🚀 Parler-TTS Mini v1 - Jenny
Parler-TTS Mini v1 - Jenny 是 Parler-TTS Mini v1 的微调版本,它基于 30 小时单说话人高质量 Jenny(她是爱尔兰人 ☘️)数据集 进行微调,适合用于训练文本转语音(TTS)模型。其使用方法与 Parler-TTS v1 大致相同,只需在语音描述中指定关键词 “Jenny” 即可。
🚀 快速开始
📦 安装指南
你可以使用以下命令安装所需库:
pip install git+https://github.com/huggingface/parler-tts.git
💻 使用示例
基础用法
安装完成后,你可以使用以下代码片段进行推理:
import torch
from parler_tts import ParlerTTSForConditionalGeneration
from transformers import AutoTokenizer
import soundfile as sf
device = "cuda:0" if torch.cuda.is_available() else "cpu"
model = ParlerTTSForConditionalGeneration.from_pretrained("parler-tts/parler-mini-v1-jenny").to(device)
tokenizer = AutoTokenizer.from_pretrained("parler-tts/parler-mini-v1-jenny")
prompt = "Hey, how are you doing today? My name is Jenny, and I'm here to help you with any questions you have."
description = "Jenny speaks at an average pace with an animated delivery in a very confined sounding environment with clear audio quality."
input_ids = tokenizer(description, return_tensors="pt").input_ids.to(device)
prompt_input_ids = tokenizer(prompt, return_tensors="pt").input_ids.to(device)
generation = model.generate(input_ids=input_ids, prompt_input_ids=prompt_input_ids)
audio_arr = generation.cpu().numpy().squeeze()
sf.write("parler_tts_out.wav", audio_arr, model.config.sampling_rate)
📚 详细文档
📄 引用说明
如果你觉得这个仓库很有用,请考虑引用以下工作以及原始的 Stability AI 论文:
@misc{lacombe-etal-2024-parler-tts,
author = {Yoach Lacombe and Vaibhav Srivastav and Sanchit Gandhi},
title = {Parler-TTS},
year = {2024},
publisher = {GitHub},
journal = {GitHub repository},
howpublished = {\url{https://github.com/huggingface/parler-tts}}
}
@misc{lyth2024natural,
title={Natural language guidance of high-fidelity text-to-speech with synthetic annotations},
author={Dan Lyth and Simon King},
year={2024},
eprint={2402.01912},
archivePrefix={arXiv},
primaryClass={cs.SD}
}
📄 许可证
使用此数据集在软件、网站、项目、界面(包括语音界面)中响应用户操作生成音频时,需要进行归因。归因意味着:语音必须被称为 “Jenny”,并且在所有实际可行的情况下,称为 “Jenny (Dioco)”。分发生成的音频片段时不需要归因(尽管欢迎这样做)。允许商业使用。请勿做不公平的事情,例如声称该数据集是你自己的。没有其他限制。