mirror of
https://github.com/w-okada/voice-changer.git
synced 2025-01-23 13:35:12 +03:00
WIP: refactoring
This commit is contained in:
wataru
parent
4ac4a225a7
commit
b3d7946592
@ -1,6 +1,11 @@
|
||||
import sys
|
||||
import os
|
||||
if sys.platform.startswith('darwin'):
|
||||
|
||||
from voice_changer.utils.LoadModelParams import LoadModelParams
|
||||
from voice_changer.utils.VoiceChangerModel import AudioInOut
|
||||
from voice_changer.utils.VoiceChangerParams import VoiceChangerParams
|
||||
|
||||
if sys.platform.startswith("darwin"):
|
||||
baseDir = [x for x in sys.path if x.endswith("Contents/MacOS")]
|
||||
if len(baseDir) != 1:
|
||||
print("baseDir should be only one ", baseDir)
|
||||
@ -12,17 +17,16 @@ else:
|
||||
|
||||
import io
|
||||
from dataclasses import dataclass, asdict, field
|
||||
from functools import reduce
|
||||
import numpy as np
|
||||
import torch
|
||||
import onnxruntime
|
||||
|
||||
# onnxruntime.set_default_logger_severity(3)
|
||||
from const import HUBERT_ONNX_MODEL_PATH
|
||||
|
||||
import pyworld as pw
|
||||
|
||||
from models import SynthesizerTrn
|
||||
import cluster
|
||||
from models import SynthesizerTrn # type:ignore
|
||||
import cluster # type:ignore
|
||||
import utils
|
||||
from fairseq import checkpoint_utils
|
||||
import librosa
|
||||
@ -30,11 +34,16 @@ import librosa
|
||||
from Exceptions import NoModeLoadedException
|
||||
|
||||
|
||||
providers = ['OpenVINOExecutionProvider', "CUDAExecutionProvider", "DmlExecutionProvider", "CPUExecutionProvider"]
|
||||
providers = [
|
||||
"OpenVINOExecutionProvider",
|
||||
"CUDAExecutionProvider",
|
||||
"DmlExecutionProvider",
|
||||
"CPUExecutionProvider",
|
||||
]
|
||||
|
||||
|
||||
@dataclass
|
||||
class SoVitsSvc40Settings():
|
||||
class SoVitsSvc40Settings:
|
||||
gpu: int = 0
|
||||
dstId: int = 0
|
||||
|
||||
@ -51,9 +60,7 @@ class SoVitsSvc40Settings():
|
||||
onnxModelFile: str = ""
|
||||
configFile: str = ""
|
||||
|
||||
speakers: dict[str, int] = field(
|
||||
default_factory=lambda: {}
|
||||
)
|
||||
speakers: dict[str, int] = field(default_factory=lambda: {})
|
||||
|
||||
# ↓mutableな物だけ列挙
|
||||
intData = ["gpu", "dstId", "tran", "predictF0", "extraConvertSize"]
|
||||
@ -62,7 +69,9 @@ class SoVitsSvc40Settings():
|
||||
|
||||
|
||||
class SoVitsSvc40:
|
||||
def __init__(self, params):
|
||||
audio_buffer: AudioInOut | None = None
|
||||
|
||||
def __init__(self, params: VoiceChangerParams):
|
||||
self.settings = SoVitsSvc40Settings()
|
||||
self.net_g = None
|
||||
self.onnx_session = None
|
||||
@ -74,32 +83,30 @@ class SoVitsSvc40:
|
||||
print("so-vits-svc40 initialization:", params)
|
||||
|
||||
# def loadModel(self, config: str, pyTorch_model_file: str = None, onnx_model_file: str = None, clusterTorchModel: str = None):
|
||||
def loadModel(self, props):
|
||||
self.settings.configFile = props["files"]["configFilename"]
|
||||
def loadModel(self, props: LoadModelParams):
|
||||
self.settings.configFile = props.files.configFilename
|
||||
self.hps = utils.get_hparams_from_file(self.settings.configFile)
|
||||
self.settings.speakers = self.hps.spk
|
||||
|
||||
self.settings.pyTorchModelFile = props["files"]["pyTorchModelFilename"]
|
||||
self.settings.onnxModelFile = props["files"]["onnxModelFilename"]
|
||||
clusterTorchModel = props["files"]["clusterTorchModelFilename"]
|
||||
self.settings.pyTorchModelFile = props.files.pyTorchModelFilename
|
||||
self.settings.onnxModelFile = props.files.onnxModelFilename
|
||||
clusterTorchModel = props.files.clusterTorchModelFilename
|
||||
|
||||
content_vec_path = self.params["content_vec_500"]
|
||||
content_vec_onnx_path = self.params["content_vec_500_onnx"]
|
||||
content_vec_onnx_on = self.params["content_vec_500_onnx_on"]
|
||||
hubert_base_path = self.params["hubert_base"]
