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
bd3667117e
commit
c96609640d
@ -16,6 +16,9 @@ from restapi.MMVC_Rest import MMVC_Rest
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from const import NATIVE_CLIENT_FILE_MAC, NATIVE_CLIENT_FILE_WIN, SSL_KEY_DIR
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import subprocess
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import multiprocessing as mp
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from misc.log_control import setup_loggers
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setup_loggers()
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def setupArgParser():
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@ -8,32 +8,31 @@ class UvicornSuppressFilter(logging.Filter):
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return False
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# logger = logging.getLogger("uvicorn.error")
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# logger.addFilter(UvicornSuppressFilter())
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def setup_loggers():
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# logger = logging.getLogger("uvicorn.error")
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# logger.addFilter(UvicornSuppressFilter())
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logger = logging.getLogger("fairseq.tasks.hubert_pretraining")
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logger.addFilter(UvicornSuppressFilter())
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logger = logging.getLogger("fairseq.tasks.hubert_pretraining")
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logger.addFilter(UvicornSuppressFilter())
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logger = logging.getLogger("fairseq.models.hubert.hubert")
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logger.addFilter(UvicornSuppressFilter())
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logger = logging.getLogger("fairseq.models.hubert.hubert")
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logger.addFilter(UvicornSuppressFilter())
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logger = logging.getLogger("fairseq.tasks.text_to_speech")
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logger.addFilter(UvicornSuppressFilter())
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logger = logging.getLogger("fairseq.tasks.text_to_speech")
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logger.addFilter(UvicornSuppressFilter())
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logger = logging.getLogger("numba.core.ssa")
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logger.addFilter(UvicornSuppressFilter())
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logger = logging.getLogger("numba.core.ssa")
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logger.addFilter(UvicornSuppressFilter())
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logger = logging.getLogger("numba.core.interpreter")
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logger.addFilter(UvicornSuppressFilter())
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logger = logging.getLogger("numba.core.interpreter")
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logger.addFilter(UvicornSuppressFilter())
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logger = logging.getLogger("numba.core.byteflow")
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logger.addFilter(UvicornSuppressFilter())
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logger = logging.getLogger("numba.core.byteflow")
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logger.addFilter(UvicornSuppressFilter())
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# logger.propagate = False
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logger = logging.getLogger("multipart.multipart")
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logger.propagate = False
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# logger.propagate = False
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logger = logging.getLogger("multipart.multipart")
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logger.propagate = False
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logging.getLogger('asyncio').setLevel(logging.WARNING)
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logging.getLogger("asyncio").setLevel(logging.WARNING)
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@ -1,6 +1,10 @@
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import sys
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import os
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if sys.platform.startswith('darwin'):
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from voice_changer.utils.LoadModelParams import LoadModelParams
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from voice_changer.utils.VoiceChangerModel import AudioInOut
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if sys.platform.startswith("darwin"):
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baseDir = [x for x in sys.path if x.endswith("Contents/MacOS")]
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if len(baseDir) != 1:
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print("baseDir should be only one ", baseDir)
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@ -12,23 +16,32 @@ else:
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sys.path.append(modulePath)
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from dataclasses import dataclass, asdict
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from dataclasses import dataclass, asdict, field
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import numpy as np
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import torch
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import onnxruntime
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import pyworld as pw
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from symbols import symbols
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from models import SynthesizerTrn
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from voice_changer.MMVCv13.TrainerFunctions import TextAudioSpeakerCollate, spectrogram_torch, load_checkpoint, get_hparams_from_file
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from symbols import symbols # type:ignore
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from models import SynthesizerTrn # type:ignore
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from voice_changer.MMVCv13.TrainerFunctions import (
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TextAudioSpeakerCollate,
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spectrogram_torch,
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load_checkpoint,
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get_hparams_from_file,
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)
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from Exceptions import NoModeLoadedException
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providers = ['OpenVINOExecutionProvider', "CUDAExecutionProvider", "DmlExecutionProvider", "CPUExecutionProvider"]
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providers = [
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"OpenVINOExecutionProvider",
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"CUDAExecutionProvider",
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"DmlExecutionProvider",
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"CPUExecutionProvider",
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]
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@dataclass
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class MMVCv13Settings():
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class MMVCv13Settings:
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gpu: int = 0
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srcId: int = 0
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dstId: int = 101
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@ -40,11 +53,13 @@ class MMVCv13Settings():
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# ↓mutableな物だけ列挙
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intData = ["gpu", "srcId", "dstId"]
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floatData = []
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floatData: list[str] = field(default_factory=lambda: [])
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strData = ["framework"]
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class MMVCv13:
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audio_buffer: AudioInOut | None = None
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def __init__(self):
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self.settings = MMVCv13Settings()
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self.net_g = None
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@ -53,51 +68,62 @@ class MMVCv13:
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self.gpu_num = torch.cuda.device_count()
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self.text_norm = torch.LongTensor([0, 6, 0])
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def loadModel(self, props):
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self.settings.configFile = props["files"]["configFilename"]
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def loadModel(self, props: LoadModelParams):
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self.settings.configFile = props.files.configFilename
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self.hps = get_hparams_from_file(self.settings.configFile)
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self.settings.pyTorchModelFile = props["files"]["pyTorchModelFilename"]
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self.settings.onnxModelFile = props["files"]["onnxModelFilename"]
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self.settings.pyTorchModelFile = props.files.pyTorchModelFilename
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self.settings.onnxModelFile = props.files.onnxModelFilename
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# PyTorchモデル生成
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if self.settings.pyTorchModelFile != None:
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if self.settings.pyTorchModelFile is not None:
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self.net_g = SynthesizerTrn(
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len(symbols),
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self.hps.data.filter_length // 2 + 1,
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self.hps.train.segment_size // self.hps.data.hop_length,
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n_speakers=self.hps.data.n_speakers,
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**self.hps.model)
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**self.hps.model
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)
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self.net_g.eval()
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load_checkpoint(self.settings.pyTorchModelFile, self.net_g, None)
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# ONNXモデル生成
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if self.settings.onnxModelFile != None:
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if self.settings.onnxModelFile is not None:
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ort_options = onnxruntime.SessionOptions()
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ort_options.intra_op_num_threads = 8
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self.onnx_session = onnxruntime.InferenceSession(
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self.settings.onnxModelFile,
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providers=providers
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self.settings.onnxModelFile, providers=providers
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)
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return self.get_info()
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def update_settings(self, key: str, val: any):
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if key == "onnxExecutionProvider" and self.onnx_session != None:
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def update_settings(self, key: str, val: int | float | str):
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if key == "onnxExecutionProvider" and self.onnx_session is not None:
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if val == "CUDAExecutionProvider":
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if self.settings.gpu < 0 or self.settings.gpu >= self.gpu_num:
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self.settings.gpu = 0
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provider_options = [{'device_id': self.settings.gpu}]
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self.onnx_session.set_providers(providers=[val], provider_options=provider_options)
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provider_options = [{"device_id": self.settings.gpu}]
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self.onnx_session.set_providers(
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providers=[val], provider_options=provider_options
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)
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else:
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self.onnx_session.set_providers(providers=[val])
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elif key in self.settings.intData:
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setattr(self.settings, key, int(val))
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if key == "gpu" and val >= 0 and val < self.gpu_num and self.onnx_session != None:
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val = int(val)
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setattr(self.settings, key, val)
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if (
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key == "gpu"
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and val >= 0
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and val < self.gpu_num
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and self.onnx_session is not None
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):
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providers = self.onnx_session.get_providers()
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print("Providers:", providers)
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if "CUDAExecutionProvider" in providers:
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provider_options = [{'device_id': self.settings.gpu}]
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self.onnx_session.set_providers(providers=["CUDAExecutionProvider"], provider_options=provider_options)
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provider_options = [{"device_id": self.settings.gpu}]
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self.onnx_session.set_providers(
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providers=["CUDAExecutionProvider"],
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provider_options=provider_options,
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)
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elif key in self.settings.floatData:
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setattr(self.settings, key, float(val))
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elif key in self.settings.strData:
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@ -110,10 +136,12 @@ class MMVCv13:
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def get_info(self):
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data = asdict(self.settings)
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data["onnxExecutionProviders"] = self.onnx_session.get_providers() if self.onnx_session != None else []
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data["onnxExecutionProviders"] = (
