mirror of
https://github.com/w-okada/voice-changer.git
synced 2025-01-23 13:35:12 +03:00
WIP: support DDSP-SVC
This commit is contained in:
wataru
parent
eb37febce8
commit
390a39fa64
4
.gitignore
vendored
4
.gitignore
vendored
@ -41,4 +41,8 @@ client/lib/worklet/dist
|
||||
docker/cudnn/
|
||||
|
||||
server/hubert_base.pt
|
||||
server/hubert-soft-0d54a1f4.pt
|
||||
server/nsf_hifigan/
|
||||
|
||||
|
||||
start_trainer.sh
|
||||
@ -66,6 +66,8 @@ Windows 版と Mac 版を提供しています。
|
||||
|
||||
- so-vits-svc 4.0/so-vits-svc 4.0v2、RVC(Retrieval-based-Voice-Conversion)の動作には hubert のモデルが必要になります。[このリポジトリ](https://huggingface.co/lj1995/VoiceConversionWebUI/tree/main)から`hubert_base.pt`をダウンロードして、バッチファイルがあるフォルダに格納してください。
|
||||
|
||||
- DDSP-SVC の動作には、hubert-soft と enhancer のモデルが必要です。hubert-soft は[このリンク](https://github.com/bshall/hubert/releases/download/v0.1/hubert-soft-0d54a1f4.pt)からダウンロードして、バッチファイルがあるフォルダに格納してください。enhancer は[このサイト](https://github.com/openvpi/vocoders/releases/tag/nsf-hifigan-v1)から`nsf_hifigan_20221211.zip`ダウンロードして下さい。解凍すると出てくる`nsf_hifigan`というフォルダをバッチファイルがあるフォルダに格納してください。
|
||||
|
||||
| Version | OS | フレームワーク | link | サポート VC | サイズ |
|
||||
| --------- | --- | --------------------------------- | ---------------------------------------------------------------------------------------- | ------------------------------------------------------------------- | ------ |
|
||||
| v.1.5.2.2 | mac | ONNX(cpu), PyTorch(cpu) | [通常](https://drive.google.com/uc?id=1dbAiGkPtGWWcQDNL0IHXl4OyTRZR8SIQ&export=download) | MMVC v.1.5.x, MMVC v.1.3.x, so-vits-svc 4.0, so-vits-svc 4.0v2, RVC | 635MB |
|
||||
|
||||
@ -40,9 +40,13 @@ def setupArgParser():
|
||||
parser.add_argument("--modelType", type=str,
|
||||
default="MMVCv15", help="model type: MMVCv13, MMVCv15, so-vits-svc-40, so-vits-svc-40v2")
|
||||
parser.add_argument("--cluster", type=str, help="path to cluster model")
|
||||
parser.add_argument("--hubert", type=str, help="path to hubert model")
|
||||
parser.add_argument("--internal", type=strtobool, default=False, help="各種パスをmac appの中身に変換")
|
||||
|
||||
parser.add_argument("--hubert", type=str, help="path to hubert model")
|
||||
parser.add_argument("--useHubertOnnx", type=strtobool, default=False, help="use hubert onnx")
|
||||
parser.add_argument("--hubertSoftPt", type=str, help="path to hubert-soft model(pytorch)")
|
||||
parser.add_argument("--enhancerPt", type=str, help="path to enhancer model(pytorch)")
|
||||
parser.add_argument("--enhancerOnnx", type=str, help="path to enhancer model(onnx)")
|
||||
|
||||
return parser
|
||||
|
||||
@ -82,12 +86,11 @@ printMessage(f"Booting PHASE :{__name__}", level=2)
|
||||
|
||||
TYPE = args.t
|
||||
PORT = args.p
|
||||
CONFIG = args.c if args.c != None else None
|
||||
MODEL = args.m if args.m != None else None
|
||||
ONNX_MODEL = args.o if args.o != None else None
|
||||
HUBERT_MODEL = args.hubert if args.hubert != None else None # hubertはユーザがダウンロードして解凍フォルダに格納する運用。
|
||||
CONFIG = args.c
|
||||
MODEL = args.m
|
||||
ONNX_MODEL = args.o
|
