SYMBOL INDEX (483 symbols across 65 files)

FILE: awesome_webui/src/App.tsx
  type AudioFormat (line 47) | type AudioFormat = 'mp3' | 'wav' | 'pcm' | 'opus'
  type LatencyMode (line 48) | type LatencyMode = 'normal' | 'balanced'
  type ControlsState (line 54) | type ControlsState = {
  type Metrics (line 65) | type Metrics = {
  type StatusState (line 71) | type StatusState = {
  type ReferenceItem (line 76) | type ReferenceItem = {
  type SpeakerGroup (line 84) | type SpeakerGroup = {
  type PendingReference (line 89) | type PendingReference = {
  function createId (line 116) | function createId() {
  function arrayBufferToBase64 (line 120) | function arrayBufferToBase64(buffer: ArrayBuffer): string {
  function createSpeakerGroup (line 129) | function createSpeakerGroup(): SpeakerGroup {
  function buildReferencesPayload (line 138) | function buildReferencesPayload(
  function buildPreviewPayload (line 152) | function buildPreviewPayload(
  function buildRequestPayload (line 171) | function buildRequestPayload(
  function createFileName (line 190) | function createFileName(inputText: string) {
  function getErrorMessage (line 195) | function getErrorMessage(error: unknown) {
  function waitForSourceBuffer (line 199) | function waitForSourceBuffer(sourceBuffer: SourceBuffer) {
  function canUseStreamingPlayback (line 214) | function canUseStreamingPlayback(format: AudioFormat) {
  type SettingSliderProps (line 219) | type SettingSliderProps = {
  function SettingSlider (line 229) | function SettingSlider({
  function App (line 262) | function App() {

FILE: awesome_webui/src/components/ui/alert.tsx
  function Alert (line 19) | function Alert({
  function AlertTitle (line 27) | function AlertTitle({ className, ...props }: React.ComponentProps<'h5'>) {
  function AlertDescription (line 31) | function AlertDescription({ className, ...props }: React.ComponentProps<...

FILE: awesome_webui/src/components/ui/badge.tsx
  function Badge (line 23) | function Badge({

FILE: awesome_webui/src/components/ui/button.tsx
  type ButtonProps (line 33) | type ButtonProps = React.ComponentProps<'button'> &
  function Button (line 38) | function Button({ className, variant, size, asChild = false, ...props }:...

FILE: awesome_webui/src/components/ui/card.tsx
  function Card (line 5) | function Card({ className, ...props }: React.ComponentProps<'div'>) {
  function CardHeader (line 15) | function CardHeader({ className, ...props }: React.ComponentProps<'div'>) {
  function CardTitle (line 19) | function CardTitle({ className, ...props }: React.ComponentProps<'div'>) {
  function CardDescription (line 23) | function CardDescription({ className, ...props }: React.ComponentProps<'...
  function CardContent (line 27) | function CardContent({ className, ...props }: React.ComponentProps<'div'...

FILE: awesome_webui/src/components/ui/dialog.tsx
  function DialogOverlay (line 12) | function DialogOverlay({
  function DialogContent (line 24) | function DialogContent({
  function DialogHeader (line 49) | function DialogHeader({ className, ...props }: React.ComponentProps<'div...
  function DialogFooter (line 53) | function DialogFooter({ className, ...props }: React.ComponentProps<'div...
  function DialogTitle (line 57) | function DialogTitle({ className, ...props }: React.ComponentProps<typeo...
  function DialogDescription (line 66) | function DialogDescription({

FILE: awesome_webui/src/components/ui/label.tsx
  function Label (line 6) | function Label({ className, ...props }: React.ComponentProps<typeof Labe...

FILE: awesome_webui/src/components/ui/scroll-area.tsx
  function ScrollArea (line 6) | function ScrollArea({
  function ScrollBar (line 22) | function ScrollBar({

FILE: awesome_webui/src/components/ui/separator.tsx
  function Separator (line 6) | function Separator({

FILE: awesome_webui/src/components/ui/slider.tsx
  function Slider (line 6) | function Slider({

FILE: awesome_webui/src/components/ui/switch.tsx
  function Switch (line 6) | function Switch({

FILE: awesome_webui/src/components/ui/textarea.tsx
  function Textarea (line 5) | function Textarea({ className, ...props }: React.ComponentProps<'textare...

