> For the complete documentation index, see [llms.txt](https://docs.clore.ai/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.clore.ai/guides/guides_v2-zh/yin-pin-yu-yu-yin/openvoice-clone.md).

# OpenVoice

在 Clore.ai 上用几秒钟的音频克隆任意声音

使用 OpenVoice 只需几秒音频即可克隆任意声音。

{% hint style="success" %}
所有示例都可以在通过以下方式租用的 GPU 服务器上运行 [CLORE.AI 市场](https://clore.ai/marketplace).
{% endhint %}

## 在 CLORE.AI 上租用

1. 访问 [CLORE.AI 市场](https://clore.ai/marketplace)
2. 按 GPU 类型、VRAM 和价格筛选
3. 选择 **按需** （固定费率）或 **竞价** （出价）
4. 配置你的订单：
   * 选择 Docker 镜像
   * 设置端口（SSH 用 TCP，Web UI 用 HTTP）
   * 如有需要，添加环境变量
   * 输入启动命令
5. 选择支付方式： **CLORE**, **BTC**，或 **USDT/USDC**
6. 创建订单并等待部署

### 访问你的服务器

* 在以下位置查找连接信息 **我的订单**
* Web 界面：使用 HTTP 端口 URL
* SSH： `ssh -p <port> root@<proxy-address>`

## 什么是 OpenVoice？

MyShell 的 OpenVoice 可以：

* 仅凭约 10 秒音频克隆声音
* 控制情感、口音和节奏
* 跨语言声音克隆
* 零样本声音转换

## 需求

| 任务   | 最低显存 | 推荐       |
| ---- | ---- | -------- |
| 推理   | 4GB  | RTX 3060 |
| 批量处理 | 6GB  | RTX 3070 |

## 快速部署

**Docker 镜像：**

```
pytorch/pytorch:2.11.0-cuda12.8-cudnn9-runtime
```

**端口：**

```
22/tcp
7860/http
```

**命令：**

```bash
pip install git+https://github.com/myshell-ai/OpenVoice.git gradio && \\
python -c "
import gradio as gr
from openvoice import se_extractor
from openvoice.api import ToneColorConverter
import torch

ckpt_converter = 'checkpoints_v2/converter'
device = 'cuda'
tone_color_converter = ToneColorConverter(f'{ckpt_converter}/config.json', device=device)
tone_color_converter.load_ckpt(f'{ckpt_converter}/checkpoint.pth')

def clone(source_audio, reference_audio):
    source_se, _ = se_extractor.get_se(source_audio, tone_color_converter, vad=False)
    target_se, _ = se_extractor.get_se(reference_audio, tone_color_converter, vad=False)

    output_path = 'output.wav'
    tone_color_converter.convert(
        audio_src_path=source_audio,
        src_se=source_se,
        tgt_se=target_se,
        output_path=output_path
    )
    return output_path

demo = gr.Interface(
    fn=clone,
    inputs=[gr.Audio(type='filepath', label='源音频'), gr.Audio(type='filepath', label='目标声音')],
    outputs=gr.Audio(label='克隆结果'),
    title='OpenVoice 克隆'
)
demo.launch(server_name='0.0.0.0', server_port=7860)
"
```

## 访问你的服务

部署后，找到你的 `http_pub` URL 在 **我的订单**:

1. 前往 **我的订单** 页面
2. 点击你的订单
3. 找到 `http_pub` URL（例如， `abc123.clorecloud.net`)

使用 `https://YOUR_HTTP_PUB_URL` 替代 `localhost` 在下面的示例中。

## 安装

```bash
git clone https://github.com/myshell-ai/OpenVoice.git
cd OpenVoice
pip install -e .

# 下载检查点
python -c "from huggingface_hub import snapshot_download; snapshot_download(repo_id='myshell-ai/OpenVoice', local_dir='checkpoints')"
```