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self.onnx_session.get_providers() if self.onnx_session is not None else []
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)
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files = ["configFile", "pyTorchModelFile", "onnxModelFile"]
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for f in files:
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if data[f] != None and os.path.exists(data[f]):
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if data[f] is not None and os.path.exists(data[f]):
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data[f] = os.path.basename(data[f])
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else:
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data[f] = ""
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@ -121,22 +149,35 @@ class MMVCv13:
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return data
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def get_processing_sampling_rate(self):
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if hasattr(self, "hps") == False:
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if hasattr(self, "hps") is False:
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raise NoModeLoadedException("config")
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return self.hps.data.sampling_rate
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def _get_spec(self, audio: any):
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spec = spectrogram_torch(audio, self.hps.data.filter_length,
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self.hps.data.sampling_rate, self.hps.data.hop_length, self.hps.data.win_length,
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center=False)
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def _get_spec(self, audio: AudioInOut):
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spec = spectrogram_torch(
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audio,
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self.hps.data.filter_length,
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self.hps.data.sampling_rate,
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self.hps.data.hop_length,
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self.hps.data.win_length,
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center=False,
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)
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spec = torch.squeeze(spec, 0)
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return spec
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def generate_input(self, newData: any, inputSize: int, crossfadeSize: int, solaSearchFrame: int = 0):
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def generate_input(
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self,
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newData: AudioInOut,
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inputSize: int,
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crossfadeSize: int,
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solaSearchFrame: int = 0,
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):
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newData = newData.astype(np.float32) / self.hps.data.max_wav_value
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if hasattr(self, "audio_buffer"):
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self.audio_buffer = np.concatenate([self.audio_buffer, newData], 0) # 過去のデータに連結
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if self.audio_buffer is not None:
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self.audio_buffer = np.concatenate(
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[self.audio_buffer, newData], 0
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) # 過去のデータに連結
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else:
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self.audio_buffer = newData
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@ -145,9 +186,12 @@ class MMVCv13:
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if convertSize < 8192:
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convertSize = 8192
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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。
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convertSize = convertSize + (self.hps.data.hop_length - (convertSize % self.hps.data.hop_length))
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convertSize = convertSize + (
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self.hps.data.hop_length - (convertSize % self.hps.data.hop_length)
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)
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self.audio_buffer = self.audio_buffer[-1 * convertSize:] # 変換対象の部分だけ抽出
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convertOffset = -1 * convertSize
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self.audio_buffer = self.audio_buffer[convertOffset:] # 変換対象の部分だけ抽出
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audio = torch.FloatTensor(self.audio_buffer)
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audio_norm = audio.unsqueeze(0) # unsqueeze
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@ -160,25 +204,29 @@ class MMVCv13:
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return data
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def _onnx_inference(self, data):
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if hasattr(self, "onnx_session") == False or self.onnx_session == None:
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if hasattr(self, "onnx_session") is False or self.onnx_session is None:
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print("[Voice Changer] No ONNX session.")
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raise NoModeLoadedException("ONNX")
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x, x_lengths, spec, spec_lengths, y, y_lengths, sid_src = [x for x in data]
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sid_tgt1 = torch.LongTensor([self.settings.dstId])
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# if spec.size()[2] >= 8:
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audio1 = self.onnx_session.run(
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["audio"],
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{
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"specs": spec.numpy(),
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"lengths": spec_lengths.numpy(),
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"sid_src": sid_src.numpy(),
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"sid_tgt": sid_tgt1.numpy()
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})[0][0, 0] * self.hps.data.max_wav_value
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audio1 = (
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self.onnx_session.run(
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["audio"],
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{
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"specs": spec.numpy(),
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"lengths": spec_lengths.numpy(),
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"sid_src": sid_src.numpy(),
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"sid_tgt": sid_tgt1.numpy(),
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},
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)[0][0, 0]
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* self.hps.data.max_wav_value
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)
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return audio1
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def _pyTorch_inference(self, data):
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if hasattr(self, "net_g") == False or self.net_g == None:
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if hasattr(self, "net_g") is False or self.net_g is None:
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print("[Voice Changer] No pyTorch session.")
