We are planning a super major update, called ESPnet2. The developing status is still under construction yet, so please be very careful to use with understanding following cautions:

  • There might be fatal bugs related to essential parts.

  • We haven’t achieved comparable results to espnet1 on each task yet.

Main changing from ESPnet1

  • Chainer free

    • Discarding Chainer completely.

    • The development of Chainer is stopped at v7:

  • Kaldi free

    • It’s not mandatory to compile Kaldi.

    • If you find some recipes requiring Kaldi mandatory, please report it. It should be dealt with as a bug in ESPnet2.

    • We still support the features made by Kaldi optionally.

    • We still follow Kaldi style. i.e. depending on utils/ of Kaldi.

  • On the fly feature extraction & text preprocessing for training

    • You don’t need to create the feature file before training, but just input wave data directly.

    • We support both raw wave input and extracted features.

    • The preprocessing for text, tokenization to characters, or sentencepieces, can be also applied during training.

    • Support self-supervised learning representations from s3prl

  • Discarding the JSON format describing the training corpus.

    • Why do we discard the JSON format? Because a dict object generated from a large JSON file requires much memory and it also takes much time to parse such a large JSON file.

  • Support distributed data-parallel training (Not enough tested)

    • Single node multi GPU training with DistributedDataParallel is also supported.

Recipes using ESPnet2

You can find the new recipes in egs2:

espnet/  # Python modules of espnet1
espnet2/ # Python modules of espnet2
egs/     # espnet1 recipes
egs2/    # espnet2 recipes

The usage of recipes is almost the same as that of ESPnet1.

  1. Change directory to the base directory

    # e.g.
    cd egs2/an4/asr1/

    an4 is a tiny corpus and can be freely obtained, so it might be suitable for this tutorial. You can perform any other recipes as the same way. e.g. wsj, librispeech, and etc.

    Keep in mind that all scripts should be ran at the level of egs2/*/{asr1,tts1,...}.

    # Doesn't work
    cd egs2/an4/
    # Doesn't work
    cd egs2/an4/asr1/local/
    # Work
    cd egs2/an4/asr1
  2. Change the configuration Describing the directory structure as follows:

     - conf/      # Configuration files for training, inference, etc.
     - scripts/   # Bash utilities of espnet2
     - pyscripts/ # Python utilities of espnet2
     - steps/     # From Kaldi utilities
     - utils/     # From Kaldi utilities
     -      # The directory path of each corpora
     -    # Setup script for environment variables
     -     # Configuration for your backend of job scheduler
     -     # Entry point
     -     # Invoked by
    • You need to modify for specifying your corpus before executing For example, when you touch the recipe of egs2/wsj, you need to change the paths of WSJ0 and WSJ1 in

    • Some corpora can be freely obtained from the WEB and they are written as “downloads/” at the initial state. You can also change them to your corpus path if it’s already downloaded.

    • is used to set up the environment for Note that the Python interpreter used for ESPnet is not the current Python of your terminal, but it’s the Python which was installed at tools/. Thus you need to source to use this Python.

    • is used for specifying the backend of the job scheduler. If you don’t have such a system in your local machine environment, you don’t need to change anything about this file. See Using Job scheduling system

  3. Run

 is an example script, which we often call as “recipe”, to run all stages related to DNN experiments; data-preparation, training, and evaluation.

See training status

Show the log file

% tail -f exp/*_train_*/train.log
[host] 2020-04-05 16:34:54,278 (trainer:192) INFO: 2/40epoch started. Estimated time to finish: 7 minutes and 58.63 seconds
[host] 2020-04-05 16:34:56,315 (trainer:453) INFO: 2epoch:train:1-10batch: iter_time=0.006, forward_time=0.076, loss=50.873, los
s_att=35.801, loss_ctc=65.945, acc=0.471, backward_time=0.072, optim_step_time=0.006, lr_0=1.000, train_time=0.203
[host] 2020-04-05 16:34:58,046 (trainer:453) INFO: 2epoch:train:11-20batch: iter_time=4.280e-05, forward_time=0.068, loss=44.369
, loss_att=28.776, loss_ctc=59.962, acc=0.506, backward_time=0.055, optim_step_time=0.006, lr_0=1.000, train_time=0.173

Show the training status in a image file

# Accuracy plot
# (eog is Eye of GNOME Image Viewer)
eog exp/*_train_*/images/acc.img
# Attention plot
eog exp/*_train_*/att_ws/<sample-id>/<param-name>.img

Use tensorboard

tensorboard --logdir exp/*_train_*/tensorboard/

Instruction for

How to parse command-line arguments in shell scripts?