||||
CLUSTER_MODEL = args.cluster if args.cluster != None else None
|
||||
USE_HUBERT_ONNX = args.useHubertOnnx
|
||||
|
||||
|
||||
if args.internal and hasattr(sys, "_MEIPASS"):
|
||||
print("use internal path")
|
||||
@ -125,7 +128,13 @@ if args.colab == True:
|
||||
os.environ["colab"] = "True"
|
||||
|
||||
if __name__ == 'MMVCServerSIO':
|
||||
voiceChangerManager = VoiceChangerManager.get_instance({"hubert": HUBERT_MODEL, "useHubertOnnx": USE_HUBERT_ONNX})
|
||||
voiceChangerManager = VoiceChangerManager.get_instance({
|
||||
"hubert": args.hubert,
|
||||
"useHubertOnnx": args.useHubertOnnx,
|
||||
"hubertSoftPt": args.hubertSoftPt,
|
||||
"enhancerPt": args.enhancerPt,
|
||||
"enhancerOnnx": args.enhancerOnnx
|
||||
})
|
||||
if CONFIG and (MODEL or ONNX_MODEL):
|
||||
if MODEL_TYPE == "MMVCv15" or MODEL_TYPE == "MMVCv13":
|
||||
voiceChangerManager.loadModel(CONFIG, MODEL, ONNX_MODEL, None)
|
||||
|
||||
@ -20,14 +20,8 @@ import pyworld as pw
|
||||
import ddsp.vocoder as vo
|
||||
from ddsp.core import upsample
|
||||
from enhancer import Enhancer
|
||||
from slicer import Slicer
|
||||
import librosa
|
||||
providers = ['OpenVINOExecutionProvider', "CUDAExecutionProvider", "DmlExecutionProvider", "CPUExecutionProvider"]
|
||||
|
||||
import resampy
|
||||
from scipy.io import wavfile
|
||||
SAMPLING_RATE = 44100
|
||||
|
||||
|
||||
@dataclass
|
||||
class DDSP_SVCSettings():
|
||||
@ -69,25 +63,31 @@ class DDSP_SVC:
|
||||
self.params = params
|
||||
print("DDSP-SVC initialization:", params)
|
||||
|
||||
def useDevice(self):
|
||||
if self.settings.gpu >= 0 and torch.cuda.is_available():
|
||||
return torch.device("cuda", index=self.settings.gpu)
|
||||
else:
|
||||
return torch.device("cpu")
|
||||
|
||||
def loadModel(self, props):
|
||||
self.settings.configFile = props["files"]["configFilename"]
|
||||
self.settings.pyTorchModelFile = props["files"]["pyTorchModelFilename"]
|
||||
# model
|
||||
model, args = vo.load_model(self.settings.pyTorchModelFile)
|
||||
model, args = vo.load_model(self.settings.pyTorchModelFile, device=self.useDevice())
|
||||
self.model = model
|
||||
self.args = args
|
||||
self.hop_size = int(self.args.data.block_size * SAMPLING_RATE / self.args.data.sampling_rate)
|
||||
# self.sampling_rate = args.data.sampling_rate
|
||||
self.sampling_rate = args.data.sampling_rate
|
||||
self.hop_size = int(self.args.data.block_size * self.sampling_rate / self.args.data.sampling_rate)
|
||||
print("-------------------hopsize", self.hop_size)
|
||||
|
||||
# hubert
|
||||
# vec_path = self.params["hubert"]
|
||||
vec_path = "./model_DDSP-SVC/hubert-soft-0d54a1f4.pt"
|
||||
self.vec_path = self.params["hubertSoftPt"]
|
||||
self.encoder = vo.Units_Encoder(
|
||||
args.data.encoder,
|
||||
vec_path,
|
||||
args.data.encoder_sample_rate,
|
||||
args.data.encoder_hop_size,
|
||||
device="cpu")
|
||||
self.args.data.encoder,
|
||||
self.vec_path,
|
||||
self.args.data.encoder_sample_rate,
|
||||
self.args.data.encoder_hop_size,
|
||||
device=self.useDevice())
