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#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
from typing import Dict, List, Optional
import torch
import torch.nn as nn
from torch import Tensor
from fairseq import search
class CTCMultiDecoderSequenceGenerator(nn.Module):
def __init__(
self,
models,
tgt_dict,
tgt_dict_mt,
beam_size=1,
beam_size_mt=1,
max_len_a=0,
max_len_b=200,
max_len_a_mt=0,
max_len_b_mt=200,
max_len=0,
min_len=1,
normalize_scores=True,
len_penalty=1.0,
len_penalty_mt=1.0,
unk_penalty=0.0,
temperature=1.0,
match_source_len=False,
no_repeat_ngram_size=0,
eos=None,
eos_mt=None,
symbols_to_strip_from_output=None,
lm_model=None,
lm_weight=1.0,
):
"""Generates translations of a given source sentence.
Args:
models (List[~fairseq.models.FairseqModel]): ensemble of models,
currently support fairseq.models.TransformerModel for scripting
beam_size (int, optional): beam width (default: 1)
max_len_a/b (int, optional): generate sequences of maximum length
ax + b, where x is the source length for the second pass
max_len_a_mt/b_mt (int, optional): generate sequences of maximum length
ax + b, where x is the source length for the first pass
max_len (int, optional): the maximum length of the generated output
(not including end-of-sentence)
min_len (int, optional): the minimum length of the generated output
(not including end-of-sentence)
normalize_scores (bool, optional): normalize scores by the length
of the output (default: True)
len_penalty (float, optional): length penalty in the second pass, where <1.0 favors
shorter, >1.0 favors longer sentences (default: 1.0)
len_penalty (float, optional): length penalty in the first pass, where <1.0 favors
shorter, >1.0 favors longer sentences (default: 1.0)
unk_penalty (float, optional): unknown word penalty, where <0
produces more unks, >0 produces fewer (default: 0.0)
temperature (float, optional): temperature, where values
>1.0 produce more uniform samples and values <1.0 produce
sharper samples (default: 1.0)
match_source_len (bool, optional): outputs should match the source
length (default: False)
"""
super().__init__()
from examples.speech_to_speech.unity.sequence_generator import SequenceGenerator
from ctc_unity.ctc_generator import CTCSequenceGenerator
from ctc_unity.ctc_decoder import CTCDecoder
# chunk_size=16
# model=models[0]
# # model.encoder.chunk_size=chunk_size
# for conv in model.encoder.subsample.conv_layers:
# conv.chunk_size=chunk_size
# for layer in model.encoder.conformer_layers:
# layer.conv_module.depthwise_conv.chunk_size=chunk_size
self.generator = SequenceGenerator(
models,
tgt_dict,
beam_size=beam_size,
max_len_a=max_len_a,
max_len_b=max_len_b,
max_len=max_len,
min_len=min_len,
normalize_scores=normalize_scores,
len_penalty=len_penalty,
unk_penalty=unk_penalty,
temperature=temperature,
match_source_len=match_source_len,
no_repeat_ngram_size=no_repeat_ngram_size,
search_strategy=search.BeamSearch(tgt_dict),
eos=eos,
symbols_to_strip_from_output=symbols_to_strip_from_output,
lm_model=lm_model,
lm_weight=lm_weight,
)
self.ctc_generator = CTCSequenceGenerator(tgt_dict, models)
self.eos = self.generator.eos
try:
src_dict_asr = (
models[0].multitask_decoders["source_unigram"].encoder.dictionary
)
self.asr_ctc_generator = CTCDecoder(src_dict_asr, models)
except:
self.asr_ctc_generator = None
try:
tgt_dict_st = (
models[0].multitask_decoders["ctc_target_unigram"].encoder.dictionary
)
self.st_ctc_generator = CTCDecoder(tgt_dict_st, models)
except:
self.st_ctc_generator = None
self.generator_mt = SequenceGenerator(
models,
tgt_dict_mt,
beam_size=beam_size_mt,
max_len_a=max_len_a_mt,
max_len_b=max_len_b_mt,
max_len=max_len,
min_len=min_len,
normalize_scores=normalize_scores,
len_penalty=len_penalty_mt,
unk_penalty=unk_penalty,
temperature=temperature,
match_source_len=match_source_len,
no_repeat_ngram_size=no_repeat_ngram_size,
search_strategy=search.BeamSearch(tgt_dict_mt),
eos=eos_mt,
symbols_to_strip_from_output=symbols_to_strip_from_output,
)
@torch.no_grad()
def generate(
self, models, sample: Dict[str, Dict[str, Tensor]], **kwargs
) -> List[List[Dict[str, Tensor]]]:
"""Generate translations. Match the api of other fairseq generators.
