config

Config File Documentation

Description of parameters available in config files for training and inference.

Training Configs

Each config should include data/hparas/model, see example on LibrSpeech.

Data

Options under this category are all data-related.

• Corpus

  For each corpus, a corresponding source python file <corpus_name>.py file should be placed at corpus/, checkout librispeech.py for example.

  Parameter	Description	Note
  name	str name of corpus (used in data.py to import the dataset defined in <corpus_name>.py)	Available: Librispeech
  path	str path to the specified corpus, parsing file structure should be handled in <corpus_name>.py
  train_split	list which includes subsets of corpus used for training, accepted partition names should be defined in <corpus_name>.py
  dev_split	list which includes subsets of corpus used for validation, accepted partition names should be defined in <corpus_name>.py
  bucketing	bool to enable bucketing, i.e. similar length in each batch, should be implemented in <corpus_name>.py	More effecient training but biased sampling
  batch_size	int Batch size for training/validation, will be send to Torch Dataloader

• Audio

  Hyperparameters of feature extraction performed on-the-fly mostly done by torchaudio, checkout audio.py for implementation.

  Parameter	Description	Note
  feat_type	str name of audio feature to be used. Please note that MFCC required latest torch audio	Available: fbank/mfcc
  feat_dim	int dimensionality of audio feature, if you are not fimiliar with audio features, 40 for fbank and 13 for mfcc generally works
  frame_length	int size of the window (millisecond) for feature extraction
  frame_shift	int hop size of the window (millisecond) for feature extraction
  dither	float dither when extracting feature	See doc
  apply_cmvn	bool to activate feature normalization	Using our own implementation
  delta_order	int to apply delta on feature. 0: do nothing, 1: add delta, 2: also add accelerate	Using our own implementation
  delta_window_size	int to specify the window size for delta calculation

• Text

  Options to specify how text should be encoded, subword models use sentencepiece

  Parameter	Description	Note
  mode	str text unit for encoding sentences	Available: character/subword/word
  vocab_file	src path to file containing vocabulary set	Please use generate_vocab_file.py to generate it

Hparas

Options under this category are all training-related.

Parameter	Description	Note
valid_step	int interval, numbers of training step for each validation
max_step	int total training step
tf_start	float init. teacher forcing probability in scheduled sampling
tf_end	float final teacher forcing probability in scheduled sampling
tf_step	int number of steps to linearly decrease teacher forcing probability
optimizer	str the name of pytorch optimizer for training	Tested: Adam/Adadelta
lr	float learning rate for optimizer
eps	float epsilon for optimizer
lr_scheduler	str learning rate scheduler	Available: fixed/warmup
curriculum	int numbers of epochs to perform curriculum learning (short uttr. first)

Model

• ctc_weight: weight of CTC in hybird CTC-Attention model (between 0~1, 0=disabled, 1 is under development)

• Encoder

  Parameter	Description	Note
  prenet	str to employ VGG/CNN based encoder before RNN	vgg/cnn
  module	str the name of recurrent unit for encoder RNN layer	Only LSTM was tested
  bidirection	bool to enable bidirectional RNN over input sequence
  dim	list of number of cells for each RNN layer (per direction)
  dropout	list of dropout probability for each RNN layer	Length must match dim
  layer_norm	list of bool to enable LayerNorm for each RNN layer	Not recommended
  proj	list of bool to enable linear projection after each RNN layer	Length must match dim
  sample_rate	list sample rate for each RNN layer. For each layer, the length of output on the time dimension will be input/sample_rate.	Length must match dim
  sample_style	str the down sampling mechanism. concat will concatenate multiple time steps according to sample rate into one vector, drop will drop the unsampled timesteps.	Available:concat/drop

• Attention

  Parameter	Description	Note
  mode	str attention mechanism, dot is the vanilla attention and loc indicates the location-based attention.	Available: dot/loc
  dim	int dimension of all networks in attention
  num_head	int number of head in multi-head attention, 1: normal attention	Performance untested
  v_proj	bool to apply additional linear transform to encoder feature before weighted sum
  temperature	float the temperature to controll sharpness of sofmax function in attention
  loc_kernel_size	int kernel size for convolution in location awared attention	For loc only
  loc_kernel_num	int number of kernel for convolution in location awared attention	For loc only

• Decoder

  Parameter	Description	Note
  module	str the name of recurrent unit for encoder RNN layer	Only LSTM was tested
  dim	int number of cells in decoder
  layer	int number of layers in decoder
  dropout	float of dropout probability

Additional Plug-ins

The following mechanisms are our proposed methods, can be activate by inserting these parameters to config file

• Emb

  Parameter	Description	Note
  enable	bool to enable word embedding regularization or fused decoding on ASR
  src	str path to pre-trained embedding table or BERT model	The bert-base-uncased model fine-tuned on librispeech text data is available here
  distance	str measurement of distance between word embedding and model output	Available: CosEmb/MSE(untested)
  weight	float $\lambda$ in paper
  fuse	float $\lambda_f$ in paper
  fuse_normalize	bool to normalize output before Cosine-Softmax in paper, should be on when distance==CosEmb
  bert	str name of BERT model if using BERT as target embedding, e.g. bert-base-uncased	mutually exclusive to fuse>0

Inference Configs

Each config should include src/decode/data, see example on LibrSpeech. Note that most of the options (audio feature, model structure, etc.) will be imported from the training config specified in src.

Src

Specify the ASR to use in decoding process.

Parameter	Description	Note
ckpt	str path to ASR checkpoint to be load
config	str path to ASR training config which belongs to the checkpoint

Data

• Corpus

  Parameter	Description	Note
  name	See corpus section in training config
  dev_split	See corpus section in training config
  test_split	Like dev set, ASR will perform exactly same decoding process on this set, should also be defined by user like train/dev set

Decode

Options for decoding that will dramatically change the decoding result.

Parameter	Description	Note
beam_size	int beam size for beam search algorithm, be careful that larger beam increases memory usage
min_len_ratio	float the minimum length of any hypothesis will be min_len_ratio $\times$ input length
max_len_ratio	float the maximum decoding time step will be max_len_ratio $\times$ input length, hypothesis will end if <eos> is predicted or maximum decoding step reached
lm_path	str the path to pre-trained LM for joint decoding, this is not language model rescoring	paper
lm_config	str the path to the config of pre-trained LM for joint decoding	paper
lm_weight	float the weight for RNNLM in joint decoding	paper, slower inference
ctc_weight	float the weight for CTC network in joint decoding, this will only be available if ctc_weight was not zero in training config	paper, slower inference
vocab_candidate	int the number of vocabulary candidates considered in CTC beam decoding, the smaller the value the faster the decoding, but it must be greater than beam_size	paper