FILE: awesome_webui/src/components/ui/toggle-group.tsx
  function ToggleGroup (line 23) | function ToggleGroup({
  function ToggleGroupItem (line 35) | function ToggleGroupItem({

FILE: awesome_webui/vite.config.ts
  function inlineEntryAssets (line 7) | function inlineEntryAssets(): Plugin {

FILE: fish_speech/callbacks/grad_norm.py
  function grad_norm (line 15) | def grad_norm(
  class GradNormMonitor (line 55) | class GradNormMonitor(Callback):
    method __init__ (line 60) | def __init__(
    method on_after_backward (line 77) | def on_after_backward(self, trainer: Trainer, model: LightningModule) ...
    method log_sub_module_grad_norm (line 100) | def log_sub_module_grad_norm(

FILE: fish_speech/content_sequence.py
  function restore_ndarray (line 14) | def restore_ndarray(obj, to_tensor: bool = False):
  class BasePart (line 25) | class BasePart:
  class VQPart (line 31) | class VQPart(BasePart):
    method __post_init__ (line 35) | def __post_init__(self: "VQPart"):
  class TextPart (line 41) | class TextPart(BasePart):
    method __post_init__ (line 46) | def __post_init__(self: "TextPart"):
  class AudioPart (line 53) | class AudioPart(BasePart):
    method __post_init__ (line 57) | def __post_init__(self: "AudioPart"):
  class EncodedMessage (line 63) | class EncodedMessage:
  class ContentSequence (line 76) | class ContentSequence:
    method __init__ (line 86) | def __init__(
    method append (line 121) | def append(
    method encode (line 154) | def encode(
    method encode_for_inference (line 282) | def encode_for_inference(
    method visualize (line 326) | def visualize(

FILE: fish_speech/conversation.py
  class Message (line 20) | class Message:
  class Conversation (line 33) | class Conversation:
    method __init__ (line 36) | def __init__(self: "Conversation", messages: list[Message] | None = No...
    method _build_content_sequence (line 39) | def _build_content_sequence(
    method encode (line 79) | def encode(
    method encode_for_inference (line 96) | def encode_for_inference(
    method visualize (line 105) | def visualize(
    method append (line 125) | def append(self: "Conversation", message: Message):
    method to_content_sequence (line 128) | def to_content_sequence(

FILE: fish_speech/datasets/concat_repeat.py
  class ConcatRepeatDataset (line 8) | class ConcatRepeatDataset(Dataset):
    method cumsum (line 14) | def cumsum(sequence, repeats):
    method __init__ (line 22) | def __init__(self, datasets: Iterable[Dataset], repeats: list[int]):
    method __len__ (line 40) | def __len__(self):
    method __getitem__ (line 43) | def __getitem__(self, idx):

FILE: fish_speech/datasets/protos/text_data_stream.py
  function read_pb_stream (line 6) | def read_pb_stream(f):
  function write_pb_stream (line 18) | def write_pb_stream(f, text_data):
  function pack_pb_stream (line 24) | def pack_pb_stream(text_data):
  function split_pb_stream (line 29) | def split_pb_stream(f):

FILE: fish_speech/datasets/semantic.py
  function split_by_rank_worker (line 32) | def split_by_rank_worker(files):
  class AutoTextSemanticInstructionIterableDataset (line 59) | class AutoTextSemanticInstructionIterableDataset(IterableDataset):
    method __init__ (line 73) | def __init__(
    method __iter__ (line 114) | def __iter__(self):
    method init_mock_data_server (line 118) | def init_mock_data_server(self):
    method sample_data (line 157) | def sample_data(self):
    method pack_sentences (line 185) | def pack_sentences(
    method augment (line 252) | def augment(self):
  class AutoTextSemanticInstructionDataset (line 286) | class AutoTextSemanticInstructionDataset(Dataset):
    method __init__ (line 300) | def __init__(
    method _init_data (line 341) | def _init_data(self):
    method __len__ (line 390) | def __len__(self):
    method __getitem__ (line 393) | def __getitem__(self, idx):
    method pack_sentences (line 396) | def pack_sentences(
  class InterleaveDataset (line 464) | class InterleaveDataset(IterableDataset):
    method __init__ (line 465) | def __init__(
    method __iter__ (line 477) | def __iter__(self):
  class TextDataCollator (line 495) | class TextDataCollator:
    method __call__ (line 499) | def __call__(self, examples):
    method batchify (line 522) | def batchify(self, examples, tokens_key="tokens", labels_key="labels"):
  class SemanticDataModule (line 568) | class SemanticDataModule(LightningDataModule):
    method __init__ (line 569) | def __init__(
    method train_dataloader (line 595) | def train_dataloader(self):
    method val_dataloader (line 604) | def val_dataloader(self):

FILE: fish_speech/datasets/vqgan.py
  class VQGANDataset (line 16) | class VQGANDataset(Dataset):
    method __init__ (line 17) | def __init__(
    method __len__ (line 38) | def __len__(self):
    method get_item (line 41) | def get_item(self, idx):
    method __getitem__ (line 67) | def __getitem__(self, idx):
  class VQGANCollator (line 79) | class VQGANCollator:
    method __call__ (line 80) | def __call__(self, batch):
  class VQGANDataModule (line 99) | class VQGANDataModule(LightningDataModule):
    method __init__ (line 100) | def __init__(
    method train_dataloader (line 116) | def train_dataloader(self):
    method val_dataloader (line 126) | def val_dataloader(self):