All shell scripts in espnet/espnet2 depend on utils/ to parase command line arguments.

e.g. If the script has ngpu option

#!/usr/bin/env bash
. utils/
echo ${ngpu}

Then you can change the value as follows:

$ ./ --ngpu 2
echo 2

You can also show the help message:

./ --help

Start from a specified stage and stop at a specified stage

The procedures in can be divided into some stages, e.g. data preparation, training, and evaluation. You can specify the starting stage and the stopping stage.

./ --stage 2 --stop-stage 6

There are also some altenative otpions to skip specified stages: --skip_data_prep true  # Skip data preparation stages. --skip_train true      # Skip training stages. --skip_eval true       # Skip decoding and evaluation stages. --skip_upload false    # Enable packing and uploading stages.

Note that skip_upload is true by default. Please change it to false when uploading your model.

Change the configuration for training

Please keep in mind that is a wrapper script of several tools including DNN training command. You need to do one of the following two ways to change the training configuration.

# Give a configuration file
./ --asr_config conf/train_asr.yaml
# Give arguments to "espnet2/bin/" directly
./ --asr_args "--foo arg --bar arg2"

e.g. To change learning rate for the LM training

./ --lm_args "--optim_conf lr=0.1"

This is the case of ASR training and you need to replace it accordingly for the other task. e.g. For TTS

./ --tts_args "--optim_conf lr=0.1"

See Change the configuration for training for more detail about the usage of training tools.

Change the number of parallel jobs

./ --nj 10             # Chnage the number of parallels for data preparation stages.
./ --inference_nj 10   # Chnage the number of parallels for inference jobs.

We also support submitting jobs to multiple hosts to accelerate your experiment: See Using Job scheduling system

Multi GPUs training and distributed training

./ --ngpu 4 # 4GPUs in a single node
./ --ngpu 2 --num_nodes 2 # 2GPUs x 2nodes

Note that you need to setup your environment correctly to use distributed training. See the following two:

Relationship between mini-batch size and number of GPUs

The behavior of batch size in ESPnet2 during multi-GPU training is different from that in ESPnet1. In ESPnet2, the total batch size is not changed regardless of the number of GPUs. Therefore, you need to manually increase the batch size if you increase the number of GPUs. Please refer to this doc for more information.

Use specified experiment directory for evaluation

If you already have trained a model, you may wonder how to give it to when you’ll evaluate it later. By default the directory name is determined according to given options, asr_args, lm_args, or etc. You can overwrite it by --asr_exp and --lm_exp.

# For ASR recipe
./ --skip_data_prep true --skip_train true --asr_exp <your_asr_exp_directory> --lm_exp <your_lm_exp_directory>

# For TTS recipe
./ --skip_data_prep true --skip_train true --tts_exp <your_tts_exp_directory>

Evaluation without training using pretrained model

./ --download_model <model_name> --skip_train true

You need to fill model_name by yourself. You can search for pretrained models on Hugging Face using the tag espnet

(Deprecated: See the following link about our pretrain models:

Evaluation using OpenAI Whisper

ESPnet2 provides a script to run inference and scoring using OpenAI’s Whisper. This can be used to evaluate speech generation models. Here is an example:

#!/usr/bin/env bash
# Set bash to 'debug' mode, it will exit on :
# -e 'error', -u 'undefined variable', -o ... 'error in pipeline', -x 'print commands',
set -e
set -u
set -o pipefail

whisper_tag=medium    # whisper model tag, e.g., small, medium, large, etc
# decode_options is used in Whisper model's transcribe method
decode_options="{language: en, task: transcribe, temperature: 0, beam_size: 10, fp16: False}"

for x in ${test_sets}; do
    wavscp=dump/raw/${x}/wav.scp    # path to wav.scp
    outdir=whisper-${whisper_tag}_outputs/${x}  # path to save output
    gt_text=dump/raw/${x}/text      # path to groundtruth text file (for scoring only)

    scripts/utils/ \
        --whisper_tag ${whisper_tag} \
        --nj ${nj} \
        --gpu_inference true \
        --stage 2 \
        --stop_stage 3 \
        --cleaner ${cleaner} \
        --hyp_cleaner ${hyp_cleaner} \
        --decode_options "${decode_options}" \
        --gt_text ${gt_text} \
        ${wavscp} \

Packing and sharing your trained model

ESPnet encourages you to share your results using platforms like Hugging Face or Zenodo (This last will become deprecated.)

