Openspeech’s configurations

This page describes all configurations in Openspeech.

audio

mfcc

• name : Name of dataset.

• sample_rate : Sampling rate of audio

• frame_length : Frame length for spectrogram

• frame_shift : Length of hop between STFT

• del_silence : Flag indication whether to apply delete silence or not

• num_mels : The number of mfc coefficients to retain.

• apply_spec_augment : Flag indication whether to apply spec augment or not

• apply_noise_augment : Flag indication whether to apply noise augment or not

• apply_time_stretch_augment : Flag indication whether to apply time stretch augment or not

• apply_joining_augment : Flag indication whether to apply audio joining augment or not

melspectrogram

• name : Name of dataset.

• sample_rate : Sampling rate of audio

• frame_length : Frame length for spectrogram

• frame_shift : Length of hop between STFT

• del_silence : Flag indication whether to apply delete silence or not

• num_mels : The number of mfc coefficients to retain.

• apply_spec_augment : Flag indication whether to apply spec augment or not

• apply_noise_augment : Flag indication whether to apply noise augment or not

• apply_time_stretch_augment : Flag indication whether to apply time stretch augment or not

• apply_joining_augment : Flag indication whether to apply audio joining augment or not

fbank

• name : Name of dataset.

• sample_rate : Sampling rate of audio

• frame_length : Frame length for spectrogram

• frame_shift : Length of hop between STFT

• del_silence : Flag indication whether to apply delete silence or not

• num_mels : The number of mfc coefficients to retain.

• apply_spec_augment : Flag indication whether to apply spec augment or not

• apply_noise_augment : Flag indication whether to apply noise augment or not

• apply_time_stretch_augment : Flag indication whether to apply time stretch augment or not

• apply_joining_augment : Flag indication whether to apply audio joining augment or not

spectrogram

• name : Name of dataset.

• sample_rate : Sampling rate of audio

• frame_length : Frame length for spectrogram

• frame_shift : Length of hop between STFT

• del_silence : Flag indication whether to apply delete silence or not

• num_mels : Spectrogram is independent of mel, but uses the ‘num_mels’ variable to unify feature size variables

• apply_spec_augment : Flag indication whether to apply spec augment or not

• apply_noise_augment : Flag indication whether to apply noise augment or not

• apply_time_stretch_augment : Flag indication whether to apply time stretch augment or not

• apply_joining_augment : Flag indication whether to apply audio joining augment or not

augment

default

• apply_spec_augment : Flag indication whether to apply spec augment or not

• apply_noise_augment : Flag indication whether to apply noise augment or not Noise augment requires noise_dataset_path. noise_dataset_dir should be contain audio files.

• apply_joining_augment : Flag indication whether to apply joining augment or not If true, create a new audio file by connecting two audio randomly

• apply_time_stretch_augment : Flag indication whether to apply spec augment or not

• freq_mask_para : Hyper Parameter for freq masking to limit freq masking length

• freq_mask_num : How many freq-masked area to make

• time_mask_num : How many time-masked area to make

• noise_dataset_dir : How many time-masked area to make

• noise_level : Noise adjustment level

• time_stretch_min_rate : Minimum rate of audio time stretch

• time_stretch_max_rate : Maximum rate of audio time stretch

dataset

kspon

• dataset : Select dataset for training (librispeech, ksponspeech, aishell, lm)

• dataset_path : Path of dataset

• test_dataset_path : Path of evaluation dataset

• manifest_file_path : Path of manifest file

• test_manifest_dir : Path of directory contains test manifest files

• preprocess_mode : KsponSpeech preprocess mode {phonetic, spelling}

libri

• dataset : Select dataset for training (librispeech, ksponspeech, aishell, lm)

• dataset_path : Path of dataset

• dataset_download : Flag indication whether to download dataset or not.

• manifest_file_path : Path of manifest file

aishell

• dataset : Select dataset for training (librispeech, ksponspeech, aishell, lm)

• dataset_path : Path of dataset

• dataset_download : Flag indication whether to download dataset or not.

• manifest_file_path : Path of manifest file

ksponspeech

• dataset : Select dataset for training (librispeech, ksponspeech, aishell, lm)

• dataset_path : Path of dataset

• test_dataset_path : Path of evaluation dataset

• manifest_file_path : Path of manifest file

• test_manifest_dir : Path of directory contains test manifest files

• preprocess_mode : KsponSpeech preprocess mode {phonetic, spelling}

librispeech

• dataset : Select dataset for training (librispeech, ksponspeech, aishell, lm)

• dataset_path : Path of dataset

• dataset_download : Flag indication whether to download dataset or not.

• manifest_file_path : Path of manifest file

lm

• dataset : Select dataset for training (librispeech, ksponspeech, aishell, lm)

• dataset_path : Path of dataset

• valid_ratio : Ratio of validation data

• test_ratio : Ratio of test data

model

listen_attend_spell

• model_name : Model name

• num_encoder_layers : The number of encoder layers.

• num_decoder_layers : The number of decoder layers.

• hidden_state_dim : The hidden state dimension of encoder.

• encoder_dropout_p : The dropout probability of encoder.

• encoder_bidirectional : If True, becomes a bidirectional encoders

• rnn_type : Type of rnn cell (rnn, lstm, gru)

• joint_ctc_attention : Flag indication joint ctc attention or not

• max_length : Max decoding length.

• num_attention_heads : The number of attention heads.

• decoder_dropout_p : The dropout probability of decoder.

• decoder_attn_mechanism : The attention mechanism for decoder.

• teacher_forcing_ratio : The ratio of teacher forcing.

• optimizer : Optimizer for training.

listen_attend_spell_with_location_aware

• model_name : Model name

• num_encoder_layers : The number of encoder layers.

• num_decoder_layers : The number of decoder layers.

• hidden_state_dim : The hidden state dimension of encoder.

• encoder_dropout_p : The dropout probability of encoder.

• encoder_bidirectional : If True, becomes a bidirectional encoders

• rnn_type : Type of rnn cell (rnn, lstm, gru)

• joint_ctc_attention : Flag indication joint ctc attention or not

• max_length : Max decoding length.

• num_attention_heads : The number of attention heads.

• decoder_dropout_p : The dropout probability of decoder.

• decoder_attn_mechanism : The attention mechanism for decoder.

• teacher_forcing_ratio : The ratio of teacher forcing.

• optimizer : Optimizer for training.

listen_attend_spell_with_multi_head

• model_name : Model name

• num_encoder_layers : The number of encoder layers.

• num_decoder_layers : The number of decoder layers.

• hidden_state_dim : The hidden state dimension of encoder.

• encoder_dropout_p : The dropout probability of encoder.

• encoder_bidirectional : If True, becomes a bidirectional encoders

• rnn_type : Type of rnn cell (rnn, lstm, gru)

• joint_ctc_attention : Flag indication joint ctc attention or not

• max_length : Max decoding length.

• num_attention_heads : The number of attention heads.

• decoder_dropout_p : The dropout probability of decoder.

• decoder_attn_mechanism : The attention mechanism for decoder.

• teacher_forcing_ratio : The ratio of teacher forcing.

• optimizer : Optimizer for training.

joint_ctc_listen_attend_spell

• model_name : Model name

• num_encoder_layers : The number of encoder layers.

• num_decoder_layers : The number of decoder layers.

• hidden_state_dim : The hidden state dimension of encoder.

• encoder_dropout_p : The dropout probability of encoder.

• encoder_bidirectional : If True, becomes a bidirectional encoders

• rnn_type : Type of rnn cell (rnn, lstm, gru)

• joint_ctc_attention : Flag indication joint ctc attention or not

• max_length : Max decoding length.

