deploy: ad475ec10dec864373099ba541cad5f743a4726b

_sources/index.rst.txt

@@ -22,6 +22,14 @@ speech recognition recipes using `k2 <https://github.com/k2-fsa/k2>`_.
 
    installation/index
    model-export/index
+
+.. toctree::
+   :maxdepth: 3
+
    recipes/index
+
+.. toctree::
+   :maxdepth: 2
+
    contributing/index
    huggingface/index

_sources/recipes/Non-streaming-ASR/index.rst.txt

@@ -0,0 +1,10 @@
+Non Streaming ASR
+=================
+
+.. toctree::
+   :maxdepth: 2
+
+   aishell/index
+   librispeech/index
+   timit/index
+   yesno/index

_sources/recipes/Non-streaming-ASR/librispeech/index.rst.txt

@@ -6,5 +6,6 @@ LibriSpeech
 
    tdnn_lstm_ctc
    conformer_ctc
+   pruned_transducer_stateless
    lstm_pruned_stateless_transducer
    zipformer_mmi

_sources/recipes/Non-streaming-ASR/librispeech/pruned_transducer_stateless.rst.txt

@@ -0,0 +1,545 @@
+Pruned transducer statelessX
+============================
+
+This tutorial shows you how to run a conformer transducer model
+with the `LibriSpeech <https://www.openslr.org/12>`_ dataset.
+
+.. Note::
+
+   The tutorial is suitable for `pruned_transducer_stateless <https://github.com/k2-fsa/icefall/tree/master/egs/librispeech/ASR/pruned_transducer_stateless>`_,
+   `pruned_transducer_stateless2 <https://github.com/k2-fsa/icefall/tree/master/egs/librispeech/ASR/pruned_transducer_stateless2>`_,
+   `pruned_transducer_stateless4 <https://github.com/k2-fsa/icefall/tree/master/egs/librispeech/ASR/pruned_transducer_stateless4>`_,
+   `pruned_transducer_stateless5 <https://github.com/k2-fsa/icefall/tree/master/egs/librispeech/ASR/pruned_transducer_stateless5>`_,
+   We will take pruned_transducer_stateless4 as an example in this tutorial.
+
+.. HINT::
+
+  We assume you have read the page :ref:`install icefall` and have setup
+  the environment for ``icefall``.
+
+.. HINT::
+
+  We recommend you to use a GPU or several GPUs to run this recipe.
+
+.. hint::
+
+   Please scroll down to the bottom of this page to find download links
+   for pretrained models if you don't want to train a model from scratch.
+
+
+We use pruned RNN-T to compute the loss.
+
+.. note::
+
+   You can find the paper about pruned RNN-T at the following address:
+
+   `<https://arxiv.org/abs/2206.13236>`_
+
+The transducer model consists of 3 parts:
+
+  - Encoder, a.k.a, the transcription network. We use a Conformer model (the reworked version by Daniel Povey)
+  - Decoder, a.k.a, the prediction network. We use a stateless model consisting of
+    ``nn.Embedding`` and ``nn.Conv1d``
+  - Joiner, a.k.a, the joint network.
+
+.. caution::
+
+   Contrary to the conventional RNN-T models, we use a stateless decoder.
+   That is, it has no recurrent connections.
+
+
+Data preparation
+----------------
+
+.. hint::
+
+   The data preparation is the same as other recipes on LibriSpeech dataset,
+   if you have finished this step, you can skip to ``Training`` directly.
+
+.. code-block:: bash
+
+  $ cd egs/librispeech/ASR
+  $ ./prepare.sh
+
+The script ``./prepare.sh`` handles the data preparation for you, **automagically**.
+All you need to do is to run it.
+
+The data preparation contains several stages, you can use the following two
+options:
+
+  - ``--stage``
+  - ``--stop-stage``
+
+to control which stage(s) should be run. By default, all stages are executed.
+
+
+For example,
+
+.. code-block:: bash
+
+  $ cd egs/librispeech/ASR
+  $ ./prepare.sh --stage 0 --stop-stage 0
+
+means to run only stage 0.
+
+To run stage 2 to stage 5, use:
+
+.. code-block:: bash
+
+  $ ./prepare.sh --stage 2 --stop-stage 5
+
+.. HINT::
+
+  If you have pre-downloaded the `LibriSpeech <https://www.openslr.org/12>`_
+  dataset and the `musan <http://www.openslr.org/17/>`_ dataset, say,
+  they are saved in ``/tmp/LibriSpeech`` and ``/tmp/musan``, you can modify
+  the ``dl_dir`` variable in ``./prepare.sh`` to point to ``/tmp`` so that
+  ``./prepare.sh`` won't re-download them.
+
+.. NOTE::
+
+  All generated files by ``./prepare.sh``, e.g., features, lexicon, etc,
+  are saved in ``./data`` directory.
+
+We provide the following YouTube video showing how to run ``./prepare.sh``.
+
+.. note::
+
+   To get the latest news of `next-gen Kaldi <https://github.com/k2-fsa>`_, please subscribe
+   the following YouTube channel by `Nadira Povey <https://www.youtube.com/channel/UC_VaumpkmINz1pNkFXAN9mw>`_:
+
+      `<https://www.youtube.com/channel/UC_VaumpkmINz1pNkFXAN9mw>`_
+
+..  youtube:: ofEIoJL-mGM
+
+
+Training
+--------
+
+Configurable options
+~~~~~~~~~~~~~~~~~~~~
+
+.. code-block:: bash
+
+  $ cd egs/librispeech/ASR
+  $ ./pruned_transducer_stateless4/train.py --help
+
+
+shows you the training options that can be passed from the commandline.
+The following options are used quite often:
+
+  - ``--exp-dir``
+
+    The directory to save checkpoints, training logs and tensorboard.
+
+  - ``--full-libri``
+
+    If it's True, the training part uses all the training data, i.e.,
+    960 hours. Otherwise, the training part uses only the subset
+    ``train-clean-100``, which has 100 hours of training data.
+
+    .. CAUTION::
+      The training set is perturbed by speed with two factors: 0.9 and 1.1.
+      If ``--full-libri`` is True, each epoch actually processes
+      ``3x960 == 2880`` hours of data.
+
+  - ``--num-epochs``
+
+    It is the number of epochs to train. For instance,
+    ``./pruned_transducer_stateless4/train.py --num-epochs 30`` trains for 30 epochs
+    and generates ``epoch-1.pt``, ``epoch-2.pt``, ..., ``epoch-30.pt``
+    in the folder ``./pruned_transducer_stateless4/exp``.
+
+  - ``--start-epoch``
+
+    It's used to resume training.
+    ``./pruned_transducer_stateless4/train.py --start-epoch 10`` loads the
+    checkpoint ``./pruned_transducer_stateless4/exp/epoch-9.pt`` and starts
+    training from epoch 10, based on the state from epoch 9.
+
+  - ``--world-size``
+
+    It is used for multi-GPU single-machine DDP training.
+
+      - (a) If it is 1, then no DDP training is used.
+
+      - (b) If it is 2, then GPU 0 and GPU 1 are used for DDP training.
+
+    The following shows some use cases with it.
+
+      **Use case 1**: You have 4 GPUs, but you only want to use GPU 0 and
+      GPU 2 for training. You can do the following:
+
+        .. code-block:: bash
+
+          $ cd egs/librispeech/ASR
+          $ export CUDA_VISIBLE_DEVICES="0,2"
+          $ ./pruned_transducer_stateless4/train.py --world-size 2
+
+      **Use case 2**: You have 4 GPUs and you want to use all of them
+      for training. You can do the following:
+
+        .. code-block:: bash
+
+          $ cd egs/librispeech/ASR
+          $ ./pruned_transducer_stateless4/train.py --world-size 4
+
+      **Use case 3**: You have 4 GPUs but you only want to use GPU 3
+      for training. You can do the following:
+
+        .. code-block:: bash
+
+          $ cd egs/librispeech/ASR
+          $ export CUDA_VISIBLE_DEVICES="3"
+          $ ./pruned_transducer_stateless4/train.py --world-size 1
+
+    .. caution::
+
+      Only multi-GPU single-machine DDP training is implemented at present.
+      Multi-GPU multi-machine DDP training will be added later.
+
+  - ``--max-duration``
+
+    It specifies the number of seconds over all utterances in a
+    batch, before **padding**.
+    If you encounter CUDA OOM, please reduce it.
+
+    .. HINT::
+
+      Due to padding, the number of seconds of all utterances in a
+      batch will usually be larger than ``--max-duration``.
+
+      A larger value for ``--max-duration`` may cause OOM during training,
+      while a smaller value may increase the training time. You have to
+      tune it.
+
+  - ``--use-fp16``
+
+    If it is True, the model will train with half precision, from our experiment
+    results, by using half precision you can train with two times larger ``--max-duration``
+    so as to get almost 2X speed up.
+
+
+Pre-configured options
+~~~~~~~~~~~~~~~~~~~~~~
+
+There are some training options, e.g., number of encoder layers,
+encoder dimension, decoder dimension, number of warmup steps etc,
+that are not passed from the commandline.
+They are pre-configured by the function ``get_params()`` in
+`pruned_transducer_stateless4/train.py <https://github.com/k2-fsa/icefall/blob/master/egs/librispeech/ASR/pruned_transducer_stateless4/train.py>`_
+
+You don't need to change these pre-configured parameters. If you really need to change
+them, please modify ``./pruned_transducer_stateless4/train.py`` directly.
+
+
+.. NOTE::
+
+  The options for `pruned_transducer_stateless5 <https://github.com/k2-fsa/icefall/blob/master/egs/librispeech/ASR/pruned_transducer_stateless5/train.py>`_ are a little different from
+  other recipes. It allows you to configure ``--num-encoder-layers``, ``--dim-feedforward``, ``--nhead``, ``--encoder-dim``, ``--decoder-dim``, ``--joiner-dim`` from commandline, so that you can train models with different size with pruned_transducer_stateless5.
+
+
+Training logs
+~~~~~~~~~~~~~
+
+Training logs and checkpoints are saved in ``--exp-dir`` (e.g. ``pruned_transducer_stateless4/exp``.
+You will find the following files in that directory:
+
+  - ``epoch-1.pt``, ``epoch-2.pt``, ...
+
+    These are checkpoint files saved at the end of each epoch, containing model
+    ``state_dict`` and optimizer ``state_dict``.
+    To resume training from some checkpoint, say ``epoch-10.pt``, you can use:
+
+      .. code-block:: bash
+
+        $ ./pruned_transducer_stateless4/train.py --start-epoch 11
+
+  - ``checkpoint-436000.pt``, ``checkpoint-438000.pt``, ...
+
+    These are checkpoint files saved every ``--save-every-n`` batches,
+    containing model ``state_dict`` and optimizer ``state_dict``.
+    To resume training from some checkpoint, say ``checkpoint-436000``, you can use:
+
+      .. code-block:: bash
+
+        $ ./pruned_transducer_stateless4/train.py --start-batch 436000
+
+  - ``tensorboard/``
+
+    This folder contains tensorBoard logs. Training loss, validation loss, learning
+    rate, etc, are recorded in these logs. You can visualize them by:
+
+      .. code-block:: bash
+
+        $ cd pruned_transducer_stateless4/exp/tensorboard
+        $ tensorboard dev upload --logdir . --description "pruned transducer training for LibriSpeech with icefall"
+
+    It will print something like below:
+
+      .. code-block::
+
+        TensorFlow installation not found - running with reduced feature set.
+        Upload started and will continue reading any new data as it's added to the logdir.
+
+        To stop uploading, press Ctrl-C.
+
+        New experiment created. View your TensorBoard at: https://tensorboard.dev/experiment/QOGSPBgsR8KzcRMmie9JGw/
+
+        [2022-11-20T15:50:50] Started scanning logdir.
+        Uploading 4468 scalars...
+        [2022-11-20T15:53:02] Total uploaded: 210171 scalars, 0 tensors, 0 binary objects
+        Listening for new data in logdir...
+
+    Note there is a URL in the above output. Click it and you will see
+    the following screenshot:
+
+      .. figure:: images/librispeech-pruned-transducer-tensorboard-log.jpg
+         :width: 600
+         :alt: TensorBoard screenshot
+         :align: center
+         :target: https://tensorboard.dev/experiment/QOGSPBgsR8KzcRMmie9JGw/
+
+         TensorBoard screenshot.
+
+  .. hint::
+
+    If you don't have access to google, you can use the following command
+    to view the tensorboard log locally:
+
+      .. code-block:: bash
+
+        cd pruned_transducer_stateless4/exp/tensorboard
+        tensorboard --logdir . --port 6008
+
+    It will print the following message:
+
+      .. code-block::
+
+        Serving TensorBoard on localhost; to expose to the network, use a proxy or pass --bind_all
+        TensorBoard 2.8.0 at http://localhost:6008/ (Press CTRL+C to quit)
+
+    Now start your browser and go to `<http://localhost:6008>`_ to view the tensorboard
+    logs.
+
+
+  - ``log/log-train-xxxx``
+
+    It is the detailed training log in text format, same as the one
+    you saw printed to the console during training.
+
+Usage example
+~~~~~~~~~~~~~
+
+You can use the following command to start the training using 6 GPUs:
+
+.. code-block:: bash
+
+  export CUDA_VISIBLE_DEVICES="0,1,2,3,4,5"
+  ./pruned_transducer_stateless4/train.py \
+     --world-size 6 \
+     --num-epochs 30 \
+     --start-epoch 1 \
+     --exp-dir pruned_transducer_stateless4/exp \
+     --full-libri 1 \
+     --max-duration 300
+
+
+Decoding
+--------
+
+The decoding part uses checkpoints saved by the training part, so you have
+to run the training part first.
+
+.. hint::
+
+   There are two kinds of checkpoints:
+
+    - (1) ``epoch-1.pt``, ``epoch-2.pt``, ..., which are saved at the end
+      of each epoch. You can pass ``--epoch`` to
+      ``pruned_transducer_stateless4/decode.py`` to use them.
+
+    - (2) ``checkpoints-436000.pt``, ``epoch-438000.pt``, ..., which are saved
+      every ``--save-every-n`` batches. You can pass ``--iter`` to
+      ``pruned_transducer_stateless4/decode.py`` to use them.
+
+    We suggest that you try both types of checkpoints and choose the one
+    that produces the lowest WERs.
+
+.. code-block:: bash
+
+  $ cd egs/librispeech/ASR
+  $ ./pruned_transducer_stateless4/decode.py --help
+
+shows the options for decoding.
+
+The following shows two examples (for two types of checkpoints):
+
+.. code-block:: bash
+
+  for m in greedy_search fast_beam_search modified_beam_search; do
+    for epoch in 25 20; do
+      for avg in 7 5 3 1; do
+        ./pruned_transducer_stateless4/decode.py \
+          --epoch $epoch \
+          --avg $avg \
+          --exp-dir pruned_transducer_stateless4/exp \
+          --max-duration 600 \
+          --decoding-method $m
+      done
+    done
+  done
+
+
+.. code-block:: bash
+
+  for m in greedy_search fast_beam_search modified_beam_search; do
+    for iter in 474000; do
+      for avg in 8 10 12 14 16 18; do
+        ./pruned_transducer_stateless4/decode.py \
+          --iter $iter \
+          --avg $avg \
+          --exp-dir pruned_transducer_stateless4/exp \
+          --max-duration 600 \
+          --decoding-method $m
+      done
+    done
+  done
+
+
+.. Note::
+
+  Supporting decoding methods are as follows:
+
+    - ``greedy_search`` : It takes the symbol with largest posterior probability
+      of each frame as the decoding result.
+
+    - ``beam_search`` :  It implements Algorithm 1 in https://arxiv.org/pdf/1211.3711.pdf and
+      `espnet/nets/beam_search_transducer.py <https://github.com/espnet/espnet/blob/master/espnet/nets/beam_search_transducer.py#L247>`_
+      is used as a reference. Basicly, it keeps topk states for each frame, and expands the kept states with their own contexts to
+      next frame.
+
+    - ``modified_beam_search`` : It implements the same algorithm as ``beam_search`` above, but it
+      runs in batch mode with ``--max-sym-per-frame=1`` being hardcoded.
+
+    - ``fast_beam_search`` : It implements graph composition between the output ``log_probs`` and
+      given ``FSAs``. It is hard to describe the details in several lines of texts, you can read
+      our paper in https://arxiv.org/pdf/2211.00484.pdf or our `rnnt decode code in k2 <https://github.com/k2-fsa/k2/blob/master/k2/csrc/rnnt_decode.h>`_. ``fast_beam_search`` can decode with ``FSAs`` on GPU efficiently.
+
+    - ``fast_beam_search_LG`` : The same as ``fast_beam_search`` above, ``fast_beam_search`` uses
+      an trivial graph that has only one state, while ``fast_beam_search_LG`` uses an LG graph
+      (with N-gram LM).
+
+    - ``fast_beam_search_nbest`` : It produces the decoding results as follows:
+
+      - (1) Use ``fast_beam_search`` to get a lattice
+      - (2) Select ``num_paths`` paths from the lattice using ``k2.random_paths()``
+      - (3) Unique the selected paths
+      - (4) Intersect the selected paths with the lattice and compute the
+            shortest path from the intersection result
+      - (5) The path with the largest score is used as the decoding output.
+
+    - ``fast_beam_search_nbest_LG`` : It implements same logic as ``fast_beam_search_nbest``, the
+      only difference is that it uses ``fast_beam_search_LG`` to generate the lattice.
+
+
+Export Model
+------------
+
+`pruned_transducer_stateless4/export.py <https://github.com/k2-fsa/icefall/blob/master/egs/librispeech/ASR/pruned_transducer_stateless4/export.py>`_ supports exporting checkpoints from ``pruned_transducer_stateless4/exp`` in the following ways.
+
+Export ``model.state_dict()``
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+Checkpoints saved by ``pruned_transducer_stateless4/train.py`` also include
+``optimizer.state_dict()``. It is useful for resuming training. But after training,
+we are interested only in ``model.state_dict()``. You can use the following
+command to extract ``model.state_dict()``.
+
+.. code-block:: bash
+
+  # Assume that --epoch 25 --avg 3 produces the smallest WER
+  # (You can get such information after running ./pruned_transducer_stateless4/decode.py)
+
+  epoch=25
+  avg=3
+
+  ./pruned_transducer_stateless4/export.py \
+    --exp-dir ./pruned_transducer_stateless4/exp \
+    --bpe-model data/lang_bpe_500/bpe.model \
+    --epoch $epoch \
+    --avg  $avg
+
+It will generate a file ``./pruned_transducer_stateless4/exp/pretrained.pt``.
+
+.. hint::
+
+   To use the generated ``pretrained.pt`` for ``pruned_transducer_stateless4/decode.py``,
+   you can run:
+
+   .. code-block:: bash
+
+      cd pruned_transducer_stateless4/exp
+      ln -s pretrained.pt epoch-999.pt
+
+   And then pass ``--epoch 999 --avg 1 --use-averaged-model 0`` to
+   ``./pruned_transducer_stateless4/decode.py``.
+
+To use the exported model with ``./pruned_transducer_stateless4/pretrained.py``, you
+can run:
+
+.. code-block:: bash
+
+  ./pruned_transducer_stateless4/pretrained.py \
+    --checkpoint ./pruned_transducer_stateless4/exp/pretrained.pt \
+    --bpe-model ./data/lang_bpe_500/bpe.model \
+    --method greedy_search \
+    /path/to/foo.wav \
+    /path/to/bar.wav
+
+
+Export model using ``torch.jit.script()``
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. code-block:: bash
+  ./pruned_transducer_stateless4/export.py \
+    --exp-dir ./pruned_transducer_stateless4/exp \
+    --bpe-model data/lang_bpe_500/bpe.model \
+    --epoch 25 \
+    --avg 3 \
+    --jit 1
+
+It will generate a file ``cpu_jit.pt`` in the given ``exp_dir``. You can later
+load it by ``torch.jit.load("cpu_jit.pt")``.
+
+Note ``cpu`` in the name ``cpu_jit.pt`` means the parameters when loaded into Python
+are on CPU. You can use ``to("cuda")`` to move them to a CUDA device.
+
+.. NOTE::
+
+   You will need this ``cpu_jit.pt`` when deploying with Sherpa framework.
+
+
+Download pretrained models
+--------------------------
+
+If you don't want to train from scratch, you can download the pretrained models
+by visiting the following links:
+
+  - `pruned_transducer_stateless <https://huggingface.co/csukuangfj/icefall-asr-librispeech-pruned-transducer-stateless-2022-03-12>`_
+
+  - `pruned_transducer_stateless2 <https://huggingface.co/csukuangfj/icefall-asr-librispeech-pruned-transducer-stateless2-2022-04-29>`_
+
+  - `pruned_transducer_stateless4 <https://huggingface.co/Zengwei/icefall-asr-librispeech-pruned-transducer-stateless4-2022-06-03>`_
+
+  - `pruned_transducer_stateless5 <https://huggingface.co/Zengwei/icefall-asr-librispeech-pruned-transducer-stateless5-2022-07-07>`_
+
+  See `<https://github.com/k2-fsa/icefall/blob/master/egs/librispeech/ASR/RESULTS.md>`_
+  for the details of the above pretrained models
+
+
+Deploy with Sherpa
+------------------
+
+Please see `<https://k2-fsa.github.io/sherpa/python/offline_asr/conformer/librispeech.html#>`_
+for how to deploy the models in ``sherpa``.

_sources/recipes/Streaming-ASR/index.rst.txt

@@ -0,0 +1,12 @@
+Streaming ASR
+=============
+
+.. toctree::
+   :maxdepth: 1
+
+   introduction
+
+.. toctree::
+   :maxdepth: 2
+
+   librispeech/index

_sources/recipes/Streaming-ASR/introduction.rst.txt

@@ -0,0 +1,52 @@
+Introduction
+============
+
+This page shows you how we implement streaming **X-former transducer** models for ASR.
+
+.. HINT::
+   X-former transducer here means the encoder of the transducer model uses Multi-Head Attention,
+   like `Conformer <https://arxiv.org/pdf/2005.08100.pdf>`_, `EmFormer <https://arxiv.org/pdf/2010.10759.pdf>`_ etc.
+
+Currently we have implemented two types of streaming models, one uses Conformer as encoder, the other uses Emformer as encoder.
+
+Streaming Conformer
+-------------------
+
+The main idea of training a streaming model is to make the model see limited contexts
+in training time, we can achieve this by applying a mask to the output of self-attention.
+In icefall, we implement the streaming conformer the way just like what `WeNet <https://arxiv.org/pdf/2012.05481.pdf>`_ did.
+
+.. NOTE::
+   The conformer-transducer recipes in LibriSpeech datasets, like, `pruned_transducer_stateless <https://github.com/k2-fsa/icefall/tree/master/egs/librispeech/ASR/pruned_transducer_stateless>`_,
+   `pruned_transducer_stateless2 <https://github.com/k2-fsa/icefall/tree/master/egs/librispeech/ASR/pruned_transducer_stateless2>`_,
+   `pruned_transducer_stateless3 <https://github.com/k2-fsa/icefall/tree/master/egs/librispeech/ASR/pruned_transducer_stateless3>`_,
+   `pruned_transducer_stateless4 <https://github.com/k2-fsa/icefall/tree/master/egs/librispeech/ASR/pruned_transducer_stateless4>`_,
+   `pruned_transducer_stateless5 <https://github.com/k2-fsa/icefall/tree/master/egs/librispeech/ASR/pruned_transducer_stateless5>`_
+   all support streaming.
+
+.. NOTE::
+   Training a streaming conformer model in ``icefall`` is almost the same as training a
+   non-streaming model, all you need to do is passing several extra arguments.
+   See :doc:`Pruned transducer statelessX <librispeech/pruned_transducer_stateless>` for more details.
+
+.. HINT::
+   If you want to adapt a non-streaming conformer model to be streaming, please refer
+   to `this pull request <https://github.com/k2-fsa/icefall/pull/454>`_.
+
+
+Streaming Emformer
+------------------
+
+The Emformer model proposed `here <https://arxiv.org/pdf/2010.10759.pdf>`_ uses more
+complicated techniques. It has a memory bank component to memorize history information,
+what' more, it also introduces right context in training time by hard-copying part of
+the input features.
+
+We have three variants of Emformer models in ``icefall``.
+
+ - ``pruned_stateless_emformer_rnnt2`` using Emformer from torchaudio, see `LibriSpeech recipe <https://github.com/k2-fsa/icefall/tree/master/egs/librispeech/ASR/pruned_stateless_emformer_rnnt2>`_.
+ - ``conv_emformer_transducer_stateless`` using ConvEmformer implemented by ourself. Different from the Emformer in torchaudio,
+   ConvEmformer has a convolution in each layer and uses the mechanisms in our reworked conformer model.
+   See `LibriSpeech recipe <https://github.com/k2-fsa/icefall/tree/master/egs/librispeech/ASR/conv_emformer_transducer_stateless>`_.
+ - ``conv_emformer_transducer_stateless2`` using ConvEmformer implemented by ourself. The only difference from the above one is that
+   it uses a simplified memory bank. See `LibriSpeech recipe <https://github.com/k2-fsa/icefall/tree/master/egs/librispeech/ASR/conv_emformer_transducer_stateless2>`_.

_sources/recipes/Streaming-ASR/librispeech/index.rst.txt

@@ -0,0 +1,9 @@
+LibriSpeech
+===========
+
+.. toctree::
+   :maxdepth: 1
+
+   pruned_transducer_stateless
+
+   lstm_pruned_stateless_transducer

