqueeze(0)
rel_length = torch.tensor([1.0])
predicted_words, predicted_tokens = self.transcribe_batch(
batch, rel_length
)
return str(predicted_words[0])
def encode_batch(self, wavs, wav_lens):
"""Encodes the input audio into a sequence of hidden states
The waveforms should already be in the model's desired format.
You can call:
``normalized = EncoderASR.normalizer(signal, sample_rate)``
to get a correctly converted signal in most cases.
Arguments
---------
wavs : torch.Tensor
Batch of waveforms [batch, time, channels] or [batch, time]
depending on the model.
wav_lens : torch.Tensor
Lengths of the waveforms relative to the longest one in the
batch, tensor of shape [batch]. The longest one should have
relative length 1.0 and others len(waveform) / max_length.
Used for ignoring padding.
Returns
-------
torch.Tensor
The encoded batch
"""
wavs = wavs.float()
wavs, wav_lens = wavs.to(self.device), wav_lens.to(self.device)
encoder_out = self.mods.encoder(wavs, wav_lens)
return encoder_out
def transcribe_batch(self, wavs, wav_lens):
"""Transcribes the input audio into a sequence of words
The waveforms should already be in the model's desired format.
You can call:
``normalized = EncoderASR.normalizer(signal, sample_rate)``
to get a correctly converted signal in most cases.
Arguments
---------
wavs : torch.Tensor
Batch of waveforms [batch, time, channels] or [batch, time]
depending on the model.
wav_lens : torch.Tensor
Lengths of the waveforms relative to the longest one in the
batch, tensor of shape [batch]. The longest one should have
relative length 1.0 and others len(waveform) / max_length.
Used for ignoring padding.
Returns
-------
list
Each waveform in the batch transcribed.
tensor
Each predicted token id.
"""
with torch.no_grad():
wav_lens = wav_lens.to(self.device)
encoder_out = self.encode_batch(wavs, wav_lens)
predictions = self.decoding_function(encoder_out, wav_lens)
is_ctc_text_encoder_tokenizer = isinstance(
self.tokenizer, speechbrain.dataio.encoder.CTCTextEncoder
)
if isinstance(self.hparams.decoding_function, functools.partial):
if is_ctc_text_encoder_tokenizer:
predicted_words = [
"".join(self.tokenizer.decode_ndim(token_seq))
for token_seq in predictions
]
else:
predicted_words = [
self.tokenizer.decode_ids(token_seq)
for token_seq in predictions
]
else:
predicted_words = [hyp[0].text for hyp in predictions]
return predicted_words, predictions
def forward(self, wavs, wav_lens):
"""Runs the encoder"""
return self.encode_batch(wavs, wav_lens)
@dataclass
class ASRWhisperSegment:
"""A single chunk of audio for Whisper ASR streaming.
This object is intended to be mutated as streaming progresses and passed across calls
to the lower-level APIs such as `encode_chunk`, `decode_chunk`, etc.
Attributes
----------
start : float
The start time of the audio chunk.
end : float
The end time of the audio chunk.
chunk : torch.Tensor
The audio chunk, shape [time, channels].
lang_id : str
The language identifier associated with the audio chunk.
words : str
The predicted words for the audio chunk.
tokens : List[int]
The predicted tokens for the audio chunk.
prompt : List[str]
The prompt associated with the audio chunk.
avg_log_probs : float
The average log probability associated with the prediction.
no_speech_prob : float
The probability of no speech in the audio chunk.
"""
start: float
end: float
chunk: torch.Tensor
lang_id: Optional[str] = None
words: Optional[str] = None
tokens: Optional[List[str]] = None
prompt: Optional[List[str]] = None
avg_log_probs: Optional[float] = None
no_speech_prob: Optional[float] = None
class WhisperASR(Pretrained):
"""A ready-to-use Whisper ASR model.
The class can be used to run the entire encoder-decoder whisper model.
The set of tasks supported are: ``transcribe``, ``translate``, and ``lang_id``.
The given YAML must contains the fields specified in the *_NEEDED[] lists.
Arguments
---------
*args : tuple
**kwargs : dict
Arguments are forwarded to ``Pretrained`` parent class.
