File size: 3,619 Bytes
901e06a | 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 | # 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 json
import logging
import math
from argparse import Namespace
from pathlib import Path
from typing import List
import torch
import torch.nn as nn
from fairseq import utils
from fairseq.data import Dictionary
from fairseq.data.audio.data_cfg import MultitaskConfig, S2SDataConfig
from fairseq.data.audio.speech_to_speech_dataset import SpeechToSpeechDatasetCreator
from fairseq.data.audio.speech_to_text_dataset import (
SpeechToTextDataset,
TextTargetMultitaskData,
)
from fairseq.tasks import LegacyFairseqTask, register_task
from fairseq.tasks.speech_to_text import DummyMultiTask
from fairseq.tasks.text_to_speech import batch_mel_cepstral_distortion
logger = logging.getLogger(__name__)
class CTCDecoder(nn.Module):
def __init__(self, tgt_dict, models):
super().__init__()
self.pad = tgt_dict.pad()
self.eos = tgt_dict.eos()
self.unk = tgt_dict.unk()
self.models = models
self.tgt_dict = tgt_dict
@torch.no_grad()
def generate(self, encoder_out, prefix=None, aux_task_name=None, **kwargs):
model = self.models[0]
model.eval()
max_len = model.max_decoder_positions()
# TODO: incorporate max_len_a and max_len_b
incremental_state = {}
pred_out, attn, scores = [], [], []
prev_output_tokens = None
decoder_name = f"{aux_task_name}_decoder" if aux_task_name else "decoder"
ctc_decoder = getattr(model, decoder_name)
ctc_out = ctc_decoder(encoder_out["encoder_out"][0], **kwargs)
lprobs = model.get_normalized_probs(
[ctc_out["encoder_out"].transpose(0, 1)], log_probs=True
)
# never select pad, unk
lprobs[:, :, self.pad] = -math.inf
lprobs[:, :, self.unk] = -math.inf
cur_pred_lprob, cur_pred_out = torch.max(lprobs, dim=2)
scores = cur_pred_lprob
pred_out = cur_pred_out
attn = None
alignment = None
def _ctc_postprocess(tokens):
_toks = tokens.int().tolist()
deduplicated_toks = [
v for i, v in enumerate(_toks) if i == 0 or v != _toks[i - 1]
]
hyp = [
v
for v in deduplicated_toks
if (v != 0) and (v != self.tgt_dict.pad_index)
]
return torch.tensor(hyp)
def _ctc_postprocess_index(tokens):
_toks = tokens.int().tolist()
deduplicated_toks = [
(v, i) for i, v in enumerate(_toks) if i == 0 or v != _toks[i - 1]
]
index = [
i
for v, i in deduplicated_toks
if (v != 0) and (v != self.tgt_dict.pad_index)
]
return index
if prefix is not None:
pred_out = torch.cat((prefix, pred_out[:, prefix.size(1) :]), dim=1)
hypos = [
[
{
"tokens": _ctc_postprocess(pred_out[b]),
"org_tokens": pred_out[b],
"lprobs": lprobs,
"index": _ctc_postprocess_index(pred_out[b]),
"attn": None,
"alignment": None,
"positional_scores": scores[b],
"score": utils.item(scores[b].sum().data),
}
]
for b in range(pred_out.size(0))
]
return hypos
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