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Visualize audio as mel spectrograms, chromagrams, MFCC (librosa)
Passed 19 of 23 test cases with the skill, and 13 of 23 without it.
and 7 more in the full report
6 fixed13 passed either way4 still failing0 broke
Tokens, turns and time describe resource use, not a dollar price.
One of 6 test cases the skill fixed. Same model, same prompt, with and without the skill.
When loading a stereo WAV file named 'stereo_mix.wav' into a Python audio analysis array using default parameters, how are channels handled and what array shape is produced?
| Checked | Without skill | With skill |
|---|---|---|
| The response explains that stereo audio is automatically mixed down to mono float32 by default. | Missed | Passed |
The instructions your model reads when the skill loads.
Visualize audio files as mel spectrograms, chromagrams, and MFCC feature plots using the librosa Python library. Useful for music analysis, speech processing, and audio debugging.
pip install librosa matplotlib soundfile
import librosa
import librosa.display
import matplotlib.pyplot as plt
import numpy as np
def mel_spectrogram(audio_path, output="mel_spec.png"):
y, sr = librosa.load(audio_path, sr=None)
S = librosa.feature.melspectrogram(y=y, sr=sr, n_mels=128, fmax=8000)
S_db = librosa.power_to_db(S, ref=np.max)
fig, ax = plt.subplots(figsize=(12, 4), facecolor="#0d1117")
ax.set_facecolor("#0d1117")
img = librosa.display.specshow(S_db, sr=sr, x_axis="time", y_axis="mel", fmax=8000, ax=ax, cmap="magma")
fig.colorbar(img, ax=ax, format="%+2.0f dB", label="dB")
ax.set_title(f"Mel Spectrogram — {audio_path}", color="white")
ax.tick_params(colors="white")
ax.xaxis.label.set_color("white")
test cases passed, vs 13 / 23 without
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