https://github.com/sovaai/sova-asr
SOVA ASR (Automatic Speech Recognition)
https://github.com/sovaai/sova-asr
asr asr-model automatic-speech-recognition speech speech-recognition speech-to-text stt wav2letter
Last synced: 7 months ago
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SOVA ASR (Automatic Speech Recognition)
- Host: GitHub
- URL: https://github.com/sovaai/sova-asr
- Owner: sovaai
- License: apache-2.0
- Created: 2020-08-18T18:24:37.000Z (almost 5 years ago)
- Default Branch: master
- Last Pushed: 2023-04-27T20:46:32.000Z (about 2 years ago)
- Last Synced: 2024-08-08T23:21:37.281Z (10 months ago)
- Topics: asr, asr-model, automatic-speech-recognition, speech, speech-recognition, speech-to-text, stt, wav2letter
- Language: Python
- Homepage: https://sova.ai
- Size: 2.32 MB
- Stars: 168
- Watchers: 13
- Forks: 20
- Open Issues: 9
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# SOVA ASR
SOVA ASR is a fast speech recognition solution based on [Wav2Letter](https://arxiv.org/abs/1609.03193) architecture. It is designed as a REST API service and it can be customized (both code and models) for your needs.
## Installation
The easiest way to deploy the service is via docker-compose, so you have to install Docker and docker-compose first. Here's a brief instruction for Ubuntu:
#### Docker installation
* Install Docker:
```bash
$ sudo apt-get update
$ sudo apt-get install \
apt-transport-https \
ca-certificates \
curl \
gnupg-agent \
software-properties-common
$ curl -fsSL https://download.docker.com/linux/ubuntu/gpg | sudo apt-key add -
$ sudo apt-key fingerprint 0EBFCD88
$ sudo add-apt-repository \
"deb [arch=amd64] https://download.docker.com/linux/ubuntu \
$(lsb_release -cs) \
stable"
$ sudo apt-get update
$ sudo apt-get install docker-ce docker-ce-cli containerd.io
$ sudo usermod -aG docker $(whoami)
```
In order to run docker commands without sudo you might need to relogin.
* Install docker-compose:
```
$ sudo curl -L "https://github.com/docker/compose/releases/download/1.25.5/docker-compose-$(uname -s)-$(uname -m)" -o /usr/local/bin/docker-compose
$ sudo chmod +x /usr/local/bin/docker-compose
```
* (Optional) If you're planning on using CUDA run these commands:
```
$ curl -s -L https://nvidia.github.io/nvidia-container-runtime/gpgkey | \
sudo apt-key add -
$ distribution=$(. /etc/os-release;echo $ID$VERSION_ID)
$ curl -s -L https://nvidia.github.io/nvidia-container-runtime/$distribution/nvidia-container-runtime.list | \
sudo tee /etc/apt/sources.list.d/nvidia-container-runtime.list
$ sudo apt-get update
$ sudo apt-get install nvidia-container-runtime
```
Add the following content to the file **/etc/docker/daemon.json**:
```json
{
"runtimes": {
"nvidia": {
"path": "nvidia-container-runtime",
"runtimeArgs": []
}
},
"default-runtime": "nvidia"
}
```
Restart the service:
```bash
$ sudo systemctl restart docker.service
```
#### Build and deploy
**In order to run service with pretrained models you will have to download http://dataset.sova.ai/SOVA-ASR/data.tar.gz.**
* Clone the repository, download the pretrained models archive and extract the contents into the project folder:
```bash
$ git clone --recursive https://github.com/sovaai/sova-asr.git
$ cd sova-asr/
$ wget http://dataset.sova.ai/SOVA-ASR/data.tar.gz
$ tar -xvf data.tar.gz && rm data.tar.gz
```
* Build docker image
* If you're planning on using GPU (it is required for training and can be used for inference): build *sova-asr* image using the following command:
```bash
$ sudo docker-compose build
```
* If you're planning on using CPU only: modify `Dockerfile`, `docker-compose.yml` (remove the runtime and environment sections) and `config.ini` (*cpu* should be set to 0) and build *sova-asr* image:
```bash
$ sudo docker-compose build
```
* Run web service in a docker container
```bash
$ sudo docker-compose up -d sova-asr
```
## Testing
To test the service you can send a POST request:
```bash
$ curl --request POST 'http://localhost:8888/asr' --form 'audio_blob=@"data/test.wav"'
```
## Finetuning acoustic model
If you want to finetune the acoustic model you can set hyperparameters and paths to your own train and validation manifest files and run the training service.
* Set training options in *Train* section of **config.ini**. Train and validation csv manifest files should contain comma-separated audio file paths and reference texts in each line. For instance:
```bash
data/audio/000000.wav,добрый день
data/audio/000001.wav,как ваши дела
...
```
* Run training in docker container:
```bash
$ sudo docker-compose up -d sova-asr-train
```
## Customizations
If you want to train your own acoustic model refer to [PuzzleLib tutorials](https://puzzlelib.org/tutorials/Wav2Letter/). Check [KenLM documentation](https://kheafield.com/code/kenlm/) for building your own language model. This repository was tested on Ubuntu 18.04 and has pre-built .so Trie decoder files for Python 3.6 running inside the Docker container, for modifications you can get your own .so files using [Wav2Letter++](https://github.com/facebookresearch/wav2letter) code for building Python bindings. Otherwise you can use a standard Greedy decoder (set in config.ini).