SYMBOL INDEX (428 symbols across 24 files) FILE: tortoise/api.py function get_model_path (line 42) | def get_model_path(model_name, models_dir=MODELS_DIR): function pad_or_truncate (line 52) | def pad_or_truncate(t, length): function load_discrete_vocoder_diffuser (line 64) | def load_discrete_vocoder_diffuser(trained_diffusion_steps=4000, desired... function format_conditioning (line 73) | def format_conditioning(clip, cond_length=132300, device="cuda" if not t... function fix_autoregressive_output (line 87) | def fix_autoregressive_output(codes, stop_token, complain=True): function do_spectrogram_diffusion (line 117) | def do_spectrogram_diffusion(diffusion_model, diffuser, latents, conditi... function classify_audio_clip (line 133) | def classify_audio_clip(clip): function pick_best_batch_size_for_gpu (line 148) | def pick_best_batch_size_for_gpu(): class TextToSpeech (line 174) | class TextToSpeech: method __init__ (line 179) | def __init__(self, autoregressive_batch_size=None, models_dir=MODELS_DIR, method temporary_cuda (line 246) | def temporary_cuda(self, model): method load_cvvp (line 252) | def load_cvvp(self): method get_conditioning_latents (line 258) | def get_conditioning_latents(self, voice_samples, return_mels=False): method get_random_conditioning_latents (line 301) | def get_random_conditioning_latents(self): method tts_with_preset (line 311) | def tts_with_preset(self, text, preset='fast', **kwargs): method tts (line 334) | def tts(self, text, voice_samples=None, conditioning_latents=None, k=1... method deterministic_state (line 598) | def deterministic_state(self, seed=None): FILE: tortoise/api_fast.py function get_model_path (line 41) | def get_model_path(model_name, models_dir=MODELS_DIR): function pad_or_truncate (line 51) | def pad_or_truncate(t, length): function load_discrete_vocoder_diffuser (line 63) | def load_discrete_vocoder_diffuser(trained_diffusion_steps=4000, desired... function format_conditioning (line 72) | def format_conditioning(clip, cond_length=132300, device="cuda" if not t... function fix_autoregressive_output (line 86) | def fix_autoregressive_output(codes, stop_token, complain=True): function do_spectrogram_diffusion (line 116) | def do_spectrogram_diffusion(diffusion_model, diffuser, latents, conditi... function classify_audio_clip (line 132) | def classify_audio_clip(clip): function pick_best_batch_size_for_gpu (line 147) | def pick_best_batch_size_for_gpu(): class TextToSpeech (line 173) | class TextToSpeech: method __init__ (line 178) | def __init__(self, autoregressive_batch_size=None, models_dir=MODELS_DIR, method get_conditioning_latents (line 230) | def get_conditioning_latents(self, voice_samples, return_mels=False): method get_random_conditioning_latents (line 253) | def get_random_conditioning_latents(self): method tts_with_preset (line 261) | def tts_with_preset(self, text, preset='fast', **kwargs): method handle_chunks (line 285) | def handle_chunks(self, wav_gen, wav_gen_prev, wav_overlap, overlap_len): method tts_stream (line 311) | def tts_stream(self, text, voice_samples=None, conditioning_latents=No... method tts (line 421) | def tts(self, text, voice_samples=None, k=1, verbose=True, use_determi... method deterministic_state (line 504) | def deterministic_state(self, seed=None): FILE: tortoise/models/arch_util.py function zero_module (line 12) | def zero_module(module): class GroupNorm32 (line 21) | class GroupNorm32(nn.GroupNorm): method forward (line 22) | def forward(self, x): function normalization (line 26) | def normalization(channels): class QKVAttentionLegacy (line 44) | class QKVAttentionLegacy(nn.Module): method __init__ (line 49) | def __init__(self, n_heads): method forward (line 53) | def forward(self, qkv, mask=None, rel_pos=None): class AttentionBlock (line 80) | class AttentionBlock(nn.Module): method __init__ (line 88) | def __init__( method forward (line 117) | def