Model Card for Qwav3-4B
Qwav3-4B is an LLM-based text-to-speech (TTS) model that uses the WavTokenizer neural codec to synthesise speech. It has been pretrained with various datasets
on Spanish, Catalan and English speech synthesis and zero-shot voice cloning.
The training recipe involves a VALL-E approach of giving 400 audio tokens as context and then having the model
perform zero-shot voice cloning. This has not been completely accomplished, as the amount of training data is insufficient. However, the model is capable of grasping
concepts such as the gender of the voice, producing male voices when given male context and viceversa.
You can visit a demo page with some audios at
https://erikupv.github.io/QWav3_Demo/Training and inference recipes will be published soon.
Model Details
Model Description
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- Developed by: Erik Beltran Lobato (ebellob)
- Model type: Text-to-Speech LLM
- Language(s) (NLP): English, Spanish and Catalan
- License: [More Information Needed]
- Finetuned from model [optional]: Qwen3-4B-Instruct-2507 https://huggingface.co/Qwen/Qwen3-4B-Instruct-2507
Model Sources [optional]
Uses
Direct Use
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Bias, Risks, and Limitations
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Recommendations
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How to Get Started with the Model
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Training Details
Training Data
TODO
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Training Procedure
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Training Hyperparameters
- Training regime: bf16 with lr = 1e-4
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Evaluation
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Testing Data, Factors & Metrics
Testing Data
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Results
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Summary
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Environmental Impact
Carbon emissions can be estimated using the
Machine Learning Impact calculator presented in
Lacoste et al. (2019).
- Hardware Type: Nvidia A40 GPU
- Hours used: 72h
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Model Architecture and Objective
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