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Minor fixes.
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@ -21,7 +21,7 @@ In this tutorial, you will learn:
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- (1) What does the transducer model look like
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- (1) What does the transducer model look like
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- (2) How to prepare data for training and decoding
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- (2) How to prepare data for training and decoding
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- (3) How to start the training, either with a single GPU or multiple GPUs
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- (3) How to start the training, either with a single GPU or with multiple GPUs
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- (4) How to do decoding after training, with greedy search, beam search and, **modified beam search**
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- (4) How to do decoding after training, with greedy search, beam search and, **modified beam search**
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- (5) How to use a pre-trained model provided by us to transcribe sound files
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- (5) How to use a pre-trained model provided by us to transcribe sound files
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@ -112,7 +112,7 @@ To prepare the data for training, please use the following commands:
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.. note::
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.. note::
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You can use ``./prepare.sh``, though it will generate FSTs that
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You can use ``./prepare.sh``, though it will generate FSTs that
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are not used in transducer traning.
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are not used in transducer training.
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When you finish running the script, you will get the following two folders:
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When you finish running the script, you will get the following two folders:
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@ -151,7 +151,7 @@ The following options are used quite often:
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- ``--world-size``
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- ``--world-size``
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It is used for multi-GPU single-machine DDP training.
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It is used for single-machine multi-GPU DDP training.
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- (a) If it is 1, then no DDP training is used.
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- (a) If it is 1, then no DDP training is used.
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@ -187,9 +187,9 @@ The following options are used quite often:
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.. CAUTION::
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.. CAUTION::
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Only multi-GPU single-machine DDP training is implemented at present.
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Only single-machine multi-GPU DDP training is implemented at present.
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There is an on-going PR `<https://github.com/k2-fsa/icefall/pull/63>`_
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There is an on-going PR `<https://github.com/k2-fsa/icefall/pull/63>`_
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that adds support for multi-GPU multi-machine DDP training.
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that adds support for multi-machine multi-GPU DDP training.
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- ``--max-duration``
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- ``--max-duration``
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@ -197,7 +197,7 @@ The following options are used quite often:
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batch **before padding**.
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batch **before padding**.
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If you encounter CUDA OOM, please reduce it. For instance, if
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If you encounter CUDA OOM, please reduce it. For instance, if
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your are using V100 NVIDIA GPU with 32 GB RAM, we recommend you
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your are using V100 NVIDIA GPU with 32 GB RAM, we recommend you
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to set it to ``300``.
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to set it to ``300`` when the vocabulary size is 500.
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.. HINT::
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.. HINT::
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@ -243,7 +243,7 @@ Training logs
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~~~~~~~~~~~~~
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~~~~~~~~~~~~~
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Training logs and checkpoints are saved in the folder set by ``--exp-dir``
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Training logs and checkpoints are saved in the folder set by ``--exp-dir``
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(default ``transducer_stateless_modified/exp``). You will find the following files in that directory:
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(defaults to ``transducer_stateless_modified/exp``). You will find the following files in that directory:
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- ``epoch-0.pt``, ``epoch-1.pt``, ...
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- ``epoch-0.pt``, ``epoch-1.pt``, ...
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@ -405,6 +405,16 @@ The commonly used options are:
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It has the same meaning as the one used in training. A larger
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It has the same meaning as the one used in training. A larger
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value may cause OOM.
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value may cause OOM.
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- ``--epoch``
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It specifies the checkpoint from which epoch that should be used for decoding.
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- ``--avg``
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It specifies the number of models to average. For instance, if it is 3 and if
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``--epoch=10``, then it averages the checkpoints ``epoch-8.pt``, ``epoch-9.pt``,
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and ``epoch-10.pt`` and the averaged checkpoint is used for decoding.
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After decoding, you can find the decoding logs and results in `exp_dir/log/<decoding_method>`, e.g.,
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After decoding, you can find the decoding logs and results in `exp_dir/log/<decoding_method>`, e.g.,
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``exp_dir/log/greedy_search``.
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``exp_dir/log/greedy_search``.
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