forward(self, x, mask=None): class Upsample (line 126) | class Upsample(nn.Module): method __init__ (line 134) | def __init__(self, channels, use_conv, out_channels=None, factor=4): method forward (line 145) | def forward(self, x): class Downsample (line 153) | class Downsample(nn.Module): method __init__ (line 161) | def __init__(self, channels, use_conv, out_channels=None, factor=4, ks... method forward (line 176) | def forward(self, x): class ResBlock (line 181) | class ResBlock(nn.Module): method __init__ (line 182) | def __init__( method forward (line 236) | def forward(self, x): class AudioMiniEncoder (line 249) | class AudioMiniEncoder(nn.Module): method __init__ (line 250) | def __init__(self, method forward (line 284) | def forward(self, x): class TorchMelSpectrogram (line 295) | class TorchMelSpectrogram(nn.Module): method __init__ (line 296) | def __init__(self, filter_length=1024, hop_length=256, win_length=1024... method forward (line 318) | def forward(self, inp): class CheckpointedLayer (line 334) | class CheckpointedLayer(nn.Module): method __init__ (line 339) | def __init__(self, wrap): method forward (line 343) | def forward(self, x, *args, **kwargs): class CheckpointedXTransformerEncoder (line 350) | class CheckpointedXTransformerEncoder(nn.Module): method __init__ (line 355) | def __init__(self, needs_permute=True, exit_permute=True, checkpoint=T... method forward (line 367) | def forward(self, x, **kwargs): FILE: tortoise/models/autoregressive.py function null_position_embeddings (line 13) | def null_position_embeddings(range, dim): class ResBlock (line 17) | class ResBlock(nn.Module): method __init__ (line 21) | def __init__(self, chan): method forward (line 31) | def forward(self, x): class GPT2InferenceModel (line 35) | class GPT2InferenceModel(GPT2PreTrainedModel): method __init__ (line 36) | def __init__(self, config, gpt, text_pos_emb, embeddings, norm, linear... method parallelize (line 49) | def parallelize(self, device_map=None): method deparallelize (line 60) | def deparallelize(self): method get_output_embeddings (line 69) | def get_output_embeddings(self): method set_output_embeddings (line 72) | def set_output_embeddings(self, new_embeddings): method store_mel_emb (line 75) | def store_mel_emb(self, mel_emb): method prepare_inputs_for_generation (line 78) | def prepare_inputs_for_generation(self, input_ids, past_key_values=Non... method forward (line 108) | def forward( method _reorder_cache (line 189) | def _reorder_cache(past, beam_idx): class ConditioningEncoder (line 204) | class ConditioningEncoder(nn.Module): method __init__ (line 205) | def __init__(self, method forward (line 222) | def forward(self, x): class LearnedPositionEmbeddings (line 231) | class LearnedPositionEmbeddings(nn.Module): method __init__ (line 232) | def __init__(self, seq_len, model_dim, init=.02): method forward (line 238) | def forward(self, x): method get_fixed_embedding (line 242) | def get_fixed_embedding(self, ind, dev): function build_hf_gpt_transformer (line 246) | def build_hf_gpt_transformer(layers, model_dim, heads, max_mel_seq_len, ... class MelEncoder (line 269) | class MelEncoder(nn.Module): method __init__ (line 270) | def __init__(self, channels, mel_channels=80, resblocks_per_reduction=2): method forward (line 287) | def forward(self, x): class UnifiedVoice (line 293) | class UnifiedVoice(nn.Module): method __init__ (line 294) | def __init__(self, layers=8, model_dim=512, heads=8, max_text_tokens=1... method post_init_gpt2_config (line 358) | def post_init_gpt2_config(self, use_deepspeed=False, kv_cache=False, h... method build_aligned_inputs_and_targets (line 398) | def build_aligned_inputs_and_targets(self, input, start_token, stop_to... method set_mel_padding (line 403) | def set_mel_padding(self, mel_input_tokens, wav_lengths): method get_logits (line 417) | def get_logits(self, speech_conditioning_inputs, first_inputs, first_h... method