Upload folder using huggingface_hub

.gitattributes

@@ -1,35 +1,35 @@
-*.7z filter=lfs diff=lfs merge=lfs -text
-*.arrow filter=lfs diff=lfs merge=lfs -text
-*.bin filter=lfs diff=lfs merge=lfs -text
-*.bz2 filter=lfs diff=lfs merge=lfs -text
-*.ckpt filter=lfs diff=lfs merge=lfs -text
-*.ftz filter=lfs diff=lfs merge=lfs -text
-*.gz filter=lfs diff=lfs merge=lfs -text
-*.h5 filter=lfs diff=lfs merge=lfs -text
-*.joblib filter=lfs diff=lfs merge=lfs -text
-*.lfs.* filter=lfs diff=lfs merge=lfs -text
-*.mlmodel filter=lfs diff=lfs merge=lfs -text
-*.model filter=lfs diff=lfs merge=lfs -text
-*.msgpack filter=lfs diff=lfs merge=lfs -text
-*.npy filter=lfs diff=lfs merge=lfs -text
-*.npz filter=lfs diff=lfs merge=lfs -text
-*.onnx filter=lfs diff=lfs merge=lfs -text
-*.ot filter=lfs diff=lfs merge=lfs -text
-*.parquet filter=lfs diff=lfs merge=lfs -text
-*.pb filter=lfs diff=lfs merge=lfs -text
-*.pickle filter=lfs diff=lfs merge=lfs -text
-*.pkl filter=lfs diff=lfs merge=lfs -text
-*.pt filter=lfs diff=lfs merge=lfs -text
-*.pth filter=lfs diff=lfs merge=lfs -text
-*.rar filter=lfs diff=lfs merge=lfs -text
-*.safetensors filter=lfs diff=lfs merge=lfs -text
-saved_model/**/* filter=lfs diff=lfs merge=lfs -text
-*.tar.* filter=lfs diff=lfs merge=lfs -text
-*.tar filter=lfs diff=lfs merge=lfs -text
-*.tflite filter=lfs diff=lfs merge=lfs -text
-*.tgz filter=lfs diff=lfs merge=lfs -text
-*.wasm filter=lfs diff=lfs merge=lfs -text
-*.xz filter=lfs diff=lfs merge=lfs -text
-*.zip filter=lfs diff=lfs merge=lfs -text
-*.zst filter=lfs diff=lfs merge=lfs -text
-*tfevents* filter=lfs diff=lfs merge=lfs -text
+*.7z filter=lfs diff=lfs merge=lfs -text
+*.arrow filter=lfs diff=lfs merge=lfs -text
+*.bin filter=lfs diff=lfs merge=lfs -text
+*.bz2 filter=lfs diff=lfs merge=lfs -text
+*.ckpt filter=lfs diff=lfs merge=lfs -text
+*.ftz filter=lfs diff=lfs merge=lfs -text
+*.gz filter=lfs diff=lfs merge=lfs -text
+*.h5 filter=lfs diff=lfs merge=lfs -text
+*.joblib filter=lfs diff=lfs merge=lfs -text
+*.lfs.* filter=lfs diff=lfs merge=lfs -text
+*.mlmodel filter=lfs diff=lfs merge=lfs -text
+*.model filter=lfs diff=lfs merge=lfs -text
+*.msgpack filter=lfs diff=lfs merge=lfs -text
+*.npy filter=lfs diff=lfs merge=lfs -text
+*.npz filter=lfs diff=lfs merge=lfs -text
+*.onnx filter=lfs diff=lfs merge=lfs -text
+*.ot filter=lfs diff=lfs merge=lfs -text
+*.parquet filter=lfs diff=lfs merge=lfs -text
+*.pb filter=lfs diff=lfs merge=lfs -text
+*.pickle filter=lfs diff=lfs merge=lfs -text
+*.pkl filter=lfs diff=lfs merge=lfs -text
+*.pt filter=lfs diff=lfs merge=lfs -text
+*.pth filter=lfs diff=lfs merge=lfs -text
+*.rar filter=lfs diff=lfs merge=lfs -text
+*.safetensors filter=lfs diff=lfs merge=lfs -text
+saved_model/**/* filter=lfs diff=lfs merge=lfs -text
+*.tar.* filter=lfs diff=lfs merge=lfs -text
+*.tar filter=lfs diff=lfs merge=lfs -text
+*.tflite filter=lfs diff=lfs merge=lfs -text
+*.tgz filter=lfs diff=lfs merge=lfs -text
+*.wasm filter=lfs diff=lfs merge=lfs -text
+*.xz filter=lfs diff=lfs merge=lfs -text
+*.zip filter=lfs diff=lfs merge=lfs -text
+*.zst filter=lfs diff=lfs merge=lfs -text
+*tfevents* filter=lfs diff=lfs merge=lfs -text

README.md

@@ -1,11 +1,335 @@
----
-license: mit
-datasets:
-- westbrook/English_Accent_DataSet
-base_model:
-- openai/whisper-small
-pipeline_tag: audio-classification
-tags:
-- accent
-- gender
----
+---
+license: mit
+datasets:
+- westbrook/English_Accent_DataSet
+base_model:
+- openai/whisper-small
+pipeline_tag: audio-classification
+tags:
+- accent
+- gender
+---
+
+# Whisper Audio Classification Model
+
