Run encoding. Args: x (Tensor): Input audio (T_wav,). Returns: Tensor: Generated codes (T_code, N_stream).

3x3 convolution with PyTorch initialization. Same as NCSNv1/NCSNv2.

Initialize state.

Read a text file having 2 columns as dict object. Only load the keys in keys_to_load if it is not None. Examples: wav.scp:

Create the reverse-time SDE/ODE. Args: score_model: A function that takes x, t and y and returns the score. probabili

Extracts the transcript from the database-reference text file Args: text_file (str) : Path to the database-reference text file

Return a compact, fully-qualified name for objects or classes. Description: Produces a stable, human-readable identifier for logging and

Return activation function.

Inverse STFT. Args: input: Tensor(batch, T, F, 2) or ComplexTensor(batch, T, F) ilens: (batch,) Returns:

Solve the linear equation ax = b.

Yield rows from one or more SCP inputs with shared utterance IDs. This helper reads one or more SCP files in lockstep. For a single key,

Read a text file indicating sequences of number Examples: key1 1 2 3 key2 34 5 6 >>> d = load_num_sequence_text('text')

Process STFT from signal.

Perform Tikhonov regularization (only modifying real part). Args: mat (torch.complex64/ComplexTensor): input matrix (..., C, C) r

Safe-dump in yaml with no anchor/alias.

Initialize No Attention.

Returns dropout proportions based on the dropout_schedule for the fraction of data seen at this stage of training. Returns a list of pairs (p

Get segments. Args: x (Tensor): Input tensor (B, C, T). start_idxs (Tensor): Start index tensor (B,). segment_size (int):

Run inference on the configured datasets.

Validate and process dialogue messages. Args: messages: List of message tuples (role, modality, content). key: The example ID for

Helper: create a dataset JSON + mock data file.

Context manager to yield a Dask client from the global singleton cluster. Args: config (DictConfig, optional): Cluster config. se

Return predicted output as a dict.

Training step logic.

Return iterator over texts.

Checks that the two dropout proportions lists are equal.

Build and return a DataLoader for the specified mode ("train" or "valid"). This method supports two modes of operation depending on the confi

Return a fully-qualified name for callables when possible. Description: Uses module + qualname for callables (functions, methods, classes

Return layer object handler from library e.g. from torch.nn E.g. if l_name=="elu", returns torch.nn.ELU. Args: l_name (string): Case

Return keys of files.

Calculate accuracy. Args: pad_outputs (Tensor): Prediction tensors (B * Lmax, D). pad_targets (LongTensor): Target label tensors

Analysis with PQMF. Args: x (Tensor): Input tensor (B, 1, T). Returns: Tensor: Output tensor (B, subbands, T

Check if the utterance is too short for subsampling.

Convert Hz to mel.

Start the training loop using Lightning's fit method. Args: *args: Positional arguments passed to `trainer.fit()`. **

Recursively finds size of objects Taken from https://github.com/bosswissam/pysize

Set the primer sequence for decoding. Used for OpenAI Whisper models.

unfolding STFT representation to add context in the mics channel. Args: mixture (torch.Tensor): 3D tensor (monaural complex STFT)

Return a linear module, initialization, and activation.

Parse data specifier strings into structured tuples. Args: task_data_factors: Space-separated unregistered data specifiers. F

Resample a list by a given factor. For integer factors, simply repeats the list. For non-integer factors, repeats the whole list for the inte

Add <sos> and <eos> labels. :param torch.Tensor ys_pad: batch of padded target sequences (B, Lmax) :param int sos: index of <sos> :param

Write matrices in kaldi style. Args: wspecifier: e.g. ark,scp:out.ark,out.scp filetype: "mat" is kaldi-martix, "hdf5": HDF5

Register a tensor with a configurable learning rate and 0 weight decay.

Score interface for both full and partial scorer. Args: y: previous char state: previous state x: encoded

Score new token. Args: y: 1D torch.int64 prefix tokens. state: Scorer state for prefix tokens x: 2D encod

Set the global Dask cluster using the provided configuration. Args: config (DictConfig): Configuration object with 'env' and cluster opti

remove wrong phoneme

first_token: first token on the xconfig line, e.g. 'affine-layer'.f key_to_value: dictionary with parameter values { '

Perform sanity checks and set combined values after initialization. This method validates that codec and SSL tokenizers are compatible

Load statistics from a JSONL file. Each line in the file should be a JSON object mapping example IDs to their lengths (in tokens or frames).

Reject unexpected positional/keyword arguments for stages.