Merge pull request #34 from linto-ai/refactor/unify_whisper_kaldi

Refactoring : unify Kaldi & Whisper STT

.github/workflows/dockerhub-description.yml

@@ -7,7 +7,7 @@ on:
       - README.md
       - .github/workflows/dockerhub-description.yml
 jobs:
-  dockerHubDescription:
+  dockerHubDescriptionKaldi:
     runs-on: ubuntu-latest
     steps:
     - uses: actions/checkout@v3
@@ -16,5 +16,16 @@ jobs:
       with:
         username: ${{ secrets.DOCKERHUB_USERNAME }}
         password: ${{ secrets.DOCKERHUB_PASSWORD }}
-        repository: lintoai/linto-platform-stt
-        readme-filepath: ./README.md
+        repository: lintoai/linto-stt-kaldi
+        readme-filepath: ./kaldi/README.md
+  dockerHubDescriptionWhisper:
+    runs-on: ubuntu-latest
+    steps:
+    - uses: actions/checkout@v3
+    - name: Docker Hub Description
+      uses: peter-evans/dockerhub-description@v3
+      with:
+        username: ${{ secrets.DOCKERHUB_USERNAME }}
+        password: ${{ secrets.DOCKERHUB_PASSWORD }}
+        repository: lintoai/linto-stt-whisper
+        readme-filepath: ./whisper/README.md

.gitignore

@@ -1,3 +1,5 @@
 start_container.sh
 .env*
 test/*
+tmp*
+__pycache__

Jenkinsfile

@@ -1,51 +1,72 @@
+def buildDockerfile(main_folder, dockerfilePath, image_name, version, changedFiles) {
+    if (changedFiles.contains(main_folder) || changedFiles.contains('celery_app') || changedFiles.contains('http_server') || changedFiles.contains('websocket') || changedFiles.contains('document')) {
+        echo "Building Dockerfile for ${image_name} with version ${version} (using ${dockerfilePath})"
+
+        script {
+            def image = docker.build(image_name, "-f ${dockerfilePath} .")
+
+            docker.withRegistry('https://registry.hub.docker.com', 'docker-hub-credentials') {
+                if (version  == 'latest-unstable') {
+                    image.push('latest-unstable')
+                } else {
+                    image.push('latest')
+                    image.push(version)
+                }
+            }
+        }
+    }
+}
+
 pipeline {
     agent any
     environment {
-        DOCKER_HUB_REPO = "lintoai/linto-platform-stt"
-        DOCKER_HUB_CRED = 'docker-hub-credentials'
-
-        VERSION = ''
+        DOCKER_HUB_REPO_KALDI   = "lintoai/linto-stt-kaldi"
+        DOCKER_HUB_REPO_WHISPER = "lintoai/linto-stt-whisper"
     }
-
-    stages{
-        stage('Docker build for master branch'){
-            when{
+    
+    stages {
+        stage('Docker build for master branch') {
+            when {
                 branch 'master'
             }
             steps {
                 echo 'Publishing latest'
                 script {
-                    image = docker.build(env.DOCKER_HUB_REPO)
-                    VERSION = sh(
+                    def changedFiles = sh(returnStdout: true, script: 'git diff --name-only HEAD^ HEAD').trim()
+                    echo "My changed files: ${changedFiles}"
+
+                    version_kaldi = sh(
+                        returnStdout: true, 
+                        script: "awk -v RS='' '/#/ {print; exit}' kaldi/RELEASE.md | head -1 | sed 's/#//' | sed 's/ //'"
+                    ).trim()
+
+                    version_whisper = sh(
                         returnStdout: true, 
-                        script: "awk -v RS='' '/#/ {print; exit}' RELEASE.md | head -1 | sed 's/#//' | sed 's/ //'"
+                        script: "awk -v RS='' '/#/ {print; exit}' whisper/RELEASE.md | head -1 | sed 's/#//' | sed 's/ //'"
                     ).trim()
 
