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180
_sources/for-dummies/data-preparation.rst.txt
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180
_sources/for-dummies/data-preparation.rst.txt
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.. _dummies_tutorial_data_preparation:
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Data Preparation
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================
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After :ref:`dummies_tutorial_environment_setup`, we can start preparing the
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data for training and decoding.
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The first step is to prepare the data for training. We have already provided
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`prepare.sh <https://github.com/k2-fsa/icefall/blob/master/egs/yesno/ASR/prepare.sh>`_
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that would prepare everything required for training.
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.. code-block::
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cd /tmp/icefall
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export PYTHONPATH=/tmp/icefall:$PYTHONPATH
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cd egs/yesno/ASR
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./prepare.sh
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Note that in each recipe from `icefall`_, there exists a file ``prepare.sh``,
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which you should run before you run anything else.
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That is all you need for data preparation.
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For the more curious
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--------------------
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If you are wondering how to prepare your own dataset, please refer to the following
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URLs for more details:
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- `<https://github.com/lhotse-speech/lhotse/tree/master/lhotse/recipes>`_
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It contains recipes for a variety of dataset. If you want to add your own
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dataset, please read recipes in this folder first.
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- `<https://github.com/lhotse-speech/lhotse/blob/master/lhotse/recipes/yesno.py>`_
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The `yesno`_ recipe in `lhotse`_.
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If you already have a `Kaldi`_ dataset directory, which contains files like
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``wav.scp``, ``feats.scp``, then you can refer to `<https://lhotse.readthedocs.io/en/latest/kaldi.html#example>`_.
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A quick look to the generated files
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-----------------------------------
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``./prepare.sh`` puts generated files into two directories:
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- ``download``
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- ``data``
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download
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^^^^^^^^
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The ``download`` directory contains downloaded dataset files:
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.. code-block:: bas
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tree -L 1 ./download/
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./download/
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|-- waves_yesno
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`-- waves_yesno.tar.gz
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.. hint::
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Please refer to `<https://github.com/lhotse-speech/lhotse/blob/master/lhotse/recipes/yesno.py#L41>`_
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for how the data is downloaded and extracted.
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data
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^^^^
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.. code-block:: bash
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tree ./data/
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./data/
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|-- fbank
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| |-- yesno_cuts_test.jsonl.gz
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| |-- yesno_cuts_train.jsonl.gz
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| |-- yesno_feats_test.lca
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| `-- yesno_feats_train.lca
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|-- lang_phone
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| |-- HLG.pt
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| |-- L.pt
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| |-- L_disambig.pt
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| |-- Linv.pt
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| |-- lexicon.txt
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| |-- lexicon_disambig.txt
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| |-- tokens.txt
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| `-- words.txt
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|-- lm
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| |-- G.arpa
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| `-- G.fst.txt
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`-- manifests
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|-- yesno_recordings_test.jsonl.gz
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|-- yesno_recordings_train.jsonl.gz
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|-- yesno_supervisions_test.jsonl.gz
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`-- yesno_supervisions_train.jsonl.gz
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4 directories, 18 files
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**data/manifests**:
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This directory contains manifests. They are used to generate files in
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``data/fbank``.
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To give you an idea of what it contains, we examine the first few lines of
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the manifests related to the ``train`` dataset.
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.. code-block:: bash
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cd data/manifests
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gunzip -c yesno_recordings_train.jsonl.gz | head -n 3
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The output is given below:
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.. code-block:: bash
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{"id": "0_0_0_0_1_1_1_1", "sources": [{"type": "file", "channels": [0], "source": "/tmp/icefall/egs/yesno/ASR/download/waves_yesno/0_0_0_0_1_1_1_1.wav"}], "sampling_rate": 8000, "num_samples": 50800, "duration": 6.35, "channel_ids": [0]}
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{"id": "0_0_0_1_0_1_1_0", "sources": [{"type": "file", "channels": [0], "source": "/tmp/icefall/egs/yesno/ASR/download/waves_yesno/0_0_0_1_0_1_1_0.wav"}], "sampling_rate": 8000, "num_samples": 48880, "duration": 6.11, "channel_ids": [0]}
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{"id": "0_0_1_0_0_1_1_0", "sources": [{"type": "file", "channels": [0], "source": "/tmp/icefall/egs/yesno/ASR/download/waves_yesno/0_0_1_0_0_1_1_0.wav"}], "sampling_rate": 8000, "num_samples": 48160, "duration": 6.02, "channel_ids": [0]}
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Please refer to `<https://github.com/lhotse-speech/lhotse/blob/master/lhotse/audio.py#L300>`_
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for the meaning of each field per line.
