56 lines
1.5 KiB
Python
56 lines
1.5 KiB
Python
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import os
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import json
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import torch
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import logging
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import concurrent.futures
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import librosa
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import torch.distributed as dist
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from typing import Collection
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import torch
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import torchaudio
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from torch import nn
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import random
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import re
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from funasr.tokenizer.cleaner import TextCleaner
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from funasr.register import tables
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@tables.register("preprocessor_classes", "SpeechPreprocessSpeedPerturb")
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class SpeechPreprocessSpeedPerturb(nn.Module):
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def __init__(self, speed_perturb: list = None, **kwargs):
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super().__init__()
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self.speed_perturb = speed_perturb
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def forward(self, waveform, fs, **kwargs):
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if self.speed_perturb is None:
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return waveform
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speed = random.choice(self.speed_perturb)
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if speed != 1.0:
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if not isinstance(waveform, torch.Tensor):
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waveform = torch.tensor(waveform)
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waveform, _ = torchaudio.sox_effects.apply_effects_tensor(
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waveform.view(1, -1), fs, [["speed", str(speed)], ["rate", str(fs)]]
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)
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waveform = waveform.view(-1)
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return waveform
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@tables.register("preprocessor_classes", "TextPreprocessSegDict")
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class TextPreprocessSegDict(nn.Module):
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def __init__(
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self,
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seg_dict: str = None,
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text_cleaner: Collection[str] = None,
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split_with_space: bool = False,
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**kwargs
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):
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super().__init__()
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self.text_cleaner = TextCleaner(text_cleaner)
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def forward(self, text, **kwargs):
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text = self.text_cleaner(text)
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return text
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