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README.md

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+---
+license: apache-2.0
+language:
+- en
+base_model: MIT/ast-finetuned-audioset-10-10-0.4593
+tags:
+- audio-classification
+- music
+- speech
+- ast
+- audio-spectrogram-transformer
+pipeline_tag: audio-classification
+datasets:
+- AIGenLab/speech-music-82k
+metrics:
+- accuracy
+library_name: transformers
+---
+
+# 🎵 AST Music vs Speech Classifier (82K)
+
+Fine-tuned Audio Spectrogram Transformer (AST) for music vs speech classification.
+
+## Model Details
+
+- **Base Model:** MIT/ast-finetuned-audioset-10-10-0.4593
+- **Task:** Binary Audio Classification (Music vs Speech)
+- **Training Dataset:** AIGenLab/speech-music-82k (82000 samples)
+- **Overall Accuracy:** 86.7% (26/30)
+
+---
+
+## 📊 Performance Results
+
+| Category | Accuracy | Correct | Total |
+|----------|----------|---------|-------|
+| Pure Music | 100.0% | 10 | 10 |
+| Pure Speech | 60.0% | 6 | 10 |
+| Speech + Music | 100.0% | 10 | 10 |
+
+### Pure Music
+
+| File | Music Score | Speech Score | Prediction | Result |
+|------|-------------|--------------|------------|--------|
+| music_1.wav | 1.000 | 0.000 | MUSIC | ✅ |
+| music_10.wav | 1.000 | 0.000 | MUSIC | ✅ |
+| music_2.wav | 1.000 | 0.000 | MUSIC | ✅ |
+| music_3.wav | 1.000 | 0.000 | MUSIC | ✅ |
+| music_4.wav | 1.000 | 0.000 | MUSIC | ✅ |
+| music_5.wav | 1.000 | 0.000 | MUSIC | ✅ |
+| music_6.wav | 1.000 | 0.000 | MUSIC | ✅ |
+| music_7.wav | 1.000 | 0.000 | MUSIC | ✅ |
+| music_8.wav | 1.000 | 0.000 | MUSIC | ✅ |
+| music_9.wav | 1.000 | 0.000 | MUSIC | ✅ |
+
+### Pure Speech
+
+| File | Music Score | Speech Score | Prediction | Result |
+|------|-------------|--------------|------------|--------|
+| speech_1.wav | 0.051 | 0.949 | SPEECH | ✅ |
+| speech_10.wav | 0.039 | 0.961 | SPEECH | ✅ |
+| speech_2.wav | 0.000 | 1.000 | SPEECH | ✅ |
+| speech_3.wav | 0.372 | 0.628 | SPEECH | ✅ |
+| speech_4.wav | 1.000 | 0.000 | MUSIC | ❌ |
+| speech_5.wav | 0.997 | 0.003 | MUSIC | ❌ |
+| speech_6.wav | 1.000 | 0.000 | MUSIC | ❌ |
+| speech_7.wav | 0.170 | 0.830 | SPEECH | ✅ |
+| speech_8.wav | 0.870 | 0.130 | MUSIC | ❌ |
+| speech_9.wav | 0.035 | 0.965 | SPEECH | ✅ |
+
+### Speech + Music
+
+| File | Music Score | Speech Score | Prediction | Result |
+|------|-------------|--------------|------------|--------|
+| speech_and_music_1.wav | 1.000 | 0.000 | MUSIC | ✅ |
+| speech_and_music_10.wav | 0.987 | 0.013 | MUSIC | ✅ |
+| speech_and_music_2.wav | 1.000 | 0.000 | MUSIC | ✅ |
+| speech_and_music_3wav.wav | 1.000 | 0.000 | MUSIC | ✅ |
+| speech_and_music_4.wav | 1.000 | 0.000 | MUSIC | ✅ |
+| speech_and_music_5.wav | 1.000 | 0.000 | MUSIC | ✅ |
+| speech_and_music_6.wav | 1.000 | 0.000 | MUSIC | ✅ |
+| speech_and_music_7.wav | 1.000 | 0.000 | MUSIC | ✅ |
+| speech_and_music_8.wav | 1.000 | 0.000 | MUSIC | ✅ |
+| speech_and_music_9.wav | 1.000 | 0.000 | MUSIC | ✅ |
+
+
+---
+
+## 🚀 Quick Start
+```python
+from transformers import pipeline
+
+# Load the model
+classifier = pipeline(
+    "audio-classification", 
+    model="AIGenLab/AST-speech-and-music-classifier-82K"
+)
+
+# Classify audio
+result = classifier("your_audio.wav")
+print(result)
+```
+
+---
+
+## 🔧 Advanced Usage
+```python
+from transformers import AutoModelForAudioClassification, AutoFeatureExtractor
+import torch
+import torchaudio
+
+# Load model and feature extractor
+model = AutoModelForAudioClassification.from_pretrained(
+    "AIGenLab/AST-speech-and-music-classifier-82K"
+)
+feature_extractor = AutoFeatureExtractor.from_pretrained(
+    "AIGenLab/AST-speech-and-music-classifier-82K"
+)
+
+# Load audio (16kHz required)
+audio, sr = torchaudio.load("audio.wav")
+if sr != 16000:
+    audio = torchaudio.functional.resample(audio, sr, 16000)
+
+# Process
+inputs = feature_extractor(
+    audio.squeeze().numpy(), 
+    sampling_rate=16000, 
+    return_tensors="pt"
+)
+
+# Predict
+with torch.no_grad():
+    outputs = model(**inputs)
+    predictions = torch.nn.functional.softmax(outputs.logits, dim=-1)
+
+music_score = predictions[0][0].item()
+speech_score = predictions[0][1].item()
+
+print(f"Music: {music_score:.3f}")
+print(f"Speech: {speech_score:.3f}")
+```
+
+---
+
+## 📊 Training Details
+
+| Parameter | Value |
+|-----------|-------|
+| Base Model | MIT/ast-finetuned-audioset-10-10-0.4593 |
+| Dataset | AIGenLab/speech-music-82k (82000 samples) |
+| Epochs | 1 |
+| Batch Size | 64 |
+| Learning Rate | 3e-5 |
+| Loss Weight | Music: 2.5x, Speech: 1.0x |
+| Optimizer | AdamW |
+| Framework | Transformers + PyTorch |