SYMBOL INDEX (417 symbols across 38 files) FILE: bigvgan/inference.py function load_bigv_model (line 12) | def load_bigv_model(checkpoint_path, model): function main (line 28) | def main(args): FILE: bigvgan/model/alias/act.py class Activation1d (line 13) | class Activation1d(nn.Module): method __init__ (line 14) | def __init__(self, method forward (line 28) | def forward(self, x): class SnakeBeta (line 36) | class SnakeBeta(nn.Module): method __init__ (line 54) | def __init__(self, in_features, alpha=1.0, alpha_trainable=True, alpha... method forward (line 79) | def forward(self, x): class Mish (line 95) | class Mish(nn.Module): method __init__ (line 102) | def __init__(self): method forward (line 105) | def forward(self, x): class SnakeAlias (line 109) | class SnakeAlias(nn.Module): method __init__ (line 110) | def __init__(self, method forward (line 124) | def forward(self, x): FILE: bigvgan/model/alias/filter.py function sinc (line 15) | def sinc(x: torch.Tensor): function kaiser_sinc_filter1d (line 28) | def kaiser_sinc_filter1d(cutoff, half_width, kernel_size): # return filt... class LowPassFilter1d (line 60) | class LowPassFilter1d(nn.Module): method __init__ (line 61) | def __init__(self, method forward (line 86) | def forward(self, x): FILE: bigvgan/model/alias/resample.py class UpSample1d (line 10) | class UpSample1d(nn.Module): method __init__ (line 11) | def __init__(self, ratio=2, kernel_size=None): method forward (line 25) | def forward(self, x): class DownSample1d (line 36) | class DownSample1d(nn.Module): method __init__ (line 37) | def __init__(self, ratio=2, kernel_size=None): method forward (line 46) | def forward(self, x): FILE: bigvgan/model/bigv.py function init_weights (line 9) | def init_weights(m, mean=0.0, std=0.01): function get_padding (line 15) | def get_padding(kernel_size, dilation=1): class AMPBlock (line 19) | class AMPBlock(torch.nn.Module): method __init__ (line 20) | def __init__(self, channels, kernel_size=3, dilation=(1, 3, 5)): method forward (line 50) | def forward(self, x): method remove_weight_norm (line 60) | def remove_weight_norm(self): FILE: bigvgan/model/generator.py class Generator (line 15) | class Generator(torch.nn.Module): method __init__ (line 17) | def __init__(self, hp): method forward (line 77) | def forward(self, x, f0, train=True): method remove_weight_norm (line 110) | def remove_weight_norm(self): method eval (line 117) | def eval(self, inference=False): method inference (line 123) | def inference(self, mel, f0): method pitch2wav (line 132) | def pitch2wav(self, f0): FILE: bigvgan/model/nsf.py class PulseGen (line 7) | class PulseGen(torch.nn.Module): method __init__ (line 14) | def __init__(self, samp_rate, pulse_amp=0.1, noise_std=0.003, voiced_t... method forward (line 29) | def forward(self, f0): class SignalsConv1d (line 76) | class SignalsConv1d(torch.nn.Module): method __init__ (line 84) | def __init__(self): method forward (line 87) | def forward(self, signal, system_ir): class CyclicNoiseGen_v1 (line 115) | class CyclicNoiseGen_v1(torch.nn.Module): method __init__ (line 121) | def __init__(self, samp_rate, noise_std=0.003, voiced_threshold=0): method noise_decay (line 135) | def noise_decay(self, beta, f0mean): method forward (line 159) | def forward(self, f0s, beta): class SineGen (line 181) | class SineGen(torch.nn.Module): method __init__ (line 199) | def __init__( method _f02uv (line 217) | def _f02uv(self, f0): method _f02sine (line 223) | def _f02sine(self, f0_values): method forward (line 284) | def forward(self, f0): class SourceModuleCycNoise_v1 (line 319) | class SourceModuleCycNoise_v1(torch.nn.Module): method __init__ (line 335) | def __init__(self, sampling_rate, noise_std=0.003, voiced_threshod=0): method forward (line 341) | def forward(self, f0_upsamped, beta): class SourceModuleHnNSF (line 