# under VoiceStar root dir
wget -O ./pretrained/encodec_6f79c6a8.th https://huggingface.co/pyp1/Encodec_VoiceStar/resolve/main/encodec_4cb2048_giga.th?download=true
wget -O ./pretrained/VoiceStar_840M_30s.pth https://huggingface.co/pyp1/VoiceStar/resolve/main/VoiceStar_840M_30s.pth?download=true
wget -O ./pretrained/VoiceStar_840M_40s.pth https://huggingface.co/pyp1/VoiceStar/resolve/main/VoiceStar_840M_40s.pth?download=trueconda create -n voicestar python=3.10
conda activate voicestar # this seems to lead to much worse results in terms of wer and spksim (comparing e9_rerun and e9_rerun_newba_upgraded)
pip install torch==2.5.1 torchaudio==2.5.1 --index-url https://download.pytorch.org/whl/cu124
pip install numpy, tqdm, fire
pip install phonemizer==3.2.1
apt-get install espeak-ng # backend for the phonemizer
pip install torchmetrics
pip install einops
pip install omegaconf==2.3.0
pip install openai-whisper
pip install gradio- avoid warnings likes [WARNING] words_mismatch.py:88 || words count mismatch on 200.0% of the lines (2/1)
# go to ~/miniconda3/envs/voicestar/lib/python3.10/site-packages/phonemizer/backend/espeak/words_mismatch.py
# pass the warning like this
def _resume(self, nmismatch: int, nlines: int):
"""Logs a high level undetailed warning"""
pass
# if nmismatch:
# self._logger.warning(
# 'words count mismatch on %s%% of the lines (%s/%s)',
# round(nmismatch / nlines, 2) * 100, nmismatch, nlines)additional packages:
pip install huggingface_hub
pip install datasets
pip install tensorboard
pip install wandb
pip install matplotlib
pip install ffmpeg-python
pip install scipy
pip install soundfilecheck signature of run_inference func in inference_commandline.py for adjustable hyperparameters
# under root dir
conda activate voicestar
python inference_commandline.py \
--reference_speech "./demo/5895_34622_000026_000002.wav" \
--target_text "I cannot believe that the same model can also do text to speech synthesis too! And you know what? this audio is 8 seconds long." \
--target_duration 8conda activate voicestar
python inference_gradio.pyCode license: MIT
Model Weights License: CC-BY-4.0 (as Emilia dataset we used is under this license)