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# --------------------------------------------------------
# The YiTrans End-to-End Speech Translation System for IWSLT 2022 Offline Shared Task (https://arxiv.org/abs/2206.05777)
# Github source: https://github.com/microsoft/SpeechT5/tree/main/YiTrans
# Copyright (c) 2022 Microsoft
# Licensed under The MIT License [see LICENSE for details]
# Based on fairseq code bases
# https://github.com/facebookresearch/fairseq
# --------------------------------------------------------
"""
Modified from
https://github.com/facebookresearch/fairseq/blob/main/fairseq/tasks/translation.py
"""
import torch
import logging
from dataclasses import dataclass, field
from typing import List, Optional, NamedTuple
from fairseq import utils
from fairseq.data import LanguagePairDataset, TransformEosLangPairDataset, FairseqDataset
from fairseq.tasks import register_task
from fairseq.tasks.translation import TranslationTask, TranslationConfig
from yitrans_iwslt22.data.concat_dataset import ConcatDataset
from yitrans_iwslt22.data.load_langpair_dataset import load_langpair_dataset
logger = logging.getLogger(__name__)
class LangPairStripDataset(FairseqDataset):
def __init__(
self,
dataset: LanguagePairDataset,
src_eos: int,
src_bos: Optional[int] = None,
noise_id: Optional[int] = -1,
mask_ratio: Optional[float] = 0,
mask_type: Optional[str] = "random",
):
self.dataset = dataset
self.src_eos = src_eos
self.src_bos = src_bos
self.noise_id = noise_id
self.mask_ratio = mask_ratio
self.mask_type = mask_type
assert mask_type in ("random", "tail")
@property
def src_sizes(self):
return self.dataset.src_sizes
@property
def tgt_sizes(self):
return self.dataset.tgt_sizes
@property
def sizes(self):
# dataset.sizes can be a dynamically computed sizes:
return self.dataset.sizes
def get_batch_shapes(self):
return self.dataset.buckets
def num_tokens_vec(self, indices):
return self.dataset.num_tokens_vec(indices)
def __len__(self):
return len(self.dataset)
def num_tokens(self, index):
return self.dataset.num_tokens(index)
def size(self, index):
return self.dataset.size(index)
def ordered_indices(self):
return self.dataset.ordered_indices()
@property
def supports_prefetch(self):
return getattr(self.dataset, "supports_prefetch", False)
def prefetch(self, indices):
return self.dataset.prefetch(indices)
def mask_src_tokens(self, sample):
src_item = sample["source"]
mask = None
if self.mask_type == "random":
mask = torch.rand(len(src_item)).le(self.mask_ratio)
else:
mask = torch.ones(len(src_item))
mask[: int(len(src_item) * (1 - self.mask_ratio))] = 0
mask = mask.eq(1)
mask[-1] = False
if src_item[0] == self.src_bos:
mask[0] = False
if src_item[-2] == self.src_eos:
mask[-2] = False
no_mask = ~mask
mask_src_item = src_item[no_mask]
smp = sample
smp["source"] = mask_src_item
print(f"{len(src_item)}: {src_item}")
print(f"{len(mask_src_item)}: {mask_src_item}")
return smp
def __getitem__(self, index):
sample = self.dataset[index]
if self.mask_ratio > 0:
sample = self.mask_src_tokens(sample)
return sample
def collater(self, samples, pad_to_length=None):
return self.dataset.collater(samples, pad_to_length=pad_to_length)
@dataclass
class AddTranslationConfig(TranslationConfig):
langs: str = ""
prepend_bos: bool = False
normalize: bool = False
append_source_id: bool = False
mask_text_ratio: float = 0
### ShrinkingDataset related, not used
shrink_start_epoch: int = 0
shrink_end_epoch: int = 0
shrink_start_ratio: float = 1.0
shrink_end_ratio: float = 1.0
@register_task("iwslt_translation_from_pretrained", dataclass=AddTranslationConfig)
class TranslationFromPretrainedTask(TranslationTask):
args: AddTranslationConfig
def __init__(self, args: AddTranslationConfig, src_dict, tgt_dict):
super().__init__(args, src_dict, tgt_dict)
self.args = args
self.langs = args.langs.split(",")
for d in [src_dict, tgt_dict]:
for l in self.langs:
d.add_symbol("[{}]".format(l))
d.add_symbol("<mask>")
def load_dataset(self, split, epoch=1, combine=False, **kwargs):
"""Load a given dataset split.
