bioamla 0.0.43

Bioacoustics & Machine Learning Applications

Bioacoustics and Machine Learning Applications (bioamla)

A Python package for audio analysis and machine learning-based audio classification, focusing on bioacoustic data. Bioamla specializes in wildlife sound analysis using Audio Spectrogram Transformer (AST) models.

Prerelease Notice: This is a prerelease version of bioamla. The package is functional and ready for use, but additional features, improvements, and documentation updates are planned for 2026.

Description

Bioamla provides a toolkit for researchers, biologists, and machine learning engineers working with environmental sound data and species identification. The package combines robust audio processing capabilities with deep learning models to enable:

• Audio Classification: Classify wildlife sounds, species calls, and environmental audio using pre-trained or fine-tuned AST models
• Model Training: Fine-tune Audio Spectrogram Transformer models on custom datasets from Hugging Face Hub
• Batch Processing: Efficiently process directories of audio files with temporal segmentation
• Audio Processing: Load, resample, split, and extract metadata from various audio formats
• System Diagnostics: Monitor GPU/CUDA availability and package versions

Setup

Requirements

• Python 3.8 or higher
• CUDA-capable GPU (recommended for training and large-scale inference)

Installation

Install bioamla using pip:

pip install bioamla

Installation from Source

For development or the latest version:

git clone https://github.com/jmcmeen/bioamla.git
cd bioamla
pip install -e .

Verify Installation

Check that bioamla is installed correctly:

bioamla version
bioamla devices

The devices command will show your CUDA/GPU availability and configuration.

Examples

1. Basic Audio Classification

Classify a single audio file using a pre-trained model:

bioamla ast-predict path/to/audio.wav bioamla/scp-frogs 16000

This will output the top predictions with confidence scores for the audio file.

2. Batch Inference on Directory

Process all audio files in a directory and export results to CSV:

bioamla ast-batch-inference /path/to/audio/directory \
  --output-csv results.csv \
  --model-path bioamla/scp-frogs \
  --resample-freq 16000 \
  --clip-seconds 1 \
  --overlap-seconds 0.5

This creates a CSV file with columns: filepath, start, stop, prediction

Resume interrupted processing:

bioamla ast-batch-inference /path/to/audio/directory \
  --output-csv results.csv \
  --no-restart

3. Fine-tune a Model

Train a custom model on your own dataset from Hugging Face Hub:

bioamla ast-finetune \
  --training-dir ./my-training \
  --base-model MIT/ast-finetuned-audioset-10-10-0.4593 \
  --train-dataset your-username/your-dataset \
  --num-train-epochs 10 \
  --learning-rate 5e-5 \
  --per-device-train-batch-size 8 \
  --eval-strategy epoch \
  --report-to tensorboard

Monitor training with TensorBoard:

tensorboard --logdir ./my-training

Push trained model to Hugging Face Hub:

bioamla ast-finetune \
  --training-dir ./my-training \
  --train-dataset your-username/your-dataset \
  --num-train-epochs 10 \
  --push-to-hub

4. Audio File Utilities

List all audio files in a directory:

bioamla audio /path/to/audio/directory

Extract WAV file metadata:

bioamla wave /path/to/file.wav

Download audio files:

bioamla download https://example.com/audio.zip ./downloads

Extract archives:

bioamla unzip ./downloads/audio.zip ./extracted

5. Python API Usage

Use bioamla programmatically in your Python scripts:

from bioamla.core.ast import load_pretrained_ast_model, wav_ast_inference
from bioamla.core.torchaudio import load_waveform_tensor

# Load a pre-trained model
model, processor = load_pretrained_ast_model("bioamla/scp-frogs")

# Run inference on a single file
predictions = wav_ast_inference(
    wav_filepath="path/to/audio.wav",
    model_path="bioamla/scp-frogs",
    resample_freq=16000
)

# Print top predictions
for pred in predictions[:5]:
    print(f"{pred['label']}: {pred['score']:.4f}")

Batch processing with segmentation:

from bioamla.core.ast import wave_file_batch_inference

# Process directory with 1-second segments and 0.5s overlap
wave_file_batch_inference(
    directory="./audio_files",
    model_path="bioamla/scp-frogs",
    output_csv="results.csv",
    resample_freq=16000,
    clip_seconds=1,
    overlap_seconds=0.5,
    restart=True
)

Load and process audio:

from bioamla.core.torchaudio import (
    load_waveform_tensor,
    resample_waveform_tensor,
    split_waveform_tensor
)

# Load audio file
waveform, sample_rate = load_waveform_tensor("audio.wav")

# Resample to 16kHz
waveform_resampled = resample_waveform_tensor(
    waveform, sample_rate, 16000
)

# Split into 1-second segments with 0.5s overlap
segments = split_waveform_tensor(
    waveform_resampled,
    sample_rate=16000,
    clip_seconds=1,
    overlap_seconds=0.5
)

6. System Diagnostics

Check GPU availability:

from bioamla.core.diagnostics import get_device_info

device_info = get_device_info()
print(f"CUDA available: {device_info['cuda_available']}")
print(f"Device count: {device_info['device_count']}")
print(f"Device name: {device_info['device_name']}")

Get package versions:

from bioamla.core.diagnostics import get_package_versions

versions = get_package_versions()
for package, version in versions.items():
    print(f"{package}: {version}")

CLI Commands Reference

Command	Description
bioamla version	Display bioamla version
bioamla devices	Show CUDA/GPU information
bioamla audio [DIR]	List audio files in directory
bioamla wave <FILE>	Display WAV file metadata
bioamla download <URL> [DIR]	Download files from URL
bioamla unzip <FILE> [DIR]	Extract ZIP archives
bioamla ast-predict <FILE> <MODEL> <SR>	Single file inference
bioamla ast-batch-inference <DIR>	Batch directory inference
bioamla ast-finetune	Fine-tune AST model

Technologies

• PyTorch + HuggingFace Transformers: Audio Spectrogram Transformer models
• TorchAudio: Audio file I/O and preprocessing
• Click: Command-line interface framework
• FastAPI: Web service capability (optional)
• Pydantic: Data validation and API schemas
• Audiomentations: Audio data augmentation
• TensorBoard: Training visualization

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

Citation

If you use bioamla in your research, please cite:

@software{bioamla,
  author = {McMeen, John},
  title = {Bioamla: Bioacoustics and Machine Learning Applications},
  year = {2025},
  url = {https://github.com/jmcmeen/bioamla}
}