|
||||
content_vec_path = self.params.content_vec_500
|
||||
content_vec_onnx_path = self.params.content_vec_500_onnx
|
||||
content_vec_onnx_on = self.params.content_vec_500_onnx_on
|
||||
hubert_base_path = self.params.hubert_base
|
||||
|
||||
# hubert model
|
||||
try:
|
||||
|
||||
if os.path.exists(content_vec_path) == False:
|
||||
if os.path.exists(content_vec_path) is False:
|
||||
content_vec_path = hubert_base_path
|
||||
|
||||
if content_vec_onnx_on == True:
|
||||
if content_vec_onnx_on is True:
|
||||
ort_options = onnxruntime.SessionOptions()
|
||||
ort_options.intra_op_num_threads = 8
|
||||
self.content_vec_onnx = onnxruntime.InferenceSession(
|
||||
content_vec_onnx_path,
|
||||
providers=providers
|
||||
content_vec_onnx_path, providers=providers
|
||||
)
|
||||
else:
|
||||
models, saved_cfg, task = checkpoint_utils.load_model_ensemble_and_task(
|
||||
@ -114,7 +121,7 @@ class SoVitsSvc40:
|
||||
|
||||
# cluster
|
||||
try:
|
||||
if clusterTorchModel != None and os.path.exists(clusterTorchModel):
|
||||
if clusterTorchModel is not None and os.path.exists(clusterTorchModel):
|
||||
self.cluster_model = cluster.get_cluster_model(clusterTorchModel)
|
||||
else:
|
||||
self.cluster_model = None
|
||||
@ -122,22 +129,22 @@ class SoVitsSvc40:
|
||||
print("EXCEPTION during loading cluster model ", e)
|
||||
|
||||
# PyTorchモデル生成
|
||||
if self.settings.pyTorchModelFile != None:
|
||||
self.net_g = SynthesizerTrn(
|
||||
if self.settings.pyTorchModelFile is not None:
|
||||
net_g = SynthesizerTrn(
|
||||
self.hps.data.filter_length // 2 + 1,
|
||||
self.hps.train.segment_size // self.hps.data.hop_length,
|
||||
**self.hps.model
|
||||
**self.hps.model,
|
||||
)
|
||||
self.net_g.eval()
|
||||
net_g.eval()
|
||||
self.net_g = net_g
|
||||
utils.load_checkpoint(self.settings.pyTorchModelFile, self.net_g, None)
|
||||
|
||||
# ONNXモデル生成
|
||||
if self.settings.onnxModelFile != None:
|
||||
if self.settings.onnxModelFile is not None:
|
||||
ort_options = onnxruntime.SessionOptions()
|
||||
ort_options.intra_op_num_threads = 8
|
||||
self.onnx_session = onnxruntime.InferenceSession(
|
||||
self.settings.onnxModelFile,
|
||||
providers=providers
|
||||
self.settings.onnxModelFile, providers=providers
|
||||
)
|
||||
# input_info = self.onnx_session.get_inputs()
|
||||
# for i in input_info:
|
||||
@ -147,30 +154,43 @@ class SoVitsSvc40:
|
||||
# print("output", i)
|
||||
return self.get_info()
|
||||
|
||||
def update_settings(self, key: str, val: any):
|
||||
if key == "onnxExecutionProvider" and self.onnx_session != None:
|
||||
def update_settings(self, key: str, val: int | float | str):
|
||||
if key == "onnxExecutionProvider" and self.onnx_session is not None:
|
||||
if val == "CUDAExecutionProvider":
|
||||
if self.settings.gpu < 0 or self.settings.gpu >= self.gpu_num:
|
||||
self.settings.gpu = 0
|
||||
provider_options = [{'device_id': self.settings.gpu}]
|
||||
self.onnx_session.set_providers(providers=[val], provider_options=provider_options)
|
||||
provider_options = [{"device_id": self.settings.gpu}]
|
||||
self.onnx_session.set_providers(
|
||||
providers=[val], provider_options=provider_options
|
||||
)
|
||||
if hasattr(self, "content_vec_onnx"):
|
||||
self.content_vec_onnx.set_providers(providers=[val], provider_options=provider_options)
|
||||
self.content_vec_onnx.set_providers(
|
||||
providers=[val], provider_options=provider_options
|
||||
)
|
||||
else:
|
||||
self.onnx_session.set_providers(providers=[val])
|
||||
if hasattr(self, "content_vec_onnx"):
|
||||
self.content_vec_onnx.set_providers(providers=[val])
|
||||
elif key == "onnxExecutionProvider" and self.onnx_session == None:
|
||||
elif key == "onnxExecutionProvider" and self.onnx_session is None:
|
||||
print("Onnx is not enabled. Please load model.")
