initial commit of model, readme.md, sample_audio, requirements


ECAPA_Acoustic_Domain_Classifier_README.md

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+# ECAPA Acoustic Domain Classifier
+
+### Subtitle
+**Speech, Music, and Noise Classification Using ECAPA-TDNN Embeddings**
+
+---   
+
+## 🧠 Overview
+This model classifies short audio clips into **Speech**, **Music**, or **Noise** domains.  
+It uses **ECAPA-TDNN embeddings**, a neural architecture optimized for speaker and acoustic feature representation.
+
+Despite being trained on a **small, human-curated dataset (5 samples per class)**, the model demonstrates **high robustness and near-perfect classification**.  
+This project serves as a **proof-of-concept** highlighting how ECAPA embeddings can generalize even in limited-data scenarios.
+
+---
+
+## 📦 Model Information
+
+- **Architecture:** ECAPA-TDNN
+- **Framework:** PyTorch (SpeechBrain-based)
+- **Input:** Mono audio waveform (16 kHz sampling rate)
+- **Output Classes:** Speech | Music | Noise
+- **Training Data:** 15 samples (5 per class), normalized and balanced
+- **Accuracy:** 100% on internal validation (small-scale)
+- **Author:** Khubaib Ahmad — AI/ML Engineer, Data Scientist
+
+---
+
+## ⚙️ Methodology
+
+1. Extract ECAPA-TDNN embeddings for all samples using SpeechBrain.  
+2. Train a simple classifier (e.g., linear or small dense network) on embeddings.  
+3. Validate predictions using held-out data.  
+4. Export trained model weights as `.pkl` file.  
+
+---
+
+## 🚀 Usage Example
+
+```python
+from speechbrain.pretrained import EncoderClassifier
+import torch
+
+# Load model
+model = torch.load("ECAPA_acoustic_domain_classifier.pkl", map_location="cpu")
+
+# Example inference (pseudo code)
+audio_tensor = load_audio("sample.wav")  # your function to load audio as torch tensor
+embedding = model.encode_batch(audio_tensor)
+prediction = model.classify(embedding)
+print(prediction)  # -> "speech", "music", or "noise"
+```
+
+---
+
+## 📂 File Information
+
+| File | Description |
+|------|--------------|
+| `ECAPA_acoustic_domain_classifier.pkl` | Trained model weights |
+| `requirements.txt` | Dependencies for inference |
+| `README.md` | Model documentation |
+| `example_audio.mp3` | Sample audio file |
+
+---
+
+## 📊 Applications
+
+- Acoustic scene classification  
+- Pre-filtering for speech recognition pipelines  
+- Smart audio event detection  
+- Sound domain separation tasks
+
+---
+
+## 🔖 Suggested Citation
+
+```
+Muhammad Khubaib Ahmad (2025). ECAPA Acoustic Domain Classifier: Differentiating Speech, Music, and Noise using ECAPA-TDNN Embeddings. Hugging Face.
+```
+
+---
+
+## 🧾 License
+MIT License — free for research and educational use.

ECAPA_acoustic_domain_classifier.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:bb06868ea2c187c8c185c2b004e948ed0105dd8988da51622c90d680b64c58b0
+size 5551

requirements.txt

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+torch
+speechbrain