voice-changer/server/voice_changer/VoiceChanger.py

from const import ERROR_NO_ONNX_SESSION
import torch
import os
import traceback
import numpy as np
from dataclasses import dataclass, asdict

import onnxruntime

from symbols import symbols
from models import SynthesizerTrn

import pyworld as pw

# from voice_changer.TrainerFunctions import TextAudioSpeakerCollate, spectrogram_torch, load_checkpoint, get_hparams_from_file

from voice_changer.client_modules import convert_continuos_f0, spectrogram_torch, TextAudioSpeakerCollate, get_hparams_from_file, load_checkpoint


providers = ['OpenVINOExecutionProvider', "CUDAExecutionProvider", "DmlExecutionProvider", "CPUExecutionProvider"]


@dataclass
class VocieChangerSettings():
    gpu: int = 0
    srcId: int = 107
    dstId: int = 100
    crossFadeOffsetRate: float = 0.1
    crossFadeEndRate: float = 0.9
    crossFadeOverlapRate: float = 0.9
    convertChunkNum: int = 32
    minConvertSize: int = 0
    framework: str = "ONNX"  # PyTorch or ONNX
    f0Factor: float = 1.0

    pyTorchModelFile: str = ""
    onnxModelFile: str = ""
    configFile: str = ""

    # ↓mutableな物だけ列挙
    intData = ["gpu", "srcId", "dstId", "convertChunkNum", "minConvertSize"]
    floatData = ["crossFadeOffsetRate", "crossFadeEndRate", "crossFadeOverlapRate", "f0Factor"]
    strData = ["framework"]


class VoiceChanger():

    def __init__(self):
        # 初期化
        self.settings = VocieChangerSettings()
        self.unpackedData_length = 0
        self.net_g = None
        self.onnx_session = None
        self.currentCrossFadeOffsetRate = 0
        self.currentCrossFadeEndRate = 0
        self.currentCrossFadeOverlapRate = 0

        self.gpu_num = torch.cuda.device_count()
        self.text_norm = torch.LongTensor([0, 6, 0])
        self.audio_buffer = torch.zeros(1, 0)
        self.prev_audio = np.zeros(1)
        self.mps_enabled = getattr(torch.backends, "mps", None) is not None and torch.backends.mps.is_available()

        print(f"VoiceChanger Initialized (GPU_NUM:{self.gpu_num}, mps_enabled:{self.mps_enabled})")

    def loadModel(self, config: str, pyTorch_model_file: str = None, onnx_model_file: str = None):
        self.settings.configFile = config
        self.hps = get_hparams_from_file(config)
        if pyTorch_model_file != None:
            self.settings.pyTorchModelFile = pyTorch_model_file
        if onnx_model_file:
            self.settings.onnxModelFile = onnx_model_file

        # PyTorchモデル生成
        if pyTorch_model_file != None:
            self.net_g = SynthesizerTrn(
                spec_channels=self.hps.data.filter_length // 2 + 1,
                segment_size=self.hps.train.segment_size // self.hps.data.hop_length,
                inter_channels=self.hps.model.inter_channels,
                hidden_channels=self.hps.model.hidden_channels,
                upsample_rates=self.hps.model.upsample_rates,
                upsample_initial_channel=self.hps.model.upsample_initial_channel,
                upsample_kernel_sizes=self.hps.model.upsample_kernel_sizes,
                n_flow=self.hps.model.n_flow,
                dec_out_channels=1,
                dec_kernel_size=7,
                n_speakers=self.hps.data.n_speakers,
                gin_channels=self.hps.model.gin_channels,
                requires_grad_pe=self.hps.requires_grad.pe,
                requires_grad_flow=self.hps.requires_grad.flow,
                requires_grad_text_enc=self.hps.requires_grad.text_enc,
                requires_grad_dec=self.hps.requires_grad.dec
            )
            self.net_g.eval()
            load_checkpoint(pyTorch_model_file, self.net_g, None)
            # utils.load_checkpoint(pyTorch_model_file, self.net_g, None)

