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import onnxruntime
import librosa
import numpy as np
import soundfile
class ContentVec:
def __init__(self, vec_path="pretrained/vec-768-layer-12.onnx", device=None):
print("load model(s) from {}".format(vec_path))
if device == "cpu" or device is None:
providers = ["CPUExecutionProvider"]
elif device == "cuda":
providers = ["CUDAExecutionProvider", "CPUExecutionProvider"]
elif device == "dml":
providers = ["DmlExecutionProvider"]
else:
raise RuntimeError("Unsportted Device")
self.model = onnxruntime.InferenceSession(vec_path, providers=providers)
def __call__(self, wav):
return self.forward(wav)
def forward(self, wav):
feats = wav
if feats.ndim == 2: # double channels
feats = feats.mean(-1)
assert feats.ndim == 1, feats.ndim
feats = np.expand_dims(np.expand_dims(feats, 0), 0)
onnx_input = {self.model.get_inputs()[0].name: feats}
logits = self.model.run(None, onnx_input)[0]
return logits.transpose(0, 2, 1)
def get_f0_predictor(f0_predictor, hop_length, sampling_rate, **kargs):
if f0_predictor == "pm":
from lib.infer_pack.modules.F0Predictor.PMF0Predictor import PMF0Predictor
f0_predictor_object = PMF0Predictor(
hop_length=hop_length, sampling_rate=sampling_rate
)
elif f0_predictor == "harvest":
from lib.infer_pack.modules.F0Predictor.HarvestF0Predictor import (
HarvestF0Predictor,
)
f0_predictor_object = HarvestF0Predictor(
hop_length=hop_length, sampling_rate=sampling_rate
)
elif f0_predictor == "dio":
from lib.infer_pack.modules.F0Predictor.DioF0Predictor import DioF0Predictor
f0_predictor_object = DioF0Predictor(
hop_length=hop_length, sampling_rate=sampling_rate
)
else:
raise Exception("Unknown f0 predictor")
return f0_predictor_object
class OnnxRVC:
def __init__(
self,
model_path,
sr=40000,
hop_size=512,
vec_path="vec-768-layer-12",
device="cpu",
):
vec_path = f"pretrained/{vec_path}.onnx"
self.vec_model = ContentVec(vec_path, device)
if device == "cpu" or device is None:
providers = ["CPUExecutionProvider"]
elif device == "cuda":
providers = ["CUDAExecutionProvider", "CPUExecutionProvider"]
elif device == "dml":
providers = ["DmlExecutionProvider"]
else:
raise RuntimeError("Unsportted Device")
self.model = onnxruntime.InferenceSession(model_path, providers=providers)
self.sampling_rate = sr
self.hop_size = hop_size
def forward(self, hubert, hubert_length, pitch, pitchf, ds, rnd):
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)
def inference(
self,
raw_path,
sid,
f0_method="dio",
f0_up_key=0,
pad_time=0.5,
cr_threshold=0.02,
):
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,
hop_length=self.hop_size,
sampling_rate=self.sampling_rate,
threshold=cr_threshold,
)
wav, sr = librosa.load(raw_path, sr=self.sampling_rate)
org_length = len(wav)
if org_length / sr > 50.0:
raise RuntimeError("Reached Max Length")
wav16k = librosa.resample(wav, orig_sr=self.sampling_rate, target_sr=16000)
wav16k = wav16k
hubert = self.vec_model(wav16k)
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_size), "constant")
return out_wav[0:org_length]
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