get_conditioning (line 444) | def get_conditioning(self, speech_conditioning_input): method forward (line 454) | def forward(self, speech_conditioning_latent, text_inputs, text_length... method compute_embeddings (line 513) | def compute_embeddings( method inference_speech (line 535) | def inference_speech(self, speech_conditioning_latent, text_inputs, in... method get_generator (line 565) | def get_generator(self, fake_inputs, **hf_generate_kwargs): FILE: tortoise/models/classifier.py class ResBlock (line 7) | class ResBlock(nn.Module): method __init__ (line 8) | def __init__( method forward (line 65) | def forward(self, x): class AudioMiniEncoder (line 78) | class AudioMiniEncoder(nn.Module): method __init__ (line 79) | def __init__(self, method forward (line 114) | def forward(self, x): class AudioMiniEncoderWithClassifierHead (line 123) | class AudioMiniEncoderWithClassifierHead(nn.Module): method __init__ (line 124) | def __init__(self, classes, distribute_zero_label=True, **kwargs): method forward (line 131) | def forward(self, x, labels=None): FILE: tortoise/models/clvp.py function exists (line 11) | def exists(val): function masked_mean (line 15) | def masked_mean(t, mask, dim = 1): class CLVP (line 19) | class CLVP(nn.Module): method __init__ (line 27) | def __init__( method forward (line 99) | def forward( FILE: tortoise/models/cvvp.py function exists (line 10) | def exists(val): function masked_mean (line 14) | def masked_mean(t, mask): class CollapsingTransformer (line 19) | class CollapsingTransformer(nn.Module): method __init__ (line 20) | def __init__(self, model_dim, output_dims, heads, dropout, depth, mask... method forward (line 43) | def forward(self, x, **transformer_kwargs): class ConvFormatEmbedding (line 54) | class ConvFormatEmbedding(nn.Module): method __init__ (line 55) | def __init__(self, *args, **kwargs): method forward (line 59) | def forward(self, x): class CVVP (line 64) | class CVVP(nn.Module): method __init__ (line 65) | def __init__( method get_grad_norm_parameter_groups (line 99) | def get_grad_norm_parameter_groups(self): method forward (line 105) | def forward( FILE: tortoise/models/diffusion_decoder.py function is_latent (line 13) | def is_latent(t): function is_sequence (line 17) | def is_sequence(t): function timestep_embedding (line 21) | def timestep_embedding(timesteps, dim, max_period=10000): class TimestepBlock (line 42) | class TimestepBlock(nn.Module): method forward (line 44) | def forward(self, x, emb): class TimestepEmbedSequential (line 50) | class TimestepEmbedSequential(nn.Sequential, TimestepBlock): method forward (line 51) | def forward(self, x, emb): class ResBlock (line 60) | class ResBlock(TimestepBlock): method __init__ (line 61) | def __init__( method forward (line 107) | def forward(self, x, emb): class DiffusionLayer (line 123) | class DiffusionLayer(TimestepBlock): method __init__ (line 124) | def __init__(self, model_channels, dropout, num_heads): method forward (line 129) | def forward(self, x, time_emb): class DiffusionTts (line 134) | class DiffusionTts(nn.Module): method __init__ (line 135) | def __init__( method get_grad_norm_parameter_groups (line 212) | def get_grad_norm_parameter_groups(self): method get_conditioning (line 222) | def get_conditioning(self, conditioning_input): method timestep_independent (line 232) | def timestep_independent(self, aligned_conditioning, conditioning_late... method forward (line 262) | def forward(self, x, timesteps, aligned_conditioning=None, conditionin... FILE: tortoise/models/hifigan_decoder.py function get_padding (line 11) | def get_padding(k, d): class ResBlock1 (line 15) | class ResBlock1(torch.nn.Module): method __init__ (line 30) | def __init__(self, channels, kernel_size=3, dilation=(1, 3, 5)): method forward (line 81) | def forward(self, x): method remove_weight_norm (line 98) | def remove_weight_norm(self): class