wehub-resource-sync
79 lines
3.0 KiB
Python
79 lines
3.0 KiB
Python
import numpy as np
|
|
import torch
|
|
import torch.nn.functional as F
|
|
from torchaudio.transforms import Resample
|
|
|
|
from basics.base_pe import BasePE
|
|
from utils.infer_utils import resample_align_curve
|
|
from utils.pitch_utils import interp_f0
|
|
from .constants import *
|
|
from .model import E2E0
|
|
from .spec import MelSpectrogram
|
|
from .utils import to_local_average_f0, to_viterbi_f0
|
|
|
|
|
|
class RMVPE(BasePE):
|
|
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.hop_length = hop_length
|
|
self.seg_length = 32 * hop_length
|
|
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='reflect')
|
|
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)
|
|
B, T = audio_res.shape
|
|
n_frames = T // self.hop_length + 1
|
|
T1 = T + self.hop_length
|
|
T_pad = self.seg_length * ((T1 - 1) // self.seg_length + 1) - T1
|
|
audio_res = F.pad(audio_res, (0, T_pad))
|
|
mel = self.mel_extractor(audio_res, center=True)
|
|
with torch.no_grad():
|
|
hidden = self.model(mel)
|
|
f0 = self.decode(hidden[:, :n_frames], thred=thred, use_viterbi=use_viterbi)
|
|
return f0
|
|
|
|
def get_pitch(
|
|
self, waveform, samplerate, length,
|
|
*, hop_size, f0_min=65, f0_max=1100,
|
|
speed=1, interp_uv=False
|
|
):
|
|
f0 = self.infer_from_audio(waveform, sample_rate=samplerate)
|
|
uv = f0 == 0
|
|
f0, uv = interp_f0(f0, uv)
|
|
|
|
hop_size = int(np.round(hop_size * speed))
|
|
time_step = hop_size / samplerate
|
|
f0_res = resample_align_curve(f0, 0.01, time_step, length)
|
|
uv_res = resample_align_curve(uv.astype(np.float32), 0.01, time_step, length) > 0.5
|
|
if not interp_uv:
|
|
f0_res[uv_res] = 0
|
|
return f0_res, uv_res
|