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WIP: support so-vits-svc, suppress log, update setting parameter

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wataru 2023-03-11 04:19:59 +09:00
parent a5c7414fc3
commit 87f7429501
2 changed files with 26 additions and 13 deletions
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13
server/misc/log_control.py
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@ -14,6 +14,19 @@ logger.addFilter(UvicornSuppressFilter())
logger = logging.getLogger("fairseq.tasks.hubert_pretraining") logger = logging.getLogger("fairseq.tasks.hubert_pretraining")
logger.addFilter(UvicornSuppressFilter()) logger.addFilter(UvicornSuppressFilter())
logger = logging.getLogger("fairseq.models.hubert.hubert")
logger.addFilter(UvicornSuppressFilter())
logger = logging.getLogger("numba.core.ssa")
logger.addFilter(UvicornSuppressFilter())
logger = logging.getLogger("numba.core.interpreter")
logger.addFilter(UvicornSuppressFilter())
logger = logging.getLogger("numba.core.byteflow")
logger.addFilter(UvicornSuppressFilter())
# logger.propagate = False # logger.propagate = False
logger = logging.getLogger("multipart.multipart") logger = logging.getLogger("multipart.multipart")

26
server/voice_changer/SoVitsSvc40v2/SoVitsSvc40v2.py
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@ -29,11 +29,13 @@ providers = ['OpenVINOExecutionProvider', "CUDAExecutionProvider", "DmlExecution
@dataclass @dataclass
class SoVitsSvc40v2Settings(): class SoVitsSvc40v2Settings():
gpu: int = 0 gpu: int = 0
srcId: int = 0
dstId: int = 101 dstId: int = 101
f0Factor: float = 1.0
f0Detector: str = "dio" # dio or harvest f0Detector: str = "dio" # dio or harvest
tran: int = 0
noiceScale: float = 0.3
predictF0: int = 0 # 0:False, 1:True
silentThreshold: float = 0.00001
framework: str = "PyTorch" # PyTorch or ONNX framework: str = "PyTorch" # PyTorch or ONNX
pyTorchModelFile: str = "" pyTorchModelFile: str = ""
@ -41,8 +43,8 @@ class SoVitsSvc40v2Settings():
configFile: str = "" configFile: str = ""
# ↓mutableな物だけ列挙 # ↓mutableな物だけ列挙
intData = ["gpu", "srcId", "dstId"] intData = ["gpu", "dstId", "tran", "predictF0"]
floatData = ["f0Factor"] floatData = ["noiceScale", "silentThreshold"]
strData = ["framework", "f0Detector"] strData = ["framework", "f0Detector"]
@ -63,7 +65,6 @@ class SoVitsSvc40v2:
# hubert model # hubert model
print("loading hubert model") print("loading hubert model")
vec_path = "hubert/checkpoint_best_legacy_500.pt" vec_path = "hubert/checkpoint_best_legacy_500.pt"
print("load model(s) from {}".format(vec_path))
models, saved_cfg, task = checkpoint_utils.load_model_ensemble_and_task( models, saved_cfg, task = checkpoint_utils.load_model_ensemble_and_task(
[vec_path], [vec_path],
suffix="", suffix="",
@ -177,9 +178,8 @@ class SoVitsSvc40v2:
rms = np.sqrt(np.square(crop).mean(axis=0)) rms = np.sqrt(np.square(crop).mean(axis=0))
vol = max(rms, self.prevVol * 0.1) vol = max(rms, self.prevVol * 0.1)
self.prevVol = vol self.prevVol = vol
# print(f" Crop:{crop.shape}, vol{vol}")
c, f0, uv = self.get_unit_f0(self.audio_buffer, 20) c, f0, uv = self.get_unit_f0(self.audio_buffer, self.settings.tran)
return (c, f0, uv, convertSize, vol) return (c, f0, uv, convertSize, vol)
def _onnx_inference(self, data): def _onnx_inference(self, data):
@ -199,17 +199,17 @@ class SoVitsSvc40v2:
vol = data[4] vol = data[4]
data = (data[0], data[1], data[2],) data = (data[0], data[1], data[2],)
# if vol < 0.00001: if vol < self.settings.silentThreshold:
# print("silcent") return np.zeros(convertSize).astype(np.int16)
# return np.zeros(convertSize).astype(np.int16)
# print(vol)
with torch.no_grad(): with torch.no_grad():
c, f0, uv = [x.to(dev)for x in data] c, f0, uv = [x.to(dev)for x in data]
sid_target = torch.LongTensor([0]).to(dev) sid_target = torch.LongTensor([self.settings.dstId]).to(dev)
self.net_g.to(dev) self.net_g.to(dev)
# audio1 = self.net_g.infer(c, f0=f0, g=sid_target, uv=uv, predict_f0=True, noice_scale=0.1)[0][0, 0].data.float() # audio1 = self.net_g.infer(c, f0=f0, g=sid_target, uv=uv, predict_f0=True, noice_scale=0.1)[0][0, 0].data.float()
audio1 = self.net_g.infer(c, f0=f0, g=sid_target, uv=uv, predict_f0=False, noice_scale=0.4)[0][0, 0].data.float() predict_f0_flag = True if self.settings.predictF0 == 1 else False
audio1 = self.net_g.infer(c, f0=f0, g=sid_target, uv=uv, predict_f0=predict_f0_flag,
noice_scale=self.settings.noiceScale)[0][0, 0].data.float()
audio1 = audio1 * self.hps.data.max_wav_value audio1 = audio1 * self.hps.data.max_wav_value
result = audio1.float().cpu().numpy() result = audio1.float().cpu().numpy()