SYMBOL INDEX (140 symbols across 11 files)

FILE: gradio_interface.py
  function load_model_if_needed (line 17) | def load_model_if_needed(model_choice: str):
  function update_ui (line 31) | def update_ui(model_choice):
  function generate_audio (line 85) | def generate_audio(
  function build_interface (line 206) | def build_interface():

FILE: zonos/autoencoder.py
  class DACAutoencoder (line 8) | class DACAutoencoder:
    method __init__ (line 9) | def __init__(self):
    method preprocess (line 17) | def preprocess(self, wav: torch.Tensor, sr: int) -> torch.Tensor:
    method encode (line 22) | def encode(self, wav: torch.Tensor) -> torch.Tensor:
    method decode (line 25) | def decode(self, codes: torch.Tensor) -> torch.Tensor:

FILE: zonos/backbone/_mamba_ssm.py
  class MambaSSMZonosBackbone (line 9) | class MambaSSMZonosBackbone(nn.Module):
    method __init__ (line 12) | def __init__(self, config: BackboneConfig):
    method allocate_inference_cache (line 38) | def allocate_inference_cache(self, batch_size: int, max_seqlen: int, d...
    method forward (line 44) | def forward(self, hidden_states: torch.Tensor, inference_params: Infer...

FILE: zonos/backbone/_torch.py
  function precompute_freqs_cis (line 9) | def precompute_freqs_cis(seq_len: int, n_elem: int, base: float = 10000)...
  function apply_rotary_emb (line 18) | def apply_rotary_emb(x: torch.Tensor, freqs_cis: torch.Tensor) -> torch....
  function _update_kv_cache (line 33) | def _update_kv_cache(
  class TorchZonosBackbone (line 52) | class TorchZonosBackbone(nn.Module):
    method __init__ (line 56) | def __init__(self, config: BackboneConfig):
    method allocate_inference_cache (line 64) | def allocate_inference_cache(self, batch_size: int, max_seqlen: int, d...
    method forward (line 73) | def forward(self, hidden_states: torch.Tensor, inference_params: Infer...
  class TransformerBlock (line 83) | class TransformerBlock(nn.Module):
    method __init__ (line 84) | def __init__(self, config: BackboneConfig, layer_idx: int) -> None:
    method allocate_inference_cache (line 96) | def allocate_inference_cache(self, batch_size: int, max_seqlen: int, d...
    method forward (line 99) | def forward(self, x: torch.Tensor, inference_params: InferenceParams, ...
  class Attention (line 105) | class Attention(nn.Module):
    method __init__ (line 106) | def __init__(self, config: BackboneConfig, layer_idx: int):
    method forward (line 117) | def forward(self, x: torch.Tensor, inference_params: InferenceParams, ...
  class FeedForward (line 144) | class FeedForward(nn.Module):
    method __init__ (line 145) | def __init__(self, config: BackboneConfig) -> None:
    method forward (line 150) | def forward(self, x: torch.Tensor) -> torch.Tensor:

FILE: zonos/codebook_pattern.py
  function apply_delay_pattern (line 5) | def apply_delay_pattern(codes: torch.Tensor, mask_token: int):
  function revert_delay_pattern (line 10) | def revert_delay_pattern(codes: torch.Tensor):

