DBGad
/

Meezan-ASR

@@ -1,199 +1,213 @@
 ---
 library_name: transformers
-tags: []
 ---
-# Model Card for Model ID
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-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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+````markdown
 ---
+language:
+- en
+license: apache-2.0
 library_name: transformers
+pipeline_tag: automatic-speech-recognition
+tags:
+- automatic-speech-recognition
+- speech
+- audio
+- whisper
+- fine-tuning
+- pytorch
+- transformers
+- huggingface
+- deep-learning
+- asr
 ---
+# Whisper Fine-Tuned Automatic Speech Recognition Model
 ## Model Details
 ### Model Description
+This model is a fine-tuned version of OpenAI's Whisper model for Automatic Speech Recognition (ASR). It has been trained on a custom speech dataset to improve transcription accuracy for domain-specific speech and conversational audio.
+The model leverages the strong multilingual capabilities of Whisper while adapting to the characteristics of the target dataset through supervised fine-tuning.
+- **Developed by:** Gad Amr
+- **Funded by:** Self-funded Academic Project
+- **Shared by:** Gad Amr
+- **Model type:** Automatic Speech Recognition (ASR)
+- **Base model:** OpenAI Whisper Medium
+- **Language(s):** Arabic / English
+- **Framework:** Hugging Face Transformers
+- **License:** Apache-2.0
+- **Fine-tuned from model:** openai/whisper-Medium
+---
+## Direct Use
+This model is designed for automatic speech recognition tasks, including:
+- Speech-to-text transcription
+---
+## Downstream Use
+The model can be integrated into:
+- Meezan Graduation Project
+---
+## Out-of-Scope Use
+# Bias, Risks, and Limitations
+Like all speech recognition models, performance depends on:
+- Audio quality
+- Background noise
+- Microphone quality
+- Accent diversity
+- Speaking speed
+- Domain mismatch
+Potential limitations include:
+- Reduced accuracy on unseen accents but covered Egyptian ones .
+- Difficulty with overlapping speakers.
+---
+## Recommendations
+# How to Get Started with the Model
+```python
+from transformers import pipeline
+pipe = pipeline(
+    "automatic-speech-recognition",
+    model="DBGad/Meezan-ASR"
+)
+result = pipe("sample.wav")
+print(result["text"])
+````
+---
+### Preprocessing
+The following preprocessing steps were applied:
+* Resampling audio to 16 kHz
+* Removing corrupted samples
+* Feature extraction using WhisperFeatureExtractor
+* Text normalization
+* Tokenization using WhisperTokenizer
+* Dynamic padding with DataCollatorSpeechSeq2SeqWithPadding
+---
+### LoRA Configuration
+| Parameter | Value |
+|------------|-------|
+| Rank (r) | 8 |
+| Alpha | 16 |
+| Dropout | 0.1 |
+Target Modules:
+- encoder_attn.q_proj
+- encoder_attn.v_proj
+- encoder_attn.out_proj
+---
+## Training Hyperparameters
+| Hyperparameter | Value |
+|----------------|-------|
+| Base Model | openai/whisper-medium |
+| Epochs | 12 |
+| Batch Size (per device) | 16 |
+| Gradient Accumulation | 8 |
+| Effective Batch Size | 128 |
+| Learning Rate | 5e-4 |
+| Warmup Steps | 150 |
+| Evaluation Strategy | Every 200 Steps |
+| Checkpoint Saving | Every 200 Steps |
+| Logging Steps | Every 50 Steps |
+| Best Model Selection Metric | Word Error Rate (WER) |
+| Load Best Model | Enabled |
+---
+## Optimization
+The model was trained using:
+- Mixed Precision (FP16/BF16)
+- Gradient Checkpointing
+- LoRA Parameter-Efficient Fine-Tuning
+These techniques significantly reduced GPU memory consumption while maintaining strong model performance.
+---
+## Evaluation
+Validation was performed every **200 training steps**.
+The best checkpoint was automatically selected using the **lowest Word Error Rate (WER)**.
+Evaluation Metric:
+- Word Error Rate (WER)
+---
+## Hardware & Software
+### Hardware
+Training was performed on an NVIDIA GPU with CUDA support.
+### Software
+- Python
+- PyTorch
+- Hugging Face Transformers
+- PEFT (LoRA)
+- Datasets
+- Evaluate
+- Accelerate
+---
+## Model Objective
+The objective of the model is to convert spoken English audio into accurate text transcriptions.
+The model minimizes sequence-to-sequence cross-entropy loss while optimizing transcription quality measured by Word Error Rate (WER).
+# Model Card Authors
+* Gad Amr
+---
+# Model Card Contact
+For questions, suggestions, or collaboration:
+* GitHub: https://github.com/yourusername
+* Email: [your.email@example.com](mailto:your.email@example.com)
+```
+```