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| # vakyansh-tts | |
| Text to Speech for Indic languages | |
| ## Pretrained Models | |
| The pretrained models are hosted in a seperate repository [here](https://github.com/Open-Speech-EkStep/vakyansh-models#tts-models-repo) | |
| ### 1. Installation and Setup for training | |
| Clone repo | |
| ``` | |
| git clone https://github.com/Open-Speech-EkStep/vakyansh-tts | |
| ``` | |
| Build conda virtual environment | |
| ``` | |
| cd ./vakyansh-tts | |
| conda create --name <env_name> python=3.7 | |
| conda activate <env_name> | |
| pip install -r requirement.txt | |
| ``` | |
| Install [apex](https://github.com/NVIDIA/apex); commit: 37cdaf4 for Mixed-precision training | |
| ``` | |
| cd .. | |
| git clone https://github.com/NVIDIA/apex | |
| cd apex | |
| git checkout 37cdaf4 | |
| pip install -v --disable-pip-version-check --no-cache-dir ./ | |
| cd ../vakyansh-tts | |
| ``` | |
| Build Monotonic Alignment Search Code (Cython) | |
| ``` | |
| bash install.sh | |
| ``` | |
| ### 1.1 Installation of tts_infer package | |
| In tts_infer package, we currently have two components: | |
| 1. Transliteration (AI4bharat's open sourced models) (Languages supported: {'hi', 'gu', 'mr', 'bn', 'te', 'ta', 'kn', 'pa', 'gom', 'mai', 'ml', 'sd', 'si', 'ur'} ) | |
| 2. Num to Word (Languages supported: {'en', 'hi', 'gu', 'mr', 'bn', 'te', 'ta', 'kn', 'or', 'pa'} ) | |
| ``` | |
| git clone https://github.com/Open-Speech-EkStep/vakyansh-tts | |
| cd vakyansh-tts | |
| bash install.sh | |
| python setup.py bdist_wheel | |
| pip install -e . | |
| cd tts_infer | |
| gsutil -m cp -r gs://vakyaansh-open-models/translit_models . | |
| ``` | |
| Usage: | |
| ``` | |
| from tts_infer.tts import TextToMel, MelToWav | |
| from tts_infer.transliterate import XlitEngine | |
| from tts_infer.num_to_word_on_sent import normalize_nums | |
| import re | |
| from scipy.io.wavfile import write | |
| text_to_mel = TextToMel(glow_model_dir='/path/to/glow-tts/checkpoint/dir', device='cuda') | |
| mel_to_wav = MelToWav(hifi_model_dir='/path/to/hifi/checkpoint/dir', device='cuda') | |
| def translit(text, lang): | |
| reg = re.compile(r'[a-zA-Z]') | |
| engine = XlitEngine(lang) | |
| words = [engine.translit_word(word, topk=1)[lang][0] if reg.match(word) else word for word in text.split()] | |
| updated_sent = ' '.join(words) | |
| return updated_sent | |
| def run_tts(text, lang): | |
| text = text.replace('।', '.') # only for hindi models | |
| text_num_to_word = normalize_nums(text, lang) # converting numbers to words in lang | |
| text_num_to_word_and_transliterated = translit(text_num_to_word, lang) # transliterating english words to lang | |
| mel = text_to_mel.generate_mel(text_num_to_word_and_transliterated) | |
| audio, sr = mel_to_wav.generate_wav(mel) | |
| write(filename='temp.wav', rate=sr, data=audio) # for saving wav file, if needed | |
| return (sr, audio) | |
| ``` | |
| ### 2. Spectogram Training (glow-tts) | |
| ``` | |
| cd ./scripts | |
| bash train_glow.sh | |
| ``` | |
| ### 3. Genrate Mels | |
| ``` | |
| cd ./scripts | |
| bash generate_mels.sh | |
| ``` | |
| ### 4. Vocoder Training (hifi-gan) | |
| ``` | |
| cd ./scripts | |
| bash train_hifi.sh | |
| ``` | |
| ### 4. Inference | |
| ``` | |
| cd ./scripts | |
| bash infer.sh | |
| ``` | |