SYMBOL INDEX (196 symbols across 14 files)

FILE: attentions.py
  class Encoder (line 13) | class Encoder(nn.Module):
    method __init__ (line 14) | def __init__(self, hidden_channels, filter_channels, n_heads, n_layers...
    method forward (line 35) | def forward(self, x, x_mask):
  class Decoder (line 50) | class Decoder(nn.Module):
    method __init__ (line 51) | def __init__(self, hidden_channels, filter_channels, n_heads, n_layers...
    method forward (line 77) | def forward(self, x, x_mask, h, h_mask):
  class MultiHeadAttention (line 101) | class MultiHeadAttention(nn.Module):
    method __init__ (line 102) | def __init__(self, channels, out_channels, n_heads, p_dropout=0., wind...
    method forward (line 138) | def forward(self, x, c, attn_mask=None):
    method attention (line 148) | def attention(self, query, key, value, mask=None):
    method _matmul_with_relative_values (line 181) | def _matmul_with_relative_values(self, x, y):
    method _matmul_with_relative_keys (line 190) | def _matmul_with_relative_keys(self, x, y):
    method _get_relative_embeddings (line 199) | def _get_relative_embeddings(self, relative_embeddings, length):
    method _relative_position_to_absolute_position (line 214) | def _relative_position_to_absolute_position(self, x):
    method _absolute_position_to_relative_position (line 231) | def _absolute_position_to_relative_position(self, x):
    method _attention_bias_proximal (line 245) | def _attention_bias_proximal(self, length):
  class FFN (line 257) | class FFN(nn.Module):
    method __init__ (line 258) | def __init__(self, in_channels, out_channels, filter_channels, kernel_...
    method forward (line 277) | def forward(self, x, x_mask):
    method _causal_padding (line 287) | def _causal_padding(self, x):
    method _same_padding (line 296) | def _same_padding(self, x):

FILE: commons.py
  function init_weights (line 8) | def init_weights(m, mean=0.0, std=0.01):
  function get_padding (line 14) | def get_padding(kernel_size, dilation=1):
  function convert_pad_shape (line 18) | def convert_pad_shape(pad_shape):
  function intersperse (line 24) | def intersperse(lst, item):
  function kl_divergence (line 30) | def kl_divergence(m_p, logs_p, m_q, logs_q):
  function rand_gumbel (line 37) | def rand_gumbel(shape):
  function rand_gumbel_like (line 43) | def rand_gumbel_like(x):
  function slice_segments (line 48) | def slice_segments(x, ids_str, segment_size=4):
  function rand_slice_segments (line 57) | def rand_slice_segments(x, x_lengths=None, segment_size=4):
  function get_timing_signal_1d (line 67) | def get_timing_signal_1d(
  function add_timing_signal_1d (line 83) | def add_timing_signal_1d(x, min_timescale=1.0, max_timescale=1.0e4):
  function cat_timing_signal_1d (line 89) | def cat_timing_signal_1d(x, min_timescale=1.0, max_timescale=1.0e4, axis...
  function subsequent_mask (line 95) | def subsequent_mask(length):
  function fused_add_tanh_sigmoid_multiply (line 101) | def fused_add_tanh_sigmoid_multiply(input_a, input_b, n_channels):
  function convert_pad_shape (line 110) | def convert_pad_shape(pad_shape):
  function shift_1d (line 116) | def shift_1d(x):
  function sequence_mask (line 121) | def sequence_mask(length, max_length=None):
  function generate_path (line 128) | def generate_path(duration, mask):
  function clip_grad_value_ (line 146) | def clip_grad_value_(parameters, clip_value, norm_type=2):

