LuxTTS

   

LuxTTS is an lightweight zipvoice based text-to-speech model designed for high quality voice cloning and realistic generation at speeds exceeding 150x realtime.

https://github.com/user-attachments/assets/a3b57152-8d97-43ce-bd99-26dc9a145c29

The main features are

• Voice cloning: SOTA voice cloning on par with models 10x larger.
• Clarity: Clear 48khz speech generation unlike most TTS models which are limited to 24khz.
• Speed: Reaches speeds of 150x realtime on a single GPU and faster then realtime on CPU's as well.
• Efficiency: Fits within 1gb vram meaning it can fit in any local gpu.

Usage

You can try it locally, colab, or spaces.

Simple installation:

git clone https://github.com/ysharma3501/LuxTTS.git
cd LuxTTS
pip install -r requirements.txt

Load model:

from zipvoice.luxvoice import LuxTTS

# load model on GPU
lux_tts = LuxTTS('YatharthS/LuxTTS', device='cuda')

# load model on CPU
# lux_tts = LuxTTS('YatharthS/LuxTTS', device='cpu', threads=2)

# load model on MPS for macs
# lux_tts = LuxTTS('YatharthS/LuxTTS', device='mps')

Simple inference

import soundfile as sf
from IPython.display import Audio

text = "Hey, what's up? I'm feeling really great if you ask me honestly!"

## change this to your reference file path, can be wav/mp3
prompt_audio = 'audio_file.wav'

## encode audio(takes 10s to init because of librosa first time)
encoded_prompt = lux_tts.encode_prompt(prompt_audio, rms=0.01)

## generate speech
final_wav = lux_tts.generate_speech(text, encoded_prompt, num_steps=4)

## save audio
final_wav = final_wav.numpy().squeeze()
sf.write('output.wav', final_wav, 48000)

## display speech
if display is not None:
  display(Audio(final_wav, rate=48000))

Inference with sampling params:

import soundfile as sf
from IPython.display import Audio

text = "Hey, what's up? I'm feeling really great if you ask me honestly!"

## change this to your reference file path, can be wav/mp3
prompt_audio = 'audio_file.wav'

rms = 0.01 ## higher makes it sound louder(0.01 or so recommended)
t_shift = 0.9 ## sampling param, higher can sound better but worse WER
num_steps = 4 ## sampling param, higher sounds better but takes longer(3-4 is best for efficiency)
speed = 1.0 ## sampling param, controls speed of audio(lower=slower)
return_smooth = False ## sampling param, makes it sound smoother possibly but less cleaner
ref_duration = 5 ## Setting it lower can speedup inference, set to 1000 if you find artifacts.

## encode audio(takes 10s to init because of librosa first time)
encoded_prompt = lux_tts.encode_prompt(prompt_audio, duration=ref_duration, rms=rms)

## generate speech
final_wav = lux_tts.generate_speech(text, encoded_prompt, num_steps=num_steps, t_shift=t_shift, speed=speed, return_smooth=return_smooth)

## save audio
final_wav = final_wav.numpy().squeeze()
sf.write('output.wav', final_wav, 48000)

## display speech
if display is not None:
  display(Audio(final_wav, rate=48000))

Tips

• Please use at minimum a 3 second audio file for voice cloning.
• You can use return_smooth = True if you hear metallic sounds.
• Lower t_shift for less possible pronunciation errors but worse quality and vice versa.

Community

• Lux-TTS-Gradio: A gradio app to use LuxTTS.
• OptiSpeech: Clean UI app to use LuxTTS.
• LuxTTS-Comfyui: Nodes to use LuxTTS in comfyui.
• FalAI hosting: Luxtts demo hosted by FalAI
• Lux-TTS ONNX: Onnx code to use luxtts

Thanks to all community contributions!

Info

Q: How is this different from ZipVoice?

A: LuxTTS uses the same architecture but distilled to 4 steps with an improved sampling technique. It also uses a custom 48khz vocoder instead of the default 24khz version.

Q: Can it be even faster?

A: Yes, currently it uses float32. Float16 should be significantly faster(almost 2x).

Roadmap

• [x] Release model and code
• [x] Huggingface spaces demo
• [x] Release MPS support (thanks to @builtbybasit)
• [ ] Release LuxTTS v1.5
• [ ] Release code for float16 inference

Acknowledgments

• ZipVoice for their excellent code and model.
• Vocos for their great vocoder.

Final Notes

The model and code are licensed under the Apache-2.0 license. See LICENSE for details.

Stars/Likes would be appreciated, thank you.

Email: yatharthsharma350@gmail.com