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https://github.com/w-okada/voice-changer.git
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restrcture
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wataru
parent
a2a12f54ad
commit
60db7877e2
@ -21,7 +21,6 @@ import uvicorn
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import socketio
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from typing import Callable
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from mods.VoiceChanger import VoiceChanger
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from mods.ssl import create_self_signed_cert
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from voice_changer.VoiceChangerManager import VoiceChangerManager
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from sio.MMVC_SocketIOApp import MMVC_SocketIOApp
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@ -29,11 +28,6 @@ from sio.MMVC_SocketIOApp import MMVC_SocketIOApp
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from restapi.MMVC_Rest import MMVC_Rest
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def setupArgParser():
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parser = argparse.ArgumentParser()
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parser.add_argument("-t", type=str, default="MMVC",
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@ -1,106 +0,0 @@
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import torch
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from scipy.io.wavfile import write, read
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import numpy as np
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import traceback
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import utils
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import commons
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from models import SynthesizerTrn
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from text.symbols import symbols
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from data_utils import TextAudioSpeakerLoader, TextAudioSpeakerCollate
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from mel_processing import spectrogram_torch
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from text import text_to_sequence, cleaned_text_to_sequence
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class VoiceChanger():
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def __init__(self, config, model):
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self.hps = utils.get_hparams_from_file(config)
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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.net_g.eval()
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self.gpu_num = torch.cuda.device_count()
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utils.load_checkpoint(model, self.net_g, None)
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text_norm = text_to_sequence("a", self.hps.data.text_cleaners)
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text_norm = commons.intersperse(text_norm, 0)
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self.text_norm = torch.LongTensor(text_norm)
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self.audio_buffer = torch.zeros(1, 0)
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self.prev_audio = np.zeros(1)
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self.mps_enabled = getattr(
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torch.backends, "mps", None) is not None and torch.backends.mps.is_available()
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print(
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f"VoiceChanger Initialized (GPU_NUM:{self.gpu_num}, mps_enabled:{self.mps_enabled})")
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def destroy(self):
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del self.net_g
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def on_request(self, gpu, srcId, dstId, timestamp, prefixChunkSize, wav):
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unpackedData = wav
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convertSize = unpackedData.shape[0] + (prefixChunkSize * 512)
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try:
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audio = torch.FloatTensor(unpackedData.astype(np.float32))
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audio_norm = audio / self.hps.data.max_wav_value
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audio_norm = audio_norm.unsqueeze(0)
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self.audio_buffer = torch.cat(
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[self.audio_buffer, audio_norm], axis=1)
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audio_norm = self.audio_buffer[:, -convertSize:]
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self.audio_buffer = audio_norm
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spec = spectrogram_torch(audio_norm, 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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spec = torch.squeeze(spec, 0)
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sid = torch.LongTensor([int(srcId)])
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data = (self.text_norm, spec, audio_norm, sid)
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data = TextAudioSpeakerCollate()([data])
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# if gpu < 0 or (self.gpu_num == 0 and not self.mps_enabled):
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if gpu < 0 or self.gpu_num == 0:
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with torch.no_grad():
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x, x_lengths, spec, spec_lengths, y, y_lengths, sid_src = [
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x.cpu() for x in data]
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sid_tgt1 = torch.LongTensor([dstId]).cpu()
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audio1 = (self.net_g.cpu().voice_conversion(spec, spec_lengths, sid_src=sid_src, sid_tgt=sid_tgt1)[
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0][0, 0].data * self.hps.data.max_wav_value).cpu().float().numpy()
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# elif self.mps_enabled == True: # MPS doesnt support aten::weight_norm_interface, and PYTORCH_ENABLE_MPS_FALLBACK=1 cause a big dely.
