# vakyansh-tts
Text to Speech for Indic languages

## 1. Installation and Setup for training

Clone repo
Note : for multspeaker glow-tts training use branch [multispeaker](https://github.com/Open-Speech-EkStep/vakyansh-tts/tree/multispeaker)
```
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 requirements.txt
```
Install [apex](https://github.com/NVIDIA/apex); commit: 37cdaf4 for Mixed-precision training

Note : used only for glow-tts
```
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)

Note : used only for glow-tts
```
bash install.sh
```

## 2. Data Resampling

The data format should have a folder containing all the .wav files for glow-tts and a text file containing filenames with their sentences.

Directory structure: 

langauge_folder_name
```
language_folder_name
|-- ./wav/*.wav
|-- ./text_file_name.txt
```
The format for text_file_name.txt (Text file is only needed for glow-tts training)

```
( audio1.wav "Sentence1." )
( audio2.wav "Sentence2." )
```

To resample the .wav files to 22050 sample rate, change the following parameters in the vakyansh-tts/scripts/data/resample.sh

```
input_wav_path : absolute path to wav file folder in vakyansh_tts/data/
output_wav_path : absolute path to vakyansh_tts/data/resampled_wav_folder_name
output_sample_rate : 22050 (or any other desired sample rate)
```

To run:  
```bash
cd scripts/data/
bash resample.sh
```


## 3. Spectogram Training (glow-tts)

### 3.1 Data Preparation


To prepare the data edit the vakyansh-tts/scripts/glow/prepare_data.sh file and change the following parameters
```
input_text_path : absolute path to vakyansh_tts/data/text_file_name.txt
input_wav_path : absolute path to vakyansh_tts/data/resampled_wav_folder_name
gender : female or male voice
```
To run:  
```bash
cd scripts/glow/
bash prepare_data.sh
```
### 3.2 Training glow-tts

To start the spectogram-training edit the vakyansh-tts/scripts/glow/train_glow.sh file and change the following parameter:
```
gender : female or male voice
```
Make sure that the gender is same as that of the prepare_data.sh file

To start the training, run:  
```bash
cd scripts/glow/
bash train_glow.sh
```
## 4. Vocoder Training (hifi-gan)

### 4.1 Data Preparation

To prepare the data edit the vakyansh-tts/scripts/hifi/prepare_data.sh file and change the following parameters
```
input_wav_path : absolute path to vakyansh_tts/data/resampled_wav_folder_name
gender : female or male voice
```
To run:  
```bash
cd scripts/hifi/
bash prepare_data.sh
```
### 4.2 Training hifi-gan

To start the spectogram-training edit the vakyansh-tts/scripts/hifi/train_hifi.sh file and change the following parameter:
```
gender : female or male voice
```
Make sure that the gender is same as that of the prepare_data.sh file

To start the training, run:  
```bash
cd scripts/hifi/
bash train_hifi.sh
```

## 5. Inference

### 5.1 Using Gradio 

To use the gradio link edit the following parameters in the vakyansh-tts/scripts/inference/gradio.sh file:
```
gender : female or male voice
device : cpu or cuda
lang : langauge code
```

To run:  
```bash
cd scripts/inference/
bash gradio.sh
```
### 5.2 Using fast API 
To use the fast api link edit the parameters in the vakyansh-tts/scripts/inference/api.sh file similar to section 5.1

To run:  
```bash
cd scripts/inference/
bash api.sh
```

### 5.3 Direct Inference using text  
To infer, edit the parameters in the vakyansh-tts/scripts/inference/infer.sh file similar to section 5.1 and set the text to the text variable

To run:  
```bash
cd scripts/inference/
bash infer.sh
```

To configure other parameters there is a version that runs the advanced inference as well. Additional Parameters:
```
noise_scale : can vary from 0 to 1 for noise factor
length_scale : can vary from 0 to 2 for changing the speed of the generated audio 
transliteration : whether to switch on/off transliteration. 1: ON, 0: OFF
number_conversion : whether to switch on/off number to words conversion. 1: ON, 0: OFF
split_sentences : whether to switch on/off splitting of sentences. 1: ON, 0: OFF
```
To run:
```
cd scripts/inference/
bash advanced_infer.sh
```

### 5.4 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: Refer to example file in tts_infer/
```
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)
```