MadLook/arabic-whisper-multidialect-processed-small

Arabic Whisper Multi-Dialect - Processed (Small)

Dataset Description

This is a preprocessed version of the Arabic multi-dialect speech dataset, ready for fine-tuning OpenAI's Whisper models. The dataset contains audio features extracted and formatted specifically for Whisper training.

• —Size: 40% subset of the full arabic-whisper-multidialect dataset
• —Total Examples: 43,091 samples
• —Format: Pre-computed Whisper input features (mel spectrograms) and tokenized labels
• —Purpose: Direct use with Whisper training pipelines without additional preprocessing

Key Features

✅ Pre-processed - Audio already converted to Whisper input features ✅ Multi-dialect - Covers various Arabic dialects ✅ Training-ready - No additional feature extraction needed ✅ Memory-efficient - Optimized for faster loading during training

Dataset Structure

Data Splits

Data Fields

• —`input_features`: Sequence[Sequence[float32]]
• —Shape: (80, 3000) - 80 mel-frequency bins × 3000 time steps
• —Pre-computed log-mel spectrogram features for Whisper

• —`labels`: Sequence[int64]
• —Tokenized Arabic transcription using Whisper's tokenizer
• —Special tokens: -100 for padding (ignored in loss computation)

Usage

Quick Start

from datasets import load_dataset

# Load the dataset
dataset = load_dataset("MadLook/arabic-whisper-multidialect-processed-small")

print(dataset)
# DatasetDict({
#     train: Dataset with 37,835 examples
#     validation: Dataset with 2,628 examples
#     test: Dataset with 2,628 examples
# })

Loading a Single Split

# Load only training data
train_data = load_dataset("MadLook/arabic-whisper-multidialect-processed-small", split="train")

# Load with streaming for large datasets
train_stream = load_dataset("MadLook/arabic-whisper-multidialect-processed-small", split="train", streaming=True)

Preprocessing Details

This dataset was created from the original Arabic multi-dialect dataset using the following preprocessing pipeline:

1. 1.Audio Loading: Resampled to 16kHz mono audio
2. 2.Feature Extraction: Converted to 80-channel log-mel spectrograms using Whisper's feature extractor
3. 3.Tokenization: Arabic text transcriptions tokenized with Whisper's multilingual tokenizer
4. 4.Normalization: Applied Whisper's standard audio normalization
5. 5.Subset Selection: Selected 40% of the original dataset

Processing Code

from transformers import WhisperProcessor

processor = WhisperProcessor.from_pretrained("openai/whisper-small", language="ar", task="transcribe")

def prepare_dataset(batch):
    # Load and resample audio
    audio = batch["audio"]
    
    # Compute input features
    batch["input_features"] = processor.feature_extractor(
        audio["array"], 
        sampling_rate=audio["sampling_rate"]
    ).input_features[0]
    
    # Tokenize transcription
    batch["labels"] = processor.tokenizer(batch["transcription"]).input_ids
    
    return batch

Source Dataset

This is a processed subset of the Arabic multi-dialect speech recognition dataset, which includes recordings from various Arabic-speaking regions and dialects.

Intended Use

Primary Use Cases

• —Fine-tuning Whisper models for Arabic speech recognition
• —Research on multi-dialect Arabic ASR
• —Benchmarking Arabic speech recognition systems
• —Transfer learning for low-resource Arabic dialects

Out-of-Scope Use

• —This dataset is already preprocessed - do not apply feature extraction again
• —Not suitable for training non-Whisper architectures without re-processing

Limitations

• —Only 40% of the full dataset (subset for faster experimentation)
• —Pre-computed features are specific to Whisper architecture
• —Fixed audio length (30 seconds max due to Whisper constraints)

Citation

If you use this dataset, please cite both this preprocessed version and the original source dataset:

@dataset{arabic_whisper_multidialect_processed,
  title={Arabic Whisper Multi-Dialect - Processed (Small)},
  author={MadLook},
  year={2025},
  publisher={Hugging Face},
  url={https://huggingface.co/datasets/MadLook/arabic-whisper-multidialect-processed-small}
}

License

Apache 2.0

Contact

For questions or issues with this dataset, please open an issue on the dataset repository.

Dataset Version: 1.0 Last Updated: 2025 Preprocessor: Whisper Feature Extractor (openai/whisper-small) Compatible Models: openai/whisper-tiny, openai/whisper-base, openai/whisper-small, openai/whisper-medium, openai/whisper-large