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rakib72642/
Custom-TTS-Bangla-Accent-VITS-XTTSv2

Name: Custom-TTS-Bangla-Accent-VITS-XTTSv2
Availability: InStock

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Bangla VITS TTS Fine-tuning

Fine-tuning Coqui TTS tts_models/bn/custom/vits-male on custom Bangla voice dataset.

Setup

1. Create Environment

bash

conda create -n tts-bn python=3.10 -y
conda activate tts-bn

2. Install PyTorch (do this first)

bash

pip install torch==2.12.0 torchaudio==2.11.0 --index-url https://download.pytorch.org/whl/cu118
or
pip install torch==2.7.1+cu118 torchaudio==2.7.1+cu118 --index-url https://download.pytorch.org/whl/cu118

3. Install Dependencies

pip install -r requirements.txt

Training

Start Fine-tuning

bash

CUDA_VISIBLE_DEVICES=0 python -m TTS.bin.train_tts \
    --config_path configs/bangla.json \
    --restore_path your_file_location/tts/tts_models--bn--custom--vits-male/model_file.pth

Continue Training from Checkpoint

bash

CUDA_VISIBLE_DEVICES=0 python -m TTS.bin.train_tts \
    --config_path configs/bangla.json \
    --continue_path outputs/YOUR_RUN_FOLDER/

inject your cleaner into the TTS package

bash

CLEANERS_PATH=$(python -c "import TTS.tts.utils.text.cleaners as c; import inspect; print(inspect.getfile(c))")

cat bangla_cleaners.py >> $CLEANERS_PATH

# Verify it worked
python -c "from TTS.tts.utils.text import cleaners; print(hasattr(cleaners, 'bangla_cleaners'))"

Monitor Training

tensorboard --logdir=outputs/ --port=8080

Inference

python inference.py

Serve Output Files

python -m http.server 8080

Tunneling with ngrok

ngrok http --domain=hawkeyes.ngrok.app 8080

rakib72642/Custom-TTS-Bangla-Accent-VITS-XTTSv2

Bangla VITS TTS Fine-tuning

Setup

1. Create Environment

2. Install PyTorch (do this first)

3. Install Dependencies

Training

Start Fine-tuning

Continue Training from Checkpoint

inject your cleaner into the TTS package

Monitor Training

Inference

Serve Output Files

Tunneling with ngrok

rakib72642/
Custom-TTS-Bangla-Accent-VITS-XTTSv2