+A fine-tuned Whisper model for multi-task audio classification, specifically trained to classify **English accents** (23 classes) and **speaker gender** (2 classes) from speech audio.
+
+## 🎯 Model Overview
+
+This model uses OpenAI's Whisper encoder as a feature extractor with custom classification heads for:
+- **Accent Classification**: Identifies 23 different English accents
+- **Gender Classification**: Classifies speaker as male or female
+
+### Model Architecture
+- **Base Model**: `openai/whisper-small.en`
+- **Encoder**: Frozen Whisper encoder (for feature extraction)
+- **Classification Heads**: Custom neural networks with dropout for robust predictions
+- **Multi-task Learning**: Jointly trained on both accent and gender classification
+
+## 🚀 Quick Start
+
+### Prerequisites
+
+```bash
+pip install torch transformers datasets numpy scikit-learn
+```
+
+### Basic Usage
+
+```python
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from transformers import WhisperFeatureExtractor, WhisperModel
+import numpy as np
+
+# Define the model class (same as training)
+class WhisperClassifier(nn.Module):
+    def __init__(self, model_name="openai/whisper-small.en", num_accent_classes=23, num_gender_classes=2, 
+                 freeze_encoder=True, dropout_rate=0.3):
+        super().__init__()
+        
+        self.whisper = WhisperModel.from_pretrained(model_name)
+        
+        if freeze_encoder:
+            for param in self.whisper.encoder.parameters():
+                param.requires_grad = False
+                
+        self.hidden_size = self.whisper.config.d_model
+        self.dropout = nn.Dropout(dropout_rate)
+        
+        # Accent classification head
+        self.accent_classifier = nn.Sequential(
+            nn.Linear(self.hidden_size, 512),
+            nn.ReLU(),
+            nn.Dropout(dropout_rate),
+            nn.Linear(512, 256),
+            nn.ReLU(),
+            nn.Dropout(dropout_rate),
+            nn.Linear(256, num_accent_classes)
+        )
+        
+        # Gender classification head
+        self.gender_classifier = nn.Sequential(
+            nn.Linear(self.hidden_size, 256),
+            nn.ReLU(),
+            nn.Dropout(dropout_rate),
+            nn.Linear(256, 128),
+            nn.ReLU(),
+            nn.Dropout(dropout_rate),
+            nn.Linear(128, num_gender_classes)
+        )
+        
+        self.num_accent_classes = num_accent_classes
+        self.num_gender_classes = num_gender_classes
+        
+    def forward(self, input_features, accent_labels=None, gender_labels=None):
+        encoder_outputs = self.whisper.encoder(input_features)
+        hidden_states = encoder_outputs.last_hidden_state
+        pooled_output = hidden_states.mean(dim=1)
+        pooled_output = self.dropout(pooled_output)
+        
+        accent_logits = self.accent_classifier(pooled_output)
+        gender_logits = self.gender_classifier(pooled_output)
+        
+        return {
+            'accent_logits': accent_logits,
+            'gender_logits': gender_logits,
+        }
+
+# Load the trained model
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+model = WhisperClassifier()
+
+# Load the trained weights
+model.load_state_dict(torch.load("./model_step1000.safetensors", map_location=device))
+model.to(device)
+model.eval()
+
+# Initialize feature extractor
+feature_extractor = WhisperFeatureExtractor.from_pretrained("openai/whisper-small.en")
+```
+
+### Making Predictions
+
+```python
+def predict_audio(audio_file_path, model, feature_extractor, device):
+    """
+    Predict accent and gender from an audio file
+    
+    Args:
+        audio_file_path: Path to audio file (.wav, .mp3, etc.)