358) | class SourceModuleHnNSF(torch.nn.Module): method __init__ (line 359) | def __init__( method forward (line 383) | def forward(self, x): FILE: grad/base.py class BaseModule (line 5) | class BaseModule(torch.nn.Module): method __init__ (line 6) | def __init__(self): method nparams (line 10) | def nparams(self): method relocate_input (line 21) | def relocate_input(self, x: list): FILE: grad/diffusion.py class Mish (line 8) | class Mish(BaseModule): method forward (line 9) | def forward(self, x): class Upsample (line 13) | class Upsample(BaseModule): method __init__ (line 14) | def __init__(self, dim): method forward (line 18) | def forward(self, x): class Downsample (line 22) | class Downsample(BaseModule): method __init__ (line 23) | def __init__(self, dim): method forward (line 27) | def forward(self, x): class Rezero (line 31) | class Rezero(BaseModule): method __init__ (line 32) | def __init__(self, fn): method forward (line 37) | def forward(self, x): class Block (line 41) | class Block(BaseModule): method __init__ (line 42) | def __init__(self, dim, dim_out, groups=8): method forward (line 48) | def forward(self, x, mask): class ResnetBlock (line 53) | class ResnetBlock(BaseModule): method __init__ (line 54) | def __init__(self, dim, dim_out, time_emb_dim, groups=8): method forward (line 66) | def forward(self, x, mask, time_emb): class LinearAttention (line 74) | class LinearAttention(BaseModule): method __init__ (line 75) | def __init__(self, dim, heads=4, dim_head=32): method forward (line 82) | def forward(self, x): class Residual (line 95) | class Residual(BaseModule): method __init__ (line 96) | def __init__(self, fn): method forward (line 100) | def forward(self, x, *args, **kwargs): class SinusoidalPosEmb (line 105) | class SinusoidalPosEmb(BaseModule): method __init__ (line 106) | def __init__(self, dim): method forward (line 110) | def forward(self, x, scale=1000): class GradLogPEstimator2d (line 120) | class GradLogPEstimator2d(BaseModule): method __init__ (line 121) | def __init__(self, dim, dim_mults=(1, 2, 4), emb_dim=64, n_mels=100, method forward (line 164) | def forward(self, spk, x, mask, mu, t): function get_noise (line 205) | def get_noise(t, beta_init, beta_term, cumulative=False): class Diffusion (line 213) | class Diffusion(BaseModule): method __init__ (line 214) | def __init__(self, n_mels, dim, emb_dim=64, method forward_diffusion (line 228) | def forward_diffusion(self, mel, mask, mu, t): method forward (line 238) | def forward(self, spk, z, mask, mu, n_timesteps, stoc=False): method loss_t (line 241) | def loss_t(self, spk, mel, mask, mu, t): method compute_loss (line 250) | def compute_loss(self, spk, mel, mask, mu, offset=1e-5): FILE: grad/encoder.py class LayerNorm (line 9) | class LayerNorm(BaseModule): method __init__ (line 10) | def __init__(self, channels, eps=1e-4): method forward (line 18) | def forward(self, x): class ConvReluNorm (line 30) | class ConvReluNorm(BaseModule): method __init__ (line 31) | def __init__(self, in_channels, hidden_channels, out_channels, kernel_... method forward (line 53) | def forward(self, x, x_mask): method instance_norm (line 61) | def instance_norm(self, x, mask, return_mean_std=False): method calc_mean_std (line 69) | def calc_mean_std(self, x, mask=None): class MultiHeadAttention (line 79) | class MultiHeadAttention(BaseModule): method __init__ (line 80) | def __init__(self, channels, out_channels, n_heads, window_size=None, method forward (line 116) | def forward(self, x, c, attn_mask=None): method attention (line 126) | def attention(self, query, key, value, mask=None): method _matmul_with_relative_values (line 157) | def _matmul_with_relative_values(self, x, y): method _matmul_with_relative_keys (line 161) | def _matmul_with_relative_keys(self, x, y): method _get_relative_embeddings (line 