_sources/recipes/Streaming-ASR/librispeech/pruned_transducer_stateless.rst.txt

@@ -0,0 +1,735 @@
+Pruned transducer statelessX
+============================
+
+This tutorial shows you how to run a **streaming** conformer transducer model
+with the `LibriSpeech <https://www.openslr.org/12>`_ dataset.
+
+.. Note::
+
+   The tutorial is suitable for `pruned_transducer_stateless <https://github.com/k2-fsa/icefall/tree/master/egs/librispeech/ASR/pruned_transducer_stateless>`_,
+   `pruned_transducer_stateless2 <https://github.com/k2-fsa/icefall/tree/master/egs/librispeech/ASR/pruned_transducer_stateless2>`_,
+   `pruned_transducer_stateless4 <https://github.com/k2-fsa/icefall/tree/master/egs/librispeech/ASR/pruned_transducer_stateless4>`_,
+   `pruned_transducer_stateless5 <https://github.com/k2-fsa/icefall/tree/master/egs/librispeech/ASR/pruned_transducer_stateless5>`_,
+   We will take pruned_transducer_stateless4 as an example in this tutorial.
+
+.. HINT::
+
+  We assume you have read the page :ref:`install icefall` and have setup
+  the environment for ``icefall``.
+
+.. HINT::
+
+  We recommend you to use a GPU or several GPUs to run this recipe.
+
+.. hint::
+
+   Please scroll down to the bottom of this page to find download links
+   for pretrained models if you don't want to train a model from scratch.
+
+
+We use pruned RNN-T to compute the loss.
+
+.. note::
+
+   You can find the paper about pruned RNN-T at the following address:
+
+   `<https://arxiv.org/abs/2206.13236>`_
+
+The transducer model consists of 3 parts:
+
+  - Encoder, a.k.a, the transcription network. We use a Conformer model (the reworked version by Daniel Povey)
+  - Decoder, a.k.a, the prediction network. We use a stateless model consisting of
+    ``nn.Embedding`` and ``nn.Conv1d``
+  - Joiner, a.k.a, the joint network.
+
+.. caution::
+
+   Contrary to the conventional RNN-T models, we use a stateless decoder.
+   That is, it has no recurrent connections.
+
+
+Data preparation
+----------------
+
+.. hint::
+
+   The data preparation is the same as other recipes on LibriSpeech dataset,
+   if you have finished this step, you can skip to ``Training`` directly.
+
+.. code-block:: bash
+
+  $ cd egs/librispeech/ASR
+  $ ./prepare.sh
+
+The script ``./prepare.sh`` handles the data preparation for you, **automagically**.
+All you need to do is to run it.
+
+The data preparation contains several stages, you can use the following two
+options:
+
+  - ``--stage``
+  - ``--stop-stage``
+
+to control which stage(s) should be run. By default, all stages are executed.
+
+
+For example,
+
+.. code-block:: bash
+
+  $ cd egs/librispeech/ASR
+  $ ./prepare.sh --stage 0 --stop-stage 0
+
+means to run only stage 0.
+
+To run stage 2 to stage 5, use:
+
+.. code-block:: bash
+
+  $ ./prepare.sh --stage 2 --stop-stage 5
+
+.. HINT::
+
+  If you have pre-downloaded the `LibriSpeech <https://www.openslr.org/12>`_
+  dataset and the `musan <http://www.openslr.org/17/>`_ dataset, say,
+  they are saved in ``/tmp/LibriSpeech`` and ``/tmp/musan``, you can modify
+  the ``dl_dir`` variable in ``./prepare.sh`` to point to ``/tmp`` so that
+  ``./prepare.sh`` won't re-download them.
+
+.. NOTE::
+
+  All generated files by ``./prepare.sh``, e.g., features, lexicon, etc,
+  are saved in ``./data`` directory.
+
+We provide the following YouTube video showing how to run ``./prepare.sh``.
+
+.. note::
+
+   To get the latest news of `next-gen Kaldi <https://github.com/k2-fsa>`_, please subscribe
+   the following YouTube channel by `Nadira Povey <https://www.youtube.com/channel/UC_VaumpkmINz1pNkFXAN9mw>`_:
+
+      `<https://www.youtube.com/channel/UC_VaumpkmINz1pNkFXAN9mw>`_
+
+..  youtube:: ofEIoJL-mGM
+
+
+Training
+--------
+
+.. NOTE::
+
+   We put the streaming and non-streaming model in one recipe, to train a streaming model you only
+   need to add **4** extra options comparing with training a non-streaming model. These options are
+   ``--dynamic-chunk-training``, ``--num-left-chunks``, ``--causal-convolution``, ``--short-chunk-size``.
+   You can see the configurable options below for their meanings or read https://arxiv.org/pdf/2012.05481.pdf for more details.
+
+Configurable options
+~~~~~~~~~~~~~~~~~~~~
+
+.. code-block:: bash
+
+  $ cd egs/librispeech/ASR
+  $ ./pruned_transducer_stateless4/train.py --help
+
+
+shows you the training options that can be passed from the commandline.
+The following options are used quite often:
+
+  - ``--exp-dir``
+
+    The directory to save checkpoints, training logs and tensorboard.
+
+  - ``--full-libri``
+
+    If it's True, the training part uses all the training data, i.e.,
+    960 hours. Otherwise, the training part uses only the subset
+    ``train-clean-100``, which has 100 hours of training data.
+
+    .. CAUTION::
+      The training set is perturbed by speed with two factors: 0.9 and 1.1.
+      If ``--full-libri`` is True, each epoch actually processes
+      ``3x960 == 2880`` hours of data.
+
+  - ``--num-epochs``
+
+    It is the number of epochs to train. For instance,
+    ``./pruned_transducer_stateless4/train.py --num-epochs 30`` trains for 30 epochs
+    and generates ``epoch-1.pt``, ``epoch-2.pt``, ..., ``epoch-30.pt``
+    in the folder ``./pruned_transducer_stateless4/exp``.
+
+  - ``--start-epoch``
+
+    It's used to resume training.
+    ``./pruned_transducer_stateless4/train.py --start-epoch 10`` loads the
+    checkpoint ``./pruned_transducer_stateless4/exp/epoch-9.pt`` and starts
+    training from epoch 10, based on the state from epoch 9.
+
+  - ``--world-size``
+
+    It is used for multi-GPU single-machine DDP training.
+
+      - (a) If it is 1, then no DDP training is used.
+
+      - (b) If it is 2, then GPU 0 and GPU 1 are used for DDP training.
+
+    The following shows some use cases with it.
+
+      **Use case 1**: You have 4 GPUs, but you only want to use GPU 0 and
+      GPU 2 for training. You can do the following:
+
+        .. code-block:: bash
+
+          $ cd egs/librispeech/ASR
+          $ export CUDA_VISIBLE_DEVICES="0,2"
+          $ ./pruned_transducer_stateless4/train.py --world-size 2
+
+      **Use case 2**: You have 4 GPUs and you want to use all of them
+      for training. You can do the following:
+
+        .. code-block:: bash
+
+          $ cd egs/librispeech/ASR
+          $ ./pruned_transducer_stateless4/train.py --world-size 4
+
+      **Use case 3**: You have 4 GPUs but you only want to use GPU 3
+      for training. You can do the following:
+
+        .. code-block:: bash
+
+          $ cd egs/librispeech/ASR
+          $ export CUDA_VISIBLE_DEVICES="3"
+          $ ./pruned_transducer_stateless4/train.py --world-size 1
+
+    .. caution::
+
+      Only multi-GPU single-machine DDP training is implemented at present.
+      Multi-GPU multi-machine DDP training will be added later.
+
+  - ``--max-duration``
+
+    It specifies the number of seconds over all utterances in a
+    batch, before **padding**.
+    If you encounter CUDA OOM, please reduce it.
+
+    .. HINT::
+
+      Due to padding, the number of seconds of all utterances in a
+      batch will usually be larger than ``--max-duration``.
+
+      A larger value for ``--max-duration`` may cause OOM during training,
+      while a smaller value may increase the training time. You have to
+      tune it.
+
+  - ``--use-fp16``
+
+    If it is True, the model will train with half precision, from our experiment
+    results, by using half precision you can train with two times larger ``--max-duration``
+    so as to get almost 2X speed up.
+
+  - ``--dynamic-chunk-training``
+
+    The flag that indicates whether to train a streaming model or not, it
+    **MUST** be True if you want to train a streaming model.
+
+  - ``--short-chunk-size``
+
+    When training a streaming attention model with chunk masking, the chunk size
+    would be either max sequence length of current batch or uniformly sampled from
+    (1, short_chunk_size). The default value is 25, you don't have to change it most of the time.
+
+  - ``--num-left-chunks``
+
+    It indicates how many left context (in chunks) that can be seen when calculating attention.
+    The default value is 4, you don't have to change it most of the time.
+
+
+  - ``--causal-convolution``
+
+    Whether to use causal convolution in conformer encoder layer, this requires
+    to be True when training a streaming model.
+
+
+Pre-configured options
+~~~~~~~~~~~~~~~~~~~~~~
+
+There are some training options, e.g., number of encoder layers,
+encoder dimension, decoder dimension, number of warmup steps etc,
+that are not passed from the commandline.
+They are pre-configured by the function ``get_params()`` in
+`pruned_transducer_stateless4/train.py <https://github.com/k2-fsa/icefall/blob/master/egs/librispeech/ASR/pruned_transducer_stateless4/train.py>`_
+
+You don't need to change these pre-configured parameters. If you really need to change
+them, please modify ``./pruned_transducer_stateless4/train.py`` directly.
+
+
+.. NOTE::
+
+  The options for `pruned_transducer_stateless5 <https://github.com/k2-fsa/icefall/blob/master/egs/librispeech/ASR/pruned_transducer_stateless5/train.py>`_ are a little different from
+  other recipes. It allows you to configure ``--num-encoder-layers``, ``--dim-feedforward``, ``--nhead``, ``--encoder-dim``, ``--decoder-dim``, ``--joiner-dim`` from commandline, so that you can train models with different size with pruned_transducer_stateless5.
+
+
+Training logs
+~~~~~~~~~~~~~
+
+Training logs and checkpoints are saved in ``--exp-dir`` (e.g. ``pruned_transducer_stateless4/exp``.
+You will find the following files in that directory:
+
+  - ``epoch-1.pt``, ``epoch-2.pt``, ...
+
+    These are checkpoint files saved at the end of each epoch, containing model
+    ``state_dict`` and optimizer ``state_dict``.
+    To resume training from some checkpoint, say ``epoch-10.pt``, you can use:
+
+      .. code-block:: bash
+
+        $ ./pruned_transducer_stateless4/train.py --start-epoch 11
+
+  - ``checkpoint-436000.pt``, ``checkpoint-438000.pt``, ...
+
+    These are checkpoint files saved every ``--save-every-n`` batches,
+    containing model ``state_dict`` and optimizer ``state_dict``.
+    To resume training from some checkpoint, say ``checkpoint-436000``, you can use:
+
+      .. code-block:: bash
+
+        $ ./pruned_transducer_stateless4/train.py --start-batch 436000
+
+  - ``tensorboard/``
+
+    This folder contains tensorBoard logs. Training loss, validation loss, learning
+    rate, etc, are recorded in these logs. You can visualize them by:
+
+      .. code-block:: bash
+
+        $ cd pruned_transducer_stateless4/exp/tensorboard
+        $ tensorboard dev upload --logdir . --description "pruned transducer training for LibriSpeech with icefall"
+
+    It will print something like below:
+
+      .. code-block::
+
+        TensorFlow installation not found - running with reduced feature set.
+        Upload started and will continue reading any new data as it's added to the logdir.
+
+        To stop uploading, press Ctrl-C.
+
+        New experiment created. View your TensorBoard at: https://tensorboard.dev/experiment/97VKXf80Ru61CnP2ALWZZg/
+
+        [2022-11-20T15:50:50] Started scanning logdir.
+        Uploading 4468 scalars...
+        [2022-11-20T15:53:02] Total uploaded: 210171 scalars, 0 tensors, 0 binary objects
+        Listening for new data in logdir...
+
+    Note there is a URL in the above output. Click it and you will see
+    the following screenshot:
+
+      .. figure:: images/streaming-librispeech-pruned-transducer-tensorboard-log.jpg
+         :width: 600
+         :alt: TensorBoard screenshot
+         :align: center
+         :target: https://tensorboard.dev/experiment/97VKXf80Ru61CnP2ALWZZg/
+
+         TensorBoard screenshot.
+
+  .. hint::
+
+    If you don't have access to google, you can use the following command
+    to view the tensorboard log locally:
+
+      .. code-block:: bash
+
+        cd pruned_transducer_stateless4/exp/tensorboard
+        tensorboard --logdir . --port 6008
+
+    It will print the following message:
+
+      .. code-block::
+
+        Serving TensorBoard on localhost; to expose to the network, use a proxy or pass --bind_all
+        TensorBoard 2.8.0 at http://localhost:6008/ (Press CTRL+C to quit)
+
+    Now start your browser and go to `<http://localhost:6008>`_ to view the tensorboard
+    logs.
+
+
+  - ``log/log-train-xxxx``
+
+    It is the detailed training log in text format, same as the one
+    you saw printed to the console during training.
+
+Usage example
+~~~~~~~~~~~~~
+
+You can use the following command to start the training using 4 GPUs:
+
+.. code-block:: bash
+
+  export CUDA_VISIBLE_DEVICES="0,1,2,3"
+  ./pruned_transducer_stateless4/train.py \
+     --world-size 4 \
+     --dynamic-chunk-training 1 \
+     --causal-convolution 1 \
+     --num-epochs 30 \
+     --start-epoch 1 \
+     --exp-dir pruned_transducer_stateless4/exp \
+     --full-libri 1 \
+     --max-duration 300
+
+.. NOTE::
+
+   Comparing with training a non-streaming model, you only need to add two extra options,
+   ``--dynamic-chunk-training 1``  and ``--causal-convolution 1`` .
+
+
+Decoding
+--------
+
+The decoding part uses checkpoints saved by the training part, so you have
+to run the training part first.
+
+.. hint::
+
+   There are two kinds of checkpoints:
+
+    - (1) ``epoch-1.pt``, ``epoch-2.pt``, ..., which are saved at the end
+      of each epoch. You can pass ``--epoch`` to
+      ``pruned_transducer_stateless4/decode.py`` to use them.
+
+    - (2) ``checkpoints-436000.pt``, ``epoch-438000.pt``, ..., which are saved
+      every ``--save-every-n`` batches. You can pass ``--iter`` to
+      ``pruned_transducer_stateless4/decode.py`` to use them.
+
+    We suggest that you try both types of checkpoints and choose the one
+    that produces the lowest WERs.
+
+.. tip::
+
+    To decode a streaming model, you can use either ``simulate streaming decoding`` in ``decode.py`` or
+    ``real streaming decoding`` in ``streaming_decode.py``, the difference between ``decode.py`` and
+    ``streaming_decode.py`` is that, ``decode.py`` processes the whole acoustic frames at one time with masking (i.e. same as training),
+    but ``streaming_decode.py`` processes the acoustic frames chunk by chunk (so it can only see limited context).
+
+.. NOTE::
+
+   ``simulate streaming decoding`` in ``decode.py`` and ``real streaming decoding`` in ``streaming_decode.py`` should
+   produce almost the same results given the same ``--decode-chunk-size`` and ``--left-context``.
+
+
+Simulate streaming decoding
+~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. code-block:: bash
+
+  $ cd egs/librispeech/ASR
+  $ ./pruned_transducer_stateless4/decode.py --help
+
+shows the options for decoding.
+The following options are important for streaming models:
+
+  ``--simulate-streaming``
+
+    If you want to decode a streaming model with ``decode.py``, you **MUST** set
+    ``--simulate-streaming`` to ``True``. ``simulate`` here means the acoustic frames
+    are not processed frame by frame (or chunk by chunk), instead, the whole sequence
+    is processed at one time with masking (the same as training).
+
+  ``--causal-convolution``
+
+    If True, the convolution module in encoder layers will be causal convolution.
+    This is **MUST** be True when decoding with a streaming model.
+
+  ``--decode-chunk-size``
+
+    For streaming models, we will calculate the chunk-wise attention, ``--decode-chunk-size``
+    indicates the chunk length (in frames after subsampling) for chunk-wise attention.
+    For ``simulate streaming decoding`` the ``decode-chunk-size`` is used to generate
+    the attention mask.
+
+  ``--left-context``
+
+    ``--left-context`` indicates how many left context frames (after subsampling) can be seen
+    for current chunk when calculating chunk-wise attention. Normally, ``left-context`` should equal
+    to ``decode-chunk-size * num-left-chunks``, where ``num-left-chunks`` is the option used
+    to train this model. For ``simulate streaming decoding`` the ``left-context`` is used to generate
+    the attention mask.
+
+
+The following shows two examples (for the two types of checkpoints):
+
+.. code-block:: bash
+
+  for m in greedy_search fast_beam_search modified_beam_search; do
+    for epoch in 25 20; do
+      for avg in 7 5 3 1; do
+        ./pruned_transducer_stateless4/decode.py \
+          --epoch $epoch \
+          --avg $avg \
+          --simulate-streaming 1 \
+          --causal-convolution 1 \
+          --decode-chunk-size 16 \
+          --left-context 64 \
+          --exp-dir pruned_transducer_stateless4/exp \
+          --max-duration 600 \
+          --decoding-method $m
+      done
+    done
+  done
+
+
+.. code-block:: bash
+
+  for m in greedy_search fast_beam_search modified_beam_search; do
+    for iter in 474000; do
+      for avg in 8 10 12 14 16 18; do
+        ./pruned_transducer_stateless4/decode.py \
+          --iter $iter \
+          --avg $avg \
+          --simulate-streaming 1 \
+          --causal-convolution 1 \
+          --decode-chunk-size 16 \
+          --left-context 64 \
+          --exp-dir pruned_transducer_stateless4/exp \
+          --max-duration 600 \
+          --decoding-method $m
+      done
+    done
+  done
+
+
+Real streaming decoding
+~~~~~~~~~~~~~~~~~~~~~~~
+
+.. code-block:: bash
+
+  $ cd egs/librispeech/ASR
+  $ ./pruned_transducer_stateless4/streaming_decode.py --help
+
+shows the options for decoding.
+The following options are important for streaming models:
+
+  ``--decode-chunk-size``
+
+    For streaming models, we will calculate the chunk-wise attention, ``--decode-chunk-size``
+    indicates the chunk length (in frames after subsampling) for chunk-wise attention.
+    For ``real streaming decoding``, we will process ``decode-chunk-size`` acoustic frames at each time.
+
+  ``--left-context``
+
+    ``--left-context`` indicates how many left context frames (after subsampling) can be seen
+    for current chunk when calculating chunk-wise attention. Normally, ``left-context`` should equal
+    to ``decode-chunk-size * num-left-chunks``, where ``num-left-chunks`` is the option used
+    to train this model.
+
+  ``--num-decode-streams``
+
+    The number of decoding streams that can be run in parallel (very similar to the ``bath size``).
+    For ``real streaming decoding``, the batches will be packed dynamically, for example, if the
+    ``num-decode-streams`` equals to 10, then, sequence 1 to 10 will be decoded at first, after a while,
+    suppose sequence 1 and 2 are done, so, sequence 3 to 12 will be processed parallelly in a batch.
+
+
+.. NOTE::
+
+   We also try adding ``--right-context`` in the real streaming decoding, but it seems not to benefit
+   the performance for all the models, the reasons might be the training and decoding mismatch. You
+   can try decoding with ``--right-context`` to see if it helps. The default value is 0.
+
+
+The following shows two examples (for the two types of checkpoints):
+
+.. code-block:: bash
+
+  for m in greedy_search fast_beam_search modified_beam_search; do
+    for epoch in 25 20; do
+      for avg in 7 5 3 1; do
+        ./pruned_transducer_stateless4/decode.py \
+          --epoch $epoch \
+          --avg $avg \
+          --decode-chunk-size 16 \
+          --left-context 64 \
+          --num-decode-streams 100 \
+          --exp-dir pruned_transducer_stateless4/exp \
+          --max-duration 600 \
+          --decoding-method $m
+      done
+    done
+  done
+
+
+.. code-block:: bash
+
+  for m in greedy_search fast_beam_search modified_beam_search; do
+    for iter in 474000; do
+      for avg in 8 10 12 14 16 18; do
+        ./pruned_transducer_stateless4/decode.py \
+          --iter $iter \
+          --avg $avg \
+          --decode-chunk-size 16 \
+          --left-context 64 \
+          --num-decode-streams 100 \
+          --exp-dir pruned_transducer_stateless4/exp \
+          --max-duration 600 \
+          --decoding-method $m
+      done
+    done
+  done
+
+
+.. tip::
+
+  Supporting decoding methods are as follows:
+
+    - ``greedy_search`` : It takes the symbol with largest posterior probability
+      of each frame as the decoding result.
+
+    - ``beam_search`` :  It implements Algorithm 1 in https://arxiv.org/pdf/1211.3711.pdf and
+      `espnet/nets/beam_search_transducer.py <https://github.com/espnet/espnet/blob/master/espnet/nets/beam_search_transducer.py#L247>`_
+      is used as a reference. Basicly, it keeps topk states for each frame, and expands the kept states with their own contexts to
+      next frame.
+
+    - ``modified_beam_search`` : It implements the same algorithm as ``beam_search`` above, but it
+      runs in batch mode with ``--max-sym-per-frame=1`` being hardcoded.
+
+    - ``fast_beam_search`` : It implements graph composition between the output ``log_probs`` and
+      given ``FSAs``. It is hard to describe the details in several lines of texts, you can read
+      our paper in https://arxiv.org/pdf/2211.00484.pdf or our `rnnt decode code in k2 <https://github.com/k2-fsa/k2/blob/master/k2/csrc/rnnt_decode.h>`_. ``fast_beam_search`` can decode with ``FSAs`` on GPU efficiently.
+
+    - ``fast_beam_search_LG`` : The same as ``fast_beam_search`` above, ``fast_beam_search`` uses
+      an trivial graph that has only one state, while ``fast_beam_search_LG`` uses an LG graph
+      (with N-gram LM).
+
+    - ``fast_beam_search_nbest`` : It produces the decoding results as follows:
+
+      - (1) Use ``fast_beam_search`` to get a lattice
+      - (2) Select ``num_paths`` paths from the lattice using ``k2.random_paths()``
+      - (3) Unique the selected paths
+      - (4) Intersect the selected paths with the lattice and compute the
+            shortest path from the intersection result
+      - (5) The path with the largest score is used as the decoding output.
+
+    - ``fast_beam_search_nbest_LG`` : It implements same logic as ``fast_beam_search_nbest``, the
+      only difference is that it uses ``fast_beam_search_LG`` to generate the lattice.
+
+.. NOTE::
+
+  The supporting decoding methods in ``streaming_decode.py`` might be less than that in ``decode.py``, if needed,
+  you can implement them by yourself or file a issue in `icefall <https://github.com/k2-fsa/icefall/issues>`_ .
+
+
+Export Model
+------------
+
+`pruned_transducer_stateless4/export.py <https://github.com/k2-fsa/icefall/blob/master/egs/librispeech/ASR/pruned_transducer_stateless4/export.py>`_ supports exporting checkpoints from ``pruned_transducer_stateless4/exp`` in the following ways.
+
+Export ``model.state_dict()``
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+Checkpoints saved by ``pruned_transducer_stateless4/train.py`` also include
+``optimizer.state_dict()``. It is useful for resuming training. But after training,
+we are interested only in ``model.state_dict()``. You can use the following
+command to extract ``model.state_dict()``.
+
+.. code-block:: bash
+
+  # Assume that --epoch 25 --avg 3 produces the smallest WER
+  # (You can get such information after running ./pruned_transducer_stateless4/decode.py)
+
+  epoch=25
+  avg=3
+
+  ./pruned_transducer_stateless4/export.py \
+    --exp-dir ./pruned_transducer_stateless4/exp \
+    --streaming-model 1 \
+    --causal-convolution 1 \
+    --bpe-model data/lang_bpe_500/bpe.model \
+    --epoch $epoch \
+    --avg  $avg
+
+.. caution::
+
+   ``--streaming-model`` and ``--causal-convolution`` require to be True to export
+   a streaming mdoel.
+
+It will generate a file ``./pruned_transducer_stateless4/exp/pretrained.pt``.
+
+.. hint::
+
+   To use the generated ``pretrained.pt`` for ``pruned_transducer_stateless4/decode.py``,
+   you can run:
+
+   .. code-block:: bash
+
+      cd pruned_transducer_stateless4/exp
+      ln -s pretrained.pt epoch-999.pt
+
+   And then pass ``--epoch 999 --avg 1 --use-averaged-model 0`` to
+   ``./pruned_transducer_stateless4/decode.py``.
+
+To use the exported model with ``./pruned_transducer_stateless4/pretrained.py``, you
+can run:
+
+.. code-block:: bash
+
+  ./pruned_transducer_stateless4/pretrained.py \
+    --checkpoint ./pruned_transducer_stateless4/exp/pretrained.pt \
+    --simulate-streaming 1 \
+    --causal-convolution 1 \
+    --bpe-model ./data/lang_bpe_500/bpe.model \
+    --method greedy_search \
+    /path/to/foo.wav \
+    /path/to/bar.wav
+
+
+Export model using ``torch.jit.script()``
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. code-block:: bash
+
+  ./pruned_transducer_stateless4/export.py \
+    --exp-dir ./pruned_transducer_stateless4/exp \
+    --streaming-model 1 \
+    --causal-convolution 1 \
+    --bpe-model data/lang_bpe_500/bpe.model \
+    --epoch 25 \
+    --avg 3 \
+    --jit 1
+
+.. caution::
+
+   ``--streaming-model`` and ``--causal-convolution`` require to be True to export
+   a streaming mdoel.
+
+It will generate a file ``cpu_jit.pt`` in the given ``exp_dir``. You can later
+load it by ``torch.jit.load("cpu_jit.pt")``.
+
+Note ``cpu`` in the name ``cpu_jit.pt`` means the parameters when loaded into Python
+are on CPU. You can use ``to("cuda")`` to move them to a CUDA device.
+
+.. NOTE::
+
+   You will need this ``cpu_jit.pt`` when deploying with Sherpa framework.
+
+
+Download pretrained models
+--------------------------
+
+If you don't want to train from scratch, you can download the pretrained models
+by visiting the following links:
+
+  - `pruned_transducer_stateless <https://huggingface.co/pkufool/icefall_librispeech_streaming_pruned_transducer_stateless_20220625>`_
+
+  - `pruned_transducer_stateless2 <https://huggingface.co/pkufool/icefall_librispeech_streaming_pruned_transducer_stateless2_20220625>`_
+
+  - `pruned_transducer_stateless4 <https://huggingface.co/pkufool/icefall_librispeech_streaming_pruned_transducer_stateless4_20220625>`_
+
+  - `pruned_transducer_stateless5 <https://huggingface.co/pkufool/icefall_librispeech_streaming_pruned_transducer_stateless5_20220729>`_
+
+  See `<https://github.com/k2-fsa/icefall/blob/master/egs/librispeech/ASR/RESULTS.md>`_
+  for the details of the above pretrained models
+
+
+Deploy with Sherpa
+------------------
+
+Please see `<https://k2-fsa.github.io/sherpa/python/streaming_asr/conformer/index.html#>`_
+for how to deploy the models in ``sherpa``.

_sources/recipes/index.rst.txt

@@ -13,7 +13,5 @@ We may add recipes for other tasks as well in the future.
    :maxdepth: 2
    :caption: Table of Contents
 
-   aishell/index
-   librispeech/index
-   timit/index
-   yesno/index
+   Non-streaming-ASR/index
+   Streaming-ASR/index

contributing/code-style.html

@@ -40,10 +40,14 @@
 </div>
         </div><div class="wy-menu wy-menu-vertical" data-spy="affix" role="navigation" aria-label="Navigation menu">
               <p class="caption" role="heading"><span class="caption-text">Contents:</span></p>
-<ul class="current">
+<ul>
 <li class="toctree-l1"><a class="reference internal" href="../installation/index.html">Installation</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../model-export/index.html">Model export</a></li>
+</ul>
+<ul>
 <li class="toctree-l1"><a class="reference internal" href="../recipes/index.html">Recipes</a></li>
+</ul>
+<ul class="current">
 <li class="toctree-l1 current"><a class="reference internal" href="index.html">Contributing</a><ul class="current">
 <li class="toctree-l2"><a class="reference internal" href="doc.html">Contributing to Documentation</a></li>
 <li class="toctree-l2 current"><a class="current reference internal" href="#">Follow the code style</a></li>
@@ -110,7 +114,7 @@ $ pre-commit install
 <div><figure class="align-center" id="id2">
 <a class="reference internal image-reference" href="../_images/pre-commit-check.png"><img alt="../_images/pre-commit-check.png" src="../_images/pre-commit-check.png" style="width: 600px;" /></a>
 <figcaption>
-<p><span class="caption-number">Fig. 8 </span><span class="caption-text">pre-commit hooks invoked by <code class="docutils literal notranslate"><span class="pre">git</span> <span class="pre">commit</span></code> (Failed).</span><a class="headerlink" href="#id2" title="Permalink to this image"></a></p>
+<p><span class="caption-number">Fig. 10 </span><span class="caption-text">pre-commit hooks invoked by <code class="docutils literal notranslate"><span class="pre">git</span> <span class="pre">commit</span></code> (Failed).</span><a class="headerlink" href="#id2" title="Permalink to this image"></a></p>
 </figcaption>
 </figure>
 </div></blockquote>
@@ -129,7 +133,7 @@ it should succeed this time:</p>
 <div><figure class="align-center" id="id3">
 <a class="reference internal image-reference" href="../_images/pre-commit-check-success.png"><img alt="../_images/pre-commit-check-success.png" src="../_images/pre-commit-check-success.png" style="width: 600px;" /></a>
 <figcaption>
-<p><span class="caption-number">Fig. 9 </span><span class="caption-text">pre-commit hooks invoked by <code class="docutils literal notranslate"><span class="pre">git</span> <span class="pre">commit</span></code> (Succeeded).</span><a class="headerlink" href="#id3" title="Permalink to this image"></a></p>
+<p><span class="caption-number">Fig. 11 </span><span class="caption-text">pre-commit hooks invoked by <code class="docutils literal notranslate"><span class="pre">git</span> <span class="pre">commit</span></code> (Succeeded).</span><a class="headerlink" href="#id3" title="Permalink to this image"></a></p>
 </figcaption>
 </figure>
 </div></blockquote>

contributing/doc.html

@@ -40,10 +40,14 @@
 </div>
         </div><div class="wy-menu wy-menu-vertical" data-spy="affix" role="navigation" aria-label="Navigation menu">
               <p class="caption" role="heading"><span class="caption-text">Contents:</span></p>
-<ul class="current">
+<ul>
 <li class="toctree-l1"><a class="reference internal" href="../installation/index.html">Installation</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../model-export/index.html">Model export</a></li>
+</ul>
+<ul>
 <li class="toctree-l1"><a class="reference internal" href="../recipes/index.html">Recipes</a></li>
+</ul>
+<ul class="current">
 <li class="toctree-l1 current"><a class="reference internal" href="index.html">Contributing</a><ul class="current">
 <li class="toctree-l2 current"><a class="current reference internal" href="#">Contributing to Documentation</a></li>
 <li class="toctree-l2"><a class="reference internal" href="code-style.html">Follow the code style</a></li>
@@ -118,7 +122,7 @@ the following:</p>
 <div><figure class="align-center" id="id1">
 <a class="reference internal image-reference" href="../_images/doc-contrib.png"><img alt="../_images/doc-contrib.png" src="../_images/doc-contrib.png" style="width: 600px;" /></a>
 <figcaption>
-<p><span class="caption-number">Fig. 7 </span><span class="caption-text">View generated documentation locally with <code class="docutils literal notranslate"><span class="pre">python3</span> <span class="pre">-m</span> <span class="pre">http.server</span></code>.</span><a class="headerlink" href="#id1" title="Permalink to this image"></a></p>
+<p><span class="caption-number">Fig. 9 </span><span class="caption-text">View generated documentation locally with <code class="docutils literal notranslate"><span class="pre">python3</span> <span class="pre">-m</span> <span class="pre">http.server</span></code>.</span><a class="headerlink" href="#id1" title="Permalink to this image"></a></p>
 </figcaption>
 </figure>
 </div></blockquote>

contributing/how-to-create-a-recipe.html

@@ -40,10 +40,14 @@
 </div>
         </div><div class="wy-menu wy-menu-vertical" data-spy="affix" role="navigation" aria-label="Navigation menu">
               <p class="caption" role="heading"><span class="caption-text">Contents:</span></p>
-<ul class="current">
+<ul>
 <li class="toctree-l1"><a class="reference internal" href="../installation/index.html">Installation</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../model-export/index.html">Model export</a></li>
+</ul>
+<ul>
 <li class="toctree-l1"><a class="reference internal" href="../recipes/index.html">Recipes</a></li>
+</ul>
+<ul class="current">
 <li class="toctree-l1 current"><a class="reference internal" href="index.html">Contributing</a><ul class="current">
 <li class="toctree-l2"><a class="reference internal" href="doc.html">Contributing to Documentation</a></li>
 <li class="toctree-l2"><a class="reference internal" href="code-style.html">Follow the code style</a></li>

contributing/index.html

@@ -21,7 +21,7 @@
     <link rel="index" title="Index" href="../genindex.html" />
     <link rel="search" title="Search" href="../search.html" />
     <link rel="next" title="Contributing to Documentation" href="doc.html" />
-    <link rel="prev" title="TDNN-CTC" href="../recipes/yesno/tdnn.html" /> 
+    <link rel="prev" title="LSTM Transducer" href="../recipes/Streaming-ASR/librispeech/lstm_pruned_stateless_transducer.html" /> 
 </head>
 
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@@ -40,10 +40,14 @@
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               <p class="caption" role="heading"><span class="caption-text">Contents:</span></p>
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+<ul>
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+</ul>
+<ul>
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+</ul>
+<ul class="current">
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@@ -120,7 +124,7 @@ and code to <code class="docutils literal notranslate"><span class="pre">icefall
            </div>
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           <footer><div class="rst-footer-buttons" role="navigation" aria-label="Footer">
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@@ -40,7 +40,11 @@
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 <li class="toctree-l1"><a class="reference internal" href="model-export/index.html">Model export</a></li>
+</ul>
+<ul>
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+</ul>
+<ul>
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               <p class="caption" role="heading"><span class="caption-text">Contents:</span></p>
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+<ul>
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+</ul>
+<ul>
 <li class="toctree-l1"><a class="reference internal" href="../recipes/index.html">Recipes</a></li>
+</ul>
+<ul class="current">
 <li class="toctree-l1"><a class="reference internal" href="../contributing/index.html">Contributing</a></li>
 <li class="toctree-l1 current"><a class="current reference internal" href="#">Huggingface</a><ul>
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huggingface/pretrained-models.html

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               <p class="caption" role="heading"><span class="caption-text">Contents:</span></p>
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+<ul>
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 <li class="toctree-l1"><a class="reference internal" href="../model-export/index.html">Model export</a></li>
+</ul>
+<ul>
 <li class="toctree-l1"><a class="reference internal" href="../recipes/index.html">Recipes</a></li>
+</ul>
+<ul class="current">
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huggingface/spaces.html

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 <li class="toctree-l1"><a class="reference internal" href="../model-export/index.html">Model export</a></li>
+</ul>
+<ul>
 <li class="toctree-l1"><a class="reference internal" href="../recipes/index.html">Recipes</a></li>
+</ul>
+<ul class="current">
 <li class="toctree-l1"><a class="reference internal" href="../contributing/index.html">Contributing</a></li>
 <li class="toctree-l1 current"><a class="reference internal" href="index.html">Huggingface</a><ul class="current">
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@@ -42,7 +42,11 @@
 <ul>
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 <li class="toctree-l1"><a class="reference internal" href="model-export/index.html">Model export</a></li>
+</ul>
+<ul>
 <li class="toctree-l1"><a class="reference internal" href="recipes/index.html">Recipes</a></li>
+</ul>
+<ul>
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 <li class="toctree-l1"><a class="reference internal" href="huggingface/index.html">Huggingface</a></li>
 </ul>
@@ -97,13 +101,29 @@ speech recognition recipes using <a class="reference external" href="https://git
 <li class="toctree-l2"><a class="reference internal" href="model-export/export-ncnn.html">Export to ncnn</a></li>
 </ul>
 </li>
+</ul>
+</div>
+<div class="toctree-wrapper compound">
+<ul>
 <li class="toctree-l1"><a class="reference internal" href="recipes/index.html">Recipes</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="recipes/aishell/index.html">aishell</a></li>
-<li class="toctree-l2"><a class="reference internal" href="recipes/librispeech/index.html">LibriSpeech</a></li>
-<li class="toctree-l2"><a class="reference internal" href="recipes/timit/index.html">TIMIT</a></li>
-<li class="toctree-l2"><a class="reference internal" href="recipes/yesno/index.html">YesNo</a></li>
+<li class="toctree-l2"><a class="reference internal" href="recipes/Non-streaming-ASR/index.html">Non Streaming ASR</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="recipes/Non-streaming-ASR/aishell/index.html">aishell</a></li>
+<li class="toctree-l3"><a class="reference internal" href="recipes/Non-streaming-ASR/librispeech/index.html">LibriSpeech</a></li>
+<li class="toctree-l3"><a class="reference internal" href="recipes/Non-streaming-ASR/timit/index.html">TIMIT</a></li>
+<li class="toctree-l3"><a class="reference internal" href="recipes/Non-streaming-ASR/yesno/index.html">YesNo</a></li>
 </ul>
 </li>
+<li class="toctree-l2"><a class="reference internal" href="recipes/Streaming-ASR/index.html">Streaming ASR</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="recipes/Streaming-ASR/introduction.html">Introduction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="recipes/Streaming-ASR/librispeech/index.html">LibriSpeech</a></li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+</div>
+<div class="toctree-wrapper compound">
+<ul>
 <li class="toctree-l1"><a class="reference internal" href="contributing/index.html">Contributing</a><ul>
 <li class="toctree-l2"><a class="reference internal" href="contributing/doc.html">Contributing to Documentation</a></li>
 <li class="toctree-l2"><a class="reference internal" href="contributing/code-style.html">Follow the code style</a></li>

installation/index.html

@@ -64,7 +64,11 @@
 </ul>
 </li>
 <li class="toctree-l1"><a class="reference internal" href="../model-export/index.html">Model export</a></li>
+</ul>
+<ul>
 <li class="toctree-l1"><a class="reference internal" href="../recipes/index.html">Recipes</a></li>
+</ul>
+<ul>
 <li class="toctree-l1"><a class="reference internal" href="../contributing/index.html">Contributing</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../huggingface/index.html">Huggingface</a></li>
 </ul>

model-export/export-model-state-dict.html

@@ -56,7 +56,11 @@
 <li class="toctree-l2"><a class="reference internal" href="export-ncnn.html">Export to ncnn</a></li>
 </ul>
 </li>
+</ul>
+<ul>
 <li class="toctree-l1"><a class="reference internal" href="../recipes/index.html">Recipes</a></li>
+</ul>
+<ul>
 <li class="toctree-l1"><a class="reference internal" href="../contributing/index.html">Contributing</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../huggingface/index.html">Huggingface</a></li>
 </ul>

model-export/export-ncnn.html

@@ -50,7 +50,11 @@
 <li class="toctree-l2 current"><a class="current reference internal" href="#">Export to ncnn</a></li>
 </ul>
 </li>
+</ul>
+<ul>
 <li class="toctree-l1"><a class="reference internal" href="../recipes/index.html">Recipes</a></li>
+</ul>
+<ul>
 <li class="toctree-l1"><a class="reference internal" href="../contributing/index.html">Contributing</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../huggingface/index.html">Huggingface</a></li>
 </ul>
@@ -83,7 +87,7 @@
   <section id="export-to-ncnn">
 <h1>Export to ncnn<a class="headerlink" href="#export-to-ncnn" title="Permalink to this heading"></a></h1>
 <p>We support exporting LSTM transducer models to <a class="reference external" href="https://github.com/tencent/ncnn">ncnn</a>.</p>
-<p>Please refer to <a class="reference internal" href="../recipes/librispeech/lstm_pruned_stateless_transducer.html#export-model-for-ncnn"><span class="std std-ref">Export model for ncnn</span></a> for details.</p>
+<p>Please refer to <a class="reference internal" href="../recipes/Streaming-ASR/librispeech/lstm_pruned_stateless_transducer.html#export-model-for-ncnn"><span class="std std-ref">Export model for ncnn</span></a> for details.</p>
 <p>We also provide <a class="reference external" href="https://github.com/k2-fsa/sherpa-ncnn">https://github.com/k2-fsa/sherpa-ncnn</a>
 performing speech recognition using <code class="docutils literal notranslate"><span class="pre">ncnn</span></code> with exported models.
 It has been tested on Linux, macOS, Windows, and Raspberry Pi. The project is

model-export/export-onnx.html

@@ -55,7 +55,11 @@
 <li class="toctree-l2"><a class="reference internal" href="export-ncnn.html">Export to ncnn</a></li>
 </ul>
 </li>
+</ul>
+<ul>
 <li class="toctree-l1"><a class="reference internal" href="../recipes/index.html">Recipes</a></li>
+</ul>
+<ul>
 <li class="toctree-l1"><a class="reference internal" href="../contributing/index.html">Contributing</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../huggingface/index.html">Huggingface</a></li>
 </ul>

