ViiTor-Voice

An LLM based TTS Engine

Viitor-Voice Cover

Features

Lightweight Design

The model is simple and efficient, compatible with most LLM inference engines. With only 0.5B parameters, it achieves extreme optimization of computational resources while maintaining high performance. This design allows the model to be deployed not only on servers but also on mobile devices and edge computing environments, meeting diverse deployment needs.
Real-time Streaming Output, Low Latency Experience

The model supports real-time speech generation, suitable for applications that demand low latency. On the Tesla T4 platform, it achieves an industry-leading first-frame latency of 200ms, providing users with nearly imperceptible instant feedback, ideal for interactive applications requiring quick response.
Rich Voice Library

Offers more than 300 different voice options, allowing you to choose the most suitable speech style according to your needs and preferences. Whether it’s a formal business presentation or casual entertainment content, the model provides perfect voice matching.
Flexible Speech Rate Adjustment

The model supports natural variations in speech rate, allowing users to easily adjust it based on content requirements and audience preferences. Whether speeding up for efficient information delivery or slowing down to enhance emotional depth, it maintains natural speech fluency.
Zero-shot Voice Cloning (Under Research)

Decoder-only architecture naturally supports Zero-shot cloning, with future support for rapid voice cloning based on minimal voice samples.

Output Samples

Below are examples of speech generated by this project:

Environment Setup

git clone https://github.com/viitor-ai/viitor-voice.git
cd viitor-voice
conda create -n viitor_voice python=3.10
conda activate viitor_voice
pip install -r requirements.txt

### Due to the issue with vllm's tokenizer length calculation, the token limit cannot take effect.
python_package_path=`pip show pip | egrep Location | awk -F ' ' '{print $2}'`
cp viitor_voice/utils/patch.py $python_package_path/vllm/entrypoints/openai/logits_processors.py

Inference

Pretrained Models

Offline Inference

from viitor_voice.utils.offline_inference import OfflineInference
import torchaudio

## English
tts_engine = OfflineInference(model_path='ZzWater/viitor-voice-en',
                              config_path='viitor_voice/inference_configs/en.json')
text_list = [
    "Isn't it fascinating to think about the endless possibilities that lie within the pages of a book. every time you turn a page, you're diving into a new world ripe with potential for discovery, and wonder what stories will you uncover today."]
# list valid speakers

print(tts_engine.prompt_map.keys())
audios = tts_engine.batch_infer(text_list=text_list, speaker=['1'], speed=2)
torchaudio.save('test.wav', audios[0], 24000)

## Chinese
tte_engine_chs = OfflineInference(model_path='ZzWater/viitor-voice-chs',
                                  config_path='viitor_voice/inference_configs/chs.json')
text_list_chs = [
    "我觉得我还是可以抢救一下的。"]
audios = tte_engine_chs.batch_infer(text_list=text_list_chs, speaker=['female1'], speed=2)
torchaudio.save('test_chs.wav', audios[0], 24000)

Demo Inference

ViiTor AI

Streaming Inference (TODO)

Training (TODO)

Join Our Community

Have questions about the project? Want to discuss new features, report bugs, or just chat with other contributors? Join our Discord community!

References

License

This project is licensed under CC BY-NC 4.0.
You are free to share and modify the code of this project for non-commercial purposes, under the following conditions:

Attribution: You must give appropriate credit, provide a link to the license, and indicate if changes were made.
Non-Commercial: You may not use the material for commercial purposes.

ZzWater
/

viitor-voice-chs

ViiTor-Voice

An LLM based TTS Engine

Features

Output Samples

English Female Voice 1:

English Male Voice 1:

Chinese Female Voice 1:

Chinese Male Voice 1: