I'll create a comprehensive web API endpoint example for audio spectrogram transformer pipelines using FastAPI and Hugging Face Transformers.I've created a comprehensive web API endpoint example for audio spectrogram transformer pipelines. This implementation includes:

## Key Features:

1. **FastAPI Framework**: Modern, fast web framework with automatic API documentation
2. **Audio Spectrogram Transformer (AST)**: Uses MIT's AST model fine-tuned on AudioSet
3. **Multiple Input Methods**:
   - File upload endpoint (`/classify`)
   - Base64 encoded audio (`/classify-base64`)
   - Batch processing (`/classify-batch`)

## Core Functionality:

- **Audio Processing**: Handles various audio formats (WAV, MP3, FLAC, OGG, M4A)
- **Automatic Resampling**: Converts audio to the required 16kHz sample rate
- **Stereo to Mono Conversion**: Automatically handles multi-channel audio
- **Length Limiting**: Trims audio to maximum duration to prevent memory issues
- **Top-K Predictions**: Returns configurable number of top predictions with confidence scores

## API Endpoints:

- `GET /`: API information and available endpoints
- `GET /health`: Health check for monitoring
- `GET /model-info`: Model configuration details
- `POST /classify`: Single audio file classification
- `POST /classify-base64`: Base64 encoded audio classification
- `POST /classify-batch`: Multiple file batch processing

## Setup Instructions:

1. **Install dependencies**:
```bash
pip install fastapi uvicorn transformers torch torchaudio numpy pydantic
```

2. **Run the server**:
```bash
python app.py
```

3. **Access the API**:
- API: `http://localhost:8000`
- Interactive docs: `http://localhost:8000/docs`
- ReDoc: `http://localhost:8000/redoc`

## Example Usage:

```python
# Using requests library
import requests
import base64

# File upload
with open("audio.wav", "rb") as f:
    response = requests.post(
        "http://localhost:8000/classify",
        files={"file": f},
        data={"top_k": 5}
    )

# Base64 encoding
with open("audio.wav", "rb") as f:
    audio_b64 = base64.b64encode(f.read()).decode()
    response = requests.post(
        "http://localhost:8000/classify-base64",
        json={"audio_base64": audio_b64, "top_k": 3}
    )
```

The API automatically handles model loading on startup, provides proper error handling, CORS support for web applications, and returns structured JSON responses with predictions, confidence scores, and metadata. The model can classify audio into over 500 AudioSet categories including speech, music, environmental sounds, and more.