TTS训练数据高效准备指南
针对数据准备的完整链路优化方案：
自动化采集
- 语音切割：使用PyAnnote切分长音频：
from pyannote.audio import Pipeline
pipeline = Pipeline.from_pretrained('pyannote/voice-activity-detection') - 文本清洗：正则表达式过滤特殊字符
import re
re.sub(r'[^ws]', '', text)
quality control
- 三阶质检法::
1. 自动过滤信噪比＜30dB的音频
2. 用Librosa检测静音片段占比
3. 人工抽查10%样本 - 标注工具链：推荐使用Audacity+JSON标注的配套方案
data enhancement
- 参数化增强：使用torchaudio动态调整：
torchaudio.sox_effects.apply_effects_file('input.wav', effects=[
['speed', '0.9'], ['pitch', '50']])
推荐将数据预处理脚本封装为Pipeline，支持增量更新。
This answer comes from the articleSpark-TTS: A Text-to-Speech Tool for Generating Natural SpeechThe
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