• num_attention_heads : The number of attention heads.

• decoder_dropout_p : The dropout probability of decoder.

• decoder_attn_mechanism : The attention mechanism for decoder.

• teacher_forcing_ratio : The ratio of teacher forcing.

• optimizer : Optimizer for training.

deep_cnn_with_joint_ctc_listen_attend_spell

• model_name : Model name

• num_encoder_layers : The number of encoder layers.

• num_decoder_layers : The number of decoder layers.

• hidden_state_dim : The hidden state dimension of encoder.

• encoder_dropout_p : The dropout probability of encoder.

• encoder_bidirectional : If True, becomes a bidirectional encoders

• rnn_type : Type of rnn cell (rnn, lstm, gru)

• extractor : The CNN feature extractor.

• activation : Type of activation function

• joint_ctc_attention : Flag indication joint ctc attention or not

• max_length : Max decoding length.

• num_attention_heads : The number of attention heads.

• decoder_dropout_p : The dropout probability of decoder.

• decoder_attn_mechanism : The attention mechanism for decoder.

• teacher_forcing_ratio : The ratio of teacher forcing.

• optimizer : Optimizer for training.

deepspeech2

• model_name : Model name

• rnn_type : Type of rnn cell (rnn, lstm, gru)

• num_rnn_layers : The number of rnn layers

• rnn_hidden_dim : Hidden state dimenstion of RNN.

• dropout_p : The dropout probability of model.

• bidirectional : If True, becomes a bidirectional encoders

• activation : Type of activation function

• optimizer : Optimizer for training.

lstm_lm

• model_name : Model name

• num_layers : The number of encoder layers.

• hidden_state_dim : The hidden state dimension of encoder.

• dropout_p : The dropout probability of encoder.

• rnn_type : Type of rnn cell (rnn, lstm, gru)

• max_length : Max decoding length.

• teacher_forcing_ratio : The ratio of teacher forcing.

• optimizer : Optimizer for training.

rnn_transducer

• model_name : Model name

• encoder_hidden_state_dim : Dimension of encoder.

• decoder_hidden_state_dim : Dimension of decoder.

• num_encoder_layers : The number of encoder layers.

• num_decoder_layers : The number of decoder layers.

• encoder_dropout_p : The dropout probability of encoder.

• decoder_dropout_p : The dropout probability of decoder.

• bidirectional : If True, becomes a bidirectional encoders

• rnn_type : Type of rnn cell (rnn, lstm, gru)

• output_dim : Dimension of outputs

• optimizer : Optimizer for training.

transformer_lm

• model_name : Model name

• num_layers : The number of encoder layers.

• d_model : The dimension of model.

• d_ff : The dimenstion of feed forward network.

• num_attention_heads : The number of attention heads.

• dropout_p : The dropout probability of encoder.

• max_length : Max decoding length.

• optimizer : Optimizer for training.

transformer

• model_name : Model name

• d_model : Dimension of model.

• d_ff : Dimenstion of feed forward network.

• num_attention_heads : The number of attention heads.

• num_encoder_layers : The number of encoder layers.

• num_decoder_layers : The number of decoder layers.

• encoder_dropout_p : The dropout probability of encoder.

• decoder_dropout_p : The dropout probability of decoder.

• ffnet_style : Style of feed forward network. (ff, conv)

• max_length : Max decoding length.

• teacher_forcing_ratio : The ratio of teacher forcing.

• joint_ctc_attention : Flag indication joint ctc attention or not

• optimizer : Optimizer for training.

joint_ctc_transformer

• model_name : Model name

• extractor : The CNN feature extractor.

• d_model : Dimension of model.

• d_ff : Dimenstion of feed forward network.

• num_attention_heads : The number of attention heads.

• num_encoder_layers : The number of encoder layers.

• num_decoder_layers : The number of decoder layers.

• encoder_dropout_p : The dropout probability of encoder.

• decoder_dropout_p : The dropout probability of decoder.