FILE: fish_speech/i18n/core.py
  function load_language_list (line 9) | def load_language_list(language):
  class I18nAuto (line 16) | class I18nAuto:
    method __init__ (line 17) | def __init__(self):
    method __call__ (line 33) | def __call__(self, key):
    method __repr__ (line 36) | def __repr__(self):

FILE: fish_speech/i18n/scan.py
  function extract_i18n_strings (line 12) | def extract_i18n_strings(node):

FILE: fish_speech/inference_engine/__init__.py
  class TTSInferenceEngine (line 22) | class TTSInferenceEngine(ReferenceLoader, VQManager):
    method __init__ (line 24) | def __init__(
    method inference (line 40) | def inference(self, req: ServeTTSRequest) -> Generator[InferenceResult...
    method send_Llama_request (line 144) | def send_Llama_request(
    method get_audio_segment (line 179) | def get_audio_segment(self, result: GenerateResponse) -> np.ndarray:

FILE: fish_speech/inference_engine/reference_loader.py
  class ReferenceLoader (line 20) | class ReferenceLoader:
    method __init__ (line 21) | def __init__(self) -> None:
    method load_by_id (line 40) | def load_by_id(
    method load_by_hash (line 75) | def load_by_hash(
    method load_audio (line 109) | def load_audio(self, reference_audio: bytes | str, sr: int):
    method list_reference_ids (line 131) | def list_reference_ids(self) -> list[str]:
    method add_reference (line 167) | def add_reference(self, id: str, wav_file_path: str, reference_text: s...
    method delete_reference (line 241) | def delete_reference(self, id: str) -> None:

FILE: fish_speech/inference_engine/utils.py
  class InferenceResult (line 10) | class InferenceResult:
  function wav_chunk_header (line 16) | def wav_chunk_header(

FILE: fish_speech/inference_engine/vq_manager.py
  class VQManager (line 9) | class VQManager:
    method __init__ (line 11) | def __init__(self):
    method decode_vq_tokens (line 16) | def decode_vq_tokens(self, codes):
    method encode_reference (line 24) | def encode_reference(self, reference_audio, enable_reference_audio):

FILE: fish_speech/models/dac/inference.py
  function load_model (line 23) | def load_model(config_name, checkpoint_path, device="cuda"):
  function main (line 71) | def main(input_path, output_path, config_name, checkpoint_path, device):

FILE: fish_speech/models/dac/modded_dac.py
  class VQResult (line 19) | class VQResult:
  function find_multiple (line 28) | def find_multiple(n: int, k: int) -> int:
  class ModelArgs (line 35) | class ModelArgs:
    method __post_init__ (line 52) | def __post_init__(self):
  class KVCache (line 65) | class KVCache(nn.Module):
    method __init__ (line 66) | def __init__(
    method update (line 74) | def update(self, input_pos, k_val, v_val):
    method clear_cache (line 88) | def clear_cache(self, prompt_len):
  class Transformer (line 97) | class Transformer(nn.Module):
    method __init__ (line 98) | def __init__(self, config: ModelArgs) -> None:
    method setup_caches (line 123) | def setup_caches(self, max_batch_size, max_seq_length):
    method forward (line 145) | def forward(
  class TransformerBlock (line 174) | class TransformerBlock(nn.Module):
    method __init__ (line 175) | def __init__(self, config: ModelArgs) -> None:
    method forward (line 184) | def forward(
  class Attention (line 198) | class Attention(nn.Module):
    method __init__ (line 199) | def __init__(self, config: ModelArgs):
    method _compute_conformer_pos_scores (line 225) | def _compute_conformer_pos_scores(self, q: Tensor, seqlen: int) -> Ten...
    method forward (line 243) | def forward(
  class FeedForward (line 308) | class FeedForward(nn.Module):
    method __init__ (line 309) | def __init__(self, config: ModelArgs) -> None:
    method forward (line 316) | def forward(self, x: Tensor) -> Tensor:
  class RMSNorm (line 320) | class RMSNorm(nn.Module):
    method __init__ (line 321) | def __init__(self, dim: int, eps: float = 1e-5):
    method _norm (line 326) | def _norm(self, x):
    method forward (line 329) | def forward(self, x: Tensor) -> Tensor:
  class LayerScale (line 334) | class LayerScale(nn.Module):
    method __init__ (line 335) | def __init__(
    method forward (line 345) | def forward(self, x: Tensor) -> Tensor:
  class WindowLimitedTransformer (line 349) | class WindowLimitedTransformer(Transformer):
    method __init__ (line 354) | def __init__(
    method make_window_limited_mask (line 380) | def make_window_limited_mask(
    method make_mask (line 400) | def make_mask(
    method forward (line 418) | def forward(
  function precompute_freqs_cis (line 442) | def precompute_freqs_cis(
  function apply_rotary_emb (line 455) | def apply_rotary_emb(x: Tensor, freqs_cis: Tensor) -> Tensor:
  function init_weights (line 470) | def init_weights(m):
  function unpad1d (line 476) | def unpad1d(x: torch.Tensor, paddings: tp.Tuple[int, int]):
  function get_extra_padding_for_conv1d (line 485) | def get_extra_padding_for_conv1d(
  function pad1d (line 495) | def pad1d(
  class CausalConvNet (line 521) | class CausalConvNet(nn.Module):
    method __init__ (line 522) | def __init__(
    method forward (line 546) | def forward(self, x):
    method weight_norm (line 554) | def weight_norm(self, name="weight", dim=0):
    method remove_weight_norm (line 558) | def remove_weight_norm(self):
  class CausalTransConvNet (line 563) | class CausalTransConvNet(nn.Module):
    method __init__ (line 564) | def __init__(
    method forward (line 574) | def forward(self, x):
    method weight_norm (line 582) | def weight_norm(self, name="weight", dim=0):
    method remove_weight_norm (line 586) | def remove_weight_norm(self):
  function CausalWNConv1d (line 591) | def CausalWNConv1d(*args, **kwargs):
  function CausalWNConvTranspose1d (line 595) | def CausalWNConvTranspose1d(*args, **kwargs):
  class ResidualUnit (line 599) | class ResidualUnit(nn.Module):
    method __init__ (line 600) | def __init__(self, dim: int = 16, dilation: int = 1, causal: bool = Fa...
    method forward (line 612) | def forward(self, x):
  class EncoderBlock (line 623) | class EncoderBlock(nn.Module):
    method __init__ (line 624) | def __init__(
    method forward (line 666) | def forward(self, x):
  class Encoder (line 670) | class Encoder(nn.Module):
    method __init__ (line 671) | def __init__(
    method forward (line 708) | def forward(self, x):
  class DecoderBlock (line 712) | class DecoderBlock(nn.Module):
    method __init__ (line 713) | def __init__(
    method forward (line 756) | def forward(self, x):
  class Decoder (line 760) | class Decoder(nn.Module):
    method __init__ (line 761) | def __init__(
    method forward (line 800) | def forward(self, x):
  class DAC (line 804) | class DAC(BaseModel, CodecMixin):
    method __init__ (line 805) | def __init__(
    method preprocess (line 863) | def preprocess(self, audio_data, sample_rate):
    method encode (line 874) | def encode(
    method from_indices (line 925) | def from_indices(self, indices: torch.Tensor):
    method decode (line 929) | def decode(self, z: torch.Tensor):
    method forward (line 948) | def forward(

