.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "auto_examples/audio/plot_loading_audio_files.py"
.. LINE NUMBERS ARE GIVEN BELOW.

.. only:: html

    .. note::
        :class: sphx-glr-download-link-note

        :ref:`Go to the end <sphx_glr_download_auto_examples_audio_plot_loading_audio_files.py>`
        to download the full example code.

.. rst-class:: sphx-glr-example-title

.. _sphx_glr_auto_examples_audio_plot_loading_audio_files.py:


Loading Audio Files
===================

This example demonstrates how to load audio files with mixref.

mixref provides a simple audio loader that handles format conversion,
mono/stereo conversion, and resampling automatically.

.. GENERATED FROM PYTHON SOURCE LINES 12-17

Basic Audio Loading
-------------------

The core function for loading audio is :func:`mixref.audio.loader.load_audio`.
It returns audio as a numpy array and the sample rate.

.. GENERATED FROM PYTHON SOURCE LINES 17-47

.. code-block:: Python


    import tempfile
    from pathlib import Path

    import numpy as np
    import soundfile as sf

    from mixref.audio.loader import load_audio

    # First, let's create some test audio files
    # In real usage, you'd have actual WAV/FLAC/MP3 files
    temp_dir = Path(tempfile.mkdtemp())

    # Create a simple sine wave
    duration = 1.0
    sample_rate = 44100
    t = np.linspace(0, duration, int(sample_rate * duration), dtype=np.float32)
    sine_wave = 0.5 * np.sin(2 * np.pi * 440 * t)

    # Save as mono WAV
    mono_file = temp_dir / "mono_track.wav"
    sf.write(mono_file, sine_wave, sample_rate)

    # Save as stereo WAV
    stereo_audio = np.stack([sine_wave, sine_wave], axis=1)
    stereo_file = temp_dir / "stereo_track.wav"
    sf.write(stereo_file, stereo_audio, sample_rate)

    print(f"Created test files in {temp_dir}")





.. rst-class:: sphx-glr-script-out

 .. code-block:: none

    Created test files in /tmp/tmp7ezzloys




.. GENERATED FROM PYTHON SOURCE LINES 48-52

Loading Mono Audio
------------------

Load a mono file and keep it mono:

.. GENERATED FROM PYTHON SOURCE LINES 52-58

.. code-block:: Python


    audio_mono, sr = load_audio(mono_file, channel_mode="auto")
    print(f"Mono audio shape: {audio_mono.shape}")
    print(f"Sample rate: {sr} Hz")
    print(f"Duration: {len(audio_mono) / sr:.2f} seconds")





.. rst-class:: sphx-glr-script-out

 .. code-block:: none

    Mono audio shape: (44100,)
    Sample rate: 44100 Hz
    Duration: 1.00 seconds




.. GENERATED FROM PYTHON SOURCE LINES 59-63

Loading Stereo Audio
--------------------

Load a stereo file:

.. GENERATED FROM PYTHON SOURCE LINES 63-69

.. code-block:: Python


    audio_stereo, sr = load_audio(stereo_file, channel_mode="auto")
    print(f"Stereo audio shape: {audio_stereo.shape}")
    print(f"Channels: {audio_stereo.shape[1]}")
    print(f"Duration: {len(audio_stereo) / sr:.2f} seconds")





.. rst-class:: sphx-glr-script-out

 .. code-block:: none

    Stereo audio shape: (44100, 2)
    Channels: 2
    Duration: 1.00 seconds




.. GENERATED FROM PYTHON SOURCE LINES 70-74

Channel Conversion
------------------

mixref can automatically convert between mono and stereo.

.. GENERATED FROM PYTHON SOURCE LINES 74-83

.. code-block:: Python


    # Convert mono to stereo (duplicates the channel)
    audio_mono_as_stereo, sr = load_audio(mono_file, channel_mode="stereo")
    print(f"Mono → Stereo: {audio_mono_as_stereo.shape}")

    # Convert stereo to mono (averages channels)
    audio_stereo_as_mono, sr = load_audio(stereo_file, channel_mode="mono")
    print(f"Stereo → Mono: {audio_stereo_as_mono.shape}")





.. rst-class:: sphx-glr-script-out

 .. code-block:: none

    Mono → Stereo: (44100, 2)
    Stereo → Mono: (44100,)




.. GENERATED FROM PYTHON SOURCE LINES 84-89

Default Behavior
----------------

By default, mixref loads audio as stereo. This is because most
production workflows work with stereo audio, even if the source is mono.

