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import os
import sys
import torch
import numpy as np
import torch.nn.functional as F
sys.path.append(os.getcwd())
from main.library.predictors.RMVPE.e2e import E2E
from main.library.predictors.RMVPE.mel import MelSpectrogram
N_MELS, N_CLASS = 128, 360
class RMVPE:
def __init__(self, model_path, is_half, device=None, providers=None, onnx=False):
self.resample_kernel = {}
self.onnx = onnx
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 = E2E(4, 1, (2, 2))
ckpt = torch.load(model_path, map_location="cpu", weights_only=True)
model.load_state_dict(ckpt)
model.eval()
if is_half: model = model.half()
self.model = model.to(device)
self.resample_kernel = {}
self.is_half = is_half
self.device = device
self.mel_extractor = MelSpectrogram(is_half, N_MELS, 16000, 1024, 160, None, 30, 8000).to(device)
cents_mapping = 20 * np.arange(N_CLASS) + 1997.3794084376191
self.cents_mapping = np.pad(cents_mapping, (4, 4))
def mel2hidden(self, mel):
with torch.no_grad():
n_frames = mel.shape[-1]
n_pad = 32 * ((n_frames - 1) // 32 + 1) - n_frames
if n_pad > 0: mel = F.pad(mel, (0, n_pad), mode="constant")
hidden = self.model.run([self.model.get_outputs()[0].name], input_feed={self.model.get_inputs()[0].name: mel.cpu().numpy().astype(np.float32)})[0] if self.onnx else self.model(mel.half() if self.is_half else mel.float())
return hidden[:, :n_frames]
def decode(self, hidden, thred=0.03):
f0 = 10 * (2 ** (self.to_local_average_cents(hidden, thred=thred) / 1200))
f0[f0 == 10] = 0
return f0
def infer_from_audio(self, audio, thred=0.03):
hidden = self.mel2hidden(self.mel_extractor(torch.from_numpy(audio).float().to(self.device).unsqueeze(0), center=True))
return self.decode((hidden.squeeze(0).cpu().numpy().astype(np.float32) if self.is_half else hidden.squeeze(0).cpu().numpy()) if not self.onnx else hidden[0], thred=thred)
def infer_from_audio_with_pitch(self, audio, thred=0.03, f0_min=50, f0_max=1100):
hidden = self.mel2hidden(self.mel_extractor(torch.from_numpy(audio).float().to(self.device).unsqueeze(0), center=True))
f0 = self.decode((hidden.squeeze(0).cpu().numpy().astype(np.float32) if self.is_half else hidden.squeeze(0).cpu().numpy()) if not self.onnx else hidden[0], thred=thred)
f0[(f0 < f0_min) | (f0 > f0_max)] = 0
return f0
def to_local_average_cents(self, salience, thred=0.05):
center = np.argmax(salience, axis=1)
salience = np.pad(salience, ((0, 0), (4, 4)))
center += 4
todo_salience, todo_cents_mapping = [], []
starts = center - 4
ends = center + 5
for idx in range(salience.shape[0]):
todo_salience.append(salience[:, starts[idx] : ends[idx]][idx])
todo_cents_mapping.append(self.cents_mapping[starts[idx] : ends[idx]])
todo_salience = np.array(todo_salience)
devided = np.sum(todo_salience * np.array(todo_cents_mapping), 1) / np.sum(todo_salience, 1)
devided[np.max(salience, axis=1) <= thred] = 0
return devided |