|
||||
|
||||
# ort_options = onnxruntime.SessionOptions()
|
||||
# ort_options.intra_op_num_threads = 8
|
||||
@ -106,13 +106,14 @@ class DDSP_SVC:
|
||||
self.f0_detector = vo.F0_Extractor(
|
||||
# "crepe",
|
||||
self.settings.f0Detector,
|
||||
SAMPLING_RATE,
|
||||
self.sampling_rate,
|
||||
self.hop_size,
|
||||
float(50),
|
||||
float(1100))
|
||||
|
||||
self.volume_extractor = vo.Volume_Extractor(self.hop_size)
|
||||
self.enhancer = Enhancer(self.args.enhancer.type, "./model_DDSP-SVC/enhancer/model", "cpu")
|
||||
self.enhancer_path = self.params["enhancerPt"]
|
||||
self.enhancer = Enhancer(self.args.enhancer.type, self.enhancer_path, device=self.useDevice())
|
||||
return self.get_info()
|
||||
|
||||
def update_settings(self, key: str, val: any):
|
||||
@ -132,6 +133,13 @@ class DDSP_SVC:
|
||||
if "CUDAExecutionProvider" in providers:
|
||||
provider_options = [{'device_id': self.settings.gpu}]
|
||||
self.onnx_session.set_providers(providers=["CUDAExecutionProvider"], provider_options=provider_options)
|
||||
if key == "gpu":
|
||||
model, _args = vo.load_model(self.settings.pyTorchModelFile, device=self.useDevice())
|
||||
self.model = model
|
||||
self.enhancer = Enhancer(self.args.enhancer.type, self.enhancer_path, device=self.useDevice())
|
||||
self.encoder = vo.Units_Encoder(self.args.data.encoder, self.vec_path, self.args.data.encoder_sample_rate,
|
||||
self.args.data.encoder_hop_size, device=self.useDevice())
|
||||
|
||||
elif key in self.settings.floatData:
|
||||
setattr(self.settings, key, float(val))
|
||||
elif key in self.settings.strData:
|
||||
@ -140,9 +148,14 @@ class DDSP_SVC:
|
||||
print("f0Detector update", val)
|
||||
if val == "dio":
|
||||
val = "parselmouth"
|
||||
|
||||
if hasattr(self, "sampling_rate") == False:
|
||||
self.sampling_rate = 44100
|
||||
self.hop_size = 512
|
||||
|
||||
self.f0_detector = vo.F0_Extractor(
|
||||
val,
|
||||
SAMPLING_RATE,
|
||||
self.sampling_rate,
|
||||
self.hop_size,
|
||||
float(50),
|
||||
float(1100))
|
||||
@ -165,7 +178,7 @@ class DDSP_SVC:
|
||||
return data
|
||||
|
||||
def get_processing_sampling_rate(self):
|
||||
return SAMPLING_RATE
|
||||
return self.sampling_rate
|
||||
|
||||
def generate_input(self, newData: any, inputSize: int, crossfadeSize: int, solaSearchFrame: int = 0):
|
||||
newData = newData.astype(np.float32) / 32768.0
|
||||
@ -197,8 +210,8 @@ class DDSP_SVC:
|
||||
volume = torch.from_numpy(volume).float().unsqueeze(-1).unsqueeze(0)
|
||||
|
||||
# embed
|
||||
audio = torch.from_numpy(self.audio_buffer).float().unsqueeze(0)
|
||||
seg_units = self.encoder.encode(audio, SAMPLING_RATE, self.hop_size)
|
||||
audio = torch.from_numpy(self.audio_buffer).float().to(self.useDevice()).unsqueeze(0)
|
||||
seg_units = self.encoder.encode(audio, self.sampling_rate, self.hop_size)
|
||||
|
||||
crop = self.audio_buffer[-1 * (inputSize + crossfadeSize):-1 * (crossfadeSize)]
|
||||
|
||||
@ -247,22 +260,19 @@ class DDSP_SVC:
|
||||
print("[Voice Changer] No pyTorch session.")
