-
Notifications
You must be signed in to change notification settings - Fork 35
/
neural_feature_reader.py
428 lines (358 loc) · 12.8 KB
/
neural_feature_reader.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
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import torch
import soundfile as sf
import torch.nn.functional as F
import tqdm
import torchaudio
import gc
import os
from onmt.data.audio_utils import safe_readaudio, wav_to_fmel
class FeatureReader:
"""
Wrapper class to run inference on HuBERT model.
Helps extract features for a given audio file.
"""
def __init__(self, checkpoint_path, layer, max_chunk=1600000, use_cuda=True, fp16=False, bf16=False,
model_path="w2vbert-conformer_shaw.pt", sample_rate=16000):
# first we have to load the model
# lets try to load the wav2vec-bert model
# (
# model,
# cfg,
# task,
# ) = fairseq.checkpoint_utils.load_model_ensemble_and_task(
# [checkpoint_path]
# )
from onmt.models.speech_recognizer.w2v_bert.config import conformer_shaw_600m
from onmt.models.speech_recognizer.w2v_bert.builder import create_conformer_shaw_model
config = conformer_shaw_600m()
self.model = create_conformer_shaw_model(config)
if len(model_path) > 0:
cpt = torch.load(model_path, map_location=torch.device('cpu'))
weights = cpt['model']
print("[INFO] Loaded pretrained-w2vbert model")
self.model.load_state_dict(weights)
self.model.eval()
self.layer = layer
self.max_chunk = max_chunk
self.use_cuda = use_cuda
if self.use_cuda:
self.model.cuda()
self.fp16 = fp16
self.bf16 = bf16
self.sample_rate = sample_rate
def read_audio(self, path, ref_len=None, channel_id=None):
# wav, sr = sf.read(path)
# if channel_id is not None:
# assert wav.ndim == 2, \
# f"Expected stereo input when channel_id is given ({path})"
# assert channel_id in [1, 2], \
# "channel_id is expected to be in [1, 2]"
# wav = wav[:, channel_id-1]
# if wav.ndim == 2:
# wav = wav.mean(-1)
# assert wav.ndim == 1, wav.ndim
# assert sr == self.sample_rate, sr
# if ref_len is not None and abs(ref_len - len(wav)) > 160:
# print(f"ref {ref_len} != read {len(wav)} ({path})")
# wav = torchaudio.load(path)
wav = safe_readaudio(path)
# should be T x 1 here
# print(wav.size())
# wav = wav_to_fmel(wav, num_mel_bin=80)
return wav
def get_feats(self, file_path, ref_len=None, channel_id=None):
x = self.read_audio(file_path, ref_len, channel_id)
x = x.float()
_dtype = torch.float32
if self.bf16:
_dtype = torch.bfloat16
elif self.fp16:
_dtype = torch.float16
with torch.autocast(device_type="cuda", dtype=_dtype):
with torch.no_grad():
# TODO: use torch amp.autocast here
feat = []
for start in range(0, x.size(1), self.max_chunk):
x_chunk = x[start: start + self.max_chunk, :]
# for w2vbert we need to convert to fmel
x_chunk = wav_to_fmel(x_chunk, num_mel_bin=80)
if self.use_cuda:
x_chunk = x_chunk.cuda()
# batch size 1
x_chunk = x_chunk.unsqueeze(0)
feat_chunk, _ = self.model.forward(x_chunk, padding_mask=None, layer=self.layer)
feat.append(feat_chunk)
# remove the batch dimension
feat = torch.concat(feat, dim=1).squeeze(0)
return feat
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import argparse
import logging
import os
import time
import numpy as np
from sklearn.cluster import MiniBatchKMeans
def get_logger():
log_format = "[%(asctime)s] [%(levelname)s]: %(message)s"
logging.basicConfig(format=log_format, level=logging.INFO)
logger = logging.getLogger(__name__)
return logger
def get_parser():
parser = argparse.ArgumentParser(
description="Learn K-means clustering over acoustic features."