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.. code-block:: bash
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gunzip -c yesno_supervisions_train.jsonl.gz | head -n 3
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The output is given below:
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.. code-block:: bash
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{"id": "0_0_0_0_1_1_1_1", "recording_id": "0_0_0_0_1_1_1_1", "start": 0.0, "duration": 6.35, "channel": 0, "text": "NO NO NO NO YES YES YES YES", "language": "Hebrew"}
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{"id": "0_0_0_1_0_1_1_0", "recording_id": "0_0_0_1_0_1_1_0", "start": 0.0, "duration": 6.11, "channel": 0, "text": "NO NO NO YES NO YES YES NO", "language": "Hebrew"}
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{"id": "0_0_1_0_0_1_1_0", "recording_id": "0_0_1_0_0_1_1_0", "start": 0.0, "duration": 6.02, "channel": 0, "text": "NO NO YES NO NO YES YES NO", "language": "Hebrew"}
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|
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Please refer to `<https://github.com/lhotse-speech/lhotse/blob/master/lhotse/supervision.py#L510>`_
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for the meaning of each field per line.
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**data/fbank**:
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This directory contains everything from ``data/manifests``. Furthermore, it also contains features
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for training.
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``data/fbank/yesno_feats_train.lca`` contains the features for the train dataset.
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Features are compressed using `lilcom`_.
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``data/fbank/yesno_cuts_train.jsonl.gz`` stores the `CutSet <https://github.com/lhotse-speech/lhotse/blob/master/lhotse/cut/set.py#L72>`_,
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which stores `RecordingSet <https://github.com/lhotse-speech/lhotse/blob/master/lhotse/audio.py#L928>`_,
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`SupervisionSet <https://github.com/lhotse-speech/lhotse/blob/master/lhotse/supervision.py#L510>`_,
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and `FeatureSet <https://github.com/lhotse-speech/lhotse/blob/master/lhotse/features/base.py#L593>`_.
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To give you an idea about what it looks like, we can run the following command:
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.. code-block:: bash
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cd data/fbank
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gunzip -c yesno_cuts_train.jsonl.gz | head -n 3
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The output is given below:
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.. code-block:: bash
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{"id": "0_0_0_0_1_1_1_1-0", "start": 0, "duration": 6.35, "channel": 0, "supervisions": [{"id": "0_0_0_0_1_1_1_1", "recording_id": "0_0_0_0_1_1_1_1", "start": 0.0, "duration": 6.35, "channel": 0, "text": "NO NO NO NO YES YES YES YES", "language": "Hebrew"}], "features": {"type": "kaldi-fbank", "num_frames": 635, "num_features": 23, "frame_shift": 0.01, "sampling_rate": 8000, "start": 0, "duration": 6.35, "storage_type": "lilcom_chunky", "storage_path": "data/fbank/yesno_feats_train.lca", "storage_key": "0,13000,3570", "channels": 0}, "recording": {"id": "0_0_0_0_1_1_1_1", "sources": [{"type": "file", "channels": [0], "source": "/tmp/icefall/egs/yesno/ASR/download/waves_yesno/0_0_0_0_1_1_1_1.wav"}], "sampling_rate": 8000, "num_samples": 50800, "duration": 6.35, "channel_ids": [0]}, "type": "MonoCut"}
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{"id": "0_0_0_1_0_1_1_0-1", "start": 0, "duration": 6.11, "channel": 0, "supervisions": [{"id": "0_0_0_1_0_1_1_0", "recording_id": "0_0_0_1_0_1_1_0", "start": 0.0, "duration": 6.11, "channel": 0, "text": "NO NO NO YES NO YES YES NO", "language": "Hebrew"}], "features": {"type": "kaldi-fbank", "num_frames": 611, "num_features": 23, "frame_shift": 0.01, "sampling_rate": 8000, "start": 0, "duration": 6.11, "storage_type": "lilcom_chunky", "storage_path": "data/fbank/yesno_feats_train.lca", "storage_key": "16570,12964,2929", "channels": 0}, "recording": {"id": "0_0_0_1_0_1_1_0", "sources": [{"type": "file", "channels": [0], "source": "/tmp/icefall/egs/yesno/ASR/download/waves_yesno/0_0_0_1_0_1_1_0.wav"}], "sampling_rate": 8000, "num_samples": 48880, "duration": 6.11, "channel_ids": [0]}, "type": "MonoCut"}
