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SadeghK/tts_fa_fastpitch_hifigan-v2.0
tts_fa_fastpitch_hifigan-v2.0 is a machine learning model from SadeghK. Use it for the machine learning task on the model card, and read the license before you ship it in a product. It is set up for nemo. The card lists the license as apache-2.0.
v2.0 of phonemizer and tokenizer. tokenzier DO SUPPORT pauses, emotion tokens etc,.
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.ckpt1.6 GB · 59%
From the Hugging Face model README
v2.0 of phonemizer and tokenizer. tokenzier DO SUPPORT pauses, emotion tokens etc,.
apt-get update && apt-get install -y libsndfile1 ffmpeg
pip install Cython packaging
rm -rf /usr/lib/python3.10/site-packages/blinker*
rm -rf /usr/local/lib/python3.10/dist-packages/blinker*
pip install --ignore-installed blinker
pip install --upgrade --force-reinstall blinker
git clone https://github.com/SadeghKrmi/NeMo.git
cd NeMo
pip install -e '.[all]'
Run the deterministic-train-test-split.py to split the train/test
using the following scripts, extract pitch statistics
tar -xzf dataset_splits.tar.gz
cd extract-supportive-data
HYDRA_FULL_ERROR=1 python3 ./scripts/extract_sup_data.py \
--config-path ../config/fastpitch/ \
--config-name ds_for_fastpitch_align.yaml \
manifest_filepath=./dataset_splits/train/train.jsonl \
sup_data_path=sup_data \
phoneme_dict_path=./persian-dict/persian-v4.0.dict \
++dataloader_params.num_workers=8
PITCH_MEAN=98.72935485839844, PITCH_STD=29.40760040283203 PITCH_MIN=65.4063949584961, PITCH_MAX=2093.004638671875
tar -czf sup_data.tar.gz sup_data
training for about 800 epochs, with CosineAnnealing sched. and max_steps 200,000 for lr to decay overtime.
val_loss didn't decrease lower that about 0.77xx
val_loss = mel_loss + dur_loss + pitch_loss + energy_loss
training for about 40 epochs, stoped the training based on quality checking by listening to audios