FILE: fish_speech/models/dac/rvq.py
  function unpad1d (line 13) | def unpad1d(x: torch.Tensor, paddings: tp.Tuple[int, int]):
  function get_extra_padding_for_conv1d (line 22) | def get_extra_padding_for_conv1d(
  function pad1d (line 32) | def pad1d(
  class CausalConvNet (line 58) | class CausalConvNet(nn.Module):
    method __init__ (line 59) | def __init__(
    method forward (line 83) | def forward(self, x):
    method weight_norm (line 91) | def weight_norm(self, name="weight", dim=0):
    method remove_weight_norm (line 95) | def remove_weight_norm(self):
  class CausalTransConvNet (line 100) | class CausalTransConvNet(nn.Module):
    method __init__ (line 101) | def __init__(
    method forward (line 111) | def forward(self, x):
    method weight_norm (line 119) | def weight_norm(self, name="weight", dim=0):
    method remove_weight_norm (line 123) | def remove_weight_norm(self):
  class ConvNeXtBlock (line 129) | class ConvNeXtBlock(nn.Module):
    method __init__ (line 143) | def __init__(
    method forward (line 173) | def forward(self, x, apply_residual: bool = True):
  class VQResult (line 195) | class VQResult:
  class DownsampleResidualVectorQuantize (line 204) | class DownsampleResidualVectorQuantize(nn.Module):
    method __init__ (line 205) | def __init__(
    method _init_weights (line 288) | def _init_weights(self, m):
    method forward (line 293) | def forward(
    method decode (line 352) | def decode(self, indices: torch.Tensor):

FILE: fish_speech/models/text2semantic/inference.py
  function multinomial_sample_one_no_sync (line 43) | def multinomial_sample_one_no_sync(probs_sort):
  function logits_to_probs (line 54) | def logits_to_probs(
  function sample (line 80) | def sample(
  function decode_one_token_ar (line 96) | def decode_one_token_ar(
  function decode_n_tokens (line 184) | def decode_n_tokens(
  function generate (line 243) | def generate(
  function init_model (line 362) | def init_model(checkpoint_path, device, precision, compile=False):
  function load_codec_model (line 396) | def load_codec_model(codec_checkpoint_path, device, precision=torch.bflo...
  function encode_audio (line 421) | def encode_audio(audio_path, codec, device):
  function decode_to_audio (line 440) | def decode_to_audio(codes, codec):
  class GenerateResponse (line 448) | class GenerateResponse:
  function split_text_by_speaker (line 454) | def split_text_by_speaker(text: str) -> list[str]:
  function group_turns_into_batches (line 485) | def group_turns_into_batches(
  function generate_long (line 523) | def generate_long(
  class WrappedGenerateResponse (line 737) | class WrappedGenerateResponse:
  class GenerateRequest (line 743) | class GenerateRequest:
  function launch_thread_safe_queue (line 748) | def launch_thread_safe_queue(
  function main (line 839) | def main(