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{"id": "0_0_1_0_0_1_1_0-2", "start": 0, "duration": 6.02, "channel": 0, "supervisions": [{"id": "0_0_1_0_0_1_1_0", "recording_id": "0_0_1_0_0_1_1_0", "start": 0.0, "duration": 6.02, "channel": 0, "text": "NO NO YES NO NO YES YES NO", "language": "Hebrew"}], "features": {"type": "kaldi-fbank", "num_frames": 602, "num_features": 23, "frame_shift": 0.01, "sampling_rate": 8000, "start": 0, "duration": 6.02, "storage_type": "lilcom_chunky", "storage_path": "data/fbank/yesno_feats_train.lca", "storage_key": "32463,12936,2696", "channels": 0}, "recording": {"id": "0_0_1_0_0_1_1_0", "sources": [{"type": "file", "channels": [0], "source": "/tmp/icefall/egs/yesno/ASR/download/waves_yesno/0_0_1_0_0_1_1_0.wav"}], "sampling_rate": 8000, "num_samples": 48160, "duration": 6.02, "channel_ids": [0]}, "type": "MonoCut"}
|
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|
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Note that ``yesno_cuts_train.jsonl.gz`` only stores the information about how to read the features.
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The actual features are stored separately in ``data/fbank/yesno_feats_train.lca``.
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**data/lang**:
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This directory contains the lexicon.
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|
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**data/lm**:
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This directory contains language models.
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39
_sources/for-dummies/decoding.rst.txt
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39
_sources/for-dummies/decoding.rst.txt
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.. _dummies_tutorial_decoding:
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Decoding
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========
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After :ref:`dummies_tutorial_training`, we can start decoding.
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|
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The command to start the decoding is quite simple:
|
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|
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.. code-block:: bash
|
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|
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cd /tmp/icefall
|
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export PYTHONPATH=/tmp/icefall:$PYTHONPATH
|
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cd egs/yesno/ASR
|
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|
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# We use CPU for decoding by setting the following environment variable
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export CUDA_VISIBLE_DEVICES=""
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|
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./tdnn/decode.py
|
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|
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The output logs are given below:
|
||||
|
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.. literalinclude:: ./code/decoding-yesno.txt
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|
||||
For the more curious
|
||||
--------------------
|
||||
|
||||
.. code-block:: bash
|
||||
|
||||
./tdnn/decode.py --help
|
||||
|
||||
will print the usage information about ``./tdnn/decode.py``. For instance, you
|
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can specify:
|
||||
|
||||
- ``--epoch`` to use which checkpoint for decoding
|
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- ``--avg`` to select how many checkpoints to use for model averaging
|
||||
|
||||
You usually try different combinations of ``--epoch`` and ``--avg`` and select
|
||||
one that leads to the lowest WER (`Word Error Rate <https://en.wikipedia.org/wiki/Word_error_rate>`_).
|
||||
121
_sources/for-dummies/environment-setup.rst.txt
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121
_sources/for-dummies/environment-setup.rst.txt
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.. _dummies_tutorial_environment_setup:
|
||||
|
||||
Environment setup
|
||||
=================
|
||||
|
||||
We will create an environment for `Next-gen Kaldi`_ that runs on ``CPU``
|
||||
in this tutorial.
|
||||
|
||||
.. note::
|
||||
|
||||
Since the `yesno`_ dataset used in this tutorial is very tiny, training on
|
||||
``CPU`` works very well for it.
|
||||
|
||||
If your dataset is very large, e.g., hundreds or thousands of hours of
|
||||
training data, please follow :ref:`install icefall` to install `icefall`_
|
||||
that works with ``GPU``.
|
||||
|
||||
|
||||
Create a virtual environment
|
||||
----------------------------
|
||||
|
||||
.. code-block:: bash
|
||||
|
||||
virtualenv -p python3 /tmp/icefall_env
|
||||
|
||||
The above command creates a virtual environment in the directory ``/tmp/icefall_env``.
|
||||
You can select any directory you want.
|
||||
|
||||
The output of the above command is given below:
|
||||
|
||||
.. code-block:: bash
|
||||
|
||||
Already using interpreter /usr/bin/python3
|
||||
Using base prefix '/usr'
|
||||
New python executable in /tmp/icefall_env/bin/python3
|
||||
Also creating executable in /tmp/icefall_env/bin/python
|
||||
Installing setuptools, pkg_resources, pip, wheel...done.
|
||||
|
||||
Now we can activate the environment using:
|
||||
|
||||
.. code-block:: bash
|
||||
|
||||
source /tmp/icefall_env/bin/activate
|
||||
|
||||
Install dependencies
|
||||
--------------------
|
||||
|
||||
.. warning::
|
||||
|
||||
Remeber to activate your virtual environment before you continue!
|
||||
|
||||
After activating the virtual environment, we can use the following command
|
||||
to install dependencies of `icefall`_:
|
||||
|
||||
.. hint::
|
||||
|
||||
Remeber that we will run this tutorial on ``CPU``, so we install
|
||||
dependencies required only by running on ``CPU``.
|
||||
|
||||
.. code-block:: bash
|
||||
|
||||
# Caution: Installation order matters!
|
||||
|
||||
# We use torch 2.0.0 and torchaduio 2.0.0 in this tutorial.
|
||||
# Other versions should also work.
|
||||
|
||||
pip install torch==2.0.0+cpu torchaudio==2.0.0+cpu -f https://download.pytorch.org/whl/torch_stable.html
|
||||
|
||||
# If you are using macOS or Windows, please use the following command to install torch and torchaudio
|
||||
# pip install torch==2.0.0 torchaudio==2.0.0 -f https://download.pytorch.org/whl/torch_stable.html
|
||||
|
||||
# Now install k2
|
||||
# Please refer to https://k2-fsa.github.io/k2/installation/from_wheels.html#linux-cpu-example
|
||||
|
||||
pip install k2==1.24.3.dev20230726+cpu.torch2.0.0 -f https://k2-fsa.github.io/k2/cpu.html
|
||||
|
||||
# Install the latest version of lhotse
|
||||
|
||||
pip install git+https://github.com/lhotse-speech/lhotse
|
||||
|
||||
|
||||
Install icefall
|
||||
---------------
|
||||
|
||||
We will put the source code of `icefall`_ into the directory ``/tmp``
|
||||
You can select any directory you want.
|
||||
|
||||
.. code-block:: bash
|
||||
|
||||
cd /tmp
|
||||
git clone https://github.com/k2-fsa/icefall
|
||||
cd icefall
|
||||
pip install -r ./requirements.txt
|
||||
|
||||
.. code-block:: bash
|
||||
|
||||
# Anytime we want to use icefall, we have to set the following
|
||||
# environment variable
|
||||
|
||||
export PYTHONPATH=/tmp/icefall:$PYTHONPATH
|
||||
|
||||
.. hint::
|
||||
|
||||
If you get the following error during this tutorial:
|
||||
|
||||
.. code-block:: bash
|
||||
|
||||
ModuleNotFoundError: No module named 'icefall'
|
||||
|
||||
please set the above environment variable to fix it.
|
||||
|
||||
|
||||
Congratulations! You have installed `icefall`_ successfully.
|
||||
|
||||
For the more curious
|
||||
--------------------
|
||||
|
||||
`icefall`_ contains a collection of Python scripts and you don't need to
|
||||
use ``python3 setup.py install`` or ``pip install icefall`` to install it.
|
||||
All you need to do is to download the code and set the environment variable
|
||||
``PYTHONPATH``.
|
||||
34
_sources/for-dummies/index.rst.txt
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34
_sources/for-dummies/index.rst.txt
Normal file
@ -0,0 +1,34 @@
|
||||
Icefall for dummies tutorial
|
||||
============================
|
||||
|
||||
This tutorial walks you step by step about how to create a simple
|
||||
ASR (`Automatic Speech Recognition <https://en.wikipedia.org/wiki/Speech_recognition>`_)
|
||||
system with `Next-gen Kaldi`_.
|
||||
|
||||
We use the `yesno`_ dataset for demonstration. We select it out of two reasons:
|
||||
|
||||
- It is quite tiny, containing only about 12 minutes of data
|
||||
- The training can be finished within 20 seconds on ``CPU``.
|
||||
|
||||
That also means you don't need a ``GPU`` to run this tutorial.
|
||||
|
||||
Let's get started!
|
||||
|
||||
Please follow items below **sequentially**.
|
||||
|
||||
.. note::
|
||||
|
||||
The :ref:`dummies_tutorial_data_preparation` runs only on Linux and on macOS.
|
||||
All other parts run on Linux, macOS, and Windows.
