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