• ffnet_style : Style of feed forward network. (ff, conv)

• max_length : Max decoding length.

• teacher_forcing_ratio : The ratio of teacher forcing.

• joint_ctc_attention : Flag indication joint ctc attention or not

• optimizer : Optimizer for training.

transformer_with_ctc

• model_name : Model name

• d_model : Dimension of model.

• d_ff : Dimenstion of feed forward network.

• num_attention_heads : The number of attention heads.

• num_encoder_layers : The number of encoder layers.

• encoder_dropout_p : The dropout probability of encoder.

• ffnet_style : Style of feed forward network. (ff, conv)

• optimizer : Optimizer for training.

vgg_transformer

• model_name : Model name

• extractor : The CNN feature extractor.

• d_model : Dimension of model.

• d_ff : Dimenstion of feed forward network.

• num_attention_heads : The number of attention heads.

• num_encoder_layers : The number of encoder layers.

• num_decoder_layers : The number of decoder layers.

• encoder_dropout_p : The dropout probability of encoder.

• decoder_dropout_p : The dropout probability of decoder.

• ffnet_style : Style of feed forward network. (ff, conv)

• max_length : Max decoding length.

• teacher_forcing_ratio : The ratio of teacher forcing.

• joint_ctc_attention : Flag indication joint ctc attention or not

• optimizer : Optimizer for training.

conformer

• model_name : Model name

• encoder_dim : Dimension of encoder.

• num_encoder_layers : The number of encoder layers.

• num_attention_heads : The number of attention heads.

• feed_forward_expansion_factor : The expansion factor of feed forward module.

• conv_expansion_factor : The expansion factor of convolution module.

• input_dropout_p : The dropout probability of inputs.

• feed_forward_dropout_p : The dropout probability of feed forward module.

• attention_dropout_p : The dropout probability of attention module.

• conv_dropout_p : The dropout probability of convolution module.

• conv_kernel_size : The kernel size of convolution.

• half_step_residual : Flag indication whether to use half step residual or not

• optimizer : Optimizer for training.

conformer_lstm

• model_name : Model name

• encoder_dim : Dimension of encoder.

• num_encoder_layers : The number of encoder layers.

• num_attention_heads : The number of attention heads.

• feed_forward_expansion_factor : The expansion factor of feed forward module.

• conv_expansion_factor : The expansion factor of convolution module.

• input_dropout_p : The dropout probability of inputs.

• feed_forward_dropout_p : The dropout probability of feed forward module.

• attention_dropout_p : The dropout probability of attention module.

• conv_dropout_p : The dropout probability of convolution module.

• conv_kernel_size : The kernel size of convolution.

• half_step_residual : Flag indication whether to use half step residual or not

• num_decoder_layers : The number of decoder layers.

• decoder_dropout_p : The dropout probability of decoder.

• max_length : Max decoding length.

• teacher_forcing_ratio : The ratio of teacher forcing.

• rnn_type : Type of rnn cell (rnn, lstm, gru)

• decoder_attn_mechanism : The attention mechanism for decoder.

• optimizer : Optimizer for training.

conformer_transducer

• model_name : Model name

• encoder_dim : Dimension of encoder.

• num_encoder_layers : The number of encoder layers.

• num_attention_heads : The number of attention heads.

• feed_forward_expansion_factor : The expansion factor of feed forward module.

• conv_expansion_factor : The expansion factor of convolution module.

• input_dropout_p : The dropout probability of inputs.

• feed_forward_dropout_p : The dropout probability of feed forward module.

• attention_dropout_p : The dropout probability of attention module.

• conv_dropout_p : The dropout probability of convolution module.

• conv_kernel_size : The kernel size of convolution.

• half_step_residual : Flag indication whether to use half step residual or not

• num_decoder_layers : The number of decoder layers.

• decoder_dropout_p : The dropout probability of decoder.