Example
-------
>>> from speechbrain.inference.ASR import WhisperASR
>>> tmpdir = getfixture("tmpdir")
>>> asr_model = WhisperASR.from_hparams(
... source="speechbrain/asr-whisper-medium-commonvoice-it",
... savedir=tmpdir,
... ) # doctest: +SKIP
>>> hyp = asr_model.transcribe_file(
... "speechbrain/asr-whisper-medium-commonvoice-it/example-it.wav"
... ) # doctest: +SKIP
>>> hyp # doctest: +SKIP
buongiorno a tutti e benvenuti a bordo
>>> _, probs = asr_model.detect_language_file(
... "speechbrain/asr-whisper-medium-commonvoice-it/example-it.wav"
... ) # doctest: +SKIP
>>> print(
... f"Detected language: {max(probs[0], key=probs[0].get)}"
... ) # doctest: +SKIP
Detected language: it
"""
HPARAMS_NEEDED = ["language", "sample_rate"]
MODULES_NEEDED = ["whisper", "decoder"]
TASKS = ["transcribe", "translate", "lang_id"]
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
self.tokenizer = self.hparams.whisper.tokenizer
@torch.no_grad()
def detect_language_file(self, path: str):
"""Detects the language of the given audiofile.
This method only works on input_file of 30 seconds or less.
Arguments
---------
path : str
Path to audio file which to transcribe.
Returns
-------
language_tokens : torch.Tensor
The detected language tokens.
language_probs : dict
The probabilities of the detected language tokens.
Raises
------
ValueError
If the model doesn't have language tokens.
"""
wavs = self.load_audio(path).float().to(self.device).unsqueeze(0)
mel = self.mods.whisper._get_mel(wavs)
language_tokens, language_probs = self.mods.whisper.detect_language(mel)
return language_tokens, language_probs
@torch.no_grad()
def detect_language_batch(self, wav: torch.Tensor):
"""Detects the language of the given wav Tensor.
This method only works on wav files of 30 seconds or less.
Arguments
---------
wav : torch.tensor
Batch of waveforms [batch, time, channels].
Returns
-------
language_tokens : torch.Tensor of shape (batch_size,)
ids of the most probable language tokens, which appears after the startoftranscript token.
language_probs : List[Dict[str, float]]
list of dictionaries containing the probability distribution over all languages.
Raises
------
ValueError
If the model doesn't have language tokens.
Example
-------
>>> from speechbrain.inference.ASR import WhisperASR
>>> from speechbrain.dataio import audio_io
>>> tmpdir = getfixture("tmpdir")
>>> asr_model = WhisperASR.from_hparams(
... source="speechbrain/asr-whisper-medium-commonvoice-it",
... savedir=tmpdir,
... ) # doctest: +SKIP
>>> wav, _ = audio_io.load("your_audio") # doctest: +SKIP
>>> language_tokens, language_probs = asr_model.detect_language(
... wav
... ) # doctest: +SKIP
"""
mel = self.mods.whisper._get_mel(wav)
language_tokens, language_probs = self.mods.whisper.detect_language(mel)
return language_tokens, language_probs
@torch.no_grad()
def _detect_language(self, mel: torch.Tensor, task: str):
"""Detects the language of the given mel spectrogram.
Arguments
---------
mel : torch.tensor
Batch of mel spectrograms [batch, time, channels].
task : str
The task to perform.
Returns
-------
language_tokens : Tensor, shape = (n_audio,)
ids of the most probable language tokens, which appears after the startoftranscript token.
language_probs : List[Dict[str, float]], length = n_audio
list of dictionaries containing the probability distribution over all languages.
"""
languages = [self.mods.whisper.language] * mel.shape[0]
lang_probs = None
if self.mods.whisper.language is None or task == "lang_id":
lang_tokens, lang_probs = self.mods.whisper.detect_language(mel)
languages = [max(probs, key=probs.get) for probs in lang_probs]
self.mods.decoder.set_lang_tokens(lang_tokens)
return languages, lang_probs
def _get_audio_stream(
self, streamer: "torchaudio.io.StreamReader", frames_per_chunk: int
):
"""From a :class:`torchaudio.io.StreamReader`, identifies the audio
stream and returns an iterable stream of chunks (after resampling and
downmixing to mono).
Arguments
---------
streamer : torchaudio.io.StreamReader
The stream object. Must hold exactly one source stream of an
audio type.
frames_per_chunk : int
The number of frames per chunk. For a streaming model, this should
be determined from the DynChunkTrain configuration.