Args:
split (str): name of the split (e.g., train, valid, test)
"""
paths = utils.split_paths(self.args.data)
assert len(paths) > 0
data_path = paths[(epoch - 1) % len(paths)]
# infer langcode
src, tgt = self.args.source_lang, self.args.target_lang
paired_datasets = []
for sub_split in split.split(","):
paired_dataset= load_langpair_dataset(
data_path,
sub_split,
src,
self.src_dict,
tgt,
self.tgt_dict,
combine=combine,
dataset_impl=self.args.dataset_impl,
upsample_primary=self.args.upsample_primary,
left_pad_source=self.args.left_pad_source,
left_pad_target=self.args.left_pad_target,
max_source_positions=getattr(self.args, "max_source_positions", 1024),
max_target_positions=getattr(self.args, "max_target_positions", 1024),
load_alignments=self.args.load_alignments,
prepend_bos=getattr(self.args, "prepend_bos", False),
append_source_id=getattr(self.args, "append_source_id", False),
)
if not split.startswith("valid") and getattr(self.args, "mask_text_ratio", 0) > 0 and not sub_split.startswith("asr_"):
mask_text_ratio = getattr(self.args, "mask_text_ratio", 0)
noise_token_id = self.src_dict.index("<mask>")
logger.info(f"Masking {sub_split} at a probability: {mask_text_ratio}")
paired_dataset = LangPairStripDataset(
paired_dataset,
src_bos=self.src_dict.bos(),
src_eos=self.src_dict.eos(),
noise_id=noise_token_id,
mask_ratio=mask_text_ratio,
)
paired_datasets.append(paired_dataset)
paired_dataset = paired_datasets[0] if len(paired_datasets) == 1 else ConcatDataset(paired_datasets, 1)
if getattr(self.args, "append_source_id", False):
logger.info(f"Appending <lang-id> to the end of samples")
self.datasets[split] = paired_dataset
else:
logger.info(f"Replacing <eos> with <lang-id> for prev_output_tokens")
self.datasets[split] = TransformEosLangPairDataset(
paired_dataset,
src_eos=self.src_dict.eos(),
tgt_bos=self.tgt_dict.eos(), # 'prev_output_tokens' starts with eos
new_tgt_bos=self.tgt_dict.index("[{}]".format(tgt)),
)
def build_generator(self, models, args, **unused):
if getattr(args, "score_reference", False):
from fairseq.sequence_scorer import SequenceScorer
return SequenceScorer(
self.target_dictionary,
eos=self.tgt_dict.index("[{}]".format(self.args.target_lang)),
)
else:
from yitrans_iwslt22.sequence_generator import SequenceGenerator
return SequenceGenerator(
models,
self.target_dictionary,
beam_size=getattr(args, "beam", 5),
max_len_a=getattr(args, "max_len_a", 0),
max_len_b=getattr(args, "max_len_b", 200),
min_len=getattr(args, "min_len", 1),
normalize_scores=(not getattr(args, "unnormalized", False)),
len_penalty=getattr(args, "lenpen", 1),
unk_penalty=getattr(args, "unkpen", 0),
temperature=getattr(args, "temperature", 1.0),
match_source_len=getattr(args, "match_source_len", False),
no_repeat_ngram_size=getattr(args, "no_repeat_ngram_size", 0),
eos=self.tgt_dict.index("[{}]".format(self.args.target_lang)) if getattr(self.args, "append_source_id", False) else None,
bos=None if getattr(self.args, "append_source_id", False) else self.tgt_dict.index("[{}]".format(self.args.target_lang))
)
def build_dataset_for_inference(self, src_tokens, src_lengths, constraints=None):
if getattr(self.args, "append_source_id", False):
src_lang_id = self.source_dictionary.index("[{}]".format(self.args.source_lang))
source_tokens = []
for s_t in src_tokens:
s_t = torch.cat([s_t, s_t.new(1).fill_(src_lang_id)])
source_tokens.append(s_t)
else:
source_tokens = src_tokens
dataset = LanguagePairDataset(
source_tokens,
src_lengths,
self.source_dictionary,
tgt_dict=self.target_dictionary,
constraints=constraints,
)
return dataset
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