Parler Tiny V1 Jenny

🚀 Parler-TTS Tiny v1 - Jenny

Parler-TTS Tiny v1 - Jenny 是 Parler-TTS Tiny v1 的微調版本，它基於 30 小時單說話人高質量 Jenny（她是愛爾蘭人 ☘️）數據集 進行微調，適用於訓練文本轉語音（TTS）模型。使用方法與 Parler-TTS v1 大致相同，只需在語音描述中指定關鍵詞 “Jenny” 即可。

• 在 Colab 上的微調指南：

🚀 快速開始

安裝

pip install git+https://github.com/huggingface/parler-tts.git

使用示例

基礎用法

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-tiny-v1-jenny").to(device)
tokenizer = AutoTokenizer.from_pretrained("parler-tts/parler-tiny-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)

📚 詳細文檔

引用

如果您覺得這個倉庫有用，請考慮引用這項工作以及原始的 Stability AI 論文：

@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}
}

許可證

在使用此數據集根據用戶操作生成音頻的軟件、網站、項目或界面（包括語音界面）中，必須進行歸因。歸因意味著：語音必須被稱為 “Jenny”，並且在所有實際可行的情況下，稱為 “Jenny (Dioco)”。在分發生成的音頻片段時，不需要進行歸因（儘管歡迎這樣做）。允許商業使用。請勿做不公平的事情，例如聲稱該數據集是您自己的。沒有其他限制。

📦 相關信息