huchenxucs

#!/usr/bin/env bash

# Define source, target, maxdepth and cd to source
source="abspath/to/source/dir"
target="abspath/to/target/dir"
depth=5
cd "${source}"

# Set the maximum number of concurrent rsync threads
maxthreads=64

huchenxucs

# open mouse mode
set-option -g mouse on

# reload config
bind r source-file ~/.tmux.conf \; display-message "Config reloaded..."

# set window split
bind-key v split-window -h
bind-key h split-window -v

huchenxucs

def process_utterance(wav_path,
                      fft_size=1024,
                      hop_size=256,
                      win_length=1024,
                      window="hann",
                      num_mels=80,
                      fmin=80,
                      fmax=7600,
                      eps=1e-10,
                      sample_rate=22050,

huchenxucs

class Conv1dWithMask(nn.Module):
    def __init__(self, in_channels, out_channels, kernel_size=3, bias=True, w_init_gain='linear'):
        super(Conv1dWithMask, self).__init__()
        assert kernel_size > 1, f"Conv1dWithMask kernel size must greater than 1"
        self.kernel_size = kernel_size
        self.out_channels = out_channels
        self.conv = torch.nn.Conv1d(in_channels, out_channels, kernel_size=kernel_size, bias=bias)
        torch.nn.init.xavier_uniform_(
            self.conv.weight, gain=torch.nn.init.calculate_gain(w_init_gain))

huchenxucs

import numpy as np
from modules.transformer_tts import TransformerEncoder, TransformerDecoder
from modules.operations import *
from modules.tts_modules import FastspeechDecoder, RefEncoder, DurationPredictor, LengthRegulator, PitchPredictor, \
    TacotronDecoder, EnergyPredictor
from modules.pos_embed import RelativePositionBias
from utils.world_utils import f0_to_coarse_torch, restore_pitch
from utils.tts_utils import sequence_mask

huchenxucs

import math
import torch
import torch.nn as nn
from torch.nn import functional as F


class RelativePositionBias(nn.Module):
    def __init__(self, bidirectional=True, num_buckets=32, max_distance=128, n_heads=2):
        super(RelativePositionBias, self).__init__()
        self.bidirectional = bidirectional