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Inference and training library for high-quality TTS models.
| Date | Stars |
|---|---|
| 2026-07-31 | 5583 |
| 2026-08-01 | 5582 |
| 2026-08-02 | 5582 |
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| 2026-08-05 | 5585 |
| 2026-08-06 | 5585 |
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# Parler-TTS
Parler-TTS is a lightweight text-to-speech (TTS) model that can generate high-quality, natural sounding speech in the style of a given speaker (gender, pitch, speaking style, etc). It 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 permissive license, enabling the community to build on our work and develop their own powerful TTS models.
This repository contains the inference and training code for Parler-TTS. It is designed to accompany the [Data-Speech](https://github.com/huggingface/dataspeech) repository for dataset annotation.
> [!IMPORTANT]
> **08/08/2024:** We are proud to release two new Parler-TTS checkpoints:
> 1. [Parler-TTS Mini](https://huggingface.co/parler-tts/parler-tts-mini-v1), an 880M parameter model.
> 2. [Parler-TTS Large](https://huggingface.co/parler-tts/parler-tts-large-v1), a 2.3B parameter model.
>
> These checkpoints have been trained on 45k hours of audiobook data.
>
> In addition, the code is optimized for much faster generation: we've added SDPA and Flash Attention 2 compatibility, as well as the ability to compile the model.
## 📖 Quick Index
* [Installation](#installation)
* [Usage](#usage)
- [🎲 Using a random voice](#-random-voice)
- [🎯 Using a specific speaker](#-using-a-specific-speaker)
* [Training](#training)
* [Demo](https://huggingface.co/spaces/parler-tts/parler_tts)
* [Model weights and datasets](https://huggingface.co/parler-tts)
* [Optimizing inference](#-optimizing-inference-speed)
## Installation
Parler-TTS has light-weight dependencies and can be installed in one line:
```sh
pip install git+https://github.com/huggingface/parler-tts.git
```
Apple Silicon users will need to run a follow-up command to make use the nightly PyTorch (2.4) build for bfloat16 support:
```sh
pip3 install --pre torch torchaudio --index-url https://download.pytorch.org/whl/nightly/cpu
```
## Usage
> [!TIP]
> You can directly try it out in an interactive demo [here](https://huggingface.co/spaces/parler-tts/parler_tts)!
Using Parler-TTS is as simple as "bonjour". Simply install the library once:
```sh
pip install git+https://github.com/huggingface/parler-tts.git
```
### 🎲 Random voice
**Parler-TTS** has been trained to generate speech with features that can be controlled with a simple text prompt, for example:
```py
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-v1").to(device)
tokenizer = AutoTokenizer.from_pretrained("parler-tts/parler-tts-mini-v1")
prompt = "Hey, how are you doing today?"
description = "A female speaker delivers a slightly expressive and animated speech with a moderate speed and pitch. The recording is of very high quality, with the speaker's voice sounding clear and very close up."
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)
```
### 🎯 Using a specific speaker
To ensure speaker consistency across generations, this checkpoint was also trained on 34 speakers, characterized by name. The full list of available speakers includes:
Laura, Gary, Jon, Lea, Karen, Rick, Brenda, David, Eileen, Jordan, Mike, Yann, Joy, James, Eric, Lauren, Rose, Will, JasonExcerpt of 9,625 characters
Read on GitHubWould you bet a product on this? Bounded 0–100 and slow moving.
matched fp:17087732b40ccf78, llm:description: 'Inference and training library for high-quality TTS models.'
matched fp:17087732b40ccf78, llm:description: 'Inference and training library for high-quality TTS models.'
matched fp:17087732b40ccf78, llm:description: 'Inference and training library for high-quality TTS models.'
