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# All rights reserved.
#
# This source code is licensed under the license found in the LICENSE file in
# the root directory of this source tree. An additional grant of patent rights
# can be found in the PATENTS file in the same directory.
import math
from argparse import Namespace
from dataclasses import dataclass, field
from omegaconf import II
from typing import Optional
import torch
import torch.nn.functional as F
from fairseq import metrics, utils
from fairseq.criterions import FairseqCriterion, register_criterion
from fairseq.dataclass import FairseqDataclass
from fairseq.data.data_utils import post_process
from fairseq.tasks import FairseqTask
from fairseq.logging.meters import safe_round
@dataclass
class CtcCriterionConfig(FairseqDataclass):
zero_infinity: bool = field(
default=False,
metadata={"help": "zero inf loss when source length <= target length"},
)
sentence_avg: bool = II("optimization.sentence_avg")
post_process: str = field(
default="letter",
metadata={
"help": "how to post process predictions into words. can be letter, "
"wordpiece, BPE symbols, etc. "
"See fairseq.data.data_utils.post_process() for full list of options"
},
)
wer_kenlm_model: Optional[str] = field(
default=None,
metadata={
"help": "if this is provided, use kenlm to compute wer (along with other wer_* args)"
},
)
wer_lexicon: Optional[str] = field(
default=None,
metadata={"help": "lexicon to use with wer_kenlm_model"},
)
wer_lm_weight: float = field(
default=2.0,
metadata={"help": "lm weight to use with wer_kenlm_model"},
)
wer_word_score: float = field(
default=-1.0,
metadata={"help": "lm word score to use with wer_kenlm_model"},
)
wer_args: Optional[str] = field(
default=None,
metadata={
"help": "DEPRECATED: tuple of (wer_kenlm_model, wer_lexicon, wer_lm_weight, wer_word_score)"
},
)
@register_criterion("ctc", dataclass=CtcCriterionConfig)
class CtcCriterion(FairseqCriterion):
def __init__(self, cfg: CtcCriterionConfig, task: FairseqTask):
super().__init__(task)
self.blank_idx = (
task.target_dictionary.index(task.blank_symbol)
if hasattr(task, "blank_symbol")
else 0
)
self.pad_idx = task.target_dictionary.pad()
self.eos_idx = task.target_dictionary.eos()
self.post_process = cfg.post_process
if cfg.wer_args is not None:
(
cfg.wer_kenlm_model,
cfg.wer_lexicon,
cfg.wer_lm_weight,
cfg.wer_word_score,
) = eval(cfg.wer_args)
if cfg.wer_kenlm_model is not None:
from examples.speech_recognition.w2l_decoder import W2lKenLMDecoder
dec_args = Namespace()
dec_args.nbest = 1
dec_args.criterion = "ctc"
dec_args.kenlm_model = cfg.wer_kenlm_model
dec_args.lexicon = cfg.wer_lexicon
dec_args.beam = 50
dec_args.beam_size_token = min(50, len(task.target_dictionary))
dec_args.beam_threshold = min(50, len(task.target_dictionary))
dec_args.lm_weight = cfg.wer_lm_weight
dec_args.word_score = cfg.wer_word_score
dec_args.unk_weight = -math.inf
dec_args.sil_weight = 0
self.w2l_decoder = W2lKenLMDecoder(dec_args, task.target_dictionary)
else:
self.w2l_decoder = None
self.zero_infinity = cfg.zero_infinity
self.sentence_avg = cfg.sentence_avg
def forward(self, model, sample, reduce=True):
net_output = model(**sample["net_input"])
lprobs = model.get_normalized_probs(
net_output, log_probs=True
).contiguous() # (T, B, C) from the encoder
if "src_lengths" in sample["net_input"]:
input_lengths = sample["net_input"]["src_lengths"]
else:
if net_output["padding_mask"] is not None:
non_padding_mask = ~net_output["padding_mask"]
input_lengths = non_padding_mask.long().sum(-1)
else:
input_lengths = lprobs.new_full(
(lprobs.size(1),), lprobs.size(0), dtype=torch.long
)
pad_mask = (sample["target"] != self.pad_idx) & (
sample["target"] != self.eos_idx
)
targets_flat = sample["target"].masked_select(pad_mask)
if "target_lengths" in sample:
target_lengths = sample["target_lengths"]
else:
target_lengths = pad_mask.sum(-1)
with torch.backends.cudnn.flags(enabled=False):
loss = F.ctc_loss(
lprobs,
targets_flat,
input_lengths,
target_lengths,
blank=self.blank_idx,
reduction="sum",
zero_infinity=self.zero_infinity,
