parler-tts-mini-v1.1-ptbr

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Parler-TTS Mini Portuguese

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Parler-TTS Mini Multilingual v1 is a multilingual extension of Parler-TTS Mini.

It is a fine-tuned version, trained on BRSpeech-TTS-Dataset. In all, this represents some 200 hours of portuguese data.

Parler-TTS Mini Portuguese can speak in Brazilian Portuguese

Thanks to its better prompt tokenizer, it can easily be extended to other languages. This tokenizer has a larger vocabulary and handles byte fallback, which simplifies multilingual training.

🚨 This work is the result of a collaboration between the HuggingFace audio team and the Quantum Squadra team. The AI4Bharat team also provided advice and assistance in improving tokenization. 🚨

📖 Quick Index

• 👨‍💻 Installation
• 🎯 Inference
• Motivation
• Optimizing inference

🛠️ Usage

🚨Unlike previous versions of Parler-TTS, here we use two tokenizers - one for the prompt and one for the description.🚨

👨‍💻 Installation

Using Parler-TTS is as simple as "bonjour". Simply install the library once:

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

Inference

Parler-TTS has been trained to generate speech with features that can be controlled with a simple text prompt, for example:

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"
Claro\! O resultado da execução do código, que cria a lista a partir dos nomes do dicionário, é:

speakers = ['Diana', 'Eduardo', 'Uriel', 'Guilherme', 'Zoe', 'Quirino', 'Enzo', 'Henrique', 'Davi', 'Thiago', 'Sofia', 'Wilson', 'Ximenia', 'Osvaldo', 'Icaro', 'Raquel', 'Carolina', 'Debora', 'Xavier', 'Olivia', 'Erick', 'Silvio', 'Daniel', 'Joao', 'Italo', 'Manuela', 'Caio', 'Yuri', 'Samuel', 'Isabela', 'Sabrina', 'Paulo', 'Victor', 'Amanda', 'Alexandre', 'Vitoria', 'Emilia', 'Diego', 'Daniela', 'Joana', 'Gabriel', 'Wanda', 'Alice', 'Valentina', 'Bruno', 'Hugo', 'Yara', 'Vinicius', 'Patricia', 'Luiz', 'Valter', 'Leticia', 'Iris', 'Fernando', 'Julio', 'Carlos', 'Lucas', 'Mateus', 'Rebeca', 'Rafael', 'Bernardo', 'Flavia']

model = ParlerTTSForConditionalGeneration.from_pretrained("freds0/parler-tts-mini-v1.1-ptbr").to(device)
tokenizer = AutoTokenizer.from_pretrained("parler-tts/parler-tts-mini-multilingual")
description_tokenizer = AutoTokenizer.from_pretrained(model.config.text_encoder._name_or_path)

prompt = "Minha terra tem palmeiras onde canta o sabiá, as aves que aqui gorjeiam não gorjeiam como lá."
description = "Diana's speech is very clear and has a consistent tone, and she is in a very confined sounding environment with clear audio quality."

input_ids = description_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:

• We've set up an inference guide to make generation faster. Think SDPA, torch.compile, batching and streaming!
• 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.