For sharing your models, the last three stages of each task simplify this process. The model is packed into a zip file and uploaded to the selected platform (one or both).

For Hugging Face, you need to first create a repository (<my_repo> = <user_name>/<repo_name>). Remember to install git-lfs before continuing. Then, execute as follows:

# For ASR recipe
./ --stage 14 --skip-upload-hf false --hf-repo <my_repo>

# For TTS recipe
./ --stage 8 --skip-upload-hf false --hf-repo <my_repo>

For Zenodo, you need to register your account first. Then, execute as follows:

# For ASR recipe
./ --stage 14 --skip-upload false

# For TTS recipe
./ --stage 8 --skip-upload false

The packed model can be uploaded to both platforms by setting the previously mentioned flags.

Usage of Self-Supervised Learning Representations as feature

ESPnet supports self-supervised learning representations (SSLR) to replace traditional spectrum features. In some cases, SSLRs can boost the performance.

To use SSLRs in your task, you need to make several modifications.


  1. Install S3PRL by tools/installers/

  2. If HuBERT / Wav2Vec is needed, fairseq should be installed by tools/installers/


  1. To reduce the time used in collect_stats step, please specify --feats_normalize uttmvn in and pass it as arguments to or other task-specific scripts. (Recommended)

  2. In the configuration file, specify the frontend and preencoder. Taking HuBERT as an example: The upstream name can be whatever supported in S3PRL. multilayer-feature=True means the final representation is a weighted-sum of all layers’ hidden states from SSLR model.

    frontend: s3prl
          upstream: hubert_large_ll60k  # Note: If the upstream is changed, please change the input_size in the preencoder.
       download_dir: ./hub
       multilayer_feature: True

    Here the preencoder is to reduce the input dimension to the encoder, to reduce the memory cost. The input_size depends on the upstream model, while the output_size can be set to any values.

    preencoder: linear
       input_size: 1024  # Note: If the upstream is changed, please change this value accordingly.
       output_size: 80
  3. Because the shift sizes of different upstream models are different, e.g. HuBERT and Wav2Vec2.0 have 20ms frameshift. Sometimes, the downsampling rate (input_layer) in the encoder configuration need to be changed. For example, using input_layer: conv2d2 will results in a total frameshift of 40ms, which is enough for some tasks.

Streaming ASR

ESPnet supports streaming Transformer/Conformer ASR with blockwise synchronous beam search.

For more details, please refer to the paper.


To achieve streaming ASR, please employ blockwise Transformer/Conformer encoder in the configuration file. Taking blockwise Transformer as an example: The encoder name can be contextual_block_transformer or contextual_block_conformer.

encoder: contextual_block_transformer
    block_size: 40         # block size for block processing
    hop_size: 16           # hop size for block processing
    look_ahead: 16         # look-ahead size for block processing
    init_average: true     # whether to use average input as initial context
    ctx_pos_enc: true      # whether to use positional encoding for the context vectors


To enable online decoding, the argument --use_streaming true should be added to

./ --stage 12 --use_streaming true


  1. Issue about 'NoneType' object has no attribute 'max' during training: Please make sure you employ forward_train function during traininig, check more details here.

  2. I successfully trained the model, but encountered the above issue during decoding: You may forget to specify --use_streaming true to select streaming inference.

Real-Time-Factor and Latency

In order to calculate real-time-factor and (non-streaming) latency the script utils/ has been reworked and can now be used for both ESPnet1 and ESPnet2. The script calculates inference times based on time markers in the decoding log files and reports the average real-time-factor (RTF) and average latency over all decoded utterances. For ESPnet2, the script will automatically be run (see Limitations section below) after the decoding stage has finished but can also be run as a stand-alone script:


usage: [-h] [--log-dir LOG_DIR]
                        [--log-name {decode,asr_inference}]
                        [--input-shift INPUT_SHIFT]
                        [--start-times-marker {input lengths,speech length}]
                        [--end-times-marker {prediction,best hypo}]

calculate real time factor (RTF)

optional arguments:
  -h, --help            show this help message and exit
  --log-dir LOG_DIR     path to logging directory
  --log-name {decode,asr_inference}
                        name of logfile, e.g., 'decode' (espnet1) and
                        'asr_inference' (espnet2)
  --input-shift INPUT_SHIFT
                        shift of inputs in milliseconds
  --start-times-marker {input lengths,speech length}
                        String marking start of decoding in logfile, e.g.,
                        'input lengths' (espnet1) and 'speech length'
  --end-times-marker {prediction,best hypo}
                        String marking end of decoding in logfile, e.g.,
                        'prediction' (espnet1) and 'best hypo' (espnet2)