+        model: Trained WhisperClassifier model
+        feature_extractor: Whisper feature extractor
+        device: torch device (cuda/cpu)
+    
+    Returns:
+        Dictionary with predictions and confidence scores
+    """
+    import librosa
+    
+    # Load audio file
+    audio, sr = librosa.load(audio_file_path, sr=16000, mono=True)
+    
+    # Extract features
+    inputs = feature_extractor(
+        audio, 
+        sampling_rate=sr, 
+        return_tensors="pt"
+    )
+    
+    # Move to device
+    input_features = inputs.input_features.to(device)
+    
+    # Get predictions
+    with torch.no_grad():
+        outputs = model(input_features=input_features)
+        
+        # Get probabilities
+        accent_probs = F.softmax(outputs["accent_logits"], dim=-1)
+        gender_probs = F.softmax(outputs["gender_logits"], dim=-1)
+        
+        # Get predictions
+        accent_pred = torch.argmax(accent_probs, dim=-1).item()
+        gender_pred = torch.argmax(gender_probs, dim=-1).item()
+        
+        # Get confidence scores
+        accent_confidence = accent_probs[0, accent_pred].item()
+        gender_confidence = gender_probs[0, gender_pred].item()
+    
+    # Map predictions to labels
+    accent_names = [
+        'african', 'australia', 'bermuda', 'canada', 'england', 'hongkong', 
+        'indian', 'ireland', 'malaysia', 'newzealand', 'philippines', 
+        'scotland', 'singapore', 'southafrica', 'us', 'wales'
+        # Add all 23 accent names based on your dataset
+    ]
+    
+    accent_name = accent_names[accent_pred] if accent_pred < len(accent_names) else f"accent_{accent_pred}"
+    gender_name = "male" if gender_pred == 0 else "female"
+    
+    return {
+        'accent': accent_name,
+        'accent_confidence': accent_confidence,
+        'gender': gender_name,
+        'gender_confidence': gender_confidence
+    }
+
+# Example usage
+result = predict_audio("path/to/your/audio.wav", model, feature_extractor, device)
+print(f"Predicted Accent: {result['accent']} (confidence: {result['accent_confidence']:.3f})")
+print(f"Predicted Gender: {result['gender']} (confidence: {result['gender_confidence']:.3f})")
+```
+
+### Batch Predictions
+
+```python
+def predict_batch(audio_files, model, feature_extractor, device, batch_size=8):
+    """
+    Predict accent and gender for multiple audio files
+    """
+    import librosa
+    from torch.utils.data import DataLoader, Dataset
+    
+    class AudioDataset(Dataset):
+        def __init__(self, audio_files):
+            self.audio_files = audio_files
+            
+        def __len__(self):
+            return len(self.audio_files)
+            
+        def __getitem__(self, idx):
+            audio, sr = librosa.load(self.audio_files[idx], sr=16000, mono=True)
+            inputs = feature_extractor(audio, sampling_rate=sr, return_tensors="pt")
+            return inputs.input_features.squeeze(0)
+    
+    dataset = AudioDataset(audio_files)
+    dataloader = DataLoader(dataset, batch_size=batch_size, shuffle=False)
+    
+    results = []
+    model.eval()
+    
+    with torch.no_grad():
+        for batch in dataloader:
+            batch = batch.to(device)
+            outputs = model(input_features=batch)
+            
+            accent_probs = F.softmax(outputs["accent_logits"], dim=-1)
+            gender_probs = F.softmax(outputs["gender_logits"], dim=-1)
+            
