Spaces:
Runtime error
Runtime error
File size: 6,148 Bytes
62e9ca6 |
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 |
# ----------------------------------------------------------------------------
# SpeechUT: Bridging Speech and Text with Hidden-Unit for Encoder-Decoder Based Speech-Text Pre-training (https://arxiv.org/abs/2210.03730)
# Github source: https://github.com/microsoft/SpeechT5/tree/main/SpeechUT
# Code based on fairseq: https://github.com/facebookresearch/fairseq/tree/272c4c5197250997148fb12c0db6306035f166a4
#
# Copyright (c) 2022 Microsoft
# Licensed under The MIT License [see LICENSE for details]
# ----------------------------------------------------------------------------
import contextlib
import torch
from dataclasses import dataclass, field
from fairseq import utils
from fairseq.models import BaseFairseqModel, register_model
from fairseq.models.fairseq_encoder import FairseqEncoder
from fairseq.models.hubert import HubertAsrConfig, HubertEncoder
from fairseq.tasks import FairseqTask
@dataclass
class SpeechUTASRConfig(HubertAsrConfig):
add_decoder: bool = field(
default=True,
metadata={"help": "add decoder for fine-tune"},
)
@register_model("speechut_asr", dataclass=SpeechUTASRConfig)
class SpeechUTASR(BaseFairseqModel):
"""
A encoder-ctc-decoder model if cfg.add_decoder is True, or a encoder-ctc model
"""
def __init__(self, cfg: SpeechUTASRConfig, encoder: FairseqEncoder):
super().__init__()
self.cfg = cfg
self.encoder = encoder
if not cfg.add_decoder:
self.encoder.w2v_model.decoder = None
def upgrade_state_dict_named(self, state_dict, name):
super().upgrade_state_dict_named(state_dict, name)
return state_dict
@classmethod
def build_model(cls, cfg: SpeechUTASRConfig, task: FairseqTask):
"""Build a new model instance."""
encoder = SpeechUTEncoder(cfg, task)
return cls(cfg, encoder)
def forward(self, source, padding_mask, prev_output_tokens, **kwargs):
encoder_out = self.encoder(source, padding_mask, **kwargs)
x = self.encoder.final_dropout(encoder_out['encoder_out'][0]) # (T, B, C)
if self.encoder.proj:
x = self.encoder.proj(x)
if self.encoder.conv_ctc_proj:
padding_mask = self.encoder.w2v_model.downsample_ctc_padding_mask(encoder_out["encoder_padding_mask"][0])
else:
padding_mask = encoder_out["encoder_padding_mask"]
decoder_out = self.decoder(
prev_output_tokens, encoder_out=encoder_out, **kwargs
) if self.cfg.add_decoder else None
return {
"encoder_out_ctc": x, # (T, B, C), for CTC loss
"padding_mask": padding_mask, # (B, T), for CTC loss
"decoder_out": decoder_out, # for ED loss
}
def forward_decoder(self, prev_output_tokens, **kwargs):
return self.decoder(prev_output_tokens, **kwargs)
def get_logits(self, net_output):
"""For CTC decoding"""
logits = net_output["encoder_out"]
padding = net_output["encoder_padding_mask"]
if padding is not None and padding.any():
padding = padding.T
logits[padding][..., 0] = 0
logits[padding][..., 1:] = float("-inf")
return logits
def get_normalized_probs(self, net_output, log_probs, sample=None):
"""For 1) computing CTC loss, 2) decoder decoding."""
if "encoder_out_ctc" in net_output:
logits = net_output["encoder_out_ctc"]
else:
return self.decoder.get_normalized_probs(net_output, log_probs, sample)
if isinstance(logits, list):
logits = logits[0]
if log_probs:
return utils.log_softmax(logits.float(), dim=-1)
else:
return utils.softmax(logits.float(), dim=-1)
@property
def decoder(self):
return self.encoder.w2v_model.decoder
class SpeechUTEncoder(HubertEncoder):
"""
Modified from fairseq.models.hubert.hubert_asr.HubertEncoder
1. make it compatible with encoder-decoder model
"""
def __init__(self, cfg: HubertAsrConfig, task):
super().__init__(cfg, task)
if (task.target_dictionary is not None) and (
hasattr(self.w2v_model, "unit_encoder_ctc_head")
):
self.proj = self.w2v_model.unit_encoder_ctc_head
self.conv_ctc_proj = True
else:
self.conv_ctc_proj = False
def forward(self, source, padding_mask, tbc=True, **kwargs):
w2v_args = {
"source": source,
"padding_mask": padding_mask,
"mask": self.apply_mask and self.training,
}
ft = self.freeze_finetune_updates <= self.num_updates
with torch.no_grad() if not ft else contextlib.ExitStack():
x, padding_mask = self.w2v_model.extract_features(**w2v_args)
if tbc:
# B x T x C -> T x B x C
x = x.transpose(0, 1)
return {
"encoder_out": [x], # T x B x C
"encoder_padding_mask": [padding_mask], # B x T
}
def forward_torchscript(self, net_input):
"""A TorchScript-compatible version of forward.
Forward the encoder out.
"""
x, padding_mask = self.w2v_model.extract_features(**net_input, mask=False)
# B x T x C -> T x B x C
x = x.transpose(0, 1)
encoder_out = {
"encoder_out" : [x],
"encoder_padding_mask" : [padding_mask],
}
if self.proj:
x = self.proj(x)
encoder_out["encoder_out_ctc"] = x
return encoder_out
def reorder_encoder_out(self, encoder_out, new_order):
if encoder_out["encoder_out"] is not None:
encoder_out["encoder_out"] = [
x.index_select(1, new_order) for x in encoder_out["encoder_out"]
]
if encoder_out["encoder_padding_mask"] is not None:
encoder_out["encoder_padding_mask"] = [
x.index_select(0, new_order) for x in encoder_out["encoder_padding_mask"]
]
return encoder_out
|