Spaces:
Sleeping
Sleeping
File size: 7,444 Bytes
83a5b06 |
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 |
import dataclasses
import pathlib
import audioflux
import librosa
import numpy as np
import parselmouth
import pyworld as pw
import resampy
import torch
import torchcrepe
import torchfcpe
from anyf0.rmvpe import RMVPE
def hz_to_cents(F, F_ref=55.0):
"""
Converts frequency in Hz to cents.
Parameters
----------
F : float or ndarray
Frequency value in Hz
F_ref : float
Reference frequency in Hz (Default value = 55.0)
Returns
-------
F_cents : float or ndarray
Frequency in cents
"""
# Avoid division by 0
F_temp = np.array(F).astype(float)
F_temp[F_temp == 0] = np.nan
F_cents = 1200 * np.log2(F_temp / F_ref)
return F_cents
@dataclasses.dataclass
class F0Extractor:
wav_path: pathlib.Path
sample_rate: int = 44100
hop_length: int = 512
f0_min: int = 50
f0_max: int = 1600
method: str = "praat_ac"
x: np.ndarray = dataclasses.field(init=False)
def __post_init__(self):
self.x, self.sample_rate = librosa.load(self.wav_path, sr=self.sample_rate)
@property
def hop_size(self) -> float:
return self.hop_length / self.sample_rate
@property
def wav16k(self) -> np.ndarray:
return resampy.resample(self.x, self.sample_rate, 16000)
def extract_f0(self) -> np.ndarray:
f0 = None
match self.method:
case "dio":
_f0, t = pw.dio(
self.x.astype("double"),
self.sample_rate,
f0_floor=self.f0_min,
f0_ceil=self.f0_max,
channels_in_octave=2,
frame_period=(1000 * self.hop_size),
)
f0 = pw.stonemask(self.x.astype("double"), _f0, t, self.sample_rate)
f0 = f0.astype("float")
case "harvest":
f0, _ = pw.harvest(
self.x.astype("double"),
self.sample_rate,
f0_floor=self.f0_min,
f0_ceil=self.f0_max,
frame_period=(1000 * self.hop_size),
)
f0 = f0.astype("float")
case "pyin":
f0, _, _ = librosa.pyin(
y=self.wav16k,
fmin=self.f0_min,
fmax=self.f0_max,
sr=16000,
hop_length=80,
)
case "piptrack":
pitches, magnitudes = librosa.piptrack(
y=self.wav16k,
fmin=self.f0_min,
fmax=self.f0_max,
sr=16000,
hop_length=80,
)
max_indexes = np.argmax(magnitudes, axis=0)
f0 = pitches[max_indexes, range(magnitudes.shape[1])]
case "cep" | "hps" | "lhs" | "ncf" | "pef":
f0 = {
"cep": audioflux.PitchCEP,
"hps": audioflux.PitchHPS,
"lhs": audioflux.PitchLHS,
"ncf": audioflux.PitchNCF,
"pef": audioflux.PitchPEF,
}[self.method](
16000,
low_fre=self.f0_min,
high_fre=self.f0_max,
slide_length=80,
).pitch(np.pad(self.wav16k, (2048, 2048)))
case "stft":
f0, _ = audioflux.PitchSTFT(
16000,
low_fre=self.f0_min,
high_fre=self.f0_max,
slide_length=80,
).pitch(np.pad(self.wav16k, (2048, 2048)))
case "yin":
f0, _, _ = audioflux.PitchYIN(
16000,
low_fre=self.f0_min,
high_fre=self.f0_max,
slide_length=80,
).pitch(np.pad(self.wav16k, (2048, 2048)))
case "torchcrepe":
device = "cuda" if torch.cuda.is_available() else "cpu"
wav16k_torch = torch.FloatTensor(self.wav16k).unsqueeze(0).to(device)
f0 = torchcrepe.predict(
wav16k_torch,
sample_rate=16000,
hop_length=80,
batch_size=1024,
fmin=self.f0_min,
fmax=self.f0_max,
device=device,
)
f0 = f0[0].cpu().numpy()
case "torchfcpe":
device = "cuda" if torch.cuda.is_available() else "cpu"
audio = librosa.to_mono(self.x)
audio_length = len(audio)
f0_target_length = (audio_length // self.hop_length) + 1
audio = torch.from_numpy(audio).float().unsqueeze(0).unsqueeze(-1).to(device)
model = torchfcpe.spawn_bundled_infer_model(device=device)
f0 = model.infer(
audio,
sr=self.sample_rate,
decoder_mode='local_argmax',
threshold=0.006,
f0_min=self.f0_min,
f0_max=self.f0_max,
interp_uv=False,
output_interp_target_length=f0_target_length,
)
f0 = f0.squeeze().cpu().numpy()
case "rmvpe":
device = "cuda" if torch.cuda.is_available() else "cpu"
model_rmvpe = RMVPE(
"rmvpe.pt",
is_half=True,
device=device,
hop_length=80
)
f0 = model_rmvpe.infer_from_audio(self.wav16k, thred=0.03)
case "praat_ac" | "praat_cc":
l_pad = int(np.ceil(1.5 / self.f0_min * self.sample_rate))
r_pad = int(self.hop_size * ((len(self.x) - 1) // self.hop_size + 1) - len(self.x) + l_pad + 1)
f0 = (
getattr(
parselmouth.Sound(np.pad(self.x, (l_pad, r_pad)), self.sample_rate),
"to_pitch_" + self.method.rpartition('_')[-1]
)(
time_step=self.hop_size,
voicing_threshold=0.6,
pitch_floor=self.f0_min,
pitch_ceiling=self.f0_max,
)
.selected_array["frequency"]
)
case "praat_shs":
l_pad = int(np.ceil(1.5 / self.f0_min * self.sample_rate))
r_pad = int(self.hop_size * ((len(self.x) - 1) // self.hop_size + 1) - len(self.x) + l_pad + 1)
f0 = parselmouth.Sound(
np.pad(self.x, (l_pad, r_pad)), self.sample_rate
).to_pitch_shs(
time_step=self.hop_size,
minimum_pitch=self.f0_min,
maximum_frequency_component=self.f0_max,
).selected_array["frequency"]
case _:
raise ValueError(f"Unknown method: {self.method}")
return hz_to_cents(f0, librosa.midi_to_hz(0))
def plot_f0(self, f0):
from matplotlib import pyplot as plt
plt.figure(figsize=(10, 4))
plt.plot(f0)
plt.title(self.method)
plt.xlabel("Time (frames)")
plt.ylabel("F0 (cents)")
plt.show() |