Parler Large V1 Jenny

🚀 Parler-TTS Large v1 - Jenny

A fine-tuned version of Parler-TTS Large v1 on a high-quality single-speaker dataset, suitable for TTS model training.

• Fine-tuning guide on Colab:

This is a fine-tuned version of Parler-TTS Large v1 on the 30-hours single-speaker high-quality Jenny (she's Irish ☘️) dataset. Its usage is similar to Parler-TTS v1, just specify the keyword “Jenny” in the voice description.

🚀 Quick Start

📦 Installation

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

💻 Usage Examples

Basic Usage

You can use the model with the following inference snippet:

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

📚 Documentation

📄 Citation

If you found this repository useful, please consider citing this work and also the original Stability AI paper:

@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}
}

📄 License

Attribution is required in software/websites/projects/interfaces (including voice interfaces) that generate audio in response to user action using this dataset. Attribution means: the voice must be referred to as "Jenny", and where at all practical, "Jenny (Dioco)". Attribution is not required when distributing the generated clips (although welcome). Commercial use is permitted. Don't do unfair things like claim the dataset is your own. No further restrictions apply.