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import pathlib |
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import click |
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import librosa |
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import matplotlib.pyplot as plt |
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import numpy as np |
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import parselmouth as pm |
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import tqdm |
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from textgrid import TextGrid |
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import distribution |
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@click.command(help='Generate word-level pitch summary') |
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@click.option('--wavs', required=True, help='Path to the segments directory') |
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@click.option('--tg', required=True, help='Path to the TextGrids directory') |
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def summary_pitch(wavs, tg): |
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wavs = pathlib.Path(wavs) |
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tg_dir = pathlib.Path(tg) |
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del tg |
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filelist = list(wavs.glob('*.wav')) |
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pit_map = {} |
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f0_min = 40. |
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f0_max = 1100. |
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voicing_thresh_vowel = 0.45 |
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for wavfile in tqdm.tqdm(filelist): |
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tg = TextGrid() |
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tg.read(tg_dir / wavfile.with_suffix('.TextGrid').name) |
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timestep = 0.01 |
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f0 = pm.Sound(str(wavfile)).to_pitch_ac( |
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time_step=timestep, |
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voicing_threshold=voicing_thresh_vowel, |
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pitch_floor=f0_min, |
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pitch_ceiling=f0_max, |
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).selected_array['frequency'] |
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pitch = 12. * np.log2(f0 / 440.) + 69. |
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for word in tg[0]: |
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if word.mark in ['AP', 'SP']: |
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continue |
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if word.maxTime - word.minTime < timestep: |
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continue |
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word_pit = pitch[int(word.minTime / timestep): int(word.maxTime / timestep)] |
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word_pit = np.extract(word_pit >= 0, word_pit) |
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if word_pit.shape[0] == 0: |
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continue |
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counts = np.bincount(word_pit.astype(np.int64)) |
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midi = counts.argmax() |
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if midi in pit_map: |
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pit_map[midi] += 1 |
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else: |
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pit_map[midi] = 1 |
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midi_keys = sorted(pit_map.keys()) |
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midi_keys = list(range(midi_keys[0], midi_keys[-1] + 1)) |
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distribution.draw_distribution( |
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title='Pitch Distribution Summary', |
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x_label='Pitch', |
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y_label='Number of occurrences', |
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items=[librosa.midi_to_note(k) for k in midi_keys], |
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values=[pit_map.get(k, 0) for k in midi_keys] |
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) |
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pitch_summary = wavs / 'pitch_distribution.jpg' |
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plt.savefig(fname=pitch_summary, |
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bbox_inches='tight', |
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pad_inches=0.25) |
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print(f'Pitch distribution summary saved to {pitch_summary}') |
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if __name__ == '__main__': |
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summary_pitch() |
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