+            accent_preds = torch.argmax(accent_probs, dim=-1)
+            gender_preds = torch.argmax(gender_probs, dim=-1)
+            
+            for i in range(len(batch)):
+                results.append({
+                    'accent_id': accent_preds[i].item(),
+                    'accent_confidence': accent_probs[i, accent_preds[i]].item(),
+                    'gender_id': gender_preds[i].item(),
+                    'gender_confidence': gender_probs[i, gender_preds[i]].item(),
+                })
+    
+    return results
+```
+
+## 📊 Model Performance
+
+The model was trained on the English Accent Dataset with the following performance:
+
+- **Accent Classification**: Achieves high accuracy across 23 English accent varieties
+- **Gender Classification**: Robust binary classification for male/female voices
+- **Multi-task Learning**: Benefits from joint training on both tasks
+
+### Supported Accent Classes
+
+The model can classify the following accent varieties:
+1. African
+2. Australian
+3. Bermuda
+4. Canadian
+5. England
+6. Hong Kong
+7. Indian
+8. Irish
+9. Malaysian
+10. New Zealand
+11. Philippines
+12. Scottish
+13. Singapore
+14. South African
+15. US American
+16. Welsh
+... (and more, totaling 23 classes)
+
+## 🔧 Advanced Usage
+
+### Custom Audio Processing
+
+```python
+def preprocess_custom_audio(audio_array, sample_rate, target_sr=16000):
+    """
+    Preprocess custom audio data
+    """
+    import librosa
+    
+    # Resample if needed
+    if sample_rate != target_sr:
+        audio_array = librosa.resample(audio_array, orig_sr=sample_rate, target_sr=target_sr)
+    
+    # Ensure mono
+    if len(audio_array.shape) > 1:
+        audio_array = librosa.to_mono(audio_array)
+    
+    # Normalize
+    audio_array = audio_array / np.max(np.abs(audio_array))
+    
+    return audio_array
+```
+
+### Getting Top-K Predictions
+
+```python
+def get_top_k_predictions(audio_file, model, feature_extractor, device, k=3):
+    """
+    Get top-k accent predictions with confidence scores
+    """
+    # ... (load and preprocess audio as above)
+    
+    with torch.no_grad():
+        outputs = model(input_features=input_features)
+        accent_probs = F.softmax(outputs["accent_logits"], dim=-1)
+        
+        # Get top-k predictions
+        top_k_probs, top_k_indices = torch.topk(accent_probs, k, dim=-1)
+        
+        results = []
+        for i in range(k):
+            results.append({
+                'accent_id': top_k_indices[0, i].item(),
+                'confidence': top_k_probs[0, i].item()
+            })
+    
+    return results
+```
+
+## 📋 Requirements
+
+- Python 3.8+
+- PyTorch 1.9+
+- Transformers 4.20+
+- librosa (for audio loading)
+- numpy
+- scikit-learn (for evaluation metrics)
+
+## 📄 License
+
+This model is based on OpenAI's Whisper and follows the same licensing terms. Please check the original Whisper repository for license details.
+
+## 🙏 Acknowledgments
+
+- OpenAI for the Whisper model
+- The English Accent Dataset creators
+- Hugging Face Transformers library
+
+---
+
+**Note**: This model is trained for research and educational purposes. Performance may vary on different audio qualities, recording conditions, and accent varieties not represented in the training data.

accent_classifier.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:9aab970ac55d278a44a5e8bc3a714604c69eb67b5ac2644ebcd2d3faa262c6d6
+size 970038528