165) | def _get_relative_embeddings(self, relative_embeddings, length): method _relative_position_to_absolute_position (line 179) | def _relative_position_to_absolute_position(self, x): method _absolute_position_to_relative_position (line 187) | def _absolute_position_to_relative_position(self, x): method _attention_bias_proximal (line 195) | def _attention_bias_proximal(self, length): class FFN (line 201) | class FFN(BaseModule): method __init__ (line 202) | def __init__(self, in_channels, out_channels, filter_channels, kernel_... method forward (line 217) | def forward(self, x, x_mask): class Encoder (line 225) | class Encoder(BaseModule): method __init__ (line 226) | def __init__(self, hidden_channels, filter_channels, n_heads, n_layers, method forward (line 250) | def forward(self, x, x_mask): class TextEncoder (line 264) | class TextEncoder(BaseModule): method __init__ (line 265) | def __init__(self, n_vecs, n_mels, n_embs, method forward (line 307) | def forward(self, x_lengths, x, pit, spk, training=False): method fine_tune (line 323) | def fine_tune(self): FILE: grad/model.py class GradTTS (line 13) | class GradTTS(BaseModule): method __init__ (line 14) | def __init__(self, n_mels, n_vecs, n_pits, n_spks, n_embs, method fine_tune (line 41) | def fine_tune(self): method forward (line 49) | def forward(self, lengths, vec, pit, spk, n_timesteps, temperature=1.0... method compute_loss (line 89) | def compute_loss(self, lengths, vec, pit, spk, mel, out_size, skip_dif... FILE: grad/reversal.py class GradientReversalFunction (line 11) | class GradientReversalFunction(Function): method forward (line 13) | def forward(ctx, x, lambda_): method backward (line 18) | def backward(ctx, grads): class GradientReversal (line 25) | class GradientReversal(torch.nn.Module): method __init__ (line 34) | def __init__(self, lambda_reversal=1): method forward (line 38) | def forward(self, x): class SpeakerClassifier (line 42) | class SpeakerClassifier(nn.Module): method __init__ (line 44) | def __init__(self, idim, odim): method forward (line 55) | def forward(self, x): FILE: grad/solver.py class NoiseScheduleVP (line 4) | class NoiseScheduleVP: method __init__ (line 6) | def __init__(self, beta_min=0.05, beta_max=20): method get_noise (line 11) | def get_noise(self, t, beta_init, beta_term, cumulative=False): method marginal_log_mean_coeff (line 18) | def marginal_log_mean_coeff(self, t): method marginal_std (line 22) | def marginal_std(self, t): method marginal_lambda (line 25) | def marginal_lambda(self, t): method inverse_lambda (line 30) | def inverse_lambda(self, lamb): method get_time_steps (line 38) | def get_time_steps(self, t_T, t_0, N): method reverse_diffusion (line 45) | def reverse_diffusion(self, estimator, spk, z, mask, mu, n_timesteps, ... class MaxLikelihood (line 75) | class MaxLikelihood: method __init__ (line 77) | def __init__(self, beta_min=0.05, beta_max=20): method get_noise (line 81) | def get_noise(self, t, beta_init, beta_term, cumulative=False): method get_gamma (line 88) | def get_gamma(self, s, t, beta_init, beta_term): method get_mu (line 93) | def get_mu(self, s, t): method get_nu (line 100) | def get_nu(self, s, t): method get_sigma (line 107) | def get_sigma(self, s, t): method get_kappa (line 114) | def get_kappa(self, t, h, noise): method get_omega (line 120) | def get_omega(self, t, h, noise): method reverse_diffusion (line 128) | def reverse_diffusion(self, estimator, spk, z, mask, mu, n_timesteps, ... class GradRaw (line 155) | class GradRaw: method __init__ (line 157) | def __init__(self, beta_min=0.05, beta_max=20): method get_noise (line 161) | def get_noise(self, t, beta_init, beta_term, cumulative=False): method reverse_diffusion (line 169) | def reverse_diffusion(self, estimator, spk, z, mask, mu, n_timesteps, ... FILE: grad/ssim.py function