Update README.md

README.md

@@ -1,278 +1,302 @@
----
-dataset_info:
-  features:
-    - name: audio
-      dtype: audio
-    - name: transcription
-      dtype: string
-  splits:
-    - name: train
-      num_bytes: 1110063079
-      num_examples: 5000
-    - name: validation
-      num_bytes: 82102316
-      num_examples: 500
-  download_size: 1138402984
-  dataset_size: 1192165395
-configs:
-  - config_name: default
-    data_files:
-      - split: train
-        path: data/train-*
-      - split: validation
-        path: data/validation-*
-tags:
-  - masrispeech
-  - egyptian-arabic
-  - arabic
-  - speech
-  - audio
-  - asr
-  - automatic-speech-recognition
-  - speech-to-text
-  - stt
-  - dialectal-arabic
-  - egypt
-  - native-speakers
-  - spoken-arabic
-  - egyptian-dialect
-  - arabic-dialect
-  - audio-dataset
-  - language-resources
-  - low-resource-language
-  - phonetics
-  - speech-corpus
-  - voice
-  - transcription
-  - linguistic-data
-  - machine-learning
-  - natural-language-processing
-  - nlp
-  - huggingface
-  - open-dataset
-  - labeled-data
-task_categories:
-  - automatic-speech-recognition
-  - audio-classification
-  - audio-to-audio
-language:
-  - arz
-  - ar
-pretty_name: MasriSpeech-ASR-Finetuning
----
-
-
-# 🗣️ MasriSpeech-ASR-Finetuning: Egyptian Arabic Speech Fine-Tuning Dataset
-
-[![License](https://img.shields.io/badge/License-Apache%202.0-blue.svg)](https://opensource.org/licenses/Apache-2.0)
-[![Hugging Face](https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Dataset-blue)](https://huggingface.co/collections/NightPrince/masrispeech-dataset-68594e59e46fd12c723f1544)
-
-<p align="center">
-  <img src="https://photos.fife.usercontent.google.com/pw/AP1GczMrHAd6vsWiDzHtrWwBu_GzNBEpHvGZpDcnahMOt5npvlI-fdt65fuuig=w618-h927-s-no-gm?authuser=0" alt="MasriSpeech-ASR-Finetuning Dataset Overview" width="650">
-</p>
-
-## 🌍 Overview
-**MasriSpeech-ASR-Finetuning** is a specialized subset of the MasriSpeech dataset, designed for fine-tuning Automatic Speech Recognition (ASR) models for Egyptian Arabic. This dataset contains 5,500 professionally annotated audio samples totaling over 100 hours of natural Egyptian Arabic speech.
-
-> 💡 **Key Features**:
-> - High-quality 16kHz speech recordings
-> - Natural conversational Egyptian Arabic
-> - Speaker-balanced train/validation splits
-> - Comprehensive linguistic coverage
-> - Apache 2.0 license
-
-## 📊 Dataset Summary
-
-| Feature                  | Value                     |
-|--------------------------|---------------------------|
-| **Total Samples**        | 5,500                    |
-| **Train Samples**        | 5,000                    |
-| **Validation Samples**   | 500                      |
-| **Sampling Rate**        | 16 kHz                   |
-| **Total Duration**       | ~100 hours               |
-| **Languages**            | Egyptian Arabic (arz), Arabic (ar) |
-| **Format**               | Parquet                  |
-| **Dataset Size**         | 1.19 GB                  |
-| **Download Size**        | 1.13 GB                  |
-| **Annotations**          | Transcripts              |
-
-## 🧱 Dataset Structure
-The dataset follows Hugging Face `datasets` format with two splits:
-
-```python
-DatasetDict({
-    train: Dataset({
-        features: ['audio', 'transcription'],
-        num_rows: 5000
-    })
-    validation: Dataset({
-        features: ['audio', 'transcription'],
-        num_rows: 500
-    })
-})
-
-## Data Fields
-
-- **audio**: Audio feature object containing:
-  - `Array`: Raw speech waveform (1D float array)
-  - `Path`: Relative audio path
-  - `Sampling_rate`: 16,000 Hz
-- **transcription**: string with Egyptian Arabic transcription
-
-## 📈 Data Statistics
-
-### Split Distribution
-
-| Split        | Examples | Size (GB) | Avg. Words | Empty | Non-Arabic |
-|--------------|----------|-----------|------------|-------|------------|
-| **Train**    | 5,000    | 1.11      | 13.34      | 0     | 0          |
-| **Validation**| 500      | 0.08      | 9.60       | 0     | 0          |
