-
Notifications
You must be signed in to change notification settings - Fork 12
/
model.py
388 lines (350 loc) · 15.8 KB
/
model.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
# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
"""EnCodec model implementation."""
import math
from pathlib import Path
import typing as tp
import numpy as np
import torch
from torch import nn
import quantization as qt
import modules as m
from utils import _check_checksum, _linear_overlap_add, _get_checkpoint_url
import random
ROOT_URL = 'https://dl.fbaipublicfiles.com/encodec/v0/'
EncodedFrame = tp.Tuple[torch.Tensor, tp.Optional[torch.Tensor]]
class LMModel(nn.Module):
"""Language Model to estimate probabilities of each codebook entry.
We predict all codebooks in parallel for a given time step.
Args:
n_q (int): number of codebooks.
card (int): codebook cardinality.
dim (int): transformer dimension.
**kwargs: passed to `encodec.modules.transformer.StreamingTransformerEncoder`.
"""
def __init__(self, n_q: int = 32, card: int = 1024, dim: int = 200, **kwargs):
super().__init__()
self.card = card
self.n_q = n_q
self.dim = dim
self.transformer = m.StreamingTransformerEncoder(dim=dim, **kwargs)
self.emb = nn.ModuleList([nn.Embedding(card + 1, dim) for _ in range(n_q)])
self.linears = nn.ModuleList([nn.Linear(dim, card) for _ in range(n_q)])
def forward(self, indices: torch.Tensor,
states: tp.Optional[tp.List[torch.Tensor]] = None, offset: int = 0):
"""
Args:
indices (torch.Tensor): indices from the previous time step. Indices
should be 1 + actual index in the codebook. The value 0 is reserved for
when the index is missing (i.e. first time step). Shape should be
`[B, n_q, T]`.
states: state for the streaming decoding.
offset: offset of the current time step.
Returns a 3-tuple `(probabilities, new_states, new_offset)` with probabilities
with a shape `[B, card, n_q, T]`.
"""
B, K, T = indices.shape
input_ = sum([self.emb[k](indices[:, k]) for k in range(K)])
out, states, offset = self.transformer(input_, states, offset)
logits = torch.stack([self.linears[k](out) for k in range(K)], dim=1).permute(0, 3, 1, 2)
return torch.softmax(logits, dim=1), states, offset
class EncodecModel(nn.Module):
"""EnCodec model operating on the raw waveform.
Args:
target_bandwidths (list of float): Target bandwidths.
encoder (nn.Module): Encoder network.
decoder (nn.Module): Decoder network.
sample_rate (int): Audio sample rate.
channels (int): Number of audio channels.
normalize (bool): Whether to apply audio normalization.
segment (float or None): segment duration in sec. when doing overlap-add.
overlap (float): overlap between segment, given as a fraction of the segment duration.
name (str): name of the model, used as metadata when compressing audio.
"""
def __init__(self,
encoder: m.SEANetEncoder,
decoder: m.SEANetDecoder,
quantizer: qt.ResidualVectorQuantizer,
target_bandwidths: tp.List[float],
sample_rate: int,
channels: int,
normalize: bool = False,
segment: tp.Optional[float] = None,
overlap: float = 0.01,
name: str = 'unset'):
super().__init__()
self.bandwidth: tp.Optional[float] = None
self.target_bandwidths = target_bandwidths
self.encoder = encoder
self.quantizer = quantizer
self.decoder = decoder
self.sample_rate = sample_rate
self.channels = channels
self.normalize = normalize
self.segment = segment
self.overlap = overlap
self.frame_rate = math.ceil(self.sample_rate / np.prod(self.encoder.ratios)) #75
self.name = name
self.bits_per_codebook = int(math.log2(self.quantizer.bins))
assert 2 ** self.bits_per_codebook == self.quantizer.bins, \
"quantizer bins must be a power of 2."
@property
def segment_length(self) -> tp.Optional[int]:
if self.segment is None:
return None
return int(self.segment * self.sample_rate)
@property
def segment_stride(self) -> tp.Optional[int]:
segment_length = self.segment_length
if segment_length is None:
return None
return max(1, int((1 - self.overlap) * segment_length))
def encode(self, x: torch.Tensor) -> tp.List[EncodedFrame]:
"""Given a tensor `x`, returns a list of frames containing
the discrete encoded codes for `x`, along with rescaling factors
for each segment, when `self.normalize` is True.
Each frames is a tuple `(codebook, scale)`, with `codebook` of
shape `[B, K, T]`, with `K` the number of codebooks.
