---
license: apache-2.0
language:
- ar
tags:
- audio
- automatic-speech-recognition
---
|
|[](https://github.com/linagora-labs/ASR_train_kaldi_tunisian?tab=readme-ov-file#acoustic-model-am)
|[](https://github.com/linagora-labs/ASR_train_kaldi_tunisian)
# LinTO ASR Arabic Tunisia v0.1
**LinTO ASR Arabic Tunisia v0.1** is an Automatic Speech Recognition (ASR) model for the Tunisian dialect,
with some capabilities of code-switching when some French or English words are used.
This repository includes two versions of the model:
- `vosk-model`: The original, comprehensive model.
- `android-model`: A lighter version with a simplified graph, optimized for deployment on Android devices or Raspberry Pi applications.
## Model Overview
- **Model type**: Kaldi TDNN
- **Language(s)**: Tunisian Dialect
- **Use cases**: Automatic Speech Recognition (ASR)
### Model Performance
The following table summarizes the performance of the **LinTO ASR Arabic Tunisia v0.1** model on various considered **test sets**:
| Dataset | CER | WER |
| :------- | :------- | :------- |
| [Youtube_TNScrapped_V1](https://huggingface.co/datasets/linagora/linto-dataset-audio-ar-tn-0.1#data-table) | `25.39%` | `37.51%` |
| [TunSwitchCS](https://huggingface.co/datasets/linagora/linto-dataset-audio-ar-tn-0.1#data-table) | `17.72%` | `20.51%` |
| [TunSwitchTO](https://huggingface.co/datasets/linagora/linto-dataset-audio-ar-tn-0.1#data-table) | `11.13%` | `22.54%` |
| [ApprendreLeTunisien](https://huggingface.co/datasets/linagora/linto-dataset-audio-ar-tn-0.1#data-table) | `11.81%` | `23.27%` |
| [TARIC](https://github.com/elyadata/TARIC-SLU) | `10.60%` | `16.06%` |
| [OneStory](https://huggingface.co/datasets/linagora/linto-dataset-audio-ar-tn-0.1#data-table)| `1.53%` | `4.47%` |
### Training code
The model was trained using the following GitHub repository: [ASR_train_kaldi_tunisian](https://github.com/linagora-labs/ASR_train_kaldi_tunisian)
### Training datasets
The model was trained using the following datasets:
- **[LinTO DataSet Audio for Arabic Tunisian v0.1](https://huggingface.co/datasets/linagora/linto-dataset-audio-ar-tn-0.1):** This dataset comprises a collection of Tunisian dialect audio recordings and their annotations for Speech-to-Text (STT) tasks. The data was collected from various sources, including Hugging Face, YouTube, and websites.
- **[LinTO DataSet Audio for Arabic Tunisian Augmented v0.1](https://huggingface.co/datasets/linagora/linto-dataset-audio-ar-tn-augmented-0.1):** This dataset is an augmented version of the LinTO DataSet Audio for Arabic Tunisian v0.1. The augmentation includes noise reduction and voice conversion.
- **[TARIC](https://github.com/elyadata/TARIC-SLU):** This dataset consists of Tunisian Arabic speech recordings collected from train stations in Tunisia.
## How to use
### 1. Download the model
You can download the model and its components directly from this repository using one of the following methods:
**Method 1: Direct Download via Browser**
1. **Visit the Repository**: Navigate to the [Hugging Face model page](https://huggingface.co/linagora/linto-asr-ar-tn-0.1).
2. **Download as Zip**: Click on the "Download" button or the "Code" button (often appearing as a dropdown). Select "Download ZIP" to get the entire repository as a zip file.
**Method 2: Using `curl` command**
You can follow the command below:
```bash
sudo apt-get install curl
curl -L https://huggingface.co/linagora/linto-asr-ar-tn-0.1/resolve/main/vosk-model.zip --output vosk-model.zip
```
(or same with `android-model.zip` instead of `vosk-model.zip`)
**Method 3: Cloning the Repository**
You can clone the repository and create a zip file of the contents if needed:
```bash
sudo apt-get install git-lfs
git lfs install
git clone https://huggingface.co/linagora/linto-asr-ar-tn-0.1.git
cd linto-asr-ar-tn-0.1
```
### 2. Unzip the model
This can be done in bash:
```bash
mkdir dir_for_zip_extract
unzip /path/to/model-name.zip -d dir_for_zip_extract
```
### 3. Python code
First, make sure to install the required dependencies:
```bash
pip install vosk
```
Then you can launch the inference script from this repository:
```bash
python inference.py
```
or use such a python code:
```python
from vosk import Model, KaldiRecognizer
import wave
import json
model_dir = "path/to/your/model"
audio_file = "path/to/your/audio/file.wav"
model = Model(model_dir)
with wave.open(audio_file, "rb") as wf:
if wf.getnchannels() != 1 or wf.getsampwidth() != 2 or wf.getcomptype() != "NONE":
raise ValueError("Audio file must be WAV format mono PCM.")
rec = KaldiRecognizer(model, wf.getframerate())
rec.AcceptWaveform(wf.readframes(wf.getnframes()))
res = rec.FinalResult()
transcript = json.loads(res)["text"]
print(f"Transcript: {transcript}")
```
## Example
Here is an example of the transcription capabilities of the model:
### Result:
بالدعم هاذايا لي بثتهولو ال berd يعني أحنا حتى ال projet متاعو تقلب حتى sur le plan حتى فال management يا سيد نحنا في تسيير الشريكة يعني تبدل مية و ثمانين درجة ماللي يعني قبل ما تجيه ال berd و بعد ما جاتو ال berd برنامج نخصص لل les startup إسمو
## WebRTC Demonstartion
Install required dependencies:
```bash
pip install vosk
pip install websockets
```
If not done, close the repostorory:
```bash
git clone https://huggingface.co/linagora/linto-asr-ar-tn-0.1.git
```
Then call the `app.py` script:
```bash
cd linto-asr-ar-tn-0.1/Demo-WebRTC
python3 app.py
```
Access the web interface at: `localhost:8010` Just start and speak.
Preview of the web app interface:
## Citation
```bibtex
@misc{linagora2024Linto-tn,
author = {Hedi Naouara and Jérôme Louradour and Jean-Pierre Lorré and Sarah zribi and Wajdi Ghezaiel},
title = {LinTO ASR AR TN 0.1},
year = {2024},
publisher = {HuggingFace},
journal = {HuggingFace},
howpublished = {\url{https://huggingface.co/linagora/linto-asr-ar-tn-0.1}},
}
```