## 基础声音克隆

```python
from openvoice import se_extractor
from openvoice.api import ToneColorConverter
import torch

# 初始化
device = "cuda" if torch.cuda.is_available() else "cpu"
ckpt_converter = 'checkpoints_v2/converter'

tone_color_converter = ToneColorConverter(
    f'{ckpt_converter}/config.json',
    device=device
)
tone_color_converter.load_ckpt(f'{ckpt_converter}/checkpoint.pth')

# 提取说话人嵌入
source_se, _ = se_extractor.get_se("source_audio.wav", tone_color_converter, vad=False)
target_se, _ = se_extractor.get_se("target_voice.wav", tone_color_converter, vad=False)

# 转换声音
tone_color_converter.convert(
    audio_src_path="source_audio.wav",
    src_se=source_se,
    tgt_se=target_se,
    output_path="output.wav"
)
```

## 结合文本转语音

用任意声音生成语音：

```python
from openvoice import se_extractor
from openvoice.api import ToneColorConverter, BaseSpeakerTTS
from melo.api import TTS

# 初始化 TTS
tts = TTS(language='EN', device=device)
speaker_ids = tts.hps.data.spk2id

# 生成基础语音
tts.tts_to_file("你好，这是一个测试。", speaker_ids['EN-US'], "base.wav")

# 克隆为目标声音
source_se, _ = se_extractor.get_se("base.wav", tone_color_converter, vad=False)
target_se, _ = se_extractor.get_se("target_voice.wav", tone_color_converter, vad=False)

tone_color_converter.convert(
    audio_src_path="base.wav",
    src_se=source_se,
    tgt_se=target_se,
    output_path="cloned_speech.wav"
)
```

## 多语言支持

```python
from melo.api import TTS

# 可用语言
languages = ['EN', 'ES', 'FR', 'ZH', 'JP', 'KR']

# 英语
tts_en = TTS(language='EN', device=device)
tts_en.tts_to_file("你好，世界", tts_en.hps.data.spk2id['EN-US'], "en.wav")

# 中文
tts_zh = TTS(language='ZH', device=device)
tts_zh.tts_to_file("你好世界", tts_zh.hps.data.spk2id['ZH'], "zh.wav")

# 日语
tts_jp = TTS(language='JP', device=device)
tts_jp.tts_to_file("こんにちは", tts_jp.hps.data.spk2id['JP'], "jp.wav")
```

## 情感控制

OpenVoice V2 支持情感/风格控制：

```python
from openvoice.api import BaseSpeakerTTS

# 带风格的基础 TTS
base_speaker_tts = BaseSpeakerTTS(
    f'{ckpt_base}/config.json',
    device=device
)
base_speaker_tts.load_ckpt(f'{ckpt_base}/checkpoint.pth')

# 可用风格
styles = ['default', 'whispering', 'cheerful', 'terrified', 'angry', 'sad', 'friendly']

for style in styles:
    base_speaker_tts.tts(
        "这是一句测试语句。",
        f"output_{style}.wav",
        speaker='default',
        language='English',
        style=style
    )
```

## 批量处理

```python
import os
from openvoice import se_extractor
from openvoice.api import ToneColorConverter

tone_color_converter = ToneColorConverter(
    f'{ckpt_converter}/config.json',
    device='cuda'
)
tone_color_converter.load_ckpt(f'{ckpt_converter}/checkpoint.pth')

# 仅获取一次目标声音嵌入
target_se, _ = se_extractor.get_se("target_voice.wav", tone_color_converter, vad=False)

input_dir = "./audio_files"
output_dir = "./cloned"
os.makedirs(output_dir, exist_ok=True)

for filename in os.listdir(input_dir):
    if filename.endswith(('.wav', '.mp3')):
        input_path = os.path.join(input_dir, filename)
        output_path = os.path.join(output_dir, f"cloned_{filename}")

        source_se, _ = se_extractor.get_se(input_path, tone_color_converter, vad=False)

        tone_color_converter.convert(
            audio_src_path=input_path,
            src_se=source_se,
            tgt_se=target_se,
            output_path=output_path
        )
        print(f"已克隆：{filename}")
```