|
||||
return np.zeros(1).astype(np.int16)
|
||||
|
||||
c = data[0]
|
||||
f0 = data[1]
|
||||
volume = data[2]
|
||||
mask = data[3]
|
||||
c = data[0].to(self.useDevice())
|
||||
f0 = data[1].to(self.useDevice())
|
||||
volume = data[2].to(self.useDevice())
|
||||
mask = data[3].to(self.useDevice())
|
||||
|
||||
convertSize = data[4]
|
||||
vol = data[5]
|
||||
|
||||
print(volume.device)
|
||||
|
||||
# if vol < self.settings.silentThreshold:
|
||||
# print("threshold")
|
||||
# return np.zeros(convertSize).astype(np.int16)
|
||||
|
||||
with torch.no_grad():
|
||||
spk_id = torch.LongTensor(np.array([[int(1)]]))
|
||||
spk_id = torch.LongTensor(np.array([[int(1)]])).to(self.useDevice())
|
||||
seg_output, _, (s_h, s_n) = self.model(c, f0, volume, spk_id=spk_id, spk_mix_dict=None)
|
||||
seg_output *= mask
|
||||
|
||||
|
||||
@ -1,257 +0,0 @@
|
||||
import sys
|
||||
import os
|
||||
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)
|
||||
sys.exit()
|
||||
modulePath = os.path.join(baseDir[0], "DDSP-SVC")
|
||||
sys.path.append(modulePath)
|
||||
else:
|
||||
sys.path.append("DDSP-SVC")
|
||||
|
||||
import io
|
||||
from dataclasses import dataclass, asdict, field
|
||||
from functools import reduce
|
||||
import numpy as np
|
||||
import torch
|
||||
import onnxruntime
|
||||
import pyworld as pw
|
||||
import ddsp.vocoder as vo
|
||||
|
||||
import librosa
|
||||
providers = ['OpenVINOExecutionProvider', "CUDAExecutionProvider", "DmlExecutionProvider", "CPUExecutionProvider"]
|
||||
|
||||
|
||||
@dataclass
|
||||
class DDSP_SVCSettings():
|
||||
gpu: int = 0
|
||||
dstId: int = 0
|
||||
|
||||
f0Detector: str = "dio" # dio or harvest
|
||||
tran: int = 20
|
||||
noiceScale: float = 0.3
|
||||
predictF0: int = 0 # 0:False, 1:True
|
||||
silentThreshold: float = 0.00001
|
||||
extraConvertSize: int = 1024 * 32
|
||||
clusterInferRatio: float = 0.1
|
||||
|
||||
framework: str = "PyTorch" # PyTorch or ONNX
|
||||
pyTorchModelFile: str = ""
|
||||
onnxModelFile: str = ""
|
||||
configFile: str = ""
|
||||
|
||||
speakers: dict[str, int] = field(
|
||||
default_factory=lambda: {}
|
||||
)
|
||||
|
||||
# ↓mutableな物だけ列挙
|
||||
intData = ["gpu", "dstId", "tran", "predictF0", "extraConvertSize"]
|
||||
floatData = ["noiceScale", "silentThreshold", "clusterInferRatio"]
|
||||
strData = ["framework", "f0Detector"]
|
||||
|
||||
|
||||
class DDSP_SVC:
|
||||
def __init__(self, params):
|
||||
self.settings = DDSP_SVCSettings()
|
||||
self.net_g = None
|
||||
self.onnx_session = None
|
||||
|
||||
self.raw_path = io.BytesIO()
|
||||
self.gpu_num = torch.cuda.device_count()
|
||||
self.prevVol = 0
|
||||
self.params = params
|
||||
print("DDSP-SVC initialization:", params)
|
||||
|
||||
def loadModel(self, config: str, pyTorch_model_file: str = None, onnx_model_file: str = None, clusterTorchModel: str = None):
|
||||
|
||||
self.settings.configFile = config
|
||||
# model
|
||||
model, args = vo.load_model(pyTorch_model_file)
|
||||
|
||||
# hubert
|
||||
self.model = model
|
||||
self.args = args
|
||||
|
||||
vec_path = self.params["hubert"]
|
||||
self.encoder = vo.Units_Encoder(
|
||||
args.data.encoder,
|
||||
vec_path,
|
||||
args.data.encoder_sample_rate,
|
||||
args.data.encoder_hop_size,
|
||||
device="cpu")
