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import json
import math
import warnings
from collections import OrderedDict
import librosa
import numpy as np
import tqdm
import pathlib
from csv import DictReader, DictWriter
import click
from get_pitch import get_pitch_parselmouth
warns = []
def get_aligned_pitch(wav_path: pathlib.Path, total_secs: float, timestep: float):
waveform, _ = librosa.load(wav_path, sr=44100, mono=True)
_, f0, _ = get_pitch_parselmouth(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]])
return pitch
def correct_cents_item(
name: str, item: OrderedDict, ref_pitch: np.ndarray,
timestep: float, error_ratio: float
):
note_seq = item['note_seq'].split()
note_dur = [float(d) for d in item['note_dur'].split()]
assert len(note_seq) == len(note_dur)
start = 0.
note_seq_correct = []
for i, (note, dur) in enumerate(zip(note_seq, note_dur)):
end = start + dur
if note == 'rest':
start = end
note_seq_correct.append('rest')
continue
midi = librosa.note_to_midi(note, round_midi=False)
start_idx = math.floor(start / timestep)
end_idx = math.ceil(end / timestep)
note_pitch = ref_pitch[start_idx: end_idx]
note_pitch_close = note_pitch[(note_pitch >= midi - 0.5) & (note_pitch < midi + 0.5)]
if len(note_pitch_close) < len(note_pitch) * error_ratio or len(note_pitch) == 0:
warns.append({
'position': name,
'note_index': i,
'note_value': note
})
if len(note_pitch) == 0 or len(note_pitch_close) == 0:
start = end
note_seq_correct.append(note)
continue
midi_correct = np.mean(note_pitch_close)
note_seq_correct.append(librosa.midi_to_note(midi_correct, cents=True, unicode=False))
start = end
item['note_seq'] = ' '.join(note_seq_correct)
def save_warnings(save_dir: pathlib.Path):
if len(warns) > 0:
save_path = save_dir.resolve() / 'warnings.csv'
with open(save_path, 'w', encoding='utf8', newline='') as f:
writer = DictWriter(f, fieldnames=['position', 'note_index', 'note_value'])
writer.writeheader()
writer.writerows(warns)
warnings.warn(
message=f'possible labeling errors saved in {save_path}',
category=UserWarning
)
warnings.filterwarnings(action='default')
@click.group(help='Apply cents correction to note sequences')
def correct_cents():
pass
@correct_cents.command(help='Apply cents correction to note sequences in transcriptions.csv')
@click.argument('transcriptions', metavar='TRANSCRIPTIONS')
@click.argument('waveforms', metavar='WAVS')
@click.option('--error_ratio', metavar='RATIO', type=float, default=0.4,
help='If the percentage of pitch points within a deviation of 50 cents compared to the note label '
'is lower than this value, a warning will be raised.')
def csv(
transcriptions,
waveforms,
error_ratio
):
transcriptions = pathlib.Path(transcriptions).resolve()
waveforms = pathlib.Path(waveforms).resolve()
with open(transcriptions, 'r', encoding='utf8') as f:
reader = DictReader(f)
items: list[OrderedDict] = []
for item in reader:
items.append(OrderedDict(item))
timestep = 512 / 44100
for item in tqdm.tqdm(items):
item: OrderedDict
ref_pitch = get_aligned_pitch(
wav_path=waveforms / (item['name'] + '.wav'),
total_secs=sum(float(d) for d in item['note_dur'].split()),
timestep=timestep
)
correct_cents_item(
name=item['name'], item=item, ref_pitch=ref_pitch,
timestep=timestep, error_ratio=error_ratio
)
with open(transcriptions, 'w', encoding='utf8', newline='') as f:
writer = DictWriter(f, fieldnames=['name', 'ph_seq', 'ph_dur', 'ph_num', 'note_seq', 'note_dur'])
writer.writeheader()
writer.writerows(items)
save_warnings(transcriptions.parent)
@correct_cents.command(help='Apply cents correction to note sequences in DS files')
@click.argument('ds_dir', metavar='DS_DIR')
@click.option('--error_ratio', metavar='RATIO', type=float, default=0.4,
help='If the percentage of pitch points within a deviation of 50 cents compared to the note label '
'is lower than this value, a warning will be raised.')
def ds(
ds_dir,
error_ratio
):
ds_dir = pathlib.Path(ds_dir).resolve()
assert ds_dir.exists(), 'The directory of DS files does not exist.'
timestep = 512 / 44100
for ds_file in tqdm.tqdm(ds_dir.glob('*.ds')):
if not ds_file.is_file():
continue
assert ds_file.with_suffix('.wav').exists(), \
f'Missing corresponding .wav file of {ds_file.name}.'
with open(ds_file, 'r', encoding='utf8') as f:
params = json.load(f)
if not isinstance(params, list):
params = [params]
params = [OrderedDict(p) for p in params]
ref_pitch = get_aligned_pitch(
wav_path=ds_file.with_suffix('.wav'),
total_secs=params[-1]['offset'] + sum(float(d) for d in params[-1]['note_dur'].split()),
timestep=timestep
)
for i, param in enumerate(params):
start_idx = math.floor(param['offset'] / timestep)
end_idx = math.ceil((param['offset'] + sum(float(d) for d in param['note_dur'].split())) / timestep)
correct_cents_item(
name=f'{ds_file.stem}#{i}', item=param, ref_pitch=ref_pitch[start_idx: end_idx],
timestep=timestep, error_ratio=error_ratio
)
with open(ds_file, 'w', encoding='utf8') as f:
json.dump(params, f, ensure_ascii=False, indent=2)
save_warnings(ds_dir)
if __name__ == '__main__':
correct_cents()
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