  • Default settings still target ESPnet1 usage:

    --log-name 'decode'
    --input-shift 10.0
    --start-times-marker 'input lengths'
    --end-times-marker 'prediction'
  • For ESPnet2, other frame shifts than 10ms are possible via different front-end/feature configurations. So different to ESPnet1, which logs the input feature frames at a fixed 10ms frame shift, in ESPnet2 the number of speech samples is logged instead and the audio sample shift in milliseconds (1/sampleRate x 1000) needs to be specified for --input-shift parameter (see --input-shift 0.0625 in example below for 16000 Hz sample rate).


From espnet/egs2/librispeech/asr1 the following call runs the decoding stage with pretrained ESPnet2 model:

./ --stage 12  --use_streaming false --skip_data_prep true --skip_train true --download_model byan/librispeech_asr_train_asr_conformer_raw_bpe_batch_bins30000000_accum_grad3_optim_conflr0.001_sp

Results for latency and rtf calculation on Librispeech test_clean subset can then be found in espnet/egs2/librispeech/asr1/exp/byan/librispeech_asr_train_asr_conformer_raw_bpe_batch_bins30000000_accum_grad3_optim_conflr0.001_sp/decode_asr_lm_lm_train_lm_transformer2_en_bpe5000_valid.loss.ave_asr_model_valid.acc.ave/test_clean/logdir/calculate_rtf.log file:

# ../../../utils/ --log-dir exp/byan/librispeech_asr_train_asr_conformer_raw_bpe_batch_bins30000000_accum_grad3_optim_conflr0.001_sp/decode_as
r_lm_lm_train_lm_transformer2_en_bpe5000_valid.loss.ave_asr_model_valid.acc.ave/test_clean/logdir --log-name asr_inference --input-shift 0.0625 --start-times-
marker "speech length" --end-times-marker "best hypo"
Total audio duration: 19452.481 [sec]
Total decoding time: 137762.231 [sec]
RTF: 7.082
Latency: 52581.004 [ms/sentence]


  • Only non-streaming inference mode is supported currently

  • The decoding stage 12 in automatically runs the rtf & latency calculation if "asr_inference_tool == "espnet2.bin.asr_inference"; other inference tools like k2 & maskctc are still left to do

Transducer ASR

Important: If you encounter any issue related to warp-transducer, please open an issue in our forked repo.

ESPnet2 supports models trained with the (RNN-)Tranducer loss, aka Transducer models. Currently, two versions of these models exist within ESPnet2: one under asr and the other under asr_transducer. The first one is designed as a supplement of CTC-Attention ASR models while the second is designed independently and purely for the Transducer task. For that, we rely on ESPnetASRTransducerModel instead of ESPnetASRModel and a new task called ASRTransducerTask is used in place of ASRTask.

For the user, it means two things. First, some features or modules may not be supported depending on the version used. Second, the usage of some common ASR features or modules may differ between the models. In addition, some core modules (e.g.: preencoder or postencoder) may be missing in the standalone version until validation.

The following sections of this tutorial are dedicated to the introduction of the version under asr_transducer. Thus, the user should keep in mind that most features described here may not be available in the other version.

General usage

To enable Transducer model training or decoding in your experiments, the following option should be supplied to in your --asr_task asr_transducer [...]

For Transducer loss computation during training, we rely by default on a fork of warp-transducer. The installation procedure is described here.