|
||||
return False
|
||||
elif key in self.settings.intData:
|
||||
setattr(self.settings, key, int(val))
|
||||
if key == "gpu" and val >= 0 and val < self.gpu_num and self.onnx_session != None:
|
||||
val = int(val)
|
||||
setattr(self.settings, key, val)
|
||||
if (
|
||||
key == "gpu"
|
||||
and val >= 0
|
||||
and val < self.gpu_num
|
||||
and self.onnx_session is not None
|
||||
):
|
||||
providers = self.onnx_session.get_providers()
|
||||
print("Providers:", providers)
|
||||
if "CUDAExecutionProvider" in providers:
|
||||
provider_options = [{'device_id': self.settings.gpu}]
|
||||
self.onnx_session.set_providers(providers=["CUDAExecutionProvider"], provider_options=provider_options)
|
||||
provider_options = [{"device_id": self.settings.gpu}]
|
||||
self.onnx_session.set_providers(
|
||||
providers=["CUDAExecutionProvider"],
|
||||
provider_options=provider_options,
|
||||
)
|
||||
elif key in self.settings.floatData:
|
||||
setattr(self.settings, key, float(val))
|
||||
elif key in self.settings.strData:
|
||||
@ -183,10 +203,12 @@ class SoVitsSvc40:
|
||||
def get_info(self):
|
||||
data = asdict(self.settings)
|
||||
|
||||
data["onnxExecutionProviders"] = self.onnx_session.get_providers() if self.onnx_session != None else []
|
||||
data["onnxExecutionProviders"] = (
|
||||
self.onnx_session.get_providers() if self.onnx_session is not None else []
|
||||
)
|
||||
files = ["configFile", "pyTorchModelFile", "onnxModelFile"]
|
||||
for f in files:
|
||||
if data[f] != None and os.path.exists(data[f]):
|
||||
if data[f] is not None and os.path.exists(data[f]):
|
||||
data[f] = os.path.basename(data[f])
|
||||
else:
|
||||
data[f] = ""
|
||||
@ -194,22 +216,30 @@ class SoVitsSvc40:
|
||||
return data
|
||||
|
||||
def get_processing_sampling_rate(self):
|
||||
if hasattr(self, "hps") == False:
|
||||
if hasattr(self, "hps") is False:
|
||||
raise NoModeLoadedException("config")
|
||||
return self.hps.data.sampling_rate
|
||||
|
||||
def get_unit_f0(self, audio_buffer, tran):
|
||||
wav_44k = audio_buffer
|
||||
# f0 = utils.compute_f0_parselmouth(wav, sampling_rate=self.target_sample, hop_length=self.hop_size)
|
||||
# f0 = utils.compute_f0_dio(wav_44k, sampling_rate=self.hps.data.sampling_rate, hop_length=self.hps.data.hop_length)
|
||||
|
||||
if self.settings.f0Detector == "dio":
|
||||
f0 = compute_f0_dio(wav_44k, sampling_rate=self.hps.data.sampling_rate, hop_length=self.hps.data.hop_length)
|
||||
f0 = compute_f0_dio(
|
||||
wav_44k,
|
||||
sampling_rate=self.hps.data.sampling_rate,
|
||||
hop_length=self.hps.data.hop_length,
|
||||
)
|
||||
else:
|
||||
f0 = compute_f0_harvest(wav_44k, sampling_rate=self.hps.data.sampling_rate, hop_length=self.hps.data.hop_length)
|
||||
f0 = compute_f0_harvest(
|
||||
wav_44k,
|
||||
sampling_rate=self.hps.data.sampling_rate,
|
||||
hop_length=self.hps.data.hop_length,
|
||||
)
|
||||
|
||||
if wav_44k.shape[0] % self.hps.data.hop_length != 0:
|
||||
print(f" !!! !!! !!! wav size not multiple of hopsize: {wav_44k.shape[0] / self.hps.data.hop_length}")
|
||||
print(
|
||||
f" !!! !!! !!! wav size not multiple of hopsize: {wav_44k.shape[0] / self.hps.data.hop_length}"
|
||||
)
|
||||
|
||||
f0, uv = utils.interpolate_f0(f0)
|
||||
f0 = torch.FloatTensor(f0)
|
||||
@ -218,11 +248,14 @@ class SoVitsSvc40:
|
||||
f0 = f0.unsqueeze(0)
|
||||