)
ntokens = (
sample["ntokens"] if "ntokens" in sample else target_lengths.sum().item()
)
sample_size = sample["target"].size(0) if self.sentence_avg else ntokens
logging_output = {
"loss": utils.item(loss.data), # * sample['ntokens'],
"ntokens": ntokens,
"nsentences": sample["id"].numel(),
"sample_size": sample_size,
}
if not model.training:
import editdistance
with torch.no_grad():
lprobs_t = lprobs.transpose(0, 1).float().contiguous().cpu()
c_err = 0
c_len = 0
w_errs = 0
w_len = 0
wv_errs = 0
for lp, t, inp_l in zip(
lprobs_t,
sample["target_label"]
if "target_label" in sample
else sample["target"],
input_lengths,
):
lp = lp[:inp_l].unsqueeze(0)
decoded = None
if self.w2l_decoder is not None:
decoded = self.w2l_decoder.decode(lp)
if len(decoded) < 1:
decoded = None
else:
decoded = decoded[0]
if len(decoded) < 1:
decoded = None
else:
decoded = decoded[0]
p = (t != self.task.target_dictionary.pad()) & (
t != self.task.target_dictionary.eos()
)
targ = t[p]
targ_units = self.task.target_dictionary.string(targ)
targ_units_arr = targ.tolist()
toks = lp.argmax(dim=-1).unique_consecutive()
pred_units_arr = toks[toks != self.blank_idx].tolist()
c_err += editdistance.eval(pred_units_arr, targ_units_arr)
c_len += len(targ_units_arr)
targ_words = post_process(targ_units, self.post_process).split()
pred_units = self.task.target_dictionary.string(pred_units_arr)
pred_words_raw = post_process(pred_units, self.post_process).split()
if decoded is not None and "words" in decoded:
pred_words = decoded["words"]
w_errs += editdistance.eval(pred_words, targ_words)
wv_errs += editdistance.eval(pred_words_raw, targ_words)
else:
dist = editdistance.eval(pred_words_raw, targ_words)
w_errs += dist
wv_errs += dist
w_len += len(targ_words)
logging_output["wv_errors"] = wv_errs
logging_output["w_errors"] = w_errs
logging_output["w_total"] = w_len
logging_output["c_errors"] = c_err
logging_output["c_total"] = c_len
return loss, sample_size, logging_output
@staticmethod
def reduce_metrics(logging_outputs) -> None:
"""Aggregate logging outputs from data parallel training."""
loss_sum = utils.item(sum(log.get("loss", 0) for log in logging_outputs))
ntokens = utils.item(sum(log.get("ntokens", 0) for log in logging_outputs))
nsentences = utils.item(
sum(log.get("nsentences", 0) for log in logging_outputs)
)
sample_size = utils.item(
sum(log.get("sample_size", 0) for log in logging_outputs)
)
metrics.log_scalar(
"loss", loss_sum / sample_size / math.log(2), sample_size, round=3
)
metrics.log_scalar("ntokens", ntokens)
metrics.log_scalar("nsentences", nsentences)
if sample_size != ntokens:
metrics.log_scalar(
"nll_loss", loss_sum / ntokens / math.log(2), ntokens, round=3
)
c_errors = sum(log.get("c_errors", 0) for log in logging_outputs)
metrics.log_scalar("_c_errors", c_errors)
c_total = sum(log.get("c_total", 0) for log in logging_outputs)
metrics.log_scalar("_c_total", c_total)
w_errors = sum(log.get("w_errors", 0) for log in logging_outputs)
metrics.log_scalar("_w_errors", w_errors)
wv_errors = sum(log.get("wv_errors", 0) for log in logging_outputs)
metrics.log_scalar("_wv_errors", wv_errors)
w_total = sum(log.get("w_total", 0) for log in logging_outputs)
metrics.log_scalar("_w_total", w_total)
if c_total > 0:
metrics.log_derived(
"uer",
lambda meters: safe_round(
meters["_c_errors"].sum * 100.0 / meters["_c_total"].sum, 3
)
if meters["_c_total"].sum > 0
else float("nan"),
)
if w_total > 0:
metrics.log_derived(
"wer",
lambda meters: safe_round(
meters["_w_errors"].sum * 100.0 / meters["_w_total"].sum, 3
)
if meters["_w_total"].sum > 0
else float("nan"),
)
metrics.log_derived(
"raw_wer",
lambda meters: safe_round(
meters["_wv_errors"].sum * 100.0 / meters["_w_total"].sum, 3
)
if meters["_w_total"].sum > 0
else float("nan"),
)
@staticmethod
def logging_outputs_can_be_summed() -> bool:
"""
Whether the logging outputs returned by `forward` can be summed
across workers prior to calling `reduce_metrics`. Setting this
to True will improves distributed training speed.
"""
return True
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