Note: We made available FastEmit regularization [Yu et al., 2021] during loss computation. To enable it, fastemit_lambda need to be set in model_conf:

  fastemit_lambda: Regularization parameter for FastEmit. (float, default = 0.0)

Optionnaly, we also support training with the Pruned RNN-T loss [Kuang et al. 2022] made available in the k2 toolkit. To use it, the parameter use_k2_pruned_loss should be set to True in model_conf. From here, the loss computation can be controlled by setting the following parameters through k2_pruned_loss_args in model_conf:

  use_k2_pruned_loss: True
    prune_range: How many tokens by frame are used compute the pruned loss. (int, default = 5)
    simple_loss_scaling: The weight to scale the simple loss after warm-up. (float, default = 0.5)
    lm_scale: The scale factor to smooth the LM part. (float, default = 0.0)
    am_scale: The scale factor to smooth the AM part. (float, default = 0.0)
    loss_type: Define the type of path to take for loss computation, either 'regular', 'smoothed' or 'constrained'. (str, default = "regular")

Note: Because the number of tokens emitted by timestep can be restricted during training with this version, we also make available the parameter validation_nstep. It let the users apply similar constraints during the validation process, when reporting CER or/and WER:

  validation_nstep: Maximum number of symbol expansions at each time step when reporting CER or/and WER using mAES.

For more information, see section Inference and “modified Adaptive Expansion Search” algorithm.


The architecture is composed of three modules: encoder, decoder and joint network. Each module has one (or three) config(s) with various parameters in order to configure the internal parts. The following sections describe the mandatory and optional parameters for each module.


For the encoder, we propose a unique encoder type encapsulating the following blocks: Branchformer, Conformer, Conv 1D and E-Branchformer. It is similar to the custom encoder in ESPnet1, meaning we don’t need to set the parameter encoder: [type] here. Instead, the encoder architecture is defined by three configurations passed to encoder_conf:

  1. input_conf (Dict): The configuration for the input block.

  2. main_conf (Dict): The main configuration for the parameters shared across all blocks.

  3. body_conf (List[Dict]): The list of configurations for each block of the encoder architecture but the input block.

The first and second configurations are optional. If needed, the following parameters can be modified in each configuration:

  pos_wise_act_type: Conformer position-wise feed-forward activation type. (str, default = "swish")
  conv_mod_act_type: Conformer convolution module activation type. (str, default = "swish")
  pos_enc_dropout_rate: Dropout rate for the positional encoding layer, if used. (float, default = 0.0)
  pos_enc_max_len: Positional encoding maximum length. (int, default = 5000)
  simplified_att_score: Whether to use simplified attention score computation. (bool, default = False)
  norm_type: X-former normalization module type. (str, default = "layer_norm")
  conv_mod_norm_type: Branchformer convolution module normalization type. (str, default = "layer_norm")
  after_norm_eps: Epsilon value for the final normalization module. (float, default = 1e-05 or 0.25 for BasicNorm)
  after_norm_partial: Partial value for the final normalization module, if norm_type = 'rms_norm'. (float, default = -1.0)
  blockdrop_rate: Probability threshold of dropping out each encoder block. (float, default = 0.0)
  # For more information on the parameters below, please refer to espnet2/asr_transducer/
  ftswish_threshold: Threshold value for FTSwish activation formulation.
  ftswish_mean_shift: Mean shifting value for FTSwish activation formulation.
  hardtanh_min_val: Minimum value of the linear region range for HardTanh activation. (float, default = -1.0)
  hardtanh_max_val: Maximum value of the linear region range for HardTanh. (float, default = 1.0)
  leakyrelu_neg_slope: Negative slope value for LeakyReLU activation formulation.
  smish_alpha: Alpha value for Smish variant activation fomulation. (float, default = 1.0)
  smish_beta: Beta value for Smish variant activation formulation. (float, default = 1.0)
  softplus_beta: Beta value for softplus activation formulation in Mish activation. (float, default = 1.0)
  softplus_threshold: Values above this revert to a linear function in Mish activation. (int, default = 20)
  swish_beta: Beta value for E-Swish activation formulation. (float, default = 20)

  block_type: Input block type, either "conv2d" or "vgg". (str, default = "conv2d")
  conv_size: Convolution output size. For "vgg", the two convolution outputs can be controlled by passing a tuple. (int, default = 256)
  subsampling_factor: Subsampling factor of the input block, either 2 (only conv2d), 4 or 6. (int, default = 4)

The only mandatory configuration is body_conf, defining the encoder body architecture block by block. Each block has its own set of mandatory and optional parameters depending on the type, defined by block_type:

