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c7f3ffb | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 | import numpy as np
import torch
import pretty_midi
def to_lf0(f0):
f0[f0 < 1.0e-5] = 1.0e-6
lf0 = f0.log() if isinstance(f0, torch.Tensor) else np.log(f0)
lf0[f0 < 1.0e-5] = - 1.0E+10
return lf0
def to_f0(lf0):
f0 = np.where(lf0 <= 0, 0.0, np.exp(lf0))
return f0.flatten()
def f0_to_coarse(f0, f0_bin=256, f0_max=900.0, f0_min=50.0):
f0_mel_min = 1127 * np.log(1 + f0_min / 700)
f0_mel_max = 1127 * np.log(1 + f0_max / 700)
is_torch = isinstance(f0, torch.Tensor)
f0_mel = 1127 * (1 + f0 / 700).log() if is_torch else 1127 * np.log(1 + f0 / 700)
f0_mel[f0_mel > 0] = (f0_mel[f0_mel > 0] - f0_mel_min) * (f0_bin - 2) / (f0_mel_max - f0_mel_min) + 1
f0_mel[f0_mel <= 1] = 1
f0_mel[f0_mel > f0_bin - 1] = f0_bin - 1
f0_coarse = (f0_mel + 0.5).long() if is_torch else np.rint(f0_mel).astype(int)
assert f0_coarse.max() <= f0_bin-1 and f0_coarse.min() >= 1, (f0_coarse.max(), f0_coarse.min(), f0.min(), f0.max())
return f0_coarse
def coarse_to_f0(f0_coarse, f0_bin=256, f0_max=900.0, f0_min=50.0):
f0_mel_min = 1127 * np.log(1 + f0_min / 700)
f0_mel_max = 1127 * np.log(1 + f0_max / 700)
uv = f0_coarse == 1
f0 = f0_mel_min + (f0_coarse - 1) * (f0_mel_max - f0_mel_min) / (f0_bin - 2)
f0 = ((f0 / 1127).exp() - 1) * 700
f0[uv] = 0
return f0
def norm_f0(f0, uv, pitch_norm='log', f0_mean=400, f0_std=100):
is_torch = isinstance(f0, torch.Tensor)
if pitch_norm == 'standard':
f0 = (f0 - f0_mean) / f0_std
if pitch_norm == 'log':
f0 = torch.log2(f0 + 1e-8) if is_torch else np.log2(f0 + 1e-8)
if uv is not None:
f0[uv > 0] = 0
return f0
def norm_interp_f0(f0, pitch_norm='log', f0_mean=None, f0_std=None):
is_torch = isinstance(f0, torch.Tensor)
if is_torch:
device = f0.device
f0 = f0.data.cpu().numpy()
uv = f0 == 0
f0 = norm_f0(f0, uv, pitch_norm, f0_mean, f0_std)
if sum(uv) == len(f0):
f0[uv] = 0
elif sum(uv) > 0:
f0[uv] = np.interp(np.where(uv)[0], np.where(~uv)[0], f0[~uv])
if is_torch:
uv = torch.FloatTensor(uv)
f0 = torch.FloatTensor(f0)
f0 = f0.to(device)
uv = uv.to(device)
return f0, uv
def denorm_f0(f0, uv, pitch_norm='log', f0_mean=400, f0_std=100, pitch_padding=None, min=50, max=900):
is_torch = isinstance(f0, torch.Tensor)
if pitch_norm == 'standard':
f0 = f0 * f0_std + f0_mean
if pitch_norm == 'log':
f0 = 2 ** f0
f0 = f0.clamp(min=min, max=max) if is_torch else np.clip(f0, a_min=min, a_max=max)
if uv is not None:
f0[uv > 0] = 0
if pitch_padding is not None:
f0[pitch_padding] = 0
return f0
def interp_f0(f0, uv=None):
if uv is None:
uv = f0 == 0
f0 = norm_f0(f0, uv)
if uv.any() and not uv.all():
f0[uv] = np.interp(np.where(uv)[0], np.where(~uv)[0], f0[~uv])
return denorm_f0(f0, uv=None), uv
def resample_align_curve(points: np.ndarray, original_timestep: float, target_timestep: float, align_length=-1):
t_max = (len(points) - 1) * original_timestep
curve_interp = np.interp(
np.arange(0, t_max, target_timestep),
original_timestep * np.arange(len(points)),
points
).astype(points.dtype)
if align_length > 0:
delta_l = align_length - len(curve_interp)
if delta_l < 0:
curve_interp = curve_interp[:align_length]
elif delta_l > 0:
curve_interp = np.concatenate((curve_interp, np.full(delta_l, fill_value=curve_interp[-1])), axis=0)
return curve_interp
def midi_to_hz(midi):
if type(midi) == np.ndarray:
non_mask = midi == 0
