Automatic Speech Recognition - LinSTT

LinSTT

Generally, Automatic Speech Recognition (ASR) is the task of recognition and translation of spoken language into text. Our ASR system takes advantages from the recent advances in machine learning technologies and in particular deep learning ones (TDNN, LSTM, attentation-based architecture). The core of our system consists of two main components: an acoustic model and a decoding graph. A high-performance ASR system relies on an accurate acoustic model as well as a perfect decoding graph.

Installation

Packaged in Docker

To start the LinSTT service on your local machine or your cloud, you need first to download the source code and set the environment file, as follows:

git clone https://github.com/linto-ai/linto-platform-stt-standalone-worker
cd linto-platform-stt-standalone-worker
mv .envdefault .env

Then, to build the docker image, execute:

docker build -t lintoai/linto-platform-stt-standalone-worker .

Or by docker-compose, by using:

docker-compose build

Or, download the pre-built image from docker-hub:

docker pull lintoai/linto-platform-stt-standalone-worker:latest

NB: You must install docker on your machine.

Configuration

The LinSTT service that will be set-up here require KALDI models, the acoustic model and the decoding graph. Indeed, these models are not included in the repository; you must download them in order to run LinSTT. You can use our pre-trained models from here: Downloads.

Outside LinTO-Platform-STT-Service-Manager

If you want to use our service alone without LinTO-Platform-STT-Service-Manager, you must unzip the files and put the extracted ones in the shared storage. For example,

1- Download the French acoustic model and the small decoding graph

wget https://dl.linto.ai/downloads/model-distribution/acoustic-models/fr-FR/linSTT_AM_fr-FR_v1.0.0.zip
wget https://dl.linto.ai/downloads/model-distribution/decoding-graphs/LVCSR/fr-FR/decoding_graph_fr-FR_Small_v1.0.0.zip

2- Uncompress both files

unzip linSTT_AM_fr-FR_v1.0.0.zip -d AM_fr-FR
unzip decoding_graph_fr-FR_Small_v1.0.0.zip -d DG_fr-FR_Small

3- Move the uncompressed files into the shared storage directory

mv AM_fr-FR ~/linto_shared/data
mv DG_fr-FR_Small ~/linto_shared/data

4- Configure the environment file .env included in this repository

AM_PATH=/full/path/to/linto_shared/data/AM_fr-FR
LM_PATH=/full/path/to/linto_shared/data/DG_fr-FR_Small

NB: if you want to use the visual user interface of the service, you need also to configure the swagger file document/swagger.yml included in this repository. Specifically, in the section host, specify the adress of the machine in which the service is deployed.

Using LinTO-Platform-STT-Service-Manager

In case you want to use LinTO-Platform-STT-Service-Manager, you need to:

1- Create an acoustic model and upload the approriate file

2- Create a language model and upload the corresponding decoding graph

3- Configure the environmenet file of this service.

For more details, see configuration instruction in LinTO - STT-Manager

Execute

In order to run the service alone, you have only to execute:

cd linto-platform-stt-standalone-worker
docker-compose up

To run and manager LinSTT under LinTO-Platform-STT-Service-Manager service, you need to create a service first and then to start it. See LinTO - STT-Manager

Our service requires an audio file in Waveform format. It should has the following parameters:

- sample rate: 16000 Hz
- number of bits per sample: 16 bits
- number of channels: 1 channel
- microphone: any type
- duration: <30 minutes

Run Example Applications

To run an automated test go to the test folder

cd linto-platform-stt-standalone-worker/test

And run the test script:

./test_deployment.sh

Or use swagger interface to perform your personal test