uv = uv.unsqueeze(0)
|
||||
|
||||
# wav16k = librosa.resample(audio_buffer, orig_sr=24000, target_sr=16000)
|
||||
wav16k_numpy = librosa.resample(audio_buffer, orig_sr=self.hps.data.sampling_rate, target_sr=16000)
|
||||
wav16k_numpy = librosa.resample(
|
||||
audio_buffer, orig_sr=self.hps.data.sampling_rate, target_sr=16000
|
||||
)
|
||||
wav16k_tensor = torch.from_numpy(wav16k_numpy)
|
||||
|
||||
if (self.settings.gpu < 0 or self.gpu_num == 0) or self.settings.framework == "ONNX":
|
||||
if (
|
||||
self.settings.gpu < 0 or self.gpu_num == 0
|
||||
) or self.settings.framework == "ONNX":
|
||||
dev = torch.device("cpu")
|
||||
else:
|
||||
dev = torch.device("cuda", index=self.settings.gpu)
|
||||
@ -232,54 +265,87 @@ class SoVitsSvc40:
|
||||
["units"],
|
||||
{
|
||||
"audio": wav16k_numpy.reshape(1, -1),
|
||||
})
|
||||
},
|
||||
)
|
||||
c = torch.from_numpy(np.array(c)).squeeze(0).transpose(1, 2)
|
||||
# print("onnx hubert:", self.content_vec_onnx.get_providers())
|
||||
else:
|
||||
if self.hps.model.ssl_dim == 768:
|
||||
self.hubert_model = self.hubert_model.to(dev)
|
||||
wav16k_tensor = wav16k_tensor.to(dev)
|
||||
c = get_hubert_content_layer9(self.hubert_model, wav_16k_tensor=wav16k_tensor)
|
||||
c = get_hubert_content_layer9(
|
||||
self.hubert_model, wav_16k_tensor=wav16k_tensor
|
||||
)
|
||||
else:
|
||||
self.hubert_model = self.hubert_model.to(dev)
|
||||
wav16k_tensor = wav16k_tensor.to(dev)
|
||||
c = utils.get_hubert_content(self.hubert_model, wav_16k_tensor=wav16k_tensor)
|
||||
c = utils.get_hubert_content(
|
||||
self.hubert_model, wav_16k_tensor=wav16k_tensor
|
||||
)
|
||||
|
||||
uv = uv.to(dev)
|
||||
f0 = f0.to(dev)
|
||||
|
||||
c = utils.repeat_expand_2d(c.squeeze(0), f0.shape[1])
|
||||
|
||||
if self.settings.clusterInferRatio != 0 and hasattr(self, "cluster_model") and self.cluster_model != None:
|
||||
speaker = [key for key, value in self.settings.speakers.items() if value == self.settings.dstId]
|
||||
if (
|
||||
self.settings.clusterInferRatio != 0
|
||||
and hasattr(self, "cluster_model")
|
||||
and self.cluster_model is not None
|
||||
):
|
||||
speaker = [
|
||||
key
|
||||
for key, value in self.settings.speakers.items()
|
||||
if value == self.settings.dstId
|
||||
]
|
||||
if len(speaker) != 1:
|
||||
pass
|
||||
# print("not only one speaker found.", speaker)
|
||||
else:
|
||||
cluster_c = cluster.get_cluster_center_result(self.cluster_model, c.cpu().numpy().T, speaker[0]).T
|
||||
cluster_c = cluster.get_cluster_center_result(
|
||||
self.cluster_model, c.cpu().numpy().T, speaker[0]
|
||||
).T
|
||||
cluster_c = torch.FloatTensor(cluster_c).to(dev)
|
||||
c = c.to(dev)
|
||||
c = self.settings.clusterInferRatio * cluster_c + (1 - self.settings.clusterInferRatio) * c
|
||||
c = (
|
||||
self.settings.clusterInferRatio * cluster_c
|
||||
+ (1 - self.settings.clusterInferRatio) * c
|
||||
)
|
||||
|
||||
c = c.unsqueeze(0)
|
||||
return c, f0, uv
|
||||
|
||||
def generate_input(self, newData: any, inputSize: int, crossfadeSize: int, solaSearchFrame: int = 0):
|
||||
def generate_input(
|
||||
self,
|
||||
newData: AudioInOut,
|
||||
inputSize: int,
|
||||
crossfadeSize: int,
|
||||
solaSearchFrame: int = 0,
|
||||
):
|
||||
newData = newData.astype(np.float32) / self.hps.data.max_wav_value
|
||||
|
||||
if hasattr(self, "audio_buffer"):
|
||||