-                    docker.withRegistry('https://registry.hub.docker.com', env.DOCKER_HUB_CRED) {
-                        image.push("${VERSION}")
-                        image.push('latest')
-                    }
+                    buildDockerfile('kaldi', 'kaldi/Dockerfile', env.DOCKER_HUB_REPO_KALDI, version_kaldi, changedFiles)
+                    buildDockerfile('whisper', 'whisper/Dockerfile.ctranslate2', env.DOCKER_HUB_REPO_WHISPER, version_whisper, changedFiles)
                 }
             }
         }
 
-        stage('Docker build for next (unstable) branch'){
-            when{
+        stage('Docker build for next (unstable) branch') {
+            when {
                 branch 'next'
             }
             steps {
                 echo 'Publishing unstable'
                 script {
-                    image = docker.build(env.DOCKER_HUB_REPO)
-                    VERSION = sh(
-                        returnStdout: true, 
-                        script: "awk -v RS='' '/#/ {print; exit}' RELEASE.md | head -1 | sed 's/#//' | sed 's/ //'"
-                    ).trim()
-                    docker.withRegistry('https://registry.hub.docker.com', env.DOCKER_HUB_CRED) {
-                        image.push('latest-unstable')
-                    }
+                    def changedFiles = sh(returnStdout: true, script: 'git diff --name-only HEAD^ HEAD').trim()
+                    echo "My changed files: ${changedFiles}"
+
+                    version = 'latest-unstable'
+
+                    buildDockerfile('kaldi', 'kaldi/Dockerfile', env.DOCKER_HUB_REPO_KALDI, version, changedFiles)
+                    buildDockerfile('whisper', 'whisper/Dockerfile.ctranslate2', env.DOCKER_HUB_REPO_WHISPER, version, changedFiles)
                 }
             }
         }
-    }// end stages
+    }
 }

Makefile

@@ -1,6 +1,6 @@
 .DEFAULT_GOAL := help
 
-target_dirs := stt http_server celery_app
+target_dirs := kaldi/stt whisper/stt http_server celery_app
 
 help:
 	@grep -E '^[a-zA-Z_-]+:.*?## .*$$' $(MAKEFILE_LIST) | sort | awk 'BEGIN {FS = ":.*?## "}; {printf "\033[36m%-30s\033[0m %s\n", $$1, $$2}'

README.md

@@ -1,224 +1,12 @@
-# LINTO-PLATFORM-STT
-LinTO-platform-stt is the transcription service within the [LinTO stack](https://github.com/linto-ai/linto-platform-stack).
+# LinTO-STT
 
-LinTO-platform-stt can either be used as a standalone transcription service or deployed within a micro-services infrastructure using a message broker connector.
+LinTO-STT is the transcription service within the [LinTO stack](https://github.com/linto-ai/linto-platform-stack),
+which can currently work with Speech-To-Text (STT) models.
+The following families of STT models are currently supported (please refer to respective documentation for more details):
+* [Kaldi models](kaldi/README.md) 
+* [Whisper models](whisper/README.md)
 
