File size: 7,729 Bytes
9a323c7 519a560 9a323c7 |
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 |
from typing import Dict, List, Optional, Union
import numpy as np
import torch
import torch.nn as nn
import torchaudio
from .encoder import ConformerEncoder
from torch import Tensor
from transformers import Wav2Vec2CTCTokenizer, Wav2Vec2Processor
from transformers.configuration_utils import PretrainedConfig
from transformers.feature_extraction_sequence_utils import \
SequenceFeatureExtractor
from transformers.feature_extraction_utils import BatchFeature
from transformers.modeling_outputs import CausalLMOutput
from transformers.modeling_utils import PreTrainedModel
class GigaAMCTC(nn.Module):
"""
GigaAM-CTC model
"""
def __init__(self, config_encoder, config_head):
super().__init__()
self.encoder = ConformerEncoder(**config_encoder)
self.head = CTCHead(**config_head)
def forward(self, input_features: Tensor, input_lengths: Tensor) -> Tensor:
encoded, encoded_lengths = self.encoder(input_features, input_lengths)
logits = self.head(encoded)
return logits, encoded_lengths
class CTCHead(nn.Module):
"""
CTC Head module for Connectionist Temporal Classification.
"""
def __init__(self, feat_in: int, num_classes: int):
super().__init__()
self.decoder_layers = nn.Sequential(
nn.Conv1d(feat_in, num_classes, kernel_size=1)
)
def forward(self, encoder_output: Tensor) -> Tensor:
# B x C x T
return self.decoder_layers(encoder_output)
class GigaAMFeatureExtractor(SequenceFeatureExtractor):
"""
Feature extractor for GigaAM.
"""
model_input_names = ["input_features"]
def __init__(
self,
feature_size=64,
sampling_rate=16000,
padding_value=0.0,
chunk_length=30.0,
**kwargs,
):
super().__init__(
feature_size=feature_size,
sampling_rate=sampling_rate,
padding_value=padding_value,
chunk_length=chunk_length,
**kwargs,
)
self.hop_length = sampling_rate // 100
self.n_samples = chunk_length * sampling_rate
self.featurizer = torchaudio.transforms.MelSpectrogram(
sample_rate=sampling_rate,
n_fft=sampling_rate // 40,
win_length=sampling_rate // 40,
hop_length=self.hop_length,
n_mels=feature_size,
)
def to_dict(self) -> Dict[str, Union[str, int, Dict]]:
dictionary = super().to_dict()
if "featurizer" in dictionary:
del dictionary["featurizer"]
dictionary["hop_length"] = self.hop_length
dictionary["n_samples"] = self.n_samples
return dictionary
def out_len(self, input_lengths: Tensor) -> Tensor:
"""
Calculates the output length after the feature extraction process.
"""
return input_lengths.div(self.hop_length, rounding_mode="floor").add(1).long()
def __call__(
self,
raw_speech: Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]],
sampling_rate: Optional[int] = None,
padding: str = "max_length",
**kwargs,
):
is_batched_numpy = (
isinstance(raw_speech, np.ndarray) and len(raw_speech.shape) > 1
)
if is_batched_numpy and len(raw_speech.shape) > 2:
raise ValueError(
f"Only mono-channel audio is supported for input to {self}"
)
is_batched = is_batched_numpy or (
isinstance(raw_speech, (list, tuple))
and (isinstance(raw_speech[0], (np.ndarray, tuple, list)))
)
if is_batched:
raw_speech = [
np.asarray([speech], dtype=np.float32).T for speech in raw_speech
]
elif not is_batched and not isinstance(raw_speech, np.ndarray):
raw_speech = np.asarray(raw_speech, dtype=np.float32)
elif isinstance(raw_speech, np.ndarray) and raw_speech.dtype is np.dtype(
np.float64
):
raw_speech = raw_speech.astype(np.float32)
# always return batch
if not is_batched:
raw_speech = [np.asarray([raw_speech]).T]
input_lengths = torch.tensor([len(speech) for speech in raw_speech])
batched_speech = BatchFeature({"input_features": raw_speech})
padded_inputs = self.pad(
batched_speech,
padding=padding,
max_length=self.n_samples,
truncation=False,
return_tensors="pt",
)
input_features = padded_inputs["input_features"].transpose(1, 2)
input_features = self.featurizer(input_features).squeeze(1)
input_features = torch.log(input_features.clamp_(1e-9, 1e9))
input_lengths = self.out_len(input_lengths)
return BatchFeature({"input_features": input_features, "input_lengths": input_lengths}, tensor_type="pt")
class GigaAMCTCTokenizer(Wav2Vec2CTCTokenizer):
"""
Char tokenizer for GigaAM-CTC model.
"""
def __init__(
self,
vocab_file,
unk_token="[BLANK]",
pad_token="[BLANK]",
bos_token=None,
eos_token=None,
word_delimiter_token=" ",
**kwargs,
):
super().__init__(
vocab_file=vocab_file,
unk_token=unk_token,
pad_token=pad_token,
bos_token=bos_token,
eos_token=eos_token,
word_delimiter_token=word_delimiter_token,
**kwargs,
)
class GigaAMProcessor(Wav2Vec2Processor):
feature_extractor_class = "GigaAMFeatureExtractor"
tokenizer_class = "GigaAMCTCTokenizer"
def __init__(self, feature_extractor, tokenizer):
# super().__init__(feature_extractor, tokenizer)
self.feature_extractor = feature_extractor
self.tokenizer = tokenizer
self.current_processor = self.feature_extractor
self._in_target_context_manager = False
@classmethod
def from_pretrained(cls, pretrained_model_name_or_path, **kwargs):
feature_extractor = GigaAMFeatureExtractor.from_pretrained(pretrained_model_name_or_path, **kwargs)
tokenizer = GigaAMCTCTokenizer.from_pretrained(pretrained_model_name_or_path, **kwargs)
return cls(feature_extractor=feature_extractor, tokenizer=tokenizer)
class GigaAMConfig(PretrainedConfig):
def __init__(self, **kwargs):
super().__init__(**kwargs)
class GigaAMCTCHF(PreTrainedModel):
"""
GigaAM-CTC model for transformers
"""
config_class = GigaAMConfig
base_model_prefix = "gigaamctc"
main_input_name = "input_features"
def __init__(self, config: GigaAMConfig):
super().__init__(config)
self.model = GigaAMCTC(config.encoder, config.head)
def forward(self, input_features, input_lengths, labels=None, **kwargs):
# B x C x T
logits, encoded_lengths = self.model(input_features, input_lengths)
# B x C x T -> B x T x C -> T x B x C
log_probs = torch.log_softmax(
logits.transpose(1, 2), dim=-1, dtype=torch.float32
).transpose(0, 1)
loss = None
if labels is not None:
labels_mask = labels >= 0
target_lengths = labels_mask.sum(-1)
flattened_targets = labels.masked_select(labels_mask)
loss = nn.functional.ctc_loss(
log_probs,
flattened_targets,
encoded_lengths,
target_lengths,
blank=self.config.blank_id,
zero_infinity=True,
)
return CausalLMOutput(loss=loss, logits=logits.transpose(1, 2))
|