FILE: data_utils.py
  class TextAudioLoader (line 14) | class TextAudioLoader(torch.utils.data.Dataset):
    method __init__ (line 20) | def __init__(self, audiopaths_and_text, hparams):
    method _filter (line 41) | def _filter(self):
    method get_audio_text_pair (line 58) | def get_audio_text_pair(self, audiopath_and_text):
    method get_audio (line 65) | def get_audio(self, filename):
    method get_text (line 83) | def get_text(self, text):
    method __getitem__ (line 93) | def __getitem__(self, index):
    method __len__ (line 96) | def __len__(self):
  class TextAudioCollate (line 100) | class TextAudioCollate():
    method __init__ (line 103) | def __init__(self, return_ids=False):
    method __call__ (line 106) | def __call__(self, batch):
  class TextAudioSpeakerLoader (line 152) | class TextAudioSpeakerLoader(torch.utils.data.Dataset):
    method __init__ (line 158) | def __init__(self, audiopaths_sid_text, hparams):
    method _filter (line 178) | def _filter(self):
    method get_audio_text_speaker_pair (line 195) | def get_audio_text_speaker_pair(self, audiopath_sid_text):
    method get_audio (line 203) | def get_audio(self, filename):
    method get_text (line 221) | def get_text(self, text):
    method get_sid (line 231) | def get_sid(self, sid):
    method __getitem__ (line 235) | def __getitem__(self, index):
    method __len__ (line 238) | def __len__(self):
  class TextAudioSpeakerCollate (line 242) | class TextAudioSpeakerCollate():
    method __init__ (line 245) | def __init__(self, return_ids=False):
    method __call__ (line 248) | def __call__(self, batch):
  class DistributedBucketSampler (line 296) | class DistributedBucketSampler(torch.utils.data.distributed.DistributedS...
    method __init__ (line 305) | def __init__(self, dataset, batch_size, boundaries, num_replicas=None,...
    method _create_buckets (line 315) | def _create_buckets(self):
    method __iter__ (line 336) | def __iter__(self):
    method _bisect (line 376) | def _bisect(self, x, lo=0, hi=None):
    method __len__ (line 391) | def __len__(self):

FILE: losses.py
  function feature_loss (line 7) | def feature_loss(fmap_r, fmap_g):
  function discriminator_loss (line 18) | def discriminator_loss(disc_real_outputs, disc_generated_outputs):
  function generator_loss (line 34) | def generator_loss(disc_outputs):
  function kl_loss (line 46) | def kl_loss(z_p, logs_q, m_p, logs_p, z_mask):

FILE: mel_processing.py
  function dynamic_range_compression_torch (line 19) | def dynamic_range_compression_torch(x, C=1, clip_val=1e-5):
  function dynamic_range_decompression_torch (line 28) | def dynamic_range_decompression_torch(x, C=1):
  function spectral_normalize_torch (line 37) | def spectral_normalize_torch(magnitudes):
  function spectral_de_normalize_torch (line 42) | def spectral_de_normalize_torch(magnitudes):
  function spectrogram_torch (line 51) | def spectrogram_torch(y, n_fft, sampling_rate, hop_size, win_size, cente...
  function spec_to_mel_torch (line 73) | def spec_to_mel_torch(spec, n_fft, num_mels, sampling_rate, fmin, fmax):
  function mel_spectrogram_torch (line 85) | def mel_spectrogram_torch(y, n_fft, num_mels, sampling_rate, hop_size, w...