"""
assert x.dim() == 3
_, channels, length = x.shape
assert channels > 0 and channels <= 2
segment_length = self.segment_length
if segment_length is None: #segment_length = 1*sample_rate
segment_length = length
stride = length
else:
stride = self.segment_stride # type: ignore
assert stride is not None
encoded_frames: tp.List[EncodedFrame] = []
for offset in range(0, length, stride): # shift windows to choose data
frame = x[:, :, offset: offset + segment_length]
encoded_frames.append(self._encode_frame(frame))
return encoded_frames
def _encode_frame(self, x: torch.Tensor) -> EncodedFrame:
length = x.shape[-1] # tensor_cut or original
duration = length / self.sample_rate
assert self.segment is None or duration <= 1e-5 + self.segment
if self.normalize:
mono = x.mean(dim=1, keepdim=True)
volume = mono.pow(2).mean(dim=2, keepdim=True).sqrt()
scale = 1e-8 + volume
x = x / scale
scale = scale.view(-1, 1)
else:
scale = None
emb = self.encoder(x) # [2,1,10000] -> [2,128,32]
#TODO: Encodec Trainer的training
if self.training:
return emb,scale
codes = self.quantizer.encode(emb, self.frame_rate, self.bandwidth)
codes = codes.transpose(0, 1)
# codes is [B, K, T], with T frames, K nb of codebooks.
return codes, scale
def decode(self, encoded_frames: tp.List[EncodedFrame]) -> torch.Tensor:
"""Decode the given frames into a waveform.
Note that the output might be a bit bigger than the input. In that case,
any extra steps at the end can be trimmed.
"""
segment_length = self.segment_length
if segment_length is None:
assert len(encoded_frames) == 1
return self._decode_frame(encoded_frames[0])
frames = [self._decode_frame(frame) for frame in encoded_frames]
return _linear_overlap_add(frames, self.segment_stride or 1)
def _decode_frame(self, encoded_frame: EncodedFrame) -> torch.Tensor:
codes, scale = encoded_frame
if self.training:
emb = codes
else:
codes = codes.transpose(0, 1)
emb = self.quantizer.decode(codes)
out = self.decoder(emb)
if scale is not None:
out = out * scale.view(-1, 1, 1)
return out
def forward(self, x: torch.Tensor) -> torch.Tensor:
frames = self.encode(x) # input_wav -> encoder , x.shape = [BatchSize,channel,tensor_cut or original length] 2,1,10000
if self.training:
# if encodec is training, input_wav -> encoder -> quantizer forward -> decode
loss_w = torch.tensor([0.0], device=x.device, requires_grad=True)
codes = []
# self.quantizer.train(self.training)
index = torch.tensor(random.randint(0,len(self.target_bandwidths)-1),device=x.device)
if torch.distributed.is_initialized():
torch.distributed.broadcast(index, src=0)
bw = self.target_bandwidths[index.item()]# fixme: variable bandwidth training, if you broadcast bd, the broadcast will encounter error
for emb,scale in frames:
qv = self.quantizer(emb,self.frame_rate,bw)
loss_w = loss_w + qv.penalty # loss_w is the sum of all quantizer forward loss (RVQ commitment loss :l_w)
codes.append((qv.quantized,scale))
return self.decode(codes)[:,:,:x.shape[-1]],loss_w,frames
else:
# if encodec is not training, input_wav -> encoder -> quantizer encode -> decode
return self.decode(frames)[:, :, :x.shape[-1]]
def set_target_bandwidth(self, bandwidth: float):
if bandwidth not in self.target_bandwidths:
raise ValueError(f"This model doesn't support the bandwidth {bandwidth}. "
f"Select one of {self.target_bandwidths}.")
self.bandwidth = bandwidth
def get_lm_model(self) -> LMModel:
"""Return the associated LM model to improve the compression rate.
"""
device = next(self.parameters()).device
lm = LMModel(self.quantizer.n_q, self.quantizer.bins, num_layers=5, dim=200,
past_context=int(3.5 * self.frame_rate)).to(device)
checkpoints = {
'encodec_24khz': 'encodec_lm_24khz-1608e3c0.th',
'encodec_48khz': 'encodec_lm_48khz-7add9fc3.th',
}
try:
checkpoint_name = checkpoints[self.name]
except KeyError:
raise RuntimeError("No LM pre-trained for the current Encodec model.")
url = _get_checkpoint_url(ROOT_URL, checkpoint_name)
state = torch.hub.load_state_dict_from_url(
url, map_location='cpu', check_hash=True) # type: ignore
lm.load_state_dict(state)
lm.eval()
return lm
@staticmethod
def _get_model(target_bandwidths: tp.List[float],
sample_rate: int = 24_000,
channels: int = 1,
causal: bool = True,
model_norm: str = 'weight_norm',
audio_normalize: bool = False,
segment: tp.Optional[float] = None,
name: str = 'unset',
ratios=[8, 5, 4, 2]):
encoder = m.SEANetEncoder(channels=channels, norm=model_norm, causal=causal,ratios=ratios)
decoder = m.SEANetDecoder(channels=channels, norm=model_norm, causal=causal,ratios=ratios)
n_q = int(1000 * target_bandwidths[-1] // (math.ceil(sample_rate / encoder.hop_length) * 10)) # int(1000*24//(math.ceil(24000/320)*10))
quantizer = qt.ResidualVectorQuantizer(
dimension=encoder.dimension,
n_q=n_q,
bins=1024,
)
model = EncodecModel(
encoder,
decoder,
quantizer,
target_bandwidths,
sample_rate,
channels,
normalize=audio_normalize,
segment=segment,
name=name,
)
return model
@staticmethod
def _get_pretrained(checkpoint_name: str, repository: tp.Optional[Path] = None):
if repository is not None:
if not repository.is_dir():
raise ValueError(f"{repository} must exist and be a directory.")