Yields
------
chunks from streamer
"""
stream_infos = [
streamer.get_src_stream_info(i)
for i in range(streamer.num_src_streams)
]
audio_stream_infos = [
(i, stream_info)
for i, stream_info in enumerate(stream_infos)
if stream_info.media_type == "audio"
]
if len(audio_stream_infos) != 1:
raise ValueError(
f"Expected stream to have only 1 stream (with any number of channels), got {len(audio_stream_infos)} (with streams: {stream_infos})"
)
# find the index of the first (and only) audio stream
audio_stream_index = audio_stream_infos[0][0]
# output stream #0
streamer.add_basic_audio_stream(
frames_per_chunk=frames_per_chunk,
stream_index=audio_stream_index,
sample_rate=self.audio_normalizer.sample_rate,
format="fltp", # torch.float32
num_channels=1,
buffer_chunk_size=-1, # avoiding the problem of dropping first chunks
)
for (chunk,) in streamer.stream():
chunk = chunk.squeeze(-1) # we deal with mono, remove that dim
chunk = chunk.unsqueeze(0) # create a fake batch dim
yield chunk
@torch.no_grad()
def transcribe_file_streaming(
self,
path: str,
task: Optional[str] = None,
initial_prompt: Optional[str] = None,
logprob_threshold: Optional[float] = -1.0,
no_speech_threshold=0.6,
condition_on_previous_text: bool = False,
verbose: bool = False,
use_torchaudio_streaming: bool = False,
chunk_size: int = 30,
**kwargs,
):
"""Transcribes the given audiofile into a sequence of words.
This method supports the following tasks: ``transcribe``, ``translate``, and ``lang_id``.
It can process an input audio file longer than 30 seconds by splitting it into chunk_size-second segments.
Arguments
---------
path : str
URI/path to the audio to transcribe. When
``use_torchaudio_streaming`` is ``False``, uses SB fetching to allow
fetching from HF or a local file. When ``True``, resolves the URI
through ffmpeg, as documented in
:class:`torchaudio.io.StreamReader`.
task : Optional[str]
The task to perform. If None, the default task is the one passed in the Whisper model.
initial_prompt : Optional[str]
The initial prompt to condition the model on.
logprob_threshold : Optional[float]
The log probability threshold to continue decoding the current segment.
no_speech_threshold : float
The threshold to skip decoding segment if the no_speech_prob is higher than this value.
condition_on_previous_text : bool
If True, the model will be condition on the last 224 tokens.
verbose : bool
If True, print the transcription of each segment.
use_torchaudio_streaming : bool
Whether the audio file can be loaded in a streaming fashion. If not,
transcription is still performed through chunks of audio, but the
entire audio file is fetched and loaded at once.
This skips the usual fetching method and instead resolves the URI
using torchaudio (via ffmpeg).
chunk_size : int
The size of the chunks to split the audio into. The default
chunk size is 30 seconds which corresponds to the maximal length
that the model can process in one go.
**kwargs : dict
Arguments forwarded to ``load_audio``
Yields
------
ASRWhisperSegment
A new ASRWhisperSegment instance initialized with the provided parameters.