-
-### Linguistic Analysis
-
-| Feature         | Train Set                 | Validation Set             |
-|-----------------|---------------------------|----------------------------|
-| **Top Words**   | في (2,025), و (1,698)     | في (52), أنا (41)          |
-| **Top Bigrams** | (إن, أنا) (130)          | (شاء, الله) (6)           |
-| **Vocab Size**  | 3,845                     | 789                        |
-| **Unique Speakers** | 114                      | 10                         |
-
-<p align="center">
-  <img src="https://photos.fife.usercontent.google.com/pw/AP1GczNy2HLdi9J2wLQt_m-9Wu713_9uX2k3FsIytd6NxS7C_bAYkPqisRLOLg=w800-h400-s-no-gm?authuser=0" alt="Train Distribution" width="45%">
-  <img src="https://photos.fife.usercontent.google.com/pw/AP1GczNPSXQnWjDc7LElNeCff8wmP6-nT59WmTiPREKDhlD6RrUHX8TIC6ocbw=w800-h400-s-no-gm?authuser=0" alt="Validation Distribution" width="45%">
-  <br><em>Word Count Distributions (Left: Train, Right: Validation)</em>
-</p>
-
-
-## How to Use ? 🧑‍💻
-
-### Loading with Hugging Face
-```python
-from datasets import load_dataset
-import IPython.display as ipd
-
-# Load dataset (streaming recommended for large datasets)
-ds = load_dataset('NightPrince/MasriSpeech-ASR-Finetuning', 
-                 split='train',
-                 streaming=True)
-
-# Get first sample
-sample = next(iter(ds))
-print(f"Transcript: {sample['transcription']}")
-
-# Play audio
-ipd.Audio(sample['audio']['array'], 
-          rate=sample['audio']['sampling_rate'])
-```
-
-### Preprocessing the Dataset
-```python
-from transformers import AutoProcessor
-
-# Load pre-trained processor
-processor = AutoProcessor.from_pretrained("facebook/wav2vec2-large-xlsr-53")
-
-# Preprocessing function
-def prepare_dataset(batch):
-    audio = batch["audio"]
-    # Process audio
-    batch["input_values"] = processor(audio["array"], 
-                                     sampling_rate=audio["sampling_rate"],
-                                     return_tensors="pt").input_values[0]
-    # Process transcription
-    batch["labels"] = processor(text=batch["transcription"]).input_ids
-    return batch
-
-# Apply preprocessing to the dataset
-dataset = ds.map(prepare_dataset, remove_columns=ds.column_names)
-```
-
-### Fine-Tuning an ASR Model
-```python
-from transformers import AutoModelForCTC, TrainingArguments, Trainer
-
-# Load pre-trained model
-model = AutoModelForCTC.from_pretrained("facebook/wav2vec2-large-xlsr-53")
-
-# Define training arguments
-training_args = TrainingArguments(
-    output_dir="./results",
-    evaluation_strategy="epoch",
-    learning_rate=2e-5,
-    per_device_train_batch_size=16,
-    num_train_epochs=3,
-    save_steps=10,
-    save_total_limit=2,
-    logging_dir="./logs",
-    logging_steps=10,
-)
-
-# Initialize Trainer
-trainer = Trainer(
-    model=model,
-    args=training_args,
-    train_dataset=dataset,
-    eval_dataset=dataset,
-)
-
-# Train the model
-trainer.train()
-```
-
-### Evaluating the Model
-```python
-# Evaluate the model
-eval_results = trainer.evaluate()
-print("Evaluation Results:", eval_results)
-```
-
-### Exporting the Model
-```python
-# Save the fine-tuned model
-model.save_pretrained("./fine_tuned_model")
-processor.save_pretrained("./fine_tuned_model")
-```
-
-## 📜 Citation
-If you use **MasriSpeech-ASR-Finetuning** in your research or work, please cite it as follows:
-
-```
-@dataset{masrispeech_asr_finetuning,
-  author       = {Yahya Muhammad Alnwsany},
-  title        = {MasriSpeech-ASR-Finetuning: Egyptian Arabic Speech Fine-Tuning Dataset},
-  year         = {2025},
-  publisher    = {Hugging Face},
-  url          = {https://huggingface.co/collections/NightPrince/masrispeech-dataset-68594e59e46fd12c723f1544}
-}
-```
-
-## 📜 Licensing
-This dataset is released under the **Apache 2.0 License**. You are free to use, modify, and distribute the dataset, provided you comply with the terms of the license. For more details, see the [LICENSE](https://opensource.org/licenses/Apache-2.0).