        # ONNXモデル生成
        if onnx_model_file != None:
            ort_options = onnxruntime.SessionOptions()
            ort_options.intra_op_num_threads = 8
            self.onnx_session = onnxruntime.InferenceSession(
                onnx_model_file,
                providers=providers
            )
        return self.get_info()

    def destroy(self):
        del self.net_g
        del self.onnx_session

    def get_info(self):
        data = asdict(self.settings)

        data["onnxExecutionProvider"] = 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 update_setteings(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)
            if key == "crossFadeOffsetRate" or key == "crossFadeEndRate":
                self.unpackedData_length = 0
        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:
            print(f"{key} is not mutalbe variable!")

        return self.get_info()

    def _generate_strength(self, unpackedData):

        if self.unpackedData_length != unpackedData.shape[0] or self.currentCrossFadeOffsetRate != self.settings.crossFadeOffsetRate or self.currentCrossFadeEndRate != self.settings.crossFadeEndRate or self.currentCrossFadeOverlapRate != self.settings.crossFadeOverlapRate:
            self.unpackedData_length = unpackedData.shape[0]
            self.currentCrossFadeOffsetRate = self.settings.crossFadeOffsetRate
            self.currentCrossFadeEndRate = self.settings.crossFadeEndRate
            self.currentCrossFadeOverlapRate = self.settings.crossFadeOverlapRate

            overlapSize = int(unpackedData.shape[0] * self.settings.crossFadeOverlapRate)

            cf_offset = int(overlapSize * self.settings.crossFadeOffsetRate)
            cf_end = int(overlapSize * self.settings.crossFadeEndRate)
            cf_range = cf_end - cf_offset
            percent = np.arange(cf_range) / cf_range

            np_prev_strength = np.cos(percent * 0.5 * np.pi) ** 2
            np_cur_strength = np.cos((1 - percent) * 0.5 * np.pi) ** 2

            self.np_prev_strength = np.concatenate([np.ones(cf_offset), np_prev_strength, np.zeros(overlapSize - cf_offset - len(np_prev_strength))])
            self.np_cur_strength = np.concatenate([np.zeros(cf_offset), np_cur_strength, np.ones(overlapSize - cf_offset - len(np_cur_strength))])

            self.prev_strength = torch.FloatTensor(self.np_prev_strength)
            self.cur_strength = torch.FloatTensor(self.np_cur_strength)

            # torch.set_printoptions(edgeitems=2100)
            print("Generated Strengths")
            # print(f"cross fade: start:{cf_offset} end:{cf_end} range:{cf_range}")
            # print(f"target_len:{unpackedData.shape[0]}, prev_len:{len(self.prev_strength)} cur_len:{len(self.cur_strength)}")
            # print("Prev", self.prev_strength)
            # print("Cur", self.cur_strength)

            # ひとつ前の結果とサイズが変わるため、記録は消去する。
            if hasattr(self, 'prev_audio1') == True:
                delattr(self, "prev_audio1")

    def _generate_input(self, unpackedData: any, convertSize: int):
        # 今回変換するデータをテンソルとして整形する
        audio = torch.FloatTensor(unpackedData.astype(np.float32))  # float32でtensorfを作成
        audio_norm = audio / self.hps.data.max_wav_value  # normalize
        audio_norm = audio_norm.unsqueeze(0)  # unsqueeze
        self.audio_buffer = torch.cat([self.audio_buffer, audio_norm], axis=1)  # 過去のデータに連結
        audio_norm = self.audio_buffer[:, -convertSize:]  # 変換対象の部分だけ抽出
        self.audio_buffer = audio_norm

        # TBD: numpy <--> pytorch変換が行ったり来たりしているが、まずは動かすことを最優先。
        audio_norm_np = audio_norm.squeeze().numpy().astype(np.double)
        _f0, _time = pw.dio(audio_norm_np, self.hps.data.sampling_rate, frame_period=5.5)
        f0 = pw.stonemask(audio_norm_np, _f0, _time, self.hps.data.sampling_rate)
        f0 = convert_continuos_f0(f0, int(audio_norm_np.shape[0] / self.hps.data.hop_length))
        f0 = torch.from_numpy(f0.astype(np.float32))

        spec = spectrogram_torch(audio_norm, self.hps.data.filter_length,
                                 self.hps.data.sampling_rate, self.hps.data.hop_length, self.hps.data.win_length,
                                 center=False)
        # dispose_stft_specs = 2
        # spec = spec[:, dispose_stft_specs:-dispose_stft_specs]
        # f0 = f0[dispose_stft_specs:-dispose_stft_specs]
        spec = torch.squeeze(spec, 0)
        sid = torch.LongTensor([int(self.settings.srcId)])