|
||||
# f0dec
|
||||
self.f0_detector = vo.F0_Extractor(
|
||||
self.settings.f0Detector,
|
||||
44100,
|
||||
512,
|
||||
float(50),
|
||||
float(1100))
|
||||
|
||||
return self.get_info()
|
||||
|
||||
def update_settings(self, key: str, val: any):
|
||||
if key == "onnxExecutionProvider" and self.onnx_session != 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)
|
||||
else:
|
||||
self.onnx_session.set_providers(providers=[val])
|
||||
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:
|
||||
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)
|
||||
elif key in self.settings.floatData:
|
||||
setattr(self.settings, key, float(val))
|
||||
elif key in self.settings.strData:
|
||||
setattr(self.settings, key, str(val))
|
||||
else:
|
||||
return False
|
||||
|
||||
return True
|
||||
|
||||
def get_info(self):
|
||||
data = asdict(self.settings)
|
||||
|
||||
data["onnxExecutionProviders"] = self.onnx_session.get_providers() if self.onnx_session != None else []
|
||||
files = ["configFile", "pyTorchModelFile", "onnxModelFile"]
|
||||
for f in files:
|
||||
if data[f] != None and os.path.exists(data[f]):
|
||||
data[f] = os.path.basename(data[f])
|
||||
else:
|
||||
data[f] = ""
|
||||
|
||||
return data
|
||||
|
||||
def get_processing_sampling_rate(self):
|
||||
return 44100
|
||||
|
||||
def get_unit_f0(self, audio_buffer, tran):
|
||||
if (self.settings.gpu < 0 or self.gpu_num == 0) or self.settings.framework == "ONNX":
|
||||
dev = torch.device("cpu")
|
||||
else:
|
||||
dev = torch.device("cpu")
|
||||
# dev = torch.device("cuda", index=self.settings.gpu)
|
||||
|
||||
wav_44k = audio_buffer
|
||||
f0 = self.f0_detector.extract(wav_44k, uv_interp=True, device=dev)
|
||||
f0 = torch.from_numpy(f0).float().to(dev).unsqueeze(-1).unsqueeze(0)
|
||||
f0 = f0 * 2 ** (float(10) / 12)
|
||||
# print("f0:", f0)
|
||||
|
||||
print("wav_44k:::", wav_44k)
|
||||
c = self.encoder.encode(torch.from_numpy(audio_buffer).float().unsqueeze(0).to(dev), 44100, 512)
|
||||
# print("c:", c)
|
||||
return c, f0
|
||||
|
||||
def generate_input(self, newData: any, inputSize: int, crossfadeSize: int):
|
||||
# newData = newData.astype(np.float32) / 32768.0
|
||||
# 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
|
||||
else:
|
||||
self.audio_buffer = newData
|
||||
|
||||
convertSize = inputSize + crossfadeSize + self.settings.extraConvertSize
|
||||
hop_size = int(self.args.data.block_size * 44100 / self.args.data.sampling_rate)
|
||||
print("hopsize", hop_size)
|
||||
if convertSize % hop_size != 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 + (hop_size - (convertSize % hop_size))
|
||||
|
||||
print("convsize", convertSize)
|
||||
self.audio_buffer = self.audio_buffer[-1 * convertSize:] # 変換対象の部分だけ抽出
|
||||
|
||||
crop = self.audio_buffer[-1 * (inputSize + crossfadeSize):-1 * (crossfadeSize)]
|
||||
|
||||
rms = np.sqrt(np.square(crop).mean(axis=0))
|
||||
vol = max(rms, self.prevVol * 0.0)
|
||||
self.prevVol = vol
|
||||
|
||||
c, f0 = self.get_unit_f0(self.audio_buffer, self.settings.tran)
|
||||
return (c, f0, convertSize, vol)
|
||||
|
||||
def _onnx_inference(self, data):
|
||||
if hasattr(self, "onnx_session") == False or self.onnx_session == None:
|
||||
print("[Voice Changer] No onnx session.")
