voice-changer/server/voice_changer/DiffusionSVC/pitchExtractor/CrepeOnnxPitchExtractor.py
2023-07-15 09:17:19 +09:00

66 lines
2.1 KiB
Python

import numpy as np
from const import PitchExtractorType
from voice_changer.DiffusionSVC.pitchExtractor.PitchExtractor import PitchExtractor
from voice_changer.RVC.deviceManager.DeviceManager import DeviceManager
import onnxruntime
import torch
from voice_changer.RVC.pitchExtractor import onnxcrepe
class CrepeOnnxPitchExtractor(PitchExtractor):
def __init__(self, pitchExtractorType: PitchExtractorType, file: str, gpu: int):
self.pitchExtractorType = pitchExtractorType
super().__init__()
(
onnxProviders,
onnxProviderOptions,
) = DeviceManager.get_instance().getOnnxExecutionProvider(gpu)
self.onnx_session = onnxruntime.InferenceSession(
file, providers=onnxProviders, provider_options=onnxProviderOptions
)
self.f0_min = 50
self.f0_max = 1100
self.sapmle_rate = 16000
self.uv_interp = True
def extract(self, audio: torch.Tensor, pitch, f0_up_key, window, silence_front=0):
start_frame = int(silence_front * self.sapmle_rate / window)
real_silence_front = start_frame * window / self.sapmle_rate
audio = audio[int(np.round(real_silence_front * self.sapmle_rate)):]
precision = (1000 * window / self.sapmle_rate)
audio_num = audio.cpu()
onnx_f0, onnx_pd = onnxcrepe.predict(
self.onnx_session,
audio_num,
self.sapmle_rate,
precision=precision,
fmin=self.f0_min,
fmax=self.f0_max,
batch_size=256,
return_periodicity=True,
decoder=onnxcrepe.decode.weighted_argmax,
)
f0 = onnxcrepe.filter.median(onnx_f0, 3)
pd = onnxcrepe.filter.median(onnx_pd, 3)
f0[pd < 0.1] = 0
f0 = f0.squeeze()
pitch[-f0.shape[0]:] = f0[:pitch.shape[0]]
f0 = pitch
if self.uv_interp:
uv = f0 == 0
if len(f0[~uv]) > 0:
f0[uv] = np.interp(np.where(uv)[0], np.where(~uv)[0], f0[~uv])
f0[f0 < self.f0_min] = self.f0_min
f0 = f0 * 2 ** (float(f0_up_key) / 12)
return f0