File size: 4,904 Bytes
c3b58fa |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 |
import typing
import os
import librosa
import numpy as np
import onnxruntime
from rvc.f0 import (
PM,
Harvest,
Dio,
F0Predictor,
)
class Model:
def __init__(
self,
path: typing.Union[str, bytes, os.PathLike],
device: typing.Literal["cpu", "cuda", "dml"] = "cpu",
):
if device == "cpu":
providers = ["CPUExecutionProvider"]
elif device == "cuda":
providers = ["CUDAExecutionProvider", "CPUExecutionProvider"]
elif device == "dml":
providers = ["DmlExecutionProvider"]
else:
raise RuntimeError("Unsportted Device")
self.model = onnxruntime.InferenceSession(path, providers=providers)
class ContentVec(Model):
def __init__(
self,
vec_path: typing.Union[str, bytes, os.PathLike],
device: typing.Literal["cpu", "cuda", "dml"] = "cpu",
):
super().__init__(vec_path, device)
def __call__(self, wav: np.ndarray[typing.Any, np.dtype]):
return self.forward(wav)
def forward(self, wav: np.ndarray[typing.Any, np.dtype]):
if wav.ndim == 2: # double channels
wav = wav.mean(-1)
assert wav.ndim == 1, wav.ndim
wav = np.expand_dims(np.expand_dims(wav, 0), 0)
onnx_input = {self.model.get_inputs()[0].name: wav}
logits = self.model.run(None, onnx_input)[0]
return logits.transpose(0, 2, 1)
predictors: typing.Dict[str, F0Predictor] = {
"pm": PM,
"harvest": Harvest,
"dio": Dio,
}
def get_f0_predictor(
f0_method: str, hop_length: int, sampling_rate: int
) -> F0Predictor:
return predictors[f0_method](hop_length=hop_length, sampling_rate=sampling_rate)
class RVC(Model):
def __init__(
self,
model_path: typing.Union[str, bytes, os.PathLike],
hop_len=512,
vec_path: typing.Union[str, bytes, os.PathLike] = "vec-768-layer-12.onnx",
device: typing.Literal["cpu", "cuda", "dml"] = "cpu",
):
super().__init__(model_path, device)
self.vec_model = ContentVec(vec_path, device)
self.hop_len = hop_len
def infer(
self,
wav: np.ndarray[typing.Any, np.dtype],
wav_sr: int,
model_sr: int = 40000,
sid: int = 0,
f0_method="dio",
f0_up_key=0,
) -> np.ndarray[typing.Any, np.dtype[np.int16]]:
f0_min = 50
f0_max = 1100
f0_mel_min = 1127 * np.log(1 + f0_min / 700)
f0_mel_max = 1127 * np.log(1 + f0_max / 700)
f0_predictor = get_f0_predictor(
f0_method,
self.hop_len,
model_sr,
)
org_length = len(wav)
if org_length / wav_sr > 50.0:
raise RuntimeError("wav max length exceeded")
hubert = self.vec_model(librosa.resample(wav, orig_sr=wav_sr, target_sr=16000))
hubert = np.repeat(hubert, 2, axis=2).transpose(0, 2, 1).astype(np.float32)
hubert_length = hubert.shape[1]
pitchf = f0_predictor.compute_f0(wav, hubert_length)
pitchf = pitchf * 2 ** (f0_up_key / 12)
pitch = pitchf.copy()
f0_mel = 1127 * np.log(1 + pitch / 700)
f0_mel[f0_mel > 0] = (f0_mel[f0_mel > 0] - f0_mel_min) * 254 / (
f0_mel_max - f0_mel_min
) + 1
f0_mel[f0_mel <= 1] = 1
f0_mel[f0_mel > 255] = 255
pitch = np.rint(f0_mel).astype(np.int64)
pitchf = pitchf.reshape(1, len(pitchf)).astype(np.float32)
pitch = pitch.reshape(1, len(pitch))
ds = np.array([sid]).astype(np.int64)
rnd = np.random.randn(1, 192, hubert_length).astype(np.float32)
hubert_length = np.array([hubert_length]).astype(np.int64)
out_wav = self.forward(hubert, hubert_length, pitch, pitchf, ds, rnd).squeeze()
out_wav = np.pad(out_wav, (0, 2 * self.hop_len), "constant")
return out_wav[0:org_length]
def forward(
self,
hubert: np.ndarray[typing.Any, np.dtype[np.float32]],
hubert_length: int,
pitch: np.ndarray[typing.Any, np.dtype[np.int64]],
pitchf: np.ndarray[typing.Any, np.dtype[np.float32]],
ds: np.ndarray[typing.Any, np.dtype[np.int64]],
rnd: np.ndarray[typing.Any, np.dtype[np.float32]],
) -> np.ndarray[typing.Any, np.dtype[np.int16]]:
onnx_input = {
self.model.get_inputs()[0].name: hubert,
self.model.get_inputs()[1].name: hubert_length,
self.model.get_inputs()[2].name: pitch,
self.model.get_inputs()[3].name: pitchf,
self.model.get_inputs()[4].name: ds,
self.model.get_inputs()[5].name: rnd,
}
return (self.model.run(None, onnx_input)[0] * 32767).astype(np.int16)
|