|
||||
return np.zeros(1).astype(np.int16)
|
||||
|
||||
c = data[0]
|
||||
f0 = data[1]
|
||||
convertSize = data[2]
|
||||
vol = data[3]
|
||||
|
||||
if vol < self.settings.silentThreshold:
|
||||
return np.zeros(convertSize).astype(np.int16)
|
||||
|
||||
c, f0, uv = [x.numpy() for x in data]
|
||||
audio1 = self.onnx_session.run(
|
||||
["audio"],
|
||||
{
|
||||
"c": c,
|
||||
"f0": f0,
|
||||
"g": np.array([self.settings.dstId]).astype(np.int64),
|
||||
"uv": np.array([self.settings.dstId]).astype(np.int64),
|
||||
"predict_f0": np.array([self.settings.dstId]).astype(np.int64),
|
||||
"noice_scale": np.array([self.settings.dstId]).astype(np.int64),
|
||||
|
||||
|
||||
})[0][0, 0] * self.hps.data.max_wav_value
|
||||
|
||||
audio1 = audio1 * vol
|
||||
|
||||
result = audio1
|
||||
|
||||
return result
|
||||
|
||||
pass
|
||||
|
||||
def _pyTorch_inference(self, data):
|
||||
|
||||
if hasattr(self, "model") == False or self.model == None:
|
||||
print("[Voice Changer] No pyTorch session.")
|
||||
return np.zeros(1).astype(np.int16)
|
||||
|
||||
if self.settings.gpu < 0 or self.gpu_num == 0:
|
||||
dev = torch.device("cpu")
|
||||
else:
|
||||
dev = torch.device("cpu")
|
||||
# dev = torch.device("cuda", index=self.settings.gpu)
|
||||
|
||||
c = data[0]
|
||||
f0 = data[1]
|
||||
convertSize = data[2]
|
||||
vol = data[3]
|
||||
if vol < self.settings.silentThreshold:
|
||||
return np.zeros(convertSize).astype(np.int16)
|
||||
|
||||
with torch.no_grad():
|
||||
c.to(dev)
|
||||
f0.to(dev)
|
||||
vol = torch.from_numpy(np.array([vol] * c.shape[1])).float().to(dev).unsqueeze(-1).unsqueeze(0)
|
||||
spk_id = torch.LongTensor(np.array([[1]])).to(dev)
|
||||
# print("vol", vol)
|
||||
print("input", c.shape, f0.shape)
|
||||
seg_output, _, (s_h, s_n) = self.model(c, f0, vol, spk_id=spk_id)
|
||||
|
||||
seg_output = seg_output.squeeze().cpu().numpy()
|
||||
print("SEG:", seg_output)
|
||||
|
||||
return seg_output
|
||||
|
||||
def inference(self, data):
|
||||
if self.settings.framework == "ONNX":
|
||||
audio = self._onnx_inference(data)
|
||||
else:
|
||||
audio = self._pyTorch_inference(data)
|
||||
return audio
|
||||
|
||||
def destroy(self):
|
||||
del self.net_g
|
||||
del self.onnx_session
|
||||
Loading…
Reference in New Issue
Block a user