|
||||
|
||||
Help from the community is appreciated to port the :ref:`dummies_tutorial_data_preparation`
|
||||
to Windows.
|
||||
|
||||
.. toctree::
|
||||
:maxdepth: 2
|
||||
|
||||
./environment-setup.rst
|
||||
./data-preparation.rst
|
||||
./training.rst
|
||||
./decoding.rst
|
||||
./model-export.rst
|
||||
310
_sources/for-dummies/model-export.rst.txt
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310
_sources/for-dummies/model-export.rst.txt
Normal file
@ -0,0 +1,310 @@
|
||||
Model Export
|
||||
============
|
||||
|
||||
There are three ways to export a pre-trained model.
|
||||
|
||||
- Export the model parameters via `model.state_dict() <https://pytorch.org/docs/stable/generated/torch.nn.Module.html?highlight=load_state_dict#torch.nn.Module.state_dict>`_
|
||||
- Export via `torchscript <https://pytorch.org/docs/stable/jit.html>`_: either `torch.jit.script() <https://pytorch.org/docs/stable/generated/torch.jit.script.html#torch.jit.script>`_ or `torch.jit.trace() <https://pytorch.org/docs/stable/generated/torch.jit.trace.html>`_
|
||||
- Export to `ONNX`_ via `torch.onnx.export() <https://pytorch.org/docs/stable/onnx.html>`_
|
||||
|
||||
Each method is explained below in detail.
|
||||
|
||||
Export the model parameters via model.state_dict()
|
||||
---------------------------------------------------
|
||||
|
||||
The command for this kind of export is
|
||||
|
||||
.. code-block:: bash
|
||||
|
||||
cd /tmp/icefall
|
||||
export PYTHONPATH=/tmp/icefall:$PYTHONPATH
|
||||
cd egs/yesno/ASR
|
||||
|
||||
# assume that "--epoch 14 --avg 2" produces the lowest WER.
|
||||
|
||||
./tdnn/export.py --epoch 14 --avg 2
|
||||
|
||||
The output logs are given below:
|
||||
|
||||
.. code-block:: bash
|
||||
|
||||
2023-08-16 20:42:03,912 INFO [export.py:76] {'exp_dir': PosixPath('tdnn/exp'), 'lang_dir': PosixPath('data/lang_phone'), 'lr': 0.01, 'feature_dim': 23, 'weight_decay': 1e-06, 'start_epoch': 0, 'best_train_loss': inf, 'best_valid_loss': inf, 'best_train_epoch': -1, 'best_valid_epoch': -1, 'batch_idx_train': 0, 'log_interval': 10, 'reset_interval': 20, 'valid_interval': 10, 'beam_size': 10, 'reduction': 'sum', 'use_double_scores': True, 'epoch': 14, 'avg': 2, 'jit': False}
|
||||
2023-08-16 20:42:03,913 INFO [lexicon.py:168] Loading pre-compiled data/lang_phone/Linv.pt
|
||||
2023-08-16 20:42:03,950 INFO [export.py:93] averaging ['tdnn/exp/epoch-13.pt', 'tdnn/exp/epoch-14.pt']
|
||||
2023-08-16 20:42:03,971 INFO [export.py:106] Not using torch.jit.script
|
||||
2023-08-16 20:42:03,974 INFO [export.py:111] Saved to tdnn/exp/pretrained.pt
|
||||
|
||||
We can see from the logs that the exported model is saved to the file ``tdnn/exp/pretrained.pt``.
|
||||
|
||||
To give you an idea of what ``tdnn/exp/pretrained.pt`` contains, we can use the following command:
|
||||
|
||||
.. code-block:: python3
|
||||
|
||||
>>> import torch
|
||||
>>> m = torch.load("tdnn/exp/pretrained.pt")
|
||||
>>> list(m.keys())
|
||||
['model']
|
||||
>>> list(m["model"].keys())
|
||||
['tdnn.0.weight', 'tdnn.0.bias', 'tdnn.2.running_mean', 'tdnn.2.running_var', 'tdnn.2.num_batches_tracked', 'tdnn.3.weight', 'tdnn.3.bias', 'tdnn.5.running_mean', 'tdnn.5.running_var', 'tdnn.5.num_batches_tracked', 'tdnn.6.weight', 'tdnn.6.bias', 'tdnn.8.running_mean', 'tdnn.8.running_var', 'tdnn.8.num_batches_tracked', 'output_linear.weight', 'output_linear.bias']
|
||||
|
||||
We can use ``tdnn/exp/pretrained.pt`` in the following way with ``./tdnn/decode.py``:
|
||||
|
||||
.. code-block:: bash
|
||||
|
||||
cd tdnn/exp
|
||||
ln -s pretrained.pt epoch-99.pt
|
||||
cd ../..
