一文梳理ChatTTS的进阶用法，手把手带你实现个性化配音，音色、语速、停顿，口语，全搞定

本文主要是介绍一文梳理ChatTTS的进阶用法，手把手带你实现个性化配音，音色、语速、停顿，口语，全搞定，希望对大家解决编程问题提供一定的参考价值，需要的开发者们随着小编来一起学习吧！

前几天和大家分享了如何从0到1搭建一套语音交互系统。

其中，语音合成（TTS）是提升用户体验的关键所在。于是，上一篇接着和大家聊了聊：全网爆火的AI语音合成工具-ChatTTS，有人已经拿它赚到了第一桶金，送增强版整合包。

后台有小伙伴反应实测中发现了一些常见的问题，今天，单独开一篇关于ChatTTS的进阶教程，手把手带你实现如何固定音色、设置语速、添加停顿词、口头语、笑声等，以及超长文本生成背后的原理。

全程干货满满，赶紧上车~

全文目录

# ChatTTS 简介
# ChatTTS 基础-环境准备
# ChatTTS 进阶-固定音色
# ChatTTS 进阶-固定语速
# ChatTTS 进阶-添加停顿
## 添加停顿词
## 添加笑声
## 添加口头语
# ChatTTS 进阶-超长文本语音生成

ChatTTS 简介

GitHub 仓库地址：https://github.com/2noise/ChatTTS

ChatTTS 是一个文本转语音的开源项目，短短2周左右的时间，在 GitHub 上已经斩获了 24.4k 的 Star，绝对的爆火级项目。
在这里插入图片描述
还没有体验过 ChatTTS 的小伙伴，出门右转看这篇👉：：全网爆火的AI语音合成工具-ChatTTS，有人已经拿它赚到了第一桶金，送增强版整合包。

下面我们直接进入实操，先准备好本地环境，然后开启进阶用法。

ChatTTS 基础-环境准备

1. 安装环境

在自己本地，打开一个终端：

git clone https://github.com/2noise/ChatTTS
cd ChatTTS
pip install -r requirements.txt

2.下载模型

最后先下载模型到本地（代码中自动下载非常慢）。

**方式一：**从huggingface下载（太慢了，很有可能失败，不推荐）

# 先安装需要用到的包
pip install huggingface-hub
pip install hf_transfer
conda install -c conda-forge pynini=2.1.5 && pip install WeTextProcessing
pip install nemo_text_processing# 编写代码
import os
os.environ['HF_ENDPOINT']="https://hf-mirror.com"
os.environ['HF_HUB_ENABLE_HF_TRANSFER']="1"
from huggingface_hub import snapshot_downloadsnapshot_download(repo_id="2Noise/ChatTTS", # 模型名称 or 地址local_dir='../models/ChatTTS', # 保存模型到指定位置，供下次加载使用cache_dir='../models' # 缓存地址)

方式二：从modelscope上下载（速度更快，强烈推荐）

git clone https://www.modelscope.cn/henjicc/ChatTTS.git

3.小试牛刀-文本转语音

进行模型推理实现中文文本转语音，注意将 local_path 改为你自己下载好的模型地址。

import torch
import torchaudio
import ChatTTStorch._dynamo.config.cache_size_limit = 64
torch._dynamo.config.suppress_errors = True
torch.set_float32_matmul_precision('high')chat = ChatTTS.Chat()
# compile=True效果好，但是更慢
chat.load_models(source='local', local_path='/path/to/models/ChatTTS', compile=False)texts = ["你好啊，我是猴哥，欢迎您关注 猴哥的AI知识库 公众号，下次更新不迷路",]
wavs = chat.infer(texts)torchaudio.save("output.wav", torch.from_numpy(wavs[0]), 24000)

如果没有报错，上述代码会将生成结果保存在当前目录，打开试试吧~

你会发现每次生成的音频音色是不一样的，这是因为 ChatTTS 每次都会随机选择一个音色进行合成。

如果想要固定音色，以及更多个性化设置，继续往下看~

ChatTTS 进阶-固定音色

在ChatTTS中，控制音色的主要是参数 spk_emb，让我们看下源码：

def sample_random_speaker(self, ):dim = self.pretrain_models['gpt'].gpt.layers[0].mlp.gate_proj.in_features# dim=768std, mean = self.pretrain_models['spk_stat'].chunk(2)# std.size:(768),mean.size(768)return torch.randn(dim, device=std.device) * std + mean

sample_random_speaker 返回的是一个 768 维的向量，也就是说如果你把这个 768 维的向量固定住，就可以获取一个固定的音色了。

为了节省大家抽卡获取音色的时间，这里猴哥梳理了两个有特点的音色。保存下来，直接取用~

音色一:男-铿锵有力:

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3,3.113,0.861,9.045,-4.593,5.160,3.660,-1.495'

音色二:女-铿锵有力:

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,-0.089,1.301,-4.817'