Args:
models (List[~fairseq.models.FairseqModel]): ensemble of models
sample (dict): batch
prefix_tokens (torch.LongTensor, optional): force decoder to begin
with these tokens
constraints (torch.LongTensor, optional): force decoder to include
the list of constraints
bos_token (int, optional): beginning of sentence token
(default: self.eos)
"""
return self._generate(sample, **kwargs)
def _generate(
self,
sample: Dict[str, Dict[str, Tensor]],
prefix_tokens: Optional[Tensor] = None,
constraints: Optional[Tensor] = None,
bos_token: Optional[int] = None,
):
net_input = sample["net_input"]
if "src_tokens" in net_input:
src_tokens = net_input["src_tokens"]
# length of the source text being the character length except EndOfSentence and pad
# if src_lengths exists in net_input (speech_to_text dataset case), then use it
if "src_lengths" in net_input:
src_lengths = net_input["src_lengths"]
else:
src_lengths = (
(
src_tokens.ne(self.generator.eos)
& src_tokens.ne(self.generator.pad)
)
.long()
.sum(dim=1)
)
else:
raise Exception(
"expected src_tokens or source in net input. input keys: "
+ str(net_input.keys())
)
if constraints is not None and not self.generator.search.supports_constraints:
raise NotImplementedError(
"Target-side constraints were provided, but search method doesn't support them"
)
# Initialize constraints, when active
self.generator.search.init_constraints(constraints, self.generator.beam_size)
self.generator_mt.search.init_constraints(
constraints, self.generator_mt.beam_size
)
# compute the encoder output for each beam
with torch.autograd.profiler.record_function("EnsembleModel: forward_encoder"):
encoder_outs = self.generator.model.forward_encoder(net_input)
if self.asr_ctc_generator is not None:
finalized_asr = self.asr_ctc_generator.generate(
encoder_outs[0], aux_task_name="source_unigram"
)
for i, hypo in enumerate(finalized_asr):
i_beam = 0
tmp = hypo[i_beam]["tokens"].int() # hyp + eos
src_ctc_indices = tmp
src_ctc_index = hypo[i_beam]["index"]
text = "".join([self.asr_ctc_generator.tgt_dict[c] for c in tmp])
text = text.replace("_", " ")
text = text.replace("▁", " ")
text = text.replace("<unk>", " ")
text = text.replace("<s>", "")
text = text.replace("</s>", "")
if len(text) > 0 and text[0] == " ":
text = text[1:]
sample_id = sample["id"].tolist()[i]
print("A-{}\t{}".format(sample_id, text))
if self.st_ctc_generator is not None:
finalized_st = self.st_ctc_generator.generate(
encoder_outs[0], aux_task_name="ctc_target_unigram"
)
for i, hypo in enumerate(finalized_st):
i_beam = 0
tmp = hypo[i_beam]["tokens"].int() # hyp + eos
tgt_ctc_indices = tmp
tgt_ctc_index = hypo[i_beam]["index"]
text = "".join([self.st_ctc_generator.tgt_dict[c] for c in tmp])
text = text.replace("_", " ")
text = text.replace("▁", " ")
text = text.replace("<unk>", " ")
text = text.replace("<s>", "")
text = text.replace("</s>", "")
if len(text) > 0 and text[0] == " ":
text = text[1:]
sample_id = sample["id"].tolist()[i]
print("S-{}\t{}".format(sample_id, text))
single_model = self.generator.model.single_model
mt_decoder = getattr(single_model, f"{single_model.mt_task_name}_decoder")
# 1. MT decoder
finalized_mt = self.generator_mt.generate_decoder(
encoder_outs,
src_tokens,
src_lengths,
sample,
prefix_tokens,
constraints,
bos_token,
aux_task_name=single_model.mt_task_name,
)
# extract decoder output corresponding to the best hypothesis
max_tgt_len = max([len(hypo[0]["tokens"]) for hypo in finalized_mt])
prev_output_tokens_mt = (
src_tokens.new_zeros(src_tokens.shape[0], max_tgt_len)
.fill_(mt_decoder.padding_idx)
.int()
) # B x T
for i, hypo in enumerate(finalized_mt):
i_beam = 0
tmp = hypo[i_beam]["tokens"].int() # hyp + eos
prev_output_tokens_mt[i, 0] = self.generator_mt.eos
if tmp[-1] == self.generator_mt.eos:
tmp = tmp[:-1]
prev_output_tokens_mt[i, 1 : len(tmp) + 1] = tmp
text = "".join([self.generator_mt.tgt_dict[c] for c in tmp])
text = text.replace("_", " ")
text = text.replace("▁", " ")
text = text.replace("<unk>", " ")
text = text.replace("<s>", "")
text = text.replace("</s>", "")
if len(text) > 0 and text[0] == " ":
text = text[1:]
sample_id = sample["id"].tolist()[i]
print("D-{}\t{}".format(sample_id, text))
x = mt_decoder(
prev_output_tokens_mt,
encoder_out=encoder_outs[0],
features_only=True,
)[0].transpose(0, 1)
if getattr(single_model, "proj", None) is not None:
x = single_model.proj(x)
mt_decoder_padding_mask = None
if prev_output_tokens_mt.eq(mt_decoder.padding_idx).any():
mt_decoder_padding_mask = prev_output_tokens_mt.eq(mt_decoder.padding_idx)
# 2. T2U encoder
if getattr(single_model, "synthesizer_encoder", None) is not None:
t2u_encoder_out = single_model.synthesizer_encoder(
x,
mt_decoder_padding_mask,
)
else:
t2u_encoder_out = {
"encoder_out": [x], # T x B x C
"encoder_padding_mask": (
[mt_decoder_padding_mask]
if mt_decoder_padding_mask is not None
else []
), # B x T
"encoder_embedding": [],
"encoder_states": [],
"src_tokens": [],
"src_lengths": [],
}
if getattr(single_model, "t2u_augmented_cross_attn", False):
encoder_outs_aug = [t2u_encoder_out]
else:
encoder_outs = [t2u_encoder_out]
encoder_outs_aug = None
finalized = self.ctc_generator.generate(encoder_outs[0])
return finalized
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