vakyansh-wav2vec2-experimentation

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vakyansh-wav2vec2-experimentation

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Repository containing experimentation platform on how to train, infer on wav2vec2 models.

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// repository documentation

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# Vakyansh Wav2Vec2 Experimentation

## Pretrained Models
We are releasing pretrained models in various Indic Languages. Please head over to [this repo](https://github.com/Open-Speech-EkStep/vakyansh-models).

## Table of contents

* [Installation and Setup](#installation-and-setup)
   * [Directory Structure](#directory-structure)
   * [Data Description](#data-description)
   * [Usage](#usage)
      * [For Pretraining](#for-pretraining)
      * [For Finetuning](#for-finetuning)
      * [For Inference](#for-inferences)
      * [For Single File Inference](#for-single-file-inference)
   * [License](#license)

## Installation and Setup 
### Create an Environment

```
conda create --name <env_name> python=3.7
conda activate <env_name>

sudo apt-get install liblzma-dev libbz2-dev libzstd-dev libsndfile1-dev libopenblas-dev libfftw3-dev libgflags-dev libgoogle-glog-dev
sudo apt install build-essential cmake libboost-system-dev libboost-thread-dev libboost-program-options-dev libboost-test-dev libeigen3-dev zlib1g-dev libbz2-dev liblzma-dev

pip install packaging soundfile swifter
pip install -r requirements.txt
```

### Install fairseq
```
git clone https://github.com/Open-Speech-EkStep/fairseq -b v2-hydra
cd fairseq
pip install -e .
cd ..
```

### Install KenLM
```
git clone https://github.com/kpu/kenlm.git
cd kenlm
mkdir -p build && cd build
cmake .. 
make -j 16
cd ..
export KENLM_ROOT=$PWD
# export USE_CUDA=0 ## for cpu
cd ..
```

### Install Flashlight
```
git clone https://github.com/flashlight/flashlight.git
cd flashlight/bindings/python
export USE_MKL=0
python setup.py install
# python criterion_example.py  ## to test

```

## Data Description

* For Audio Files.
    * Sample Rate [Hz] = 16000
    * Channels = 'mono'
    * Bit Rate [kbit/s] = 256
    * Precision [bits] = 16
    * Audio length should be less than 30 seconds otherwise it will be ignored during data preparation
    * After scripts/finetuning/prepare_data.sh is run, analysis will be generated which can be used to tune min/max_sample_size in the config files
* For Text Files
    * Corresponding text file of each audio file must be on the same directory as its audio
    * Text file should not contain any punctuation characters
    * Check dict.ltr.txt file generated after prepare_data so that it does not contain any foreign language character
* For Language Model
    * Character set of text used for language model should be same as character set used for training
    * Sample code for cleaning text file for english language is given here [clean_text.py](utils/lm/clean_text.py)
    * Sample code for removing duplicate line from text file is given here [remove_duplicate_lines.py](utils/lm/remove_duplicate_lines.py)

# Usage

## For Pretraining
Edit the path to data in the scripts/pretraining/prepare_data.sh file. To prepare the data:
```
$ cd scripts/pretraining
$ bash prepare_data.sh
```
Edit the config/pretraining_base.yaml or config/pretraining_large.yaml for different parameter configurations.Check the required paths and values in start_pretraining_base.sh or start_pretraining_large.sh. Refer to [config README](config/README.md)
To start run:
```

$ bash start_pretraining_base.sh
```
Refer [this](scripts/pretraining/README.md) for pretraining parameters.

## For Finetuning
Edit the path to data in the scripts/finetuning/prepare_data.sh file. To prepare the data:
```
$ cd scripts/finetuning
$ bash prepare_data.sh
```
Edit the config/finetuning.yaml for different parameter configurations.Check the required paths and values in start_finetuning.sh. Refer to [config README](config/README.md)
To start run:
```
$ bash start_finetuning.sh
```

Refer [this](scripts/finetuning/README.md) for finetuning parameters.

## For Inference
Edit the path to data in the scripts/inference/prepare_data.sh file. To prepare the test data run:
```
$ cd scripts/inference/
$ bash prepare_data.sh
```
Edit the infer.sh file for required paths. To start inference run:
```
$ bash infer.sh
```
Refer [this](scripts/inference/README.md) for inference parameters.

## For Single File Inference
For single file inference, there are different models. Please head over to this (repo)[https://github.com/Open-Speech-EkStep/vakyansh-models#finetuned-models] to download the same

To generate custom model, run: 
```
$ cd scripts/inference
$ bash generate_custom_model.sh
```
To infer for single file, change path in single_file_inference.sh. Then run:
```
$ bash single_file_inference.sh
```

## For generating LM
Edit the run_lm_pipeline.sh variables as required, then run:
```
$ cd scripts/lm
$ bash run_lm_pipeline.sh
```
Refer [this](scripts/lm/README.md) for LM pipeline.

# License

fairseq(-py) is MIT-licensed. The license applies to the pre-trained models as well.

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