model-export/export-with-torch-jit-script.html

@@ -55,7 +55,11 @@
 <li class="toctree-l2"><a class="reference internal" href="export-ncnn.html">Export to ncnn</a></li>
 </ul>
 </li>
+</ul>
+<ul>
 <li class="toctree-l1"><a class="reference internal" href="../recipes/index.html">Recipes</a></li>
+</ul>
+<ul>
 <li class="toctree-l1"><a class="reference internal" href="../contributing/index.html">Contributing</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../huggingface/index.html">Huggingface</a></li>
 </ul>

model-export/export-with-torch-jit-trace.html

@@ -55,7 +55,11 @@
 <li class="toctree-l2"><a class="reference internal" href="export-ncnn.html">Export to ncnn</a></li>
 </ul>
 </li>
+</ul>
+<ul>
 <li class="toctree-l1"><a class="reference internal" href="../recipes/index.html">Recipes</a></li>
+</ul>
+<ul>
 <li class="toctree-l1"><a class="reference internal" href="../contributing/index.html">Contributing</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../huggingface/index.html">Huggingface</a></li>
 </ul>

model-export/index.html

@@ -50,7 +50,11 @@
 <li class="toctree-l2"><a class="reference internal" href="export-ncnn.html">Export to ncnn</a></li>
 </ul>
 </li>
+</ul>
+<ul>
 <li class="toctree-l1"><a class="reference internal" href="../recipes/index.html">Recipes</a></li>
+</ul>
+<ul>
 <li class="toctree-l1"><a class="reference internal" href="../contributing/index.html">Contributing</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../huggingface/index.html">Huggingface</a></li>
 </ul>

recipes/Non-streaming-ASR/aishell/conformer_ctc.html

@@ -5,21 +5,21 @@
 
   <meta name="viewport" content="width=device-width, initial-scale=1.0" />
   <title>Conformer CTC &mdash; icefall 0.1 documentation</title>
-      <link rel="stylesheet" href="../../_static/pygments.css" type="text/css" />
-      <link rel="stylesheet" href="../../_static/css/theme.css" type="text/css" />
+      <link rel="stylesheet" href="../../../_static/pygments.css" type="text/css" />
+      <link rel="stylesheet" href="../../../_static/css/theme.css" type="text/css" />
   <!--[if lt IE 9]>
-    <script src="../../_static/js/html5shiv.min.js"></script>
+    <script src="../../../_static/js/html5shiv.min.js"></script>
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-        <script src="../../_static/_sphinx_javascript_frameworks_compat.js"></script>
-        <script src="../../_static/doctools.js"></script>
-        <script src="../../_static/sphinx_highlight.js"></script>
-    <script src="../../_static/js/theme.js"></script>
-    <link rel="index" title="Index" href="../../genindex.html" />
-    <link rel="search" title="Search" href="../../search.html" />
+        <script data-url_root="../../../" id="documentation_options" src="../../../_static/documentation_options.js"></script>
+        <script src="../../../_static/jquery.js"></script>
+        <script src="../../../_static/underscore.js"></script>
+        <script src="../../../_static/_sphinx_javascript_frameworks_compat.js"></script>
+        <script src="../../../_static/doctools.js"></script>
+        <script src="../../../_static/sphinx_highlight.js"></script>
+    <script src="../../../_static/js/theme.js"></script>
+    <link rel="index" title="Index" href="../../../genindex.html" />
+    <link rel="search" title="Search" href="../../../search.html" />
     <link rel="next" title="Stateless Transducer" href="stateless_transducer.html" />
     <link rel="prev" title="TDNN-LSTM CTC" href="tdnn_lstm_ctc.html" /> 
 </head>
@@ -29,10 +29,10 @@
     <nav data-toggle="wy-nav-shift" class="wy-nav-side">
       <div class="wy-side-scroll">
         <div class="wy-side-nav-search" >
-            <a href="../../index.html" class="icon icon-home"> icefall
+            <a href="../../../index.html" class="icon icon-home"> icefall
           </a>
 <div role="search">
-  <form id="rtd-search-form" class="wy-form" action="../../search.html" method="get">
+  <form id="rtd-search-form" class="wy-form" action="../../../search.html" method="get">
     <input type="text" name="q" placeholder="Search docs" />
     <input type="hidden" name="check_keywords" value="yes" />
     <input type="hidden" name="area" value="default" />
@@ -40,31 +40,31 @@
 </div>
         </div><div class="wy-menu wy-menu-vertical" data-spy="affix" role="navigation" aria-label="Navigation menu">
               <p class="caption" role="heading"><span class="caption-text">Contents:</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../../../installation/index.html">Installation</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../../model-export/index.html">Model export</a></li>
+</ul>
 <ul class="current">
-<li class="toctree-l1"><a class="reference internal" href="../../installation/index.html">Installation</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../../model-export/index.html">Model export</a></li>
-<li class="toctree-l1 current"><a class="reference internal" href="../index.html">Recipes</a><ul class="current">
-<li class="toctree-l2 current"><a class="reference internal" href="index.html">aishell</a><ul class="current">
-<li class="toctree-l3"><a class="reference internal" href="tdnn_lstm_ctc.html">TDNN-LSTM CTC</a></li>
-<li class="toctree-l3 current"><a class="current reference internal" href="#">Conformer CTC</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="#data-preparation">Data preparation</a></li>
-<li class="toctree-l4"><a class="reference internal" href="#training">Training</a></li>
-<li class="toctree-l4"><a class="reference internal" href="#decoding">Decoding</a></li>
-<li class="toctree-l4"><a class="reference internal" href="#pre-trained-model">Pre-trained Model</a></li>
-<li class="toctree-l4"><a class="reference internal" href="#colab-notebook">Colab notebook</a></li>
-<li class="toctree-l4"><a class="reference internal" href="#deployment-with-c">Deployment with C++</a></li>
+<li class="toctree-l1 current"><a class="reference internal" href="../../index.html">Recipes</a><ul class="current">
+<li class="toctree-l2 current"><a class="reference internal" href="../index.html">Non Streaming ASR</a><ul class="current">
+<li class="toctree-l3 current"><a class="reference internal" href="index.html">aishell</a><ul class="current">
+<li class="toctree-l4"><a class="reference internal" href="tdnn_lstm_ctc.html">TDNN-LSTM CTC</a></li>
+<li class="toctree-l4 current"><a class="current reference internal" href="#">Conformer CTC</a></li>
+<li class="toctree-l4"><a class="reference internal" href="stateless_transducer.html">Stateless Transducer</a></li>
 </ul>
 </li>
-<li class="toctree-l3"><a class="reference internal" href="stateless_transducer.html">Stateless Transducer</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../librispeech/index.html">LibriSpeech</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../timit/index.html">TIMIT</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../yesno/index.html">YesNo</a></li>
 </ul>
 </li>
-<li class="toctree-l2"><a class="reference internal" href="../librispeech/index.html">LibriSpeech</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../timit/index.html">TIMIT</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../yesno/index.html">YesNo</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../Streaming-ASR/index.html">Streaming ASR</a></li>
 </ul>
 </li>
-<li class="toctree-l1"><a class="reference internal" href="../../contributing/index.html">Contributing</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../../huggingface/index.html">Huggingface</a></li>
+</ul>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../../../contributing/index.html">Contributing</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../../huggingface/index.html">Huggingface</a></li>
 </ul>
 
         </div>
@@ -73,19 +73,20 @@
 
     <section data-toggle="wy-nav-shift" class="wy-nav-content-wrap"><nav class="wy-nav-top" aria-label="Mobile navigation menu" >
           <i data-toggle="wy-nav-top" class="fa fa-bars"></i>
-          <a href="../../index.html">icefall</a>
+          <a href="../../../index.html">icefall</a>
       </nav>
 
       <div class="wy-nav-content">
         <div class="rst-content">
           <div role="navigation" aria-label="Page navigation">
   <ul class="wy-breadcrumbs">
-      <li><a href="../../index.html" class="icon icon-home"></a></li>
-          <li class="breadcrumb-item"><a href="../index.html">Recipes</a></li>
+      <li><a href="../../../index.html" class="icon icon-home"></a></li>
+          <li class="breadcrumb-item"><a href="../../index.html">Recipes</a></li>
+          <li class="breadcrumb-item"><a href="../index.html">Non Streaming ASR</a></li>
           <li class="breadcrumb-item"><a href="index.html">aishell</a></li>
       <li class="breadcrumb-item active">Conformer CTC</li>
       <li class="wy-breadcrumbs-aside">
-              <a href="https://github.com/k2-fsa/icefall/blob/master/docs/source/recipes/aishell/conformer_ctc.rst" class="fa fa-github"> Edit on GitHub</a>
+              <a href="https://github.com/k2-fsa/icefall/blob/master/docs/source/recipes/Non-streaming-ASR/aishell/conformer_ctc.rst" class="fa fa-github"> Edit on GitHub</a>
       </li>
   </ul>
   <hr/>
@@ -99,7 +100,7 @@
 with the <a class="reference external" href="https://www.openslr.org/33">Aishell</a> dataset.</p>
 <div class="admonition hint">
 <p class="admonition-title">Hint</p>
-<p>We assume you have read the page <a class="reference internal" href="../../installation/index.html#install-icefall"><span class="std std-ref">Installation</span></a> and have setup
+<p>We assume you have read the page <a class="reference internal" href="../../../installation/index.html#install-icefall"><span class="std std-ref">Installation</span></a> and have setup
 the environment for <code class="docutils literal notranslate"><span class="pre">icefall</span></code>.</p>
 </div>
 <div class="admonition hint">
@@ -331,7 +332,7 @@ $ tensorboard dev upload --logdir . --name <span class="s2">&quot;Aishell confor
 the following screenshot:</p>
 <blockquote>
 <div><figure class="align-center" id="id2">
-<a class="reference external image-reference" href="https://tensorboard.dev/experiment/WE1DocDqRRCOSAgmGyClhg/"><img alt="TensorBoard screenshot" src="../../_images/aishell-conformer-ctc-tensorboard-log.jpg" style="width: 600px;" /></a>
+<a class="reference external image-reference" href="https://tensorboard.dev/experiment/WE1DocDqRRCOSAgmGyClhg/"><img alt="TensorBoard screenshot" src="../../../_images/aishell-conformer-ctc-tensorboard-log.jpg" style="width: 600px;" /></a>
 <figcaption>
 <p><span class="caption-number">Fig. 2 </span><span class="caption-text">TensorBoard screenshot.</span><a class="headerlink" href="#id2" title="Permalink to this image"></a></p>
 </figcaption>

recipes/Non-streaming-ASR/aishell/index.html

@@ -5,23 +5,23 @@
 
   <meta name="viewport" content="width=device-width, initial-scale=1.0" />
   <title>aishell &mdash; icefall 0.1 documentation</title>
-      <link rel="stylesheet" href="../../_static/pygments.css" type="text/css" />
-      <link rel="stylesheet" href="../../_static/css/theme.css" type="text/css" />
+      <link rel="stylesheet" href="../../../_static/pygments.css" type="text/css" />
+      <link rel="stylesheet" href="../../../_static/css/theme.css" type="text/css" />
   <!--[if lt IE 9]>
-    <script src="../../_static/js/html5shiv.min.js"></script>
+    <script src="../../../_static/js/html5shiv.min.js"></script>
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-        <script src="../../_static/sphinx_highlight.js"></script>
-    <script src="../../_static/js/theme.js"></script>
-    <link rel="index" title="Index" href="../../genindex.html" />
-    <link rel="search" title="Search" href="../../search.html" />
+        <script data-url_root="../../../" id="documentation_options" src="../../../_static/documentation_options.js"></script>
+        <script src="../../../_static/jquery.js"></script>
+        <script src="../../../_static/underscore.js"></script>
+        <script src="../../../_static/_sphinx_javascript_frameworks_compat.js"></script>
+        <script src="../../../_static/doctools.js"></script>
+        <script src="../../../_static/sphinx_highlight.js"></script>
+    <script src="../../../_static/js/theme.js"></script>
+    <link rel="index" title="Index" href="../../../genindex.html" />
+    <link rel="search" title="Search" href="../../../search.html" />
     <link rel="next" title="TDNN-LSTM CTC" href="tdnn_lstm_ctc.html" />
-    <link rel="prev" title="Recipes" href="../index.html" /> 
+    <link rel="prev" title="Non Streaming ASR" href="../index.html" /> 
 </head>
 
 <body class="wy-body-for-nav"> 
@@ -29,10 +29,10 @@
     <nav data-toggle="wy-nav-shift" class="wy-nav-side">
       <div class="wy-side-scroll">
         <div class="wy-side-nav-search" >
-            <a href="../../index.html" class="icon icon-home"> icefall
+            <a href="../../../index.html" class="icon icon-home"> icefall
           </a>
 <div role="search">
-  <form id="rtd-search-form" class="wy-form" action="../../search.html" method="get">
+  <form id="rtd-search-form" class="wy-form" action="../../../search.html" method="get">
     <input type="text" name="q" placeholder="Search docs" />
     <input type="hidden" name="check_keywords" value="yes" />
     <input type="hidden" name="area" value="default" />
@@ -40,23 +40,31 @@
 </div>
         </div><div class="wy-menu wy-menu-vertical" data-spy="affix" role="navigation" aria-label="Navigation menu">
               <p class="caption" role="heading"><span class="caption-text">Contents:</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../../../installation/index.html">Installation</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../../model-export/index.html">Model export</a></li>
+</ul>
 <ul class="current">
-<li class="toctree-l1"><a class="reference internal" href="../../installation/index.html">Installation</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../../model-export/index.html">Model export</a></li>
-<li class="toctree-l1 current"><a class="reference internal" href="../index.html">Recipes</a><ul class="current">
-<li class="toctree-l2 current"><a class="current reference internal" href="#">aishell</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="tdnn_lstm_ctc.html">TDNN-LSTM CTC</a></li>
-<li class="toctree-l3"><a class="reference internal" href="conformer_ctc.html">Conformer CTC</a></li>
-<li class="toctree-l3"><a class="reference internal" href="stateless_transducer.html">Stateless Transducer</a></li>
+<li class="toctree-l1 current"><a class="reference internal" href="../../index.html">Recipes</a><ul class="current">
+<li class="toctree-l2 current"><a class="reference internal" href="../index.html">Non Streaming ASR</a><ul class="current">
+<li class="toctree-l3 current"><a class="current reference internal" href="#">aishell</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="tdnn_lstm_ctc.html">TDNN-LSTM CTC</a></li>
+<li class="toctree-l4"><a class="reference internal" href="conformer_ctc.html">Conformer CTC</a></li>
+<li class="toctree-l4"><a class="reference internal" href="stateless_transducer.html">Stateless Transducer</a></li>
 </ul>
 </li>
-<li class="toctree-l2"><a class="reference internal" href="../librispeech/index.html">LibriSpeech</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../timit/index.html">TIMIT</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../yesno/index.html">YesNo</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../librispeech/index.html">LibriSpeech</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../timit/index.html">TIMIT</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../yesno/index.html">YesNo</a></li>
 </ul>
 </li>
-<li class="toctree-l1"><a class="reference internal" href="../../contributing/index.html">Contributing</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../../huggingface/index.html">Huggingface</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../Streaming-ASR/index.html">Streaming ASR</a></li>
+</ul>
+</li>
+</ul>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../../../contributing/index.html">Contributing</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../../huggingface/index.html">Huggingface</a></li>
 </ul>
 
         </div>
@@ -65,18 +73,19 @@
 
     <section data-toggle="wy-nav-shift" class="wy-nav-content-wrap"><nav class="wy-nav-top" aria-label="Mobile navigation menu" >
           <i data-toggle="wy-nav-top" class="fa fa-bars"></i>
-          <a href="../../index.html">icefall</a>
+          <a href="../../../index.html">icefall</a>
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       <div class="wy-nav-content">
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           <div role="navigation" aria-label="Page navigation">
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-      <li><a href="../../index.html" class="icon icon-home"></a></li>
-          <li class="breadcrumb-item"><a href="../index.html">Recipes</a></li>
+      <li><a href="../../../index.html" class="icon icon-home"></a></li>
+          <li class="breadcrumb-item"><a href="../../index.html">Recipes</a></li>
+          <li class="breadcrumb-item"><a href="../index.html">Non Streaming ASR</a></li>
       <li class="breadcrumb-item active">aishell</li>
       <li class="wy-breadcrumbs-aside">
-              <a href="https://github.com/k2-fsa/icefall/blob/master/docs/source/recipes/aishell/index.rst" class="fa fa-github"> Edit on GitHub</a>
+              <a href="https://github.com/k2-fsa/icefall/blob/master/docs/source/recipes/Non-streaming-ASR/aishell/index.rst" class="fa fa-github"> Edit on GitHub</a>
       </li>
   </ul>
   <hr/>
@@ -108,7 +117,7 @@ amount of data for new researchers in the field of speech recognition.</p>
            </div>
           </div>
           <footer><div class="rst-footer-buttons" role="navigation" aria-label="Footer">
-        <a href="../index.html" class="btn btn-neutral float-left" title="Recipes" accesskey="p" rel="prev"><span class="fa fa-arrow-circle-left" aria-hidden="true"></span> Previous</a>
+        <a href="../index.html" class="btn btn-neutral float-left" title="Non Streaming ASR" accesskey="p" rel="prev"><span class="fa fa-arrow-circle-left" aria-hidden="true"></span> Previous</a>
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recipes/Non-streaming-ASR/aishell/stateless_transducer.html

@@ -5,21 +5,21 @@
 
   <meta name="viewport" content="width=device-width, initial-scale=1.0" />
   <title>Stateless Transducer &mdash; icefall 0.1 documentation</title>
-      <link rel="stylesheet" href="../../_static/pygments.css" type="text/css" />
-      <link rel="stylesheet" href="../../_static/css/theme.css" type="text/css" />
+      <link rel="stylesheet" href="../../../_static/pygments.css" type="text/css" />
+      <link rel="stylesheet" href="../../../_static/css/theme.css" type="text/css" />
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-    <script src="../../_static/js/theme.js"></script>
-    <link rel="index" title="Index" href="../../genindex.html" />
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+        <script data-url_root="../../../" id="documentation_options" src="../../../_static/documentation_options.js"></script>
+        <script src="../../../_static/jquery.js"></script>
+        <script src="../../../_static/underscore.js"></script>
+        <script src="../../../_static/_sphinx_javascript_frameworks_compat.js"></script>
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+    <script src="../../../_static/js/theme.js"></script>
+    <link rel="index" title="Index" href="../../../genindex.html" />
+    <link rel="search" title="Search" href="../../../search.html" />
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@@ -29,10 +29,10 @@
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         <div class="wy-side-nav-search" >
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+            <a href="../../../index.html" class="icon icon-home"> icefall
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@@ -40,32 +40,31 @@
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+<li class="toctree-l1"><a class="reference internal" href="../../../installation/index.html">Installation</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../../model-export/index.html">Model export</a></li>
+</ul>
 <ul class="current">
-<li class="toctree-l1"><a class="reference internal" href="../../installation/index.html">Installation</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../../model-export/index.html">Model export</a></li>
-<li class="toctree-l1 current"><a class="reference internal" href="../index.html">Recipes</a><ul class="current">
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-<li class="toctree-l3"><a class="reference internal" href="conformer_ctc.html">Conformer CTC</a></li>
-<li class="toctree-l3 current"><a class="current reference internal" href="#">Stateless Transducer</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="#the-model">The Model</a></li>
-<li class="toctree-l4"><a class="reference internal" href="#the-loss">The Loss</a></li>
-<li class="toctree-l4"><a class="reference internal" href="#data-preparation">Data Preparation</a></li>
-<li class="toctree-l4"><a class="reference internal" href="#training">Training</a></li>
-<li class="toctree-l4"><a class="reference internal" href="#decoding">Decoding</a></li>
-<li class="toctree-l4"><a class="reference internal" href="#pre-trained-model">Pre-trained Model</a></li>
-<li class="toctree-l4"><a class="reference internal" href="#colab-notebook">Colab notebook</a></li>
+<li class="toctree-l1 current"><a class="reference internal" href="../../index.html">Recipes</a><ul class="current">
+<li class="toctree-l2 current"><a class="reference internal" href="../index.html">Non Streaming ASR</a><ul class="current">
+<li class="toctree-l3 current"><a class="reference internal" href="index.html">aishell</a><ul class="current">
+<li class="toctree-l4"><a class="reference internal" href="tdnn_lstm_ctc.html">TDNN-LSTM CTC</a></li>
+<li class="toctree-l4"><a class="reference internal" href="conformer_ctc.html">Conformer CTC</a></li>
+<li class="toctree-l4 current"><a class="current reference internal" href="#">Stateless Transducer</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../librispeech/index.html">LibriSpeech</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../timit/index.html">TIMIT</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../yesno/index.html">YesNo</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../Streaming-ASR/index.html">Streaming ASR</a></li>
 </ul>
 </li>
 </ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../librispeech/index.html">LibriSpeech</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../timit/index.html">TIMIT</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../yesno/index.html">YesNo</a></li>
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+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../../../contributing/index.html">Contributing</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../../huggingface/index.html">Huggingface</a></li>
 </ul>
 
         </div>
@@ -74,19 +73,20 @@
 
     <section data-toggle="wy-nav-shift" class="wy-nav-content-wrap"><nav class="wy-nav-top" aria-label="Mobile navigation menu" >
           <i data-toggle="wy-nav-top" class="fa fa-bars"></i>
-          <a href="../../index.html">icefall</a>
+          <a href="../../../index.html">icefall</a>
       </nav>
 
       <div class="wy-nav-content">
         <div class="rst-content">
           <div role="navigation" aria-label="Page navigation">
   <ul class="wy-breadcrumbs">
-      <li><a href="../../index.html" class="icon icon-home"></a></li>
-          <li class="breadcrumb-item"><a href="../index.html">Recipes</a></li>
+      <li><a href="../../../index.html" class="icon icon-home"></a></li>
+          <li class="breadcrumb-item"><a href="../../index.html">Recipes</a></li>
+          <li class="breadcrumb-item"><a href="../index.html">Non Streaming ASR</a></li>
           <li class="breadcrumb-item"><a href="index.html">aishell</a></li>
       <li class="breadcrumb-item active">Stateless Transducer</li>
       <li class="wy-breadcrumbs-aside">
-              <a href="https://github.com/k2-fsa/icefall/blob/master/docs/source/recipes/aishell/stateless_transducer.rst" class="fa fa-github"> Edit on GitHub</a>
+              <a href="https://github.com/k2-fsa/icefall/blob/master/docs/source/recipes/Non-streaming-ASR/aishell/stateless_transducer.rst" class="fa fa-github"> Edit on GitHub</a>
       </li>
   </ul>
   <hr/>
@@ -104,7 +104,7 @@ here. As you will see, there are no RNNs in the model.</p>
 </div>
 <div class="admonition hint">
 <p class="admonition-title">Hint</p>
-<p>We assume you have read the page <a class="reference internal" href="../../installation/index.html#install-icefall"><span class="std std-ref">Installation</span></a> and have setup
+<p>We assume you have read the page <a class="reference internal" href="../../../installation/index.html#install-icefall"><span class="std std-ref">Installation</span></a> and have setup
 the environment for <code class="docutils literal notranslate"><span class="pre">icefall</span></code>.</p>
 </div>
 <div class="admonition hint">
@@ -391,7 +391,7 @@ $ tensorboard dev upload --logdir . --name <span class="s2">&quot;Aishell transd
 above output, click it and you will see the following screenshot:</p>
 <blockquote>
 <div><figure class="align-center" id="id3">
-<a class="reference external image-reference" href="https://tensorboard.dev/experiment/laGZ6HrcQxOigbFD5E0Y3Q"><img alt="TensorBoard screenshot" src="../../_images/aishell-transducer_stateless_modified-tensorboard-log.png" style="width: 600px;" /></a>
+<a class="reference external image-reference" href="https://tensorboard.dev/experiment/laGZ6HrcQxOigbFD5E0Y3Q"><img alt="TensorBoard screenshot" src="../../../_images/aishell-transducer_stateless_modified-tensorboard-log.png" style="width: 600px;" /></a>
 <figcaption>
 <p><span class="caption-number">Fig. 3 </span><span class="caption-text">TensorBoard screenshot.</span><a class="headerlink" href="#id3" title="Permalink to this image"></a></p>
 </figcaption>

recipes/Non-streaming-ASR/aishell/tdnn_lstm_ctc.html

@@ -5,21 +5,21 @@
 
   <meta name="viewport" content="width=device-width, initial-scale=1.0" />
   <title>TDNN-LSTM CTC &mdash; icefall 0.1 documentation</title>
-      <link rel="stylesheet" href="../../_static/pygments.css" type="text/css" />
-      <link rel="stylesheet" href="../../_static/css/theme.css" type="text/css" />
+      <link rel="stylesheet" href="../../../_static/pygments.css" type="text/css" />
+      <link rel="stylesheet" href="../../../_static/css/theme.css" type="text/css" />
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-    <link rel="index" title="Index" href="../../genindex.html" />
-    <link rel="search" title="Search" href="../../search.html" />
+        <script data-url_root="../../../" id="documentation_options" src="../../../_static/documentation_options.js"></script>
+        <script src="../../../_static/jquery.js"></script>
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+    <link rel="index" title="Index" href="../../../genindex.html" />
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     <link rel="next" title="Conformer CTC" href="conformer_ctc.html" />
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@@ -29,10 +29,10 @@
     <nav data-toggle="wy-nav-shift" class="wy-nav-side">
       <div class="wy-side-scroll">
         <div class="wy-side-nav-search" >
-            <a href="../../index.html" class="icon icon-home"> icefall
+            <a href="../../../index.html" class="icon icon-home"> icefall
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 <div role="search">
-  <form id="rtd-search-form" class="wy-form" action="../../search.html" method="get">
+  <form id="rtd-search-form" class="wy-form" action="../../../search.html" method="get">
     <input type="text" name="q" placeholder="Search docs" />
     <input type="hidden" name="check_keywords" value="yes" />
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@@ -40,30 +40,31 @@
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         </div><div class="wy-menu wy-menu-vertical" data-spy="affix" role="navigation" aria-label="Navigation menu">
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+<li class="toctree-l1"><a class="reference internal" href="../../../installation/index.html">Installation</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../../model-export/index.html">Model export</a></li>
+</ul>
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-<li class="toctree-l2 current"><a class="reference internal" href="index.html">aishell</a><ul class="current">
-<li class="toctree-l3 current"><a class="current reference internal" href="#">TDNN-LSTM CTC</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="#data-preparation">Data preparation</a></li>
-<li class="toctree-l4"><a class="reference internal" href="#training">Training</a></li>
-<li class="toctree-l4"><a class="reference internal" href="#decoding">Decoding</a></li>
-<li class="toctree-l4"><a class="reference internal" href="#pre-trained-model">Pre-trained Model</a></li>
-<li class="toctree-l4"><a class="reference internal" href="#colab-notebook">Colab notebook</a></li>
+<li class="toctree-l1 current"><a class="reference internal" href="../../index.html">Recipes</a><ul class="current">
+<li class="toctree-l2 current"><a class="reference internal" href="../index.html">Non Streaming ASR</a><ul class="current">
+<li class="toctree-l3 current"><a class="reference internal" href="index.html">aishell</a><ul class="current">
+<li class="toctree-l4 current"><a class="current reference internal" href="#">TDNN-LSTM CTC</a></li>
+<li class="toctree-l4"><a class="reference internal" href="conformer_ctc.html">Conformer CTC</a></li>
+<li class="toctree-l4"><a class="reference internal" href="stateless_transducer.html">Stateless Transducer</a></li>
 </ul>
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-<li class="toctree-l3"><a class="reference internal" href="conformer_ctc.html">Conformer CTC</a></li>
-<li class="toctree-l3"><a class="reference internal" href="stateless_transducer.html">Stateless Transducer</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../librispeech/index.html">LibriSpeech</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../timit/index.html">TIMIT</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../yesno/index.html">YesNo</a></li>
 </ul>
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-<li class="toctree-l2"><a class="reference internal" href="../librispeech/index.html">LibriSpeech</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../yesno/index.html">YesNo</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../Streaming-ASR/index.html">Streaming ASR</a></li>
 </ul>
 </li>
-<li class="toctree-l1"><a class="reference internal" href="../../contributing/index.html">Contributing</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../../huggingface/index.html">Huggingface</a></li>
+</ul>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../../../contributing/index.html">Contributing</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../../huggingface/index.html">Huggingface</a></li>
 </ul>
 
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@@ -72,19 +73,20 @@
 
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-          <a href="../../index.html">icefall</a>
+          <a href="../../../index.html">icefall</a>
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-      <li><a href="../../index.html" class="icon icon-home"></a></li>
-          <li class="breadcrumb-item"><a href="../index.html">Recipes</a></li>
+      <li><a href="../../../index.html" class="icon icon-home"></a></li>
+          <li class="breadcrumb-item"><a href="../../index.html">Recipes</a></li>
+          <li class="breadcrumb-item"><a href="../index.html">Non Streaming ASR</a></li>
           <li class="breadcrumb-item"><a href="index.html">aishell</a></li>
       <li class="breadcrumb-item active">TDNN-LSTM CTC</li>
       <li class="wy-breadcrumbs-aside">
-              <a href="https://github.com/k2-fsa/icefall/blob/master/docs/source/recipes/aishell/tdnn_lstm_ctc.rst" class="fa fa-github"> Edit on GitHub</a>
+              <a href="https://github.com/k2-fsa/icefall/blob/master/docs/source/recipes/Non-streaming-ASR/aishell/tdnn_lstm_ctc.rst" class="fa fa-github"> Edit on GitHub</a>
       </li>
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   <hr/>
@@ -98,7 +100,7 @@
 with the <a class="reference external" href="https://www.openslr.org/33">Aishell</a> dataset.</p>
 <div class="admonition hint">
 <p class="admonition-title">Hint</p>
-<p>We assume you have read the page <a class="reference internal" href="../../installation/index.html#install-icefall"><span class="std std-ref">Installation</span></a> and have setup
+<p>We assume you have read the page <a class="reference internal" href="../../../installation/index.html#install-icefall"><span class="std std-ref">Installation</span></a> and have setup
 the environment for <code class="docutils literal notranslate"><span class="pre">icefall</span></code>.</p>
 </div>
 <div class="admonition hint">
@@ -326,7 +328,7 @@ $ tensorboard dev upload --logdir . --description <span class="s2">&quot;TDNN-LS
 the following screenshot:</p>
 <blockquote>
 <div><figure class="align-center" id="id2">
-<a class="reference external image-reference" href="https://tensorboard.dev/experiment/LJI9MWUORLOw3jkdhxwk8A/"><img alt="TensorBoard screenshot" src="../../_images/aishell-tdnn-lstm-ctc-tensorboard-log.jpg" style="width: 600px;" /></a>
+<a class="reference external image-reference" href="https://tensorboard.dev/experiment/LJI9MWUORLOw3jkdhxwk8A/"><img alt="TensorBoard screenshot" src="../../../_images/aishell-tdnn-lstm-ctc-tensorboard-log.jpg" style="width: 600px;" /></a>
 <figcaption>
 <p><span class="caption-number">Fig. 1 </span><span class="caption-text">TensorBoard screenshot.</span><a class="headerlink" href="#id2" title="Permalink to this image"></a></p>
 </figcaption>

recipes/Non-streaming-ASR/index.html

@@ -0,0 +1,151 @@
+<!DOCTYPE html>
+<html class="writer-html5" lang="en" >
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+  <meta charset="utf-8" /><meta name="generator" content="Docutils 0.17.1: http://docutils.sourceforge.net/" />
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+  <meta name="viewport" content="width=device-width, initial-scale=1.0" />
+  <title>Non Streaming ASR &mdash; icefall 0.1 documentation</title>
+      <link rel="stylesheet" href="../../_static/pygments.css" type="text/css" />
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+  <section id="non-streaming-asr">
+<h1>Non Streaming ASR<a class="headerlink" href="#non-streaming-asr" title="Permalink to this heading"></a></h1>
+<div class="toctree-wrapper compound">
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+<li class="toctree-l2"><a class="reference internal" href="aishell/stateless_transducer.html">Stateless Transducer</a></li>
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+<li class="toctree-l2"><a class="reference internal" href="librispeech/tdnn_lstm_ctc.html">TDNN-LSTM-CTC</a></li>
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recipes/Non-streaming-ASR/librispeech/conformer_ctc.html

