Mikkeli Matlock
a322f08d0c
- SpectrogramMetric: log-frequency STFT power heatmap over time, magma colormap, -80 dB floor. Adaptive hop caps time bins at ~4000 so long tracks stay responsive on redraw; N_FFT=4096 keeps low-freq resolution. - master_core: load audio at native sample rate (librosa.load sr=None) instead of librosa's 22050 Hz default, so the full band up to the file's own nyquist (~22 kHz at 44.1 kHz) is analysed. ~2x heavier on 44.1/48 kHz files, by design. - metrics: shared _show_axis_extents helper forces each axis's exact min/max onto the tick list with compact labels (_fmt_tick), so the true range is always readable -- notably the spectrogram's 22 kHz top, which otherwise sits unlabelled between log-scale decade ticks. Applied to all metric renders. - Docs: README + CLAUDE updated for the new metric, native-rate loading, and axis-readability behaviour. Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
68 lines
2.2 KiB
Python
68 lines
2.2 KiB
Python
import os
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import librosa
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import numpy as np
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from mutagen.mp3 import MP3
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from mutagen.easyid3 import EasyID3
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from font_manager import safe_title
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def _try_mp3_tags(file_path):
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try:
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return MP3(file_path, ID3=EasyID3)
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except Exception:
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return None
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class AudioFile:
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def __init__(self, file_path):
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self.file_path = file_path
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audio = _try_mp3_tags(self.file_path)
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artist = audio.get('artist', [None])[0] if audio is not None else None
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title = audio.get('title', [None])[0] if audio is not None else None
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if artist and title:
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self.song_name = safe_title(f"{artist} - {title}")
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else:
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self.song_name = safe_title(os.path.basename(self.file_path))
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# librosa.load normalises to [-1.0, 1.0]. sr=None preserves the file's
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# native sample rate; without it librosa resamples to 22050 Hz, which would
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# discard everything above ~11 kHz (the entire top octave) before analysis.
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self.y, self.sr = librosa.load(file_path, sr=None)
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self.y_mono = librosa.to_mono(self.y)
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self.max_amplitude = np.max(np.abs(self.y_mono))
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self.avg_amplitude = np.mean(np.abs(self.y_mono))
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self.bpm, _ = librosa.beat.beat_track(y=self.y_mono, sr=self.sr)
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def get_bpm(self):
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# librosa.beat.beat_track returns numpy array - extract scalar value
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if isinstance(self.bpm, np.ndarray):
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return float(self.bpm[0]) if len(self.bpm) > 0 else 0.0
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return float(self.bpm)
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def get_energy_levels_over_time(self, window=10, hop=2):
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"""Compute rolling RMS power.
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Args:
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window: Rolling window length in seconds.
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hop: Hop length in seconds.
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"""
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if (not hasattr(self, 'window')) or (self.window != window) or (self.hop != hop):
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self.window, self.hop = window, hop
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if not hasattr(self, 'rms_array'):
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window_samples = window * self.sr
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hop_samples = hop * self.sr
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self.rms_array = librosa.feature.rms(
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y=self.y, frame_length=window_samples, hop_length=hop_samples
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)
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def get_times(self):
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"""Time-axis values matching the RMS frames."""
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if not hasattr(self, 'rms_array'):
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self.get_energy_levels_over_time()
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return librosa.frames_to_time(
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np.arange(self.rms_array.shape[1]), sr=self.sr, hop_length=self.hop * self.sr
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)
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