## API 服务器

```python
from fastapi import FastAPI, UploadFile
from fastapi.responses import FileResponse
from openvoice import se_extractor
from openvoice.api import ToneColorConverter
import tempfile
import shutil

app = FastAPI()

tone_color_converter = ToneColorConverter(
    'checkpoints_v2/converter/config.json',
    device='cuda'
)
tone_color_converter.load_ckpt('checkpoints_v2/converter/checkpoint.pth')

@app.post("/clone")
async def clone_voice(source: UploadFile, target: UploadFile):
    # 保存上传的文件
    with tempfile.NamedTemporaryFile(suffix=".wav", delete=False) as src_tmp:
        shutil.copyfileobj(source.file, src_tmp)
        src_path = src_tmp.name

    with tempfile.NamedTemporaryFile(suffix=".wav", delete=False) as tgt_tmp:
        shutil.copyfileobj(target.file, tgt_tmp)
        tgt_path = tgt_tmp.name

    # 提取嵌入
    source_se, _ = se_extractor.get_se(src_path, tone_color_converter, vad=False)
    target_se, _ = se_extractor.get_se(tgt_path, tone_color_converter, vad=False)

    # 转换
    output_path = tempfile.mktemp(suffix=".wav")
    tone_color_converter.convert(
        audio_src_path=src_path,
        src_se=source_se,
        tgt_se=target_se,
        output_path=output_path
    )

    return FileResponse(output_path, media_type="audio/wav")

# 运行：uvicorn server:app --host 0.0.0.0 --port 8000
```

## 质量提示

### 为了获得最佳效果

* 使用 10–30 秒清晰的参考音频
* 避免背景噪音
* 参考音频中只保留单一说话人
* 尽量匹配说话速度

### 音频预处理

```python
import librosa
import soundfile as sf

def preprocess_audio(input_path, output_path, target_sr=22050):
    audio, sr = librosa.load(input_path, sr=target_sr)

    # 裁剪静音
    audio, _ = librosa.effects.trim(audio, top_db=20)

    # 归一化
    audio = librosa.util.normalize(audio)

    sf.write(output_path, audio, target_sr)
    return output_path

preprocess_audio("raw_reference.wav", "clean_reference.wav")
```

## 与其他工具的对比

| 功能   | OpenVoice | RVC     | Bark |
| ---- | --------- | ------- | ---- |
| 参考音频 | 10–30 秒   | 10 分钟以上 | 不适用  |
| 训练   | 不需要       | 必需      | 不适用  |
| 速度   | 快         | 中等      | 慢    |
| 质量   | 很高        | 最佳      | 好    |
| 跨语言  | 是         | 有限      | 是    |

## 性能

| 任务        | GPU      | 时间    |
| --------- | -------- | ----- |
| 提取嵌入      | RTX 3090 | 约 1 秒 |
| 转换 10 秒音频 | RTX 3090 | \~2s  |
| 转换 1 分钟音频 | RTX 3090 | \~8s  |

## 故障排查

### 声音匹配不佳

* 使用更长的参考音频
* 确保音频质量清晰
* 检查是否有背景噪音

### 音频瑕疵

* 降低语速/重音设置
* 使用一致的音频格式
* 检查采样率是否匹配

### 内存不足

* 处理更短的片段
* 减小批量大小
* 清除 CUDA 缓存

## 成本估算

CLORE.AI 市场常见费率（截至 2024 年）：

| GPU       | 小时费率    | 日费率     | 4 小时会话  |
| --------- | ------- | ------- | ------- |
| RTX 3060  | \~$0.03 | \~$0.70 | \~$0.12 |
| RTX 3090  | \~$0.06 | \~$1.50 | \~$0.25 |
| RTX 4090  | \~$0.10 | \~$2.30 | \~$0.40 |
| A100 40GB | \~$0.17 | \~$4.00 | \~$0.70 |
| A100 80GB | \~$0.25 | \~$6.00 | \~$1.00 |

*价格因提供商和需求而异。请查看* [*CLORE.AI 市场*](https://clore.ai/marketplace) *以获取当前费率。*

**节省费用：**

* 使用 **竞价** 可中断工作市场——约三分之一的服务器将现货价格定得低于按需价格（中位数约优惠 13%），其余则与按需价格持平
* 使用 **CLORE** 代币支付
* 比较不同提供商的价格

## 下一步

* [Bark TTS](/guides/guides_v2-zh/yin-pin-yu-yu-yin/bark-tts.md) - 文本转语音
* [RVC 变声克隆](/guides/guides_v2-zh/yin-pin-yu-yu-yin/rvc-voice-clone.md) - 基于训练的克隆
* [Whisper Transcription](/guides/guides_v2-zh/yin-pin-yu-yu-yin/whisper-transcription.md) - 语音转文本


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