• max_length : Max decoding length.

• teacher_forcing_ratio : The ratio of teacher forcing.

• rnn_type : Type of rnn cell (rnn, lstm, gru)

• decoder_hidden_state_dim : Hidden state dimension of decoder.

• decoder_output_dim : Output dimension of decoder.

• optimizer : Optimizer for training.

joint_ctc_conformer_lstm

• model_name : Model name

• encoder_dim : Dimension of encoder.

• num_encoder_layers : The number of encoder layers.

• num_attention_heads : The number of attention heads.

• feed_forward_expansion_factor : The expansion factor of feed forward module.

• conv_expansion_factor : The expansion factor of convolution module.

• input_dropout_p : The dropout probability of inputs.

• feed_forward_dropout_p : The dropout probability of feed forward module.

• attention_dropout_p : The dropout probability of attention module.

• conv_dropout_p : The dropout probability of convolution module.

• conv_kernel_size : The kernel size of convolution.

• half_step_residual : Flag indication whether to use half step residual or not

• num_decoder_layers : The number of decoder layers.

• decoder_dropout_p : The dropout probability of decoder.

• num_decoder_attention_heads : The number of decoder attention heads.

• max_length : Max decoding length.

• teacher_forcing_ratio : The ratio of teacher forcing.

• rnn_type : Type of rnn cell (rnn, lstm, gru)

• decoder_attn_mechanism : The attention mechanism for decoder.

• optimizer : Optimizer for training.

transformer_transducer

• model_name : Model name

• encoder_dim : Dimension of encoder name

• d_ff : Dimension of feed forward network

• num_audio_layers : Number of audio layers

• num_label_layers : Number of label layers

• num_attention_heads : Number of attention heads

• audio_dropout_p : Dropout probability of audio layer

• label_dropout_p : Dropout probability of label layer

• decoder_hidden_state_dim : Hidden state dimension of decoder

• decoder_output_dim : Dimension of model output.

• conv_kernel_size : Kernel size of convolution layer.

• max_positional_length : Max length of positional encoding.

• optimizer : Optimizer for training.

quartznet5x5

• model_name : Model name

• num_blocks : Number of quartznet blocks

• num_sub_blocks : Number of quartznet sub blocks

• in_channels : Input channels of jasper blocks

• out_channels : Output channels of jasper block’s convolution

• kernel_size : Kernel size of jasper block’s convolution

• dilation : Dilation of jasper block’s convolution

• dropout_p : Dropout probability

• optimizer : Optimizer for training.

quartznet10x5

• model_name : Model name

• num_blocks : Number of quartznet blocks

• num_sub_blocks : Number of quartznet sub blocks

• in_channels : Input channels of jasper blocks

• out_channels : Output channels of jasper block’s convolution

• kernel_size : Kernel size of jasper block’s convolution

• dilation : Dilation of jasper block’s convolution

• dropout_p : Dropout probability

• optimizer : Optimizer for training.

quartznet15x5

• model_name : Model name

• num_blocks : Number of quartznet5x5 blocks

• num_sub_blocks : Number of quartznet5x5 sub blocks

• in_channels : Input channels of jasper blocks

• out_channels : Output channels of jasper block’s convolution

• kernel_size : Kernel size of jasper block’s convolution

• dilation : Dilation of jasper block’s convolution

• dropout_p : Dropout probability

• optimizer : Optimizer for training.

contextnet

• model_name : Model name

• model_size : Model size

• input_dim : Dimension of input vector

• num_encoder_layers : The number of convolution layers

• kernel_size : Value of convolution kernel size

• num_channels : The number of channels in the convolution filter

• encoder_dim : Dimension of encoder output vector

• optimizer : Optimizer for training

contextnet_lstm

• model_name : Model name

• model_size : Model size

• input_dim : Dimension of input vector

• num_encoder_layers : The number of convolution layers

• num_decoder_layers : The number of decoder layers.