freq_hz = 440.0 * 2.0 ** ((midi - 69.0) / 12.0)
freq_hz[non_mask] = 0
else:
freq_hz = 440.0 * 2.0 ** ((midi - 69.0) / 12.0)
return freq_hz
def hz_to_midi(hz):
if type(hz) == torch.Tensor:
non_mask = hz == 0
midi = 69.0 + 12.0 * (torch.log2(hz) - torch.log2(torch.Tensor(440.0)))
midi[non_mask] = 0
elif type(hz) == np.ndarray:
non_mask = hz == 0
midi = 69.0 + 12.0 * (np.log2(hz) - np.log2(440.0))
midi[non_mask] = 0
else:
midi = 69.0 + 12.0 * (np.log2(hz) - np.log2(440.0))
if hz == 0:
midi = 0
return midi
def boundary2Interval(bd):
# bd has a shape of [T] with T frames
is_torch = isinstance(bd, torch.Tensor)
if is_torch:
device = bd.device
bd = bd.data.cpu().numpy()
assert len(bd.shape) == 1
# force valid begin and end
# bd[0] = 0 # took care of in regulate_boundary()
# bd[-1] = 0
ret = np.zeros(shape=(bd.sum() + 1, 2), dtype=int)
ret_idx = 0
ret[0, 0] = 0
for i, u in enumerate(bd):
if i == 0:
continue
if u == 1:
ret[ret_idx, 1] = i
ret[ret_idx+1, 0] = i
ret_idx += 1
ret[-1, 1] = bd.shape[0] - 1
if is_torch:
ret = torch.LongTensor(ret).to(device)
return ret
def validate_pitch_and_itv(notes, note_itv):
# notes [T]
# note_itv [T, 2]
assert notes.shape[0] == note_itv.shape[0]
res_notes = []
res_note_itv = []
for idx in range(notes.shape[0]):
pitch, itv = notes[idx], note_itv[idx]
if itv[0] >= itv[1]:
raise RuntimeError("The note duration should be positive")
if pitch == 0:
continue
res_notes.append(pitch)
res_note_itv.append([itv[0], itv[1]])
res_notes = np.array(res_notes)
res_note_itv = np.array(res_note_itv)
return res_notes, res_note_itv
def save_midi(notes, note_itv, midi_path):
# notes [T]
# note_itv [T, 2]
notes, note_itv = validate_pitch_and_itv(notes, note_itv)
if notes.shape == (0,):
return None
assert notes.shape[0] == note_itv.shape[0]
piano_chord = pretty_midi.PrettyMIDI()
piano_program = pretty_midi.instrument_name_to_program('Acoustic Grand Piano')
piano = pretty_midi.Instrument(program=piano_program)
for idx in range(notes.shape[0]):
pitch, itv = notes[idx], note_itv[idx]
note = pretty_midi.Note(velocity=120, pitch=pitch, start=itv[0], end=itv[1])
piano.notes.append(note)
piano_chord.remove_invalid_notes()
piano_chord.instruments.append(piano)
piano_chord.write(midi_path)
return piano_chord
def midi2NoteInterval(mid):
assert type(mid) == pretty_midi.PrettyMIDI
if len(mid.instruments) == 0 or len(mid.instruments[0].notes) == 0:
return None
ret = np.zeros(shape=(len(mid.instruments[0].notes), 2))
for i, note in enumerate(mid.instruments[0].notes):
ret[i, 0] = note.start
ret[i, 1] = note.end
return ret
def midi2NotePitch(mid):
assert type(mid) == pretty_midi.PrettyMIDI
if len(mid.instruments) == 0 or len(mid.instruments[0].notes) == 0:
return None
ret = np.zeros(shape=len(mid.instruments[0].notes))
for i, note in enumerate(mid.instruments[0].notes):
ret[i] = note.pitch
return ret
def midi_onset_eval(mid_gt, mid_pred):
import mir_eval
interval_true = midi2NoteInterval(mid_gt)
if interval_true is None:
raise RuntimeError('Midi ground truth is None')
interval_pred = midi2NoteInterval(mid_pred)
if interval_pred is None:
return 0, 0, 0
onset_p, onset_r, onset_f = mir_eval.transcription.onset_precision_recall_f1(
interval_true, interval_pred, onset_tolerance=0.05, strict=False, beta=1.0)
return onset_p, onset_r, onset_f
def midi_offset_eval(mid_gt, mid_pred):
import mir_eval
interval_true = midi2NoteInterval(mid_gt)
if interval_true is None:
