Whisper Medium — Fine-tuned for Arabic ASR on SADA

openai/whisper-medium fine-tuned on the full SADA22 dataset (~420 hours of Saudi Arabic speech) for Arabic automatic speech recognition (ASR).

Used as Baseline 2 in experiments on predicting the Arabic Level of Dialectness (ALDi) from speech: the transcript produced by this model is fed into a text-based ALDi classifier to obtain a dialect score.

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Training details

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Quick Start

1. Install dependencies

pip install torch "transformers>=4.27" torchaudio safetensors

2. Transcribe an audio file

import torch
import torchaudio
from transformers import WhisperForConditionalGeneration, WhisperProcessor

model = WhisperForConditionalGeneration.from_pretrained("wageehkhad/whisper-medium-finetuned-sada-asr")
processor = WhisperProcessor.from_pretrained("wageehkhad/whisper-medium-finetuned-sada-asr")


def transcribe(audio_path: str, device: str = "cpu") -> str:
    """
    Transcribe an Arabic audio file.
    Accepts any format supported by torchaudio (WAV, FLAC, MP3, etc.).
    """
    wav, sr = torchaudio.load(audio_path)
    wav = torchaudio.functional.resample(wav, sr, 16_000).mean(0).numpy()

    inputs = processor(
        wav,
        sampling_rate=16_000,
        return_tensors="pt",
        return_attention_mask=True,
    ).to(device)
    model.to(device)

    with torch.no_grad():
        token_ids = model.generate(
            inputs.input_features,
            attention_mask=inputs.attention_mask,
            language="arabic",
            task="transcribe",
        )

    return processor.batch_decode(token_ids, skip_special_tokens=True)[0]

# Example
print(transcribe("example.wav"))

If you get the error:

ImportError: TorchCodec is required for load_with_torchcodec

make sure to run

pip install torchcodec

3. Using as part of the Baseline 2 ALDi pipeline

This model is the ASR component of a two-step pipeline:

Audio → [this model] → Arabic transcript → [AMR-KELEG/Sentence-ALDi] → ALDi score

from transformers import pipeline, AutoModelForSequenceClassification, AutoTokenizer
import torch

# Step 1: transcribe
asr = pipeline("automatic-speech-recognition",
               model="wageehkhad/whisper-medium-finetuned-sada-asr")
result = asr("speech.wav", generate_kwargs={"language": "arabic", "task": "transcribe"})
transcript = result["text"]

# Step 2: score dialect level
aldi_tok = AutoTokenizer.from_pretrained("AMR-KELEG/Sentence-ALDi")
aldi_mdl = AutoModelForSequenceClassification.from_pretrained("AMR-KELEG/Sentence-ALDi")
aldi_mdl.eval()

enc = aldi_tok(transcript, return_tensors="pt", truncation=True, max_length=256)
with torch.no_grad():
    score = float(aldi_mdl(**enc).logits.squeeze())

print(f"Transcript : {transcript}")
print(f"ALDi score : {score:.3f}")  # 0.0 = MSA, 1.0 = heavy dialect

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Limitations

• Fine-tuned on Saudi Arabic (SADA22). WER on other Arabic dialects or MSA broadcasts will be higher.
• Optimised for speech segments up to 30 seconds (Whisper's native window). Longer files are chunked automatically by the pipeline.

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Citation

If you use this model, please cite the ALDi paper and the SADA dataset:

@inproceedings{keleg2023aldi,
  title     = {ALDi: Quantifying the Arabic Level of Dialectness of Text},
  author    = {Keleg, Amr and Goldwater, Sharon and Magdy, Walid},
  booktitle = {Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing (EMNLP)},
  year      = {2023},
  publisher = {Association for Computational Linguistics},
  address   = {Singapore},
  url       = {https://aclanthology.org/2023.emnlp-main.655}
}

@misc{sada22,
  author       = {Al-Gamdi, Ahmed and others},
  title        = {SADA: Saudi Audio Dataset for Arabic},
  year         = {2022},
  howpublished = {\url{https://huggingface.co/datasets/MohamedRashad/SADA22}},
  note         = {Accessed 2026}
}