For getting encoding in a streaming fashion Attention!!!!! we apply dropout only once at the whole utterance level in a none

For getting encoding in a streaming fashion Attention!!!!! we apply dropout only once at the whole utterance level in a none

For getting encoding in a streaming fashion Attention!!!!! we apply dropout only once at the whole utterance level in a none

prefix beam search also see wenet.transducer.transducer.beam_search

[mat] = read_mat(file_or_fd) Reads single kaldi matrix, supports ascii and binary. file_or_fd : file, gzipped file, pipe or opened file descrip

read non-linguistic symbol from file. The file format is like below: {NOISE}\n {BRK}\n ... Args: non_lang_sym_path: no

read non-linguistic symbol from file. The file format is like below: {NOISE}\n {BRK}\n ... Args: non_lang_sym_path: no

[flt-vec] = read_vec_flt(file_or_fd) Read kaldi float vector, ascii or binary input,

[int-vec] = read_vec_int(file_or_fd) Read kaldi integer vector, ascii or binary input,

r"""Refer to https://github.com/Outsider565/LoRA-GA/blob/ c185846309ea9012d0bcd46ebd30347dda1c592c/run_exp.py#L67 Reinitialize the lora model

Sample data according to rank/world_size/num_workers Args: data(List): input data list Returns:

Args: infos (dict or None): any info you want to save.

Args: infos (dict or None): any info you want to save.

Fixed euler solver for ODEs. Args: x (torch.Tensor): random noise t_span (torch.Tensor): n_timesteps interpol

Export interface for c++ call, return sos symbol id of the model

Static batch the data by `batch_size` Args: data: Iterable[{key, feat, label}] batch_size: batch size Retur

Export interface for c++ call, return subsampling_rate of the model

Create mask for subsequent steps (size, size) with chunk size, this is for streaming encoder Args: size (int): size of mask

Create mask for subsequent steps (size, size) with chunk size, this is for streaming encoder Args: size (int): size of mask

Create mask for subsequent steps (size, size) with chunk size, this is for streaming encoder Args: size (int): size of mask

Create mask for subsequent steps (size, size). This mask is used only in decoder which works in an auto-regressive mode. This means the curre

Tie or clone module weights (between word_emb and output_layer) depending of whether we are using TorchScript or not

Tie or clone module weights (between word_emb and output_layer) depending of whether we are using TorchScript or not

Tie or clone module weights (between word_emb and output_layer) depending of whether we are using TorchScript or not

Decode text to chars or BPE Inplace operation Args: data: Iterable[{key, wav, txt, sample_rate}] Returns:

Construct dataset from arguments We have two shuffle stage in the Dataset. The first is global shuffle at shard tar/raw file level.

Construct dataset from arguments We have two shuffle stage in the Dataset. The first is global shuffle at shards tar/raw file level.

Construct dataset from arguments for ssl model We have two shuffle stage in the Dataset. The first is global shuffle at shards tar/r

Construct an MultiHeadedAttention object.

Construct an EncoderLayer object.

Construct CTC module Args: odim: dimension of outputs encoder_output_size: number of encoder projection units

Construct an EncoderLayer object.

Construct an EncoderLayer object.

Construct DualConformerEncoder Support both the full context mode and the streaming mode separately

Perform batch beam search Args: logit: shape (batch_size, seq_length, vocab_size) masks: shape (batch_size, seq_leng

This function fills the fail arc for each trie node, it can be computed in linear time by performing a breadth-first search starting from the

This function fills the fail arc for each trie node, it can be computed in linear time by performing a breadth-first search starting from the