"""
if task is not None:
if task in self.TASKS:
if task != "lang_id":
self.mods.decoder.set_task(task)
else:
raise ValueError(
f"Task {task} not supported. Supported tasks are {self.TASKS}"
)
# create chunks of chunk_size seconds
num_frames_per_chunk = chunk_size * self.hparams.sample_rate
if use_torchaudio_streaming:
streamer = torchaudio.io.StreamReader(path)
segments = self._get_audio_stream(streamer, num_frames_per_chunk)
else:
waveform = self.load_audio(path, **kwargs)
batch = waveform.unsqueeze(0)
segments = split_fixed_chunks(batch, num_frames_per_chunk)
rel_length = torch.tensor([1.0])
all_tokens = []
prompt_reset_since = 0
if initial_prompt is not None:
initial_prompt_tokens = self.whisper.tokenizer.encode(
" " + initial_prompt.strip()
)
all_tokens.extend(initial_prompt_tokens)
else:
initial_prompt_tokens = []
for i, segment in enumerate(tqdm(segments, disable=verbose)):
# move the segment on the device
segment = segment.to(self.device)
# extract mel spectrogram
mel_segment = self.mods.whisper._get_mel(segment)
start = i * chunk_size
end = (i + 1) * chunk_size
encoder_out = self.mods.whisper.forward_encoder(mel_segment)
languages, _ = self._detect_language(mel_segment, task)
if task == "lang_id":
yield ASRWhisperSegment(
start=start,
end=end,
chunk=segment,
lang_id=languages[0],
)
continue
prompt = all_tokens[prompt_reset_since:]
self.mods.decoder.set_prompt(prompt)
predicted_tokens, _, scores, _ = self.mods.decoder(
encoder_out, rel_length
)
avg_log_probs = scores.sum() / (len(predicted_tokens[0]) + 1)
if no_speech_threshold is not None:
should_skip = (
self.mods.decoder.no_speech_probs[0] > no_speech_threshold
)
if (
logprob_threshold is not None
and avg_log_probs > logprob_threshold
):
# don't skip if the logprob is high enough, despite the no_speech_prob
should_skip = False
if should_skip:
yield ASRWhisperSegment(
start=start,
end=end,
chunk=segment,
lang_id=languages[0],
words="",
tokens=[],
prompt=prompt,
avg_log_probs=avg_log_probs.item(),
no_speech_prob=self.mods.decoder.no_speech_probs[0],
)
continue
predicted_words = [
self.tokenizer.decode(t, skip_special_tokens=True).strip()
for t in predicted_tokens
]
yield ASRWhisperSegment(
start=start,
end=end,
chunk=segment,
lang_id=languages[0],
words=predicted_words[0],
tokens=predicted_tokens[0],
prompt=prompt,
avg_log_probs=avg_log_probs.item(),
no_speech_prob=self.mods.decoder.no_speech_probs[0],
)
all_tokens.extend(predicted_tokens[0])
if (
not condition_on_previous_text
or self.mods.decoder.temperature > 0.5
):
prompt_reset_since = len(all_tokens)
def transcribe_file(
self,
path: str,
task: Optional[str] = None,
initial_prompt: Optional[str] = None,
logprob_threshold: Optional[float] = -1.0,
no_speech_threshold=0.6,
condition_on_previous_text: bool = False,
verbose: bool = False,
use_torchaudio_streaming: bool = False,
chunk_size: Optional[int] = 30,
**kwargs,
) -> List[ASRWhisperSegment]:
"""Run the Whisper model using the specified task on the given audio file and return the ``ASRWhisperSegment`` objects
for each segment.
This method supports the following tasks: ``transcribe``, ``translate``, and ``lang_id``.
It can process an input audio file longer than 30 seconds by splitting it into chunk_size-second segments.
Arguments
---------
path : str
URI/path to the audio to transcribe. When
``use_torchaudio_streaming`` is ``False``, uses SB fetching to allow
fetching from HF or a local file. When ``True``, resolves the URI
through ffmpeg, as documented in
:class:`torchaudio.io.StreamReader`.
task : Optional[str]
The task to perform. If None, the default task is the one passed in the Whisper model.
It can be one of the following: ``transcribe``, ``translate``, ``lang_id``.
initial_prompt : Optional[str]
The initial prompt to condition the model on.
logprob_threshold : Optional[float]
The log probability threshold to continue decoding the current segment.
no_speech_threshold : float
The threshold to skip decoding segment if the no_speech_prob is higher than this value.
condition_on_previous_text : bool
If True, the model will be condition on the last 224 tokens.
verbose : bool
If True, print the details of each segment.
use_torchaudio_streaming : bool
Whether the audio file can be loaded in a streaming fashion. If not,
transcription is still performed through chunks of audio, but the
entire audio file is fetched and loaded at once.
This skips the usual fetching method and instead resolves the URI
using torchaudio (via ffmpeg).
chunk_size : Optional[int]
The size of the chunks to split the audio into. The default
chunk size is 30 seconds which corresponds to the maximal length
that the model can process in one go.
**kwargs : dict
Arguments forwarded to ``load_audio``
Returns
-------
results : list
A list of ``WhisperASRChunk`` objects, each containing the task result.