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# x, x_lengths, spec, spec_lengths, y, y_lengths, sid_src = [
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# x.to("mps") for x in data]
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# sid_tgt1 = torch.LongTensor([dstId]).to("mps")
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# audio1 = (self.net_g.to("mps").voice_conversion(spec, spec_lengths, sid_src=sid_src, sid_tgt=sid_tgt1)[
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# 0][0, 0].data * self.hps.data.max_wav_value).cpu().float().numpy()
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else:
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with torch.no_grad():
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x, x_lengths, spec, spec_lengths, y, y_lengths, sid_src = [
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x.cuda(gpu) for x in data]
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sid_tgt1 = torch.LongTensor([dstId]).cuda(gpu)
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audio1 = (self.net_g.cuda(gpu).voice_conversion(spec, spec_lengths, sid_src=sid_src, sid_tgt=sid_tgt1)[
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0][0, 0].data * self.hps.data.max_wav_value).cpu().float().numpy()
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# if len(self.prev_audio) > unpackedData.shape[0]:
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# prevLastFragment = self.prev_audio[-unpackedData.shape[0]:]
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# curSecondLastFragment = audio1[-unpackedData.shape[0]*2:-unpackedData.shape[0]]
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# print("prev, cur", prevLastFragment.shape, curSecondLastFragment.shape)
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# self.prev_audio = audio1
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# print("self.prev_audio", self.prev_audio.shape)
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audio1 = audio1[-unpackedData.shape[0]*2:]
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except Exception as e:
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print("VC PROCESSING!!!! EXCEPTION!!!", e)
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print(traceback.format_exc())
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audio1 = audio1.astype(np.int16)
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return audio1
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@ -1,36 +0,0 @@
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import whisper
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import numpy as np
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import torchaudio
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from scipy.io.wavfile import write
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_MODELS = {
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"tiny": "/whisper/tiny.pt",
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"base": "/whisper/base.pt",
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"small": "/whisper/small.pt",
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"medium": "/whisper/medium.pt",
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}
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class Whisper():
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def __init__(self):
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self.storedSizeFromTry = 0
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def loadModel(self, model):
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# self.model = whisper.load_model(_MODELS[model], device="cpu")
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self.model = whisper.load_model(_MODELS[model])
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self.data = np.zeros(1).astype(np.float)
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def addData(self, unpackedData):
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self.data = np.concatenate([self.data, unpackedData], 0)
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def transcribe(self, audio):
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received_data_file = "received_data.wav"
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write(received_data_file, 24000, self.data.astype(np.int16))
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source, sr = torchaudio.load(received_data_file)
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target = torchaudio.functional.resample(source, 24000, 16000)
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result = self.model.transcribe(received_data_file)
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print("WHISPER1:::", result["text"])
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print("WHISPER2:::", result["segments"])
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self.data = np.zeros(1).astype(np.float)
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return result["text"]
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@ -5,16 +5,16 @@ from fastapi.encoders import jsonable_encoder
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from fastapi.responses import JSONResponse
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from mods.Trainer_Speakers import mod_get_speakers
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from mods.Trainer_Training import mod_post_pre_training, mod_post_start_training, mod_post_stop_training, mod_get_related_files, mod_get_tail_training_log
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from mods.Trainer_Model import mod_get_model, mod_delete_model
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from restapi.mods.Trainer_Speakers import mod_get_speakers
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from restapi.mods.Trainer_Training import mod_post_pre_training, mod_post_start_training, mod_post_stop_training, mod_get_related_files, mod_get_tail_training_log
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from restapi.mods.Trainer_Model import mod_get_model, mod_delete_model
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from mods.Trainer_Models import mod_get_models
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from mods.Trainer_MultiSpeakerSetting import mod_get_multi_speaker_setting, mod_post_multi_speaker_setting
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from mods.Trainer_Speaker_Voice import mod_get_speaker_voice
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from mods.Trainer_Speaker_Voices import mod_get_speaker_voices
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from restapi.mods.Trainer_Models import mod_get_models
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from restapi.mods.Trainer_MultiSpeakerSetting import mod_get_multi_speaker_setting, mod_post_multi_speaker_setting
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from restapi.mods.Trainer_Speaker_Voice import mod_get_speaker_voice
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from restapi.mods.Trainer_Speaker_Voices import mod_get_speaker_voices
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from mods.Trainer_Speaker import mod_delete_speaker
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from restapi.mods.Trainer_Speaker import mod_delete_speaker
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from dataclasses import dataclass
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INFO_DIR = "info"
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@ -1,5 +1,5 @@
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import shutil
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from mods.Trainer_MultiSpeakerSetting import MULTI_SPEAKER_SETTING_PATH
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from restapi.mods.Trainer_MultiSpeakerSetting import MULTI_SPEAKER_SETTING_PATH
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def mod_delete_speaker(speaker:str):
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shutil.rmtree(f"MMVC_Trainer/dataset/textful/{speaker}")
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