@@ -5,22 +5,22 @@
 
   <meta name="viewport" content="width=device-width, initial-scale=1.0" />
   <title>Conformer CTC &mdash; icefall 0.1 documentation</title>
-      <link rel="stylesheet" href="../../_static/pygments.css" type="text/css" />
-      <link rel="stylesheet" href="../../_static/css/theme.css" type="text/css" />
+      <link rel="stylesheet" href="../../../_static/pygments.css" type="text/css" />
+      <link rel="stylesheet" href="../../../_static/css/theme.css" type="text/css" />
   <!--[if lt IE 9]>
-    <script src="../../_static/js/html5shiv.min.js"></script>
+    <script src="../../../_static/js/html5shiv.min.js"></script>
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-        <script src="../../_static/sphinx_highlight.js"></script>
-    <script src="../../_static/js/theme.js"></script>
-    <link rel="index" title="Index" href="../../genindex.html" />
-    <link rel="search" title="Search" href="../../search.html" />
-    <link rel="next" title="LSTM Transducer" href="lstm_pruned_stateless_transducer.html" />
+        <script data-url_root="../../../" id="documentation_options" src="../../../_static/documentation_options.js"></script>
+        <script src="../../../_static/jquery.js"></script>
+        <script src="../../../_static/underscore.js"></script>
+        <script src="../../../_static/_sphinx_javascript_frameworks_compat.js"></script>
+        <script src="../../../_static/doctools.js"></script>
+        <script src="../../../_static/sphinx_highlight.js"></script>
+    <script src="../../../_static/js/theme.js"></script>
+    <link rel="index" title="Index" href="../../../genindex.html" />
+    <link rel="search" title="Search" href="../../../search.html" />
+    <link rel="next" title="Pruned transducer statelessX" href="pruned_transducer_stateless.html" />
     <link rel="prev" title="TDNN-LSTM-CTC" href="tdnn_lstm_ctc.html" /> 
 </head>
 
@@ -29,10 +29,10 @@
     <nav data-toggle="wy-nav-shift" class="wy-nav-side">
       <div class="wy-side-scroll">
         <div class="wy-side-nav-search" >
-            <a href="../../index.html" class="icon icon-home"> icefall
+            <a href="../../../index.html" class="icon icon-home"> icefall
           </a>
 <div role="search">
-  <form id="rtd-search-form" class="wy-form" action="../../search.html" method="get">
+  <form id="rtd-search-form" class="wy-form" action="../../../search.html" method="get">
     <input type="text" name="q" placeholder="Search docs" />
     <input type="hidden" name="check_keywords" value="yes" />
     <input type="hidden" name="area" value="default" />
@@ -40,32 +40,32 @@
 </div>
         </div><div class="wy-menu wy-menu-vertical" data-spy="affix" role="navigation" aria-label="Navigation menu">
               <p class="caption" role="heading"><span class="caption-text">Contents:</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../../../installation/index.html">Installation</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../../model-export/index.html">Model export</a></li>
+</ul>
 <ul class="current">
-<li class="toctree-l1"><a class="reference internal" href="../../installation/index.html">Installation</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../../model-export/index.html">Model export</a></li>
-<li class="toctree-l1 current"><a class="reference internal" href="../index.html">Recipes</a><ul class="current">
-<li class="toctree-l2"><a class="reference internal" href="../aishell/index.html">aishell</a></li>
-<li class="toctree-l2 current"><a class="reference internal" href="index.html">LibriSpeech</a><ul class="current">
-<li class="toctree-l3"><a class="reference internal" href="tdnn_lstm_ctc.html">TDNN-LSTM-CTC</a></li>
-<li class="toctree-l3 current"><a class="current reference internal" href="#">Conformer CTC</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="#data-preparation">Data preparation</a></li>
-<li class="toctree-l4"><a class="reference internal" href="#training">Training</a></li>
-<li class="toctree-l4"><a class="reference internal" href="#decoding">Decoding</a></li>
-<li class="toctree-l4"><a class="reference internal" href="#pre-trained-model">Pre-trained Model</a></li>
-<li class="toctree-l4"><a class="reference internal" href="#colab-notebook">Colab notebook</a></li>
-<li class="toctree-l4"><a class="reference internal" href="#deployment-with-c">Deployment with C++</a></li>
+<li class="toctree-l1 current"><a class="reference internal" href="../../index.html">Recipes</a><ul class="current">
+<li class="toctree-l2 current"><a class="reference internal" href="../index.html">Non Streaming ASR</a><ul class="current">
+<li class="toctree-l3"><a class="reference internal" href="../aishell/index.html">aishell</a></li>
+<li class="toctree-l3 current"><a class="reference internal" href="index.html">LibriSpeech</a><ul class="current">
+<li class="toctree-l4"><a class="reference internal" href="tdnn_lstm_ctc.html">TDNN-LSTM-CTC</a></li>
+<li class="toctree-l4 current"><a class="current reference internal" href="#">Conformer CTC</a></li>
+<li class="toctree-l4"><a class="reference internal" href="pruned_transducer_stateless.html">Pruned transducer statelessX</a></li>
+<li class="toctree-l4"><a class="reference internal" href="zipformer_mmi.html">Zipformer MMI</a></li>
 </ul>
 </li>
-<li class="toctree-l3"><a class="reference internal" href="lstm_pruned_stateless_transducer.html">LSTM Transducer</a></li>
-<li class="toctree-l3"><a class="reference internal" href="zipformer_mmi.html">Zipformer MMI</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../timit/index.html">TIMIT</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../yesno/index.html">YesNo</a></li>
 </ul>
 </li>
-<li class="toctree-l2"><a class="reference internal" href="../timit/index.html">TIMIT</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../yesno/index.html">YesNo</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../Streaming-ASR/index.html">Streaming ASR</a></li>
 </ul>
 </li>
-<li class="toctree-l1"><a class="reference internal" href="../../contributing/index.html">Contributing</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../../huggingface/index.html">Huggingface</a></li>
+</ul>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../../../contributing/index.html">Contributing</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../../huggingface/index.html">Huggingface</a></li>
 </ul>
 
         </div>
@@ -74,19 +74,20 @@
 
     <section data-toggle="wy-nav-shift" class="wy-nav-content-wrap"><nav class="wy-nav-top" aria-label="Mobile navigation menu" >
           <i data-toggle="wy-nav-top" class="fa fa-bars"></i>
-          <a href="../../index.html">icefall</a>
+          <a href="../../../index.html">icefall</a>
       </nav>
 
       <div class="wy-nav-content">
         <div class="rst-content">
           <div role="navigation" aria-label="Page navigation">
   <ul class="wy-breadcrumbs">
-      <li><a href="../../index.html" class="icon icon-home"></a></li>
-          <li class="breadcrumb-item"><a href="../index.html">Recipes</a></li>
+      <li><a href="../../../index.html" class="icon icon-home"></a></li>
+          <li class="breadcrumb-item"><a href="../../index.html">Recipes</a></li>
+          <li class="breadcrumb-item"><a href="../index.html">Non Streaming ASR</a></li>
           <li class="breadcrumb-item"><a href="index.html">LibriSpeech</a></li>
       <li class="breadcrumb-item active">Conformer CTC</li>
       <li class="wy-breadcrumbs-aside">
-              <a href="https://github.com/k2-fsa/icefall/blob/master/docs/source/recipes/librispeech/conformer_ctc.rst" class="fa fa-github"> Edit on GitHub</a>
+              <a href="https://github.com/k2-fsa/icefall/blob/master/docs/source/recipes/Non-streaming-ASR/librispeech/conformer_ctc.rst" class="fa fa-github"> Edit on GitHub</a>
       </li>
   </ul>
   <hr/>
@@ -100,7 +101,7 @@
 with the <a class="reference external" href="https://www.openslr.org/12">LibriSpeech</a> dataset.</p>
 <div class="admonition hint">
 <p class="admonition-title">Hint</p>
-<p>We assume you have read the page <a class="reference internal" href="../../installation/index.html#install-icefall"><span class="std std-ref">Installation</span></a> and have setup
+<p>We assume you have read the page <a class="reference internal" href="../../../installation/index.html#install-icefall"><span class="std std-ref">Installation</span></a> and have setup
 the environment for <code class="docutils literal notranslate"><span class="pre">icefall</span></code>.</p>
 </div>
 <div class="admonition hint">
@@ -337,7 +338,7 @@ $ tensorboard dev upload --logdir . --description <span class="s2">&quot;Conform
 the following screenshot:</p>
 <blockquote>
 <div><figure class="align-center" id="id2">
-<a class="reference external image-reference" href="https://tensorboard.dev/experiment/lzGnETjwRxC3yghNMd4kPw/"><img alt="TensorBoard screenshot" src="../../_images/librispeech-conformer-ctc-tensorboard-log.png" style="width: 600px;" /></a>
+<a class="reference external image-reference" href="https://tensorboard.dev/experiment/lzGnETjwRxC3yghNMd4kPw/"><img alt="TensorBoard screenshot" src="../../../_images/librispeech-conformer-ctc-tensorboard-log.png" style="width: 600px;" /></a>
 <figcaption>
 <p><span class="caption-number">Fig. 4 </span><span class="caption-text">TensorBoard screenshot.</span><a class="headerlink" href="#id2" title="Permalink to this image"></a></p>
 </figcaption>
@@ -1090,7 +1091,7 @@ Please see <a class="reference external" href="https://colab.research.google.com
           </div>
           <footer><div class="rst-footer-buttons" role="navigation" aria-label="Footer">
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+        <a href="pruned_transducer_stateless.html" class="btn btn-neutral float-right" title="Pruned transducer statelessX" accesskey="n" rel="next">Next <span class="fa fa-arrow-circle-right" aria-hidden="true"></span></a>
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   <hr/>

recipes/Non-streaming-ASR/librispeech/index.html

@@ -5,21 +5,21 @@
 
   <meta name="viewport" content="width=device-width, initial-scale=1.0" />
   <title>LibriSpeech &mdash; icefall 0.1 documentation</title>
-      <link rel="stylesheet" href="../../_static/pygments.css" type="text/css" />
-      <link rel="stylesheet" href="../../_static/css/theme.css" type="text/css" />
+      <link rel="stylesheet" href="../../../_static/pygments.css" type="text/css" />
+      <link rel="stylesheet" href="../../../_static/css/theme.css" type="text/css" />
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-    <script src="../../_static/js/html5shiv.min.js"></script>
+    <script src="../../../_static/js/html5shiv.min.js"></script>
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-    <script src="../../_static/js/theme.js"></script>
-    <link rel="index" title="Index" href="../../genindex.html" />
-    <link rel="search" title="Search" href="../../search.html" />
+        <script data-url_root="../../../" id="documentation_options" src="../../../_static/documentation_options.js"></script>
+        <script src="../../../_static/jquery.js"></script>
+        <script src="../../../_static/underscore.js"></script>
+        <script src="../../../_static/_sphinx_javascript_frameworks_compat.js"></script>
+        <script src="../../../_static/doctools.js"></script>
+        <script src="../../../_static/sphinx_highlight.js"></script>
+    <script src="../../../_static/js/theme.js"></script>
+    <link rel="index" title="Index" href="../../../genindex.html" />
+    <link rel="search" title="Search" href="../../../search.html" />
     <link rel="next" title="TDNN-LSTM-CTC" href="tdnn_lstm_ctc.html" />
     <link rel="prev" title="Stateless Transducer" href="../aishell/stateless_transducer.html" /> 
 </head>
@@ -29,10 +29,10 @@
     <nav data-toggle="wy-nav-shift" class="wy-nav-side">
       <div class="wy-side-scroll">
         <div class="wy-side-nav-search" >
-            <a href="../../index.html" class="icon icon-home"> icefall
+            <a href="../../../index.html" class="icon icon-home"> icefall
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-  <form id="rtd-search-form" class="wy-form" action="../../search.html" method="get">
+  <form id="rtd-search-form" class="wy-form" action="../../../search.html" method="get">
     <input type="text" name="q" placeholder="Search docs" />
     <input type="hidden" name="check_keywords" value="yes" />
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@@ -40,24 +40,32 @@
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         </div><div class="wy-menu wy-menu-vertical" data-spy="affix" role="navigation" aria-label="Navigation menu">
               <p class="caption" role="heading"><span class="caption-text">Contents:</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../../../installation/index.html">Installation</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../../model-export/index.html">Model export</a></li>
+</ul>
 <ul class="current">
-<li class="toctree-l1"><a class="reference internal" href="../../installation/index.html">Installation</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../../model-export/index.html">Model export</a></li>
-<li class="toctree-l1 current"><a class="reference internal" href="../index.html">Recipes</a><ul class="current">
-<li class="toctree-l2"><a class="reference internal" href="../aishell/index.html">aishell</a></li>
-<li class="toctree-l2 current"><a class="current reference internal" href="#">LibriSpeech</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="tdnn_lstm_ctc.html">TDNN-LSTM-CTC</a></li>
-<li class="toctree-l3"><a class="reference internal" href="conformer_ctc.html">Conformer CTC</a></li>
-<li class="toctree-l3"><a class="reference internal" href="lstm_pruned_stateless_transducer.html">LSTM Transducer</a></li>
-<li class="toctree-l3"><a class="reference internal" href="zipformer_mmi.html">Zipformer MMI</a></li>
+<li class="toctree-l1 current"><a class="reference internal" href="../../index.html">Recipes</a><ul class="current">
+<li class="toctree-l2 current"><a class="reference internal" href="../index.html">Non Streaming ASR</a><ul class="current">
+<li class="toctree-l3"><a class="reference internal" href="../aishell/index.html">aishell</a></li>
+<li class="toctree-l3 current"><a class="current reference internal" href="#">LibriSpeech</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="tdnn_lstm_ctc.html">TDNN-LSTM-CTC</a></li>
+<li class="toctree-l4"><a class="reference internal" href="conformer_ctc.html">Conformer CTC</a></li>
+<li class="toctree-l4"><a class="reference internal" href="pruned_transducer_stateless.html">Pruned transducer statelessX</a></li>
+<li class="toctree-l4"><a class="reference internal" href="zipformer_mmi.html">Zipformer MMI</a></li>
 </ul>
 </li>
-<li class="toctree-l2"><a class="reference internal" href="../timit/index.html">TIMIT</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../yesno/index.html">YesNo</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../timit/index.html">TIMIT</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../yesno/index.html">YesNo</a></li>
 </ul>
 </li>
-<li class="toctree-l1"><a class="reference internal" href="../../contributing/index.html">Contributing</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../../huggingface/index.html">Huggingface</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../Streaming-ASR/index.html">Streaming ASR</a></li>
+</ul>
+</li>
+</ul>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../../../contributing/index.html">Contributing</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../../huggingface/index.html">Huggingface</a></li>
 </ul>
 
         </div>
@@ -66,18 +74,19 @@
 
     <section data-toggle="wy-nav-shift" class="wy-nav-content-wrap"><nav class="wy-nav-top" aria-label="Mobile navigation menu" >
           <i data-toggle="wy-nav-top" class="fa fa-bars"></i>
-          <a href="../../index.html">icefall</a>
+          <a href="../../../index.html">icefall</a>
       </nav>
 
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           <div role="navigation" aria-label="Page navigation">
   <ul class="wy-breadcrumbs">
-      <li><a href="../../index.html" class="icon icon-home"></a></li>
-          <li class="breadcrumb-item"><a href="../index.html">Recipes</a></li>
+      <li><a href="../../../index.html" class="icon icon-home"></a></li>
+          <li class="breadcrumb-item"><a href="../../index.html">Recipes</a></li>
+          <li class="breadcrumb-item"><a href="../index.html">Non Streaming ASR</a></li>
       <li class="breadcrumb-item active">LibriSpeech</li>
       <li class="wy-breadcrumbs-aside">
-              <a href="https://github.com/k2-fsa/icefall/blob/master/docs/source/recipes/librispeech/index.rst" class="fa fa-github"> Edit on GitHub</a>
+              <a href="https://github.com/k2-fsa/icefall/blob/master/docs/source/recipes/Non-streaming-ASR/librispeech/index.rst" class="fa fa-github"> Edit on GitHub</a>
       </li>
   </ul>
   <hr/>
@@ -91,7 +100,7 @@
 <ul>
 <li class="toctree-l1"><a class="reference internal" href="tdnn_lstm_ctc.html">TDNN-LSTM-CTC</a></li>
 <li class="toctree-l1"><a class="reference internal" href="conformer_ctc.html">Conformer CTC</a></li>
-<li class="toctree-l1"><a class="reference internal" href="lstm_pruned_stateless_transducer.html">LSTM Transducer</a></li>
+<li class="toctree-l1"><a class="reference internal" href="pruned_transducer_stateless.html">Pruned transducer statelessX</a></li>
 <li class="toctree-l1"><a class="reference internal" href="zipformer_mmi.html">Zipformer MMI</a></li>
 </ul>
 </div>

recipes/Non-streaming-ASR/librispeech/pruned_transducer_stateless.html

@@ -0,0 +1,644 @@
+<!DOCTYPE html>
+<html class="writer-html5" lang="en" >
+<head>
+  <meta charset="utf-8" /><meta name="generator" content="Docutils 0.17.1: http://docutils.sourceforge.net/" />
+
+  <meta name="viewport" content="width=device-width, initial-scale=1.0" />
+  <title>Pruned transducer statelessX &mdash; icefall 0.1 documentation</title>
+      <link rel="stylesheet" href="../../../_static/pygments.css" type="text/css" />
+      <link rel="stylesheet" href="../../../_static/css/theme.css" type="text/css" />
+  <!--[if lt IE 9]>
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+        <script src="../../../_static/underscore.js"></script>
+        <script src="../../../_static/_sphinx_javascript_frameworks_compat.js"></script>
+        <script src="../../../_static/doctools.js"></script>
+        <script src="../../../_static/sphinx_highlight.js"></script>
+    <script src="../../../_static/js/theme.js"></script>
+    <link rel="index" title="Index" href="../../../genindex.html" />
+    <link rel="search" title="Search" href="../../../search.html" />
+    <link rel="next" title="Zipformer MMI" href="zipformer_mmi.html" />
+    <link rel="prev" title="Conformer CTC" href="conformer_ctc.html" /> 
+</head>
+
+<body class="wy-body-for-nav"> 
+  <div class="wy-grid-for-nav">
+    <nav data-toggle="wy-nav-shift" class="wy-nav-side">
+      <div class="wy-side-scroll">
+        <div class="wy-side-nav-search" >
+            <a href="../../../index.html" class="icon icon-home"> icefall
+          </a>
+<div role="search">
+  <form id="rtd-search-form" class="wy-form" action="../../../search.html" method="get">
+    <input type="text" name="q" placeholder="Search docs" />
+    <input type="hidden" name="check_keywords" value="yes" />
+    <input type="hidden" name="area" value="default" />
+  </form>
+</div>
+        </div><div class="wy-menu wy-menu-vertical" data-spy="affix" role="navigation" aria-label="Navigation menu">
+              <p class="caption" role="heading"><span class="caption-text">Contents:</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../../../installation/index.html">Installation</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../../model-export/index.html">Model export</a></li>
+</ul>
+<ul class="current">
+<li class="toctree-l1 current"><a class="reference internal" href="../../index.html">Recipes</a><ul class="current">
+<li class="toctree-l2 current"><a class="reference internal" href="../index.html">Non Streaming ASR</a><ul class="current">
+<li class="toctree-l3"><a class="reference internal" href="../aishell/index.html">aishell</a></li>
+<li class="toctree-l3 current"><a class="reference internal" href="index.html">LibriSpeech</a><ul class="current">
+<li class="toctree-l4"><a class="reference internal" href="tdnn_lstm_ctc.html">TDNN-LSTM-CTC</a></li>
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+<li class="toctree-l4 current"><a class="current reference internal" href="#">Pruned transducer statelessX</a></li>
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+           <div itemprop="articleBody">
+             
+  <section id="pruned-transducer-statelessx">
+<h1>Pruned transducer statelessX<a class="headerlink" href="#pruned-transducer-statelessx" title="Permalink to this heading"></a></h1>
+<p>This tutorial shows you how to run a conformer transducer model
+with the <a class="reference external" href="https://www.openslr.org/12">LibriSpeech</a> dataset.</p>
+<div class="admonition note">
+<p class="admonition-title">Note</p>
+<p>The tutorial is suitable for <a class="reference external" href="https://github.com/k2-fsa/icefall/tree/master/egs/librispeech/ASR/pruned_transducer_stateless">pruned_transducer_stateless</a>,
+<a class="reference external" href="https://github.com/k2-fsa/icefall/tree/master/egs/librispeech/ASR/pruned_transducer_stateless2">pruned_transducer_stateless2</a>,
+<a class="reference external" href="https://github.com/k2-fsa/icefall/tree/master/egs/librispeech/ASR/pruned_transducer_stateless4">pruned_transducer_stateless4</a>,
+<a class="reference external" href="https://github.com/k2-fsa/icefall/tree/master/egs/librispeech/ASR/pruned_transducer_stateless5">pruned_transducer_stateless5</a>,
+We will take pruned_transducer_stateless4 as an example in this tutorial.</p>
+</div>
+<div class="admonition hint">
+<p class="admonition-title">Hint</p>
+<p>We assume you have read the page <a class="reference internal" href="../../../installation/index.html#install-icefall"><span class="std std-ref">Installation</span></a> and have setup
+the environment for <code class="docutils literal notranslate"><span class="pre">icefall</span></code>.</p>
+</div>
+<div class="admonition hint">
+<p class="admonition-title">Hint</p>
+<p>We recommend you to use a GPU or several GPUs to run this recipe.</p>
+</div>
+<div class="admonition hint">
+<p class="admonition-title">Hint</p>
+<p>Please scroll down to the bottom of this page to find download links
+for pretrained models if you don’t want to train a model from scratch.</p>
+</div>
+<p>We use pruned RNN-T to compute the loss.</p>
+<div class="admonition note">
+<p class="admonition-title">Note</p>
+<p>You can find the paper about pruned RNN-T at the following address:</p>
+<p><a class="reference external" href="https://arxiv.org/abs/2206.13236">https://arxiv.org/abs/2206.13236</a></p>
+</div>
+<p>The transducer model consists of 3 parts:</p>
+<blockquote>
+<div><ul class="simple">
+<li><p>Encoder, a.k.a, the transcription network. We use a Conformer model (the reworked version by Daniel Povey)</p></li>
+<li><p>Decoder, a.k.a, the prediction network. We use a stateless model consisting of
+<code class="docutils literal notranslate"><span class="pre">nn.Embedding</span></code> and <code class="docutils literal notranslate"><span class="pre">nn.Conv1d</span></code></p></li>
+<li><p>Joiner, a.k.a, the joint network.</p></li>
+</ul>
+</div></blockquote>
+<div class="admonition caution">
+<p class="admonition-title">Caution</p>
+<p>Contrary to the conventional RNN-T models, we use a stateless decoder.
+That is, it has no recurrent connections.</p>
+</div>
+<section id="data-preparation">
+<h2>Data preparation<a class="headerlink" href="#data-preparation" title="Permalink to this heading"></a></h2>
+<div class="admonition hint">
+<p class="admonition-title">Hint</p>
+<p>The data preparation is the same as other recipes on LibriSpeech dataset,
+if you have finished this step, you can skip to <code class="docutils literal notranslate"><span class="pre">Training</span></code> directly.</p>
+</div>
+<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>$ <span class="nb">cd</span> egs/librispeech/ASR
+$ ./prepare.sh
+</pre></div>
+</div>
+<p>The script <code class="docutils literal notranslate"><span class="pre">./prepare.sh</span></code> handles the data preparation for you, <strong>automagically</strong>.
+All you need to do is to run it.</p>
+<p>The data preparation contains several stages, you can use the following two
+options:</p>
+<blockquote>
+<div><ul class="simple">
+<li><p><code class="docutils literal notranslate"><span class="pre">--stage</span></code></p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">--stop-stage</span></code></p></li>
+</ul>
+</div></blockquote>
+<p>to control which stage(s) should be run. By default, all stages are executed.</p>
+<p>For example,</p>
+<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>$ <span class="nb">cd</span> egs/librispeech/ASR
+$ ./prepare.sh --stage <span class="m">0</span> --stop-stage <span class="m">0</span>
+</pre></div>
+</div>
+<p>means to run only stage 0.</p>
+<p>To run stage 2 to stage 5, use:</p>
+<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>$ ./prepare.sh --stage <span class="m">2</span> --stop-stage <span class="m">5</span>
+</pre></div>
+</div>
+<div class="admonition hint">
+<p class="admonition-title">Hint</p>
+<p>If you have pre-downloaded the <a class="reference external" href="https://www.openslr.org/12">LibriSpeech</a>
+dataset and the <a class="reference external" href="http://www.openslr.org/17/">musan</a> dataset, say,
+they are saved in <code class="docutils literal notranslate"><span class="pre">/tmp/LibriSpeech</span></code> and <code class="docutils literal notranslate"><span class="pre">/tmp/musan</span></code>, you can modify
+the <code class="docutils literal notranslate"><span class="pre">dl_dir</span></code> variable in <code class="docutils literal notranslate"><span class="pre">./prepare.sh</span></code> to point to <code class="docutils literal notranslate"><span class="pre">/tmp</span></code> so that
+<code class="docutils literal notranslate"><span class="pre">./prepare.sh</span></code> won’t re-download them.</p>
+</div>
+<div class="admonition note">
+<p class="admonition-title">Note</p>
+<p>All generated files by <code class="docutils literal notranslate"><span class="pre">./prepare.sh</span></code>, e.g., features, lexicon, etc,
+are saved in <code class="docutils literal notranslate"><span class="pre">./data</span></code> directory.</p>
+</div>
+<p>We provide the following YouTube video showing how to run <code class="docutils literal notranslate"><span class="pre">./prepare.sh</span></code>.</p>
+<div class="admonition note">
+<p class="admonition-title">Note</p>
+<p>To get the latest news of <a class="reference external" href="https://github.com/k2-fsa">next-gen Kaldi</a>, please subscribe
+the following YouTube channel by <a class="reference external" href="https://www.youtube.com/channel/UC_VaumpkmINz1pNkFXAN9mw">Nadira Povey</a>:</p>
+<blockquote>
+<div><p><a class="reference external" href="https://www.youtube.com/channel/UC_VaumpkmINz1pNkFXAN9mw">https://www.youtube.com/channel/UC_VaumpkmINz1pNkFXAN9mw</a></p>
+</div></blockquote>
+</div>
+<div class="video_wrapper" style="">
+<iframe allowfullscreen="true" src="https://www.youtube.com/embed/ofEIoJL-mGM" style="border: 0; height: 345px; width: 560px">
+</iframe></div></section>
+<section id="training">
+<h2>Training<a class="headerlink" href="#training" title="Permalink to this heading"></a></h2>
+<section id="configurable-options">
+<h3>Configurable options<a class="headerlink" href="#configurable-options" title="Permalink to this heading"></a></h3>
+<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>$ <span class="nb">cd</span> egs/librispeech/ASR
+$ ./pruned_transducer_stateless4/train.py --help
+</pre></div>
+</div>
+<p>shows you the training options that can be passed from the commandline.
+The following options are used quite often:</p>
+<blockquote>
+<div><ul>
+<li><p><code class="docutils literal notranslate"><span class="pre">--exp-dir</span></code></p>
+<p>The directory to save checkpoints, training logs and tensorboard.</p>
+</li>
+<li><p><code class="docutils literal notranslate"><span class="pre">--full-libri</span></code></p>
+<p>If it’s True, the training part uses all the training data, i.e.,
+960 hours. Otherwise, the training part uses only the subset
+<code class="docutils literal notranslate"><span class="pre">train-clean-100</span></code>, which has 100 hours of training data.</p>
+<div class="admonition caution">
+<p class="admonition-title">Caution</p>
+<p>The training set is perturbed by speed with two factors: 0.9 and 1.1.
+If <code class="docutils literal notranslate"><span class="pre">--full-libri</span></code> is True, each epoch actually processes
+<code class="docutils literal notranslate"><span class="pre">3x960</span> <span class="pre">==</span> <span class="pre">2880</span></code> hours of data.</p>
+</div>
+</li>
+<li><p><code class="docutils literal notranslate"><span class="pre">--num-epochs</span></code></p>
+<p>It is the number of epochs to train. For instance,
+<code class="docutils literal notranslate"><span class="pre">./pruned_transducer_stateless4/train.py</span> <span class="pre">--num-epochs</span> <span class="pre">30</span></code> trains for 30 epochs
+and generates <code class="docutils literal notranslate"><span class="pre">epoch-1.pt</span></code>, <code class="docutils literal notranslate"><span class="pre">epoch-2.pt</span></code>, …, <code class="docutils literal notranslate"><span class="pre">epoch-30.pt</span></code>
+in the folder <code class="docutils literal notranslate"><span class="pre">./pruned_transducer_stateless4/exp</span></code>.</p>
+</li>
+<li><p><code class="docutils literal notranslate"><span class="pre">--start-epoch</span></code></p>
+<p>It’s used to resume training.
+<code class="docutils literal notranslate"><span class="pre">./pruned_transducer_stateless4/train.py</span> <span class="pre">--start-epoch</span> <span class="pre">10</span></code> loads the
+checkpoint <code class="docutils literal notranslate"><span class="pre">./pruned_transducer_stateless4/exp/epoch-9.pt</span></code> and starts
+training from epoch 10, based on the state from epoch 9.</p>
+</li>
+<li><p><code class="docutils literal notranslate"><span class="pre">--world-size</span></code></p>
+<p>It is used for multi-GPU single-machine DDP training.</p>
+<blockquote>
+<div><ul class="simple">
+<li><ol class="loweralpha simple">
+<li><p>If it is 1, then no DDP training is used.</p></li>
+</ol>
+</li>
+<li><ol class="loweralpha simple" start="2">
+<li><p>If it is 2, then GPU 0 and GPU 1 are used for DDP training.</p></li>
+</ol>
+</li>
+</ul>
+</div></blockquote>
+<p>The following shows some use cases with it.</p>
+<blockquote>
+<div><p><strong>Use case 1</strong>: You have 4 GPUs, but you only want to use GPU 0 and
+GPU 2 for training. You can do the following:</p>
+<blockquote>
+<div><div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>$ <span class="nb">cd</span> egs/librispeech/ASR
+$ <span class="nb">export</span> <span class="nv">CUDA_VISIBLE_DEVICES</span><span class="o">=</span><span class="s2">&quot;0,2&quot;</span>
+$ ./pruned_transducer_stateless4/train.py --world-size <span class="m">2</span>
+</pre></div>
+</div>
+</div></blockquote>
+<p><strong>Use case 2</strong>: You have 4 GPUs and you want to use all of them
+for training. You can do the following:</p>
+<blockquote>
+<div><div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>$ <span class="nb">cd</span> egs/librispeech/ASR
+$ ./pruned_transducer_stateless4/train.py --world-size <span class="m">4</span>
+</pre></div>
+</div>
+</div></blockquote>
+<p><strong>Use case 3</strong>: You have 4 GPUs but you only want to use GPU 3
+for training. You can do the following:</p>
+<blockquote>
+<div><div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>$ <span class="nb">cd</span> egs/librispeech/ASR
+$ <span class="nb">export</span> <span class="nv">CUDA_VISIBLE_DEVICES</span><span class="o">=</span><span class="s2">&quot;3&quot;</span>
+$ ./pruned_transducer_stateless4/train.py --world-size <span class="m">1</span>
+</pre></div>
+</div>
+</div></blockquote>
+</div></blockquote>
+<div class="admonition caution">
+<p class="admonition-title">Caution</p>
+<p>Only multi-GPU single-machine DDP training is implemented at present.
+Multi-GPU multi-machine DDP training will be added later.</p>
+</div>
+</li>
+<li><p><code class="docutils literal notranslate"><span class="pre">--max-duration</span></code></p>
+<p>It specifies the number of seconds over all utterances in a
+batch, before <strong>padding</strong>.
+If you encounter CUDA OOM, please reduce it.</p>
+<div class="admonition hint">
+<p class="admonition-title">Hint</p>
+<p>Due to padding, the number of seconds of all utterances in a
+batch will usually be larger than <code class="docutils literal notranslate"><span class="pre">--max-duration</span></code>.</p>
+<p>A larger value for <code class="docutils literal notranslate"><span class="pre">--max-duration</span></code> may cause OOM during training,
+while a smaller value may increase the training time. You have to
+tune it.</p>
+</div>
+</li>
+<li><p><code class="docutils literal notranslate"><span class="pre">--use-fp16</span></code></p>
+<p>If it is True, the model will train with half precision, from our experiment
+results, by using half precision you can train with two times larger <code class="docutils literal notranslate"><span class="pre">--max-duration</span></code>
+so as to get almost 2X speed up.</p>
+</li>
+</ul>
+</div></blockquote>
+</section>
+<section id="pre-configured-options">
+<h3>Pre-configured options<a class="headerlink" href="#pre-configured-options" title="Permalink to this heading"></a></h3>
+<p>There are some training options, e.g., number of encoder layers,
+encoder dimension, decoder dimension, number of warmup steps etc,
+that are not passed from the commandline.
+They are pre-configured by the function <code class="docutils literal notranslate"><span class="pre">get_params()</span></code> in
+<a class="reference external" href="https://github.com/k2-fsa/icefall/blob/master/egs/librispeech/ASR/pruned_transducer_stateless4/train.py">pruned_transducer_stateless4/train.py</a></p>
+<p>You don’t need to change these pre-configured parameters. If you really need to change
+them, please modify <code class="docutils literal notranslate"><span class="pre">./pruned_transducer_stateless4/train.py</span></code> directly.</p>
+<div class="admonition note">
+<p class="admonition-title">Note</p>
+<p>The options for <a class="reference external" href="https://github.com/k2-fsa/icefall/blob/master/egs/librispeech/ASR/pruned_transducer_stateless5/train.py">pruned_transducer_stateless5</a> are a little different from
+other recipes. It allows you to configure <code class="docutils literal notranslate"><span class="pre">--num-encoder-layers</span></code>, <code class="docutils literal notranslate"><span class="pre">--dim-feedforward</span></code>, <code class="docutils literal notranslate"><span class="pre">--nhead</span></code>, <code class="docutils literal notranslate"><span class="pre">--encoder-dim</span></code>, <code class="docutils literal notranslate"><span class="pre">--decoder-dim</span></code>, <code class="docutils literal notranslate"><span class="pre">--joiner-dim</span></code> from commandline, so that you can train models with different size with pruned_transducer_stateless5.</p>
+</div>
+</section>
+<section id="training-logs">
+<h3>Training logs<a class="headerlink" href="#training-logs" title="Permalink to this heading"></a></h3>
+<p>Training logs and checkpoints are saved in <code class="docutils literal notranslate"><span class="pre">--exp-dir</span></code> (e.g. <code class="docutils literal notranslate"><span class="pre">pruned_transducer_stateless4/exp</span></code>.
+You will find the following files in that directory:</p>
+<blockquote>
+<div><ul>
+<li><p><code class="docutils literal notranslate"><span class="pre">epoch-1.pt</span></code>, <code class="docutils literal notranslate"><span class="pre">epoch-2.pt</span></code>, …</p>
+<p>These are checkpoint files saved at the end of each epoch, containing model
+<code class="docutils literal notranslate"><span class="pre">state_dict</span></code> and optimizer <code class="docutils literal notranslate"><span class="pre">state_dict</span></code>.
+To resume training from some checkpoint, say <code class="docutils literal notranslate"><span class="pre">epoch-10.pt</span></code>, you can use:</p>
+<blockquote>
+<div><div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>$ ./pruned_transducer_stateless4/train.py --start-epoch <span class="m">11</span>
+</pre></div>
+</div>
+</div></blockquote>
+</li>
+<li><p><code class="docutils literal notranslate"><span class="pre">checkpoint-436000.pt</span></code>, <code class="docutils literal notranslate"><span class="pre">checkpoint-438000.pt</span></code>, …</p>
+<p>These are checkpoint files saved every <code class="docutils literal notranslate"><span class="pre">--save-every-n</span></code> batches,
+containing model <code class="docutils literal notranslate"><span class="pre">state_dict</span></code> and optimizer <code class="docutils literal notranslate"><span class="pre">state_dict</span></code>.
+To resume training from some checkpoint, say <code class="docutils literal notranslate"><span class="pre">checkpoint-436000</span></code>, you can use:</p>
+<blockquote>
+<div><div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>$ ./pruned_transducer_stateless4/train.py --start-batch <span class="m">436000</span>
+</pre></div>
+</div>
+</div></blockquote>
+</li>
+<li><p><code class="docutils literal notranslate"><span class="pre">tensorboard/</span></code></p>
+<p>This folder contains tensorBoard logs. Training loss, validation loss, learning
+rate, etc, are recorded in these logs. You can visualize them by:</p>
+<blockquote>
+<div><div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>$ <span class="nb">cd</span> pruned_transducer_stateless4/exp/tensorboard
+$ tensorboard dev upload --logdir . --description <span class="s2">&quot;pruned transducer training for LibriSpeech with icefall&quot;</span>
+</pre></div>
+</div>
+</div></blockquote>
+<p>It will print something like below:</p>
+<blockquote>
+<div><div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">TensorFlow</span> <span class="n">installation</span> <span class="ow">not</span> <span class="n">found</span> <span class="o">-</span> <span class="n">running</span> <span class="k">with</span> <span class="n">reduced</span> <span class="n">feature</span> <span class="nb">set</span><span class="o">.</span>
+<span class="n">Upload</span> <span class="n">started</span> <span class="ow">and</span> <span class="n">will</span> <span class="k">continue</span> <span class="n">reading</span> <span class="nb">any</span> <span class="n">new</span> <span class="n">data</span> <span class="k">as</span> <span class="n">it</span><span class="s1">&#39;s added to the logdir.</span>
+
+<span class="n">To</span> <span class="n">stop</span> <span class="n">uploading</span><span class="p">,</span> <span class="n">press</span> <span class="n">Ctrl</span><span class="o">-</span><span class="n">C</span><span class="o">.</span>
+
+<span class="n">New</span> <span class="n">experiment</span> <span class="n">created</span><span class="o">.</span> <span class="n">View</span> <span class="n">your</span> <span class="n">TensorBoard</span> <span class="n">at</span><span class="p">:</span> <span class="n">https</span><span class="p">:</span><span class="o">//</span><span class="n">tensorboard</span><span class="o">.</span><span class="n">dev</span><span class="o">/</span><span class="n">experiment</span><span class="o">/</span><span class="n">QOGSPBgsR8KzcRMmie9JGw</span><span class="o">/</span>
+
+<span class="p">[</span><span class="mi">2022</span><span class="o">-</span><span class="mi">11</span><span class="o">-</span><span class="mi">20</span><span class="n">T15</span><span class="p">:</span><span class="mi">50</span><span class="p">:</span><span class="mi">50</span><span class="p">]</span> <span class="n">Started</span> <span class="n">scanning</span> <span class="n">logdir</span><span class="o">.</span>
+<span class="n">Uploading</span> <span class="mi">4468</span> <span class="n">scalars</span><span class="o">...</span>
+<span class="p">[</span><span class="mi">2022</span><span class="o">-</span><span class="mi">11</span><span class="o">-</span><span class="mi">20</span><span class="n">T15</span><span class="p">:</span><span class="mi">53</span><span class="p">:</span><span class="mi">02</span><span class="p">]</span> <span class="n">Total</span> <span class="n">uploaded</span><span class="p">:</span> <span class="mi">210171</span> <span class="n">scalars</span><span class="p">,</span> <span class="mi">0</span> <span class="n">tensors</span><span class="p">,</span> <span class="mi">0</span> <span class="n">binary</span> <span class="n">objects</span>
+<span class="n">Listening</span> <span class="k">for</span> <span class="n">new</span> <span class="n">data</span> <span class="ow">in</span> <span class="n">logdir</span><span class="o">...</span>
+</pre></div>
+</div>
+</div></blockquote>
+<p>Note there is a URL in the above output. Click it and you will see
+the following screenshot:</p>
+<blockquote>
+<div><figure class="align-center" id="id7">
+<a class="reference external image-reference" href="https://tensorboard.dev/experiment/QOGSPBgsR8KzcRMmie9JGw/"><img alt="TensorBoard screenshot" src="../../../_images/librispeech-pruned-transducer-tensorboard-log.jpg" style="width: 600px;" /></a>
+<figcaption>
+<p><span class="caption-number">Fig. 5 </span><span class="caption-text">TensorBoard screenshot.</span><a class="headerlink" href="#id7" title="Permalink to this image"></a></p>
+</figcaption>
+</figure>
+</div></blockquote>
+</li>
+</ul>
+<div class="admonition hint">
+<p class="admonition-title">Hint</p>
+<p>If you don’t have access to google, you can use the following command
+to view the tensorboard log locally:</p>
+<blockquote>
+<div><div class="highlight-bash notranslate"><div class="highlight"><pre><span></span><span class="nb">cd</span> pruned_transducer_stateless4/exp/tensorboard
+tensorboard --logdir . --port <span class="m">6008</span>
+</pre></div>
+</div>
+</div></blockquote>
+<p>It will print the following message:</p>
+<blockquote>
+<div><div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">Serving</span> <span class="n">TensorBoard</span> <span class="n">on</span> <span class="n">localhost</span><span class="p">;</span> <span class="n">to</span> <span class="n">expose</span> <span class="n">to</span> <span class="n">the</span> <span class="n">network</span><span class="p">,</span> <span class="n">use</span> <span class="n">a</span> <span class="n">proxy</span> <span class="ow">or</span> <span class="k">pass</span> <span class="o">--</span><span class="n">bind_all</span>
+<span class="n">TensorBoard</span> <span class="mf">2.8.0</span> <span class="n">at</span> <span class="n">http</span><span class="p">:</span><span class="o">//</span><span class="n">localhost</span><span class="p">:</span><span class="mi">6008</span><span class="o">/</span> <span class="p">(</span><span class="n">Press</span> <span class="n">CTRL</span><span class="o">+</span><span class="n">C</span> <span class="n">to</span> <span class="n">quit</span><span class="p">)</span>
+</pre></div>
+</div>
+</div></blockquote>
+<p>Now start your browser and go to <a class="reference external" href="http://localhost:6008">http://localhost:6008</a> to view the tensorboard
+logs.</p>
+</div>
+<ul>
+<li><p><code class="docutils literal notranslate"><span class="pre">log/log-train-xxxx</span></code></p>
+<p>It is the detailed training log in text format, same as the one
+you saw printed to the console during training.</p>
+</li>
+</ul>
+</div></blockquote>
+</section>
+<section id="usage-example">
+<h3>Usage example<a class="headerlink" href="#usage-example" title="Permalink to this heading"></a></h3>
+<p>You can use the following command to start the training using 6 GPUs:</p>
+<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span><span class="nb">export</span> <span class="nv">CUDA_VISIBLE_DEVICES</span><span class="o">=</span><span class="s2">&quot;0,1,2,3,4,5&quot;</span>
+./pruned_transducer_stateless4/train.py <span class="se">\</span>
+   --world-size <span class="m">6</span> <span class="se">\</span>
+   --num-epochs <span class="m">30</span> <span class="se">\</span>
+   --start-epoch <span class="m">1</span> <span class="se">\</span>
+   --exp-dir pruned_transducer_stateless4/exp <span class="se">\</span>
+   --full-libri <span class="m">1</span> <span class="se">\</span>
+   --max-duration <span class="m">300</span>
+</pre></div>
+</div>
+</section>
+</section>
+<section id="decoding">
+<h2>Decoding<a class="headerlink" href="#decoding" title="Permalink to this heading"></a></h2>
+<p>The decoding part uses checkpoints saved by the training part, so you have
+to run the training part first.</p>
+<div class="admonition hint">
+<p class="admonition-title">Hint</p>
+<p>There are two kinds of checkpoints:</p>
+<blockquote>
+<div><ul class="simple">
+<li><p>(1) <code class="docutils literal notranslate"><span class="pre">epoch-1.pt</span></code>, <code class="docutils literal notranslate"><span class="pre">epoch-2.pt</span></code>, …, which are saved at the end
+of each epoch. You can pass <code class="docutils literal notranslate"><span class="pre">--epoch</span></code> to
+<code class="docutils literal notranslate"><span class="pre">pruned_transducer_stateless4/decode.py</span></code> to use them.</p></li>
+<li><p>(2) <code class="docutils literal notranslate"><span class="pre">checkpoints-436000.pt</span></code>, <code class="docutils literal notranslate"><span class="pre">epoch-438000.pt</span></code>, …, which are saved
+every <code class="docutils literal notranslate"><span class="pre">--save-every-n</span></code> batches. You can pass <code class="docutils literal notranslate"><span class="pre">--iter</span></code> to
+<code class="docutils literal notranslate"><span class="pre">pruned_transducer_stateless4/decode.py</span></code> to use them.</p></li>
+</ul>
+<p>We suggest that you try both types of checkpoints and choose the one
+that produces the lowest WERs.</p>
+</div></blockquote>
+</div>
+<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>$ <span class="nb">cd</span> egs/librispeech/ASR
+$ ./pruned_transducer_stateless4/decode.py --help
+</pre></div>
+</div>
+<p>shows the options for decoding.</p>
+<p>The following shows two examples (for two types of checkpoints):</p>
+<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span><span class="k">for</span> m <span class="k">in</span> greedy_search fast_beam_search modified_beam_search<span class="p">;</span> <span class="k">do</span>
+  <span class="k">for</span> epoch <span class="k">in</span> <span class="m">25</span> <span class="m">20</span><span class="p">;</span> <span class="k">do</span>
+    <span class="k">for</span> avg <span class="k">in</span> <span class="m">7</span> <span class="m">5</span> <span class="m">3</span> <span class="m">1</span><span class="p">;</span> <span class="k">do</span>
+      ./pruned_transducer_stateless4/decode.py <span class="se">\</span>
+        --epoch <span class="nv">$epoch</span> <span class="se">\</span>
+        --avg <span class="nv">$avg</span> <span class="se">\</span>
+        --exp-dir pruned_transducer_stateless4/exp <span class="se">\</span>
+        --max-duration <span class="m">600</span> <span class="se">\</span>
+        --decoding-method <span class="nv">$m</span>
+    <span class="k">done</span>
+  <span class="k">done</span>
+<span class="k">done</span>
+</pre></div>
+</div>
+<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span><span class="k">for</span> m <span class="k">in</span> greedy_search fast_beam_search modified_beam_search<span class="p">;</span> <span class="k">do</span>
+  <span class="k">for</span> iter <span class="k">in</span> <span class="m">474000</span><span class="p">;</span> <span class="k">do</span>
+    <span class="k">for</span> avg <span class="k">in</span> <span class="m">8</span> <span class="m">10</span> <span class="m">12</span> <span class="m">14</span> <span class="m">16</span> <span class="m">18</span><span class="p">;</span> <span class="k">do</span>
+      ./pruned_transducer_stateless4/decode.py <span class="se">\</span>
+        --iter <span class="nv">$iter</span> <span class="se">\</span>
+        --avg <span class="nv">$avg</span> <span class="se">\</span>
+        --exp-dir pruned_transducer_stateless4/exp <span class="se">\</span>
+        --max-duration <span class="m">600</span> <span class="se">\</span>
+        --decoding-method <span class="nv">$m</span>
+    <span class="k">done</span>
+  <span class="k">done</span>
+<span class="k">done</span>
+</pre></div>
+</div>
+<div class="admonition note">
+<p class="admonition-title">Note</p>
+<p>Supporting decoding methods are as follows:</p>
+<blockquote>
+<div><ul class="simple">
+<li><p><code class="docutils literal notranslate"><span class="pre">greedy_search</span></code> : It takes the symbol with largest posterior probability
+of each frame as the decoding result.</p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">beam_search</span></code> :  It implements Algorithm 1 in <a class="reference external" href="https://arxiv.org/pdf/1211.3711.pdf">https://arxiv.org/pdf/1211.3711.pdf</a> and
+<a class="reference external" href="https://github.com/espnet/espnet/blob/master/espnet/nets/beam_search_transducer.py#L247">espnet/nets/beam_search_transducer.py</a>
+is used as a reference. Basicly, it keeps topk states for each frame, and expands the kept states with their own contexts to
+next frame.</p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">modified_beam_search</span></code> : It implements the same algorithm as <code class="docutils literal notranslate"><span class="pre">beam_search</span></code> above, but it
+runs in batch mode with <code class="docutils literal notranslate"><span class="pre">--max-sym-per-frame=1</span></code> being hardcoded.</p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">fast_beam_search</span></code> : It implements graph composition between the output <code class="docutils literal notranslate"><span class="pre">log_probs</span></code> and
+given <code class="docutils literal notranslate"><span class="pre">FSAs</span></code>. It is hard to describe the details in several lines of texts, you can read
+our paper in <a class="reference external" href="https://arxiv.org/pdf/2211.00484.pdf">https://arxiv.org/pdf/2211.00484.pdf</a> or our <a class="reference external" href="https://github.com/k2-fsa/k2/blob/master/k2/csrc/rnnt_decode.h">rnnt decode code in k2</a>. <code class="docutils literal notranslate"><span class="pre">fast_beam_search</span></code> can decode with <code class="docutils literal notranslate"><span class="pre">FSAs</span></code> on GPU efficiently.</p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">fast_beam_search_LG</span></code> : The same as <code class="docutils literal notranslate"><span class="pre">fast_beam_search</span></code> above, <code class="docutils literal notranslate"><span class="pre">fast_beam_search</span></code> uses
+an trivial graph that has only one state, while <code class="docutils literal notranslate"><span class="pre">fast_beam_search_LG</span></code> uses an LG graph
+(with N-gram LM).</p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">fast_beam_search_nbest</span></code> : It produces the decoding results as follows:</p>
+<ul>
+<li><ol class="arabic simple">
+<li><p>Use <code class="docutils literal notranslate"><span class="pre">fast_beam_search</span></code> to get a lattice</p></li>
+</ol>
+</li>
+<li><ol class="arabic simple" start="2">
+<li><p>Select <code class="docutils literal notranslate"><span class="pre">num_paths</span></code> paths from the lattice using <code class="docutils literal notranslate"><span class="pre">k2.random_paths()</span></code></p></li>
+</ol>
+</li>
+<li><ol class="arabic simple" start="3">
+<li><p>Unique the selected paths</p></li>
+</ol>
+</li>
+<li><ol class="arabic simple" start="4">
+<li><p>Intersect the selected paths with the lattice and compute the
+shortest path from the intersection result</p></li>
+</ol>
+</li>
+<li><ol class="arabic simple" start="5">
+<li><p>The path with the largest score is used as the decoding output.</p></li>
+</ol>
+</li>
+</ul>
+</li>
+<li><p><code class="docutils literal notranslate"><span class="pre">fast_beam_search_nbest_LG</span></code> : It implements same logic as <code class="docutils literal notranslate"><span class="pre">fast_beam_search_nbest</span></code>, the
+only difference is that it uses <code class="docutils literal notranslate"><span class="pre">fast_beam_search_LG</span></code> to generate the lattice.</p></li>
+</ul>
+</div></blockquote>
+</div>
+</section>
+<section id="export-model">
+<h2>Export Model<a class="headerlink" href="#export-model" title="Permalink to this heading"></a></h2>
+<p><a class="reference external" href="https://github.com/k2-fsa/icefall/blob/master/egs/librispeech/ASR/pruned_transducer_stateless4/export.py">pruned_transducer_stateless4/export.py</a> supports exporting checkpoints from <code class="docutils literal notranslate"><span class="pre">pruned_transducer_stateless4/exp</span></code> in the following ways.</p>
+<section id="export-model-state-dict">
+<h3>Export <code class="docutils literal notranslate"><span class="pre">model.state_dict()</span></code><a class="headerlink" href="#export-model-state-dict" title="Permalink to this heading"></a></h3>
+<p>Checkpoints saved by <code class="docutils literal notranslate"><span class="pre">pruned_transducer_stateless4/train.py</span></code> also include
+<code class="docutils literal notranslate"><span class="pre">optimizer.state_dict()</span></code>. It is useful for resuming training. But after training,
+we are interested only in <code class="docutils literal notranslate"><span class="pre">model.state_dict()</span></code>. You can use the following
+command to extract <code class="docutils literal notranslate"><span class="pre">model.state_dict()</span></code>.</p>
+<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span><span class="c1"># Assume that --epoch 25 --avg 3 produces the smallest WER</span>
+<span class="c1"># (You can get such information after running ./pruned_transducer_stateless4/decode.py)</span>
+
+<span class="nv">epoch</span><span class="o">=</span><span class="m">25</span>
+<span class="nv">avg</span><span class="o">=</span><span class="m">3</span>
+
+./pruned_transducer_stateless4/export.py <span class="se">\</span>
+  --exp-dir ./pruned_transducer_stateless4/exp <span class="se">\</span>
+  --bpe-model data/lang_bpe_500/bpe.model <span class="se">\</span>
+  --epoch <span class="nv">$epoch</span> <span class="se">\</span>
+  --avg  <span class="nv">$avg</span>
+</pre></div>
+</div>
+<p>It will generate a file <code class="docutils literal notranslate"><span class="pre">./pruned_transducer_stateless4/exp/pretrained.pt</span></code>.</p>
+<div class="admonition hint">
+<p class="admonition-title">Hint</p>
+<p>To use the generated <code class="docutils literal notranslate"><span class="pre">pretrained.pt</span></code> for <code class="docutils literal notranslate"><span class="pre">pruned_transducer_stateless4/decode.py</span></code>,
+you can run:</p>
+<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span><span class="nb">cd</span> pruned_transducer_stateless4/exp
+ln -s pretrained.pt epoch-999.pt
+</pre></div>
+</div>
+<p>And then pass <code class="docutils literal notranslate"><span class="pre">--epoch</span> <span class="pre">999</span> <span class="pre">--avg</span> <span class="pre">1</span> <span class="pre">--use-averaged-model</span> <span class="pre">0</span></code> to
+<code class="docutils literal notranslate"><span class="pre">./pruned_transducer_stateless4/decode.py</span></code>.</p>
+</div>
+<p>To use the exported model with <code class="docutils literal notranslate"><span class="pre">./pruned_transducer_stateless4/pretrained.py</span></code>, you
+can run:</p>
+<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>./pruned_transducer_stateless4/pretrained.py <span class="se">\</span>
+  --checkpoint ./pruned_transducer_stateless4/exp/pretrained.pt <span class="se">\</span>
+  --bpe-model ./data/lang_bpe_500/bpe.model <span class="se">\</span>
+  --method greedy_search <span class="se">\</span>
+  /path/to/foo.wav <span class="se">\</span>
+  /path/to/bar.wav
+</pre></div>
+</div>
+</section>
+<section id="export-model-using-torch-jit-script">
+<h3>Export model using <code class="docutils literal notranslate"><span class="pre">torch.jit.script()</span></code><a class="headerlink" href="#export-model-using-torch-jit-script" title="Permalink to this heading"></a></h3>
+<p>It will generate a file <code class="docutils literal notranslate"><span class="pre">cpu_jit.pt</span></code> in the given <code class="docutils literal notranslate"><span class="pre">exp_dir</span></code>. You can later
+load it by <code class="docutils literal notranslate"><span class="pre">torch.jit.load(&quot;cpu_jit.pt&quot;)</span></code>.</p>
+<p>Note <code class="docutils literal notranslate"><span class="pre">cpu</span></code> in the name <code class="docutils literal notranslate"><span class="pre">cpu_jit.pt</span></code> means the parameters when loaded into Python
+are on CPU. You can use <code class="docutils literal notranslate"><span class="pre">to(&quot;cuda&quot;)</span></code> to move them to a CUDA device.</p>
+<div class="admonition note">
+<p class="admonition-title">Note</p>
+<p>You will need this <code class="docutils literal notranslate"><span class="pre">cpu_jit.pt</span></code> when deploying with Sherpa framework.</p>
+</div>
+</section>
+</section>
+<section id="download-pretrained-models">
+<h2>Download pretrained models<a class="headerlink" href="#download-pretrained-models" title="Permalink to this heading"></a></h2>
+<p>If you don’t want to train from scratch, you can download the pretrained models
+by visiting the following links:</p>
+<blockquote>
+<div><ul class="simple">
+<li><p><a class="reference external" href="https://huggingface.co/csukuangfj/icefall-asr-librispeech-pruned-transducer-stateless-2022-03-12">pruned_transducer_stateless</a></p></li>
+<li><p><a class="reference external" href="https://huggingface.co/csukuangfj/icefall-asr-librispeech-pruned-transducer-stateless2-2022-04-29">pruned_transducer_stateless2</a></p></li>
+<li><p><a class="reference external" href="https://huggingface.co/Zengwei/icefall-asr-librispeech-pruned-transducer-stateless4-2022-06-03">pruned_transducer_stateless4</a></p></li>
+<li><p><a class="reference external" href="https://huggingface.co/Zengwei/icefall-asr-librispeech-pruned-transducer-stateless5-2022-07-07">pruned_transducer_stateless5</a></p></li>
+</ul>
+<p>See <a class="reference external" href="https://github.com/k2-fsa/icefall/blob/master/egs/librispeech/ASR/RESULTS.md">https://github.com/k2-fsa/icefall/blob/master/egs/librispeech/ASR/RESULTS.md</a>
+for the details of the above pretrained models</p>
+</div></blockquote>
+</section>
+<section id="deploy-with-sherpa">
+<h2>Deploy with Sherpa<a class="headerlink" href="#deploy-with-sherpa" title="Permalink to this heading"></a></h2>
+<p>Please see <a class="reference external" href="https://k2-fsa.github.io/sherpa/python/offline_asr/conformer/librispeech.html#">https://k2-fsa.github.io/sherpa/python/offline_asr/conformer/librispeech.html#</a>
+for how to deploy the models in <code class="docutils literal notranslate"><span class="pre">sherpa</span></code>.</p>
+</section>
+</section>
+
+
+           </div>
+          </div>
+          <footer><div class="rst-footer-buttons" role="navigation" aria-label="Footer">
+        <a href="conformer_ctc.html" class="btn btn-neutral float-left" title="Conformer CTC" accesskey="p" rel="prev"><span class="fa fa-arrow-circle-left" aria-hidden="true"></span> Previous</a>
+        <a href="zipformer_mmi.html" class="btn btn-neutral float-right" title="Zipformer MMI" accesskey="n" rel="next">Next <span class="fa fa-arrow-circle-right" aria-hidden="true"></span></a>
+    </div>
+
+  <hr/>
+
+  <div role="contentinfo">
+    <p>&#169; Copyright 2021, icefall development team.</p>
+  </div>
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-    <link rel="index" title="Index" href="../../genindex.html" />
-    <link rel="search" title="Search" href="../../search.html" />
+        <script data-url_root="../../../" id="documentation_options" src="../../../_static/documentation_options.js"></script>
+        <script src="../../../_static/jquery.js"></script>
+        <script src="../../../_static/underscore.js"></script>
+        <script src="../../../_static/_sphinx_javascript_frameworks_compat.js"></script>
+        <script src="../../../_static/doctools.js"></script>
+        <script src="../../../_static/sphinx_highlight.js"></script>
+    <script src="../../../_static/js/theme.js"></script>
+    <link rel="index" title="Index" href="../../../genindex.html" />
+    <link rel="search" title="Search" href="../../../search.html" />
     <link rel="next" title="Conformer CTC" href="conformer_ctc.html" />
     <link rel="prev" title="LibriSpeech" href="index.html" /> 
 </head>
@@ -29,10 +29,10 @@
     <nav data-toggle="wy-nav-shift" class="wy-nav-side">
       <div class="wy-side-scroll">
         <div class="wy-side-nav-search" >
-            <a href="../../index.html" class="icon icon-home"> icefall
+            <a href="../../../index.html" class="icon icon-home"> icefall
           </a>
 <div role="search">
-  <form id="rtd-search-form" class="wy-form" action="../../search.html" method="get">
+  <form id="rtd-search-form" class="wy-form" action="../../../search.html" method="get">
     <input type="text" name="q" placeholder="Search docs" />
     <input type="hidden" name="check_keywords" value="yes" />
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@@ -40,31 +40,32 @@
 </div>
         </div><div class="wy-menu wy-menu-vertical" data-spy="affix" role="navigation" aria-label="Navigation menu">
               <p class="caption" role="heading"><span class="caption-text">Contents:</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../../../installation/index.html">Installation</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../../model-export/index.html">Model export</a></li>
+</ul>
 <ul class="current">
-<li class="toctree-l1"><a class="reference internal" href="../../installation/index.html">Installation</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../../model-export/index.html">Model export</a></li>
-<li class="toctree-l1 current"><a class="reference internal" href="../index.html">Recipes</a><ul class="current">
-<li class="toctree-l2"><a class="reference internal" href="../aishell/index.html">aishell</a></li>
-<li class="toctree-l2 current"><a class="reference internal" href="index.html">LibriSpeech</a><ul class="current">
-<li class="toctree-l3 current"><a class="current reference internal" href="#">TDNN-LSTM-CTC</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="#data-preparation">Data preparation</a></li>
-<li class="toctree-l4"><a class="reference internal" href="#training">Training</a></li>
-<li class="toctree-l4"><a class="reference internal" href="#decoding">Decoding</a></li>
-<li class="toctree-l4"><a class="reference internal" href="#pre-trained-model">Pre-trained Model</a></li>
-<li class="toctree-l4"><a class="reference internal" href="#colab-notebook">Colab notebook</a></li>
+<li class="toctree-l1 current"><a class="reference internal" href="../../index.html">Recipes</a><ul class="current">
+<li class="toctree-l2 current"><a class="reference internal" href="../index.html">Non Streaming ASR</a><ul class="current">
+<li class="toctree-l3"><a class="reference internal" href="../aishell/index.html">aishell</a></li>
+<li class="toctree-l3 current"><a class="reference internal" href="index.html">LibriSpeech</a><ul class="current">
+<li class="toctree-l4 current"><a class="current reference internal" href="#">TDNN-LSTM-CTC</a></li>
+<li class="toctree-l4"><a class="reference internal" href="conformer_ctc.html">Conformer CTC</a></li>
+<li class="toctree-l4"><a class="reference internal" href="pruned_transducer_stateless.html">Pruned transducer statelessX</a></li>
+<li class="toctree-l4"><a class="reference internal" href="zipformer_mmi.html">Zipformer MMI</a></li>
 </ul>
 </li>
-<li class="toctree-l3"><a class="reference internal" href="conformer_ctc.html">Conformer CTC</a></li>
-<li class="toctree-l3"><a class="reference internal" href="lstm_pruned_stateless_transducer.html">LSTM Transducer</a></li>
-<li class="toctree-l3"><a class="reference internal" href="zipformer_mmi.html">Zipformer MMI</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../timit/index.html">TIMIT</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../yesno/index.html">YesNo</a></li>
 </ul>
 </li>
-<li class="toctree-l2"><a class="reference internal" href="../timit/index.html">TIMIT</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../yesno/index.html">YesNo</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../Streaming-ASR/index.html">Streaming ASR</a></li>
 </ul>
 </li>
-<li class="toctree-l1"><a class="reference internal" href="../../contributing/index.html">Contributing</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../../huggingface/index.html">Huggingface</a></li>
+</ul>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../../../contributing/index.html">Contributing</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../../huggingface/index.html">Huggingface</a></li>
 </ul>
 
         </div>
@@ -73,19 +74,20 @@
 
     <section data-toggle="wy-nav-shift" class="wy-nav-content-wrap"><nav class="wy-nav-top" aria-label="Mobile navigation menu" >
           <i data-toggle="wy-nav-top" class="fa fa-bars"></i>
-          <a href="../../index.html">icefall</a>
+          <a href="../../../index.html">icefall</a>
       </nav>
 
       <div class="wy-nav-content">
         <div class="rst-content">
           <div role="navigation" aria-label="Page navigation">
   <ul class="wy-breadcrumbs">
-      <li><a href="../../index.html" class="icon icon-home"></a></li>
-          <li class="breadcrumb-item"><a href="../index.html">Recipes</a></li>
+      <li><a href="../../../index.html" class="icon icon-home"></a></li>
+          <li class="breadcrumb-item"><a href="../../index.html">Recipes</a></li>
+          <li class="breadcrumb-item"><a href="../index.html">Non Streaming ASR</a></li>
           <li class="breadcrumb-item"><a href="index.html">LibriSpeech</a></li>
       <li class="breadcrumb-item active">TDNN-LSTM-CTC</li>
       <li class="wy-breadcrumbs-aside">
-              <a href="https://github.com/k2-fsa/icefall/blob/master/docs/source/recipes/librispeech/tdnn_lstm_ctc.rst" class="fa fa-github"> Edit on GitHub</a>
+              <a href="https://github.com/k2-fsa/icefall/blob/master/docs/source/recipes/Non-streaming-ASR/librispeech/tdnn_lstm_ctc.rst" class="fa fa-github"> Edit on GitHub</a>
       </li>
   </ul>
   <hr/>
@@ -98,7 +100,7 @@
 <p>This tutorial shows you how to run a TDNN-LSTM-CTC model with the <a class="reference external" href="https://www.openslr.org/12">LibriSpeech</a> dataset.</p>
 <div class="admonition hint">
 <p class="admonition-title">Hint</p>
-<p>We assume you have read the page <a class="reference internal" href="../../installation/index.html#install-icefall"><span class="std std-ref">Installation</span></a> and have setup
+<p>We assume you have read the page <a class="reference internal" href="../../../installation/index.html#install-icefall"><span class="std std-ref">Installation</span></a> and have setup
 the environment for <code class="docutils literal notranslate"><span class="pre">icefall</span></code>.</p>
 </div>
 <section id="data-preparation">

recipes/Non-streaming-ASR/librispeech/zipformer_mmi.html

@@ -5,23 +5,23 @@
 
   <meta name="viewport" content="width=device-width, initial-scale=1.0" />
   <title>Zipformer MMI &mdash; icefall 0.1 documentation</title>
-      <link rel="stylesheet" href="../../_static/pygments.css" type="text/css" />
-      <link rel="stylesheet" href="../../_static/css/theme.css" type="text/css" />
+      <link rel="stylesheet" href="../../../_static/pygments.css" type="text/css" />
+      <link rel="stylesheet" href="../../../_static/css/theme.css" type="text/css" />
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-    <script src="../../_static/js/theme.js"></script>
-    <link rel="index" title="Index" href="../../genindex.html" />
-    <link rel="search" title="Search" href="../../search.html" />
+        <script data-url_root="../../../" id="documentation_options" src="../../../_static/documentation_options.js"></script>
+        <script src="../../../_static/jquery.js"></script>
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+    <link rel="index" title="Index" href="../../../genindex.html" />
+    <link rel="search" title="Search" href="../../../search.html" />
     <link rel="next" title="TIMIT" href="../timit/index.html" />
-    <link rel="prev" title="LSTM Transducer" href="lstm_pruned_stateless_transducer.html" /> 
+    <link rel="prev" title="Pruned transducer statelessX" href="pruned_transducer_stateless.html" /> 
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@@ -29,10 +29,10 @@
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       <div class="wy-side-scroll">
         <div class="wy-side-nav-search" >
-            <a href="../../index.html" class="icon icon-home"> icefall
+            <a href="../../../index.html" class="icon icon-home"> icefall
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+  <form id="rtd-search-form" class="wy-form" action="../../../search.html" method="get">
     <input type="text" name="q" placeholder="Search docs" />
     <input type="hidden" name="check_keywords" value="yes" />
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@@ -40,31 +40,32 @@
 </div>
         </div><div class="wy-menu wy-menu-vertical" data-spy="affix" role="navigation" aria-label="Navigation menu">
               <p class="caption" role="heading"><span class="caption-text">Contents:</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../../../installation/index.html">Installation</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../../model-export/index.html">Model export</a></li>
+</ul>
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-<li class="toctree-l1"><a class="reference internal" href="../../installation/index.html">Installation</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../../model-export/index.html">Model export</a></li>
-<li class="toctree-l1 current"><a class="reference internal" href="../index.html">Recipes</a><ul class="current">
-<li class="toctree-l2"><a class="reference internal" href="../aishell/index.html">aishell</a></li>
-<li class="toctree-l2 current"><a class="reference internal" href="index.html">LibriSpeech</a><ul class="current">
-<li class="toctree-l3"><a class="reference internal" href="tdnn_lstm_ctc.html">TDNN-LSTM-CTC</a></li>
-<li class="toctree-l3"><a class="reference internal" href="conformer_ctc.html">Conformer CTC</a></li>
-<li class="toctree-l3"><a class="reference internal" href="lstm_pruned_stateless_transducer.html">LSTM Transducer</a></li>
-<li class="toctree-l3 current"><a class="current reference internal" href="#">Zipformer MMI</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="#data-preparation">Data preparation</a></li>
-<li class="toctree-l4"><a class="reference internal" href="#training">Training</a></li>
-<li class="toctree-l4"><a class="reference internal" href="#decoding">Decoding</a></li>
-<li class="toctree-l4"><a class="reference internal" href="#export-models">Export models</a></li>
-<li class="toctree-l4"><a class="reference internal" href="#download-pretrained-models">Download pretrained models</a></li>
+<li class="toctree-l1 current"><a class="reference internal" href="../../index.html">Recipes</a><ul class="current">
+<li class="toctree-l2 current"><a class="reference internal" href="../index.html">Non Streaming ASR</a><ul class="current">
+<li class="toctree-l3"><a class="reference internal" href="../aishell/index.html">aishell</a></li>
+<li class="toctree-l3 current"><a class="reference internal" href="index.html">LibriSpeech</a><ul class="current">
+<li class="toctree-l4"><a class="reference internal" href="tdnn_lstm_ctc.html">TDNN-LSTM-CTC</a></li>
+<li class="toctree-l4"><a class="reference internal" href="conformer_ctc.html">Conformer CTC</a></li>
+<li class="toctree-l4"><a class="reference internal" href="pruned_transducer_stateless.html">Pruned transducer statelessX</a></li>
+<li class="toctree-l4 current"><a class="current reference internal" href="#">Zipformer MMI</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../timit/index.html">TIMIT</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../yesno/index.html">YesNo</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../Streaming-ASR/index.html">Streaming ASR</a></li>
 </ul>
 </li>
 </ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../timit/index.html">TIMIT</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../yesno/index.html">YesNo</a></li>
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-<li class="toctree-l1"><a class="reference internal" href="../../contributing/index.html">Contributing</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../../huggingface/index.html">Huggingface</a></li>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../../../contributing/index.html">Contributing</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../../huggingface/index.html">Huggingface</a></li>
 </ul>
 
