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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
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