self.audio_buffer = np.concatenate([self.audio_buffer, newData], 0) # 過去のデータに連結
|
||||
if self.audio_buffer is not None:
|
||||
self.audio_buffer = np.concatenate(
|
||||
[self.audio_buffer, newData], 0
|
||||
) # 過去のデータに連結
|
||||
else:
|
||||
self.audio_buffer = newData
|
||||
|
||||
convertSize = inputSize + crossfadeSize + solaSearchFrame + self.settings.extraConvertSize
|
||||
convertSize = (
|
||||
inputSize + crossfadeSize + solaSearchFrame + self.settings.extraConvertSize
|
||||
)
|
||||
|
||||
if convertSize % self.hps.data.hop_length != 0: # モデルの出力のホップサイズで切り捨てが発生するので補う。
|
||||
convertSize = convertSize + (self.hps.data.hop_length - (convertSize % self.hps.data.hop_length))
|
||||
convertSize = convertSize + (
|
||||
self.hps.data.hop_length - (convertSize % self.hps.data.hop_length)
|
||||
)
|
||||
|
||||
self.audio_buffer = self.audio_buffer[-1 * convertSize:] # 変換対象の部分だけ抽出
|
||||
convertOffset = -1 * convertSize
|
||||
self.audio_buffer = self.audio_buffer[convertOffset:] # 変換対象の部分だけ抽出
|
||||
|
||||
crop = self.audio_buffer[-1 * (inputSize + crossfadeSize):-1 * (crossfadeSize)]
|
||||
cropOffset = -1 * (inputSize + crossfadeSize)
|
||||
cropEnd = -1 * (crossfadeSize)
|
||||
crop = self.audio_buffer[cropOffset:cropEnd]
|
||||
|
||||
rms = np.sqrt(np.square(crop).mean(axis=0))
|
||||
vol = max(rms, self.prevVol * 0.0)
|
||||
@ -289,38 +355,46 @@ class SoVitsSvc40:
|
||||
return (c, f0, uv, convertSize, vol)
|
||||
|
||||
def _onnx_inference(self, data):
|
||||
if hasattr(self, "onnx_session") == False or self.onnx_session == None:
|
||||
if hasattr(self, "onnx_session") is False or self.onnx_session is None:
|
||||
print("[Voice Changer] No onnx session.")
|
||||
raise NoModeLoadedException("ONNX")
|
||||
|
||||
convertSize = data[3]
|
||||
vol = data[4]
|
||||
data = (data[0], data[1], data[2],)
|
||||
data = (
|
||||
data[0],
|
||||
data[1],
|
||||
data[2],
|
||||
)
|
||||
|
||||
if vol < self.settings.silentThreshold:
|
||||
return np.zeros(convertSize).astype(np.int16)
|
||||
|
||||
c, f0, uv = [x.numpy() for x in data]
|
||||
sid_target = torch.LongTensor([self.settings.dstId]).unsqueeze(0).numpy()
|
||||
audio1 = self.onnx_session.run(
|
||||
["audio"],
|
||||
{
|
||||
"c": c.astype(np.float32),
|
||||
"f0": f0.astype(np.float32),
|
||||
"uv": uv.astype(np.float32),
|
||||
"g": sid_target.astype(np.int64),
|
||||
"noise_scale": np.array([self.settings.noiseScale]).astype(np.float32),
|
||||
# "predict_f0": np.array([self.settings.dstId]).astype(np.int64),
|
||||
|
||||
|
||||
})[0][0, 0] * self.hps.data.max_wav_value
|
||||
audio1 = (
|
||||
self.onnx_session.run(
|
||||
["audio"],
|
||||
{
|
||||
"c": c.astype(np.float32),
|
||||
"f0": f0.astype(np.float32),
|
||||
"uv": uv.astype(np.float32),
|
||||
"g": sid_target.astype(np.int64),
|
||||
"noise_scale": np.array([self.settings.noiseScale]).astype(
|
||||
np.float32
|
||||
),
|
||||
# "predict_f0": np.array([self.settings.dstId]).astype(np.int64),
|
||||
},
|
||||
)[0][0, 0]
|
||||
* self.hps.data.max_wav_value
|
||||
)
|
||||
|
||||
audio1 = audio1 * vol
|
||||
result = audio1
|
||||
return result
|
||||
|
||||
def _pyTorch_inference(self, data):
|
||||
if hasattr(self, "net_g") == False or self.net_g == None:
|
||||
if hasattr(self, "net_g") is False or self.net_g is None:
|
||||
print("[Voice Changer] No pyTorch session.")
