aicinema69
/

audio-emotion-detector-v1.0

@@ -1,199 +1,117 @@
 ---
 library_name: transformers
-tags: []
 ---
-# Model Card for Model ID
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-## Model Details
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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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 ---
 library_name: transformers
+tags:
+- emotion-detection
+- audio-emotion-detection
+base_model:
+- MIT/ast-finetuned-audioset-14-14-0.443
+pipeline_tag: audio-classification
 ---
+## 📊 Model Performance
+| Metric     | Score     |
+|------------|-----------|
+| Accuracy   | 0.38      |
+| Precision  | 0.3075    |
+| Recall     | 0.38      |
+| F1-Score   | 0.2871    |
+> **Note**: This model is a first iteration and may benefit from further fine-tuning and data augmentation.
+---
+## 🗂️ Emotion Classes
+The model classifies audio samples into the following 9 emotions:
+```
+0 - angry
+1 - apologetic
+2 - base
+3 - calm
+4 - excited
+5 - fear
+6 - happy
+7 - sad
+8 - surprise
+```
+---
+## 🏋️‍♂️ Training Details
+- **Dataset**: Custom dataset with `audio_path` and `emotion` columns.
+- **Sampling Rate**: Resampled to 16kHz
+- **Max Audio Length**: 10 seconds
+- **Training Epochs**: 2
+- **Training/Validation Split**: 80/20
+- **Optimization**: AdamW
+- **Precision**: Full (fp32)
+---
+## 🧪 Training Logs (Loss)
+| Step | Training Loss | Validation Loss |
+|------|----------------|------------------|
+| 0    | 2.23          | 1.91             |
+| 350  | 0.88          | 0.86             |
+| 525  | 0.98          | 0.53             |
+| 750  | 0.22          | 0.35             |
+| 1125 | 0.25          | 0.30             |
+> Full logs available in training script output.
+---
+## 🚀 Usage
+```python
+from transformers import AutoFeatureExtractor, AutoModelForAudioClassification
+import torchaudio
+model = AutoModelForAudioClassification.from_pretrained("aicinema69/audio-emotion-detector-v1.0")
+feature_extractor = AutoFeatureExtractor.from_pretrained("aicinema69/audio-emotion-detector-v1.0")
+# Load your audio (16kHz recommended)
+waveform, sample_rate = torchaudio.load("your_audio.wav")
+# Preprocess
+inputs = feature_extractor(waveform.squeeze().numpy(), sampling_rate=sample_rate, return_tensors="pt", padding=True)
+# Predict
+with torch.no_grad():
+    logits = model(**inputs).logits
+    predicted_class = logits.argmax(-1).item()
+print("Predicted emotion:", model.config.id2label[predicted_class])
+```
+---
+## 🛠️ Model Card Notes
+- You **should fine-tune this model on your downstream task** for better performance.
+- Feature extractor is stored in `trainer.tokenizer` (note: `tokenizer` is deprecated in future 🤗 releases, use `processing_class`).
+- Model and extractor pushed to Hub using `push_to_hub`.
+---
+## 📦 Deployment
+After training:
+```python
+model.push_to_hub("aicinema69/audio-emotion-detector-v1.0")
+feature_extractor.push_to_hub("aicinema69/audio-emotion-detector-v1.0")
+```
+---
+## ✍️ Author
+Satyam Singh
+GitHub: [SatyamSingh8306](https://github.com/SatyamSingh8306)
+Hugging Face: [aicinema69](https://huggingface.co/aicinema69)
+---