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Update train.py

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zr_jin 2024-11-04 14:26:51 +08:00
parent 8eb160e287
commit 729e86edb9
1 changed files with 4 additions and 33 deletions
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37
egs/ljspeech/TTS/matcha/train.py
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@ -7,7 +7,7 @@ import json
import logging import logging
from pathlib import Path from pathlib import Path
from shutil import copyfile from shutil import copyfile
from typing import Any, Dict, Optional, Tuple, Union from typing import Any, Dict, Optional, Union
import k2 import k2
import numpy as np import numpy as np
@ -344,7 +344,7 @@ def compute_validation_loss(
valid_dl: torch.utils.data.DataLoader, valid_dl: torch.utils.data.DataLoader,
world_size: int = 1, world_size: int = 1,
rank: int = 0, rank: int = 0,
) -> Tuple[MetricsTracker, Tuple[np.ndarray, np.ndarray]]: ) -> MetricsTracker:
"""Run the validation process.""" """Run the validation process."""
model.eval() model.eval()
device = model.device if isinstance(model, DDP) else next(model.parameters()).device device = model.device if isinstance(model, DDP) else next(model.parameters()).device
@ -392,23 +392,6 @@ def compute_validation_loss(
# summary stats # summary stats
tot_loss = tot_loss + loss_info tot_loss = tot_loss + loss_info
# infer for first batch:
if batch_idx == 0 and rank == 0:
inner_model = model.module if isinstance(model, DDP) else model
audio_pred, _, duration = inner_model.inference(
text=tokens[0, : tokens_lens[0].item()]
)
audio_pred = audio_pred.data.cpu().numpy()
audio_len_pred = (
(duration.sum(0) * params.frame_shift).to(dtype=torch.int64).item()
)
assert audio_len_pred == len(audio_pred), (
audio_len_pred,
len(audio_pred),
)
audio_gt = audio[0, : audio_lens[0].item()].data.cpu().numpy()
returned_sample = (audio_pred, audio_gt)
if world_size > 1: if world_size > 1:
tot_loss.reduce(device) tot_loss.reduce(device)
@ -417,7 +400,7 @@ def compute_validation_loss(
params.best_valid_epoch = params.cur_epoch params.best_valid_epoch = params.cur_epoch
params.best_valid_loss = loss_value params.best_valid_loss = loss_value
return tot_loss, returned_sample return tot_loss
def train_one_epoch( def train_one_epoch(
@ -577,7 +560,7 @@ def train_one_epoch(
if params.batch_idx_train % params.valid_interval == 1: if params.batch_idx_train % params.valid_interval == 1:
logging.info("Computing validation loss") logging.info("Computing validation loss")
valid_info, (speech_hat, speech) = compute_validation_loss( valid_info = compute_validation_loss(
params=params, params=params,
model=model, model=model,
tokenizer=tokenizer, tokenizer=tokenizer,
@ -595,18 +578,6 @@ def train_one_epoch(
valid_info.write_summary( valid_info.write_summary(
tb_writer, "train/valid_", params.batch_idx_train tb_writer, "train/valid_", params.batch_idx_train
) )
tb_writer.add_audio(
"train/valid_speech_hat",
speech_hat,
params.batch_idx_train,
params.sampling_rate,
)
tb_writer.add_audio(
"train/valid_speech",
speech,
params.batch_idx_train,
params.sampling_rate,
)
loss_value = tot_loss["tot_loss"] / tot_loss["samples"] loss_value = tot_loss["tot_loss"] / tot_loss["samples"]
params.train_loss = loss_value params.train_loss = loss_value