RVC/Retrieval-based-Voice-Conversion-WebUI
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Format code (#384)

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2023-05-30 15:22:53 +08:00
committed by GitHub
parent 5284e38c3d
commit 89afd017ba
7 changed files with 181 additions and 133 deletions
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94
infer_pack/onnx_inference.py
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@@ -3,13 +3,14 @@ import librosa
import numpy as np
import soundfile
class ContentVec():
def __init__(self, vec_path = "pretrained/vec-768-layer-12.onnx",device=None):
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']
if device == "cpu" or device is None:
providers = ["CPUExecutionProvider"]
elif device == "cuda":
providers = ["CUDAExecutionProvider", "CPUExecutionProvider"]
else:
raise RuntimeError("Unsportted Device")
self.model = onnxruntime.InferenceSession(vec_path, providers=providers)
@@ -20,7 +21,7 @@ class ContentVec():
def forward(self, wav):
feats = wav
if feats.ndim == 2: # double channels
feats = feats.mean(-1)
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}
@@ -31,33 +32,42 @@ class ContentVec():
def get_f0_predictor(f0_predictor, hop_length, sampling_rate, **kargs):
if f0_predictor == "pm":
from infer_pack.modules.F0Predictor.PMF0Predictor import PMF0Predictor
f0_predictor_object = PMF0Predictor(hop_length=hop_length,sampling_rate=sampling_rate)
f0_predictor_object = PMF0Predictor(
hop_length=hop_length, sampling_rate=sampling_rate
)
elif f0_predictor == "harvest":
from infer_pack.modules.F0Predictor.HarvestF0Predictor import HarvestF0Predictor
f0_predictor_object = HarvestF0Predictor(hop_length=hop_length,sampling_rate=sampling_rate)
f0_predictor_object = HarvestF0Predictor(
hop_length=hop_length, sampling_rate=sampling_rate
)
elif f0_predictor == "dio":
from infer_pack.modules.F0Predictor.DioF0Predictor import DioF0Predictor
f0_predictor_object = DioF0Predictor(hop_length=hop_length,sampling_rate=sampling_rate)
f0_predictor_object = DioF0Predictor(
hop_length=hop_length, sampling_rate=sampling_rate
)
else:
raise Exception("Unknown f0 predictor")
return f0_predictor_object
class OnnxRVC():
class OnnxRVC:
def __init__(
self,
model_path,
sr=40000,
hop_size=512,
vec_path="vec-768-layer-12",
device="cpu"
):
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']
if device == "cpu" or device is None:
providers = ["CPUExecutionProvider"]
elif device == "cuda":
providers = ["CUDAExecutionProvider", "CPUExecutionProvider"]
else:
raise RuntimeError("Unsportted Device")
self.model = onnxruntime.InferenceSession(model_path, providers=providers)
@@ -66,29 +76,37 @@ class OnnxRVC():
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
}
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):
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
)
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.:
if org_length / sr > 50.0:
raise RuntimeError("Reached Max Length")
wav16k = librosa.resample(wav, orig_sr=self.sampling_rate, target_sr=16000)
@@ -117,5 +135,5 @@ class OnnxRVC():
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]
out_wav = np.pad(out_wav, (0, 2 * self.hop_size), "constant")
return out_wav[0:org_length]