         </div>
@@ -73,19 +74,20 @@
 
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-          <a href="../../index.html">icefall</a>
+          <a href="../../../index.html">icefall</a>
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-      <li><a href="../../index.html" class="icon icon-home"></a></li>
-          <li class="breadcrumb-item"><a href="../index.html">Recipes</a></li>
+      <li><a href="../../../index.html" class="icon icon-home"></a></li>
+          <li class="breadcrumb-item"><a href="../../index.html">Recipes</a></li>
+          <li class="breadcrumb-item"><a href="../index.html">Non Streaming ASR</a></li>
           <li class="breadcrumb-item"><a href="index.html">LibriSpeech</a></li>
       <li class="breadcrumb-item active">Zipformer MMI</li>
       <li class="wy-breadcrumbs-aside">
-              <a href="https://github.com/k2-fsa/icefall/blob/master/docs/source/recipes/librispeech/zipformer_mmi.rst" class="fa fa-github"> Edit on GitHub</a>
+              <a href="https://github.com/k2-fsa/icefall/blob/master/docs/source/recipes/Non-streaming-ASR/librispeech/zipformer_mmi.rst" class="fa fa-github"> Edit on GitHub</a>
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@@ -493,7 +495,7 @@ for the details of the above pretrained models</p>
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recipes/Non-streaming-ASR/timit/index.html

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+<html class="writer-html5" lang="en" >
+<head>
+  <meta charset="utf-8" /><meta name="generator" content="Docutils 0.17.1: http://docutils.sourceforge.net/" />
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+  <title>TIMIT &mdash; icefall 0.1 documentation</title>
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+<li class="toctree-l1 current"><a class="reference internal" href="../../index.html">Recipes</a><ul class="current">
+<li class="toctree-l2 current"><a class="reference internal" href="../index.html">Non Streaming ASR</a><ul class="current">
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recipes/Non-streaming-ASR/timit/tdnn_ligru_ctc.html

@@ -5,21 +5,21 @@
 
   <meta name="viewport" content="width=device-width, initial-scale=1.0" />
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-      <link rel="stylesheet" href="../../_static/pygments.css" type="text/css" />
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+        <script data-url_root="../../../" id="documentation_options" src="../../../_static/documentation_options.js"></script>
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@@ -29,10 +29,10 @@
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         </div><div class="wy-menu wy-menu-vertical" data-spy="affix" role="navigation" aria-label="Navigation menu">
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+<li class="toctree-l1"><a class="reference internal" href="../../../model-export/index.html">Model export</a></li>
+</ul>
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-<li class="toctree-l1"><a class="reference internal" href="../../installation/index.html">Installation</a></li>
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-<li class="toctree-l2 current"><a class="reference internal" href="index.html">TIMIT</a><ul class="current">
-<li class="toctree-l3 current"><a class="current reference internal" href="#">TDNN-LiGRU-CTC</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="#data-preparation">Data preparation</a></li>
-<li class="toctree-l4"><a class="reference internal" href="#training">Training</a></li>
-<li class="toctree-l4"><a class="reference internal" href="#decoding">Decoding</a></li>
-<li class="toctree-l4"><a class="reference internal" href="#pre-trained-model">Pre-trained Model</a></li>
-<li class="toctree-l4"><a class="reference internal" href="#colab-notebook">Colab notebook</a></li>
+<li class="toctree-l1 current"><a class="reference internal" href="../../index.html">Recipes</a><ul class="current">
+<li class="toctree-l2 current"><a class="reference internal" href="../index.html">Non Streaming ASR</a><ul class="current">
+<li class="toctree-l3"><a class="reference internal" href="../aishell/index.html">aishell</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../librispeech/index.html">LibriSpeech</a></li>
+<li class="toctree-l3 current"><a class="reference internal" href="index.html">TIMIT</a><ul class="current">
+<li class="toctree-l4 current"><a class="current reference internal" href="#">TDNN-LiGRU-CTC</a></li>
+<li class="toctree-l4"><a class="reference internal" href="tdnn_lstm_ctc.html">TDNN-LSTM-CTC</a></li>
 </ul>
 </li>
-<li class="toctree-l3"><a class="reference internal" href="tdnn_lstm_ctc.html">TDNN-LSTM-CTC</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../yesno/index.html">YesNo</a></li>
 </ul>
 </li>
-<li class="toctree-l2"><a class="reference internal" href="../yesno/index.html">YesNo</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../Streaming-ASR/index.html">Streaming ASR</a></li>
 </ul>
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-<li class="toctree-l1"><a class="reference internal" href="../../contributing/index.html">Contributing</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../../huggingface/index.html">Huggingface</a></li>
+</ul>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../../../contributing/index.html">Contributing</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../../huggingface/index.html">Huggingface</a></li>
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@@ -71,19 +72,20 @@
 
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       </nav>
 
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   <ul class="wy-breadcrumbs">
-      <li><a href="../../index.html" class="icon icon-home"></a></li>
-          <li class="breadcrumb-item"><a href="../index.html">Recipes</a></li>
+      <li><a href="../../../index.html" class="icon icon-home"></a></li>
+          <li class="breadcrumb-item"><a href="../../index.html">Recipes</a></li>
+          <li class="breadcrumb-item"><a href="../index.html">Non Streaming ASR</a></li>
           <li class="breadcrumb-item"><a href="index.html">TIMIT</a></li>
       <li class="breadcrumb-item active">TDNN-LiGRU-CTC</li>
       <li class="wy-breadcrumbs-aside">
-              <a href="https://github.com/k2-fsa/icefall/blob/master/docs/source/recipes/timit/tdnn_ligru_ctc.rst" class="fa fa-github"> Edit on GitHub</a>
+              <a href="https://github.com/k2-fsa/icefall/blob/master/docs/source/recipes/Non-streaming-ASR/timit/tdnn_ligru_ctc.rst" class="fa fa-github"> Edit on GitHub</a>
       </li>
   </ul>
   <hr/>
@@ -96,7 +98,7 @@
 <p>This tutorial shows you how to run a TDNN-LiGRU-CTC model with the <a class="reference external" href="https://data.deepai.org/timit.zip">TIMIT</a> dataset.</p>
 <div class="admonition hint">
 <p class="admonition-title">Hint</p>
-<p>We assume you have read the page <a class="reference internal" href="../../installation/index.html#install-icefall"><span class="std std-ref">Installation</span></a> and have setup
+<p>We assume you have read the page <a class="reference internal" href="../../../installation/index.html#install-icefall"><span class="std std-ref">Installation</span></a> and have setup
 the environment for <code class="docutils literal notranslate"><span class="pre">icefall</span></code>.</p>
 </div>
 <section id="data-preparation">

recipes/Non-streaming-ASR/timit/tdnn_lstm_ctc.html

@@ -5,21 +5,21 @@
 
   <meta name="viewport" content="width=device-width, initial-scale=1.0" />
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-      <link rel="stylesheet" href="../../_static/pygments.css" type="text/css" />
-      <link rel="stylesheet" href="../../_static/css/theme.css" type="text/css" />
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-    <link rel="index" title="Index" href="../../genindex.html" />
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+        <script data-url_root="../../../" id="documentation_options" src="../../../_static/documentation_options.js"></script>
+        <script src="../../../_static/jquery.js"></script>
+        <script src="../../../_static/underscore.js"></script>
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+    <link rel="index" title="Index" href="../../../genindex.html" />
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@@ -29,10 +29,10 @@
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       <div class="wy-side-scroll">
         <div class="wy-side-nav-search" >
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     <input type="hidden" name="check_keywords" value="yes" />
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@@ -40,29 +40,30 @@
 </div>
         </div><div class="wy-menu wy-menu-vertical" data-spy="affix" role="navigation" aria-label="Navigation menu">
               <p class="caption" role="heading"><span class="caption-text">Contents:</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../../../installation/index.html">Installation</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../../model-export/index.html">Model export</a></li>
+</ul>
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-<li class="toctree-l1"><a class="reference internal" href="../../installation/index.html">Installation</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../../model-export/index.html">Model export</a></li>
-<li class="toctree-l1 current"><a class="reference internal" href="../index.html">Recipes</a><ul class="current">
-<li class="toctree-l2"><a class="reference internal" href="../aishell/index.html">aishell</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../librispeech/index.html">LibriSpeech</a></li>
-<li class="toctree-l2 current"><a class="reference internal" href="index.html">TIMIT</a><ul class="current">
-<li class="toctree-l3"><a class="reference internal" href="tdnn_ligru_ctc.html">TDNN-LiGRU-CTC</a></li>
-<li class="toctree-l3 current"><a class="current reference internal" href="#">TDNN-LSTM-CTC</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="#data-preparation">Data preparation</a></li>
-<li class="toctree-l4"><a class="reference internal" href="#training">Training</a></li>
-<li class="toctree-l4"><a class="reference internal" href="#decoding">Decoding</a></li>
-<li class="toctree-l4"><a class="reference internal" href="#pre-trained-model">Pre-trained Model</a></li>
-<li class="toctree-l4"><a class="reference internal" href="#colab-notebook">Colab notebook</a></li>
+<li class="toctree-l1 current"><a class="reference internal" href="../../index.html">Recipes</a><ul class="current">
+<li class="toctree-l2 current"><a class="reference internal" href="../index.html">Non Streaming ASR</a><ul class="current">
+<li class="toctree-l3"><a class="reference internal" href="../aishell/index.html">aishell</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../librispeech/index.html">LibriSpeech</a></li>
+<li class="toctree-l3 current"><a class="reference internal" href="index.html">TIMIT</a><ul class="current">
+<li class="toctree-l4"><a class="reference internal" href="tdnn_ligru_ctc.html">TDNN-LiGRU-CTC</a></li>
+<li class="toctree-l4 current"><a class="current reference internal" href="#">TDNN-LSTM-CTC</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="../yesno/index.html">YesNo</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../../Streaming-ASR/index.html">Streaming ASR</a></li>
 </ul>
 </li>
 </ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../yesno/index.html">YesNo</a></li>
-</ul>
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-<li class="toctree-l1"><a class="reference internal" href="../../contributing/index.html">Contributing</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../../huggingface/index.html">Huggingface</a></li>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../../../contributing/index.html">Contributing</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../../huggingface/index.html">Huggingface</a></li>
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@@ -71,19 +72,20 @@
 
     <section data-toggle="wy-nav-shift" class="wy-nav-content-wrap"><nav class="wy-nav-top" aria-label="Mobile navigation menu" >
           <i data-toggle="wy-nav-top" class="fa fa-bars"></i>
-          <a href="../../index.html">icefall</a>
+          <a href="../../../index.html">icefall</a>
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       <div class="wy-nav-content">
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-      <li><a href="../../index.html" class="icon icon-home"></a></li>
-          <li class="breadcrumb-item"><a href="../index.html">Recipes</a></li>
+      <li><a href="../../../index.html" class="icon icon-home"></a></li>
+          <li class="breadcrumb-item"><a href="../../index.html">Recipes</a></li>
+          <li class="breadcrumb-item"><a href="../index.html">Non Streaming ASR</a></li>
           <li class="breadcrumb-item"><a href="index.html">TIMIT</a></li>
       <li class="breadcrumb-item active">TDNN-LSTM-CTC</li>
       <li class="wy-breadcrumbs-aside">
-              <a href="https://github.com/k2-fsa/icefall/blob/master/docs/source/recipes/timit/tdnn_lstm_ctc.rst" class="fa fa-github"> Edit on GitHub</a>
+              <a href="https://github.com/k2-fsa/icefall/blob/master/docs/source/recipes/Non-streaming-ASR/timit/tdnn_lstm_ctc.rst" class="fa fa-github"> Edit on GitHub</a>
       </li>
   </ul>
   <hr/>
@@ -96,7 +98,7 @@
 <p>This tutorial shows you how to run a TDNN-LSTM-CTC model with the <a class="reference external" href="https://data.deepai.org/timit.zip">TIMIT</a> dataset.</p>
 <div class="admonition hint">
 <p class="admonition-title">Hint</p>
-<p>We assume you have read the page <a class="reference internal" href="../../installation/index.html#install-icefall"><span class="std std-ref">Installation</span></a> and have setup
+<p>We assume you have read the page <a class="reference internal" href="../../../installation/index.html#install-icefall"><span class="std std-ref">Installation</span></a> and have setup
 the environment for <code class="docutils literal notranslate"><span class="pre">icefall</span></code>.</p>
 </div>
 <section id="data-preparation">

recipes/Non-streaming-ASR/yesno/index.html

@@ -5,21 +5,21 @@
 
   <meta name="viewport" content="width=device-width, initial-scale=1.0" />
   <title>YesNo &mdash; icefall 0.1 documentation</title>
-      <link rel="stylesheet" href="../../_static/pygments.css" type="text/css" />
-      <link rel="stylesheet" href="../../_static/css/theme.css" type="text/css" />
+      <link rel="stylesheet" href="../../../_static/pygments.css" type="text/css" />
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-    <link rel="index" title="Index" href="../../genindex.html" />
-    <link rel="search" title="Search" href="../../search.html" />
+        <script data-url_root="../../../" id="documentation_options" src="../../../_static/documentation_options.js"></script>
+        <script src="../../../_static/jquery.js"></script>
+        <script src="../../../_static/underscore.js"></script>
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+    <link rel="index" title="Index" href="../../../genindex.html" />
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@@ -29,10 +29,10 @@
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       <div class="wy-side-scroll">
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     <input type="text" name="q" placeholder="Search docs" />
     <input type="hidden" name="check_keywords" value="yes" />
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@@ -40,21 +40,29 @@
 </div>
         </div><div class="wy-menu wy-menu-vertical" data-spy="affix" role="navigation" aria-label="Navigation menu">
               <p class="caption" role="heading"><span class="caption-text">Contents:</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../../../installation/index.html">Installation</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../../model-export/index.html">Model export</a></li>
+</ul>
 <ul class="current">
-<li class="toctree-l1"><a class="reference internal" href="../../installation/index.html">Installation</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../../model-export/index.html">Model export</a></li>
-<li class="toctree-l1 current"><a class="reference internal" href="../index.html">Recipes</a><ul class="current">
-<li class="toctree-l2"><a class="reference internal" href="../aishell/index.html">aishell</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../librispeech/index.html">LibriSpeech</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../timit/index.html">TIMIT</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="tdnn.html">TDNN-CTC</a></li>
+<li class="toctree-l1 current"><a class="reference internal" href="../../index.html">Recipes</a><ul class="current">
+<li class="toctree-l2 current"><a class="reference internal" href="../index.html">Non Streaming ASR</a><ul class="current">
+<li class="toctree-l3"><a class="reference internal" href="../aishell/index.html">aishell</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../librispeech/index.html">LibriSpeech</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../timit/index.html">TIMIT</a></li>
+<li class="toctree-l3 current"><a class="current reference internal" href="#">YesNo</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="tdnn.html">TDNN-CTC</a></li>
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-<li class="toctree-l1"><a class="reference internal" href="../../contributing/index.html">Contributing</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../../huggingface/index.html">Huggingface</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../Streaming-ASR/index.html">Streaming ASR</a></li>
+</ul>
+</li>
+</ul>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../../../contributing/index.html">Contributing</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../../huggingface/index.html">Huggingface</a></li>
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@@ -63,18 +71,19 @@
 
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           <i data-toggle="wy-nav-top" class="fa fa-bars"></i>
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+          <a href="../../../index.html">icefall</a>
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+      <li><a href="../../../index.html" class="icon icon-home"></a></li>
+          <li class="breadcrumb-item"><a href="../../index.html">Recipes</a></li>
+          <li class="breadcrumb-item"><a href="../index.html">Non Streaming ASR</a></li>
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+              <a href="https://github.com/k2-fsa/icefall/blob/master/docs/source/recipes/Non-streaming-ASR/yesno/index.rst" class="fa fa-github"> Edit on GitHub</a>
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recipes/Non-streaming-ASR/yesno/tdnn.html

@@ -5,22 +5,22 @@
 
   <meta name="viewport" content="width=device-width, initial-scale=1.0" />
   <title>TDNN-CTC &mdash; icefall 0.1 documentation</title>
-      <link rel="stylesheet" href="../../_static/pygments.css" type="text/css" />
-      <link rel="stylesheet" href="../../_static/css/theme.css" type="text/css" />
+      <link rel="stylesheet" href="../../../_static/pygments.css" type="text/css" />
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+        <script data-url_root="../../../" id="documentation_options" src="../../../_static/documentation_options.js"></script>
+        <script src="../../../_static/jquery.js"></script>
+        <script src="../../../_static/underscore.js"></script>
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+    <link rel="index" title="Index" href="../../../genindex.html" />
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+    <link rel="next" title="Streaming ASR" href="../../Streaming-ASR/index.html" />
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@@ -29,10 +29,10 @@
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         <div class="wy-side-nav-search" >
-            <a href="../../index.html" class="icon icon-home"> icefall
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+  <form id="rtd-search-form" class="wy-form" action="../../../search.html" method="get">
     <input type="text" name="q" placeholder="Search docs" />
     <input type="hidden" name="check_keywords" value="yes" />
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@@ -40,28 +40,29 @@
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         </div><div class="wy-menu wy-menu-vertical" data-spy="affix" role="navigation" aria-label="Navigation menu">
               <p class="caption" role="heading"><span class="caption-text">Contents:</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../../../installation/index.html">Installation</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../../model-export/index.html">Model export</a></li>
+</ul>
 <ul class="current">
-<li class="toctree-l1"><a class="reference internal" href="../../installation/index.html">Installation</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../../model-export/index.html">Model export</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../aishell/index.html">aishell</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../timit/index.html">TIMIT</a></li>
-<li class="toctree-l2 current"><a class="reference internal" href="index.html">YesNo</a><ul class="current">
-<li class="toctree-l3 current"><a class="current reference internal" href="#">TDNN-CTC</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="#data-preparation">Data preparation</a></li>
-<li class="toctree-l4"><a class="reference internal" href="#training">Training</a></li>
-<li class="toctree-l4"><a class="reference internal" href="#decoding">Decoding</a></li>
-<li class="toctree-l4"><a class="reference internal" href="#pre-trained-model">Pre-trained Model</a></li>
-<li class="toctree-l4"><a class="reference internal" href="#colab-notebook">Colab notebook</a></li>
+<li class="toctree-l1 current"><a class="reference internal" href="../../index.html">Recipes</a><ul class="current">
+<li class="toctree-l2 current"><a class="reference internal" href="../index.html">Non Streaming ASR</a><ul class="current">
+<li class="toctree-l3"><a class="reference internal" href="../aishell/index.html">aishell</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../librispeech/index.html">LibriSpeech</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../timit/index.html">TIMIT</a></li>
+<li class="toctree-l3 current"><a class="reference internal" href="index.html">YesNo</a><ul class="current">
+<li class="toctree-l4 current"><a class="current reference internal" href="#">TDNN-CTC</a></li>
 </ul>
 </li>
 </ul>
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+<li class="toctree-l2"><a class="reference internal" href="../../Streaming-ASR/index.html">Streaming ASR</a></li>
 </ul>
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-<li class="toctree-l1"><a class="reference internal" href="../../contributing/index.html">Contributing</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../../huggingface/index.html">Huggingface</a></li>
+</ul>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../../../contributing/index.html">Contributing</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../../huggingface/index.html">Huggingface</a></li>
 </ul>
 
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@@ -70,19 +71,20 @@
 
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+          <a href="../../../index.html">icefall</a>
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-      <li><a href="../../index.html" class="icon icon-home"></a></li>
-          <li class="breadcrumb-item"><a href="../index.html">Recipes</a></li>
+      <li><a href="../../../index.html" class="icon icon-home"></a></li>
+          <li class="breadcrumb-item"><a href="../../index.html">Recipes</a></li>
+          <li class="breadcrumb-item"><a href="../index.html">Non Streaming ASR</a></li>
           <li class="breadcrumb-item"><a href="index.html">YesNo</a></li>
       <li class="breadcrumb-item active">TDNN-CTC</li>
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-              <a href="https://github.com/k2-fsa/icefall/blob/master/docs/source/recipes/yesno/tdnn.rst" class="fa fa-github"> Edit on GitHub</a>
+              <a href="https://github.com/k2-fsa/icefall/blob/master/docs/source/recipes/Non-streaming-ASR/yesno/tdnn.rst" class="fa fa-github"> Edit on GitHub</a>
       </li>
   </ul>
   <hr/>
@@ -188,7 +190,7 @@ for training as well as for decoding.</p>
 </div></blockquote>
 <div class="admonition hint">
 <p class="admonition-title">Hint</p>
-<p>We assume you have read the page <a class="reference internal" href="../../installation/index.html#install-icefall"><span class="std std-ref">Installation</span></a> and have setup
+<p>We assume you have read the page <a class="reference internal" href="../../../installation/index.html#install-icefall"><span class="std std-ref">Installation</span></a> and have setup
 the environment for <code class="docutils literal notranslate"><span class="pre">icefall</span></code>.</p>
 </div>
 <div class="admonition hint">
@@ -281,7 +283,7 @@ $ tensorboard dev upload --logdir . --description <span class="s2">&quot;TDNN tr
 the following screenshot:</p>
 <blockquote>
 <div><figure class="align-center" id="id1">
-<a class="reference external image-reference" href="https://tensorboard.dev/experiment/yKUbhb5wRmOSXYkId1z9eg/"><img alt="TensorBoard screenshot" src="../../_images/tdnn-tensorboard-log.png" style="width: 600px;" /></a>
+<a class="reference external image-reference" href="https://tensorboard.dev/experiment/yKUbhb5wRmOSXYkId1z9eg/"><img alt="TensorBoard screenshot" src="../../../_images/tdnn-tensorboard-log.png" style="width: 600px;" /></a>
 <figcaption>
 <p><span class="caption-number">Fig. 6 </span><span class="caption-text">TensorBoard screenshot.</span><a class="headerlink" href="#id1" title="Permalink to this image"></a></p>
 </figcaption>
@@ -548,7 +550,7 @@ $ ./tdnn/pretrained.py --help
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@@ -4,7 +4,7 @@
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   <meta name="viewport" content="width=device-width, initial-scale=1.0" />
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+  <title>Streaming ASR &mdash; icefall 0.1 documentation</title>
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+    <link rel="next" title="Introduction" href="introduction.html" />
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-<li class="toctree-l3"><a class="reference internal" href="tdnn_lstm_ctc.html">TDNN-LSTM-CTC</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../Non-streaming-ASR/index.html">Non Streaming ASR</a></li>
+<li class="toctree-l2 current"><a class="current reference internal" href="#">Streaming ASR</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="introduction.html">Introduction</a></li>
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           <li class="breadcrumb-item"><a href="../index.html">Recipes</a></li>
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+      <li class="breadcrumb-item active">Streaming ASR</li>
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+              <a href="https://github.com/k2-fsa/icefall/blob/master/docs/source/recipes/Streaming-ASR/index.rst" class="fa fa-github"> Edit on GitHub</a>
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@@ -83,12 +85,20 @@
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            <div itemprop="articleBody">
              
-  <section id="timit">
-<h1>TIMIT<a class="headerlink" href="#timit" title="Permalink to this heading"></a></h1>
+  <section id="streaming-asr">
+<h1>Streaming ASR<a class="headerlink" href="#streaming-asr" title="Permalink to this heading"></a></h1>
 <div class="toctree-wrapper compound">
 <ul>
-<li class="toctree-l1"><a class="reference internal" href="tdnn_ligru_ctc.html">TDNN-LiGRU-CTC</a></li>
-<li class="toctree-l1"><a class="reference internal" href="tdnn_lstm_ctc.html">TDNN-LSTM-CTC</a></li>
+<li class="toctree-l1"><a class="reference internal" href="introduction.html">Introduction</a></li>
+</ul>
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+<div class="toctree-wrapper compound">
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="librispeech/index.html">LibriSpeech</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="librispeech/pruned_transducer_stateless.html">Pruned transducer statelessX</a></li>
+<li class="toctree-l2"><a class="reference internal" href="librispeech/lstm_pruned_stateless_transducer.html">LSTM Transducer</a></li>
+</ul>
+</li>
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@@ -97,8 +107,8 @@
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recipes/Streaming-ASR/introduction.html

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+<!DOCTYPE html>
+<html class="writer-html5" lang="en" >
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+<li class="toctree-l1 current"><a class="reference internal" href="../index.html">Recipes</a><ul class="current">
+<li class="toctree-l2"><a class="reference internal" href="../Non-streaming-ASR/index.html">Non Streaming ASR</a></li>
+<li class="toctree-l2 current"><a class="reference internal" href="index.html">Streaming ASR</a><ul class="current">
+<li class="toctree-l3 current"><a class="current reference internal" href="#">Introduction</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="#streaming-conformer">Streaming Conformer</a></li>
+<li class="toctree-l4"><a class="reference internal" href="#streaming-emformer">Streaming Emformer</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="librispeech/index.html">LibriSpeech</a></li>
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+<li class="toctree-l1"><a class="reference internal" href="../../contributing/index.html">Contributing</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../huggingface/index.html">Huggingface</a></li>
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+      <li class="breadcrumb-item active">Introduction</li>
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+      </li>
+  </ul>
+  <hr/>
+</div>
+          <div role="main" class="document" itemscope="itemscope" itemtype="http://schema.org/Article">
+           <div itemprop="articleBody">
+             
+  <section id="introduction">
+<h1>Introduction<a class="headerlink" href="#introduction" title="Permalink to this heading"></a></h1>
+<p>This page shows you how we implement streaming <strong>X-former transducer</strong> models for ASR.</p>
+<div class="admonition hint">
+<p class="admonition-title">Hint</p>
+<p>X-former transducer here means the encoder of the transducer model uses Multi-Head Attention,
+like <a class="reference external" href="https://arxiv.org/pdf/2005.08100.pdf">Conformer</a>, <a class="reference external" href="https://arxiv.org/pdf/2010.10759.pdf">EmFormer</a> etc.</p>
+</div>
+<p>Currently we have implemented two types of streaming models, one uses Conformer as encoder, the other uses Emformer as encoder.</p>
+<section id="streaming-conformer">
+<h2>Streaming Conformer<a class="headerlink" href="#streaming-conformer" title="Permalink to this heading"></a></h2>
+<p>The main idea of training a streaming model is to make the model see limited contexts
+in training time, we can achieve this by applying a mask to the output of self-attention.
+In icefall, we implement the streaming conformer the way just like what <a class="reference external" href="https://arxiv.org/pdf/2012.05481.pdf">WeNet</a> did.</p>
+<div class="admonition note">
+<p class="admonition-title">Note</p>
+<p>The conformer-transducer recipes in LibriSpeech datasets, like, <a class="reference external" href="https://github.com/k2-fsa/icefall/tree/master/egs/librispeech/ASR/pruned_transducer_stateless">pruned_transducer_stateless</a>,
+<a class="reference external" href="https://github.com/k2-fsa/icefall/tree/master/egs/librispeech/ASR/pruned_transducer_stateless2">pruned_transducer_stateless2</a>,
+<a class="reference external" href="https://github.com/k2-fsa/icefall/tree/master/egs/librispeech/ASR/pruned_transducer_stateless3">pruned_transducer_stateless3</a>,
+<a class="reference external" href="https://github.com/k2-fsa/icefall/tree/master/egs/librispeech/ASR/pruned_transducer_stateless4">pruned_transducer_stateless4</a>,
+<a class="reference external" href="https://github.com/k2-fsa/icefall/tree/master/egs/librispeech/ASR/pruned_transducer_stateless5">pruned_transducer_stateless5</a>
+all support streaming.</p>
+</div>
+<div class="admonition note">
+<p class="admonition-title">Note</p>
+<p>Training a streaming conformer model in <code class="docutils literal notranslate"><span class="pre">icefall</span></code> is almost the same as training a
+non-streaming model, all you need to do is passing several extra arguments.
+See <a class="reference internal" href="librispeech/pruned_transducer_stateless.html"><span class="doc">Pruned transducer statelessX</span></a> for more details.</p>
+</div>
+<div class="admonition hint">
+<p class="admonition-title">Hint</p>
+<p>If you want to adapt a non-streaming conformer model to be streaming, please refer
+to <a class="reference external" href="https://github.com/k2-fsa/icefall/pull/454">this pull request</a>.</p>
+</div>
+</section>
+<section id="streaming-emformer">
+<h2>Streaming Emformer<a class="headerlink" href="#streaming-emformer" title="Permalink to this heading"></a></h2>
+<p>The Emformer model proposed <a class="reference external" href="https://arxiv.org/pdf/2010.10759.pdf">here</a> uses more
+complicated techniques. It has a memory bank component to memorize history information,
+what’ more, it also introduces right context in training time by hard-copying part of
+the input features.</p>
+<p>We have three variants of Emformer models in <code class="docutils literal notranslate"><span class="pre">icefall</span></code>.</p>
+<blockquote>
+<div><ul class="simple">
+<li><p><code class="docutils literal notranslate"><span class="pre">pruned_stateless_emformer_rnnt2</span></code> using Emformer from torchaudio, see <a class="reference external" href="https://github.com/k2-fsa/icefall/tree/master/egs/librispeech/ASR/pruned_stateless_emformer_rnnt2">LibriSpeech recipe</a>.</p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">conv_emformer_transducer_stateless</span></code> using ConvEmformer implemented by ourself. Different from the Emformer in torchaudio,
+ConvEmformer has a convolution in each layer and uses the mechanisms in our reworked conformer model.
+See <a class="reference external" href="https://github.com/k2-fsa/icefall/tree/master/egs/librispeech/ASR/conv_emformer_transducer_stateless">LibriSpeech recipe</a>.</p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">conv_emformer_transducer_stateless2</span></code> using ConvEmformer implemented by ourself. The only difference from the above one is that
+it uses a simplified memory bank. See <a class="reference external" href="https://github.com/k2-fsa/icefall/tree/master/egs/librispeech/ASR/conv_emformer_transducer_stateless2">LibriSpeech recipe</a>.</p></li>
+</ul>
+</div></blockquote>
+</section>
+</section>
+
+
+           </div>
+          </div>
+          <footer><div class="rst-footer-buttons" role="navigation" aria-label="Footer">
+        <a href="index.html" class="btn btn-neutral float-left" title="Streaming ASR" accesskey="p" rel="prev"><span class="fa fa-arrow-circle-left" aria-hidden="true"></span> Previous</a>
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recipes/Streaming-ASR/librispeech/index.html

