> For the complete documentation index, see [llms.txt](https://whitepaper.virtuals.io/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://whitepaper.virtuals.io/virtuals-bai-pi-shu/guan-yu-virtuals-1/xie-yi/ai-zhi-neng-ti-gong-tong-gong-xian-yu-su-yuan/mian-xiang-ai-zhi-neng-ti-zhi-li-de-mo-kuai-hua-gong-shi-kuang-jia/qu-zhong-xin-hua-ai-zhi-neng-ti-gong-xian-yu-nft/ai-zhi-neng-ti-de-yu-yin-he-xin.md).

# AI 智能体的语音核心

探索 Virtuals Protocol 的语音核心，涵盖 AI 智能体语音、语音转文本、文本转语音、VITS 语音合成以及音频数据预处理。

Voice Core 为每个虚拟智能体提供独特且与其个性相匹配的声音。AI 语音模型训练会为每个智能体和角色生成真实、一致的语音。

### AI 智能体语音模块

**语音转文本（STT）：** STT 模块基于多样化的语音数据进行训练。它能够准确转写不同用户场景中的口音、方言和说话模式。

**文本转语音（TTS）：** TTS 模块使用用于文本转语音的变分推断（VITS）进行训练。VITS 能生成高质量、自然的语音，并支持针对每个 AI 智能体个性定制的语音合成。

语音模型训练之前会进行音频数据预处理。

### 语音模型的音频数据预处理

1. **音频格式一致性：** 采用 22050 Hz 的单声道 WAV 文件可创建一致的训练输入。一致的输入数据有助于机器学习语音模型稳定可靠地运行。
2. **采样率归一化：** 22050 Hz 的采样率既能捕获人类语音频率，又能保持文件大小可控。根据奈奎斯特定理，它可捕获最高 11025 Hz 的频率。
3. **单声道音频通道：** 将立体声或多声道音频转换为单声道，可为语音模型提供单一训练通道，并简化学习过程。

<details>

<summary>示例代码</summary>

```python
import os
from pydub import AudioSegment

upload_dir = 'upload_dir'
output_dir = 'out'

# 确保输出目录存在
os.makedirs(output_dir, exist_ok=True)

extensions = ['wav', 'mp3', 'ogg']

# 处理上传目录中的所有文件
for filename in os.listdir(upload_dir):
    if any(filename.lower().endswith(ext) for ext in extensions):
        # 构建文件路径
        file_path = os.path.join(upload_dir, filename)
        output_path = os.path.join(output_dir, os.path.splitext(filename)[0] + '.wav')

        # 加载音频文件
        audio = AudioSegment.from_file(file_path)

        # 转换为 WAV、22050 Hz、单声道
        audio = audio.set_frame_rate(22050).set_channels(1)

        # 导出处理后的音频
        audio.export(output_path, format='wav')

```

</details>

[<mark style="color:红色;">进一步了解如何为 Voice Core 贡献内容。</mark>](/virtuals-bai-pi-shu/gou-jian-zhe-zhong-xin/shi-yong-virtuals-gou-jian/zhi-neng-ti-gong-xian/gong-xian-gei-yu-yin-he-xin.md)


---

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