• kernel_size : Value of convolution kernel size

• num_channels : The number of channels in the convolution filter

• encoder_dim : Dimension of encoder output vector

• num_attention_heads : The number of attention heads.

• attention_dropout_p : The dropout probability of attention module.

• decoder_dropout_p : The dropout probability of decoder.

• max_length : Max decoding length.

• teacher_forcing_ratio : The ratio of teacher forcing.

• rnn_type : Type of rnn cell (rnn, lstm, gru)

• decoder_attn_mechanism : The attention mechanism for decoder.

• optimizer : Optimizer for training.

contextnet_transducer

• model_name : Model name

• model_size : Model size

• input_dim : Dimension of input vector

• num_encoder_layers : The number of convolution layers

• num_decoder_layers : The number of rnn layers

• kernel_size : Value of convolution kernel size

• num_channels : The number of channels in the convolution filter

• hidden_dim : The number of features in the decoder hidden state

• encoder_dim : Dimension of encoder output vector

• decoder_output_dim : Dimension of decoder output vector

• dropout : Dropout probability of decoder

• rnn_type : Type of rnn cell

• optimizer : Optimizer for training

jasper5x3

• model_name : Model name

• num_blocks : Number of jasper blocks

• num_sub_blocks : Number of jasper sub blocks

• in_channels : Input channels of jasper blocks

• out_channels : Output channels of jasper block’s convolution

• kernel_size : Kernel size of jasper block’s convolution

• dilation : Dilation of jasper block’s convolution

• dropout_p : Dropout probability

• optimizer : Optimizer for training.

jasper10x5

• model_name : Model name

• num_blocks : Number of jasper blocks

• num_sub_blocks : Number of jasper sub blocks

• in_channels : Input channels of jasper blocks

• out_channels : Output channels of jasper block’s convolution

• kernel_size : Kernel size of jasper block’s convolution

• dilation : Dilation of jasper block’s convolution

• dropout_p : Dropout probability

• optimizer : Optimizer for training.

criterion

label_smoothed_cross_entropy

• criterion_name : Criterion name for training.

• reduction : Reduction method of criterion

• smoothing : Ratio of smoothing loss (confidence = 1.0 - smoothing)

joint_ctc_cross_entropy

• criterion_name : Criterion name for training.

• reduction : Reduction method of criterion

• ctc_weight : Weight of ctc loss for training.

• cross_entropy_weight : Weight of cross entropy loss for training.

• smoothing : Ratio of smoothing loss (confidence = 1.0 - smoothing)

• zero_infinity : Whether to zero infinite losses and the associated gradients.

perplexity

• criterion_name : Criterion name for training

• reduction : Reduction method of criterion

transducer

• criterion_name : Criterion name for training.

• reduction : Reduction method of criterion

• gather : Reduce memory consumption.

ctc

• criterion_name : Criterion name for training

• reduction : Reduction method of criterion

• zero_infinity : Whether to zero infinite losses and the associated gradients.

cross_entropy

• criterion_name : Criterion name for training

• reduction : Reduction method of criterion

lr_scheduler

reduce_lr_on_plateau

• lr : Learning rate

• scheduler_name : Name of learning rate scheduler.

• lr_patience : Number of epochs with no improvement after which learning rate will be reduced.

• lr_factor : Factor by which the learning rate will be reduced. new_lr = lr * factor.

warmup

• lr : Learning rate

• scheduler_name : Name of learning rate scheduler.

• peak_lr : Maximum learning rate.

• init_lr : Initial learning rate.

• warmup_steps : Warmup the learning rate linearly for the first N updates

• total_steps : Total training steps.

warmup_reduce_lr_on_plateau

• lr : Learning rate

• scheduler_name : Name of learning rate scheduler.

• lr_patience : Number of epochs with no improvement after which learning rate will be reduced.

• lr_factor : Factor by which the learning rate will be reduced. new_lr = lr * factor.

