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import os
import sys
import math
import torch
import librosa
import torchaudio
import numpy as np
sys.path.append(os.getcwd())
from main.library.predictors.FCN.model import MODEL
from main.library.predictors.FCN.convert import frequency_to_bins, seconds_to_samples, bins_to_frequency
CENTS_PER_BIN, PITCH_BINS, SAMPLE_RATE, WINDOW_SIZE = 5, 1440, 16000, 1024
class FCN:
def __init__(self, model_path, hop_length=160, batch_size=None, f0_min=50, f0_max=1100, device=None, sample_rate=16000, providers=None, onnx=False):
self.device = device or ("cuda" if torch.cuda.is_available() else "cpu")
self.hopsize = hop_length / SAMPLE_RATE
self.batch_size = batch_size
self.sample_rate = sample_rate
self.onnx = onnx
self.f0_min = f0_min
self.f0_max = f0_max
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:
model = MODEL()
ckpt = torch.load(model_path, map_location="cpu", weights_only=True)
model.load_state_dict(ckpt['model'])
model.eval()
self.model = model.to(device)
def entropy(self, logits):
distribution = torch.nn.functional.softmax(logits, dim=1)
return (1 + 1 / math.log(PITCH_BINS) * (distribution * torch.log(distribution + 1e-7)).sum(dim=1))
def expected_frames(self, samples, center):
hopsize_resampled = seconds_to_samples(self.hopsize, self.sample_rate)
if center == 'half-window':
window_size_resampled = WINDOW_SIZE / SAMPLE_RATE * self.sample_rate
samples = samples - (window_size_resampled - hopsize_resampled)
elif center == 'half-hop':
samples = samples
elif center == 'zero':
samples = samples + hopsize_resampled
else: raise ValueError
return max(1, int(samples / hopsize_resampled))
def resample(self, audio, target_rate=SAMPLE_RATE):
if self.sample_rate == target_rate: return audio
resampler = torchaudio.transforms.Resample(self.sample_rate, target_rate)
resampler = resampler.to(audio.device)
return resampler(audio)
def preprocess(self, audio, center='half-window'):
total_frames = self.expected_frames(audio.shape[-1], center)
if self.sample_rate != SAMPLE_RATE: audio = self.resample(audio)
hopsize = seconds_to_samples(self.hopsize, SAMPLE_RATE)
if center in ['half-hop', 'zero']:
if center == 'half-hop': padding = int((WINDOW_SIZE - hopsize) / 2)
else: padding = int(WINDOW_SIZE / 2)
audio = torch.nn.functional.pad(audio, (padding, padding), mode='reflect')
if isinstance(hopsize, int) or hopsize.is_integer():
hopsize = int(round(hopsize))
start_idxs = None
else:
start_idxs = torch.round(torch.tensor([hopsize * i for i in range(total_frames + 1)])).int()
batch_size = total_frames if self.batch_size is None else self.batch_size
for i in range(0, total_frames, batch_size):
batch = min(total_frames - i, batch_size)
if start_idxs is None:
start = i * hopsize
end = start + int((batch - 1) * hopsize) + WINDOW_SIZE
end = min(end, audio.shape[-1])
batch_audio = audio[:, start:end]
if end - start < WINDOW_SIZE:
padding = WINDOW_SIZE - (end - start)
remainder = (end - start) % hopsize
if remainder: padding += end - start - hopsize
batch_audio = torch.nn.functional.pad(batch_audio, (0, padding))
frames = torch.nn.functional.unfold(batch_audio[:, None, None], kernel_size=(1, WINDOW_SIZE), stride=(1, hopsize)).permute(2, 0, 1)
else:
frames = torch.zeros(batch, 1, WINDOW_SIZE)
for j in range(batch):
start = start_idxs[i + j]
end = min(start + WINDOW_SIZE, audio.shape[-1])
frames[j, :, : end - start] = audio[:, start:end]
yield frames
def viterbi(self, logits):
if not hasattr(self, 'transition'):
xx, yy = np.meshgrid(range(PITCH_BINS), range(PITCH_BINS))
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_to_frequency(bins)
def postprocess(self, logits):
with torch.inference_mode():
minidx = frequency_to_bins(torch.tensor(self.f0_min))
maxidx = frequency_to_bins(torch.tensor(self.f0_max), torch.ceil)
logits[:, :minidx] = -float('inf')
logits[:, maxidx:] = -float('inf')
pitch = self.viterbi(logits)
periodicity = self.entropy(logits)
return pitch.T, periodicity.T
def compute_f0(self, audio, center = 'half-window'):
if self.batch_size is not None: logits = []
for frames in self.preprocess(audio, center):
if self.onnx:
inferred = torch.tensor(
self.model.run(
[self.model.get_outputs()[0].name],
{
self.model.get_inputs()[0].name: frames.cpu().numpy()
}
)[0]
).detach()
else:
with torch.no_grad():
inferred = self.model(frames.to(self.device)).detach()
logits.append(inferred)
pitch, periodicity = self.postprocess(torch.cat(logits, 0).to(self.device))
return pitch, periodicity |