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Testing Audio Processing Implementation in OpenAI Whisper

This test suite validates the audio processing functionality in Whisper’s audio module, focusing on loading audio files and generating mel spectrograms. It ensures proper audio file handling, signal processing, and spectrogram generation for speech recognition tasks.

Test Coverage Overview

The test coverage focuses on core audio processing capabilities:

Audio file loading and format validation
Sample rate and duration verification
Audio signal normalization checks
Mel spectrogram generation consistency
Signal amplitude and range validation

Implementation Analysis

The testing approach employs NumPy-based comparisons for audio processing verification. It uses a reference audio file (jfk.flac) to test both direct audio loading and mel spectrogram generation, ensuring consistent results across different input methods.

The implementation validates both the dimensional correctness of audio arrays and the numerical properties of processed signals.

Technical Details

Testing tools and components:

NumPy for array operations and comparisons
Custom audio loading function (load_audio)
Mel spectrogram generation (log_mel_spectrogram)
FLAC audio format support
Fixed sample rate constant (SAMPLE_RATE)

Best Practices Demonstrated

The test suite demonstrates several testing best practices:

Isolation of audio processing components
Verification of both file-based and array-based inputs
Numerical tolerance handling in floating-point comparisons
Range validation for audio signals
Path handling using os.path for cross-platform compatibility

openai/whisper

tests/test_audio.py

            
import os.path

import numpy as np

from whisper.audio import SAMPLE_RATE, load_audio, log_mel_spectrogram


def test_audio():
    audio_path = os.path.join(os.path.dirname(__file__), "jfk.flac")
    audio = load_audio(audio_path)
    assert audio.ndim == 1
    assert SAMPLE_RATE * 10 < audio.shape[0] < SAMPLE_RATE * 12
    assert 0 < audio.std() < 1

    mel_from_audio = log_mel_spectrogram(audio)
    mel_from_file = log_mel_spectrogram(audio_path)

    assert np.allclose(mel_from_audio, mel_from_file)
    assert mel_from_audio.max() - mel_from_audio.min() <= 2.0