Linto-Platform-Stt-Standalone-Worker

This service is mandatory in a LinTO platform stack as the main worker for speech to text toolkit.

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.

Usage

See documentation : doc.linto.ai

Deploy

With our proposed stack linto-platform-stack

Hardware requirements

In order to install and run this service, you need to have at least:

• 5Go available on your hard drive for the installation, and

• 500Mo/3Go/7Go of RAM memory available for models loading and decoding. The size depends mainly on the choosed decoding model (small, medium or big).

While there is no specific minimal requirement on the CPU, speech recognition is a computationally task.

—The better your hardware performance, the lower your decoding time—

Develop

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
git submodule update --init
mv .envdefault .env

Then, to build the docker image, execute:

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

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 and docker-compose 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.

1- Download the French acoustic model and the small decoding graph (linstt.v1). You can download the latest version for optimal performance and you should make sure that you have the hardware requirement in terms of RAM.

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_v1.1.0.zip

2- Uncompress both files

unzip linSTT_AM_fr-FR_v1.0.0.zip -d AM_fr-FR
unzip decoding_graph_fr-FR_Small_v1.1.0.zip -d DG_fr-FR_Small

3- Move the uncompressed files into the shared storage directory

mkdir ~/linstt_model_storage
mv AM_fr-FR ~/linstt_model_storage
mv DG_fr-FR ~/linstt_model_storage

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

AM_PATH=~/linstt_model_storage/AM_fr-FR
LM_PATH=~/linstt_model_storage/DG_fr-FR

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.

Execute

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

cd linto-platform-stt-standalone-worker
docker run -p 8888:80 -v /full/path/to/linstt_model_storage/AM_fr-FR:/opt/models/AM -v /full/path/to/linstt_model_storage/DG_fr-FR:/opt/models/LM -v /full/path/to/linto-platform-stt-standalone-worker/document/swagger.yml:/opt/swagger.yml -e SWAGGER_PATH="/opt/swagger.yml" lintoai/linto-platform-stt-standalone-worker:latest

or simply by executing:

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

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

API

/transcribe

Convert a speech to text

Functionality

post
Make a POST request

Arguments :

• {File} file Audio File - Waveform Audio File Format is required

Header :

• {String} Accept: response content type (text/plain|application/json)

{text|Json} : Return the full transcription or a json object with metadata

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

To run personal test, you can use swagger interface: localhost:8888/api-doc/