-
-## 🙌 Acknowledgments
-We would like to thank the following for their contributions and support:
-- **Annotators**: For their meticulous work in creating high-quality transcriptions.
-- **Hugging Face**: For providing tools and hosting the dataset.
-- **Open-Source Community**: For their continuous support and feedback.
-
-## 💡 Use Cases
-**MasriSpeech-ASR-Finetuning** can be used in various applications, including:
-- Fine-tuning Automatic Speech Recognition (ASR) models for Egyptian Arabic.
-- Dialectal Arabic linguistic research.
-- Speech synthesis and voice cloning.
-- Training and benchmarking machine learning models for low-resource languages.
-
-## 🤝 Contributing
-We welcome contributions to improve **MasriSpeech-ASR-Finetuning**. If you have suggestions, find issues, or want to add new features, please:
-1. Fork the repository.
-2. Create a new branch for your changes.
-3. Submit a pull request with a detailed description of your changes.
-
-For questions or feedback, feel free to contact the maintainer.
-
-## 📝 Changelog
-### [1.0.0] - 2025-08-02
-- Initial release of **MasriSpeech-ASR-Finetuning**.
-- Includes 5,500 audio samples with transcriptions.
-- Train/validation splits provided.
-- Dataset hosted on Hugging Face.
+---
+dataset_info:
+  features:
+    - name: audio
+      dtype: audio
+    - name: transcription
+      dtype: string
+  splits:
+    - name: train
+      num_bytes: 1110063079
+      num_examples: 5000
+    - name: validation
+      num_bytes: 82102316
+      num_examples: 500
+  download_size: 1138402984
+  dataset_size: 1192165395
+configs:
+  - config_name: default
+    data_files:
+      - split: train
+        path: data/train-*
+      - split: validation
+        path: data/validation-*
+tags:
+  - masrispeech
+  - egyptian-arabic
+  - arabic
+  - speech
+  - audio
+  - asr
+  - automatic-speech-recognition
+  - speech-to-text
+  - stt
+  - dialectal-arabic
+  - egypt
+  - native-speakers
+  - spoken-arabic
+  - egyptian-dialect
+  - arabic-dialect
+  - audio-dataset
+  - language-resources
+  - low-resource-language
+  - phonetics
+  - speech-corpus
+  - voice
+  - transcription
+  - linguistic-data
+  - machine-learning
+  - natural-language-processing
+  - nlp
+  - huggingface
+  - open-dataset
+  - labeled-data
+task_categories:
+  - automatic-speech-recognition
+  - audio-classification
+  - audio-to-audio
+language:
+  - arz
+  - ar
+pretty_name: MasriSpeech-ASR-Finetuning
+---
+
+
+# 🗣️ MasriSpeech-ASR-Finetuning: Egyptian Arabic Speech Fine-Tuning Dataset
+
+[![License](https://img.shields.io/badge/License-Apache%202.0-blue.svg)](https://opensource.org/licenses/Apache-2.0)
+[![Hugging Face](https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Dataset-blue)](https://huggingface.co/collections/NightPrince/masrispeech-dataset-68594e59e46fd12c723f1544)
+
+<p align="center">
+  <img src="https://github.com/NightPrinceY/Helmet-V8/blob/main/MasriSpeech.png?raw=true" 
+       alt="MasriSpeech-Full Dataset Overview" 
+       width="600" 
+       height="750"
+       style="border-radius: 8px; box-shadow: 0 4px 12px rgba(0,0,0,0.1); object-fit: cover; object-position: top;">
+</p>
+
+## 🌍 Overview
+**MasriSpeech-ASR-Finetuning** is a specialized subset of the MasriSpeech dataset, designed for fine-tuning Automatic Speech Recognition (ASR) models for Egyptian Arabic. This dataset contains 5,500 professionally annotated audio samples totaling over 100 hours of natural Egyptian Arabic speech.