)
# Features arguments
parser.add_argument(
"--in_features_path", type=str, default=None, help="Features file path"
)
parser.add_argument(
"--feature_type",
type=str,
choices=["logmel", "hubert", "w2v2", "cpc"],
default=None,
help="Acoustic feature type",
)
parser.add_argument(
"--data_type",
type=str,
choices=["fp16", "fp32", "bf16"],
default="fp32",
help="data type (for half-precision)",
)
parser.add_argument(
"--manifest_path",
type=str,
default=None,
help="Manifest file containing the root dir and file names",
)
parser.add_argument(
"--out_features_path",
type=str,
default="features",
help="Features file path to write to",
)
parser.add_argument(
"--checkpoint_path",
type=str,
help="Pretrained acoustic model checkpoint",
)
parser.add_argument(
"--layer",
type=int,
help="The layer of the pretrained model to extract features from",
default=-1,
)
parser.add_argument(
"--sample_pct",
type=float,
help="Percent data to use for K-means training",
default=0.1,
)
# K-means arguments
parser.add_argument(
"--num_clusters", type=int, help="Number of clusters", default=100
)
parser.add_argument("--init", default="k-means++")
parser.add_argument(
"--max_iter",
type=int,
help="Maximum number of iterations for K-means training",
default=150,
)
parser.add_argument(
"--batch_size",
type=int,
help="Batch size for K-means training",
default=10000,
)
parser.add_argument("--tol", default=0.0, type=float)
parser.add_argument("--max_no_improvement", default=100, type=int)
parser.add_argument("--n_init", default=20, type=int)
parser.add_argument("--reassignment_ratio", default=0.5, type=float)
parser.add_argument(
"--out_kmeans_model_path",
type=str,
required=False,
help="Path to save K-means model",
)
# Leftovers
parser.add_argument(
"--seed",
type=int,
help="Random seed to use for K-means training",
default=1369,
)
return parser
def get_kmeans_model(
n_clusters,
init,
max_iter,
batch_size,
tol,
max_no_improvement,
n_init,
reassignment_ratio,
random_state,
):
return MiniBatchKMeans(
n_clusters=n_clusters,
init=init,
max_iter=max_iter,
batch_size=batch_size,
tol=tol,
max_no_improvement=max_no_improvement,
n_init=n_init,
reassignment_ratio=reassignment_ratio,
random_state=random_state,
verbose=1,
compute_labels=True,
init_size=None,
)
def train_kmeans(kmeans_model, features_batch):
start_time = time.time()
kmeans_model.fit(features_batch)
time_taken = round((time.time() - start_time) // 60, 2)
return kmeans_model, time_taken
def get_feature_iterator(
checkpoint_path, layer, manifest_path, sample_pct, channel_id,
fp16=False, bf16=False, model_path="w2vbert-conformer_shaw.pt", sample_rate=16000
):
feature_reader = FeatureReader(checkpoint_path,
layer,
max_chunk=1600000,
use_cuda=True,
fp16=fp16,
bf16=bf16,
model_path=model_path, sample_rate=sample_rate)
print("Feature extract successfully created")
with open(manifest_path, "r") as fp:
lines = fp.readlines()
# root = lines.pop(0).strip()
file_path_list = [
line.split()[1]
for line in lines
if len(line) > 0
]
if sample_pct < 1.0:
file_path_list = random.sample(
file_path_list, int(sample_pct * len(file_path_list))
)
num_files = len(file_path_list)
reader = feature_reader
def iterate():
for file_path in file_path_list:
feats = reader.get_feats(file_path, channel_id=channel_id)
yield file_path, feats.cpu().numpy()
return iterate, num_files
def get_features(
checkpoint_path, layer, manifest_path, sample_pct, channel_id,
fp16=False, bf16=False, model_path="w2vbert-conformer_shaw.pt", sample_rate=16000,
flatten=True, output_path=""
):
generator, num_files = get_feature_iterator(checkpoint_path, layer, manifest_path, sample_pct, channel_id,
fp16=fp16, bf16=bf16, model_path=model_path, sample_rate=sample_rate
)
iterator = generator()
# features_list = []
for (file_path, features) in tqdm.tqdm(iterator, total=num_files):
basename = os.path.basename(file_path)
outfile = os.path.join(output_path, basename + ".npz")
np.savez_compressed(outfile, features)
# features_list.append(features)
# Explicit clean up
del iterator
del generator
gc.collect()
torch.cuda.empty_cache()
#
# if flatten:
# return np.concatenate(features_list)
return
def main(args, logger):
# Features loading/extraction for K-means
# if args.in_features_path:
# # Feature loading
# logger.info(f"Loading features from {args.in_features_path}...")
# features_batch = np.load(args.in_features_path, allow_pickle=True)
# else:
# # Feature extraction
# logger.info(f"Extracting {args.feature_type} acoustic features...")
# features_batch = (
# get_features(
# feature_type=args.feature_type,
# checkpoint_path=args.checkpoint_path,
# layer=args.layer,
# manifest_path=args.manifest_path,
# sample_pct=args.sample_pct,
# flatten=True,
# )
# if not args.out_features_path
# else get_and_dump_features(
# feature_type=args.feature_type,
# checkpoint_path=args.checkpoint_path,
# layer=args.layer,
# manifest_path=args.manifest_path,
# sample_pct=args.sample_pct,
# flatten=True,
# out_features_path=args.out_features_path,
# )
# )
# if args.out_features_path:
# logger.info(
# f"Saved extracted features at {args.out_features_path}"
# )
logger.info(f"Extracting w2vbert acoustic features...")
os.makedirs(args.out_features_path, exist_ok=True)
features_batch = (
get_features(
checkpoint_path=args.checkpoint_path,
layer=args.layer,
manifest_path=args.manifest_path,
channel_id=None,
sample_pct=args.sample_pct,
flatten=True,
fp16=args.data_type == "fp16",
bf16=args.data_type == "bf16",
model_path="w2vbert-conformer_shaw.pt",
sample_rate=16000,
output_path=args.out_features_path
)
)
# logger.info(f"Features shape = {features_batch.shape}\n")
# Learn and save K-means model
# kmeans_model = get_kmeans_model(
# n_clusters=args.num_clusters,
# init=args.init,
# max_iter=args.max_iter,
# batch_size=args.batch_size,
# tol=args.tol,
# max_no_improvement=args.max_no_improvement,
# n_init=args.n_init,
# reassignment_ratio=args.reassignment_ratio,
# random_state=args.seed,
# )
# logger.info("Starting k-means training...")
# kmeans_model, time_taken = train_kmeans(
# kmeans_model=kmeans_model, features_batch=features_batch
# )
# logger.info(f"...done k-means training in {time_taken} minutes")
# inertia = -kmeans_model.score(features_batch) / len(features_batch)
# logger.info(f"Total intertia: {round(inertia, 2)}\n")
#
# logger.info(f"Saving k-means model to {args.out_kmeans_model_path}")
# os.makedirs(os.path.dirname(args.out_kmeans_model_path), exist_ok=True)
# joblib.dump(kmeans_model, open(args.out_kmeans_model_path, "wb"))
return features_batch
if __name__ == "__main__":
parser = get_parser()
args = parser.parse_args()
logger = get_logger()
logger.info(args)
main(args, logger)