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72c09ec0b7* indextts2 * update lfs for audio files --------- Co-authored-by: wangyining02 <wangyining02@bilibili.com>
93 lines
3.1 KiB
Python
93 lines
3.1 KiB
Python
import os
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import re
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import random
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import torch
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import torchaudio
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MATPLOTLIB_FLAG = False
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def load_audio(audiopath, sampling_rate):
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audio, sr = torchaudio.load(audiopath)
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#print(f"wave shape: {audio.shape}, sample_rate: {sr}")
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if audio.size(0) > 1: # mix to mono
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audio = audio[0].unsqueeze(0)
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if sr != sampling_rate:
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try:
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audio = torchaudio.functional.resample(audio, sr, sampling_rate)
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except Exception as e:
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print(f"Warning: {audiopath}, wave shape: {audio.shape}, sample_rate: {sr}")
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return None
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# clip audio invalid values
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audio.clip_(-1, 1)
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return audio
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def tokenize_by_CJK_char(line: str) -> str:
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"""
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Tokenize a line of text with CJK char.
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Note: All return charaters will be upper case.
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Example:
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input = "你好世界是 hello world 的中文"
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output = "你 好 世 界 是 HELLO WORLD 的 中 文"
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Args:
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line:
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The input text.
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Return:
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A new string tokenize by CJK char.
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"""
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# The CJK ranges is from https://github.com/alvations/nltk/blob/79eed6ddea0d0a2c212c1060b477fc268fec4d4b/nltk/tokenize/util.py
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pattern = re.compile(
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r"([\u1100-\u11ff\u2e80-\ua4cf\ua840-\uD7AF\uF900-\uFAFF\uFE30-\uFE4F\uFF65-\uFFDC\U00020000-\U0002FFFF])"
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)
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chars = pattern.split(line.strip().upper())
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return " ".join([w.strip() for w in chars if w.strip()])
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def make_pad_mask(lengths: torch.Tensor, max_len: int = 0) -> torch.Tensor:
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"""Make mask tensor containing indices of padded part.
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See description of make_non_pad_mask.
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Args:
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lengths (torch.Tensor): Batch of lengths (B,).
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Returns:
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torch.Tensor: Mask tensor containing indices of padded part.
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Examples:
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>>> lengths = [5, 3, 2]
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>>> make_pad_mask(lengths)
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masks = [[0, 0, 0, 0 ,0],
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[0, 0, 0, 1, 1],
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[0, 0, 1, 1, 1]]
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"""
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batch_size = lengths.size(0)
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max_len = max_len if max_len > 0 else lengths.max().item()
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seq_range = torch.arange(0,
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max_len,
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dtype=torch.int64,
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device=lengths.device)
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seq_range_expand = seq_range.unsqueeze(0).expand(batch_size, max_len)
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seq_length_expand = lengths.unsqueeze(-1)
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mask = seq_range_expand >= seq_length_expand
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return mask
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def safe_log(x: torch.Tensor, clip_val: float = 1e-7) -> torch.Tensor:
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"""
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Computes the element-wise logarithm of the input tensor with clipping to avoid near-zero values.
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Args:
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x (Tensor): Input tensor.
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clip_val (float, optional): Minimum value to clip the input tensor. Defaults to 1e-7.
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Returns:
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Tensor: Element-wise logarithm of the input tensor with clipping applied.
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"""
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return torch.log(torch.clip(x, min=clip_val))
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