-## Pre-requisites
-
-### Hardware
-To run the transcription models you'll need:
-* At least 7Go of disk space to build the docker image.
-* Up to 7GB of RAM depending on the model used.
-* One CPU per worker. Inference time scales on CPU performances. 
-
-### Model
-LinTO-Platform-STT accepts two kinds of models:
-* LinTO Acoustic and Languages models.
-* Vosk models.
-
-We provide home-cured models (v2) on [dl.linto.ai](https://doc.linto.ai/docs/developpers/apis/ASR/models).
-Or you can also use Vosk models available [here](https://alphacephei.com/vosk/models).
-
-### Docker
-The transcription service requires docker up and running.
-
-### (micro-service) Service broker and shared folder
-The STT only entry point in task mode are tasks posted on a message broker. Supported message broker are RabbitMQ, Redis, Amazon SQS.
-On addition, as to prevent large audio from transiting through the message broker, STT-Worker use a shared storage folder (SHARED_FOLDER).
-
-## Deploy linto-platform-stt
-
-**1- First step is to build or pull the image:**
-
-```bash
-git clone https://github.com/linto-ai/linto-platform-stt.git
-cd linto-platform-stt
-docker build . -t linto-platform-stt:latest
-```
-or
-
-```bash
-docker pull lintoai/linto-platform-stt
-```
-
-**2- Download the models**
-
-Have the acoustic and language model ready at AM_PATH and LM_PATH if you are using LinTO models. If you are using a Vosk model, have it ready at MODEL.
-
-**3- Fill the .env**
-
-```bash
-cp .envdefault .env
-```
-
-| PARAMETER | DESCRIPTION | EXEMPLE |
-|---|---|---|
-| SERVICE_MODE | STT serving mode see [Serving mode](#serving-mode) | http\|task\|websocket |
-| MODEL_TYPE | Type of STT model used. | lin\|vosk |
-| ENABLE_STREAMING | Using http serving mode, enable the /streaming websocket route | true\|false |
-| SERVICE_NAME | Using the task mode, set the queue's name for task processing | my-stt |
-| SERVICE_BROKER | Using the task mode, URL of the message broker | redis://my-broker:6379 |
-| BROKER_PASS | Using the task mode, broker password | my-password |
-| STREAMING_PORT | Using the websocket mode, the listening port for ingoing WS connexions.  | 80 |
-| CONCURRENCY | Maximum number of parallel requests | >1 |
-
-### Serving mode 
-![Serving Modes](https://i.ibb.co/qrtv3Z6/platform-stt.png)
-
-STT can be used three ways:
-* Through an [HTTP API](#http-server) using the **http**'s mode.
-* Through a [message broker](#micro-service-within-linto-platform-stack) using the **task**'s mode.
-* Through a [websocket server](#websocket-server) **websocket**'s mode.
-
-Mode is specified using the .env value or environment variable ```SERVING_MODE```.
-```bash
-SERVICE_MODE=http
-```
-### HTTP Server
-The HTTP serving mode deploys a HTTP server and a swagger-ui to allow transcription request on a dedicated route.
-
-The SERVICE_MODE value in the .env should be set to ```http```.
-
-```bash
-docker run --rm \
--p HOST_SERVING_PORT:80 \
--v AM_PATH:/opt/AM \
--v LM_PATH:/opt/LM \
---env-file .env \
-linto-platform-stt:latest
-```
-
-This will run a container providing an [HTTP API](#http-api) binded on the host HOST_SERVING_PORT port.
-
-**Parameters:**
-| Variables | Description | Example |
-|:-|:-|:-|
-| HOST_SERVING_PORT | Host serving port | 80 |
-| AM_PATH | Path to the acoustic model on the host machine mounted to /opt/AM | /my/path/to/models/AM_fr-FR_v2.2.0 |
-| LM_PATH | Path to the language model on the host machine mounted to /opt/LM | /my/path/to/models/fr-FR_big-v2.2.0 |
-| MODEL_PATH | Path to the model (using MODEL_TYPE=vosk) mounted to /opt/model | /my/path/to/models/vosk-model |
-
-### Micro-service within LinTO-Platform stack
-The HTTP serving mode connect a celery worker to a message broker.
-
-The SERVICE_MODE value in the .env should be set to ```task```.
-
->LinTO-platform-stt can be deployed within the linto-platform-stack through the use of linto-platform-services-manager. Used this way, the container spawn celery worker waiting for transcription task on a message broker.
