This is the recipe of Kazakh text-to-speech model based on KazakhTTS corpus.

Our code builds upon ESPnet, and requires prior installation of the framework. Please follow the installation guide and put the KazakhTTS folder inside espnet/egs2/ directory:

cd espnet/egs2
git clone https://github.com/IS2AI/Kazakh_TTS.git

Go to Kazakh_TTS/tts1 folder and create links to the dependencies:

ln -s ../../TEMPLATE/tts1/path.sh .
ln -s ../../TEMPLATE/asr1/pyscripts .
ln -s ../../TEMPLATE/asr1/scripts .
ln -s ../../../tools/kaldi/egs/wsj/s5/steps .
ln -s ../../TEMPLATE/tts1/tts.sh .
ln -s ../../../tools/kaldi/egs/wsj/s5/utils .

Download KazakhTTS dataset and untar in the directory of your choice. Specify the path to the dataset inside KazakhTTS/tts1/local/data.sh script:

db_root=/path-to-speaker-folder

For example db_root=/home/datasets/ISSAI_KazakhTTS/M1_Iseke/

To train the models, run the script ./run.sh inside KazakhTTS/tts1/ folder. GPU and RAM specifications can be found in the configuration (conf/) folder.

./run.sh --stage 1 --stop_stage 6 --train_config conf/train.yaml 

If you would like to train fastspeech/transformer models, change train_config=conf/train.yaml accordingly. The detailed description of each stage are documented in ESPNet's repository.

If you want to use pretrained models, download them from the links below and unzip inside KazakhTTS/tts1/ folder.

• https://issai.nu.edu.kz/wp-content/uploads/2021/04/kaztts_male1_tacotron2_train.loss.ave.zip

• https://issai.nu.edu.kz/wp-content/uploads/2021/04/kaztts_female1_tacotron2_train.loss.ave.zip

You would also need the pre-trained vocoder to convert generated mel-spectrogram to wav. This repository used ParallelWaveGAN to train the vocoders on the same KazakhTTS corpus.

• https://issai.nu.edu.kz/wp-content/uploads/2021/04/parallelwavegan_male1_checkpoint.zip

• https://issai.nu.edu.kz/wp-content/uploads/2021/04/parallelwavegan_female1_checkpoint.zip

You can synthesize an arbitrary text using synthesize.py script. Modify the following lines in the script:

## specify the path to vocoder's checkpoint, i.e
vocoder_checkpoint="exp/vocoder/checkpoint-400000steps.pkl"

## specify path to the main model(transformer/tacotron2/fastspeech) and its config file
config_file = "exp/tts_train_raw_char/config.yaml"
model_path = "exp/tts_train_raw_char/train.loss.ave_5best.pth"

Now you can run the script using an arbitrary text, for example:

python synthesize.py --text "бүгінде өңірде тағы бес жобаның құрылысы жүргізілуде."

The generated file will be saved in tts1/synthesized_wavs folder.