FILE: models.py
  class StochasticDurationPredictor (line 17) | class StochasticDurationPredictor(nn.Module):
    method __init__ (line 18) | def __init__(self, in_channels, filter_channels, kernel_size, p_dropou...
    method forward (line 50) | def forward(self, x, x_mask, w=None, g=None, reverse=False, noise_scal...
  class DurationPredictor (line 98) | class DurationPredictor(nn.Module):
    method __init__ (line 99) | def __init__(self, in_channels, filter_channels, kernel_size, p_dropou...
    method forward (line 118) | def forward(self, x, x_mask, g=None):
  class TextEncoder (line 135) | class TextEncoder(nn.Module):
    method __init__ (line 136) | def __init__(self,
    method forward (line 167) | def forward(self, x, x_lengths):
  class ResidualCouplingBlock (line 179) | class ResidualCouplingBlock(nn.Module):
    method __init__ (line 180) | def __init__(self,
    method forward (line 202) | def forward(self, x, x_mask, g=None, reverse=False):
  class PosteriorEncoder (line 212) | class PosteriorEncoder(nn.Module):
    method __init__ (line 213) | def __init__(self,
    method forward (line 234) | def forward(self, x, x_lengths, g=None):
  class Generator (line 244) | class Generator(torch.nn.Module):
    method __init__ (line 245) | def __init__(self, initial_channel, resblock, resblock_kernel_sizes, r...
    method forward (line 270) | def forward(self, x, g=None):
    method remove_weight_norm (line 291) | def remove_weight_norm(self):
  class DiscriminatorP (line 299) | class DiscriminatorP(torch.nn.Module):
    method __init__ (line 300) | def __init__(self, period, kernel_size=5, stride=3, use_spectral_norm=...
    method forward (line 314) | def forward(self, x):
  class DiscriminatorS (line 336) | class DiscriminatorS(torch.nn.Module):
    method __init__ (line 337) | def __init__(self, use_spectral_norm=False):
    method forward (line 350) | def forward(self, x):
  class MultiPeriodDiscriminator (line 364) | class MultiPeriodDiscriminator(torch.nn.Module):
    method __init__ (line 365) | def __init__(self, use_spectral_norm=False):
    method forward (line 373) | def forward(self, y, y_hat):
  class SynthesizerTrn (line 390) | class SynthesizerTrn(nn.Module):
    method __init__ (line 395) | def __init__(self,
    method forward (line 459) | def forward(self, x, x_lengths, y, y_lengths, sid=None):
    method infer (line 499) | def infer(self, x, x_lengths, sid=None, noise_scale=1, length_scale=1,...
    method voice_conversion (line 525) | def voice_conversion(self, y, y_lengths, sid_src, sid_tgt):

FILE: modules.py
  class LayerNorm (line 20) | class LayerNorm(nn.Module):
    method __init__ (line 21) | def __init__(self, channels, eps=1e-5):
    method forward (line 29) | def forward(self, x):
  class ConvReluNorm (line 35) | class ConvReluNorm(nn.Module):
    method __init__ (line 36) | def __init__(self, in_channels, hidden_channels, out_channels, kernel_...
    method forward (line 60) | def forward(self, x, x_mask):
  class DDSConv (line 70) | class DDSConv(nn.Module):
    method __init__ (line 74) | def __init__(self, channels, kernel_size, n_layers, p_dropout=0.):
    method forward (line 96) | def forward(self, x, x_mask, g=None):
  class WN (line 111) | class WN(torch.nn.Module):
    method __init__ (line 112) | def __init__(self, hidden_channels, kernel_size, dilation_rate, n_laye...
    method forward (line 148) | def forward(self, x, x_mask, g=None, **kwargs):
    method remove_weight_norm (line 178) | def remove_weight_norm(self):
  class ResBlock1 (line 187) | class ResBlock1(torch.nn.Module):
    method __init__ (line 188) | def __init__(self, channels, kernel_size=3, dilation=(1, 3, 5)):
    method forward (line 210) | def forward(self, x, x_mask=None):
    method remove_weight_norm (line 225) | def remove_weight_norm(self):
  class ResBlock2 (line 232) | class ResBlock2(torch.nn.Module):
    method __init__ (line 233) | def __init__(self, channels, kernel_size=3, dilation=(1, 3)):
    method forward (line 243) | def forward(self, x, x_mask=None):
    method remove_weight_norm (line 254) | def remove_weight_norm(self):
  class Log (line 259) | class Log(nn.Module):
    method forward (line 260) | def forward(self, x, x_mask, reverse=False, **kwargs):
  class Flip (line 270) | class Flip(nn.Module):
    method forward (line 271) | def forward(self, x, *args, reverse=False, **kwargs):
  class ElementwiseAffine (line 280) | class ElementwiseAffine(nn.Module):
    method __init__ (line 281) | def __init__(self, channels):
    method forward (line 287) | def forward(self, x, x_mask, reverse=False, **kwargs):
  class ResidualCouplingLayer (line 298) | class ResidualCouplingLayer(nn.Module):
    method __init__ (line 299) | def __init__(self,
    method forward (line 324) | def forward(self, x, x_mask, g=None, reverse=False):
  class ConvFlow (line 346) | class ConvFlow(nn.Module):
    method __init__ (line 347) | def __init__(self, in_channels, filter_channels, kernel_size, n_layers...
    method forward (line 363) | def forward(self, x, x_mask, g=None, reverse=False):

