Model Card for RawandLaouini/ArabicEchoV1
Model Details
Model Description
This is a fine-tuned version of the openai/whisper-medium model, adapted for Arabic Automatic Speech Recognition (ASR) using LoRA (Low-Rank Adaptation). The model was trained to transcribe Arabic audio from the Common Voice 11.0 dataset, focusing on improving performance for the Arabic language.
- Developed by: Rawand Laouini
- Finetuned from model:
openai/whisper-medium
- Model type: Transformer-based ASR model with LoRA
- Language(s): Arabic
- License: MIT (or specify your preferred license)
- Shared by: Rawand Laouini
Uses
Direct Use
This model can be used for transcribing Arabic speech to text, suitable for applications like voice assistants, subtitle generation, or educational tools for Arabic speakers.
Out-of-Scope Use
The model should not be used for real-time transcription without further optimization, nor for languages other than Arabic without retraining.
Bias, Risks, and Limitations
The model was trained on the Common Voice 11.0 Arabic dataset, which may contain biases or limited dialectal coverage. Performance may vary across different Arabic dialects or noisy environments. Users should validate outputs for critical applications.
Recommendations
Users should test the model on their specific use case and consider augmenting the training data for better dialectal or noise robustness.
How to Get Started with the Model
Use the following code to load and use the model:
1from transformers import WhisperProcessor, WhisperForConditionalGeneration
2from peft import PeftModel
3
4processor = WhisperProcessor.from_pretrained("openai/whisper-medium")
5model = WhisperForConditionalGeneration.from_pretrained("openai/whisper-medium")
6model = PeftModel.from_pretrained(model, "RawandLaouini/ArabicEchoV1")
7
8model.eval()
9input_features = processor(audio, return_tensors="pt").input_features
10predicted_ids = model.generate(input_features)
11transcription = processor.tokenizer.batch_decode(predicted_ids, skip_special_tokens=True)[0]
12print(transcription)
Training Details
Training Data
- Dataset:
mozilla-foundation/common_voice_11_0 (Arabic split)
- Training split: 90% of train+validation (approx. 25 238 examples)
- Validation split: Manual evaluation on 200 examples from test split
Training Procedure
Preprocessing
Audio was resampled to 16 kHz to match Whisper's requirements, and input features were extracted using the Whisper processor.
Training Hyperparameters
- Batch size: 1 (per device)
- Gradient accumulation steps: 1
- Learning rate: 1e-4
- Warmup steps: 100
- Max steps: 300
- Optimizer: AdamW
- Mixed precision: FP16
Speeds, Sizes, Times
- Training time: ~2.46 minutes for 300 steps
- Model size: ~18.9 MB (LoRA adapters)
Evaluation
Testing Data
Manual evaluation on 200 examples from the Common Voice test split.
Metrics
- Word Error Rate (WER): 0.4425
- Character Error Rate (CER): 0.1187
Results
The model achieves a WER of 44.25% and CER of 11.87% on the manual evaluation set, indicating moderate transcription accuracy for Arabic speech.
Environmental Impact
- Hardware Type: NVIDIA GPU (14.74 GiB)
- Hours used: ~0.04 hours (2.46 minutes)
- Cloud Provider: Local/Colab (unspecified)
- Compute Region: Unspecified
- Carbon Emitted: Minimal (estimated < 0.01 kg CO2e using Lacoste et al., 2019)
Citation
BibTeX:
1@misc{laouini2025arabicechov1,
2 author = {Rawand Laouini},
3 title = {ArabicEchoV1: Fine-tuned Whisper-medium for Arabic ASR with LoRA},
4 year = {2025},
5 howpublished = {\url{https://huggingface.co/RawandLaouini/ArabicEchoV1}}
6}
APA:
Laouini, R. (2025).
ArabicEchoV1: Fine-tuned Whisper-medium for Arabic ASR with LoRA. Retrieved from
https://huggingface.co/RawandLaouini/ArabicEchoV1Model Card Authors
Model Card Contact
- Email: [awini.rawand21@gmail.com] (replace with your email if needed)
