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ylacombe / parler_tts_mini_v0.1
Model's Last Updated: May 29 2024
text-to-speech
Introduction of parler_tts_mini_v0.1
Model Details of parler_tts_mini_v0.1
Parler-TTS Mini v0.1
- Fine-tuning guide on Colab:
Parler-TTS Mini v0.1 is a lightweight text-to-speech (TTS) model, trained on 10.5K hours of audio data, that can generate high-quality, natural sounding speech with features that 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, which aims to provide the community with TTS training resources and dataset pre-processing code.
Usage
Using Parler-TTS is as simple as "bonjour". Simply install the library once:
pip install git+https://github.com/huggingface/parler-tts.git
You can then 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)
Tips :
- 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
Motivation
Parler-TTS is a reproduction of work from the paper Natural language guidance of high-fidelity text-to-speech with synthetic annotations 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. Parler-TTS was released alongside:
- The Parler-TTS repository - you can train and fine-tuned 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.
Runs of ylacombe parler_tts_mini_v0.1 on huggingface.co
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More Information About parler_tts_mini_v0.1 huggingface.co Model
Url of parler_tts_mini_v0.1
parler_tts_mini_v0.1 huggingface.co Url
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