File size: 6,446 Bytes
2dcbf9e |
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 |
import os
import sys
import torch
import librosa
import scipy.stats
import numpy as np
sys.path.append(os.getcwd())
CENTS_PER_BIN, PITCH_BINS, SAMPLE_RATE, WINDOW_SIZE = 20, 360, 16000, 1024
class CREPE:
def __init__(
self,
model_path,
model_size="full",
hop_length=512,
batch_size=None,
f0_min=50,
f0_max=1100,
device=None,
sample_rate=16000,
providers=None,
onnx=False,
return_periodicity=False
):
self.device = device or ("cuda" if torch.cuda.is_available() else "cpu")
self.hop_length = hop_length
self.batch_size = batch_size
self.sample_rate = sample_rate
self.onnx = onnx
self.f0_min = f0_min
self.f0_max = f0_max
self.return_periodicity = return_periodicity
if self.onnx:
import onnxruntime as ort
sess_options = ort.SessionOptions()
sess_options.log_severity_level = 3
self.model = ort.InferenceSession(model_path, sess_options=sess_options, providers=providers)
else:
from main.library.predictors.CREPE.model import CREPEE
model = CREPEE(model_size)
model.load_state_dict(torch.load(model_path, map_location="cpu", weights_only=True))
model.eval()
self.model = model.to(device)
def bins_to_frequency(self, bins):
if str(bins.device).startswith(("ocl", "privateuseone")): bins = bins.to(torch.float32)
cents = CENTS_PER_BIN * bins + 1997.3794084376191
cents = (
cents + cents.new_tensor(
scipy.stats.triang.rvs(
c=0.5,
loc=-CENTS_PER_BIN,
scale=2 * CENTS_PER_BIN,
size=cents.size()
)
)
) / 1200
return 10 * 2 ** cents
def frequency_to_bins(self, frequency, quantize_fn=torch.floor):
return quantize_fn(((1200 * (frequency / 10).log2()) - 1997.3794084376191) / CENTS_PER_BIN).int()
def viterbi(self, logits):
if not hasattr(self, 'transition'):
xx, yy = np.meshgrid(range(360), range(360))
transition = np.maximum(12 - abs(xx - yy), 0)
self.transition = transition / transition.sum(axis=1, keepdims=True)
with torch.no_grad():
probs = torch.nn.functional.softmax(logits, dim=1)
bins = torch.tensor(
np.array([
librosa.sequence.viterbi(sequence, self.transition).astype(np.int64)
for sequence in probs.cpu().numpy()
]),
device=probs.device
)
return bins, self.bins_to_frequency(bins)
def preprocess(self, audio, pad=True):
hop_length = (self.sample_rate // 100) if self.hop_length is None else self.hop_length
if self.sample_rate != SAMPLE_RATE:
audio = torch.tensor(
librosa.resample(
audio.detach().cpu().numpy().squeeze(0),
orig_sr=self.sample_rate,
target_sr=SAMPLE_RATE,
res_type="soxr_vhq"
),
device=audio.device
).unsqueeze(0)
hop_length = int(hop_length * SAMPLE_RATE / self.sample_rate)
if pad:
total_frames = 1 + int(audio.size(1) // hop_length)
audio = torch.nn.functional.pad(audio, (WINDOW_SIZE // 2, WINDOW_SIZE // 2))
else: total_frames = 1 + int((audio.size(1) - WINDOW_SIZE) // hop_length)
batch_size = total_frames if self.batch_size is None else self.batch_size
for i in range(0, total_frames, batch_size):
frames = torch.nn.functional.unfold(
audio[:, None, None, max(0, i * hop_length):min(audio.size(1), (i + batch_size - 1) * hop_length + WINDOW_SIZE)],
kernel_size=(1, WINDOW_SIZE),
stride=(1, hop_length)
)
if self.device.startswith(("ocl", "privateuseone")):
frames = frames.transpose(1, 2).contiguous().reshape(-1, WINDOW_SIZE).to(self.device)
else:
frames = frames.transpose(1, 2).reshape(-1, WINDOW_SIZE).to(self.device)
frames -= frames.mean(dim=1, keepdim=True)
frames /= torch.tensor(1e-10, device=frames.device).max(frames.std(dim=1, keepdim=True))
yield frames
def periodicity(self, probabilities, bins):
probs_stacked = probabilities.transpose(1, 2).reshape(-1, PITCH_BINS)
periodicity = probs_stacked.gather(1, bins.reshape(-1, 1).to(torch.int64))
return periodicity.reshape(probabilities.size(0), probabilities.size(2))
def postprocess(self, probabilities):
probabilities = probabilities.detach()
probabilities[:, :self.frequency_to_bins(torch.tensor(self.f0_min))] = -float('inf')
probabilities[:, self.frequency_to_bins(torch.tensor(self.f0_max), torch.ceil):] = -float('inf')
bins, pitch = self.viterbi(probabilities)
if not self.return_periodicity: return pitch
return pitch, self.periodicity(probabilities, bins)
def compute_f0(self, audio, pad=True):
results = []
for frames in self.preprocess(audio, pad):
if self.onnx:
model = torch.tensor(
self.model.run(
[self.model.get_outputs()[0].name],
{
self.model.get_inputs()[0].name: frames.cpu().numpy()
}
)[0].transpose(1, 0)[None],
device=self.device
)
else:
with torch.no_grad():
model = self.model(
frames,
embed=False
).reshape(audio.size(0), -1, PITCH_BINS).transpose(1, 2)
result = self.postprocess(model)
results.append(
(result[0].to(audio.device), result[1].to(audio.device)) if isinstance(result, tuple) else result.to(audio.device)
)
if self.return_periodicity:
pitch, periodicity = zip(*results)
return torch.cat(pitch, 1), torch.cat(periodicity, 1)
return torch.cat(results, 1) |