# Branchformer
- block_type: branchformer
  hidden_size: Hidden (and output) dimension. (int)
  linear_size: Dimension of the Linear layers. (int)
  conv_mod_kernel_size: Size of the convolving kernel in the ConvolutionalSpatialGatingUnit module. (int)
  heads (optional): Number of heads in multi-head attention. (int, default = 4)
  norm_eps (optional): Epsilon value for the normalization module. (float, default = 1e-05 or 0.25 for BasicNorm)
  norm_partial (optional): Partial value for the normalization module, if norm_type = 'rms_norm'. (float, default = -1.0)
  conv_mod_norm_eps (optional): Epsilon value for ConvolutionalSpatialGatingUnit module normalization. (float, default = 1e-05 or 0.25 for BasicNorm)
  conv_mod_norm_partial (optional): Partial value for the ConvolutionalSpatialGatingUnit module normalization, if conv_norm_type = 'rms_norm'. (float, default = -1.0)
  dropout_rate (optional): Dropout rate for some intermediate layers. (float, default = 0.0)
  att_dropout_rate (optional): Dropout rate for the attention module. (float, default = 0.0)

# Conformer
- block_type: conformer
  hidden_size: Hidden (and output) dimension. (int)
  linear_size: Dimension of feed-forward module. (int)
  conv_mod_kernel_size: Size of the convolving kernel in the ConformerConvolution module. (int)
  heads (optional): Number of heads in multi-head attention. (int, default = 4)
  norm_eps (optional): Epsilon value for normalization module. (float, default = 1e-05 or 0.25 for BasicNorm)
  norm_partial (optional): Partial value for the normalization module, if norm_type = 'rms_norm'. (float, default = -1.0)
  conv_mod_norm_eps (optional): Epsilon value for Batchnorm1d in the ConformerConvolution module. (float, default = 1e-05)
  conv_mod_norm_momentum (optional): Momentum value for Batchnorm1d in the ConformerConvolution module. (float, default = 0.1)
  dropout_rate (optional): Dropout rate for some intermediate layers. (float, default = 0.0)
  att_dropout_rate (optional): Dropout rate for the attention module. (float, default = 0.0)
  pos_wise_dropout_rate (optional): Dropout rate for the position-wise feed-forward module. (float, default = 0.0)

# Conv 1D
- block_type: conv1d
  output_size: Output size. (int)
  kernel_size: Size of the convolving kernel. (int or Tuple)
  stride (optional): Stride of the sliding blocks. (int or tuple, default = 1)
  dilation (optional): Parameter to control the stride of elements within the neighborhood. (int or tuple, default = 1)
  groups (optional): Number of blocked connections from input channels to output channels. (int, default = 1)
  bias (optional): Whether to add a learnable bias to the output. (bool, default = True)
  relu (optional): Whether to use a ReLU activation after convolution. (bool, default = True)
  batch_norm: Whether to use batch normalization after convolution. (bool, default = False)
  dropout_rate (optional): Dropout rate for the Conv1d outputs. (float, default = 0.0)

# E-Branchformer
- block_type: ebranchformer
  hidden_size: Hidden (and output) dimension. (int)
  linear_size: Dimension of the feed-forward module and othger linear layers. (int)
  conv_mod_kernel_size: Size of the convolving kernel in the ConvolutionalSpatialGatingUnit module. (int)
  depthwise_conv_kernel_size: Size of the convolving kernel in the DepthwiseConvolution module. (int, default = conv_mod_kernel_size)
  heads (optional): Number of heads in multi-head attention. (int, default = 4)
  norm_eps (optional): Epsilon value for the normalization module. (float, default = 1e-05 or 0.25 for BasicNorm)
  norm_partial (optional): Partial value for the normalization module, if norm_type = 'rms_norm'. (float, default = -1.0)
  conv_mod_norm_eps (optional): Epsilon value for ConvolutionalSpatialGatingUnit module normalization. (float, default = 1e-05 or 0.25 for BasicNorm)
  conv_mod_norm_partial (optional): Partial value for the ConvolutionalSpatialGatingUnit module normalization, if conv_norm_type = 'rms_norm'. (float, default = -1.0)
  dropout_rate (optional): Dropout rate for some intermediate layers. (float, default = 0.0)
  att_dropout_rate (optional): Dropout rate for the attention module. (float, default = 0.0)

In addition, each block has a parameter num_blocks to build N times the defined block (int, default = 1). This is useful if you want to use a group of blocks sharing the same parameters without writing each configuration.

