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minor fixes

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zr_jin 2024-12-06 11:51:40 +08:00
parent ce73643af6
commit 2504036f5b
2 changed files with 8 additions and 6 deletions
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7
egs/wenetspeech4tts/TTS/valle/train.py
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@ -4,6 +4,7 @@
# Mingshuang Luo)
# Copyright 2023 (authors: Feiteng Li)
# Copyright 2024 (authors: Yuekai Zhang)
# Copyright 2024 Tsinghua University (authors: Zengrui Jin,)
#
# See ../../../../LICENSE for clarification regarding multiple authors
#
@ -48,10 +49,8 @@ python3 valle/train.py --max-duration 160 --filter-min-duration 0.5 --filter-max
import argparse
import copy
import logging
import os
import random
import warnings
from contextlib import nullcontext
from pathlib import Path
from shutil import copyfile
from typing import Any, Dict, Optional, Tuple, Union
@ -686,9 +685,9 @@ def compute_validation_loss(
output_dir = Path(f"{params.exp_dir}/eval/step-{params.batch_idx_train:06d}")
output_dir.mkdir(parents=True, exist_ok=True)
if isinstance(model, DDP):
model.module.visualize(predicts, batch, output_dir=output_dir)
model.module.visualize(predicts, batch, tokenizer, output_dir=output_dir)
else:
model.visualize(predicts, batch, output_dir=output_dir)
model.visualize(predicts, batch, tokenizer, output_dir=output_dir)
return tot_loss

7
egs/wenetspeech4tts/TTS/valle/valle.py
View File

@ -23,6 +23,7 @@ import matplotlib.pyplot as plt
import numpy as np
import torch
import torch.nn as nn
from tokenizer import TextTokenCollater
from torch import Tensor
from torch.nn import Linear, Module
from torch.nn import functional as F
@ -1664,13 +1665,15 @@ class VALLE(nn.Module):
self,
predicts: Tuple[torch.Tensor],
batch: Dict[str, Union[List, torch.Tensor]],
tokenizer: TextTokenCollater,
output_dir: str,
limit: int = 4,
) -> None:
text_tokens = batch["text_tokens"].to("cpu").detach().numpy()
text_tokens_lens = batch["text_tokens_lens"].to("cpu").detach().numpy()
audio_features = batch["audio_features"].to("cpu").detach().numpy()
audio_features_lens = batch["audio_features_lens"].to("cpu").detach().numpy()
tokens = batch["tokens"]
text_tokens, text_tokens_lens = tokenizer(tokens)
assert text_tokens.ndim == 2
utt_ids, texts = batch["utt_id"], batch["text"]