gaussian (line 10) | def gaussian(window_size, sigma): function create_window (line 15) | def create_window(window_size, channel): function _ssim (line 22) | def _ssim(img1, img2, window, window_size, channel, size_average=True): class SSIM (line 45) | class SSIM(torch.nn.Module): method __init__ (line 46) | def __init__(self, window_size=11, size_average=True): method forward (line 53) | def forward(self, fake, real, mask, bias=6.0): FILE: grad/utils.py function sequence_mask (line 6) | def sequence_mask(length, max_length=None): function fix_len_compatibility (line 13) | def fix_len_compatibility(length, num_downsamplings_in_unet=2): function convert_pad_shape (line 20) | def convert_pad_shape(pad_shape): function generate_path (line 26) | def generate_path(duration, mask): function duration_loss (line 42) | def duration_loss(logw, logw_, lengths): function f0_to_coarse (line 54) | def f0_to_coarse(f0): function rand_ids_segments (line 70) | def rand_ids_segments(lengths, segment_size=200): function slice_segments (line 77) | def slice_segments(x, ids_str, segment_size=200): function retrieve_name (line 86) | def retrieve_name(var): function debug_shapes (line 97) | def debug_shapes(var): FILE: grad_extend/data.py class TextMelSpeakerDataset (line 11) | class TextMelSpeakerDataset(torch.utils.data.Dataset): method __init__ (line 12) | def __init__(self, filelist_path): method _filter (line 18) | def _filter(self): method get_triplet (line 41) | def get_triplet(self, item): method __getitem__ (line 83) | def __getitem__(self, index): method __len__ (line 88) | def __len__(self): method sample_test_batch (line 91) | def sample_test_batch(self, size): class TextMelSpeakerBatchCollate (line 99) | class TextMelSpeakerBatchCollate(object): method __call__ (line 104) | def __call__(self, batch): FILE: grad_extend/train.py function train (line 19) | def train(hps, chkpt_path=None): FILE: grad_extend/utils.py function parse_filelist (line 9) | def parse_filelist(filelist_path, split_char="|"): function load_model (line 15) | def load_model(model, saved_state_dict): function latest_checkpoint_path (line 28) | def latest_checkpoint_path(dir_path, regex="grad_svc_*.pt"): function load_checkpoint (line 35) | def load_checkpoint(logdir, model, num=None): function save_figure_to_numpy (line 46) | def save_figure_to_numpy(fig): function plot_tensor (line 52) | def plot_tensor(tensor): function save_plot (line 64) | def save_plot(tensor, savepath): function print_error (line 76) | def print_error(info): FILE: gvc_export.py function load_model (line 9) | def load_model(checkpoint_path, model): function main (line 25) | def main(args): FILE: gvc_inference.py function load_gvc_model (line 18) | def load_gvc_model(checkpoint_path, model): function load_bigv_model (line 34) | def load_bigv_model(checkpoint_path, model): function gvc_main (line 51) | def gvc_main(device, model, _vec, _pit, spk, rature=1.015): function main (line 66) | def main(args): FILE: hubert/hubert_model.py class Hubert (line 10) | class Hubert(nn.Module): method __init__ (line 11) | def __init__(self, num_label_embeddings: int = 100, mask: bool = True): method mask (line 30) | def mask(self, x: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]: method encode (line 37) | def encode( method logits (line 48) | def logits(self, x: torch.Tensor) -> torch.Tensor: method forward (line 56) | def forward(self, x: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]: class HubertSoft (line 63) | class HubertSoft(Hubert): method __init__ (line 64) | def __init__(self): method units (line 68) | def units(self, wav: torch.Tensor) -> torch.Tensor: class FeatureExtractor (line 74) | class FeatureExtractor(nn.Module): method __init__ (line 75) | def __init__(self): method forward (line 86) | def forward(self, x: torch.Tensor) -> torch.Tensor: class FeatureProjection (line 97) | class FeatureProjection(nn.Module): method __init__ (line 98) | def __init__(self): method forward (line 104) | def forward(self, x: torch.Tensor) -> torch.Tensor: class PositionalConvEmbedding (line 111) | class PositionalConvEmbedding(nn.Module): method __init__ (line 112) | def __init__(self): method forward (line 123) | def forward(self, x: torch.Tensor) -> torch.Tensor: class TransformerEncoder (line 129) | class TransformerEncoder(nn.Module): method __init__ (line 130) | def __init__( method forward (line 139) | def forward( function _compute_mask (line 154) | def _compute_mask( function consume_prefix (line 209) | def consume_prefix(state_dict, prefix: str) -> None: function hubert_soft (line 217) | def hubert_soft( FILE: hubert/inference.py function load_audio (line 11) | def load_audio(file: str, sr: int = 16000): function load_model (line 16) | def load_model(path, device): function pred_vec (line 25) | def pred_vec(model, wavPath, vecPath, device): FILE: pitch/inference.py function compute_f0_mouth (line 9) | def compute_f0_mouth(path): function compute_f0_crepe (line 23) | def compute_f0_crepe(filename): function save_csv_pitch (line 55) | def save_csv_pitch(pitch, path): function load_csv_pitch (line 66) | def load_csv_pitch(path): FILE: prepare/preprocess_a.py function resample_wave (line 10) | def resample_wave(wav_in, wav_out, sample_rate): function process_file (line 17) | def process_file(file, wavPath, spks, outPath, sr): function process_files_with_thread_pool (line 23) | def process_files_with_thread_pool(wavPath, spks, outPath, sr, thread_nu... FILE: prepare/preprocess_f0.py function compute_f0 (line 11) | def compute_f0(path, save): function process_file (line 27) | def process_file(file, wavPath, spks, pitPath): function process_files_with_process_pool (line 33) | def process_files_with_process_pool(wavPath, spks, pitPath, process_num=... FILE: prepare/preprocess_hubert.py function load_audio (line 12) | def load_audio(file: str, sr: int = 16000): function load_model (line 17) | def load_model(path, device): function pred_vec (line 25) | def pred_vec(model, wavPath, vecPath, device): FILE: prepare/preprocess_speaker.py function get_spk_wavs (line 18) | def get_spk_wavs(dataset_path, output_path): function process_wav (line 32) | def process_wav(wav_file, dataset_path, output_path, args, speaker_encod... function extract_speaker_embeddings (line 48) | def extract_speaker_embeddings(wav_files, dataset_path, output_path, arg... FILE: prepare/preprocess_spec.py function compute_spec (line 11) | def compute_spec(hps, filename, specname): function process_file (line 18) | def process_file(file): function process_files_with_thread_pool (line 24) | def process_files_with_thread_pool(wavPath, spks, thread_num): FILE: prepare/preprocess_train.py function print_error (line 5) | def print_error(info): FILE: speaker/config.py class SpeakerEncoderConfig (line 9) | class SpeakerEncoderConfig(BaseTrainingConfig): method check_values (line 59) | def check_values(self): FILE: speaker/infer.py function read_json (line 14) | def read_json(json_path): function read_json_with_comments (line 26) | def read_json_with_comments(json_path): FILE: speaker/models/lstm.py class LSTMWithProjection (line 8) | class LSTMWithProjection(nn.Module): method __init__ (line 9) | def __init__(self, input_size, hidden_size, proj_size): method forward (line 17) | def forward(self, x): class LSTMWithoutProjection (line 23) | class LSTMWithoutProjection(nn.Module): method __init__ (line 24) | def __init__(self, input_dim, lstm_dim, proj_dim, num_lstm_layers): method forward (line 30) | def forward(self, x): class LSTMSpeakerEncoder (line 35) | class LSTMSpeakerEncoder(nn.Module): method __init__ (line 36) | def __init__(self, input_dim, proj_dim=256, lstm_dim=768, num_lstm_lay... method _init_layers (line 51) | def _init_layers(self): method forward (line 58) | def forward(self, x): method inference (line 68) | def inference(self, x): method compute_embedding (line 76) | def compute_embedding(self, x, num_frames=250, num_eval=10, return_mea... method batch_compute_embedding (line 103) | def batch_compute_embedding(self, x, seq_lens, num_frames=160, overlap... method load_checkpoint (line 124) | def load_checkpoint(self, checkpoint_path: str, eval: bool = False, us... FILE: speaker/models/resnet.py class SELayer (line 8) | class SELayer(nn.Module): method __init__ (line 9) | def __init__(self, channel, reduction=8): method forward (line 19) | def forward(self, x): class SEBasicBlock (line 26) | class SEBasicBlock(nn.Module): method __init__ (line 29) | def __init__(self, inplanes, planes, stride=1, downsample=None, reduct... method forward (line 40) | def forward(self, x): class ResNetSpeakerEncoder (line 59) | class ResNetSpeakerEncoder(nn.Module): method __init__ (line 65) | def __init__( method _init_layers (line 112) | def _init_layers(self): method create_layer (line 120) | def create_layer(self, block, planes, blocks, stride=1): method new_parameter (line 137) | def new_parameter(self, *size): method forward (line 142) | def forward(self, x, l2_norm=False): method compute_embedding (line 178) | def compute_embedding(self, x, num_frames=250, num_eval=10, return_mea... method load_checkpoint (line 205) | def load_checkpoint(self, config: dict, checkpoint_path: str, eval: bo... FILE: speaker/utils/audio.py class StandardScaler (line 12) | class StandardScaler: method __init__ (line 15) | def __init__(self, mean: np.ndarray = None, scale: np.ndarray = None) ... method set_stats (line 19) | def set_stats(self, mean, scale): method reset_stats (line 23) | def reset_stats(self): method transform (line 27) | def transform(self, X): method inverse_transform (line 33) | def inverse_transform(self, X): class TorchSTFT (line 39) | class TorchSTFT(nn.Module): # pylint: disable=abstract-method method __init__ (line 45) | def __init__( method __call__ (line 77) | def __call__(self, x): method _build_mel_basis (line 116) | def _build_mel_basis(self): method _amp_to_db (line 123) | def _amp_to_db(x, spec_gain=1.0): method _db_to_amp (line 127) | def _db_to_amp(x, spec_gain=1.0): class AudioProcessor (line 132) | class AudioProcessor(object): method __init__ (line 229) | def __init__( method _build_mel_basis (line 325) | def _build_mel_basis( method _stft_parameters (line 339) | def _stft_parameters( method normalize (line 354) | def normalize(self, S: np.ndarray) -> np.ndarray: method denormalize (line 395) | def denormalize(self, S: np.ndarray) -> np.ndarray: method load_stats (line 434) | def load_stats(self, stats_path: str) -> Tuple[np.array, np.array, np.... method setup_scaler (line 462) | def setup_scaler( method _amp_to_db (line 480) | def _amp_to_db(self, x: np.ndarray) -> np.ndarray: method _db_to_amp (line 492) | def _db_to_amp(self, x: np.ndarray) -> np.ndarray: method apply_preemphasis (line 504) | def apply_preemphasis(self, x: np.ndarray) -> np.ndarray: method apply_inv_preemphasis (line 520) | def apply_inv_preemphasis(self, x: np.ndarray) -> np.ndarray: method _linear_to_mel (line 527) | def _linear_to_mel(self, spectrogram: np.ndarray) -> np.ndarray: method _mel_to_linear (line 538) | def _mel_to_linear(self, mel_spec: np.ndarray) -> np.ndarray: method spectrogram (line 542) | def spectrogram(self, y: np.ndarray) -> np.ndarray: method melspectrogram (line 561) | def melspectrogram(self, y: np.ndarray) -> np.ndarray: method inv_spectrogram (line 573) | def inv_spectrogram(self, spectrogram: np.ndarray) -> np.ndarray: method inv_melspectrogram (line 582) | def inv_melspectrogram(self, mel_spectrogram: np.ndarray) -> np.ndarray: method out_linear_to_mel (line 591) | def out_linear_to_mel(self, linear_spec: np.ndarray) -> np.ndarray: method _stft (line 608) | def _stft(self, y: np.ndarray) -> np.ndarray: method _istft (line 627) | def _istft(self, y: np.ndarray) -> np.ndarray: method _griffin_lim (line 631) | def _griffin_lim(self, S): method compute_stft_paddings (line 643) | def compute_stft_paddings(self, x, pad_sides=1): method compute_f0 (line 652) | def compute_f0(self, x: np.ndarray) -> np.ndarray: method find_endpoint (line 695) | def find_endpoint(self, wav: np.ndarray, threshold_db=-40, min_silence... method trim_silence (line 714) | def trim_silence(self, wav): method sound_norm (line 723) | def sound_norm(x: np.ndarray) -> np.ndarray: method load_wav (line 735) | def load_wav(self, filename: str, sr: int = None) -> np.ndarray: method save_wav (line 761) | def save_wav(self, wav: np.ndarray, path: str, sr: int = None) -> None: method mulaw_encode (line 773) | def mulaw_encode(wav: np.ndarray, qc: int) -> np.ndarray: method mulaw_decode (line 784) | def mulaw_decode(wav, qc): method encode_16bits (line 791) | def encode_16bits(x): method quantize (line 795) | def quantize(x: np.ndarray, bits: int) -> np.ndarray: method dequantize (line 808) | def dequantize(x, bits): function _log (line 813) | def _log(x, base): function _exp (line 819) | def _exp(x, base): FILE: speaker/utils/coqpit.py class _NoDefault (line 17) | class _NoDefault(Generic[T]): function is_primitive_type (line 25) | def is_primitive_type(arg_type: Any) -> bool: function is_list (line 40) | def is_list(arg_type: Any) -> bool: function is_dict (line 55) | def is_dict(arg_type: Any) -> bool: function is_union (line 70) | def is_union(arg_type: Any) -> bool: function safe_issubclass (line 85) | def safe_issubclass(cls, classinfo) -> bool: function _coqpit_json_default (line 103) | def _coqpit_json_default(obj: Any) -> Any: function _default_value (line 109) | def _default_value(x: Field): function _is_optional_field (line 125) | def _is_optional_field(field) -> bool: function my_get_type_hints (line 138) | def my_get_type_hints( function _serialize (line 155) | def _serialize(x): function _deserialize_dict (line 177) | def _deserialize_dict(x: Dict) -> Dict: function _deserialize_list (line 195) | def _deserialize_list(x: List, field_type: Type) -> List: function _deserialize_union (line 225) | def _deserialize_union(x: Any, field_type: Type) -> Any: function _deserialize_primitive_types (line 245) | def _deserialize_primitive_types(x: Union[int, float, str, bool], field_... function _deserialize (line 269) | def _deserialize(x: Any, field_type: Any) -> Any: function rsetattr (line 295) | def rsetattr(obj, attr, val): function rgetattr (line 306) | def rgetattr(obj, attr, *args): function rsetitem (line 318) | def rsetitem(obj, attr, val): function rgetitem (line 324) | def rgetitem(obj, attr, *args): class Serializable (line 332) | class Serializable: method __post_init__ (line 335) | def __post_init__(self): method _validate_contracts (line 341) | def _validate_contracts(self): method validate (line 358) | def validate(self): method to_dict (line 366) | def to_dict(self) -> dict: method serialize (line 374) | def serialize(self) -> dict: method deserialize (line 389) | def deserialize(self, data: dict) -> "Serializable": method deserialize_immutable (line 419) | def deserialize_immutable(cls, data: dict) -> "Serializable": function _get_help (line 457) | def _get_help(field): function _init_argparse (line 465) | def _init_argparse( class Coqpit (line 580) | class Coqpit(Serializable, MutableMapping): method _is_initialized (line 589) | def _is_initialized(self): method __post_init__ (line 594) | def __post_init__(self): method __iter__ (line 603) | def __iter__(self): method __len__ (line 606) | def __len__(self): method __setitem__ (line 609) | def __setitem__(self, arg: str, value: Any): method __getitem__ (line 612) | def __getitem__(self, arg: str): method __delitem__ (line 616) | def __delitem__(self, arg: str): method _keytransform (line 619) | def _keytransform(self, key): # pylint: disable=no-self-use method __getattribute__ (line 624) | def __getattribute__(self, arg: str): # pylint: disable=no-self-use method __contains__ (line 631) | def __contains__(self, arg: str): method get (line 634) | def get(self, key: str, default: Any = None): method items (line 639) | def items(self): method merge (line 642) | def merge(self, coqpits: Union["Coqpit", List["Coqpit"]]): method check_values (line 663) | def check_values(self): method has (line 666) | def has(self, arg: str) -> bool: method copy (line 669) | def copy(self): method update (line 672) | def update(self, new: dict, allow_new=False) -> None: method pprint (line 688) | def pprint(self) -> None: method to_dict (line 692) | def to_dict(self) -> dict: method from_dict (line 696) | def from_dict(self, data: dict) -> None: method new_from_dict (line 700) | def new_from_dict(cls: Serializable, data: dict) -> "Coqpit": method to_json (line 703) | def to_json(self) -> str: method save_json (line 707) | def save_json(self, file_name: str) -> None: method load_json (line 716) | def load_json(self, file_name: str) -> None: method init_from_argparse (line 734) | def init_from_argparse( method parse_args (line 782) | def parse_args( method parse_known_args (line 814) | def parse_known_args( method init_argparse (line 843) | def init_argparse( function check_argument (line 889) | def check_argument( FILE: speaker/utils/io.py class RenamingUnpickler (line 13) | class RenamingUnpickler(pickle_tts.Unpickler): method find_class (line 16) | def find_class(self, module, name): class AttrDict (line 20) | class AttrDict(dict): method __init__ (line 24) | def __init__(self, *args, **kwargs): function copy_model_files (line 29) | def copy_model_files(config: Coqpit, out_path, new_fields): function load_fsspec (line 56) | def load_fsspec( function load_checkpoint (line 75) | def load_checkpoint(model, checkpoint_path, use_cuda=False, eval=False):... function save_fsspec (line 89) | def save_fsspec(state: Any, path: str, **kwargs): function save_model (line 101) | def save_model(config, model, optimizer, scaler, current_step, epoch, ou... function save_checkpoint (line 132) | def save_checkpoint( function save_best_model (line 157) | def save_best_model( FILE: speaker/utils/shared_configs.py class BaseAudioConfig (line 8) | class BaseAudioConfig(Coqpit): method check_values (line 137) | def check_values( class BaseDatasetConfig (line 173) | class BaseDatasetConfig(Coqpit): method check_values (line 205) | def check_values( class BaseTrainingConfig (line 218) | class BaseTrainingConfig(Coqpit): FILE: spec/inference.py function load_wav_to_torch (line 13) | def load_wav_to_torch(full_path, sample_rate): function dynamic_range_compression (line 19) | def dynamic_range_compression(x, C=1, clip_val=1e-5): function dynamic_range_decompression (line 23) | def dynamic_range_decompression(x, C=1): function dynamic_range_compression_torch (line 27) | def dynamic_range_compression_torch(x, C=1, clip_val=1e-5): function dynamic_range_decompression_torch (line 31) | def dynamic_range_decompression_torch(x, C=1): function spectral_normalize_torch (line 35) | def spectral_normalize_torch(magnitudes): function spectral_de_normalize_torch (line 40) | def spectral_de_normalize_torch(magnitudes): function mel_spectrogram (line 49) | def mel_spectrogram(y, n_fft, num_mels, sampling_rate, hop_size, win_siz... function mel_spectrogram_file (line 76) | def mel_spectrogram_file(path, hps): function print_mel (line 88) | def print_mel(mel, path="mel.png"):