Parler Tts Mini V0.1

🚀 Parler-TTS Mini v0.1

Parler-TTS Mini v0.1 is a lightweight text-to-speech (TTS) model. Trained on 10.5K hours of audio data, it can generate high-quality, natural - sounding speech. Its features can be controlled using a simple text prompt (e.g., gender, background noise, speaking rate, pitch, and reverberation). It is the first release model from the Parler-TTS project, aiming to provide the community with TTS training resources and dataset pre - processing code.

• Fine - tuning guide on Colab:

🚀 Quick Start

Using Parler-TTS is as simple as "bonjour". First, install the library:

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

Then, 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_tts_mini_v0.1").to(device)
tokenizer = AutoTokenizer.from_pretrained("parler-tts/parler_tts_mini_v0.1")

prompt = "Hey, how are you doing today?"
description = "A female speaker with a slightly low - pitched voice delivers her words quite expressively, in a very confined sounding environment with clear audio quality. She speaks very fast."

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)

💡 Usage Tip

• Include the term "very clear audio" to generate the highest quality audio, and "very noisy audio" for high levels of background noise.
• Punctuation can be used to control the prosody of the generations, e.g., use commas to add small breaks in speech.
• The remaining speech features (gender, speaking rate, pitch, and reverberation) can be controlled directly through the prompt.

✨ Features

• Trained on 10.5K hours of audio data.
• Can generate high - quality, natural - sounding speech.
• Allows control of speech features (gender, background noise, speaking rate, pitch, and reverberation) using a simple text prompt.

📦 Installation

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

💻 Usage Examples

Basic Usage

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_tts_mini_v0.1").to(device)
tokenizer = AutoTokenizer.from_pretrained("parler-tts/parler_tts_mini_v0.1")

prompt = "Hey, how are you doing today?"
description = "A female speaker with a slightly low - pitched voice delivers her words quite expressively, in a very confined sounding environment with clear audio quality. She speaks very fast."

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

Motivation

Parler-TTS is a reproduction of work from the paper [Natural language guidance of high - fidelity text - to - speech with synthetic annotations](https://www.text - description - to - speech.com) by Dan Lyth and Simon King, from Stability AI and Edinburgh University respectively.

Contrarily to other TTS models, Parler-TTS is a fully open - source release. All of the datasets, pre - processing, training code, and weights are released publicly under a permissive license, enabling the community to build on our work and develop their own powerful TTS models. Parler-TTS was released alongside:

• The Parler - TTS repository - You can train and fine - tune your own version of the model.
• The Data - Speech repository - A suite of utility scripts designed to annotate speech datasets.
• The Parler - TTS organization - Where you can find the annotated datasets as well as the future checkpoints.

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

This model is permissively licensed under the Apache 2.0 license.

📦 Datasets