->LinTO-platform-stt in task mode is not intended to be launch manually.
->However, if you intent to connect it to your custom message's broker here are the parameters:
-
-You need a message broker up and running at MY_SERVICE_BROKER.
-
-```bash
-docker run --rm \
--v AM_PATH:/opt/AM \
--v LM_PATH:/opt/LM \
--v SHARED_AUDIO_FOLDER:/opt/audio \
---env-file .env \
-linto-platform-stt:latest
-```
-
-**Parameters:**
-| Variables | Description | Example |
-|:-|:-|:-|
-| AM_PATH | Path to the acoustic model on the host machine mounted to /opt/AM | /my/path/to/models/AM_fr-FR_v2.2.0 |
-| LM_PATH | Path to the language model on the host machine mounted to /opt/LM | /my/path/to/models/fr-FR_big-v2.2.0 |
-| MODEL_PATH | Path to the model (using MODEL_TYPE=vosk) mounted to /opt/model | /my/path/to/models/vosk-model |
-| SHARED_AUDIO_FOLDER | Shared audio folder mounted to /opt/audio | /my/path/to/models/vosk-model |
-
-
-### Websocket Server
-Websocket server's mode deploy a streaming transcription service only.
-
-The SERVICE_MODE value in the .env should be set to ```websocket```.
-
-Usage is the same as the [http streaming API](#/streaming)
-
-## Usages
-### HTTP API
-#### /healthcheck
-Returns the state of the API
-
-Method: GET
-
-Returns "1" if healthcheck passes.
-
-#### /transcribe
-Transcription API
-
-* Method: POST
-* Response content: text/plain or application/json
-* File: An Wave file 16b 16Khz
-
-Return the transcripted text using "text/plain" or a json object when using "application/json" structure as followed:
-```json
-{
-  "text" : "This is the transcription",
-  "words" : [
-    {"word":"This", "start": 0.123, "end": 0.453, "conf": 0.9},
-    ...
-  ]
-  "confidence-score": 0.879
-}
-```
-
-#### /streaming
-The /streaming route is accessible if the ENABLE_STREAMING environment variable is set to true.
-
-The route accepts websocket connexions. Exchanges are structured as followed:
-1. Client send a json {"config": {"sample_rate":16000}}.
-2. Client send audio chunk (go to 3- ) or {"eof" : 1} (go to 5-).
-3. Server send either a partial result {"partial" : "this is a "} or a final result {"text": "this is a transcription"}.
-4. Back to 2-
-5. Server send a final result and close the connexion.
-
-> Connexion will be closed and the worker will be freed if no chunk are received for 10s. 
-
-#### /docs
-The /docs route offers a OpenAPI/swagger interface.
-
-### Through the message broker
-
-STT-Worker accepts requests with the following arguments:
-```file_path: str, with_metadata: bool```
-
-* <ins>file_path</ins>: Is the location of the file within the shared_folder. /.../SHARED_FOLDER/{file_path}
-* <ins>with_metadata</ins>: If True, words timestamps and confidence will be computed and returned. If false, the fields will be empty.
-
-#### Return format
-On a successfull transcription the returned object is a json object structured as follow:
-```json
-{
-    "text" : "this is the transcription as text",
-    "words": [
-        {
-        "word" : "this",
-        "start": 0.0,
-        "end": 0.124,
-        "conf": 1.0
-        },
-        ...
-    ],
-    "confidence-score": ""
-}
-```
-
-* The <ins>text</ins> field contains the raw transcription.
-* The <ins>word</ins> field contains each word with their time stamp and individual confidence. (Empty if with_metadata=False)
-* The <ins>confidence</ins> field contains the overall confidence for the transcription. (0.0 if with_metadata=False)
-
-
-## Test
-### Curl
-You can test you http API using curl:
-```bash 
-curl -X POST "http://YOUR_SERVICE:YOUR_PORT/transcribe" -H  "accept: application/json" -H  "Content-Type: multipart/form-data" -F "file=@YOUR_FILE;type=audio/x-wav"
-```
+LinTO-STT can either be used as a standalone transcription service or deployed within a micro-services infrastructure using a message broker connector.
 
 ## License
 This project is developped under the AGPLv3 License (see LICENSE).
-
-## Acknowlegment.
-
-* [Vosk, speech recognition toolkit](https://alphacephei.com/vosk/).
-* [Kaldi Speech Recognition Toolkit](https://github.com/kaldi-asr/kaldi)