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raise NoModeLoadedException("pytorch")
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@ -188,11 +236,19 @@ class MMVCv13:
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dev = torch.device("cuda", index=self.settings.gpu)
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with torch.no_grad():
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x, x_lengths, spec, spec_lengths, y, y_lengths, sid_src = [x.to(dev) for x in data]
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x, x_lengths, spec, spec_lengths, y, y_lengths, sid_src = [
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x.to(dev) for x in data
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]
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sid_target = torch.LongTensor([self.settings.dstId]).to(dev)
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audio1 = (self.net_g.to(dev).voice_conversion(spec, spec_lengths, sid_src=sid_src,
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sid_tgt=sid_target)[0, 0].data * self.hps.data.max_wav_value)
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audio1 = (
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self.net_g.to(dev)
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.voice_conversion(
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spec, spec_lengths, sid_src=sid_src, sid_tgt=sid_target
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)[0, 0]
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.data
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* self.hps.data.max_wav_value
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)
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result = audio1.float().cpu().numpy()
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return result
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@ -208,7 +264,7 @@ class MMVCv13:
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del self.net_g
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del self.onnx_session
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remove_path = os.path.join("MMVC_Client_v13", "python")
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sys.path = [x for x in sys.path if x.endswith(remove_path) == False]
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sys.path = [x for x in sys.path if x.endswith(remove_path) is False]
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for key in list(sys.modules):
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val = sys.modules.get(key)
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@ -217,5 +273,5 @@ class MMVCv13:
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if file_path.find(remove_path + os.path.sep) >= 0:
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print("remove", key, file_path)
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sys.modules.pop(key)
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except Exception as e:
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except: # type:ignore
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pass
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@ -1,36 +1,58 @@
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import torch
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import os, sys, json
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import os
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import sys
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import json
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import logging
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logging.basicConfig(stream=sys.stdout, level=logging.DEBUG)
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logger = logging
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hann_window = {}
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def spectrogram_torch(y, n_fft, sampling_rate, hop_size, win_size, center=False):
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if torch.min(y) < -1.:
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print('min value is ', torch.min(y))
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if torch.max(y) > 1.:
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print('max value is ', torch.max(y))
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if torch.min(y) < -1.0:
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print("min value is ", torch.min(y))
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if torch.max(y) > 1.0:
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print("max value is ", torch.max(y))
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global hann_window
