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https://github.com/w-okada/voice-changer.git
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update
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w-okada
parent
56e26d438e
commit
2c8597d742
@ -1,120 +1,31 @@
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"""
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"""
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from dataclasses import asdict
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from typing import Union
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import os
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import numpy as np
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from const import MODEL_DIR_STATIC
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from data.ModelSlot import BeatriceModelSlot
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from mods.log_control import VoiceChangaerLogger
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from voice_changer.Beatrice.BeatriceSettings import BeatriceSettings
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from voice_changer.utils.VoiceChangerModel import AudioInOut, VoiceChangerModel
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from voice_changer.utils.VoiceChangerParams import VoiceChangerParams
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from beatrice_internal_api import BeatriceInternalAPI
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logger = VoiceChangaerLogger.get_instance().getLogger()
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class BeatriceAPI(BeatriceInternalAPI):
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def __init__(self, sample_rate: float = 48000.0):
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if sample_rate < 1000.0:
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raise ValueError(sample_rate)
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super().__init__(float(sample_rate))
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def get_n_speakers(self):
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return 500
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def get_target_speaker_names(self):
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names = []
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for i in range(1, 101):
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names.append(f"[商用不可] jvs{i:03d}")
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names.append(f"[商用不可] jvs{i:03d} -1")
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names.append(f"[商用不可] jvs{i:03d} -2")
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names.append(f"[商用不可] jvs{i:03d} +1")
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names.append(f"[商用不可] jvs{i:03d} +2")
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return names
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def set_sample_rate(self, sample_rate: float):
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if sample_rate < 1000.0:
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raise ValueError(sample_rate)
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super().set_sample_rate(float(sample_rate))
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def set_target_speaker_id(self, target_speaker_id: int):
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if not 0 <= target_speaker_id < self.get_n_speakers():
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raise ValueError(target_speaker_id)
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super().set_target_speaker_id(int(target_speaker_id))
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def read_parameters(self, filename: Union[str, bytes, os.PathLike]):
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super().read_parameters(filename)
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def convert(self, in_wav: np.ndarray) -> np.ndarray:
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if in_wav.ndim != 1:
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raise ValueError(in_wav.ndim)
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if in_wav.dtype != np.float32:
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raise ValueError(in_wav.dtype)
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out_wav = super().convert(in_wav)
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assert in_wav.shape == out_wav.shape
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return out_wav
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class Beatrice(VoiceChangerModel):
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def __init__(self, params: VoiceChangerParams, slotInfo: BeatriceModelSlot, static: bool = False):
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logger.info("[Voice Changer] [Beatrice] Creating instance ")
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self.settings = BeatriceSettings()
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self.params = params
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self.prevVol = 0.0
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self.slotInfo = slotInfo
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self.audio_buffer: AudioInOut | None = None
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self.static = static
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def __init__(self, params: VoiceChangerParams, slotInfo: BeatriceModelSlot):
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raise RuntimeError("not implemented")
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def initialize(self):
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logger.info("[Voice Changer] [Beatrice] Initializing... ")
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self.beatrice_api = BeatriceAPI()
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if self.static:
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modelPath = os.path.join(MODEL_DIR_STATIC, str(self.slotInfo.slotIndex), os.path.basename(self.slotInfo.modelFile))
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else:
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modelPath = os.path.join(self.params.model_dir, str(self.slotInfo.slotIndex), os.path.basename(self.slotInfo.modelFile))
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self.beatrice_api.read_parameters(modelPath)
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self.beatrice_api.set_sample_rate(self.inputSampleRate)
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# その他の設定
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self.settings.dstId = self.slotInfo.dstId
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logger.info("[Voice Changer] [Beatrice] Initializing... done")
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raise RuntimeError("not implemented")
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def setSamplingRate(self, inputSampleRate, outputSampleRate):
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if inputSampleRate == outputSampleRate:
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self.inputSampleRate = inputSampleRate
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self.outputSampleRate = outputSampleRate
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self.initialize()
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else:
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print("inputSampleRate, outputSampleRate", inputSampleRate, outputSampleRate)
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raise RuntimeError("not implemented")
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def update_settings(self, key: str, val: int | float | str):
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logger.info(f"[Voice Changer][Beatrice]: update_settings {key}:{val}")
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if key in self.settings.intData:
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setattr(self.settings, key, int(val))
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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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setattr(self.settings, key, str(val))
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else:
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return False
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return True
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raise RuntimeError("not implemented")
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def get_info(self):
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data = asdict(self.settings)
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return data
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raise RuntimeError("not implemented")
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def get_processing_sampling_rate(self):
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return self.inputSampleRate
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raise RuntimeError("not implemented")
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def generate_input(
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self,
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@ -122,61 +33,14 @@ class Beatrice(VoiceChangerModel):
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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) / 32768.0
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# 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([self.audio_buffer, newData], 0)
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else:
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self.audio_buffer = newData
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convertSize = newData.shape[0] + crossfadeSize + solaSearchFrame
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# バッファがたまっていない場合はzeroで補う
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if self.audio_buffer.shape[0] < convertSize:
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self.audio_buffer = np.concatenate([np.zeros([convertSize]), self.audio_buffer])
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# 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:]
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return (self.audio_buffer,)
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raise RuntimeError("not implemented")
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def inference(self, receivedData: AudioInOut, crossfade_frame: int, sola_search_frame: int):
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data = self.generate_input(receivedData, crossfade_frame, sola_search_frame)
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audio = (data[0]).astype(np.float32)
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self.beatrice_api.set_target_speaker_id(self.settings.dstId)
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block_size = 500
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out_wav_blocks = []
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head = 0
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while head < len(audio):
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in_wav_block = audio[head : head + block_size]
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out_wav_block = self.beatrice_api.convert(in_wav_block)
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out_wav_blocks.append(out_wav_block)
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head += block_size
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out_wav = np.concatenate(out_wav_blocks)
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assert audio.shape == out_wav.shape
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return (out_wav * 32767.0).astype(np.int16)
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raise RuntimeError("not implemented")
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def __del__(self):
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del self.pipeline
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# def export2onnx(self):
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# modelSlot = self.slotInfo
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# if modelSlot.isONNX:
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# print("[Voice Changer] export2onnx, No pyTorch filepath.")
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# return {"status": "ng", "path": ""}
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# output_file_simple = export2onnx(self.settings.gpu, modelSlot)
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# return {
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# "status": "ok",
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# "path": f"/tmp/{output_file_simple}",
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# "filename": output_file_simple,
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# }
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def get_model_current(self):
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return [
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{
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