file = repository / checkpoint_name
checksum = file.stem.split('-')[1]
_check_checksum(file, checksum)
return torch.load(file)
else:
url = _get_checkpoint_url(ROOT_URL, checkpoint_name)
return torch.hub.load_state_dict_from_url(url, map_location='cpu', check_hash=True) # type:ignore
@staticmethod
def encodec_model_24khz(pretrained: bool = True, repository: tp.Optional[Path] = None):
"""Return the pretrained causal 24khz model.
"""
if repository:
assert pretrained
target_bandwidths = [1.5, 3., 6, 12., 24.]
checkpoint_name = 'encodec_24khz-d7cc33bc.th'
sample_rate = 24_000
channels = 1
model = EncodecModel._get_model(
target_bandwidths, sample_rate, channels,
causal=True, model_norm='weight_norm', audio_normalize=False,
name='encodec_24khz' if pretrained else 'unset')
if pretrained:
state_dict = EncodecModel._get_pretrained(checkpoint_name, repository)
model.load_state_dict(state_dict)
model.eval()
return model
@staticmethod
def encodec_model_48khz(pretrained: bool = True, repository: tp.Optional[Path] = None):
"""Return the pretrained 48khz model.
"""
if repository:
assert pretrained
target_bandwidths = [3., 6., 12., 24.]
checkpoint_name = 'encodec_48khz-7e698e3e.th'
sample_rate = 48_000
channels = 2
model = EncodecModel._get_model(
target_bandwidths, sample_rate, channels,
causal=False, model_norm='time_group_norm', audio_normalize=True,
segment=1., name='encodec_48khz' if pretrained else 'unset')
if pretrained:
state_dict = EncodecModel._get_pretrained(checkpoint_name, repository)
model.load_state_dict(state_dict)
model.eval()
return model
#TODO: 自己实现一个encodec的model
@staticmethod
def my_encodec_model(checkpoint: str,ratios=[8,5,4,2]):
"""Return the pretrained 24khz model.
"""
import os
assert os.path.exists(checkpoint), "checkpoint not exists"
print("loading model from: ",checkpoint)
target_bandwidths = [1.5, 3., 6, 12., 24.]
sample_rate = 24_000
channels = 1
model = EncodecModel._get_model(
target_bandwidths, sample_rate, channels,
causal=False, model_norm='time_group_norm', audio_normalize=True,
segment=None, name='my_encodec',ratios=ratios)
pre_dic = torch.load(checkpoint)['model_state_dict']
model.load_state_dict({k.replace('quantizer.model','quantizer.vq'):v for k,v in pre_dic.items()})
model.eval()
return model
@staticmethod
def encodec_model_bw(checkpoint: str, bandwidth: float):
"""Return target bw model, if you train a model in a single bandwidth
"""
import os
assert os.path.exists(checkpoint), "checkpoint not exists"
print("loading model from: ",checkpoint)
target_bandwidths = bandwidth
sample_rate = 24_000
channels = 1
model = EncodecModel._get_model(
target_bandwidths, sample_rate, channels,
causal=False, model_norm='time_group_norm', audio_normalize=True,
segment=1., name='my_encodec')
pre_dic = torch.load(checkpoint)['model_state_dict']
model.load_state_dict({k.replace('quantizer.model','quantizer.vq'):v for k,v in pre_dic.items()})
model.eval()
return model
def test():
from itertools import product
import torchaudio
bandwidths = [3, 6, 12, 24]
models = {
'encodec_24khz': EncodecModel.encodec_model_24khz,
'encodec_48khz': EncodecModel.encodec_model_48khz,
"my_encodec": EncodecModel.my_encodec_model,
"encodec_bw": EncodecModel.encodec_model_bw,
}
for model_name, bw in product(models.keys(), bandwidths):
model = models[model_name]()
model.set_target_bandwidth(bw)
audio_suffix = model_name.split('_')[1][:3]
wav, sr = torchaudio.load(f"test_{audio_suffix}.wav")
wav = wav[:, :model.sample_rate * 2]
wav_in = wav.unsqueeze(0)
wav_dec = model(wav_in)[0]
assert wav.shape == wav_dec.shape, (wav.shape, wav_dec.shape)
if __name__ == '__main__':
test()