AST-Music-Classifier-1K

🎵 AST Music vs Speech Classifier

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-1k (1000 samples)
• Overall Accuracy: 90.0% (27/30)

---

📊 Performance Results

Pure Music

Pure Speech

Speech + Music

---

🚀 Quick Start

from transformers import pipeline

# Load the model
classifier = pipeline(
    "audio-classification", 
    model="AIGenLab/AST-speech-and-music-classifier-1K"
)

# Classify audio
result = classifier("your_audio.wav")
print(result)

---

🔧 Advanced Usage

from transformers import AutoModelForAudioClassification, AutoFeatureExtractor
import torch
import torchaudio

# Load model and feature extractor
model = AutoModelForAudioClassification.from_pretrained(
    "AIGenLab/AST-speech-and-music-classifier-1K"
)
feature_extractor = AutoFeatureExtractor.from_pretrained(
    "AIGenLab/AST-speech-and-music-classifier-1K"
)

# 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