将音色向量放到代码中，就可以实现生成固定音色的音频，代码奉上：

speaker_vector = ' xxx ' # 768维向量
speaker = torch.tensor([float(x) for x in speaker_vector.split(',')])
my_text = """你好啊，我是猴哥，欢迎您关注 猴哥的AI知识库 公众号，下次更新不迷路"""
params_infer_code = {'prompt':'[speed_2]', 'temperature':.1,'top_P':0.9, 'top_K':1, 'spk_emb' : speaker}
params_refine_text = {'prompt':'[oral_0][laugh_0][break_6]'}wavs = chat.infer([my_text],do_text_normalization=True,params_refine_text=params_refine_text,params_infer_code=params_infer_code)torchaudio.save("output.wav", torch.from_numpy(wavs[0]), 24000)

ChatTTS 进阶-固定语速

有心的同学可能已经发现了，上述代码中多了两个参数。

没错，ChatTTS 中，语速是 speed 参数实现的，设置播放的语速，从慢到快，共有10个等级[speed_0]~[speed_9]。

params_infer_code = {'prompt':'[speed_2]'}

ChatTTS 进阶-添加停顿

ChatTTS 中，主要的停顿有三种，分别是：

停顿词
笑声
口头语

添加停顿词

在ChatTTS中，停顿词主要有10个等级 [break_0]~[break_9] 。其中[break_0]是完全没有停顿，[break_9] 停顿最长。当然，模型会根据文本内容自行添加停顿，你也可以在文本中手动添加停顿 [uv_break]。

如果需要 ChatTTS 模型自动添加停顿，只需加上参数 refine_text_only=True：

my_text="""不跟生活认输，不向岁月低头。如果风景不够好，就让我们多走几步。如果道路不好走，就让我们放慢脚步。只要还在路上，总能看到想要的风景，总能找到属于自己的方向。"""
params_infer_code = {'prompt':'[speed_4]', 'temperature':.1,'top_P':0.9, 'top_K':1, 'spk_emb' : girl_speaker}
params_refine_text = {'prompt':'[oral_0][laugh_0][break_6]'}
text = chat.infer([my_text], skip_refine_text=False,refine_text_only=True,params_refine_text=params_refine_text,params_infer_code=params_infer_code)
print("\n".join(text))

输出效果如下：

'不 跟 生 活 认 输 ， 不 向 岁 月 低 头 [uv_break] 。 如 果 风 景 不 够 好 [uv_break] ， 就 让 我 们 多 走 几 步 。 如 果 道 路 不 好 走 ， 就 让 我 们 放 慢 脚 步 [uv_break] 。 只 要 还 在 路 上 ， 总 能 看 到 想 要 的 风 景 [uv_break] ， 总 能 找 到 属 于 自 己 的 方 向 。'

如果需要自行添加停顿，只需在文本中添加 [uv_break]。

示例如下：

my_text="不跟生活[uv_break]认输，不向岁月低头[laugh]。"

添加笑声

在ChatTTS中，添加笑声主要是通过 [laugh]，共有10个等级[laugh_0]~[laugh_9]。其中[laugh_0]是没有笑声，[laugh_9]笑声较多。当然，模型会根据文本自动添加笑声，也可以像上面的示例一样手动添加 [laugh].

添加口头语

口头语，就是：“那么，就是，嗯” 等语气词。

在ChatTTS中，添加口头语主要是通过 [oral]，共有10个等级[oral_0]~[oral_9]。其中[oral-0]基本不添加口头语，[oral_9]添加最多口头语。

举个例子而言，你可以将下面这个参数放入模型试试看哈：

params_refine_text = {'prompt':'[oral_9][laugh_9][break_6]'}

ChatTTS 进阶-超长文本语音生成

目前 ChatTTS ，受模型限制，一次性只支持 30 秒以内的语音生成，当你一次生成文本过多时，模型只会截取前30秒的，之后的文本将被截断。

而要实现超长文本语音生成，只能通过过将文本拆分成多个句子，然后在合并音频的方式。

比如按行拆分，代码实现如下：

text = """如果本文对你有帮助，欢迎点赞收藏备用！猴哥一直在做 AI 领域的研发和探索，会陆续跟大家分享路上的思考和心得。最近开始运营一个公众号，旨在分享关于AI效率工具、自媒体副业的一切。用心做内容，不辜负每一份关注。新朋友欢迎关注 “猴哥的AI知识库” 公众号，下次更新不迷路。"""
params_infer_code = {'prompt':'[speed_2]', 'temperature':.1,'top_P':0.9, 'top_K':1, 'spk_emb' : speaker}
params_refine_text = {'prompt':'[oral_6][laugh_3][break_6]'}
# 将文本按照句号拆分为句子
inputs_text = text.split("\n")
wavs = chat.infer(inputs_text,do_text_normalization=True,params_refine_text=params_refine_text,params_infer_code=params_infer_code)
# 合并音频
finally_wavs = torch.tensor(np.concatenate(wavs,axis=-1))
# 将输出的语音保存为音频文件
torchaudio.save("output.wav",finally_wavs, 24000)