raise RuntimeError('Midi ground truth is None')
interval_pred = midi2NoteInterval(mid_pred)
if interval_pred is None:
return 0, 0, 0
offset_p, offset_r, offset_f = mir_eval.transcription.offset_precision_recall_f1(
interval_true, interval_pred, offset_ratio=0.2, offset_min_tolerance=0.05, strict=False, beta=1.0)
return offset_p, offset_r, offset_f
def midi_pitch_eval(mid_gt, mid_pred, offset_ratio=0.2):
import mir_eval
interval_true = midi2NoteInterval(mid_gt)
pitch_true = midi_to_hz(midi2NotePitch(mid_gt))
if interval_true is None or pitch_true is None:
raise RuntimeError('Midi ground truth is None')
interval_pred = midi2NoteInterval(mid_pred)
pitch_pred = midi2NotePitch(mid_pred)
if interval_pred is None:
return 0, 0, 0, 0
if pitch_pred is None:
pitch_pred = np.zeros(interval_pred.shape[0])
pitch_pred = midi_to_hz(pitch_pred)
overlap_p, overlap_r, overlap_f, avg_overlap_ratio = mir_eval.transcription.precision_recall_f1_overlap(
interval_true, pitch_true, interval_pred, pitch_pred, onset_tolerance=0.05, pitch_tolerance=50.0,
offset_ratio=offset_ratio, offset_min_tolerance=0.05, strict=False, beta=1.0)
return overlap_p, overlap_r, overlap_f, avg_overlap_ratio
def midi_COn_eval(mid_gt, mid_pred):
return midi_onset_eval(mid_gt, mid_pred)
def midi_COnP_eval(mid_gt, mid_pred):
return midi_pitch_eval(mid_gt, mid_pred, offset_ratio=None)
def midi_COnPOff_eval(mid_gt, mid_pred):
return midi_pitch_eval(mid_gt, mid_pred)
def midi_melody_eval(mid_gt, mid_pred, hop_size=256, sample_rate=48000):
interval_true = midi2NoteInterval(mid_gt)
pitch_true = midi_to_hz(midi2NotePitch(mid_gt))
if interval_true is None or pitch_true is None:
raise RuntimeError('Midi ground truth is None')
interval_pred = midi2NoteInterval(mid_pred)
pitch_pred = midi2NotePitch(mid_pred)
if interval_pred is None:
return 0, 0, 0, 0
if pitch_pred is None:
pitch_pred = np.zeros(interval_pred.shape[0])
pitch_pred = midi_to_hz(pitch_pred)
vr, vfa, rpa, rca, oa = melody_eval_pitch_and_itv(
pitch_true, interval_true, pitch_pred, interval_pred, hop_size, sample_rate)
return vr, vfa, rpa, rca, oa
def melody_eval_pitch_and_itv(pitch_true, interval_true, pitch_pred, interval_pred, hop_size=256, sample_rate=48000):
import mir_eval
t_gt = np.arange(0, interval_true[-1][1], hop_size / sample_rate)
freq_gt = np.zeros_like(t_gt)
for idx in range(len(pitch_true)):
freq_gt[min(len(freq_gt) - 1, round(interval_true[idx][0] * sample_rate / hop_size)): round(
interval_true[idx][1] * sample_rate / hop_size)] = pitch_true[idx]
t_pred = np.arange(0, interval_pred[-1][1], hop_size / sample_rate)
freq_pred = np.zeros_like(t_pred)
for idx in range(len(pitch_pred)):
freq_pred[min(len(freq_pred) - 1, round(interval_pred[idx][0] * sample_rate / hop_size)): round(
interval_pred[idx][1] * sample_rate / hop_size)] = pitch_pred[idx]
ref_voicing, ref_cent, est_voicing, est_cent = mir_eval.melody.to_cent_voicing(t_gt, freq_gt,
t_pred, freq_pred)
vr, vfa = mir_eval.melody.voicing_measures(ref_voicing,
est_voicing) # voicing recall, voicing false alarm
rpa = mir_eval.melody.raw_pitch_accuracy(ref_voicing, ref_cent, est_voicing, est_cent)
rca = mir_eval.melody.raw_chroma_accuracy(ref_voicing, ref_cent, est_voicing, est_cent)
oa = mir_eval.melody.overall_accuracy(ref_voicing, ref_cent, est_voicing, est_cent)
return vr, vfa, rpa, rca, oa
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