FILE: fish_speech/models/text2semantic/lit_module.py
  class TextToSemantic (line 16) | class TextToSemantic(L.LightningModule):
    method __init__ (line 17) | def __init__(
    method forward (line 29) | def forward(self, x):
    method on_save_checkpoint (line 32) | def on_save_checkpoint(self, checkpoint):
    method configure_optimizers (line 43) | def configure_optimizers(self) -> OptimizerLRScheduler:
    method get_batch_logps (line 76) | def get_batch_logps(
    method _step (line 109) | def _step(self, batch, batch_idx, stage: str):
    method get_accuracy (line 193) | def get_accuracy(self, logits, labels):
    method training_step (line 206) | def training_step(self, batch, batch_idx):
    method validation_step (line 209) | def validation_step(self, batch, batch_idx):

FILE: fish_speech/models/text2semantic/llama.py
  function find_multiple (line 21) | def find_multiple(n: int, k: int) -> int:
  class BaseModelArgs (line 28) | class BaseModelArgs:
    method __post_init__ (line 65) | def __post_init__(self):
    method from_pretrained (line 76) | def from_pretrained(path: str):
    method _from_fish_qwen3_omni (line 102) | def _from_fish_qwen3_omni(data: dict) -> "DualARModelArgs":
    method save (line 145) | def save(self, path: str):
  class NaiveModelArgs (line 151) | class NaiveModelArgs(BaseModelArgs):
  class DualARModelArgs (line 156) | class DualARModelArgs(BaseModelArgs):
    method __post_init__ (line 169) | def __post_init__(self):
  class KVCache (line 196) | class KVCache(nn.Module):
    method __init__ (line 197) | def __init__(
    method update (line 205) | def update(self, input_pos, k_val, v_val):
  class TransformerForwardResult (line 218) | class TransformerForwardResult:
  class BaseTransformerForwardResult (line 224) | class BaseTransformerForwardResult:
  function _remap_fish_qwen3_omni_keys (line 229) | def _remap_fish_qwen3_omni_keys(weights: OrderedDict) -> OrderedDict:
  class BaseTransformer (line 249) | class BaseTransformer(nn.Module):
    method __init__ (line 250) | def __init__(
    method setup_caches (line 307) | def setup_caches(
    method embed (line 326) | def embed(self, inp: Tensor) -> Tensor:
    method forward (line 347) | def forward(
    method forward_generate (line 390) | def forward_generate(
    method _init_weights (line 468) | def _init_weights(self, module):
    method from_pretrained (line 480) | def from_pretrained(
    method save_pretrained (line 595) | def save_pretrained(self, path: str, drop_lora: bool = False):
  class NaiveTransformer (line 613) | class NaiveTransformer(BaseTransformer):
    method __init__ (line 614) | def __init__(self, config: NaiveModelArgs) -> None:
    method decode (line 626) | def decode(self, result: BaseTransformerForwardResult) -> TransformerF...
    method forward (line 641) | def forward(
    method forward_generate (line 652) | def forward_generate(
  class DualARTransformer (line 659) | class DualARTransformer(BaseTransformer):
    method __init__ (line 660) | def __init__(self, config: NaiveModelArgs) -> None:
    method setup_caches (line 707) | def setup_caches(
    method forward (line 723) | def forward(
    method forward_generate_fast (line 798) | def forward_generate_fast(
    method forward_generate (line 818) | def forward_generate(
  class TransformerBlock (line 830) | class TransformerBlock(nn.Module):
    method __init__ (line 831) | def __init__(self, config: BaseModelArgs, use_sdpa: bool = True) -> None:
    method forward (line 838) | def forward(
  class Attention (line 846) | class Attention(nn.Module):
    method __init__ (line 847) | def __init__(self, config: BaseModelArgs, use_sdpa: bool = True):
    method load_hook (line 876) | def load_hook(self, state_dict, prefix, *args):
    method forward (line 883) | def forward(
    method eq_scaled_dot_product_attention (line 947) | def eq_scaled_dot_product_attention(
  class FeedForward (line 978) | class FeedForward(nn.Module):
    method __init__ (line 979) | def __init__(self, config: BaseModelArgs) -> None:
    method forward (line 985) | def forward(self, x: Tensor) -> Tensor:
  class RMSNorm (line 989) | class RMSNorm(nn.Module):
    method __init__ (line 990) | def __init__(self, dim: int, eps: float = 1e-5):
    method _norm (line 995) | def _norm(self, x):
    method forward (line 998) | def forward(self, x: Tensor) -> Tensor:
  function precompute_freqs_cis (line 1003) | def precompute_freqs_cis(seq_len: int, n_elem: int, base: int = 10000) -...
  function apply_rotary_emb (line 1025) | def apply_rotary_emb(x: Tensor, freqs_cis: Tensor) -> Tensor:

FILE: fish_speech/models/text2semantic/lora.py
  class LoraConfig (line 7) | class LoraConfig:
  function _replace_embedding (line 13) | def _replace_embedding(old_embed, lora_config):
  function setup_lora (line 25) | def setup_lora(model, lora_config):
  function get_merged_state_dict (line 81) | def get_merged_state_dict(model):

FILE: fish_speech/scheduler.py
  function get_cosine_schedule_with_warmup_lr_lambda (line 4) | def get_cosine_schedule_with_warmup_lr_lambda(
  function get_constant_schedule_with_warmup_lr_lambda (line 28) | def get_constant_schedule_with_warmup_lr_lambda(

FILE: fish_speech/text/clean.py
  function clean_text (line 24) | def clean_text(text):

FILE: fish_speech/tokenizer.py
  class FishTokenizer (line 55) | class FishTokenizer:
    method __init__ (line 56) | def __init__(self, model_path: str):
    method vocab_size (line 91) | def vocab_size(self):
    method pad_token_id (line 95) | def pad_token_id(self):
    method eos_token_id (line 99) | def eos_token_id(self):
    method get_token_id (line 102) | def get_token_id(self, token: str) -> int:
    method encode (line 105) | def encode(
    method decode (line 118) | def decode(self, tokens: Union[List[int], int], **kwargs) -> str:
    method save_pretrained (line 121) | def save_pretrained(self, path: str):
    method from_pretrained (line 125) | def from_pretrained(cls, path: str):
    method __getattr__ (line 128) | def __getattr__(self, name):

FILE: fish_speech/train.py
  function train (line 36) | def train(cfg: DictConfig) -> tuple[dict, dict]:
  function main (line 135) | def main(cfg: DictConfig) -> Optional[float]:

FILE: fish_speech/utils/braceexpand.py
  class UnbalancedBracesError (line 15) | class UnbalancedBracesError(ValueError):
  function braceexpand (line 26) | def braceexpand(pattern: str, escape: bool = True) -> Iterator[str]:
  function parse_pattern (line 105) | def parse_pattern(pattern: str, escape: bool) -> Iterator[str]:
  function parse_expression (line 144) | def parse_expression(expr: str, escape: bool) -> Optional[Iterable[str]]:
  function parse_sequence (line 156) | def parse_sequence(seq: str, escape: bool) -> Optional[Iterator[str]]:
  function make_int_range (line 187) | def make_int_range(left: str, right: str, incr: Optional[str] = None) ->...
  function make_char_range (line 200) | def make_char_range(left: str, right: str, incr: Optional[str] = None) -...

FILE: fish_speech/utils/context.py
  function autocast_exclude_mps (line 6) | def autocast_exclude_mps(

FILE: fish_speech/utils/file.py
  function get_latest_checkpoint (line 27) | def get_latest_checkpoint(path: Path | str) -> Path | None:
  function audio_to_bytes (line 41) | def audio_to_bytes(file_path):
  function read_ref_text (line 49) | def read_ref_text(ref_text):
  function list_files (line 57) | def list_files(
  function load_filelist (line 89) | def load_filelist(path: Path | str) -> list[tuple[Path, str, str, str]]:

FILE: fish_speech/utils/instantiators.py
  function instantiate_callbacks (line 13) | def instantiate_callbacks(callbacks_cfg: DictConfig) -> List[Callback]:
  function instantiate_loggers (line 33) | def instantiate_loggers(logger_cfg: DictConfig) -> List[Logger]:

FILE: fish_speech/utils/logger.py
  class RankedLogger (line 7) | class RankedLogger(logging.LoggerAdapter):
    method __init__ (line 10) | def __init__(
    method log (line 27) | def log(

FILE: fish_speech/utils/logging_utils.py
  function log_hyperparameters (line 7) | def log_hyperparameters(object_dict: dict) -> None:

FILE: fish_speech/utils/rich_utils.py
  function print_config_tree (line 16) | def print_config_tree(
  function enforce_tags (line 82) | def enforce_tags(cfg: DictConfig, save_to_file: bool = False) -> None:

FILE: fish_speech/utils/schema.py
  class ServeVQPart (line 15) | class ServeVQPart(BaseModel):
  class ServeTextPart (line 20) | class ServeTextPart(BaseModel):
  class ServeAudioPart (line 25) | class ServeAudioPart(BaseModel):
  class ServeRequest (line 30) | class ServeRequest(BaseModel):
  class ServeVQGANEncodeRequest (line 42) | class ServeVQGANEncodeRequest(BaseModel):
  class ServeVQGANEncodeResponse (line 47) | class ServeVQGANEncodeResponse(BaseModel):
  class ServeVQGANDecodeRequest (line 51) | class ServeVQGANDecodeRequest(BaseModel):
  class ServeVQGANDecodeResponse (line 55) | class ServeVQGANDecodeResponse(BaseModel):
  class ServeReferenceAudio (line 60) | class ServeReferenceAudio(BaseModel):
    method decode_audio (line 65) | def decode_audio(cls, values):
    method __repr__ (line 77) | def __repr__(self) -> str:
  class ServeTTSRequest (line 81) | class ServeTTSRequest(BaseModel):
    class Config (line 105) | class Config:
  class AddReferenceRequest (line 110) | class AddReferenceRequest(BaseModel):
  class AddReferenceResponse (line 116) | class AddReferenceResponse(BaseModel):
  class ListReferencesResponse (line 122) | class ListReferencesResponse(BaseModel):
  class DeleteReferenceResponse (line 128) | class DeleteReferenceResponse(BaseModel):
  class UpdateReferenceResponse (line 134) | class UpdateReferenceResponse(BaseModel):

FILE: fish_speech/utils/spectrogram.py
  class LinearSpectrogram (line 7) | class LinearSpectrogram(nn.Module):
    method __init__ (line 8) | def __init__(
    method forward (line 27) | def forward(self, y: Tensor) -> Tensor:
  class LogMelSpectrogram (line 62) | class LogMelSpectrogram(nn.Module):
    method __init__ (line 63) | def __init__(
    method compress (line 102) | def compress(self, x: Tensor) -> Tensor:
    method decompress (line 105) | def decompress(self, x: Tensor) -> Tensor:
    method apply_mel_scale (line 108) | def apply_mel_scale(self, x: Tensor) -> Tensor:
    method forward (line 111) | def forward(

FILE: fish_speech/utils/utils.py
  function extras (line 16) | def extras(cfg: DictConfig) -> None:
  function task_wrapper (line 46) | def task_wrapper(task_func: Callable) -> Callable:
  function get_metric_value (line 100) | def get_metric_value(metric_dict: dict, metric_name: str) -> float:
  function set_seed (line 120) | def set_seed(seed: int):

FILE: tools/api_client.py
  function parse_args (line 16) | def parse_args():

FILE: tools/api_server.py
  class API (line 29) | class API(ExceptionHandler):
    method __init__ (line 30) | def __init__(self):
    method initialize_app (line 81) | async def initialize_app(self, app: Kui):

FILE: tools/llama/build_dataset.py
  function task_generator_folder (line 23) | def task_generator_folder(root: Path, text_extension: str):
  function task_generator_filelist (line 55) | def task_generator_filelist(filelist):
  function run_task (line 65) | def run_task(task):
  function main (line 127) | def main(input, output, num_workers, text_extension, shard_size):

FILE: tools/llama/eval_in_context.py
  function smooth (line 16) | def smooth(
  function analyze_one_model (line 30) | def analyze_one_model(loader, config, weight, max_length):
  function main (line 115) | def main():

FILE: tools/llama/merge_lora.py
  function merge (line 21) | def merge(lora_config, base_weight, lora_weight, output):

FILE: tools/llama/quantize.py
  function dynamically_quantize_per_channel (line 22) | def dynamically_quantize_per_channel(x, quant_min, quant_max, target_dty...
  function get_group_qparams (line 57) | def get_group_qparams(w, n_bit=4, groupsize=128):
  function pack_scales_and_zeros (line 78) | def pack_scales_and_zeros(scales, zeros):
  function unpack_scales_and_zeros (line 95) | def unpack_scales_and_zeros(scales_and_zeros):
  function group_quantize_tensor_from_qparams (line 101) | def group_quantize_tensor_from_qparams(w, scales, zeros, n_bit=4, groups...
  function group_quantize_tensor (line 130) | def group_quantize_tensor(w, n_bit=4, groupsize=128):
  function group_dequantize_tensor_from_qparams (line 137) | def group_dequantize_tensor_from_qparams(
  function group_dequantize_tensor (line 157) | def group_dequantize_tensor(w_int32, scales_and_zeros, n_bit=4, groupsiz...
  class QuantHandler (line 164) | class QuantHandler:
    method __init__ (line 165) | def __init__(self, mod):
    method create_quantized_state_dict (line 168) | def create_quantized_state_dict(self) -> "StateDict":
    method convert_for_runtime (line 171) | def convert_for_runtime(self) -> "nn.Module":
  function replace_linear_weight_only_int8_per_channel (line 178) | def replace_linear_weight_only_int8_per_channel(module):
  class WeightOnlyInt8QuantHandler (line 190) | class WeightOnlyInt8QuantHandler:
    method __init__ (line 191) | def __init__(self, mod):
    method create_quantized_state_dict (line 195) | def create_quantized_state_dict(self):
    method convert_for_runtime (line 207) | def convert_for_runtime(self):
  class WeightOnlyInt8Linear (line 212) | class WeightOnlyInt8Linear(torch.nn.Module):
    method __init__ (line 218) | def __init__(
    method forward (line 235) | def forward(self, input: torch.Tensor) -> torch.Tensor:
  function prepare_int4_weight_and_scales_and_zeros (line 242) | def prepare_int4_weight_and_scales_and_zeros(weight_bf16, groupsize, inn...
  function linear_forward_int4 (line 252) | def linear_forward_int4(x, weight_int4pack, scales_and_zeros, out_featur...
  function _check_linear_int4_k (line 263) | def _check_linear_int4_k(k, groupsize=1, inner_k_tiles=1):
  function replace_linear_int4 (line 267) | def replace_linear_int4(module, groupsize, inner_k_tiles, padding):
  class WeightOnlyInt4QuantHandler (line 300) | class WeightOnlyInt4QuantHandler:
    method __init__ (line 301) | def __init__(self, mod, groupsize=128, inner_k_tiles=8, padding=True):
    method create_quantized_state_dict (line 310) | def create_quantized_state_dict(self):
    method convert_for_runtime (line 353) | def convert_for_runtime(self):
  class WeightOnlyInt4Linear (line 358) | class WeightOnlyInt4Linear(torch.nn.Module):
    method __init__ (line 364) | def __init__(
    method forward (line 410) | def forward(self, input: torch.Tensor) -> torch.Tensor:
  function generate_folder_name (line 421) | def generate_folder_name():
  function quantize (line 440) | def quantize(checkpoint_path: Path, mode: str, groupsize: int, timestamp...

