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Refactor folder structure
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@ -1,8 +1,8 @@
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from toolbox.ui import UI
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from encoder import inference as encoder
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from synthesizer.inference import Synthesizer
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from vocoder import inference as rnn_vocoder
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from hifigan import inference as gan_vocoder
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from vocoder.wavernn import inference as rnn_vocoder
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from vocoder.hifigan import inference as gan_vocoder
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from pathlib import Path
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from time import perf_counter as timer
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from toolbox.utterance import Utterance
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@ -50,13 +50,6 @@ MAX_WAVES = 15
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class Toolbox:
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def __init__(self, datasets_root, enc_models_dir, syn_models_dir, voc_models_dir, seed, no_mp3_support):
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if not no_mp3_support:
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try:
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librosa.load("samples/6829_00000.mp3")
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except NoBackendError:
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print("Librosa will be unable to open mp3 files if additional software is not installed.\n"
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"Please install ffmpeg or add the '--no_mp3_support' option to proceed without support for mp3 files.")
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exit(-1)
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self.no_mp3_support = no_mp3_support
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sys.excepthook = self.excepthook
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self.datasets_root = datasets_root
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@ -1,15 +1,12 @@
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from __future__ import absolute_import, division, print_function, unicode_literals
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import glob
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import os
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import argparse
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import json
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import torch
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import numpy as np
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from scipy.io.wavfile import write
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from hifigan.env import AttrDict
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from hifigan.meldataset import mel_spectrogram, MAX_WAV_VALUE, load_wav
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from hifigan.models import Generator
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from vocoder.hifigan.env import AttrDict
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from vocoder.hifigan.meldataset import mel_spectrogram, MAX_WAV_VALUE, load_wav
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from vocoder.hifigan.models import Generator
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import soundfile as sf
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@ -31,7 +28,7 @@ def load_model(weights_fpath, verbose=True):
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if verbose:
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print("Building hifigan")
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with open("./hifigan/config_16k_.json") as f:
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with open("./vocoder/hifigan/config_16k_.json") as f:
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data = f.read()
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json_config = json.loads(data)
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h = AttrDict(json_config)
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@ -3,7 +3,7 @@ import torch.nn.functional as F
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import torch.nn as nn
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from torch.nn import Conv1d, ConvTranspose1d, AvgPool1d, Conv2d
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from torch.nn.utils import weight_norm, remove_weight_norm, spectral_norm
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from hifigan.utils import init_weights, get_padding
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from vocoder.hifigan.utils import init_weights, get_padding
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LRELU_SLOPE = 0.1
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@ -1,7 +1,7 @@
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import math
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import numpy as np
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import librosa
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import vocoder.hparams as hp
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import vocoder.wavernn.hparams as hp
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from scipy.signal import lfilter
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import soundfile as sf
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@ -1,5 +1,5 @@
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from vocoder.models.fatchord_version import WaveRNN
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from vocoder.audio import *
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from vocoder.wavernn.models.fatchord_version import WaveRNN
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from vocoder.wavernn.audio import *
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def gen_testset(model: WaveRNN, test_set, samples, batched, target, overlap, save_path):
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@ -1,5 +1,5 @@
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from vocoder.models.fatchord_version import WaveRNN
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from vocoder import hparams as hp
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from vocoder.wavernn.models.fatchord_version import WaveRNN
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from vocoder.wavernn import hparams as hp
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import torch
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@ -3,7 +3,7 @@ import torch.nn as nn
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import torch.nn.functional as F
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from vocoder.distribution import sample_from_discretized_mix_logistic
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from vocoder.display import *
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from vocoder.audio import *
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from vocoder.wavernn.audio import *
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class ResBlock(nn.Module):
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@ -1,13 +1,13 @@
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from vocoder.models.fatchord_version import WaveRNN
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from vocoder.wavernn.models.fatchord_version import WaveRNN
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from vocoder.vocoder_dataset import VocoderDataset, collate_vocoder
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from vocoder.distribution import discretized_mix_logistic_loss
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from vocoder.display import stream, simple_table
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from vocoder.gen_wavernn import gen_testset
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from vocoder.wavernn.gen_wavernn import gen_testset
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from torch.utils.data import DataLoader
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from pathlib import Path
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from torch import optim
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import torch.nn.functional as F
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import vocoder.hparams as hp
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import vocoder.wavernn.hparams as hp
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import numpy as np
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import time
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import torch
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@ -1,5 +1,5 @@
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from utils.argutils import print_args
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from vocoder.train import train
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from vocoder.wavernn.train import train
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from pathlib import Path
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import argparse
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