FILE: zonos/conditioning.py
  class Conditioner (line 11) | class Conditioner(nn.Module):
    method __init__ (line 12) | def __init__(
    method apply_cond (line 41) | def apply_cond(self, *inputs: Any) -> torch.Tensor:
    method forward (line 44) | def forward(self, inputs: tuple[Any, ...] | None) -> torch.Tensor:
  function _remove_commas (line 81) | def _remove_commas(m: re.Match) -> str:
  function _expand_decimal_point (line 85) | def _expand_decimal_point(m: re.Match) -> str:
  function _expand_dollars (line 89) | def _expand_dollars(m: re.Match) -> str:
  function _expand_ordinal (line 110) | def _expand_ordinal(m: re.Match) -> str:
  function _expand_number (line 114) | def _expand_number(m: re.Match) -> str:
  function normalize_numbers (line 129) | def normalize_numbers(text: str) -> str:
  function _get_symbol_id (line 155) | def _get_symbol_id(s: str) -> int:
  function get_symbol_ids (line 159) | def get_symbol_ids(text: str) -> list[int]:
  function tokenize_phonemes (line 163) | def tokenize_phonemes(phonemes: list[str]) -> tuple[torch.Tensor, list[i...
  function normalize_jp_text (line 171) | def normalize_jp_text(text: str, tokenizer=Dictionary(dict="full").creat...
  function clean (line 178) | def clean(texts: list[str], languages: list[str]) -> list[str]:
  function get_backend (line 190) | def get_backend(language: str) -> "EspeakBackend":
  function phonemize (line 207) | def phonemize(texts: list[str], languages: list[str]) -> list[str]:
  class EspeakPhonemeConditioner (line 219) | class EspeakPhonemeConditioner(Conditioner):
    method __init__ (line 220) | def __init__(self, output_dim: int, **kwargs):
    method apply_cond (line 224) | def apply_cond(self, texts: list[str], languages: list[str]) -> torch....
  class FourierConditioner (line 242) | class FourierConditioner(Conditioner):
    method __init__ (line 243) | def __init__(
    method apply_cond (line 257) | def apply_cond(self, x: torch.Tensor) -> torch.Tensor:
  class IntegerConditioner (line 264) | class IntegerConditioner(Conditioner):
    method __init__ (line 265) | def __init__(self, output_dim: int, min_val: int = 0, max_val: int = 5...
    method apply_cond (line 271) | def apply_cond(self, x: torch.Tensor) -> torch.Tensor:
  class PassthroughConditioner (line 276) | class PassthroughConditioner(Conditioner):
    method __init__ (line 277) | def __init__(self, output_dim: int, **kwargs):
    method apply_cond (line 280) | def apply_cond(self, x: torch.Tensor) -> torch.Tensor:
  function build_conditioners (line 293) | def build_conditioners(conditioners: list[dict], output_dim: int) -> lis...
  class PrefixConditioner (line 297) | class PrefixConditioner(Conditioner):
    method __init__ (line 298) | def __init__(self, config: PrefixConditionerConfig, output_dim: int):
    method forward (line 304) | def forward(self, cond_dict: dict) -> torch.Tensor:
  function make_cond_dict (line 330) | def make_cond_dict(

FILE: zonos/config.py
  class InferenceParams (line 9) | class InferenceParams:
    method reset (line 20) | def reset(self, max_seqlen, max_batch_size):
  class BackboneConfig (line 29) | class BackboneConfig:
  class PrefixConditionerConfig (line 43) | class PrefixConditionerConfig:
  class ZonosConfig (line 49) | class ZonosConfig:
    method from_dict (line 57) | def from_dict(cls, d: dict) -> "ZonosConfig":

FILE: zonos/model.py
  class Zonos (line 22) | class Zonos(nn.Module):
    method __init__ (line 23) | def __init__(self, config: ZonosConfig, backbone_cls=DEFAULT_BACKBONE_...
    method _pad_embeddings_and_heads (line 49) | def _pad_embeddings_and_heads(self, *args, **kwargs):
    method device (line 54) | def device(self) -> torch.device:
    method from_pretrained (line 58) | def from_pretrained(
    method from_local (line 66) | def from_local(
    method make_speaker_embedding (line 90) | def make_speaker_embedding(self, wav: torch.Tensor, sr: int) -> torch....
    method embed_codes (line 97) | def embed_codes(self, codes: torch.Tensor) -> torch.Tensor:
    method apply_heads (line 100) | def apply_heads(self, hidden_states: torch.Tensor) -> torch.Tensor:
    method _compute_logits (line 103) | def _compute_logits(
    method _decode_one_token (line 118) | def _decode_one_token(
    method _prefill (line 181) | def _prefill(
    method setup_cache (line 198) | def setup_cache(self, batch_size: int, max_seqlen: int, dtype: torch.d...
    method prepare_conditioning (line 204) | def prepare_conditioning(self, cond_dict: dict, uncond_dict: dict | No...
    method can_use_cudagraphs (line 214) | def can_use_cudagraphs(self) -> bool:
    method generate (line 219) | def generate(