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dtype_device = str(y.dtype) + '_' + str(y.device)
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wnsize_dtype_device = str(win_size) + '_' + dtype_device
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dtype_device = str(y.dtype) + "_" + str(y.device)
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wnsize_dtype_device = str(win_size) + "_" + dtype_device
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if wnsize_dtype_device not in hann_window:
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hann_window[wnsize_dtype_device] = torch.hann_window(win_size).to(dtype=y.dtype, device=y.device)
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hann_window[wnsize_dtype_device] = torch.hann_window(win_size).to(
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dtype=y.dtype, device=y.device
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)
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y = torch.nn.functional.pad(y.unsqueeze(1), (int((n_fft-hop_size)/2), int((n_fft-hop_size)/2)), mode='reflect')
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y = torch.nn.functional.pad(
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y.unsqueeze(1),
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(int((n_fft - hop_size) / 2), int((n_fft - hop_size) / 2)),
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mode="reflect",
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)
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y = y.squeeze(1)
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spec = torch.stft(y, n_fft, hop_length=hop_size, win_length=win_size, window=hann_window[wnsize_dtype_device],
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center=center, pad_mode='reflect', normalized=False, onesided=True, return_complex=True)
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spec = torch.stft(
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y,
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n_fft,
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hop_length=hop_size,
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win_length=win_size,
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window=hann_window[wnsize_dtype_device],
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center=center,
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pad_mode="reflect",
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normalized=False,
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onesided=True,
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return_complex=True,
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)
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spec = torch.view_as_real(spec)
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|
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spec = torch.sqrt(spec.pow(2).sum(-1) + 1e-6)
|
||||
return spec
|
||||
|
||||
class TextAudioSpeakerCollate():
|
||||
""" Zero-pads model inputs and targets
|
||||
"""
|
||||
def __init__(self, return_ids=False, no_text = False):
|
||||
|
||||
class TextAudioSpeakerCollate:
|
||||
"""Zero-pads model inputs and targets"""
|
||||
|
||||
def __init__(self, return_ids=False, no_text=False):
|
||||
self.return_ids = return_ids
|
||||
self.no_text = no_text
|
||||
|
||||
@ -42,8 +64,8 @@ class TextAudioSpeakerCollate():
|
||||
"""
|
||||
# Right zero-pad all one-hot text sequences to max input length
|
||||
_, ids_sorted_decreasing = torch.sort(
|
||||
torch.LongTensor([x[1].size(1) for x in batch]),
|
||||
dim=0, descending=True)
|
||||
torch.LongTensor([x[1].size(1) for x in batch]), dim=0, descending=True
|
||||
)
|
||||
|
||||
max_text_len = max([len(x[0]) for x in batch])
|
||||
max_spec_len = max([x[1].size(1) for x in batch])
|
||||
@ -64,88 +86,108 @@ class TextAudioSpeakerCollate():
|
||||
row = batch[ids_sorted_decreasing[i]]
|
||||
|
||||
text = row[0]
|
||||
text_padded[i, :text.size(0)] = text
|
||||
text_padded[i, : text.size(0)] = text
|
||||
text_lengths[i] = text.size(0)
|
||||
|
||||
spec = row[1]
|
||||
spec_padded[i, :, :spec.size(1)] = spec
|
||||
spec_padded[i, :, : spec.size(1)] = spec
|
||||
spec_lengths[i] = spec.size(1)
|
||||
|
||||
wav = row[2]
|
||||
wav_padded[i, :, :wav.size(1)] = wav
|
||||
wav_padded[i, :, : wav.size(1)] = wav
|
||||
wav_lengths[i] = wav.size(1)
|
||||
|
||||
sid[i] = row[3]
|
||||
|
||||
if self.return_ids:
|
||||
return text_padded, text_lengths, spec_padded, spec_lengths, wav_padded, wav_lengths, sid, ids_sorted_decreasing
|
||||
return text_padded, text_lengths, spec_padded, spec_lengths, wav_padded, wav_lengths, sid
|
||||
return (
|
||||
text_padded,
|
||||
text_lengths,
|
||||