• peak_lr : Maximum learning rate.

• init_lr : Initial learning rate.

• warmup_steps : Warmup the learning rate linearly for the first N updates

tri_stage

• lr : Learning rate

• scheduler_name : Name of learning rate scheduler.

• init_lr : Initial learning rate.

• init_lr_scale : Initial learning rate scale.

• final_lr_scale : Final learning rate scale

• phase_ratio : Automatically sets warmup/hold/decay steps to the ratio specified here from max_updates. the ratios must add up to 1.0

• total_steps : Total training steps.

transformer

• lr : Learning rate

• scheduler_name : Name of learning rate scheduler.

• peak_lr : Maximum learning rate.

• final_lr : Final learning rate.

• final_lr_scale : Final learning rate scale

• warmup_steps : Warmup the learning rate linearly for the first N updates

• decay_steps : Steps in decay stages

trainer

cpu

• seed : Seed for training.

• accelerator : Previously known as distributed_backend (dp, ddp, ddp2, etc…).

• accumulate_grad_batches : Accumulates grads every k batches or as set up in the dict.

• num_workers : The number of cpu cores

• batch_size : Size of batch

• check_val_every_n_epoch : Check val every n train epochs.

• gradient_clip_val : 0 means don’t clip.

• logger : Training logger. {wandb, tensorboard}

• max_epochs : Stop training once this number of epochs is reached.

• auto_scale_batch_size : If set to True, will initially run a batch size finder trying to find the largest batch size that fits into memory.

• name : Trainer name

• device : Training device.

• use_cuda : If set True, will train with GPU

gpu

• seed : Seed for training.

• accelerator : Previously known as distributed_backend (dp, ddp, ddp2, etc…).

• accumulate_grad_batches : Accumulates grads every k batches or as set up in the dict.

• num_workers : The number of cpu cores

• batch_size : Size of batch

• check_val_every_n_epoch : Check val every n train epochs.

• gradient_clip_val : 0 means don’t clip.

• logger : Training logger. {wandb, tensorboard}

• max_epochs : Stop training once this number of epochs is reached.

• auto_scale_batch_size : If set to True, will initially run a batch size finder trying to find the largest batch size that fits into memory.

• name : Trainer name

• device : Training device.

• use_cuda : If set True, will train with GPU

• auto_select_gpus : If enabled and gpus is an integer, pick available gpus automatically.

tpu

• seed : Seed for training.

• accelerator : Previously known as distributed_backend (dp, ddp, ddp2, etc…).

• accumulate_grad_batches : Accumulates grads every k batches or as set up in the dict.

• num_workers : The number of cpu cores

• batch_size : Size of batch

• check_val_every_n_epoch : Check val every n train epochs.

• gradient_clip_val : 0 means don’t clip.

• logger : Training logger. {wandb, tensorboard}

• max_epochs : Stop training once this number of epochs is reached.

• auto_scale_batch_size : If set to True, will initially run a batch size finder trying to find the largest batch size that fits into memory.

• name : Trainer name

• device : Training device.

• use_cuda : If set True, will train with GPU

• use_tpu : If set True, will train with GPU

• tpu_cores : Number of TPU cores

gpu-fp16

• seed : Seed for training.

• accelerator : Previously known as distributed_backend (dp, ddp, ddp2, etc…).

• accumulate_grad_batches : Accumulates grads every k batches or as set up in the dict.

• num_workers : The number of cpu cores

• batch_size : Size of batch

• check_val_every_n_epoch : Check val every n train epochs.

• gradient_clip_val : 0 means don’t clip.

• logger : Training logger. {wandb, tensorboard}

• max_epochs : Stop training once this number of epochs is reached.

• auto_scale_batch_size : If set to True, will initially run a batch size finder trying to find the largest batch size that fits into memory.

• name : Trainer name

• device : Training device.

