mirror of
https://github.com/modelscope/FunASR
synced 2025-09-15 14:48:36 +08:00
83 lines
2.2 KiB
Python
83 lines
2.2 KiB
Python
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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waveform, _ = torchaudio.sox_effects.apply_effects_tensor(
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torch.tensor(waveform).view(1, -1), fs, [['speed', str(speed)], ['rate', str(fs)]])
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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__(self, 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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super().__init__()
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self.seg_dict = None
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if seg_dict is not None:
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self.seg_dict = {}
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with open(seg_dict, "r", encoding="utf8") as f:
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lines = f.readlines()
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for line in lines:
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s = line.strip().split()
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key = s[0]
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value = s[1:]
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self.seg_dict[key] = " ".join(value)
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self.text_cleaner = TextCleaner(text_cleaner)
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self.split_with_space = split_with_space
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def forward(self, text, **kwargs):
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if self.seg_dict is not None:
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text = self.text_cleaner(text)
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if self.split_with_space:
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tokens = text.strip().split(" ")
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if self.seg_dict is not None:
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text = seg_tokenize(tokens, self.seg_dict)
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return text
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def seg_tokenize(txt, seg_dict):
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pattern = re.compile(r'^[\u4E00-\u9FA50-9]+$')
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out_txt = ""
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for word in txt:
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word = word.lower()
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if word in seg_dict:
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out_txt += seg_dict[word] + " "
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else:
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if pattern.match(word):
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for char in word:
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if char in seg_dict:
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out_txt += seg_dict[char] + " "
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else:
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out_txt += "<unk>" + " "
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else:
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out_txt += "<unk>" + " "
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return out_txt.strip().split() |