spec_padded,
|
||||
spec_lengths,
|
||||
wav_padded,
|
||||
wav_lengths,
|
||||
sid,
|
||||
ids_sorted_decreasing,
|
||||
)
|
||||
return (
|
||||
text_padded,
|
||||
text_lengths,
|
||||
spec_padded,
|
||||
spec_lengths,
|
||||
wav_padded,
|
||||
wav_lengths,
|
||||
sid,
|
||||
)
|
||||
|
||||
|
||||
def load_checkpoint(checkpoint_path, model, optimizer=None):
|
||||
assert os.path.isfile(checkpoint_path), f"No such file or directory: {checkpoint_path}"
|
||||
checkpoint_dict = torch.load(checkpoint_path, map_location='cpu')
|
||||
iteration = checkpoint_dict['iteration']
|
||||
learning_rate = checkpoint_dict['learning_rate']
|
||||
if optimizer is not None:
|
||||
optimizer.load_state_dict(checkpoint_dict['optimizer'])
|
||||
saved_state_dict = checkpoint_dict['model']
|
||||
if hasattr(model, 'module'):
|
||||
state_dict = model.module.state_dict()
|
||||
else:
|
||||
state_dict = model.state_dict()
|
||||
new_state_dict= {}
|
||||
for k, v in state_dict.items():
|
||||
try:
|
||||
new_state_dict[k] = saved_state_dict[k]
|
||||
except:
|
||||
logger.info("%s is not in the checkpoint" % k)
|
||||
new_state_dict[k] = v
|
||||
if hasattr(model, 'module'):
|
||||
model.module.load_state_dict(new_state_dict)
|
||||
else:
|
||||
model.load_state_dict(new_state_dict)
|
||||
logger.info("Loaded checkpoint '{}' (iteration {})" .format(
|
||||
checkpoint_path, iteration))
|
||||
return model, optimizer, learning_rate, iteration
|
||||
assert os.path.isfile(
|
||||
checkpoint_path
|
||||
), f"No such file or directory: {checkpoint_path}"
|
||||
checkpoint_dict = torch.load(checkpoint_path, map_location="cpu")
|
||||
iteration = checkpoint_dict["iteration"]
|
||||
learning_rate = checkpoint_dict["learning_rate"]
|
||||
if optimizer is not None:
|
||||
optimizer.load_state_dict(checkpoint_dict["optimizer"])
|
||||
saved_state_dict = checkpoint_dict["model"]
|
||||
if hasattr(model, "module"):
|
||||
state_dict = model.module.state_dict()
|
||||
else:
|
||||
state_dict = model.state_dict()
|
||||
new_state_dict = {}
|
||||
for k, v in state_dict.items():
|
||||
try:
|
||||
new_state_dict[k] = saved_state_dict[k]
|
||||
except:
|
||||
logger.info("%s is not in the checkpoint" % k)
|
||||
new_state_dict[k] = v
|
||||
if hasattr(model, "module"):
|
||||
model.module.load_state_dict(new_state_dict)
|
||||
else:
|
||||
model.load_state_dict(new_state_dict)
|
||||
logger.info(
|
||||
"Loaded checkpoint '{}' (iteration {})".format(checkpoint_path, iteration)
|
||||
)
|
||||
return model, optimizer, learning_rate, iteration
|
||||
|
||||
|
||||
def get_hparams_from_file(config_path):
|
||||
with open(config_path, "r") as f:
|
||||
data = f.read()
|
||||
config = json.loads(data)
|
||||
with open(config_path, "r") as f:
|
||||
data = f.read()
|
||||
config = json.loads(data)
|
||||
|
||||
hparams =HParams(**config)
|
||||
return hparams
|
||||
hparams = HParams(**config)
|
||||
return hparams
|
||||
|
||||
class HParams():
|
||||
def __init__(self, **kwargs):
|
||||
for k, v in kwargs.items():
|
||||
if type(v) == dict:
|
||||
v = HParams(**v)
|
||||
self[k] = v
|
||||
|
||||
def keys(self):
|
||||
return self.__dict__.keys()
|
||||
|
||||
def items(self):
|
||||
return self.__dict__.items()
|
||||
class HParams:
|
||||
def __init__(self, **kwargs):
|
||||
for k, v in kwargs.items():
|
||||
if type(v) == dict:
|
||||
v = HParams(**v)
|
||||
self[k] = v
|
||||
|
||||
def values(self):
|
||||
return self.__dict__.values()
|
||||
def keys(self):
|
||||
return self.__dict__.keys()
|
||||
|
||||
def __len__(self):
|
||||
return len(self.__dict__)
|
||||
def items(self):
|
||||
return self.__dict__.items()
|
||||
|
||||
def __getitem__(self, key):
|
||||
return getattr(self, key)
|
||||
def values(self):
|
||||
return self.__dict__.values()
|
||||
|
||||
def __setitem__(self, key, value):
|
||||
return setattr(self, key, value)
|
||||
def __len__(self):
|
||||
return len(self.__dict__)
|
||||
|
||||
def __contains__(self, key):
|
||||
return key in self.__dict__
|
||||
def __getitem__(self, key):
|
||||
return getattr(self, key)
|
||||
|
||||
def __repr__(self):
|
||||
return self.__dict__.__repr__()
|
||||
def __setitem__(self, key, value):
|
||||
return setattr(self, key, value)
|
||||
|
||||
def __contains__(self, key):
|
||||
return key in self.__dict__
|
||||
|
||||
def __repr__(self):
|
||||
return self.__dict__.__repr__()
|
||||
|
||||
@ -124,6 +124,7 @@ class VoiceChanger:
|
||||
try:
|
||||
return self.voiceChanger.loadModel(props)
|
||||
except Exception as e:
|
||||
print(traceback.format_exc())
|
||||
print("[Voice Changer] Model Load Error! Check your model is valid.", e)
|
||||
return {"status": "NG"}
|
||||
|
||||
|
||||
Loading…
Reference in New Issue
Block a user