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+<html class="writer-html5" lang="en" >
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+</ul>
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+<li class="toctree-l3 current"><a class="current reference internal" href="#">LibriSpeech</a><ul>
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+<li class="toctree-l4"><a class="reference internal" href="lstm_pruned_stateless_transducer.html">LSTM Transducer</a></li>
+</ul>
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+<li class="toctree-l1"><a class="reference internal" href="../../../huggingface/index.html">Huggingface</a></li>
+</ul>
+
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+      </div>
+    </nav>
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+      </nav>
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+        <div class="rst-content">
+          <div role="navigation" aria-label="Page navigation">
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+          <li class="breadcrumb-item"><a href="../../index.html">Recipes</a></li>
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+      <li class="breadcrumb-item active">LibriSpeech</li>
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+          <div role="main" class="document" itemscope="itemscope" itemtype="http://schema.org/Article">
+           <div itemprop="articleBody">
+             
+  <section id="librispeech">
+<h1>LibriSpeech<a class="headerlink" href="#librispeech" title="Permalink to this heading"></a></h1>
+<div class="toctree-wrapper compound">
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="pruned_transducer_stateless.html">Pruned transducer statelessX</a></li>
+<li class="toctree-l1"><a class="reference internal" href="lstm_pruned_stateless_transducer.html">LSTM Transducer</a></li>
+</ul>
+</div>
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+
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recipes/Streaming-ASR/librispeech/lstm_pruned_stateless_transducer.html

@@ -5,23 +5,23 @@
 
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-      <link rel="stylesheet" href="../../_static/pygments.css" type="text/css" />
-      <link rel="stylesheet" href="../../_static/css/theme.css" type="text/css" />
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+      <link rel="stylesheet" href="../../../_static/css/theme.css" type="text/css" />
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-    <link rel="next" title="Zipformer MMI" href="zipformer_mmi.html" />
-    <link rel="prev" title="Conformer CTC" href="conformer_ctc.html" /> 
+        <script data-url_root="../../../" id="documentation_options" src="../../../_static/documentation_options.js"></script>
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+    <link rel="index" title="Index" href="../../../genindex.html" />
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+    <link rel="next" title="Contributing" href="../../../contributing/index.html" />
+    <link rel="prev" title="Pruned transducer statelessX" href="pruned_transducer_stateless.html" /> 
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@@ -29,10 +29,10 @@
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       <div class="wy-side-scroll">
         <div class="wy-side-nav-search" >
-            <a href="../../index.html" class="icon icon-home"> icefall
+            <a href="../../../index.html" class="icon icon-home"> icefall
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-  <form id="rtd-search-form" class="wy-form" action="../../search.html" method="get">
+  <form id="rtd-search-form" class="wy-form" action="../../../search.html" method="get">
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@@ -40,32 +40,28 @@
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         </div><div class="wy-menu wy-menu-vertical" data-spy="affix" role="navigation" aria-label="Navigation menu">
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+<ul>
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+<li class="toctree-l1"><a class="reference internal" href="../../../model-export/index.html">Model export</a></li>
+</ul>
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-<li class="toctree-l1"><a class="reference internal" href="../../installation/index.html">Installation</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../../model-export/index.html">Model export</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../aishell/index.html">aishell</a></li>
-<li class="toctree-l2 current"><a class="reference internal" href="index.html">LibriSpeech</a><ul class="current">
-<li class="toctree-l3"><a class="reference internal" href="tdnn_lstm_ctc.html">TDNN-LSTM-CTC</a></li>
-<li class="toctree-l3"><a class="reference internal" href="conformer_ctc.html">Conformer CTC</a></li>
-<li class="toctree-l3 current"><a class="current reference internal" href="#">LSTM Transducer</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="#which-model-to-use">Which model to use</a></li>
-<li class="toctree-l4"><a class="reference internal" href="#data-preparation">Data preparation</a></li>
-<li class="toctree-l4"><a class="reference internal" href="#training">Training</a></li>
-<li class="toctree-l4"><a class="reference internal" href="#decoding">Decoding</a></li>
-<li class="toctree-l4"><a class="reference internal" href="#export-models">Export models</a></li>
-<li class="toctree-l4"><a class="reference internal" href="#download-pretrained-models">Download pretrained models</a></li>
+<li class="toctree-l1 current"><a class="reference internal" href="../../index.html">Recipes</a><ul class="current">
+<li class="toctree-l2"><a class="reference internal" href="../../Non-streaming-ASR/index.html">Non Streaming ASR</a></li>
+<li class="toctree-l2 current"><a class="reference internal" href="../index.html">Streaming ASR</a><ul class="current">
+<li class="toctree-l3"><a class="reference internal" href="../introduction.html">Introduction</a></li>
+<li class="toctree-l3 current"><a class="reference internal" href="index.html">LibriSpeech</a><ul class="current">
+<li class="toctree-l4"><a class="reference internal" href="pruned_transducer_stateless.html">Pruned transducer statelessX</a></li>
+<li class="toctree-l4 current"><a class="current reference internal" href="#">LSTM Transducer</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="zipformer_mmi.html">Zipformer MMI</a></li>
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+<li class="toctree-l1"><a class="reference internal" href="../../../huggingface/index.html">Huggingface</a></li>
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@@ -74,19 +70,20 @@
 
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+      <li><a href="../../../index.html" class="icon icon-home"></a></li>
+          <li class="breadcrumb-item"><a href="../../index.html">Recipes</a></li>
+          <li class="breadcrumb-item"><a href="../index.html">Streaming ASR</a></li>
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       <li class="breadcrumb-item active">LSTM Transducer</li>
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+              <a href="https://github.com/k2-fsa/icefall/blob/master/docs/source/recipes/Streaming-ASR/librispeech/lstm_pruned_stateless_transducer.rst" class="fa fa-github"> Edit on GitHub</a>
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   <hr/>
@@ -379,9 +376,9 @@ $ tensorboard dev upload --logdir . --description <span class="s2">&quot;LSTM tr
 the following screenshot:</p>
 <blockquote>
 <div><figure class="align-center" id="id3">
-<a class="reference external image-reference" href="https://tensorboard.dev/experiment/lzGnETjwRxC3yghNMd4kPw/"><img alt="TensorBoard screenshot" src="../../_images/librispeech-lstm-transducer-tensorboard-log.png" style="width: 600px;" /></a>
+<a class="reference external image-reference" href="https://tensorboard.dev/experiment/lzGnETjwRxC3yghNMd4kPw/"><img alt="TensorBoard screenshot" src="../../../_images/librispeech-lstm-transducer-tensorboard-log.png" style="width: 600px;" /></a>
 <figcaption>
-<p><span class="caption-number">Fig. 5 </span><span class="caption-text">TensorBoard screenshot.</span><a class="headerlink" href="#id3" title="Permalink to this image"></a></p>
+<p><span class="caption-number">Fig. 8 </span><span class="caption-text">TensorBoard screenshot.</span><a class="headerlink" href="#id3" title="Permalink to this image"></a></p>
 </figcaption>
 </figure>
 </div></blockquote>
@@ -701,8 +698,8 @@ for the details of the above pretrained models</p>
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recipes/Streaming-ASR/librispeech/pruned_transducer_stateless.html