FILE: tools/run_webui.py
  function parse_args (line 22) | def parse_args():

FILE: tools/server/api_utils.py
  function parse_args (line 21) | def parse_args():
  class MsgPackRequest (line 46) | class MsgPackRequest(HttpRequest):
    method data (line 47) | async def data(
  function inference_async (line 72) | async def inference_async(req: ServeTTSRequest, engine: TTSInferenceEngi...
  function buffer_to_async_generator (line 79) | async def buffer_to_async_generator(buffer):
  function get_content_type (line 83) | def get_content_type(audio_format):
  function wants_json (line 96) | def wants_json(req):
  function format_response (line 116) | def format_response(response: BaseModel, status_code=200):

FILE: tools/server/exception_handler.py
  class ExceptionHandler (line 7) | class ExceptionHandler:
    method http_exception_handler (line 9) | async def http_exception_handler(self, exc: HTTPException):
    method other_exception_handler (line 20) | async def other_exception_handler(self, exc: Exception):

FILE: tools/server/inference.py
  function inference_wrapper (line 12) | def inference_wrapper(req: ServeTTSRequest, engine: TTSInferenceEngine):

FILE: tools/server/model_manager.py
  class ModelManager (line 11) | class ModelManager:
    method __init__ (line 12) | def __init__(
    method load_llama_model (line 56) | def load_llama_model(
    method load_decoder_model (line 72) | def load_decoder_model(self, config_name, checkpoint_path, device) -> ...
    method warm_up (line 80) | def warm_up(self, tts_inference_engine) -> None:

FILE: tools/server/model_utils.py
  function batch_encode (line 17) | def batch_encode(model, audios_list: list[bytes]):
  function cached_vqgan_batch_encode (line 55) | def cached_vqgan_batch_encode(model, audios: list[bytes]):
  function batch_vqgan_decode (line 61) | def batch_vqgan_decode(model, features):

FILE: tools/server/views.py
  class WebUI (line 62) | class WebUI(HttpView):
    method get (line 64) | async def get(cls):
  class Health (line 76) | class Health(HttpView):
    method get (line 78) | async def get(cls):
    method post (line 82) | async def post(cls):
  function vqgan_encode (line 87) | async def vqgan_encode(req: Annotated[ServeVQGANEncodeRequest, Body(excl...
  function vqgan_decode (line 116) | async def vqgan_decode(req: Annotated[ServeVQGANDecodeRequest, Body(excl...
  function tts (line 147) | async def tts(req: Annotated[ServeTTSRequest, Body(exclusive=True)]):
  function add_reference (line 209) | async def add_reference(
  function list_references (line 290) | async def list_references():
  function delete_reference (line 319) | async def delete_reference(reference_id: str = Body(...)):
  function update_reference (line 381) | async def update_reference(

FILE: tools/vqgan/create_train_split.py
  function main (line 20) | def main(root, val_ratio, val_count, filelist, min_duration, max_duration):

FILE: tools/vqgan/extract_vq.py
  function get_model (line 48) | def get_model(
  function process_batch (line 80) | def process_batch(files: list[Path], model) -> float:
  function main (line 143) | def main(

FILE: tools/webui/__init__.py
  function build_app (line 9) | def build_app(inference_fct: Callable, theme: str = "light") -> gr.Blocks:

FILE: tools/webui/inference.py
  function inference_wrapper (line 9) | def inference_wrapper(
  function get_reference_audio (line 58) | def get_reference_audio(reference_audio: str, reference_text: str) -> list:
  function build_html_error_message (line 69) | def build_html_error_message(error: Any) -> str:
  function get_inference_wrapper (line 81) | def get_inference_wrapper(engine) -> Callable: