README.md

---
language:
- ar
metrics:
- wer
base_model:
- openai/whisper-medium
pipeline_tag: automatic-speech-recognition
tags:
- whisper
- arabic
- pytorch
license: apache-2.0
---
# WhisperLevantineArabic

**Fine-tuned Whisper model for the Levantine Dialect (Israeli-Arabic)**

## Model Description

This model is a fine-tuned version of [Whisper Medium](https://github.com/openai/whisper) tailored specifically for transcribing Levantine Arabic, focusing on the Israeli dialect. It is designed to improve automatic speech recognition (ASR) performance for this particular variant of Arabic.

- **Base Model**: Whisper Medium
- **Fine-tuned for**: Levantine Arabic (Israeli Dialect)
- **WER on test set**: 10%

## Training Data

The dataset used for training and fine-tuning this model consists of approximately 2,200 hours of transcribed audio, primarily featuring Israeli Levantine Arabic, along with some general Levantine Arabic content. The data sources include:

1. **Self-maintained Collection**: 2,000 hours of audio data curated by the team, covering a wide range of Israeli Levantine Arabic speech.
2. **[MGB-2 Corpus (Filtered)](https://huggingface.co/datasets/BelalElhossany/mgb2_audios_transcriptions_preprocessed)**: 200 hours of broadcast media in Arabic.
3. **[CommonVoice18 (Filtered)](https://huggingface.co/datasets/fsicoli/common_voice_18_0)**: A filtered portion of the CommonVoice18 dataset.

Filtering was applied using the [AlcLaM](https://arxiv.org/abs/2407.13097) Arabic language model to ensure relevance to Levantine Arabic.

- **Total Dataset Size**: ~2,200 hours
- **Sampling Rate**: 16kHz
- **Annotation**: Human-transcribed and annotated for high accuracy.

## How to Use

The model is compatible with 16kHz audio input. Ensure your files are at the same sample rate for optimal results. You can load the model as follows:

```python
from transformers import WhisperProcessor, WhisperForConditionalGeneration
import torch

# Load the model and processor
processor = WhisperProcessor.from_pretrained("HebArabNlpProject/whisperLevantine")
model = WhisperForConditionalGeneration.from_pretrained("HebArabNlpProject/whisperLevantine").to("cuda" if torch.cuda.is_available() else "cpu")

# Example usage: processing audio input
file_path = ...  # wav filepath goes here
audio_input, samplerate = torchaudio.load(file_path)
inputs = processor(audio_input.squeeze(), return_tensors="pt", sampling_rate=samplerate).to("cuda" if torch.cuda.is_available() else "cpu")

# Run inference
with torch.no_grad():
    generated_ids = model.generate(inputs["input_features"])
transcription = processor.batch_decode(generated_ids, skip_special_tokens=True)
print(transcription[0])