"""
results = []
for whisper_segment in self.transcribe_file_streaming(
path,
task=task,
initial_prompt=initial_prompt,
logprob_threshold=logprob_threshold,
no_speech_threshold=no_speech_threshold,
condition_on_previous_text=condition_on_previous_text,
verbose=verbose,
use_torchaudio_streaming=use_torchaudio_streaming,
chunk_size=chunk_size,
**kwargs,
):
results.append(whisper_segment)
if verbose:
pred = (
whisper_segment.words
if task != "lang_id"
else whisper_segment.lang_id
)
print(
f"[{whisper_segment.start}s --> {whisper_segment.end}s] {pred}"
)
return results
def encode_batch(self, wavs, wav_lens):
"""Encodes the input audio into a sequence of hidden states
The waveforms should already be in the model's desired format.
You can call:
``normalized = EncoderDecoderASR.normalizer(signal, sample_rate)``
to get a correctly converted signal in most cases.
Arguments
---------
wavs : torch.tensor
Batch of waveforms [batch, time, channels].
wav_lens : torch.tensor
Lengths of the waveforms relative to the longest one in the
batch, tensor of shape [batch]. The longest one should have
relative length 1.0 and others len(waveform) / max_length.
Used for ignoring padding.
Returns
-------
torch.tensor
The encoded batch
"""
wavs = wavs.to(device=self.device, dtype=torch.float32)
mel = self.mods.whisper._get_mel(wavs)
encoder_out = self.mods.whisper.forward_encoder(mel)
return encoder_out
@torch.no_grad()
def transcribe_batch(self, wavs, wav_lens):
"""Transcribes the input audio into a sequence of words
The waveforms should already be in the model's desired format.
You can call:
``normalized = EncoderDecoderASR.normalizer(signal, sample_rate)``
to get a correctly converted signal in most cases.
Arguments
---------
wavs : torch.tensor
Batch of waveforms [batch, time, channels].
wav_lens : torch.tensor
Lengths of the waveforms relative to the longest one in the
batch, tensor of shape [batch]. The longest one should have
relative length 1.0 and others len(waveform) / max_length.
Used for ignoring padding.
Returns
-------
list
Each waveform in the batch transcribed.
tensor
Each predicted token id.
"""
wav_lens = wav_lens.float().to(self.device)
encoder_out = self.encode_batch(wavs, wav_lens)
predicted_tokens, _, _, _ = self.mods.decoder(encoder_out, wav_lens)
predicted_words = [
self.tokenizer.decode(t, skip_special_tokens=True).strip()
for t in predicted_tokens
]
if self.hparams.normalized_transcripts:
predicted_words = [
self.tokenizer.normalize(text).split(" ")
for text in predicted_words
]
return predicted_words, predicted_tokens
def forward(self, wavs, wav_lens):
"""Runs full transcription - note: no gradients through decoding"""
return self.transcribe_batch(wavs, wav_lens)
@dataclass
class ASRStreamingContext:
"""Streaming metadata, initialized by
:meth:`~StreamingASR.make_streaming_context` (see there for details on
initialization of fields here).
This object is intended to be mutate: the same object should be passed
across calls as streaming progresses (namely when using the lower-level
:meth:`~StreamingASR.encode_chunk`, etc. APIs).
Holds some references to opaque streaming contexts, so the context is
model-agnostic to an extent."""
config: DynChunkTrainConfig
"""Dynamic chunk training configuration used to initialize the streaming
context. Cannot be modified on the fly."""
fea_extractor_context: Any
"""Opaque feature extractor streaming context."""
encoder_context: Any
"""Opaque encoder streaming context."""
decoder_context: Any
"""Opaque decoder streaming context."""
tokenizer_context: Optional[List[Any]]
"""Opaque streaming context for the tokenizer. Initially `None`. Initialized
to a list of tokenizer contexts once batch size can be determined."""
class StreamingASR(Pretrained):
"""A ready-to-use, streaming-capable ASR model.
Arguments
---------
*args : tuple
**kwargs : dict
Arguments are forwarded to ``Pretrained`` parent class.
Example
-------
>>> from speechbrain.inference.ASR import StreamingASR
>>> from speechbrain.utils.dynamic_chunk_training import DynChunkTrainConfig
>>> tmpdir = getfixture("tmpdir")
>>> asr_model = StreamingASR.from_hparams(
... source="speechbrain/asr-conformer-streaming-librispeech",
... savedir=tmpdir,
... ) # doctest: +SKIP
>>> asr_model.transcribe_file(
... "speechbrain/asr-conformer-streaming-librispeech/test-en.wav",
... DynChunkTrainConfig(24, 8),
... ) # doctest: +SKIP
"""