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import torch |
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import torch.nn.functional as F |
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from torchaudio.transforms import Resample |
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from .constants import * |
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from .model import E2E0 |
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from .spec import MelSpectrogram |
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from .utils import to_local_average_f0, to_viterbi_f0 |
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class RMVPE: |
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def __init__(self, model_path, hop_length=160): |
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self.resample_kernel = {} |
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self.device = 'cuda' if torch.cuda.is_available() else 'cpu' |
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self.model = E2E0(4, 1, (2, 2)).eval().to(self.device) |
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ckpt = torch.load(model_path, map_location=self.device) |
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self.model.load_state_dict(ckpt['model'], strict=False) |
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self.mel_extractor = MelSpectrogram( |
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N_MELS, SAMPLE_RATE, WINDOW_LENGTH, hop_length, None, MEL_FMIN, MEL_FMAX |
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).to(self.device) |
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@torch.no_grad() |
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def mel2hidden(self, mel): |
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n_frames = mel.shape[-1] |
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mel = F.pad(mel, (0, 32 * ((n_frames - 1) // 32 + 1) - n_frames), mode='constant') |
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hidden = self.model(mel) |
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return hidden[:, :n_frames] |
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def decode(self, hidden, thred=0.03, use_viterbi=False): |
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if use_viterbi: |
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f0 = to_viterbi_f0(hidden, thred=thred) |
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else: |
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f0 = to_local_average_f0(hidden, thred=thred) |
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return f0 |
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def infer_from_audio(self, audio, sample_rate=16000, thred=0.03, use_viterbi=False): |
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audio = torch.from_numpy(audio).float().unsqueeze(0).to(self.device) |
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if sample_rate == 16000: |
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audio_res = audio |
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else: |
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key_str = str(sample_rate) |
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if key_str not in self.resample_kernel: |
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self.resample_kernel[key_str] = Resample(sample_rate, 16000, lowpass_filter_width=128) |
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self.resample_kernel[key_str] = self.resample_kernel[key_str].to(self.device) |
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audio_res = self.resample_kernel[key_str](audio) |
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mel = self.mel_extractor(audio_res, center=True) |
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hidden = self.mel2hidden(mel) |
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f0 = self.decode(hidden, thred=thred, use_viterbi=use_viterbi) |
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return f0 |
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