| import torch |
| import torch.nn.functional as F |
| from torchaudio.transforms import Resample |
|
|
| from .constants import * |
| from .model import E2E0 |
| from .spec import MelSpectrogram |
| from .utils import to_local_average_f0, to_viterbi_f0 |
|
|
|
|
| class RMVPE: |
| def __init__(self, model_path, hop_length=160): |
| self.resample_kernel = {} |
| self.device = 'cuda' if torch.cuda.is_available() else 'cpu' |
| self.model = E2E0(4, 1, (2, 2)).eval().to(self.device) |
| ckpt = torch.load(model_path, map_location=self.device) |
| self.model.load_state_dict(ckpt['model'], strict=False) |
| self.mel_extractor = MelSpectrogram( |
| N_MELS, SAMPLE_RATE, WINDOW_LENGTH, hop_length, None, MEL_FMIN, MEL_FMAX |
| ).to(self.device) |
|
|
| @torch.no_grad() |
| def mel2hidden(self, mel): |
| 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: |
| f0 = to_viterbi_f0(hidden, thred=thred) |
| else: |
| f0 = to_local_average_f0(hidden, thred=thred) |
| return f0 |
|
|
| def infer_from_audio(self, audio, sample_rate=16000, thred=0.03, use_viterbi=False): |
| audio = torch.from_numpy(audio).float().unsqueeze(0).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.device) |
| audio_res = self.resample_kernel[key_str](audio) |
| mel = self.mel_extractor(audio_res, center=True) |
| hidden = self.mel2hidden(mel) |
| f0 = self.decode(hidden, thred=thred, use_viterbi=use_viterbi) |
| return f0 |
|
|