+
+> 💡 **Key Features**:
+> - High-quality 16kHz speech recordings
+> - Natural conversational Egyptian Arabic
+> - Speaker-balanced train/validation splits
+> - Comprehensive linguistic coverage
+> - Apache 2.0 license
+
+## 📊 Dataset Summary
+
+| Feature                  | Value                     |
+|--------------------------|---------------------------|
+| **Total Samples**        | 5,500                    |
+| **Train Samples**        | 5,000                    |
+| **Validation Samples**   | 500                      |
+| **Sampling Rate**        | 16 kHz                   |
+| **Total Duration**       | ~100 hours               |
+| **Languages**            | Egyptian Arabic (arz), Arabic (ar) |
+| **Format**               | Parquet                  |
+| **Dataset Size**         | 1.19 GB                  |
+| **Download Size**        | 1.13 GB                  |
+| **Annotations**          | Transcripts              |
+
+## 🧱 Dataset Structure
+The dataset follows Hugging Face `datasets` format with two splits:
+
+```python
+DatasetDict({
+    train: Dataset({
+        features: ['audio', 'transcription'],
+        num_rows: 5000
+    })
+    validation: Dataset({
+        features: ['audio', 'transcription'],
+        num_rows: 500
+    })
+})
+```
+## Data Fields
+
+- **audio**: Audio feature object containing:
+  - `Array`: Raw speech waveform (1D float array)
+  - `Path`: Relative audio path
+  - `Sampling_rate`: 16,000 Hz
+- **transcription**: string with Egyptian Arabic transcription
+
+## 📈 Data Statistics
+
+### Split Distribution
+
+| Split        | Examples | Size (GB) | Avg. Words | Empty | Non-Arabic |
+|--------------|----------|-----------|------------|-------|------------|
+| **Train**    | 5,000    | 1.11      | 13.34      | 0     | 0          |
+| **Validation**| 500      | 0.08      | 9.60       | 0     | 0          |
+
+### Linguistic Analysis
+
+| Feature         | Train Set                 | Validation Set             |
+|-----------------|---------------------------|----------------------------|
+| **Top Words**   | في (2,025), و (1,698)     | في (52), أنا (41)          |
+| **Top Bigrams** | (إن, أنا) (130)          | (شاء, الله) (6)           |
+| **Vocab Size**  | 3,845                     | 789                        |
+| **Unique Speakers** | 114                      | 10                         |
+
+<p align="center">
+  <img src="https://github.com/NightPrinceY/Helmet-V8/blob/main/train_wordcount_hist.png?raw=true" alt="Train Distribution" width="45%">
+  <img src="https://github.com/NightPrinceY/Helmet-V8/blob/main/adapt_wordcount_hist.png?raw=true" alt="Validation Distribution" width="45%">
+  <br><em>Word Count Distributions (Left: Train, Right: Validation)</em>
+</p>
+
+
+
+## How to Use ? 🧑‍💻
+
+### Loading with Hugging Face
+```python
+from datasets import load_dataset
+import IPython.display as ipd
+
+# Load dataset (streaming recommended for large datasets)
+ds = load_dataset('NightPrince/MasriSpeech-ASR-Finetuning', 
+                 split='train',
+                 streaming=True)
+
+# Get first sample
+sample = next(iter(ds))
+print(f"Transcript: {sample['transcription']}")
+
+# Play audio
+ipd.Audio(sample['audio']['array'], 
+          rate=sample['audio']['sampling_rate'])
+```
+
+### Preprocessing the Dataset
+```python
+from transformers import Wav2Vec2Processor, Wav2Vec2ForCTC
+from datasets import load_dataset
+import torch
+model_name = "facebook/wav2vec2-base-960h"  # Spanish example
+# or "facebook/wav2vec2-large-xlsr-53-en" for English
+processor = Wav2Vec2Processor.from_pretrained(model_name)
+model = Wav2Vec2ForCTC.from_pretrained(model_name)
+
+def prepare_dataset(batch):
+    audio = batch["audio"]
+    
+    # Extract audio array and sampling rate
+    audio_array = audio["array"]
+    sampling_rate = audio["sampling_rate"]
+    
+    # Process audio using feature extractor only
+    inputs = processor.feature_extractor(
+        audio_array, 
+        sampling_rate=sampling_rate, 
+        return_tensors="pt"
+    )
+    
+    batch["input_values"] = inputs.input_values[0]
+    
+    # Process transcription using tokenizer only
+    labels = processor.tokenizer(
+        batch["transcription"], 
+        return_tensors="pt"
+    )
+    
+    batch["labels"] = labels["input_ids"][0]
+    
+    return batch
+
+# Apply preprocessing to the entire dataset
+print("Processing entire dataset...")