FILE: zonos/sampling.py
  function multinomial (line 4) | def multinomial(input: torch.Tensor, num_samples: int, replacement=False...
  function apply_unified (line 29) | def apply_unified(probs: torch.Tensor, linear: float, conf: float, quad:...
  function apply_top_k (line 45) | def apply_top_k(
  function apply_top_p (line 64) | def apply_top_p(probs: torch.Tensor, p: float) -> torch.Tensor:
  function apply_min_p (line 82) | def apply_min_p(probs: torch.Tensor, min_p: float) -> torch.Tensor:
  function modify_logit_for_repetition_penalty (line 99) | def modify_logit_for_repetition_penalty(
  function sample_from_logits (line 117) | def sample_from_logits(

FILE: zonos/speaker_cloning.py
  class logFbankCal (line 13) | class logFbankCal(nn.Module):
    method __init__ (line 14) | def __init__(
    method forward (line 31) | def forward(self, x):
  class ASP (line 38) | class ASP(nn.Module):
    method __init__ (line 40) | def __init__(self, in_planes, acoustic_dim):
    method forward (line 53) | def forward(self, x):
  class SimAMBasicBlock (line 64) | class SimAMBasicBlock(nn.Module):
    method __init__ (line 67) | def __init__(self, ConvLayer, NormLayer, in_planes, planes, stride=1, ...
    method forward (line 83) | def forward(self, x):
    method SimAM (line 91) | def SimAM(self, X, lambda_p=1e-4):
  class BasicBlock (line 99) | class BasicBlock(nn.Module):
    method __init__ (line 102) | def __init__(self, ConvLayer, NormLayer, in_planes, planes, stride=1, ...
    method forward (line 117) | def forward(self, x):
  class Bottleneck (line 125) | class Bottleneck(nn.Module):
    method __init__ (line 128) | def __init__(self, ConvLayer, NormLayer, in_planes, planes, stride=1, ...
    method forward (line 144) | def forward(self, x):
  class ResNet (line 153) | class ResNet(nn.Module):
    method __init__ (line 154) | def __init__(self, in_planes, block, num_blocks, in_ch=1, feat_dim="2d...
    method _make_layer (line 178) | def _make_layer(self, block, planes, num_blocks, stride, block_id=1):
    method forward (line 186) | def forward(self, x):
  function ResNet293 (line 195) | def ResNet293(in_planes: int, **kwargs):
  class ResNet293_based (line 199) | class ResNet293_based(nn.Module):
    method __init__ (line 200) | def __init__(
    method forward (line 217) | def forward(self, x):
  class SEModule (line 227) | class SEModule(nn.Module):
    method __init__ (line 228) | def __init__(self, channels, bottleneck=128):
    method forward (line 239) | def forward(self, input):
  class Bottle2neck (line 244) | class Bottle2neck(nn.Module):
    method __init__ (line 245) | def __init__(self, inplanes, planes, kernel_size=None, dilation=None, ...
    method forward (line 265) | def forward(self, x):
  class ECAPA_TDNN (line 295) | class ECAPA_TDNN(nn.Module):
    method __init__ (line 296) | def __init__(self, C, featCal):
    method forward (line 319) | def forward(self, x):
  class SpeakerEmbedding (line 356) | class SpeakerEmbedding(nn.Module):
    method __init__ (line 357) | def __init__(self, ckpt_path: str = "ResNet293_SimAM_ASP_base.pt", dev...
    method dtype (line 369) | def dtype(self):
    method _get_resampler (line 373) | def _get_resampler(self, orig_sample_rate: int):
    method prepare_input (line 376) | def prepare_input(self, wav: torch.Tensor, sample_rate: int) -> torch....
    method forward (line 383) | def forward(self, wav: torch.Tensor, sample_rate: int):
  class SpeakerEmbeddingLDA (line 388) | class SpeakerEmbeddingLDA(nn.Module):
    method __init__ (line 389) | def __init__(self, device: str = DEFAULT_DEVICE):
    method forward (line 410) | def forward(self, wav: torch.Tensor, sample_rate: int):

FILE: zonos/utils.py
  function find_multiple (line 6) | def find_multiple(n: int, k: int) -> int:
  function pad_weight_ (line 12) | def pad_weight_(w: nn.Embedding | nn.Linear, multiple: int):
  function get_device (line 30) | def get_device() -> torch.device: