ac10b5febe
Corrected comment about speed parameter to clarify that lower values result in slower audio.
120 lines
4.3 KiB
Markdown
120 lines
4.3 KiB
Markdown
# LuxTTS
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<p align="center">
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<a href="https://huggingface.co/YatharthS/LuxTTS">
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<img src="https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Model-FFD21E" alt="Hugging Face Model">
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</a>
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<a href="https://huggingface.co/spaces/YatharthS/LuxTTS">
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<img src="https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Space-blue" alt="Hugging Face Space">
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</a>
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<a href="https://colab.research.google.com/drive/1cDaxtbSDLRmu6tRV_781Of_GSjHSo1Cu?usp=sharing">
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<img src="https://img.shields.io/badge/Colab-Notebook-F9AB00?logo=googlecolab&logoColor=white" alt="Colab Notebook">
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</a>
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</p>
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LuxTTS is an lightweight zipvoice based text-to-speech model designed for high quality voice cloning and realistic generation at speeds exceeding 150x realtime.
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https://github.com/user-attachments/assets/a3b57152-8d97-43ce-bd99-26dc9a145c29
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### The main features are
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- Voice cloning: SOTA voice cloning on par with models 10x larger.
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- Clarity: Clear 48khz speech generation unlike most TTS models which are limited to 24khz.
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- Speed: Reaches speeds of 150x realtime on a single GPU and faster then realtime on CPU's as well.
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- Efficiency: Fits within 1gb vram meaning it can fit in any local gpu.
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## Usage
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You can try it locally, colab, or spaces.
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[](https://colab.research.google.com/drive/1cDaxtbSDLRmu6tRV_781Of_GSjHSo1Cu?usp=sharing)
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[](https://huggingface.co/spaces/YatharthS/LuxTTS)
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#### Simple installation:
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```
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git clone https://github.com/ysharma3501/LuxTTS.git
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cd LuxTTS
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pip install -r requirements.txt
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```
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#### Load model:
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```python
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from zipvoice.luxvoice import LuxTTS
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lux_tts = LuxTTS('YatharthS/LuxTTS', device='cuda', threads=2) ## change device to cpu for cpu usage
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```
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#### Simple inference
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```python
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from IPython.display import Audio
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text = "Hey, what's up? I'm feeling really great if you ask me honestly!"
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prompt_audio = 'audio_file.wav'
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## encode audio(takes 10s to init because of librosa first time)
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encoded_prompt = lux_tts.encode_prompt(prompt_audio, rms=rms)
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## generate speech
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final_wav = lux_tts.generate_speech(text, encoded_prompt, num_steps=num_steps)
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## display speech
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display(Audio(final_wav, rate=48000))
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```
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#### Inference with sampling params:
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```python
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from IPython.display import Audio
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text = "Hey, what's up? I'm feeling really great if you ask me honestly!"
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prompt_audio = 'audio_file.wav'
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rms = 0.01 ## higher makes it sound louder(0.01 or so recommended)
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t_shift = 0.9 ## sampling param, higher can sound better but worse WER
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num_steps = 4 ## sampling param, higher sounds better but takes longer(3-4 is best for efficiency)
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speed = 1.0 ## sampling param, controls speed of audio(lower=slower)
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return_smooth = False ## sampling param, makes it sound smoother possibly but less cleaner
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## encode audio(takes 10s to init because of librosa first time)
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encoded_prompt = lux_tts.encode_prompt(prompt_audio, rms=rms)
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## generate speech
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final_wav = lux_tts.generate_speech(text, encoded_prompt, num_steps=num_steps, t_shift=t_shift, speed=speed, return_smooth=return_smooth)
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## display speech
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display(Audio(final_wav, rate=48000))
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```
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## Tips
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- Please use at minimum a 3 second audio file for voice cloning.
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- You can use return_smooth = True if you hear metallic sounds.
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- Lower t_shift for less possible pronunciation errors but worse quality and vice versa.
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## Info
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Q: How is this different from ZipVoice?
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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.
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Q: Can it be even faster?
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A: Yes, currently it uses float32. Float16 should be significantly faster(almost 2x).
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## Roadmap
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- [x] Release model and code
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- [x] Huggingface spaces demo
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- [ ] Release mps support
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- [ ] Release code for float16 inference
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## Acknowledgments
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- [ZipVoice](https://github.com/k2-fsa/ZipVoice) for their excellent code and model.
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- [Vocos](https://github.com/gemelo-ai/vocos.git) for their great vocoder.
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## Final Notes
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The model and code are licensed under the Apache-2.0 license. See LICENSE for details.
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Stars/Likes would be appreciated, thank you.
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Email: yatharthsharma350@gmail.com
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