xc-api-py 0.1.19

An intuitive, type-safe Python wrapper for the Xeno-Canto API

Xeno Canto API Client for Python

An intuitive, type-safe Python wrapper for the Xeno-Canto API.
Streamline your bioacoustics and machine learning workflows by searching, filtering, and processing wildlife recordings with full validation and IDE autocompletion.

Project Sponsors

Disclaimer

This project is a free and open-source tool intended for educational purposes only;
This project is provided AS IS without any warranty - use at your own risk;
This project is not associated with, maintained by, or endorsed by the Xeno-Canto Foundation;
Users are responsible for adhering to the Xeno-Canto Terms of Use and respecting the licensing of individual recordings.

Quickstart

Installation

It is highly recommended to use a virtual environment to manage your project's dependencies and avoid conflicts with system-wide packages.

Creating a virtual environment:

# Create the environment
python -m venv .venv

# Activate it (macOS/Linux)
source .venv/bin/activate

# Activate it (Windows)
.venv\Scripts\activate

For more details, see the official Python guide on Virtual Environments.

PIP

pip install xc-api-py

Basic Usage

from xeno_canto import Client

client = Client(api_key='YOUR_API_KEY')

# Search for multiple species at once
recordings = client.search(
    species_list=['Grus grus', 'Acrocephalus melanopogon'], 
    limit=50
)

# Download and organize automatically
client.download(
    recordings, 
    grouping='species', 
    naming='catalogue'
)

Advanced Features

Bulk Downloading & Organization

The download method supports advanced path management and naming conventions to keep your datasets organized for machine learning or archival.

Argument	Options	Description
grouping	'flat', 'species', 'recordist'	Nest files in subdirs (e.g., genus-species/subspecies/file.mp3).
naming	'original', 'catalogue'	Use uploader filename or standardized xc12345.mp3.
target_dir	str or Path	If None, creates a timestamped folder: xc-recordings-YYYY-MM-DD...

# Create a recordist-based dataset with standardized names
client.download(
    recordings, 
    target_dir='./my_dataset',
    grouping='recordist',
    naming='catalogue'
)

Flexible Search

The search method supports iterative species lists, coordinate filtering, and streaming for large datasets.

# Search across multiple species with a global limit and streaming
rs = client.search(
    species_list=['Passer domesticus', 'Passer montanus'],
    country='israel',
    limit=100,
    stream=True
)

Data Return Modes & "Lean" Objects

You can control exactly what kind of objects the client returns using the mode and lean parameters.

The lean=True flag optimizes performance by filtering out dozens of metadata fields (like location descriptions, temperature, or uploader comments) and returning only the critical core needed for identification and file retrieval.

Mode	Return Type	Best For
dataclass (default)	XenoCantoRecording	Fast script performance
pydantic	XenoCantoRecordingSchema	Strict runtime validation
audio	XenoCantoAudio	Direct playback and processing
dict	dict	Loading into Pandas DataFrames
json	str	Raw archival/caching

# High-speed search returning lean dataclasses
recordings = client.search(genus='apus', mode='dataclass', lean=True)

SoundDevice Playback

import sounddevice as sd
from xeno_canto import Client

client = Client('API_KEY')
# mode='audio' returns XenoCantoAudio objects
recording = client.get_by_id('XC125492', mode='audio')

with recording.load() as a:
    sd.play(a.to_numpy(), a.sample_rate)
    sd.wait()

BirdNET Integration

from birdnetlib.analyzer import Analyzer
from birdnetlib import RecordingBuffer

analyzer = Analyzer()
recording = client.get_by_id('XC779948', mode='audio')

with recording.load() as a:
    # Segment audio for BirdNET (expects 48kHz)
    arr = a.to_birdnet() 
    
buffer = RecordingBuffer(analyzer, arr)
buffer.analyze()
print(buffer.detections)

License

Distributed under the MIT License. See LICENSE for more information.