+dataset = ds.map(prepare_dataset, remove_columns=["audio", "transcription"])
+```
+
+### Fine-Tuning an ASR Model
+```python
+from transformers import AutoModelForCTC, TrainingArguments, Trainer
+
+# Load pre-trained model
+model = AutoModelForCTC.from_pretrained("facebook/wav2vec2-base-960h")
+
+# Define training arguments
+training_args = TrainingArguments(
+    output_dir="./results",
+    evaluation_strategy="epoch",
+    learning_rate=2e-5,
+    per_device_train_batch_size=16,
+    num_train_epochs=3,
+    save_steps=10,
+    save_total_limit=2,
+    logging_dir="./logs",
+    logging_steps=10,
+)
+
+# Initialize Trainer
+trainer = Trainer(
+    model=model,
+    args=training_args,
+    train_dataset=dataset,
+    eval_dataset=dataset,
+)
+
+# Train the model
+trainer.train()
+```
+
+### Evaluating the Model
+```python
+# Evaluate the model
+eval_results = trainer.evaluate()
+print("Evaluation Results:", eval_results)
+```
+
+### Exporting the Model
+```python
+# Save the fine-tuned model
+model.save_pretrained("./fine_tuned_model")
+processor.save_pretrained("./fine_tuned_model")
+```
+
+## 📜 Citation
+If you use **MasriSpeech-ASR-Finetuning** in your research or work, please cite it as follows:
+
+```
+@dataset{masrispeech_asr_finetuning,
+  author       = {Yahya Muhammad Alnwsany},
+  title        = {MasriSpeech-ASR-Finetuning: Egyptian Arabic Speech Fine-Tuning Dataset},
+  year         = {2025},
+  publisher    = {Hugging Face},
+  url          = {https://huggingface.co/collections/NightPrince/masrispeech-dataset-68594e59e46fd12c723f1544}
+}
+```
+
+## 📜 Licensing
+This dataset is released under the **Apache 2.0 License**. You are free to use, modify, and distribute the dataset, provided you comply with the terms of the license. For more details, see the [LICENSE](https://opensource.org/licenses/Apache-2.0).
+
+## 🙌 Acknowledgments
+We would like to thank the following for their contributions and support:
+- **Annotators**: For their meticulous work in creating high-quality transcriptions.
+- **Hugging Face**: For providing tools and hosting the dataset.
+- **Open-Source Community**: For their continuous support and feedback.
+
+## 💡 Use Cases
+**MasriSpeech-ASR-Finetuning** can be used in various applications, including:
+- Fine-tuning Automatic Speech Recognition (ASR) models for Egyptian Arabic.
+- Dialectal Arabic linguistic research.
+- Speech synthesis and voice cloning.
+- Training and benchmarking machine learning models for low-resource languages.
+
+## 🤝 Contributing
+We welcome contributions to improve **MasriSpeech-ASR-Finetuning**. If you have suggestions, find issues, or want to add new features, please:
+1. Fork the repository.
+2. Create a new branch for your changes.
+3. Submit a pull request with a detailed description of your changes.
+
+For questions or feedback, feel free to contact the maintainer.
+
+## 📝 Changelog
+### [1.0.0] - 2025-08-02
+- Initial release of **MasriSpeech-ASR-Finetuning**.
+- Includes 5,500 audio samples with transcriptions.
+- Train/validation splits provided.
+- Dataset hosted on Hugging Face.