| 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 |