ResBlock2 (line 105) | class ResBlock2(torch.nn.Module): method __init__ (line 120) | def __init__(self, channels, kernel_size=3, dilation=(1, 3)): method forward (line 147) | def forward(self, x): method remove_weight_norm (line 154) | def remove_weight_norm(self): class HifiganGenerator (line 159) | class HifiganGenerator(torch.nn.Module): method __init__ (line 160) | def __init__( method forward (line 237) | def forward(self, x, g=None): method inference (line 269) | def inference(self, c, g=None): method remove_weight_norm (line 296) | def remove_weight_norm(self): FILE: tortoise/models/random_latent_generator.py function fused_leaky_relu (line 8) | def fused_leaky_relu(input, bias=None, negative_slope=0.2, scale=2 ** 0.5): class EqualLinear (line 21) | class EqualLinear(nn.Module): method __init__ (line 22) | def __init__( method forward (line 34) | def forward(self, input): class RandomLatentConverter (line 40) | class RandomLatentConverter(nn.Module): method __init__ (line 41) | def __init__(self, channels): method forward (line 47) | def forward(self, ref): FILE: tortoise/models/stream_generator.py function setup_seed (line 26) | def setup_seed(seed): class StreamGenerationConfig (line 37) | class StreamGenerationConfig(GenerationConfig): method __init__ (line 38) | def __init__(self, **kwargs): class NewGenerationMixin (line 43) | class NewGenerationMixin(GenerationMixin): method generate (line 45) | def generate( method sample_stream (line 722) | def sample_stream( function init_stream_support (line 1003) | def init_stream_support(): FILE: tortoise/models/transformer.py function exists (line 13) | def exists(val): function default (line 17) | def default(val, d): function cast_tuple (line 21) | def cast_tuple(val, depth = 1): function max_neg_value (line 27) | def max_neg_value(t): function stable_softmax (line 31) | def stable_softmax(t, dim = -1, alpha = 32 ** 2): function route_args (line 37) | def route_args(router, args, depth): class SequentialSequence (line 50) | class SequentialSequence(nn.Module): method __init__ (line 51) | def __init__(self, layers, args_route = {}, layer_dropout = 0.): method forward (line 58) | def forward(self, x, **kwargs): class DivideMax (line 68) | class DivideMax(nn.Module): method __init__ (line 69) | def __init__(self, dim): method forward (line 73) | def forward(self, x): class LayerScale (line 79) | class LayerScale(nn.Module): method __init__ (line 80) | def __init__(self, dim, depth, fn): method forward (line 92) | def forward(self, x, **kwargs): class PreNorm (line 98) | class PreNorm(nn.Module): method __init__ (line 99) | def __init__(self, dim, fn, sandwich = False): method forward (line 105) | def forward(self, x, **kwargs): class GEGLU (line 113) | class GEGLU(nn.Module): method forward (line 114) | def forward(self, x): class FeedForward (line 119) | class FeedForward(nn.Module): method __init__ (line 120) | def __init__(self, dim, dropout = 0., mult = 4.): method forward (line 129) | def forward(self, x): class Attention (line 135) | class Attention(nn.Module): method __init__ (line 136) | def __init__(self, dim, seq_len, causal = True, heads = 8, dim_head = ... method forward (line 151) | def forward(self, x, mask = None): class Transformer (line 182) | class Transformer(nn.Module): method __init__ (line 183) | def __init__( method forward (line 218) | def forward(self, x, **kwargs): FILE: tortoise/models/vocoder.py class KernelPredictor (line 7) | class KernelPredictor(torch.nn.Module): method __init__ (line 10) | def __init__( method forward (line 66) | def forward(self, c): method remove_weight_norm (line 95) | def remove_weight_norm(self): class LVCBlock (line 104) | class LVCBlock(torch.nn.Module): method __init__ (line 107) | def __init__( method forward (line 155) | def forward(self, x, c): method location_variable_convolution (line 182) | def