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README.md

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-A Vietnamese TTS
-================
-
-Duration model + Acoustic model + HiFiGAN vocoder for vietnamese text-to-speech application.
-
-Online demo at https://huggingface.co/spaces/ntt123/vietTTS.
-
-A synthesized audio clip: [clip.wav](assets/infore/clip.wav). A colab notebook: [notebook](https://colab.research.google.com/drive/1oczrWOQOr1Y_qLdgis1twSlNZlfPVXoY?usp=sharing).
-
-
-🔔Checkout the experimental `multi-speaker` branch (`git checkout multi-speaker`) for multi-speaker support.🔔
-
-Install
--------
-
-
-```sh
-git clone https://github.com/NTT123/vietTTS.git
-cd vietTTS 
-pip3 install -e .
-```
-
-
-Quick start using pretrained models
-----------------------------------
-```sh
-bash ./scripts/quick_start.sh
-```
-
-
-Download InfoRe dataset
------------------------
-
-```sh
-python ./scripts/download_aligned_infore_dataset.py
-```
-
-**Note**: this is a denoised and aligned version of the original dataset which is donated by the InfoRe Technology company (see [here](https://www.facebook.com/groups/j2team.community/permalink/1010834009248719/)). You can download the original dataset (**InfoRe Technology 1**) at [here](https://github.com/TensorSpeech/TensorFlowASR/blob/main/README.md#vietnamese).
-
-See `notebooks/denoise_infore_dataset.ipynb` for instructions on how to denoise the dataset. We use the Montreal Forced Aligner (MFA) to align transcript and speech (textgrid files). 
-See `notebooks/align_text_audio_infore_mfa.ipynb` for instructions on how to create textgrid files.
-
-Train duration model
---------------------
-
-```sh
-python -m vietTTS.nat.duration_trainer
-```
-
-
-Train acoustic model
---------------------
-```sh
-python -m vietTTS.nat.acoustic_trainer
-```
-
-
-
-Train HiFiGAN vocoder
--------------
-
-We use the original implementation from HiFiGAN authors at https://github.com/jik876/hifi-gan. Use the config file at `assets/hifigan/config.json` to train your model.
-
-```sh
-git clone https://github.com/jik876/hifi-gan.git
-
-# create dataset in hifi-gan format
-ln -sf `pwd`/train_data hifi-gan/data
-cd hifi-gan/data
-ls -1 *.TextGrid | sed -e 's/\.TextGrid$//' > files.txt
-cd ..
-head -n 100 data/files.txt > val_files.txt
-tail -n +101 data/files.txt > train_files.txt
-rm data/files.txt
-
-# training
-python train.py \
-  --config ../assets/hifigan/config.json \
-  --input_wavs_dir=data \
-  --input_training_file=train_files.txt \
-  --input_validation_file=val_files.txt
-```
-
-Finetune on Ground-Truth Aligned melspectrograms:
-```sh
-cd /path/to/vietTTS # go to vietTTS directory
-python -m vietTTS.nat.zero_silence_segments -o train_data # zero all [sil, sp, spn] segments
-python -m vietTTS.nat.gta -o /path/to/hifi-gan/ft_dataset  # create gta melspectrograms at hifi-gan/ft_dataset directory
-
-# turn on finetune
-cd /path/to/hifi-gan
-python train.py \
-  --fine_tuning True \
-  --config ../assets/hifigan/config.json \
-  --input_wavs_dir=data \
-  --input_training_file=train_files.txt \
-  --input_validation_file=val_files.txt
-```
-
-Then, use the following command to convert pytorch model to haiku format:
-```sh
-cd ..
-python -m vietTTS.hifigan.convert_torch_model_to_haiku \
-  --config-file=assets/hifigan/config.json \
-  --checkpoint-file=hifi-gan/cp_hifigan/g_[latest_checkpoint]
-```
-
-Synthesize speech
------------------
-
-```sh
-python -m vietTTS.synthesizer \
-  --lexicon-file=train_data/lexicon.txt \
-  --text="hôm qua em tới trường" \
-  --output=clip.wav
-```