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| import torch | |
| import torch.nn.functional as F | |
| from torchaudio.transforms import Resample | |
| from .constants import * # noqa: F403 | |
| from .model import E2E0 | |
| from .spec import MelSpectrogram | |
| from .utils import to_local_average_cents, to_viterbi_cents | |
| class RMVPE: | |
| def __init__(self, model_path, device=None, dtype = torch.float32, hop_length=160): | |
| self.resample_kernel = {} | |
| if device is None: | |
| self.device = 'cuda' if torch.cuda.is_available() else 'cpu' | |
| else: | |
| self.device = device | |
| model = E2E0(4, 1, (2, 2)) | |
| ckpt = torch.load(model_path, map_location=torch.device(self.device)) | |
| model.load_state_dict(ckpt['model']) | |
| model = model.to(dtype).to(self.device) | |
| model.eval() | |
| self.model = model | |
| self.dtype = dtype | |
| self.mel_extractor = MelSpectrogram(N_MELS, SAMPLE_RATE, WINDOW_LENGTH, hop_length, None, MEL_FMIN, MEL_FMAX) # noqa: F405 | |
| self.resample_kernel = {} | |
| def mel2hidden(self, mel): | |
| with torch.no_grad(): | |
| n_frames = mel.shape[-1] | |
| mel = F.pad(mel, (0, 32 * ((n_frames - 1) // 32 + 1) - n_frames), mode='constant') | |
| hidden = self.model(mel) | |
| return hidden[:, :n_frames] | |
| def decode(self, hidden, thred=0.03, use_viterbi=False): | |
| if use_viterbi: | |
| cents_pred = to_viterbi_cents(hidden, thred=thred) | |
| else: | |
| cents_pred = to_local_average_cents(hidden, thred=thred) | |
| f0 = torch.Tensor([10 * (2 ** (cent_pred / 1200)) if cent_pred else 0 for cent_pred in cents_pred]).to(self.device) | |
| return f0 | |
| def infer_from_audio(self, audio, sample_rate=16000, thred=0.05, use_viterbi=False): | |
| audio = audio.unsqueeze(0).to(self.dtype).to(self.device) | |
| if sample_rate == 16000: | |
| audio_res = audio | |
| else: | |
| key_str = str(sample_rate) | |
| if key_str not in self.resample_kernel: | |
| self.resample_kernel[key_str] = Resample(sample_rate, 16000, lowpass_filter_width=128) | |
| self.resample_kernel[key_str] = self.resample_kernel[key_str].to(self.dtype).to(self.device) | |
| audio_res = self.resample_kernel[key_str](audio) | |
| mel_extractor = self.mel_extractor.to(self.device) | |
| mel = mel_extractor(audio_res, center=True).to(self.dtype) | |
| hidden = self.mel2hidden(mel) | |
| f0 = self.decode(hidden.squeeze(0), thred=thred, use_viterbi=use_viterbi) | |
| return f0 | |