Example 1: conv 2D + 2x Conv 1D + 14x Conformer.

      pos_wise_act_type: swish
      pos_enc_dropout_rate: 0.1
      conv_mod_act_type: swish
      block_type: conv2d
      conv_size: 256
      subsampling_factor: 4
    - block_type: conv1d
      output_size: 128
      kernel_size: 3
    - block_type: conv1d
      output_size: 256
      kernel_size: 2
    - block_type: conformer
      linear_size: 1024
      hidden_size: 256
      heads: 8
      dropout_rate: 0.1
      pos_wise_dropout_rate: 0.1
      att_dropout_rate: 0.1
      conv_mod_kernel_size: 31
      num_blocks: 14


For the decoder, four types of blocks are available: stateless (’stateless’), RNN (’rnn’), MEGA (’mega’) or RWKV (’rwkv’). Contrary to the encoder, the parameters are shared across the blocks, meaning we only define one block in the configuration. The type of the stack of blocks is defined by passing the corresponding type string to the parameter decoder. The internal parts are defined through the field decoder_conf containing the following controlable parameters:

  embed_size: Size of the embedding layer (int, default = 256).
  num_blocks: Number of decoder blocks/layers (int, default = 4 for MEGA or 1 for RNN).
  rnn_type (RNN only): Type of RNN cells (int, default = "lstm").
  hidden_size (RNN only): Size of the hidden layers (int, default = 256).
  block_size (MEGA/RWKV only): Size of the block's input/output (int, default = 512).
  linear_size (MEGA/RWKV only): Feed-Forward module hidden size (int, default = 1024).
  attention_size (RWKV only): Hidden-size of the attention module. (int, default = None).
  context_size (RWKV only): Context size for the WKV kernel module (int, default = 1024).
  qk_size (MEGA only): Shared query and key size for attention module (int, default = 128).
  v_size (MEGA only): Value size for attention module (int, default = 1024).
  chunk_size (MEGA only): Chunk size for attention computation (int, default = -1, i.e. full context).
  num_heads (MEGA only): Number of EMA heads (int, default = 4).
  rel_pos_bias (MEGA only): Type of relative position bias in attention module (str, default = "simple").
  max_positions (MEGA only): Maximum number of position for RelativePositionBias (int, default = 2048).
  truncation_length (MEGA only): Maximum length for truncation in EMA module (int, default = None).
  normalization_type (MEGA/RWKV only): Normalization layer type (str, default = "layer_norm").
  normalization_args (MEGA/RKWV only): Normalization layer arguments (dict, default = {}).
  activation_type (MEGA only): Activation function type (str, default = "swish").
  activation_args (MEGA only): Activation function arguments (dict, default = {}).
  rescale_every (RWKV only): Whether to rescale input every N blocks during inference (int, default = 0)
  dropout_rate (excl. RWKV): Dropout rate for main block modules (float, default = 0.0).
  embed_dropout_rate: Dropout rate for embedding layer (float, default = 0.0).
  att_dropout_rate (MEGA/RWKV only): Dropout rate for the attention module.
  ema_dropout_rate (MEGA only): Dropout rate for the EMA module.
  ffn_dropout_rate (MEGA/RWKV only): Dropout rate for the feed-forward module.

Example 1: RNN decoder.

decoder: rnn
    rnn_type: lstm
    num_layers: 2
    embed_size: 256
    hidden_size: 256
    dropout_rate: 0.1
    embed_dropout_rate: 0.1

Example 2: MEGA decoder.

decoder: mega
    block_size: 256
    linear_size: 2048
    qk_size: 128
    v_size: 1024
    max_positions: 1024
    num_heads: 4
    rel_pos_bias_type: "rotary"
    chunk_size: 256
    num_blocks: 6
    dropout_rate: 0.1
    ffn_dropout_rate: 0.1
    att_dropout_rate: 0.1
    embed_dropout_rate: 0.1

Joint network

Currently, we only propose the standard joint network module composed of three linear layers and an activation function. The module definition is optional but the following parameters can be modified through the configuration parameter joint_network_conf:

  joint_space_size: Size of the joint space (int, default = 256).
  joint_act_type: Type of activation in the joint network (str, default = "tanh").

The options related to the activation functions can also be modified through the parameters introduced in the Encoder section (See main_conf description).

