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import csv |
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import math |
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import pathlib |
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import click |
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import librosa |
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import numpy as np |
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import tqdm |
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from typing import List |
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from get_pitch import get_pitch |
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@click.command(help='Estimate note pitch from transcriptions and corresponding waveforms') |
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@click.argument('transcriptions', metavar='TRANSCRIPTIONS') |
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@click.argument('waveforms', metavar='WAVS') |
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@click.option('--pe', metavar='ALGORITHM', default='parselmouth', |
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help='Pitch extractor (parselmouth, rmvpe)') |
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@click.option('--rest_uv_ratio', metavar='RATIO', type=float, default=0.85, |
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help='The minimum percentage of unvoiced length for a note to be regarded as rest') |
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def estimate_midi( |
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transcriptions: str, |
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waveforms: str, |
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pe: str = 'parselmouth', |
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rest_uv_ratio: float = 0.85 |
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): |
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transcriptions = pathlib.Path(transcriptions).resolve() |
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waveforms = pathlib.Path(waveforms).resolve() |
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with open(transcriptions, 'r', encoding='utf8') as f: |
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reader = csv.DictReader(f) |
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items: List[dict] = [] |
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for item in reader: |
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items.append(item) |
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timestep = 512 / 44100 |
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for item in tqdm.tqdm(items): |
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item: dict |
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ph_dur = [float(d) for d in item['ph_dur'].split()] |
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ph_num = [int(n) for n in item['ph_num'].split()] |
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assert sum(ph_num) == len(ph_dur), f'ph_num does not sum to number of phones in \'{item["name"]}\'.' |
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word_dur = [] |
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i = 0 |
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for num in ph_num: |
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word_dur.append(sum(ph_dur[i: i + num])) |
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i += num |
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total_secs = sum(ph_dur) |
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waveform, _ = librosa.load(waveforms / (item['name'] + '.wav'), sr=44100, mono=True) |
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_, f0, uv = get_pitch(pe, waveform, 512, 44100) |
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pitch = librosa.hz_to_midi(f0) |
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if pitch.shape[0] < total_secs / timestep: |
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pad = math.ceil(total_secs / timestep) - pitch.shape[0] |
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pitch = np.pad(pitch, [0, pad], mode='constant', constant_values=[0, pitch[-1]]) |
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uv = np.pad(uv, [0, pad], mode='constant') |
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note_seq = [] |
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note_dur = [] |
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start = 0. |
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for dur in word_dur: |
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end = start + dur |
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start_idx = math.floor(start / timestep) |
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end_idx = math.ceil(end / timestep) |
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word_pitch = pitch[start_idx: end_idx] |
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word_uv = uv[start_idx: end_idx] |
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word_valid_pitch = np.extract(~word_uv & (word_pitch >= 0), word_pitch) |
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if len(word_valid_pitch) < (1 - rest_uv_ratio) * (end_idx - start_idx): |
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note_seq.append('rest') |
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else: |
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counts = np.bincount(np.round(word_valid_pitch).astype(np.int64)) |
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midi = counts.argmax() |
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midi = np.mean(word_valid_pitch[(word_valid_pitch >= midi - 0.5) & (word_valid_pitch < midi + 0.5)]) |
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note_seq.append(librosa.midi_to_note(midi, cents=True, unicode=False)) |
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note_dur.append(dur) |
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start = end |
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item['note_seq'] = ' '.join(note_seq) |
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item['note_dur'] = ' '.join([str(round(d, 6)) for d in note_dur]) |
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with open(transcriptions, 'w', encoding='utf8', newline='') as f: |
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writer = csv.DictWriter(f, fieldnames=['name', 'ph_seq', 'ph_dur', 'ph_num', 'note_seq', 'note_dur']) |
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writer.writeheader() |
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writer.writerows(items) |
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if __name__ == '__main__': |
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estimate_midi() |
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