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+             
+  <section id="pruned-transducer-statelessx">
+<h1>Pruned transducer statelessX<a class="headerlink" href="#pruned-transducer-statelessx" title="Permalink to this heading"></a></h1>
+<p>This tutorial shows you how to run a <strong>streaming</strong> conformer transducer model
+with the <a class="reference external" href="https://www.openslr.org/12">LibriSpeech</a> dataset.</p>
+<div class="admonition note">
+<p class="admonition-title">Note</p>
+<p>The tutorial is suitable for <a class="reference external" href="https://github.com/k2-fsa/icefall/tree/master/egs/librispeech/ASR/pruned_transducer_stateless">pruned_transducer_stateless</a>,
+<a class="reference external" href="https://github.com/k2-fsa/icefall/tree/master/egs/librispeech/ASR/pruned_transducer_stateless2">pruned_transducer_stateless2</a>,
+<a class="reference external" href="https://github.com/k2-fsa/icefall/tree/master/egs/librispeech/ASR/pruned_transducer_stateless4">pruned_transducer_stateless4</a>,
+<a class="reference external" href="https://github.com/k2-fsa/icefall/tree/master/egs/librispeech/ASR/pruned_transducer_stateless5">pruned_transducer_stateless5</a>,
+We will take pruned_transducer_stateless4 as an example in this tutorial.</p>
+</div>
+<div class="admonition hint">
+<p class="admonition-title">Hint</p>
+<p>We assume you have read the page <a class="reference internal" href="../../../installation/index.html#install-icefall"><span class="std std-ref">Installation</span></a> and have setup
+the environment for <code class="docutils literal notranslate"><span class="pre">icefall</span></code>.</p>
+</div>
+<div class="admonition hint">
+<p class="admonition-title">Hint</p>
+<p>We recommend you to use a GPU or several GPUs to run this recipe.</p>
+</div>
+<div class="admonition hint">
+<p class="admonition-title">Hint</p>
+<p>Please scroll down to the bottom of this page to find download links
+for pretrained models if you don’t want to train a model from scratch.</p>
+</div>
+<p>We use pruned RNN-T to compute the loss.</p>
+<div class="admonition note">
+<p class="admonition-title">Note</p>
+<p>You can find the paper about pruned RNN-T at the following address:</p>
+<p><a class="reference external" href="https://arxiv.org/abs/2206.13236">https://arxiv.org/abs/2206.13236</a></p>
+</div>
+<p>The transducer model consists of 3 parts:</p>
+<blockquote>
+<div><ul class="simple">
+<li><p>Encoder, a.k.a, the transcription network. We use a Conformer model (the reworked version by Daniel Povey)</p></li>
+<li><p>Decoder, a.k.a, the prediction network. We use a stateless model consisting of
+<code class="docutils literal notranslate"><span class="pre">nn.Embedding</span></code> and <code class="docutils literal notranslate"><span class="pre">nn.Conv1d</span></code></p></li>
+<li><p>Joiner, a.k.a, the joint network.</p></li>
+</ul>
+</div></blockquote>
+<div class="admonition caution">
+<p class="admonition-title">Caution</p>
+<p>Contrary to the conventional RNN-T models, we use a stateless decoder.
+That is, it has no recurrent connections.</p>
+</div>
+<section id="data-preparation">
+<h2>Data preparation<a class="headerlink" href="#data-preparation" title="Permalink to this heading"></a></h2>
+<div class="admonition hint">
+<p class="admonition-title">Hint</p>
+<p>The data preparation is the same as other recipes on LibriSpeech dataset,
+if you have finished this step, you can skip to <code class="docutils literal notranslate"><span class="pre">Training</span></code> directly.</p>
+</div>
+<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>$ <span class="nb">cd</span> egs/librispeech/ASR
+$ ./prepare.sh
+</pre></div>
+</div>
+<p>The script <code class="docutils literal notranslate"><span class="pre">./prepare.sh</span></code> handles the data preparation for you, <strong>automagically</strong>.
+All you need to do is to run it.</p>
+<p>The data preparation contains several stages, you can use the following two
+options:</p>
+<blockquote>
+<div><ul class="simple">
+<li><p><code class="docutils literal notranslate"><span class="pre">--stage</span></code></p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">--stop-stage</span></code></p></li>
+</ul>
+</div></blockquote>
+<p>to control which stage(s) should be run. By default, all stages are executed.</p>
+<p>For example,</p>
+<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>$ <span class="nb">cd</span> egs/librispeech/ASR
+$ ./prepare.sh --stage <span class="m">0</span> --stop-stage <span class="m">0</span>
+</pre></div>
+</div>
+<p>means to run only stage 0.</p>
+<p>To run stage 2 to stage 5, use:</p>
+<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>$ ./prepare.sh --stage <span class="m">2</span> --stop-stage <span class="m">5</span>
+</pre></div>
+</div>
+<div class="admonition hint">
+<p class="admonition-title">Hint</p>
+<p>If you have pre-downloaded the <a class="reference external" href="https://www.openslr.org/12">LibriSpeech</a>
+dataset and the <a class="reference external" href="http://www.openslr.org/17/">musan</a> dataset, say,
+they are saved in <code class="docutils literal notranslate"><span class="pre">/tmp/LibriSpeech</span></code> and <code class="docutils literal notranslate"><span class="pre">/tmp/musan</span></code>, you can modify
+the <code class="docutils literal notranslate"><span class="pre">dl_dir</span></code> variable in <code class="docutils literal notranslate"><span class="pre">./prepare.sh</span></code> to point to <code class="docutils literal notranslate"><span class="pre">/tmp</span></code> so that
+<code class="docutils literal notranslate"><span class="pre">./prepare.sh</span></code> won’t re-download them.</p>
+</div>
+<div class="admonition note">
+<p class="admonition-title">Note</p>
+<p>All generated files by <code class="docutils literal notranslate"><span class="pre">./prepare.sh</span></code>, e.g., features, lexicon, etc,
+are saved in <code class="docutils literal notranslate"><span class="pre">./data</span></code> directory.</p>
+</div>
+<p>We provide the following YouTube video showing how to run <code class="docutils literal notranslate"><span class="pre">./prepare.sh</span></code>.</p>
+<div class="admonition note">
+<p class="admonition-title">Note</p>
+<p>To get the latest news of <a class="reference external" href="https://github.com/k2-fsa">next-gen Kaldi</a>, please subscribe
+the following YouTube channel by <a class="reference external" href="https://www.youtube.com/channel/UC_VaumpkmINz1pNkFXAN9mw">Nadira Povey</a>:</p>
+<blockquote>
+<div><p><a class="reference external" href="https://www.youtube.com/channel/UC_VaumpkmINz1pNkFXAN9mw">https://www.youtube.com/channel/UC_VaumpkmINz1pNkFXAN9mw</a></p>
+</div></blockquote>
+</div>
+<div class="video_wrapper" style="">
+<iframe allowfullscreen="true" src="https://www.youtube.com/embed/ofEIoJL-mGM" style="border: 0; height: 345px; width: 560px">
+</iframe></div></section>
+<section id="training">
+<h2>Training<a class="headerlink" href="#training" title="Permalink to this heading"></a></h2>
+<div class="admonition note">
+<p class="admonition-title">Note</p>
+<p>We put the streaming and non-streaming model in one recipe, to train a streaming model you only
+need to add <strong>4</strong> extra options comparing with training a non-streaming model. These options are
+<code class="docutils literal notranslate"><span class="pre">--dynamic-chunk-training</span></code>, <code class="docutils literal notranslate"><span class="pre">--num-left-chunks</span></code>, <code class="docutils literal notranslate"><span class="pre">--causal-convolution</span></code>, <code class="docutils literal notranslate"><span class="pre">--short-chunk-size</span></code>.
+You can see the configurable options below for their meanings or read <a class="reference external" href="https://arxiv.org/pdf/2012.05481.pdf">https://arxiv.org/pdf/2012.05481.pdf</a> for more details.</p>
+</div>
+<section id="configurable-options">
+<h3>Configurable options<a class="headerlink" href="#configurable-options" title="Permalink to this heading"></a></h3>
+<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>$ <span class="nb">cd</span> egs/librispeech/ASR
+$ ./pruned_transducer_stateless4/train.py --help
+</pre></div>
+</div>
+<p>shows you the training options that can be passed from the commandline.
+The following options are used quite often:</p>
+<blockquote>
+<div><ul>
+<li><p><code class="docutils literal notranslate"><span class="pre">--exp-dir</span></code></p>
+<p>The directory to save checkpoints, training logs and tensorboard.</p>
+</li>
+<li><p><code class="docutils literal notranslate"><span class="pre">--full-libri</span></code></p>
+<p>If it’s True, the training part uses all the training data, i.e.,
+960 hours. Otherwise, the training part uses only the subset
+<code class="docutils literal notranslate"><span class="pre">train-clean-100</span></code>, which has 100 hours of training data.</p>
+<div class="admonition caution">
+<p class="admonition-title">Caution</p>
+<p>The training set is perturbed by speed with two factors: 0.9 and 1.1.
+If <code class="docutils literal notranslate"><span class="pre">--full-libri</span></code> is True, each epoch actually processes
+<code class="docutils literal notranslate"><span class="pre">3x960</span> <span class="pre">==</span> <span class="pre">2880</span></code> hours of data.</p>
+</div>
+</li>
+<li><p><code class="docutils literal notranslate"><span class="pre">--num-epochs</span></code></p>
+<p>It is the number of epochs to train. For instance,
+<code class="docutils literal notranslate"><span class="pre">./pruned_transducer_stateless4/train.py</span> <span class="pre">--num-epochs</span> <span class="pre">30</span></code> trains for 30 epochs
+and generates <code class="docutils literal notranslate"><span class="pre">epoch-1.pt</span></code>, <code class="docutils literal notranslate"><span class="pre">epoch-2.pt</span></code>, …, <code class="docutils literal notranslate"><span class="pre">epoch-30.pt</span></code>
+in the folder <code class="docutils literal notranslate"><span class="pre">./pruned_transducer_stateless4/exp</span></code>.</p>
+</li>
+<li><p><code class="docutils literal notranslate"><span class="pre">--start-epoch</span></code></p>
+<p>It’s used to resume training.
+<code class="docutils literal notranslate"><span class="pre">./pruned_transducer_stateless4/train.py</span> <span class="pre">--start-epoch</span> <span class="pre">10</span></code> loads the
+checkpoint <code class="docutils literal notranslate"><span class="pre">./pruned_transducer_stateless4/exp/epoch-9.pt</span></code> and starts
+training from epoch 10, based on the state from epoch 9.</p>
+</li>
+<li><p><code class="docutils literal notranslate"><span class="pre">--world-size</span></code></p>
+<p>It is used for multi-GPU single-machine DDP training.</p>
+<blockquote>
+<div><ul class="simple">
+<li><ol class="loweralpha simple">
+<li><p>If it is 1, then no DDP training is used.</p></li>
+</ol>
+</li>
+<li><ol class="loweralpha simple" start="2">
+<li><p>If it is 2, then GPU 0 and GPU 1 are used for DDP training.</p></li>
+</ol>
+</li>
+</ul>
+</div></blockquote>
+<p>The following shows some use cases with it.</p>
+<blockquote>
+<div><p><strong>Use case 1</strong>: You have 4 GPUs, but you only want to use GPU 0 and
+GPU 2 for training. You can do the following:</p>
+<blockquote>
+<div><div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>$ <span class="nb">cd</span> egs/librispeech/ASR
+$ <span class="nb">export</span> <span class="nv">CUDA_VISIBLE_DEVICES</span><span class="o">=</span><span class="s2">&quot;0,2&quot;</span>
+$ ./pruned_transducer_stateless4/train.py --world-size <span class="m">2</span>
+</pre></div>
+</div>
+</div></blockquote>
+<p><strong>Use case 2</strong>: You have 4 GPUs and you want to use all of them
+for training. You can do the following:</p>
+<blockquote>
+<div><div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>$ <span class="nb">cd</span> egs/librispeech/ASR
+$ ./pruned_transducer_stateless4/train.py --world-size <span class="m">4</span>
+</pre></div>
+</div>
+</div></blockquote>
+<p><strong>Use case 3</strong>: You have 4 GPUs but you only want to use GPU 3
+for training. You can do the following:</p>
+<blockquote>
+<div><div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>$ <span class="nb">cd</span> egs/librispeech/ASR
+$ <span class="nb">export</span> <span class="nv">CUDA_VISIBLE_DEVICES</span><span class="o">=</span><span class="s2">&quot;3&quot;</span>
+$ ./pruned_transducer_stateless4/train.py --world-size <span class="m">1</span>
+</pre></div>
+</div>
+</div></blockquote>
+</div></blockquote>
+<div class="admonition caution">
+<p class="admonition-title">Caution</p>
+<p>Only multi-GPU single-machine DDP training is implemented at present.
+Multi-GPU multi-machine DDP training will be added later.</p>
+</div>
+</li>
+<li><p><code class="docutils literal notranslate"><span class="pre">--max-duration</span></code></p>
+<p>It specifies the number of seconds over all utterances in a
+batch, before <strong>padding</strong>.
+If you encounter CUDA OOM, please reduce it.</p>
+<div class="admonition hint">
+<p class="admonition-title">Hint</p>
+<p>Due to padding, the number of seconds of all utterances in a
+batch will usually be larger than <code class="docutils literal notranslate"><span class="pre">--max-duration</span></code>.</p>
+<p>A larger value for <code class="docutils literal notranslate"><span class="pre">--max-duration</span></code> may cause OOM during training,
+while a smaller value may increase the training time. You have to
+tune it.</p>
+</div>
+</li>
+<li><p><code class="docutils literal notranslate"><span class="pre">--use-fp16</span></code></p>
+<p>If it is True, the model will train with half precision, from our experiment
+results, by using half precision you can train with two times larger <code class="docutils literal notranslate"><span class="pre">--max-duration</span></code>
+so as to get almost 2X speed up.</p>
+</li>
+<li><p><code class="docutils literal notranslate"><span class="pre">--dynamic-chunk-training</span></code></p>
+<p>The flag that indicates whether to train a streaming model or not, it
+<strong>MUST</strong> be True if you want to train a streaming model.</p>
+</li>
+<li><p><code class="docutils literal notranslate"><span class="pre">--short-chunk-size</span></code></p>
+<p>When training a streaming attention model with chunk masking, the chunk size
+would be either max sequence length of current batch or uniformly sampled from
+(1, short_chunk_size). The default value is 25, you don’t have to change it most of the time.</p>
+</li>
+<li><p><code class="docutils literal notranslate"><span class="pre">--num-left-chunks</span></code></p>
+<p>It indicates how many left context (in chunks) that can be seen when calculating attention.
+The default value is 4, you don’t have to change it most of the time.</p>
+</li>
+<li><p><code class="docutils literal notranslate"><span class="pre">--causal-convolution</span></code></p>
+<p>Whether to use causal convolution in conformer encoder layer, this requires
+to be True when training a streaming model.</p>
+</li>
+</ul>
+</div></blockquote>
+</section>
+<section id="pre-configured-options">
+<h3>Pre-configured options<a class="headerlink" href="#pre-configured-options" title="Permalink to this heading"></a></h3>
+<p>There are some training options, e.g., number of encoder layers,
+encoder dimension, decoder dimension, number of warmup steps etc,
+that are not passed from the commandline.
+They are pre-configured by the function <code class="docutils literal notranslate"><span class="pre">get_params()</span></code> in
+<a class="reference external" href="https://github.com/k2-fsa/icefall/blob/master/egs/librispeech/ASR/pruned_transducer_stateless4/train.py">pruned_transducer_stateless4/train.py</a></p>
+<p>You don’t need to change these pre-configured parameters. If you really need to change
+them, please modify <code class="docutils literal notranslate"><span class="pre">./pruned_transducer_stateless4/train.py</span></code> directly.</p>
+<div class="admonition note">
+<p class="admonition-title">Note</p>
+<p>The options for <a class="reference external" href="https://github.com/k2-fsa/icefall/blob/master/egs/librispeech/ASR/pruned_transducer_stateless5/train.py">pruned_transducer_stateless5</a> are a little different from
+other recipes. It allows you to configure <code class="docutils literal notranslate"><span class="pre">--num-encoder-layers</span></code>, <code class="docutils literal notranslate"><span class="pre">--dim-feedforward</span></code>, <code class="docutils literal notranslate"><span class="pre">--nhead</span></code>, <code class="docutils literal notranslate"><span class="pre">--encoder-dim</span></code>, <code class="docutils literal notranslate"><span class="pre">--decoder-dim</span></code>, <code class="docutils literal notranslate"><span class="pre">--joiner-dim</span></code> from commandline, so that you can train models with different size with pruned_transducer_stateless5.</p>
+</div>
+</section>
+<section id="training-logs">
+<h3>Training logs<a class="headerlink" href="#training-logs" title="Permalink to this heading"></a></h3>
+<p>Training logs and checkpoints are saved in <code class="docutils literal notranslate"><span class="pre">--exp-dir</span></code> (e.g. <code class="docutils literal notranslate"><span class="pre">pruned_transducer_stateless4/exp</span></code>.
+You will find the following files in that directory:</p>
+<blockquote>
+<div><ul>
+<li><p><code class="docutils literal notranslate"><span class="pre">epoch-1.pt</span></code>, <code class="docutils literal notranslate"><span class="pre">epoch-2.pt</span></code>, …</p>
+<p>These are checkpoint files saved at the end of each epoch, containing model
+<code class="docutils literal notranslate"><span class="pre">state_dict</span></code> and optimizer <code class="docutils literal notranslate"><span class="pre">state_dict</span></code>.
+To resume training from some checkpoint, say <code class="docutils literal notranslate"><span class="pre">epoch-10.pt</span></code>, you can use:</p>
+<blockquote>
+<div><div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>$ ./pruned_transducer_stateless4/train.py --start-epoch <span class="m">11</span>
+</pre></div>
+</div>
+</div></blockquote>
+</li>
+<li><p><code class="docutils literal notranslate"><span class="pre">checkpoint-436000.pt</span></code>, <code class="docutils literal notranslate"><span class="pre">checkpoint-438000.pt</span></code>, …</p>
+<p>These are checkpoint files saved every <code class="docutils literal notranslate"><span class="pre">--save-every-n</span></code> batches,
+containing model <code class="docutils literal notranslate"><span class="pre">state_dict</span></code> and optimizer <code class="docutils literal notranslate"><span class="pre">state_dict</span></code>.
+To resume training from some checkpoint, say <code class="docutils literal notranslate"><span class="pre">checkpoint-436000</span></code>, you can use:</p>
+<blockquote>
+<div><div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>$ ./pruned_transducer_stateless4/train.py --start-batch <span class="m">436000</span>
+</pre></div>
+</div>
+</div></blockquote>
+</li>
+<li><p><code class="docutils literal notranslate"><span class="pre">tensorboard/</span></code></p>
+<p>This folder contains tensorBoard logs. Training loss, validation loss, learning
+rate, etc, are recorded in these logs. You can visualize them by:</p>
+<blockquote>
+<div><div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>$ <span class="nb">cd</span> pruned_transducer_stateless4/exp/tensorboard
+$ tensorboard dev upload --logdir . --description <span class="s2">&quot;pruned transducer training for LibriSpeech with icefall&quot;</span>
+</pre></div>
+</div>
+</div></blockquote>
+<p>It will print something like below:</p>
+<blockquote>
+<div><div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">TensorFlow</span> <span class="n">installation</span> <span class="ow">not</span> <span class="n">found</span> <span class="o">-</span> <span class="n">running</span> <span class="k">with</span> <span class="n">reduced</span> <span class="n">feature</span> <span class="nb">set</span><span class="o">.</span>
+<span class="n">Upload</span> <span class="n">started</span> <span class="ow">and</span> <span class="n">will</span> <span class="k">continue</span> <span class="n">reading</span> <span class="nb">any</span> <span class="n">new</span> <span class="n">data</span> <span class="k">as</span> <span class="n">it</span><span class="s1">&#39;s added to the logdir.</span>
+
+<span class="n">To</span> <span class="n">stop</span> <span class="n">uploading</span><span class="p">,</span> <span class="n">press</span> <span class="n">Ctrl</span><span class="o">-</span><span class="n">C</span><span class="o">.</span>
+
+<span class="n">New</span> <span class="n">experiment</span> <span class="n">created</span><span class="o">.</span> <span class="n">View</span> <span class="n">your</span> <span class="n">TensorBoard</span> <span class="n">at</span><span class="p">:</span> <span class="n">https</span><span class="p">:</span><span class="o">//</span><span class="n">tensorboard</span><span class="o">.</span><span class="n">dev</span><span class="o">/</span><span class="n">experiment</span><span class="o">/</span><span class="mi">97</span><span class="n">VKXf80Ru61CnP2ALWZZg</span><span class="o">/</span>
+
+<span class="p">[</span><span class="mi">2022</span><span class="o">-</span><span class="mi">11</span><span class="o">-</span><span class="mi">20</span><span class="n">T15</span><span class="p">:</span><span class="mi">50</span><span class="p">:</span><span class="mi">50</span><span class="p">]</span> <span class="n">Started</span> <span class="n">scanning</span> <span class="n">logdir</span><span class="o">.</span>
+<span class="n">Uploading</span> <span class="mi">4468</span> <span class="n">scalars</span><span class="o">...</span>
+<span class="p">[</span><span class="mi">2022</span><span class="o">-</span><span class="mi">11</span><span class="o">-</span><span class="mi">20</span><span class="n">T15</span><span class="p">:</span><span class="mi">53</span><span class="p">:</span><span class="mi">02</span><span class="p">]</span> <span class="n">Total</span> <span class="n">uploaded</span><span class="p">:</span> <span class="mi">210171</span> <span class="n">scalars</span><span class="p">,</span> <span class="mi">0</span> <span class="n">tensors</span><span class="p">,</span> <span class="mi">0</span> <span class="n">binary</span> <span class="n">objects</span>
+<span class="n">Listening</span> <span class="k">for</span> <span class="n">new</span> <span class="n">data</span> <span class="ow">in</span> <span class="n">logdir</span><span class="o">...</span>
+</pre></div>
+</div>
+</div></blockquote>
+<p>Note there is a URL in the above output. Click it and you will see
+the following screenshot:</p>
+<blockquote>
+<div><figure class="align-center" id="id7">
+<a class="reference external image-reference" href="https://tensorboard.dev/experiment/97VKXf80Ru61CnP2ALWZZg/"><img alt="TensorBoard screenshot" src="../../../_images/streaming-librispeech-pruned-transducer-tensorboard-log.jpg" style="width: 600px;" /></a>
+<figcaption>
+<p><span class="caption-number">Fig. 7 </span><span class="caption-text">TensorBoard screenshot.</span><a class="headerlink" href="#id7" title="Permalink to this image"></a></p>
+</figcaption>
+</figure>
+</div></blockquote>
+</li>
+</ul>
+<div class="admonition hint">
+<p class="admonition-title">Hint</p>
+<p>If you don’t have access to google, you can use the following command
+to view the tensorboard log locally:</p>
+<blockquote>
+<div><div class="highlight-bash notranslate"><div class="highlight"><pre><span></span><span class="nb">cd</span> pruned_transducer_stateless4/exp/tensorboard
+tensorboard --logdir . --port <span class="m">6008</span>
+</pre></div>
+</div>
+</div></blockquote>
+<p>It will print the following message:</p>
+<blockquote>
+<div><div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">Serving</span> <span class="n">TensorBoard</span> <span class="n">on</span> <span class="n">localhost</span><span class="p">;</span> <span class="n">to</span> <span class="n">expose</span> <span class="n">to</span> <span class="n">the</span> <span class="n">network</span><span class="p">,</span> <span class="n">use</span> <span class="n">a</span> <span class="n">proxy</span> <span class="ow">or</span> <span class="k">pass</span> <span class="o">--</span><span class="n">bind_all</span>
+<span class="n">TensorBoard</span> <span class="mf">2.8.0</span> <span class="n">at</span> <span class="n">http</span><span class="p">:</span><span class="o">//</span><span class="n">localhost</span><span class="p">:</span><span class="mi">6008</span><span class="o">/</span> <span class="p">(</span><span class="n">Press</span> <span class="n">CTRL</span><span class="o">+</span><span class="n">C</span> <span class="n">to</span> <span class="n">quit</span><span class="p">)</span>
+</pre></div>
+</div>
+</div></blockquote>
+<p>Now start your browser and go to <a class="reference external" href="http://localhost:6008">http://localhost:6008</a> to view the tensorboard
+logs.</p>
+</div>
+<ul>
+<li><p><code class="docutils literal notranslate"><span class="pre">log/log-train-xxxx</span></code></p>
+<p>It is the detailed training log in text format, same as the one
+you saw printed to the console during training.</p>
+</li>
+</ul>
+</div></blockquote>
+</section>
+<section id="usage-example">
+<h3>Usage example<a class="headerlink" href="#usage-example" title="Permalink to this heading"></a></h3>
+<p>You can use the following command to start the training using 4 GPUs:</p>
+<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span><span class="nb">export</span> <span class="nv">CUDA_VISIBLE_DEVICES</span><span class="o">=</span><span class="s2">&quot;0,1,2,3&quot;</span>
+./pruned_transducer_stateless4/train.py <span class="se">\</span>
+   --world-size <span class="m">4</span> <span class="se">\</span>
+   --dynamic-chunk-training <span class="m">1</span> <span class="se">\</span>
+   --causal-convolution <span class="m">1</span> <span class="se">\</span>
+   --num-epochs <span class="m">30</span> <span class="se">\</span>
+   --start-epoch <span class="m">1</span> <span class="se">\</span>
+   --exp-dir pruned_transducer_stateless4/exp <span class="se">\</span>
+   --full-libri <span class="m">1</span> <span class="se">\</span>
+   --max-duration <span class="m">300</span>
+</pre></div>
+</div>
+<div class="admonition note">
+<p class="admonition-title">Note</p>
+<p>Comparing with training a non-streaming model, you only need to add two extra options,
+<code class="docutils literal notranslate"><span class="pre">--dynamic-chunk-training</span> <span class="pre">1</span></code>  and <code class="docutils literal notranslate"><span class="pre">--causal-convolution</span> <span class="pre">1</span></code> .</p>
+</div>
+</section>
+</section>
+<section id="decoding">
+<h2>Decoding<a class="headerlink" href="#decoding" title="Permalink to this heading"></a></h2>
+<p>The decoding part uses checkpoints saved by the training part, so you have
+to run the training part first.</p>
+<div class="admonition hint">
+<p class="admonition-title">Hint</p>
+<p>There are two kinds of checkpoints:</p>
+<blockquote>
+<div><ul class="simple">
+<li><p>(1) <code class="docutils literal notranslate"><span class="pre">epoch-1.pt</span></code>, <code class="docutils literal notranslate"><span class="pre">epoch-2.pt</span></code>, …, which are saved at the end
+of each epoch. You can pass <code class="docutils literal notranslate"><span class="pre">--epoch</span></code> to
+<code class="docutils literal notranslate"><span class="pre">pruned_transducer_stateless4/decode.py</span></code> to use them.</p></li>
+<li><p>(2) <code class="docutils literal notranslate"><span class="pre">checkpoints-436000.pt</span></code>, <code class="docutils literal notranslate"><span class="pre">epoch-438000.pt</span></code>, …, which are saved
+every <code class="docutils literal notranslate"><span class="pre">--save-every-n</span></code> batches. You can pass <code class="docutils literal notranslate"><span class="pre">--iter</span></code> to
+<code class="docutils literal notranslate"><span class="pre">pruned_transducer_stateless4/decode.py</span></code> to use them.</p></li>
+</ul>
+<p>We suggest that you try both types of checkpoints and choose the one
+that produces the lowest WERs.</p>
+</div></blockquote>
+</div>
+<div class="admonition tip">
+<p class="admonition-title">Tip</p>
+<p>To decode a streaming model, you can use either <code class="docutils literal notranslate"><span class="pre">simulate</span> <span class="pre">streaming</span> <span class="pre">decoding</span></code> in <code class="docutils literal notranslate"><span class="pre">decode.py</span></code> or
+<code class="docutils literal notranslate"><span class="pre">real</span> <span class="pre">streaming</span> <span class="pre">decoding</span></code> in <code class="docutils literal notranslate"><span class="pre">streaming_decode.py</span></code>, the difference between <code class="docutils literal notranslate"><span class="pre">decode.py</span></code> and
+<code class="docutils literal notranslate"><span class="pre">streaming_decode.py</span></code> is that, <code class="docutils literal notranslate"><span class="pre">decode.py</span></code> processes the whole acoustic frames at one time with masking (i.e. same as training),
+but <code class="docutils literal notranslate"><span class="pre">streaming_decode.py</span></code> processes the acoustic frames chunk by chunk (so it can only see limited context).</p>
+</div>
+<div class="admonition note">
+<p class="admonition-title">Note</p>
+<p><code class="docutils literal notranslate"><span class="pre">simulate</span> <span class="pre">streaming</span> <span class="pre">decoding</span></code> in <code class="docutils literal notranslate"><span class="pre">decode.py</span></code> and <code class="docutils literal notranslate"><span class="pre">real</span> <span class="pre">streaming</span> <span class="pre">decoding</span></code> in <code class="docutils literal notranslate"><span class="pre">streaming_decode.py</span></code> should
+produce almost the same results given the same <code class="docutils literal notranslate"><span class="pre">--decode-chunk-size</span></code> and <code class="docutils literal notranslate"><span class="pre">--left-context</span></code>.</p>
+</div>
+<section id="simulate-streaming-decoding">
+<h3>Simulate streaming decoding<a class="headerlink" href="#simulate-streaming-decoding" title="Permalink to this heading"></a></h3>
+<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>$ <span class="nb">cd</span> egs/librispeech/ASR
+$ ./pruned_transducer_stateless4/decode.py --help
+</pre></div>
+</div>
+<p>shows the options for decoding.
+The following options are important for streaming models:</p>
+<blockquote>
+<div><p><code class="docutils literal notranslate"><span class="pre">--simulate-streaming</span></code></p>
+<blockquote>
+<div><p>If you want to decode a streaming model with <code class="docutils literal notranslate"><span class="pre">decode.py</span></code>, you <strong>MUST</strong> set
+<code class="docutils literal notranslate"><span class="pre">--simulate-streaming</span></code> to <code class="docutils literal notranslate"><span class="pre">True</span></code>. <code class="docutils literal notranslate"><span class="pre">simulate</span></code> here means the acoustic frames
+are not processed frame by frame (or chunk by chunk), instead, the whole sequence
+is processed at one time with masking (the same as training).</p>
+</div></blockquote>
+<p><code class="docutils literal notranslate"><span class="pre">--causal-convolution</span></code></p>
+<blockquote>
+<div><p>If True, the convolution module in encoder layers will be causal convolution.
+This is <strong>MUST</strong> be True when decoding with a streaming model.</p>
+</div></blockquote>
+<p><code class="docutils literal notranslate"><span class="pre">--decode-chunk-size</span></code></p>
+<blockquote>
+<div><p>For streaming models, we will calculate the chunk-wise attention, <code class="docutils literal notranslate"><span class="pre">--decode-chunk-size</span></code>
+indicates the chunk length (in frames after subsampling) for chunk-wise attention.
+For <code class="docutils literal notranslate"><span class="pre">simulate</span> <span class="pre">streaming</span> <span class="pre">decoding</span></code> the <code class="docutils literal notranslate"><span class="pre">decode-chunk-size</span></code> is used to generate
+the attention mask.</p>
+</div></blockquote>
+<p><code class="docutils literal notranslate"><span class="pre">--left-context</span></code></p>
+<blockquote>
+<div><p><code class="docutils literal notranslate"><span class="pre">--left-context</span></code> indicates how many left context frames (after subsampling) can be seen
+for current chunk when calculating chunk-wise attention. Normally, <code class="docutils literal notranslate"><span class="pre">left-context</span></code> should equal
+to <code class="docutils literal notranslate"><span class="pre">decode-chunk-size</span> <span class="pre">*</span> <span class="pre">num-left-chunks</span></code>, where <code class="docutils literal notranslate"><span class="pre">num-left-chunks</span></code> is the option used
+to train this model. For <code class="docutils literal notranslate"><span class="pre">simulate</span> <span class="pre">streaming</span> <span class="pre">decoding</span></code> the <code class="docutils literal notranslate"><span class="pre">left-context</span></code> is used to generate
+the attention mask.</p>
+</div></blockquote>
+</div></blockquote>
+<p>The following shows two examples (for the two types of checkpoints):</p>
+<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span><span class="k">for</span> m <span class="k">in</span> greedy_search fast_beam_search modified_beam_search<span class="p">;</span> <span class="k">do</span>
+  <span class="k">for</span> epoch <span class="k">in</span> <span class="m">25</span> <span class="m">20</span><span class="p">;</span> <span class="k">do</span>
+    <span class="k">for</span> avg <span class="k">in</span> <span class="m">7</span> <span class="m">5</span> <span class="m">3</span> <span class="m">1</span><span class="p">;</span> <span class="k">do</span>
+      ./pruned_transducer_stateless4/decode.py <span class="se">\</span>
+        --epoch <span class="nv">$epoch</span> <span class="se">\</span>
+        --avg <span class="nv">$avg</span> <span class="se">\</span>
+        --simulate-streaming <span class="m">1</span> <span class="se">\</span>
+        --causal-convolution <span class="m">1</span> <span class="se">\</span>
+        --decode-chunk-size <span class="m">16</span> <span class="se">\</span>
+        --left-context <span class="m">64</span> <span class="se">\</span>
+        --exp-dir pruned_transducer_stateless4/exp <span class="se">\</span>
+        --max-duration <span class="m">600</span> <span class="se">\</span>
+        --decoding-method <span class="nv">$m</span>
+    <span class="k">done</span>
+  <span class="k">done</span>
+<span class="k">done</span>
+</pre></div>
+</div>
+<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span><span class="k">for</span> m <span class="k">in</span> greedy_search fast_beam_search modified_beam_search<span class="p">;</span> <span class="k">do</span>
+  <span class="k">for</span> iter <span class="k">in</span> <span class="m">474000</span><span class="p">;</span> <span class="k">do</span>
+    <span class="k">for</span> avg <span class="k">in</span> <span class="m">8</span> <span class="m">10</span> <span class="m">12</span> <span class="m">14</span> <span class="m">16</span> <span class="m">18</span><span class="p">;</span> <span class="k">do</span>
+      ./pruned_transducer_stateless4/decode.py <span class="se">\</span>
+        --iter <span class="nv">$iter</span> <span class="se">\</span>
+        --avg <span class="nv">$avg</span> <span class="se">\</span>
+        --simulate-streaming <span class="m">1</span> <span class="se">\</span>
+        --causal-convolution <span class="m">1</span> <span class="se">\</span>
+        --decode-chunk-size <span class="m">16</span> <span class="se">\</span>
+        --left-context <span class="m">64</span> <span class="se">\</span>
+        --exp-dir pruned_transducer_stateless4/exp <span class="se">\</span>
+        --max-duration <span class="m">600</span> <span class="se">\</span>
+        --decoding-method <span class="nv">$m</span>
+    <span class="k">done</span>
+  <span class="k">done</span>
+<span class="k">done</span>
+</pre></div>
+</div>
+</section>
+<section id="real-streaming-decoding">
+<h3>Real streaming decoding<a class="headerlink" href="#real-streaming-decoding" title="Permalink to this heading"></a></h3>
+<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>$ <span class="nb">cd</span> egs/librispeech/ASR
+$ ./pruned_transducer_stateless4/streaming_decode.py --help
+</pre></div>
+</div>
+<p>shows the options for decoding.
+The following options are important for streaming models:</p>
+<blockquote>
+<div><p><code class="docutils literal notranslate"><span class="pre">--decode-chunk-size</span></code></p>
+<blockquote>
+<div><p>For streaming models, we will calculate the chunk-wise attention, <code class="docutils literal notranslate"><span class="pre">--decode-chunk-size</span></code>
+indicates the chunk length (in frames after subsampling) for chunk-wise attention.
+For <code class="docutils literal notranslate"><span class="pre">real</span> <span class="pre">streaming</span> <span class="pre">decoding</span></code>, we will process <code class="docutils literal notranslate"><span class="pre">decode-chunk-size</span></code> acoustic frames at each time.</p>
+</div></blockquote>
+<p><code class="docutils literal notranslate"><span class="pre">--left-context</span></code></p>
+<blockquote>
+<div><p><code class="docutils literal notranslate"><span class="pre">--left-context</span></code> indicates how many left context frames (after subsampling) can be seen
+for current chunk when calculating chunk-wise attention. Normally, <code class="docutils literal notranslate"><span class="pre">left-context</span></code> should equal
+to <code class="docutils literal notranslate"><span class="pre">decode-chunk-size</span> <span class="pre">*</span> <span class="pre">num-left-chunks</span></code>, where <code class="docutils literal notranslate"><span class="pre">num-left-chunks</span></code> is the option used
+to train this model.</p>
+</div></blockquote>
+<p><code class="docutils literal notranslate"><span class="pre">--num-decode-streams</span></code></p>
+<blockquote>
+<div><p>The number of decoding streams that can be run in parallel (very similar to the <code class="docutils literal notranslate"><span class="pre">bath</span> <span class="pre">size</span></code>).
+For <code class="docutils literal notranslate"><span class="pre">real</span> <span class="pre">streaming</span> <span class="pre">decoding</span></code>, the batches will be packed dynamically, for example, if the
+<code class="docutils literal notranslate"><span class="pre">num-decode-streams</span></code> equals to 10, then, sequence 1 to 10 will be decoded at first, after a while,
+suppose sequence 1 and 2 are done, so, sequence 3 to 12 will be processed parallelly in a batch.</p>
+</div></blockquote>
+</div></blockquote>
+<div class="admonition note">
+<p class="admonition-title">Note</p>
+<p>We also try adding <code class="docutils literal notranslate"><span class="pre">--right-context</span></code> in the real streaming decoding, but it seems not to benefit
+the performance for all the models, the reasons might be the training and decoding mismatch. You
+can try decoding with <code class="docutils literal notranslate"><span class="pre">--right-context</span></code> to see if it helps. The default value is 0.</p>
+</div>
+<p>The following shows two examples (for the two types of checkpoints):</p>
+<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span><span class="k">for</span> m <span class="k">in</span> greedy_search fast_beam_search modified_beam_search<span class="p">;</span> <span class="k">do</span>
+  <span class="k">for</span> epoch <span class="k">in</span> <span class="m">25</span> <span class="m">20</span><span class="p">;</span> <span class="k">do</span>
+    <span class="k">for</span> avg <span class="k">in</span> <span class="m">7</span> <span class="m">5</span> <span class="m">3</span> <span class="m">1</span><span class="p">;</span> <span class="k">do</span>
+      ./pruned_transducer_stateless4/decode.py <span class="se">\</span>
+        --epoch <span class="nv">$epoch</span> <span class="se">\</span>
+        --avg <span class="nv">$avg</span> <span class="se">\</span>
+        --decode-chunk-size <span class="m">16</span> <span class="se">\</span>
+        --left-context <span class="m">64</span> <span class="se">\</span>
+        --num-decode-streams <span class="m">100</span> <span class="se">\</span>
+        --exp-dir pruned_transducer_stateless4/exp <span class="se">\</span>
+        --max-duration <span class="m">600</span> <span class="se">\</span>
+        --decoding-method <span class="nv">$m</span>
+    <span class="k">done</span>
+  <span class="k">done</span>
+<span class="k">done</span>
+</pre></div>
+</div>
+<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span><span class="k">for</span> m <span class="k">in</span> greedy_search fast_beam_search modified_beam_search<span class="p">;</span> <span class="k">do</span>
+  <span class="k">for</span> iter <span class="k">in</span> <span class="m">474000</span><span class="p">;</span> <span class="k">do</span>
+    <span class="k">for</span> avg <span class="k">in</span> <span class="m">8</span> <span class="m">10</span> <span class="m">12</span> <span class="m">14</span> <span class="m">16</span> <span class="m">18</span><span class="p">;</span> <span class="k">do</span>
+      ./pruned_transducer_stateless4/decode.py <span class="se">\</span>
+        --iter <span class="nv">$iter</span> <span class="se">\</span>
+        --avg <span class="nv">$avg</span> <span class="se">\</span>
+        --decode-chunk-size <span class="m">16</span> <span class="se">\</span>
+        --left-context <span class="m">64</span> <span class="se">\</span>
+        --num-decode-streams <span class="m">100</span> <span class="se">\</span>
+        --exp-dir pruned_transducer_stateless4/exp <span class="se">\</span>
+        --max-duration <span class="m">600</span> <span class="se">\</span>
+        --decoding-method <span class="nv">$m</span>
+    <span class="k">done</span>
+  <span class="k">done</span>
+<span class="k">done</span>
+</pre></div>
+</div>
+<div class="admonition tip">
+<p class="admonition-title">Tip</p>
+<p>Supporting decoding methods are as follows:</p>
+<blockquote>
+<div><ul class="simple">
+<li><p><code class="docutils literal notranslate"><span class="pre">greedy_search</span></code> : It takes the symbol with largest posterior probability
+of each frame as the decoding result.</p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">beam_search</span></code> :  It implements Algorithm 1 in <a class="reference external" href="https://arxiv.org/pdf/1211.3711.pdf">https://arxiv.org/pdf/1211.3711.pdf</a> and
+<a class="reference external" href="https://github.com/espnet/espnet/blob/master/espnet/nets/beam_search_transducer.py#L247">espnet/nets/beam_search_transducer.py</a>
+is used as a reference. Basicly, it keeps topk states for each frame, and expands the kept states with their own contexts to
+next frame.</p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">modified_beam_search</span></code> : It implements the same algorithm as <code class="docutils literal notranslate"><span class="pre">beam_search</span></code> above, but it
+runs in batch mode with <code class="docutils literal notranslate"><span class="pre">--max-sym-per-frame=1</span></code> being hardcoded.</p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">fast_beam_search</span></code> : It implements graph composition between the output <code class="docutils literal notranslate"><span class="pre">log_probs</span></code> and
+given <code class="docutils literal notranslate"><span class="pre">FSAs</span></code>. It is hard to describe the details in several lines of texts, you can read
+our paper in <a class="reference external" href="https://arxiv.org/pdf/2211.00484.pdf">https://arxiv.org/pdf/2211.00484.pdf</a> or our <a class="reference external" href="https://github.com/k2-fsa/k2/blob/master/k2/csrc/rnnt_decode.h">rnnt decode code in k2</a>. <code class="docutils literal notranslate"><span class="pre">fast_beam_search</span></code> can decode with <code class="docutils literal notranslate"><span class="pre">FSAs</span></code> on GPU efficiently.</p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">fast_beam_search_LG</span></code> : The same as <code class="docutils literal notranslate"><span class="pre">fast_beam_search</span></code> above, <code class="docutils literal notranslate"><span class="pre">fast_beam_search</span></code> uses
+an trivial graph that has only one state, while <code class="docutils literal notranslate"><span class="pre">fast_beam_search_LG</span></code> uses an LG graph
+(with N-gram LM).</p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">fast_beam_search_nbest</span></code> : It produces the decoding results as follows:</p>
+<ul>
+<li><ol class="arabic simple">
+<li><p>Use <code class="docutils literal notranslate"><span class="pre">fast_beam_search</span></code> to get a lattice</p></li>
+</ol>
+</li>
+<li><ol class="arabic simple" start="2">
+<li><p>Select <code class="docutils literal notranslate"><span class="pre">num_paths</span></code> paths from the lattice using <code class="docutils literal notranslate"><span class="pre">k2.random_paths()</span></code></p></li>
+</ol>
+</li>
+<li><ol class="arabic simple" start="3">
+<li><p>Unique the selected paths</p></li>
+</ol>
+</li>
+<li><ol class="arabic simple" start="4">
+<li><p>Intersect the selected paths with the lattice and compute the
+shortest path from the intersection result</p></li>
+</ol>
+</li>
+<li><ol class="arabic simple" start="5">
+<li><p>The path with the largest score is used as the decoding output.</p></li>
+</ol>
+</li>
+</ul>
+</li>
+<li><p><code class="docutils literal notranslate"><span class="pre">fast_beam_search_nbest_LG</span></code> : It implements same logic as <code class="docutils literal notranslate"><span class="pre">fast_beam_search_nbest</span></code>, the
+only difference is that it uses <code class="docutils literal notranslate"><span class="pre">fast_beam_search_LG</span></code> to generate the lattice.</p></li>
+</ul>
+</div></blockquote>
+</div>
+<div class="admonition note">
+<p class="admonition-title">Note</p>
+<p>The supporting decoding methods in <code class="docutils literal notranslate"><span class="pre">streaming_decode.py</span></code> might be less than that in <code class="docutils literal notranslate"><span class="pre">decode.py</span></code>, if needed,
+you can implement them by yourself or file a issue in <a class="reference external" href="https://github.com/k2-fsa/icefall/issues">icefall</a> .</p>
+</div>
+</section>
+</section>
+<section id="export-model">
+<h2>Export Model<a class="headerlink" href="#export-model" title="Permalink to this heading"></a></h2>
+<p><a class="reference external" href="https://github.com/k2-fsa/icefall/blob/master/egs/librispeech/ASR/pruned_transducer_stateless4/export.py">pruned_transducer_stateless4/export.py</a> supports exporting checkpoints from <code class="docutils literal notranslate"><span class="pre">pruned_transducer_stateless4/exp</span></code> in the following ways.</p>
+<section id="export-model-state-dict">
+<h3>Export <code class="docutils literal notranslate"><span class="pre">model.state_dict()</span></code><a class="headerlink" href="#export-model-state-dict" title="Permalink to this heading"></a></h3>
+<p>Checkpoints saved by <code class="docutils literal notranslate"><span class="pre">pruned_transducer_stateless4/train.py</span></code> also include
+<code class="docutils literal notranslate"><span class="pre">optimizer.state_dict()</span></code>. It is useful for resuming training. But after training,
+we are interested only in <code class="docutils literal notranslate"><span class="pre">model.state_dict()</span></code>. You can use the following
+command to extract <code class="docutils literal notranslate"><span class="pre">model.state_dict()</span></code>.</p>
+<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span><span class="c1"># Assume that --epoch 25 --avg 3 produces the smallest WER</span>
+<span class="c1"># (You can get such information after running ./pruned_transducer_stateless4/decode.py)</span>
+
+<span class="nv">epoch</span><span class="o">=</span><span class="m">25</span>
+<span class="nv">avg</span><span class="o">=</span><span class="m">3</span>
+
+./pruned_transducer_stateless4/export.py <span class="se">\</span>
+  --exp-dir ./pruned_transducer_stateless4/exp <span class="se">\</span>
+  --streaming-model <span class="m">1</span> <span class="se">\</span>
+  --causal-convolution <span class="m">1</span> <span class="se">\</span>
+  --bpe-model data/lang_bpe_500/bpe.model <span class="se">\</span>
+  --epoch <span class="nv">$epoch</span> <span class="se">\</span>
+  --avg  <span class="nv">$avg</span>
+</pre></div>
+</div>
+<div class="admonition caution">
+<p class="admonition-title">Caution</p>
+<p><code class="docutils literal notranslate"><span class="pre">--streaming-model</span></code> and <code class="docutils literal notranslate"><span class="pre">--causal-convolution</span></code> require to be True to export
+a streaming mdoel.</p>
+</div>
+<p>It will generate a file <code class="docutils literal notranslate"><span class="pre">./pruned_transducer_stateless4/exp/pretrained.pt</span></code>.</p>
+<div class="admonition hint">
+<p class="admonition-title">Hint</p>
+<p>To use the generated <code class="docutils literal notranslate"><span class="pre">pretrained.pt</span></code> for <code class="docutils literal notranslate"><span class="pre">pruned_transducer_stateless4/decode.py</span></code>,
+you can run:</p>
+<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span><span class="nb">cd</span> pruned_transducer_stateless4/exp
+ln -s pretrained.pt epoch-999.pt
+</pre></div>
+</div>
+<p>And then pass <code class="docutils literal notranslate"><span class="pre">--epoch</span> <span class="pre">999</span> <span class="pre">--avg</span> <span class="pre">1</span> <span class="pre">--use-averaged-model</span> <span class="pre">0</span></code> to
+<code class="docutils literal notranslate"><span class="pre">./pruned_transducer_stateless4/decode.py</span></code>.</p>
+</div>
+<p>To use the exported model with <code class="docutils literal notranslate"><span class="pre">./pruned_transducer_stateless4/pretrained.py</span></code>, you
+can run:</p>
+<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>./pruned_transducer_stateless4/pretrained.py <span class="se">\</span>
+  --checkpoint ./pruned_transducer_stateless4/exp/pretrained.pt <span class="se">\</span>
+  --simulate-streaming <span class="m">1</span> <span class="se">\</span>
+  --causal-convolution <span class="m">1</span> <span class="se">\</span>
+  --bpe-model ./data/lang_bpe_500/bpe.model <span class="se">\</span>
+  --method greedy_search <span class="se">\</span>
+  /path/to/foo.wav <span class="se">\</span>
+  /path/to/bar.wav
+</pre></div>
+</div>
+</section>
+<section id="export-model-using-torch-jit-script">
+<h3>Export model using <code class="docutils literal notranslate"><span class="pre">torch.jit.script()</span></code><a class="headerlink" href="#export-model-using-torch-jit-script" title="Permalink to this heading"></a></h3>
+<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>./pruned_transducer_stateless4/export.py <span class="se">\</span>
+  --exp-dir ./pruned_transducer_stateless4/exp <span class="se">\</span>
+  --streaming-model <span class="m">1</span> <span class="se">\</span>
+  --causal-convolution <span class="m">1</span> <span class="se">\</span>
+  --bpe-model data/lang_bpe_500/bpe.model <span class="se">\</span>
+  --epoch <span class="m">25</span> <span class="se">\</span>
+  --avg <span class="m">3</span> <span class="se">\</span>
+  --jit <span class="m">1</span>
+</pre></div>
+</div>
+<div class="admonition caution">
+<p class="admonition-title">Caution</p>
+<p><code class="docutils literal notranslate"><span class="pre">--streaming-model</span></code> and <code class="docutils literal notranslate"><span class="pre">--causal-convolution</span></code> require to be True to export
+a streaming mdoel.</p>
+</div>
+<p>It will generate a file <code class="docutils literal notranslate"><span class="pre">cpu_jit.pt</span></code> in the given <code class="docutils literal notranslate"><span class="pre">exp_dir</span></code>. You can later
+load it by <code class="docutils literal notranslate"><span class="pre">torch.jit.load(&quot;cpu_jit.pt&quot;)</span></code>.</p>
+<p>Note <code class="docutils literal notranslate"><span class="pre">cpu</span></code> in the name <code class="docutils literal notranslate"><span class="pre">cpu_jit.pt</span></code> means the parameters when loaded into Python
+are on CPU. You can use <code class="docutils literal notranslate"><span class="pre">to(&quot;cuda&quot;)</span></code> to move them to a CUDA device.</p>
+<div class="admonition note">
+<p class="admonition-title">Note</p>
+<p>You will need this <code class="docutils literal notranslate"><span class="pre">cpu_jit.pt</span></code> when deploying with Sherpa framework.</p>
+</div>
+</section>
+</section>
+<section id="download-pretrained-models">
+<h2>Download pretrained models<a class="headerlink" href="#download-pretrained-models" title="Permalink to this heading"></a></h2>
+<p>If you don’t want to train from scratch, you can download the pretrained models
+by visiting the following links:</p>
+<blockquote>
+<div><ul class="simple">
+<li><p><a class="reference external" href="https://huggingface.co/pkufool/icefall_librispeech_streaming_pruned_transducer_stateless_20220625">pruned_transducer_stateless</a></p></li>
+<li><p><a class="reference external" href="https://huggingface.co/pkufool/icefall_librispeech_streaming_pruned_transducer_stateless2_20220625">pruned_transducer_stateless2</a></p></li>
+<li><p><a class="reference external" href="https://huggingface.co/pkufool/icefall_librispeech_streaming_pruned_transducer_stateless4_20220625">pruned_transducer_stateless4</a></p></li>
+<li><p><a class="reference external" href="https://huggingface.co/pkufool/icefall_librispeech_streaming_pruned_transducer_stateless5_20220729">pruned_transducer_stateless5</a></p></li>
+</ul>
+<p>See <a class="reference external" href="https://github.com/k2-fsa/icefall/blob/master/egs/librispeech/ASR/RESULTS.md">https://github.com/k2-fsa/icefall/blob/master/egs/librispeech/ASR/RESULTS.md</a>
+for the details of the above pretrained models</p>
+</div></blockquote>
+</section>
+<section id="deploy-with-sherpa">
+<h2>Deploy with Sherpa<a class="headerlink" href="#deploy-with-sherpa" title="Permalink to this heading"></a></h2>
+<p>Please see <a class="reference external" href="https://k2-fsa.github.io/sherpa/python/streaming_asr/conformer/index.html#">https://k2-fsa.github.io/sherpa/python/streaming_asr/conformer/index.html#</a>
+for how to deploy the models in <code class="docutils literal notranslate"><span class="pre">sherpa</span></code>.</p>
+</section>
+</section>
+
+
+           </div>
+          </div>
+          <footer><div class="rst-footer-buttons" role="navigation" aria-label="Footer">
+        <a href="index.html" class="btn btn-neutral float-left" title="LibriSpeech" accesskey="p" rel="prev"><span class="fa fa-arrow-circle-left" aria-hidden="true"></span> Previous</a>
+        <a href="lstm_pruned_stateless_transducer.html" class="btn btn-neutral float-right" title="LSTM Transducer" accesskey="n" rel="next">Next <span class="fa fa-arrow-circle-right" aria-hidden="true"></span></a>
+    </div>
+
+  <hr/>
+
+  <div role="contentinfo">
+    <p>&#169; Copyright 2021, icefall development team.</p>
+  </div>
+
+  Built with <a href="https://www.sphinx-doc.org/">Sphinx</a> using a
+    <a href="https://github.com/readthedocs/sphinx_rtd_theme">theme</a>
+    provided by <a href="https://readthedocs.org">Read the Docs</a>.
+   
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+      jQuery(function () {
+          SphinxRtdTheme.Navigation.enable(true);
+      });
+  </script> 
+
+</body>
+</html>

recipes/index.html

@@ -20,7 +20,7 @@
     <script src="../_static/js/theme.js"></script>
     <link rel="index" title="Index" href="../genindex.html" />
     <link rel="search" title="Search" href="../search.html" />
-    <link rel="next" title="aishell" href="aishell/index.html" />
+    <link rel="next" title="Non Streaming ASR" href="Non-streaming-ASR/index.html" />
     <link rel="prev" title="Export to ncnn" href="../model-export/export-ncnn.html" /> 
 </head>
 
@@ -40,16 +40,18 @@
 </div>
         </div><div class="wy-menu wy-menu-vertical" data-spy="affix" role="navigation" aria-label="Navigation menu">
               <p class="caption" role="heading"><span class="caption-text">Contents:</span></p>
-<ul class="current">
+<ul>
 <li class="toctree-l1"><a class="reference internal" href="../installation/index.html">Installation</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../model-export/index.html">Model export</a></li>
+</ul>
+<ul class="current">
 <li class="toctree-l1 current"><a class="current reference internal" href="#">Recipes</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="aishell/index.html">aishell</a></li>
-<li class="toctree-l2"><a class="reference internal" href="librispeech/index.html">LibriSpeech</a></li>
-<li class="toctree-l2"><a class="reference internal" href="timit/index.html">TIMIT</a></li>
-<li class="toctree-l2"><a class="reference internal" href="yesno/index.html">YesNo</a></li>
+<li class="toctree-l2"><a class="reference internal" href="Non-streaming-ASR/index.html">Non Streaming ASR</a></li>
+<li class="toctree-l2"><a class="reference internal" href="Streaming-ASR/index.html">Streaming ASR</a></li>
 </ul>
 </li>
+</ul>
+<ul>
 <li class="toctree-l1"><a class="reference internal" href="../contributing/index.html">Contributing</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../huggingface/index.html">Huggingface</a></li>
 </ul>
@@ -86,26 +88,16 @@ Currently, only speech recognition recipes are provided.</p>
 <div class="toctree-wrapper compound">
 <p class="caption" role="heading"><span class="caption-text">Table of Contents</span></p>
 <ul>
-<li class="toctree-l1"><a class="reference internal" href="aishell/index.html">aishell</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="aishell/tdnn_lstm_ctc.html">TDNN-LSTM CTC</a></li>
-<li class="toctree-l2"><a class="reference internal" href="aishell/conformer_ctc.html">Conformer CTC</a></li>
-<li class="toctree-l2"><a class="reference internal" href="aishell/stateless_transducer.html">Stateless Transducer</a></li>
+<li class="toctree-l1"><a class="reference internal" href="Non-streaming-ASR/index.html">Non Streaming ASR</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="Non-streaming-ASR/aishell/index.html">aishell</a></li>
+<li class="toctree-l2"><a class="reference internal" href="Non-streaming-ASR/librispeech/index.html">LibriSpeech</a></li>
+<li class="toctree-l2"><a class="reference internal" href="Non-streaming-ASR/timit/index.html">TIMIT</a></li>
+<li class="toctree-l2"><a class="reference internal" href="Non-streaming-ASR/yesno/index.html">YesNo</a></li>
 </ul>
 </li>
-<li class="toctree-l1"><a class="reference internal" href="librispeech/index.html">LibriSpeech</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="librispeech/tdnn_lstm_ctc.html">TDNN-LSTM-CTC</a></li>
-<li class="toctree-l2"><a class="reference internal" href="librispeech/conformer_ctc.html">Conformer CTC</a></li>
-<li class="toctree-l2"><a class="reference internal" href="librispeech/lstm_pruned_stateless_transducer.html">LSTM Transducer</a></li>
-<li class="toctree-l2"><a class="reference internal" href="librispeech/zipformer_mmi.html">Zipformer MMI</a></li>
-</ul>
-</li>
-<li class="toctree-l1"><a class="reference internal" href="timit/index.html">TIMIT</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="timit/tdnn_ligru_ctc.html">TDNN-LiGRU-CTC</a></li>
-<li class="toctree-l2"><a class="reference internal" href="timit/tdnn_lstm_ctc.html">TDNN-LSTM-CTC</a></li>
-</ul>
-</li>
-<li class="toctree-l1"><a class="reference internal" href="yesno/index.html">YesNo</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="yesno/tdnn.html">TDNN-CTC</a></li>
+<li class="toctree-l1"><a class="reference internal" href="Streaming-ASR/index.html">Streaming ASR</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="Streaming-ASR/introduction.html">Introduction</a></li>
+<li class="toctree-l2"><a class="reference internal" href="Streaming-ASR/librispeech/index.html">LibriSpeech</a></li>
 </ul>
 </li>
 </ul>
@@ -117,7 +109,7 @@ Currently, only speech recognition recipes are provided.</p>
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           <footer><div class="rst-footer-buttons" role="navigation" aria-label="Footer">
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-        <a href="aishell/index.html" class="btn btn-neutral float-right" title="aishell" accesskey="n" rel="next">Next <span class="fa fa-arrow-circle-right" aria-hidden="true"></span></a>
+        <a href="Non-streaming-ASR/index.html" class="btn btn-neutral float-right" title="Non Streaming ASR" accesskey="n" rel="next">Next <span class="fa fa-arrow-circle-right" aria-hidden="true"></span></a>
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search.html

@@ -43,7 +43,11 @@
 <ul>
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+</ul>
+<ul>
 <li class="toctree-l1"><a class="reference internal" href="recipes/index.html">Recipes</a></li>
+</ul>
+<ul>
 <li class="toctree-l1"><a class="reference internal" href="contributing/index.html">Contributing</a></li>
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