Multi-task learning

We also support multi-task learning with two auxiliary tasks: CTC and cross-entropy w/ label smoothing option (called LM loss here). The auxiliary tasks contribute to the overal task defined as:

L_tot = (λ_trans x L_trans) + (λ_auxCTC x L_auxCTC) + (λ_auxLM x L_auxLM)

where the losses (L_*) are respectively, in order: The Transducer loss, the CTC loss and the LM loss. Lambda values define their respective contribution to the total loss. Each task can be parameterized using the following options, passed to model_conf:

  transducer_weight: Weight of the Transducer loss (float, default = 1.0)
  auxiliary_ctc_weight: Weight of the CTC loss. (float, default = 0.0)
  auxiliary_ctc_dropout_rate: Dropout rate for the CTC loss inputs. (float, default = 0.0)
  auxiliary_lm_loss_weight: Weight of the LM loss. (float, default = 0.2)
  auxiliary_lm_loss_smoothing: Smoothing rate for LM loss. If > 0, label smoothing is enabled. (float, default = 0.0)

Note: We do not support other auxiliary tasks in ESPnet2 yet.


Various decoding algorithms are also available for Transducer by setting search_type parameter in your decode config:

The algorithms share two parameters to control the beam size (beam_size) and the partial/final hypotheses normalization (score_norm). In addition, three algorithms have specific parameters:

# Time-synchronous decoding
search_type: tsd
max_sym_exp : Number of maximum symbol expansions at each time step. (int > 1, default = 3)

# Alignement-Length Synchronous decoding
search_type: alsd
u_max: Maximum expected target sequence length. (int, default = 50)

# modified Adaptive Expansion Search
search_type: maes
nstep: Number of maximum expansion steps at each time step (int, default = 2)
expansion_gamma: Number of additional candidates in expanded hypotheses selection. (int, default = 2)
expansion_beta: Allowed logp difference for prune-by-value method. (float, default = 2.3)

Note: Except for the default algorithm, the described parameters are used to control the performance and decoding speed. The optimal values for each parameter are task-dependent; a high value will typically increase decoding time to focus on performance while a low value will improve decoding time at the expense of performance.

Note 2: The algorithms in the standalone version are the same as the one in the other version.. However, due to design choices, some parts were reworked and minor optimizations were added in the same time.


To enable streaming capabilities for Transducer models, we support dynamic chunk training and chunk-by-chunk decoding as proposed in [Zhang et al., 2021]. Our implementation is based on the version proposed in Icefall, based itself on the original WeNet one.

For a complete explanation on the different procedure and parameters, we refer the reader to the corresponding paper.


To train a streaming model, the parameter dynamic_chunk_training should be set to True in main_conf (See section Encoder. From here, the user has access to two parameters in order to control the dynamic chunk selection (short_chunk_threshold and short_chunk_size) and another one to control the left context in the causal convolution and the attention module (num_left_chunks).

All these parameters can be configured through main_conf, introduced in the Encoder section:

dynamic_chunk_training: Whether to train streaming model with dynamic chunks. (bool, default = False)
short_chunk_threshold: Chunk length threshold (in percent) for dynamic chunk selection. (int, default = 0.75)
short_chunk_size: Minimum number of frames during dynamic chunk training. (int, default = 25)
num_left_chunks: The number of left chunks the attention module can see during training, where the actual size is defined by `short_chunk_threshold` and `short_chunk_size`. (int, default = 0, i.e. full context)


To perform chunk-by-chunk inference, the parameter streaming should be set to True in the decoding configuration (otherwise, offline decoding will be performed). Two parameters are available to control the decoding process:

decoding_window: The input audio length, in milliseconds, to process during decoding. (int, default = 640)
left_context: Number of previous frames (AFTER subsampling) the attention module can see in current chunk. (int, default = 32)

Note: All search algorithms but ALSD are available with chunk-by-chunk inference.


How to add a new block type to the custom encoder?

Provided paths are relative to the directory: espnet2/asr_transducer/encoder/

Adding support to a new block type can be achieved in three main steps:

  1. Write your need block class in encoder/blocks/. The class should have the following methods: __init__(...), forward(...) (training + offline), chunk_forward(...) (online decoding), reset_streaming_cache(...) (online cache definition). For more details on implementing internal parts, we refer the user to the existing block definition and the Streaming section.

  2. In, write a block constructor method and add a new condition in build_body_blocks(...) for your block type, calling the constructor method. If you need additional parameters to share across blocks, you can add them in build_main_parameters(...) and pass main_conf to your constructor.

  3. In, add new conditions to `validate_block_arguments(…) in order to set and validate the mandatory block parameters before building (if not already covered).

For additional information or examples, please refer to the named files. If you need to add other classes related to the new block, they should be added within the block class or in modules/.