        # data = (self.text_norm, spec, audio_norm, sid)
        # data = TextAudioSpeakerCollate()([data])
        data = TextAudioSpeakerCollate(
            sample_rate=self.hps.data.sampling_rate,
            hop_size=self.hps.data.hop_length,
            f0_factor=self.settings.f0Factor  # TBD: parameter
            # f0_factor=2.4  # TBD: parameter
        )([(spec, sid, f0)])

        return data

    def _onnx_inference(self, data, inputSize):
        if hasattr(self, "onnx_session") == False or self.onnx_session == None:
            print("[Voice Changer] No ONNX session.")
            return np.zeros(1).astype(np.int16)

        x, x_lengths, spec, spec_lengths, y, y_lengths, sid_src = [x for x in data]
        sid_tgt1 = torch.LongTensor([self.settings.dstId])
        # if spec.size()[2] >= 8:
        audio1 = self.onnx_session.run(
            ["audio"],
            {
                "specs": spec.numpy(),
                "lengths": spec_lengths.numpy(),
                "sid_src": sid_src.numpy(),
                "sid_tgt": sid_tgt1.numpy()
            })[0][0, 0] * self.hps.data.max_wav_value
        if hasattr(self, 'np_prev_audio1') == True:
            overlapSize = int(inputSize * self.settings.crossFadeOverlapRate)
            prev_overlap = self.np_prev_audio1[-1 * overlapSize:]
            cur_overlap = audio1[-1 * (inputSize + overlapSize):-1 * inputSize]
            # print(prev_overlap.shape, self.np_prev_strength.shape, cur_overlap.shape, self.np_cur_strength.shape)
            # print(">>>>>>>>>>>", -1*(inputSize + overlapSize) , -1*inputSize)
            powered_prev = prev_overlap * self.np_prev_strength
            powered_cur = cur_overlap * self.np_cur_strength
            powered_result = powered_prev + powered_cur

            cur = audio1[-1 * inputSize:-1 * overlapSize]
            result = np.concatenate([powered_result, cur], axis=0)
        else:
            result = np.zeros(1).astype(np.int16)
        self.np_prev_audio1 = audio1
        return result

    def _pyTorch_inference(self, data, inputSize):
        if hasattr(self, "net_g") == False or self.net_g == None:
            print("[Voice Changer] No pyTorch session.")
            return np.zeros(1).astype(np.int16)

        if self.settings.gpu < 0 or self.gpu_num == 0:
            with torch.no_grad():
                spec, spec_lengths, sid_src, sin, d = data
                spec = spec.cpu()
                spec_lengths = spec_lengths.cpu()
                sid_src = sid_src.cpu()
                sin = sin.cpu()
                d = tuple([d[:1].cpu() for d in d])
                sid_target = torch.LongTensor([self.settings.dstId]).cpu()

                audio1 = self.net_g.cpu().voice_conversion(spec, spec_lengths, sin, d, sid_src, sid_target)[0, 0].data * self.hps.data.max_wav_value

                if self.prev_strength.device != torch.device('cpu'):
                    print(f"prev_strength move from {self.prev_strength.device} to cpu")
                    self.prev_strength = self.prev_strength.cpu()
                if self.cur_strength.device != torch.device('cpu'):
                    print(f"cur_strength move from {self.cur_strength.device} to cpu")
                    self.cur_strength = self.cur_strength.cpu()

                if hasattr(self, 'prev_audio1') == True and self.prev_audio1.device == torch.device('cpu'):  # prev_audio1が所望のデバイスに無い場合は一回休み。
                    overlapSize = int(inputSize * self.settings.crossFadeOverlapRate)
                    prev_overlap = self.prev_audio1[-1 * overlapSize:]
                    cur_overlap = audio1[-1 * (inputSize + overlapSize):-1 * inputSize]
                    powered_prev = prev_overlap * self.prev_strength
                    powered_cur = cur_overlap * self.cur_strength
                    powered_result = powered_prev + powered_cur

