Update README.md

README.md

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 ---
-dataset_info:
-  features:
-  - name: input_features
-    sequence:
-      sequence: float32
-  - name: labels
-    sequence: int64
-  splits:
-  - name: train
-    num_bytes: 36341853032
-    num_examples: 37835
-  - name: validation
-    num_bytes: 2524287408
-    num_examples: 2628
-  - name: test
-    num_bytes: 2524301720
-    num_examples: 2628
-  download_size: 5837478573
-  dataset_size: 41390442160
-configs:
-- config_name: default
-  data_files:
-  - split: train
-    path: data/train-*
-  - split: validation
-    path: data/validation-*
-  - split: test
-    path: data/test-*
+language:
+- ar
+license: apache-2.0
+task_categories:
+- automatic-speech-recognition
+tags:
+- whisper
+- arabic
+- speech
+- asr
+- multidialect
+pretty_name: Arabic Whisper Multi-Dialect (Processed) - Small
+size_categories:
+- 10K<n<100K
 ---
+
+# 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
+
+| Split | Examples | Size (GB) |
+|-------|----------|-----------|
+| Train | 37,835 | 33.8 |
+| Validation | 2,628 | 2.35 |
+| Test | 2,628 | 2.35 |
+| **Total** | **43,091** | **38.5** |
+
+### 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
+
+```python
+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
+
+```python
+# 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. **Audio Loading**: Resampled to 16kHz mono audio
+2. **Feature Extraction**: Converted to 80-channel log-mel spectrograms using Whisper's feature extractor
+3. **Tokenization**: Arabic text transcriptions tokenized with Whisper's multilingual tokenizer
+4. **Normalization**: Applied Whisper's standard audio normalization
+5. **Subset Selection**: Selected 40% of the original dataset
+
+### Processing Code
+
+```python
+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:
+
+```bibtex
+@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**: 2024  
+**Preprocessor**: Whisper Feature Extractor (openai/whisper-small)  
+**Compatible Models**: openai/whisper-tiny, openai/whisper-base, openai/whisper-small, openai/whisper-medium, openai/whisper-large