• use_cuda : If set True, will train with GPU

• auto_select_gpus : If enabled and gpus is an integer, pick available gpus automatically.

• precision : Double precision (64), full precision (32) or half precision (16). Can be used on CPU, GPU or TPUs.

• amp_backend : The mixed precision backend to use (“native” or “apex”)

tpu-fp16

• seed : Seed for training.

• accelerator : Previously known as distributed_backend (dp, ddp, ddp2, etc…).

• accumulate_grad_batches : Accumulates grads every k batches or as set up in the dict.

• num_workers : The number of cpu cores

• batch_size : Size of batch

• check_val_every_n_epoch : Check val every n train epochs.

• gradient_clip_val : 0 means don’t clip.

• logger : Training logger. {wandb, tensorboard}

• max_epochs : Stop training once this number of epochs is reached.

• auto_scale_batch_size : If set to True, will initially run a batch size finder trying to find the largest batch size that fits into memory.

• name : Trainer name

• device : Training device.

• use_cuda : If set True, will train with GPU

• use_tpu : If set True, will train with GPU

• tpu_cores : Number of TPU cores

• precision : Double precision (64), full precision (32) or half precision (16). Can be used on CPU, GPU or TPUs.

• amp_backend : The mixed precision backend to use (“native” or “apex”)

cpu-fp64

• seed : Seed for training.

• accelerator : Previously known as distributed_backend (dp, ddp, ddp2, etc…).

• accumulate_grad_batches : Accumulates grads every k batches or as set up in the dict.

• num_workers : The number of cpu cores

• batch_size : Size of batch

• check_val_every_n_epoch : Check val every n train epochs.

• gradient_clip_val : 0 means don’t clip.

• logger : Training logger. {wandb, tensorboard}

• max_epochs : Stop training once this number of epochs is reached.

• auto_scale_batch_size : If set to True, will initially run a batch size finder trying to find the largest batch size that fits into memory.

• name : Trainer name

• device : Training device.

• use_cuda : If set True, will train with GPU

• precision : Double precision (64), full precision (32) or half precision (16). Can be used on CPU, GPU or TPUs.

• amp_backend : The mixed precision backend to use (“native” or “apex”)

tokenizer

libri_subword

• sos_token : Start of sentence token

• eos_token : End of sentence token

• pad_token : Pad token

• blank_token : Blank token (for CTC training)

• encoding : Encoding of vocab

• unit : Unit of vocabulary.

• vocab_size : Size of vocabulary.

• vocab_path : Path of vocabulary file.

libri_character

• sos_token : Start of sentence token

• eos_token : End of sentence token

• pad_token : Pad token

• blank_token : Blank token (for CTC training)

• encoding : Encoding of vocab

• unit : Unit of vocabulary.

• vocab_path : Path of vocabulary file.

aishell_character

• sos_token : Start of sentence token

• eos_token : End of sentence token

• pad_token : Pad token

• blank_token : Blank token (for CTC training)

• encoding : Encoding of vocab

• unit : Unit of vocabulary.

• vocab_path : Path of vocabulary file.

kspon_subword

• sos_token : Start of sentence token

• eos_token : End of sentence token

• pad_token : Pad token

• blank_token : Blank token (for CTC training)

• encoding : Encoding of vocab

• unit : Unit of vocabulary.

• sp_model_path : Path of sentencepiece model.

• vocab_size : Size of vocabulary.

kspon_grapheme

• sos_token : Start of sentence token

• eos_token : End of sentence token

• pad_token : Pad token

• blank_token : Blank token (for CTC training)

• encoding : Encoding of vocab

• unit : Unit of vocabulary.

• vocab_path : Path of vocabulary file.

kspon_character

• sos_token : Start of sentence token

• eos_token : End of sentence token

• pad_token : Pad token

• blank_token : Blank token (for CTC training)

• encoding : Encoding of vocab

• unit : Unit of vocabulary.

• vocab_path : Path of vocabulary file.