location_variable_convolution(self, x, kernel, bias, dilation=1, h... method remove_weight_norm (line 218) | def remove_weight_norm(self): class UnivNetGenerator (line 225) | class UnivNetGenerator(nn.Module): method __init__ (line 232) | def __init__(self, noise_dim=64, channel_size=32, dilations=[1,3,9,27]... method forward (line 267) | def forward(self, c, z): method eval (line 284) | def eval(self, inference=False): method remove_weight_norm (line 290) | def remove_weight_norm(self): method inference (line 300) | def inference(self, c, z=None): FILE: tortoise/models/xtransformers.py function exists (line 27) | def exists(val): function default (line 31) | def default(val, d): function cast_tuple (line 37) | def cast_tuple(val, depth): class always (line 41) | class always(): method __init__ (line 42) | def __init__(self, val): method __call__ (line 45) | def __call__(self, *args, **kwargs): class not_equals (line 49) | class not_equals(): method __init__ (line 50) | def __init__(self, val): method __call__ (line 53) | def __call__(self, x, *args, **kwargs): class equals (line 57) | class equals(): method __init__ (line 58) | def __init__(self, val): method __call__ (line 61) | def __call__(self, x, *args, **kwargs): function max_neg_value (line 65) | def max_neg_value(tensor): function l2norm (line 69) | def l2norm(t): function init_zero_ (line 75) | def init_zero_(layer): function pick_and_pop (line 83) | def pick_and_pop(keys, d): function group_dict_by_key (line 88) | def group_dict_by_key(cond, d): function string_begins_with (line 97) | def string_begins_with(prefix, str): function group_by_key_prefix (line 101) | def group_by_key_prefix(prefix, d): function groupby_prefix_and_trim (line 105) | def groupby_prefix_and_trim(prefix, d): class ReluSquared (line 113) | class ReluSquared(nn.Module): method forward (line 114) | def forward(self, x): class AbsolutePositionalEmbedding (line 120) | class AbsolutePositionalEmbedding(nn.Module): method __init__ (line 121) | def __init__(self, dim, max_seq_len): method forward (line 126) | def forward(self, x): class FixedPositionalEmbedding (line 133) | class FixedPositionalEmbedding(nn.Module): method __init__ (line 134) | def __init__(self, dim): method forward (line 139) | def forward(self, x, seq_dim=1, offset=0): class RelativePositionBias (line 146) | class RelativePositionBias(nn.Module): method __init__ (line 147) | def __init__(self, scale, causal=False, num_buckets=32, max_distance=1... method _relative_position_bucket (line 156) | def _relative_position_bucket(relative_position, causal=True, num_buck... method forward (line 177) | def forward(self, qk_dots): class AlibiPositionalBias (line 189) | class AlibiPositionalBias(nn.Module): method __init__ (line 190) | def __init__(self, heads, **kwargs): method _get_slopes (line 199) | def _get_slopes(heads): method forward (line 212) | def forward(self, qk_dots): class LearnedAlibiPositionalBias (line 229) | class LearnedAlibiPositionalBias(AlibiPositionalBias): method __init__ (line 230) | def __init__(self, heads, bidirectional=False): method forward (line 239) | def forward(self, qk_dots): class RotaryEmbedding (line 264) | class RotaryEmbedding(nn.Module): method __init__ (line 265) | def __init__(self, dim): method forward (line 270) | def forward(self, max_seq_len, device): function rotate_half (line 277) | def rotate_half(x): function apply_rotary_pos_emb (line 283) | def apply_rotary_pos_emb(t, freqs): class Scale (line 291) | class Scale(nn.Module): method __init__ (line 292) | def __init__(self, value, fn): method forward (line 297) | def forward(self, x, **kwargs): class Rezero (line 307) | class Rezero(nn.Module): method __init__ (line 308) | def __init__(self, fn): method forward (line 313) | def forward(self, x, **kwargs): class ScaleNorm (line 323) | class ScaleNorm(nn.Module): method __init__ (line 324) | def __init__(self, dim, eps=1e-5): method forward (line 330) | def forward(self, x): class RMSNorm (line 335) | class RMSNorm(nn.Module): method __init__ (line 336) | def __init__(self, dim, eps=1e-8): method forward (line 342) | def forward(self, x): class RMSScaleShiftNorm (line 347) | class RMSScaleShiftNorm(nn.Module): method __init__ (line 348) | def __init__(self, dim, eps=1e-8): method forward (line 355) | def forward(self, x, norm_scale_shift_inp): class Residual (line 367) | class Residual(nn.Module): method __init__ (line 368) | def __init__(self, dim, scale_residual=False): method forward (line 372) | def forward(self, x, residual): class GRUGating (line 379) | class GRUGating(nn.Module): method __init__ (line 380) | def __init__(self, dim, scale_residual=False): method forward (line 385) | def forward(self, x, residual): function shift (line 399) | def shift(t, amount, mask=None): class ShiftTokens (line 409) | class ShiftTokens(nn.Module): method __init__ (line 410) | def __init__(self, shifts, fn): method forward (line 415) | def forward(self, x, **kwargs): class GLU (line 429) | class GLU(nn.Module): method __init__ (line 430) | def __init__(self, dim_in, dim_out, activation): method forward (line 435) | def forward(self, x): class FeedForward (line 440) | class FeedForward(nn.Module): method __init__ (line 441) | def __init__( method forward (line 473) | def forward(self, x): class Attention (line 479) | class Attention(nn.Module): method __init__ (line 480) | def __init__( method forward (line 576) | def forward( class AttentionLayers (line 731) | class AttentionLayers(nn.Module): method __init__ (line 732) | def __init__( method forward (line 906) | def forward( class Encoder (line 1016) | class Encoder(AttentionLayers): method __init__ (line 1017) | def __init__(self, **kwargs): class Decoder (line 1022) | class Decoder(AttentionLayers): method __init__ (line 1023) | def __init__(self, **kwargs): class CrossAttender (line 1028) | class CrossAttender(AttentionLayers): method __init__ (line 1029) | def __init__(self, **kwargs): class ViTransformerWrapper (line 1033) | class ViTransformerWrapper(nn.Module): method __init__ (line 1034) | def __init__( method forward (line 1062) | def forward( class TransformerWrapper (line 1087) | class TransformerWrapper(nn.Module): method __init__ (line 1088) | def __init__( method init_ (line 1131) | def init_(self): method forward (line 1134) | def forward( class ContinuousTransformerWrapper (line 1187) | class ContinuousTransformerWrapper(nn.Module): method __init__ (line 1188) | def __init__( method forward (line 1217) | def forward( FILE: tortoise/socket_client.py function play_audio_stream (line 5) | def play_audio_stream(client_socket): function send_text_to_server (line 31) | def send_text_to_server(character_name, text, server_ip='localhost', ser... FILE: tortoise/socket_server.py function generate_audio_stream (line 11) | def generate_audio_stream(text, tts, voice_samples): function split_text (line 25) | def split_text(text, max_length=200): function handle_client (line 46) | def handle_client(client_socket, tts): function start_server (line 69) | def start_server(): FILE: tortoise/tts_stream.py function play_audio (line 14) | def play_audio(audio_queue): FILE: tortoise/utils/audio.py function load_wav_to_torch (line 16) | def load_wav_to_torch(full_path): function load_audio (line 29) | def load_audio(audiopath, sampling_rate): function denormalize_tacotron_mel (line 63) | def denormalize_tacotron_mel(norm_mel): function normalize_tacotron_mel (line 67) | def normalize_tacotron_mel(mel): function dynamic_range_compression (line 71) | def dynamic_range_compression(x, C=1, clip_val=1e-5): function dynamic_range_decompression (line 80) | def dynamic_range_decompression(x, C=1): function get_voices (line 89) | def get_voices(extra_voice_dirs=[]): function save_pth (line 100) | def save_pth(conds, save_path): function load_voice (line 104) | def load_voice(voice, extra_voice_dirs=[]): function load_voices (line 127) | def load_voices(voices, extra_voice_dirs=[]): class TacotronSTFT (line 151) | class TacotronSTFT(torch.nn.Module): method __init__ (line 152) | def __init__(self, filter_length=1024, hop_length=256, win_length=1024, method spectral_normalize (line 165) | def spectral_normalize(self, magnitudes): method spectral_de_normalize (line 169) | def spectral_de_normalize(self, magnitudes): method mel_spectrogram (line 173) | def mel_spectrogram(self, y): function wav_to_univnet_mel (line 194) | def wav_to_univnet_mel(wav, do_normalization=False, FILE: tortoise/utils/diffusion.py function normal_kl (line 19) | def normal_kl(mean1, logvar1, mean2, logvar2): function approx_standard_normal_cdf (line 49) | def approx_standard_normal_cdf(x): function discretized_gaussian_log_likelihood (line 57) | def discretized_gaussian_log_likelihood(x, *, means, log_scales): function mean_flat (line 87) | def mean_flat(tensor): function get_named_beta_schedule (line 94) | def get_named_beta_schedule(schedule_name, num_diffusion_timesteps): function betas_for_alpha_bar (line 121) | def betas_for_alpha_bar(num_diffusion_timesteps, alpha_bar, max_beta=0.9... class ModelMeanType (line 141) | class ModelMeanType(enum.Enum): class ModelVarType (line 151) | class ModelVarType(enum.Enum): class LossType (line 165) | class LossType(enum.Enum): method is_vb (line 171) | def is_vb(self): class GaussianDiffusion (line 175) | class GaussianDiffusion: method __init__ (line 192) | def __init__( method q_mean_variance (line 251) | def q_mean_variance(self, x_start, t): method q_sample (line 268) | def q_sample(self, x_start, t, noise=None): method q_posterior_mean_variance (line 288) | def q_posterior_mean_variance(self, x_start, x_t, t): method p_mean_variance (line 312) | def p_mean_variance( method _predict_xstart_from_eps (line 420) | def _predict_xstart_from_eps(self, x_t, t, eps): method _predict_xstart_from_xprev (line 427) | def _predict_xstart_from_xprev(self, x_t, t, xprev): method _predict_eps_from_xstart (line 437) | def _predict_eps_from_xstart(self, x_t, t, pred_xstart): method _scale_timesteps (line 443) | def _scale_timesteps(self, t): method condition_mean (line 448) | def condition_mean(self, cond_fn, p_mean_var, x, t, model_kwargs=None): method condition_score (line 463) | def condition_score(self, cond_fn, p_mean_var, x, t, model_kwargs=None): method p_sample (line 487) | def p_sample( method p_sample_loop (line 533) | def p_sample_loop( method p_sample_loop_progressive (line 579) | def p_sample_loop_progressive( method ddim_sample (line 623) | def ddim_sample( method ddim_reverse_sample (line 673) | def ddim_reverse_sample( method ddim_sample_loop (line 711) | def ddim_sample_loop( method ddim_sample_loop_progressive (line 745) | def ddim_sample_loop_progressive( method _vb_terms_bpd (line 795) | def _vb_terms_bpd( method training_losses (line 830) | def training_losses(self, model, x_start, t, model_kwargs=None, noise=... method autoregressive_training_losses (line 918) | def autoregressive_training_losses(self, model, x_start, t, model_outp... method _prior_bpd (line 990) | def _prior_bpd(self, x_start): method calc_bpd_loop (line 1008) | def calc_bpd_loop(self, model, x_start, clip_denoised=True, model_kwar... function get_named_beta_schedule (line 1066) | def get_named_beta_schedule(schedule_name, num_diffusion_timesteps): class SpacedDiffusion (line 1093) | class SpacedDiffusion(GaussianDiffusion): method __init__ (line 1102) | def __init__(self, use_timesteps, **kwargs): method p_mean_variance (line 1118) | def p_mean_variance( method training_losses (line 1123) | def training_losses( method autoregressive_training_losses (line 1128) | def autoregressive_training_losses( method condition_mean (line 1133) | def condition_mean(self, cond_fn, *args, **kwargs): method condition_score (line 1136) | def condition_score(self, cond_fn, *args, **kwargs): method _wrap_model (line 1139) | def _wrap_model(self, model, autoregressive=False): method _scale_timesteps (line 1147) | def _scale_timesteps(self, t): function space_timesteps (line 1152) | def space_timesteps(num_timesteps, section_counts): class _WrappedModel (line 1208) | class _WrappedModel: method __init__ (line 1209) | def __init__(self, model, timestep_map, rescale_timesteps, original_nu... method __call__ (line 1215) | def __call__(self, x, ts, **kwargs): class _WrappedAutoregressiveModel (line 1223) | class _WrappedAutoregressiveModel: method __init__ (line 1224) | def __init__(self, model, timestep_map, rescale_timesteps, original_nu... method __call__ (line 1230) | def __call__(self, x, x0, ts, **kwargs): function _extract_into_tensor (line 1237) | def _extract_into_tensor(arr, timesteps, broadcast_shape): FILE: tortoise/utils/stft.py function window_sumsquare (line 42) | def window_sumsquare(window, n_frames, hop_length=200, win_length=800, class STFT (line 94) | class STFT(torch.nn.Module): method __init__ (line 96) | def __init__(self, filter_length=800, hop_length=200, win_length=800, method transform (line 129) | def transform(self, input_data): method inverse (line 159) | def inverse(self, magnitude, phase): method forward (line 190) | def forward(self, input_data): FILE: tortoise/utils/text.py function split_and_recombine_text (line 4) | def split_and_recombine_text(text, desired_length=200, max_length=300): class Test (line 80) | class Test(unittest.TestCase): method test_split_and_recombine_text (line 81) | def test_split_and_recombine_text(self): method test_split_and_recombine_text_2 (line 96) | def test_split_and_recombine_text_2(self): method test_split_and_recombine_text_3 (line 107) | def test_split_and_recombine_text_3(self): FILE: tortoise/utils/tokenizer.py function expand_abbreviations (line 38) | def expand_abbreviations(text): function _remove_commas (line 53) | def _remove_commas(m): function _expand_decimal_point (line 57) | def _expand_decimal_point(m): function _expand_dollars (line 61) | def _expand_dollars(m): function _expand_ordinal (line 82) | def _expand_ordinal(m): function _expand_number (line 86) | def _expand_number(m): function normalize_numbers (line 101) | def normalize_numbers(text): function expand_numbers (line 111) | def expand_numbers(text): function lowercase (line 115) | def lowercase(text): function collapse_whitespace (line 119) | def collapse_whitespace(text): function convert_to_ascii (line 123) | def convert_to_ascii(text): function basic_cleaners (line 127) | def basic_cleaners(text): function transliteration_cleaners (line 134) | def transliteration_cleaners(text): function english_cleaners (line 142) | def english_cleaners(text): function lev_distance (line 153) | def lev_distance(s1, s2): class VoiceBpeTokenizer (line 172) | class VoiceBpeTokenizer: method __init__ (line 173) | def __init__(self, vocab_file=None, use_basic_cleaners=False): method encode (line 182) | def encode(self, txt): method decode (line 187) | def decode(self, seq): FILE: tortoise/utils/typical_sampling.py class TypicalLogitsWarper (line 5) | class TypicalLogitsWarper(LogitsWarper): method __init__ (line 6) | def __init__(self, mass: float = 0.9, filter_value: float = -float("In... method __call__ (line 11) | def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTen... FILE: tortoise/utils/wav2vec_alignment.py function max_alignment (line 10) | def max_alignment(s1, s2, skip_character='~', record=None): class Wav2VecAlignment (line 48) | class Wav2VecAlignment: method __init__ (line 52) | def __init__(self, device='cuda' if not torch.backends.mps.is_availabl... method align (line 58) | def align(self, audio, expected_text, audio_sample_rate=24000): method redact (line 125) | def redact(self, audio, expected_text, audio_sample_rate=24000):