mesolitica/Malaysian-F5-TTS-v3

Full Parameter Finetuning Malaysian Emilia F5-TTS v3

Continue training from SWivid/F5-TTS F5TTS_v1_Base checkpoint on Malaysian-Emilia,

with total 15631 hours included 600 hours Mandarin sampled from amphion/Emilia-Dataset.

Checkpoints

We uploaded full checkpoints with optimizer states at checkpoints.

How to

You can use Gradio app from official F5-TTS,

git clone https://github.com/SWivid/F5-TTS
cd F5-TTS
GRADIO_SERVER_NAME="0.0.0.0" python3 src/f5_tts/infer/infer_gradio.py

After that, use hf://mesolitica/Malaysian-F5-TTS-v3/checkpoints/model_220000.pt in custom model path,

• The model able to generate filler such as erm, uhm if the reference speaker also has the filler.
• The model able to generate emotion representation if the reference speaker also has the same emotion.
• The model able to generate multi-lingual (malay, local english and mainland Mandarin) plus context switching even though the reference speaker is mono-speaker.

Dataset

We train on postfilter Malaysian-Emilia called Malaysian-Voice-Conversion

Source code

All source code at https://github.com/mesolitica/malaya-speech/tree/master/session/f5-tts