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# ----------------------------------------------------------------------------
# SpeechLM: Enhanced Speech Pre-Training with Unpaired Textual Data (https://arxiv.org/abs/2209.15329)
# Github source: https://github.com/microsoft/SpeechT5/tree/main/SpeechLM
# Code based on fairseq: https://github.com/facebookresearch/fairseq/tree/272c4c5197250997148fb12c0db6306035f166a4
#
# Copyright (c) 2022 Microsoft
# Licensed under The MIT License [see LICENSE for details]
# ----------------------------------------------------------------------------
from typing import List, Dict, Any
from dataclasses import dataclass, field
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 lengths_to_mask
from fairseq.models.fairseq_model import FairseqEncoderModel
def label_smoothed_nll_loss(lprobs, target, epsilon, ignore_index=None, reduce=True):
if target.dim() == lprobs.dim() - 1:
target = target.unsqueeze(-1)
nll_loss = -lprobs.gather(dim=-1, index=target)
smooth_loss = -lprobs.sum(dim=-1, keepdim=True)
if ignore_index is not None:
pad_mask = target.eq(ignore_index)
nll_loss.masked_fill_(pad_mask, 0.0)
smooth_loss.masked_fill_(pad_mask, 0.0)
else:
nll_loss = nll_loss.squeeze(-1)
smooth_loss = smooth_loss.squeeze(-1)
if reduce:
ntokens = (~pad_mask).sum()
nll_loss = nll_loss.sum() / ntokens
smooth_loss = smooth_loss.sum() / ntokens
eps_i = epsilon / (lprobs.size(-1) - 1)
loss = (1.0 - epsilon - eps_i) * nll_loss + eps_i * smooth_loss
return loss, nll_loss
@dataclass
class FastText2UnitCriterionConfig(FairseqDataclass):
label_smoothing: float = field(
default=0.0,
metadata={"help": "epsilon for label smoothing, 0 means no label smoothing"},
)
dur_loss_weight: float = field(
default=1.0,
metadata={"help": "scale of duration loss"},
)
report_accuracy: bool = field(
default=True,
metadata={"help": "report decoder accuracy metric"},
)
@register_criterion("fasttext2unit_criterion", dataclass=FastText2UnitCriterionConfig)
class FastText2UnitLoss(FairseqCriterion):
def __init__(self,
task,
label_smoothing=0,
dur_loss_weight=1.0,
report_accuracy=False,
):
super().__init__(task)
self.eps = label_smoothing
self.dur_loss_weight = dur_loss_weight
self.pad_idx = task.tgt_dict.pad()
self.report_accuracy = report_accuracy
def forward(self, model: FairseqEncoderModel, sample, reduction="mean"):
src_tokens = sample["net_input"]["src_tokens"]
src_lens = sample["net_input"]["src_lengths"]
tgt_lens = sample["target_lengths"]
_feat_out, _feat_out_post, out_lens, log_dur_out, pitch_out, energy_out = model(
src_tokens=src_tokens,
src_lengths=src_lens,
prev_output_tokens=sample["net_input"]["prev_output_tokens"],
incremental_state=None,
target_lengths=tgt_lens,
speaker=sample["speaker"],
durations=sample["durations"],
pitches=sample["pitches"],
energies=sample["energies"],
)
src_mask = lengths_to_mask(sample["net_input"]["src_lengths"])
tgt_mask = lengths_to_mask(sample["target_lengths"])
lprobs = model.get_normalized_probs((_feat_out,), log_probs=True)
target = sample["target"].long()
ce_loss, nll_loss = label_smoothed_nll_loss(lprobs, target, self.eps, self.padding_idx, reduce=True)
pitches, energies = sample["pitches"], sample["energies"]
if pitches is not None:
pitch_out, pitches = pitch_out[src_mask], pitches[src_mask]
pitch_loss = F.mse_loss(pitch_out, pitches, reduction=reduction)
else:
pitch_loss = 0
if energies is not None:
energy_out, energies = energy_out[src_mask], energies[src_mask]
energy_loss = F.mse_loss(energy_out, energies, reduction=reduction)
else:
energy_loss = 0
log_dur_out = log_dur_out[src_mask]
dur = sample["durations"].float()
dur = dur.half() if log_dur_out.type().endswith(".HalfTensor") else dur
log_dur = torch.log(dur + 1)[src_mask]
dur_loss = F.mse_loss(log_dur_out, log_dur, reduction=reduction)
dur_loss = self.dur_loss_weight * dur_loss
loss = ce_loss + dur_loss + pitch_loss + energy_loss
sample_size = sample["nsentences"]
logging_output = {
"loss": utils.item(loss.data),
"ntokens": sample["ntokens"],
"nsentences": sample["nsentences"],
"sample_size": sample_size,
"ce_loss": utils.item(ce_loss.data),
"dur_loss": utils.item(dur_loss.data),
"pitch_loss": utils.item(pitch_loss),
"energy_loss": utils.item(energy_loss),
}
if self.report_accuracy:
n_correct = lprobs.argmax(-1).masked_select(tgt_mask).eq(target.masked_select(tgt_mask)).sum()
logging_output["n_correct"] = utils.item(n_correct.data)
logging_output["total"] = tgt_mask.sum()
return loss, 1, logging_output
@classmethod
def reduce_metrics(cls, logging_outputs: List[Dict[str, Any]]) -> None:
ns = [log.get("sample_size", 0) for log in logging_outputs]
ntot = sum(ns)
ws = [n / (ntot + 1e-8) for n in ns]
for key in [
"loss",
"ce_loss",
"dur_loss",
"pitch_loss",
"energy_loss",
]:
vals = [log.get(key, 0) for log in logging_outputs]
val = sum(val * w for val, w in zip(vals, ws))
metrics.log_scalar(key, val, ntot, round=3)
metrics.log_scalar("sample_size", ntot, len(logging_outputs))
total = utils.item(sum(log.get("total", 0) for log in logging_outputs))
if total > 0:
metrics.log_scalar("total", total)
n_correct = utils.item(
sum(log.get("n_correct", 0) for log in logging_outputs)
)
metrics.log_scalar("n_correct", n_correct)
metrics.log_derived(
"accuracy",
lambda meters: round(
meters["n_correct"].sum * 100.0 / meters["total"].sum, 3
)
if meters["total"].sum > 0
else float("nan"),
)
# inference metrics
if "targ_frames" not in logging_outputs[0]:
return
n = sum(log.get("targ_frames", 0) for log in logging_outputs)
for key, new_key in [
("mcd_loss", "mcd_loss"),
("pred_frames", "pred_ratio"),
("nins", "ins_rate"),
("ndel", "del_rate"),
]:
val = sum(log.get(key, 0) for log in logging_outputs)
metrics.log_scalar(new_key, val / n, n, round=3)
@staticmethod
def logging_outputs_can_be_summed() -> bool:
return False
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