FILE: monotonic_align/__init__.py
  function maximum_path (line 6) | def maximum_path(neg_cent, mask):

FILE: text/__init__.py
  function text_to_sequence (line 11) | def text_to_sequence(text, cleaner_names):
  function cleaned_text_to_sequence (line 28) | def cleaned_text_to_sequence(cleaned_text):
  function sequence_to_text (line 39) | def sequence_to_text(sequence):
  function _clean_text (line 48) | def _clean_text(text, cleaner_names):

FILE: text/cleaners.py
  function expand_abbreviations (line 46) | def expand_abbreviations(text):
  function expand_numbers (line 52) | def expand_numbers(text):
  function lowercase (line 56) | def lowercase(text):
  function collapse_whitespace (line 60) | def collapse_whitespace(text):
  function convert_to_ascii (line 64) | def convert_to_ascii(text):
  function basic_cleaners (line 68) | def basic_cleaners(text):
  function transliteration_cleaners (line 75) | def transliteration_cleaners(text):
  function english_cleaners (line 83) | def english_cleaners(text):
  function english_cleaners2 (line 93) | def english_cleaners2(text):

FILE: train.py
  function main (line 41) | def main():
  function run (line 53) | def run(rank, n_gpus, hps):
  function train_and_evaluate (line 124) | def train_and_evaluate(rank, epoch, hps, nets, optims, schedulers, scale...
  function evaluate (line 233) | def evaluate(hps, generator, eval_loader, writer_eval):

FILE: train_ms.py
  function main (line 41) | def main():
  function run (line 53) | def run(rank, n_gpus, hps):
  function train_and_evaluate (line 125) | def train_and_evaluate(rank, epoch, hps, nets, optims, schedulers, scale...
  function evaluate (line 235) | def evaluate(hps, generator, eval_loader, writer_eval):

FILE: transforms.py
  function piecewise_rational_quadratic_transform (line 12) | def piecewise_rational_quadratic_transform(inputs,
  function searchsorted (line 47) | def searchsorted(bin_locations, inputs, eps=1e-6):
  function unconstrained_rational_quadratic_spline (line 55) | def unconstrained_rational_quadratic_spline(inputs,
  function rational_quadratic_spline (line 96) | def rational_quadratic_spline(inputs,

FILE: utils.py
  function load_checkpoint (line 18) | def load_checkpoint(checkpoint_path, model, optimizer=None):
  function save_checkpoint (line 46) | def save_checkpoint(model, optimizer, learning_rate, iteration, checkpoi...
  function summarize (line 59) | def summarize(writer, global_step, scalars={}, histograms={}, images={},...
  function latest_checkpoint_path (line 70) | def latest_checkpoint_path(dir_path, regex="G_*.pth"):
  function plot_spectrogram_to_numpy (line 78) | def plot_spectrogram_to_numpy(spectrogram):
  function plot_alignment_to_numpy (line 104) | def plot_alignment_to_numpy(alignment, info=None):
  function load_wav_to_torch (line 133) | def load_wav_to_torch(full_path):
  function load_filepaths_and_text (line 138) | def load_filepaths_and_text(filename, split="|"):
  function get_hparams (line 144) | def get_hparams(init=True):
  function get_hparams_from_dir (line 174) | def get_hparams_from_dir(model_dir):
  function get_hparams_from_file (line 185) | def get_hparams_from_file(config_path):
  function check_git_hash (line 194) | def check_git_hash(model_dir):
  function get_logger (line 214) | def get_logger(model_dir, filename="train.log"):
  class HParams (line 229) | class HParams():
    method __init__ (line 230) | def __init__(self, **kwargs):
    method keys (line 236) | def keys(self):
    method items (line 239) | def items(self):
    method values (line 242) | def values(self):
    method __len__ (line 245) | def __len__(self):
    method __getitem__ (line 248) | def __getitem__(self, key):
    method __setitem__ (line 251) | def __setitem__(self, key, value):
    method __contains__ (line 254) | def __contains__(self, key):
    method __repr__ (line 257) | def __repr__(self):