.. GENERATED FROM PYTHON SOURCE LINES 89-96

.. code-block:: Python


    audio_default, sr = load_audio(mono_file)  # No channel_mode specified
    print(f"Default loading (mono file): {audio_default.shape}")

    audio_default2, sr = load_audio(stereo_file)  # No channel_mode specified
    print(f"Default loading (stereo file): {audio_default2.shape}")





.. rst-class:: sphx-glr-script-out

 .. code-block:: none

    Default loading (mono file): (44100, 2)
    Default loading (stereo file): (44100, 2)




.. GENERATED FROM PYTHON SOURCE LINES 97-101

Resampling
----------

Load audio and resample to a target sample rate:

.. GENERATED FROM PYTHON SOURCE LINES 101-112

.. code-block:: Python


    # Create a 48kHz file
    audio_48k = 0.3 * np.sin(2 * np.pi * 440 * np.linspace(0, 1, 48000, dtype=np.float32))
    file_48k = temp_dir / "audio_48khz.wav"
    sf.write(file_48k, audio_48k, 48000)

    # Load and resample to 44.1kHz
    audio_resampled, sr_new = load_audio(file_48k, sample_rate=44100)
    print(f"\nOriginal: 48000 Hz, {len(audio_48k)} samples")
    print(f"Resampled: {sr_new} Hz, {len(audio_resampled)} samples")





.. rst-class:: sphx-glr-script-out

 .. code-block:: none


    Original: 48000 Hz, 48000 samples
    Resampled: 44100 Hz, 44100 samples




.. GENERATED FROM PYTHON SOURCE LINES 113-117

Producer Workflow Example
--------------------------

Here's a typical workflow for a producer analyzing multiple tracks:

.. GENERATED FROM PYTHON SOURCE LINES 117-136

.. code-block:: Python


    print("\n" + "=" * 50)
    print("Analyzing Multiple Tracks")
    print("=" * 50)

    tracks = [mono_file, stereo_file, file_48k]

    for track_path in tracks:
        # Load as stereo, normalize to 44.1kHz
        audio, sr = load_audio(track_path, sample_rate=44100, channel_mode="stereo")

        # Get some basic info
        duration = len(audio) / sr
        peak = np.abs(audio).max()
        rms = np.sqrt(np.mean(audio**2))

        print(f"\n📁 {track_path.name}")
        print(f"   Duration: {duration:.2f}s | Peak: {peak:.3f} | RMS: {rms:.3f}")





.. rst-class:: sphx-glr-script-out

 .. code-block:: none


    ==================================================
    Analyzing Multiple Tracks
    ==================================================

    📁 mono_track.wav
       Duration: 1.00s | Peak: 0.500 | RMS: 0.354

    📁 stereo_track.wav
       Duration: 1.00s | Peak: 0.500 | RMS: 0.354

    📁 audio_48khz.wav
       Duration: 1.00s | Peak: 0.300 | RMS: 0.212




.. GENERATED FROM PYTHON SOURCE LINES 137-141

Error Handling
--------------

The loader provides clear errors for common issues:

.. GENERATED FROM PYTHON SOURCE LINES 141-147

.. code-block:: Python


    try:
        load_audio("nonexistent_file.wav")
    except FileNotFoundError as e:
        print(f"\n❌ File not found: {e}")





.. rst-class:: sphx-glr-script-out

 .. code-block:: none


    ❌ File not found: Audio file not found: nonexistent_file.wav




.. GENERATED FROM PYTHON SOURCE LINES 148-149

Cleanup

.. GENERATED FROM PYTHON SOURCE LINES 149-153

.. code-block:: Python

    import shutil

    shutil.rmtree(temp_dir)
    print("\n✅ Audio loading examples complete!")




.. rst-class:: sphx-glr-script-out

 .. code-block:: none


    ✅ Audio loading examples complete!





.. rst-class:: sphx-glr-timing

   **Total running time of the script:** (0 minutes 16.334 seconds)


.. _sphx_glr_download_auto_examples_audio_plot_loading_audio_files.py:

.. only:: html

  .. container:: sphx-glr-footer sphx-glr-footer-example

    .. container:: sphx-glr-download sphx-glr-download-jupyter

      :download:`Download Jupyter notebook: plot_loading_audio_files.ipynb <plot_loading_audio_files.ipynb>`

    .. container:: sphx-glr-download sphx-glr-download-python

      :download:`Download Python source code: plot_loading_audio_files.py <plot_loading_audio_files.py>`

    .. container:: sphx-glr-download sphx-glr-download-zip

      :download:`Download zipped: plot_loading_audio_files.zip <plot_loading_audio_files.zip>`


.. only:: html

 .. rst-class:: sphx-glr-signature

    `Gallery generated by Sphinx-Gallery <https://sphinx-gallery.github.io>`_