|
||||
raise NoModeLoadedException("pytorch")
|
||||
|
||||
@ -331,19 +405,29 @@ class SoVitsSvc40:
|
||||
|
||||
convertSize = data[3]
|
||||
vol = data[4]
|
||||
data = (data[0], data[1], data[2],)
|
||||
data = (
|
||||
data[0],
|
||||
data[1],
|
||||
data[2],
|
||||
)
|
||||
|
||||
if vol < self.settings.silentThreshold:
|
||||
return np.zeros(convertSize).astype(np.int16)
|
||||
|
||||
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([self.settings.dstId]).to(dev).unsqueeze(0)
|
||||
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()
|
||||
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.noiseScale)
|
||||
audio1 = self.net_g.infer(
|
||||
c,
|
||||
f0=f0,
|
||||
g=sid_target,
|
||||
uv=uv,
|
||||
predict_f0=predict_f0_flag,
|
||||
noice_scale=self.settings.noiseScale,
|
||||
)
|
||||
audio1 = audio1[0][0].data.float()
|
||||
# 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()
|
||||
@ -368,7 +452,7 @@ class SoVitsSvc40:
|
||||
del self.net_g
|
||||
del self.onnx_session
|
||||
remove_path = os.path.join("so-vits-svc-40")
|
||||
sys.path = [x for x in sys.path if x.endswith(remove_path) == False]
|
||||
sys.path = [x for x in sys.path if x.endswith(remove_path) is False]
|
||||
|
||||
for key in list(sys.modules):
|
||||
val = sys.modules.get(key)
|
||||
@ -377,14 +461,18 @@ class SoVitsSvc40:
|
||||
if file_path.find("so-vits-svc-40" + os.path.sep) >= 0:
|
||||
print("remove", key, file_path)
|
||||
sys.modules.pop(key)
|
||||
except Exception as e:
|
||||
except Exception: # type:ignore
|
||||
pass
|
||||
|
||||
|
||||
def resize_f0(x, target_len):
|
||||
source = np.array(x)
|
||||
source[source < 0.001] = np.nan
|
||||
target = np.interp(np.arange(0, len(source) * target_len, len(source)) / target_len, np.arange(0, len(source)), source)
|
||||
target = np.interp(
|
||||
np.arange(0, len(source) * target_len, len(source)) / target_len,
|
||||
np.arange(0, len(source)),
|
||||
source,
|
||||
)
|
||||
res = np.nan_to_num(target)
|
||||
return res
|
||||
|
||||
@ -407,7 +495,13 @@ def compute_f0_dio(wav_numpy, p_len=None, sampling_rate=44100, hop_length=512):
|
||||
def compute_f0_harvest(wav_numpy, p_len=None, sampling_rate=44100, hop_length=512):
|
||||
if p_len is None:
|
||||
p_len = wav_numpy.shape[0] // hop_length
|
||||
f0, t = pw.harvest(wav_numpy.astype(np.double), fs=sampling_rate, frame_period=5.5, f0_floor=71.0, f0_ceil=1000.0)
|
||||
f0, t = pw.harvest(
|
||||
wav_numpy.astype(np.double),
|
||||
fs=sampling_rate,
|
||||
frame_period=5.5,
|
||||
f0_floor=71.0,
|
||||
f0_ceil=1000.0,
|
||||
)
|
||||
|
||||
for index, pitch in enumerate(f0):
|
||||
f0[index] = round(pitch, 1)
|
||||
|
||||
Loading…
Reference in New Issue
Block a user