                    cur = audio1[-1 * inputSize:-1 * overlapSize]  # 今回のインプットの生部分。(インプット - 次回のCrossfade部分)。
                    result = torch.cat([powered_result, cur], axis=0)  # Crossfadeと今回のインプットの生部分を結合

                else:
                    cur = audio1[-2 * inputSize:-1 * inputSize]
                    result = cur

                self.prev_audio1 = audio1
                result = result.cpu().float().numpy()

        else:
            with torch.no_grad():
                spec, spec_lengths, sid_src, sin, d = data
                spec = spec.cuda(self.settings.gpu)
                spec_lengths = spec_lengths.cuda(self.settings.gpu)
                sid_src = sid_src.cuda(self.settings.gpu)
                sin = sin.cuda(self.settings.gpu)
                d = tuple([d[:1].cuda(self.settings.gpu) for d in d])
                sid_target = torch.LongTensor([self.settings.dstId]).cuda(self.settings.gpu)

                # audio1 = self.net_g.cuda(self.settings.gpu).voice_conversion(spec, spec_lengths, sid_src=sid_src,
                #  sid_tgt=sid_tgt1)[0, 0].data * self.hps.data.max_wav_value

                audio1 = self.net_g.cuda(self.settings.gpu).voice_conversion(spec, spec_lengths, sin, d,
                                                                             sid_src, sid_target)[0, 0].data * self.hps.data.max_wav_value

                # audio1 = audio1[10:-10]
                if self.prev_strength.device != torch.device('cuda', self.settings.gpu):
                    print(f"prev_strength move from {self.prev_strength.device} to gpu{self.settings.gpu}")
                    self.prev_strength = self.prev_strength.cuda(self.settings.gpu)
                if self.cur_strength.device != torch.device('cuda', self.settings.gpu):
                    print(f"cur_strength move from {self.cur_strength.device} to gpu{self.settings.gpu}")
                    self.cur_strength = self.cur_strength.cuda(self.settings.gpu)

                if hasattr(self, 'prev_audio1') == True and self.prev_audio1.device == torch.device('cuda', self.settings.gpu):
                    overlapSize = int(inputSize * self.settings.crossFadeOverlapRate)
                    prev_overlap = self.prev_audio1[-1 * overlapSize:]
                    cur_overlap = audio1[-1 * (inputSize + overlapSize):-1 * inputSize]
                    powered_prev = prev_overlap * self.prev_strength
                    powered_cur = cur_overlap * self.cur_strength
                    powered_result = powered_prev + powered_cur

                    cur = audio1[-1 * inputSize:-1 * overlapSize]  # 今回のインプットの生部分。(インプット - 次回のCrossfade部分)。
                    result = torch.cat([powered_result, cur], axis=0)  # Crossfadeと今回のインプットの生部分を結合

                else:
                    cur = audio1[-2 * inputSize:-1 * inputSize]
                    result = cur
                self.prev_audio1 = audio1

                result = result.cpu().float().numpy()
        return result

    def on_request(self, unpackedData: any):
        convertSize = self.settings.convertChunkNum * 128  # 128sample/1chunk

        if unpackedData.shape[0] * (1 + self.settings.crossFadeOverlapRate) + 1024 > convertSize:
            convertSize = int(unpackedData.shape[0] * (1 + self.settings.crossFadeOverlapRate)) + 1024
        if convertSize < self.settings.minConvertSize:
            convertSize = self.settings.minConvertSize
        # print("convert Size", unpackedData.shape[0], unpackedData.shape[0]*(1 + self.settings.crossFadeOverlapRate), convertSize, self.settings.minConvertSize)

        self._generate_strength(unpackedData)
        data = self._generate_input(unpackedData, convertSize)

        try:
            if self.settings.framework == "ONNX":
                result = self._onnx_inference(data, unpackedData.shape[0])
            else:
                result = self._pyTorch_inference(data, unpackedData.shape[0])

        except Exception as e:
            print("VC PROCESSING!!!! EXCEPTION!!!", e)
            print(traceback.format_exc())
            if hasattr(self, "np_prev_audio1"):
                del self.np_prev_audio1
            if hasattr(self, "prev_audio1"):
                del self.prev_audio1
            return np.zeros(1).astype(np.int16)

        result = result.astype(np.int16)
        # print("on_request result size:",result.shape)
        return result