|
||||
|
||||
./tdnn/decode.py --epoch 99 --avg 1
|
||||
|
||||
The output logs of the above command are given below:
|
||||
|
||||
.. code-block:: bash
|
||||
|
||||
2023-08-16 20:45:48,089 INFO [decode.py:262] Decoding started
|
||||
2023-08-16 20:45:48,090 INFO [decode.py:263] {'exp_dir': PosixPath('tdnn/exp'), 'lang_dir': PosixPath('data/lang_phone'), 'feature_dim': 23, 'search_beam': 20, 'output_beam': 8, 'min_active_states': 30, 'max_active_states': 10000, 'use_double_scores': True, 'epoch': 99, 'avg': 1, 'export': False, 'feature_dir': PosixPath('data/fbank'), 'max_duration': 30.0, 'bucketing_sampler': False, 'num_buckets': 10, 'concatenate_cuts': False, 'duration_factor': 1.0, 'gap': 1.0, 'on_the_fly_feats': False, 'shuffle': False, 'return_cuts': True, 'num_workers': 2, 'env_info': {'k2-version': '1.24.3', 'k2-build-type': 'Release', 'k2-with-cuda': False, 'k2-git-sha1': 'ad79f1c699c684de9785ed6ca5edb805a41f78c3', 'k2-git-date': 'Wed Jul 26 09:30:42 2023', 'lhotse-version': '1.16.0.dev+git.aa073f6.clean', 'torch-version': '2.0.0', 'torch-cuda-available': False, 'torch-cuda-version': None, 'python-version': '3.1', 'icefall-git-branch': 'master', 'icefall-git-sha1': '9a47c08-clean', 'icefall-git-date': 'Mon Aug 14 22:10:50 2023', 'icefall-path': '/private/tmp/icefall', 'k2-path': '/private/tmp/icefall_env/lib/python3.11/site-packages/k2/__init__.py', 'lhotse-path': '/private/tmp/icefall_env/lib/python3.11/site-packages/lhotse/__init__.py', 'hostname': 'fangjuns-MacBook-Pro.local', 'IP address': '127.0.0.1'}}
|
||||
2023-08-16 20:45:48,092 INFO [lexicon.py:168] Loading pre-compiled data/lang_phone/Linv.pt
|
||||
2023-08-16 20:45:48,103 INFO [decode.py:272] device: cpu
|
||||
2023-08-16 20:45:48,109 INFO [checkpoint.py:112] Loading checkpoint from tdnn/exp/epoch-99.pt
|
||||
2023-08-16 20:45:48,115 INFO [asr_datamodule.py:218] About to get test cuts
|
||||
2023-08-16 20:45:48,115 INFO [asr_datamodule.py:253] About to get test cuts
|
||||
2023-08-16 20:45:50,386 INFO [decode.py:203] batch 0/?, cuts processed until now is 4
|
||||
2023-08-16 20:45:50,556 INFO [decode.py:240] The transcripts are stored in tdnn/exp/recogs-test_set.txt
|
||||
2023-08-16 20:45:50,557 INFO [utils.py:564] [test_set] %WER 0.42% [1 / 240, 0 ins, 1 del, 0 sub ]
|
||||
2023-08-16 20:45:50,558 INFO [decode.py:248] Wrote detailed error stats to tdnn/exp/errs-test_set.txt
|
||||
2023-08-16 20:45:50,559 INFO [decode.py:315] Done!
|
||||
|
||||
We can see that it produces an identical WER as before.
|
||||
|
||||
We can also use it to decode files with the following command:
|
||||
|
||||
.. code-block:: bash
|
||||
|
||||
# ./tdnn/pretrained.py requires kaldifeat
|
||||
#
|
||||
# Please refer to https://csukuangfj.github.io/kaldifeat/installation/from_wheels.html
|
||||
# for how to install kaldifeat
|
||||
|
||||
pip install kaldifeat==1.25.0.dev20230726+cpu.torch2.0.0 -f https://csukuangfj.github.io/kaldifeat/cpu.html
|
||||
|
||||
./tdnn/pretrained.py \
|
||||
--checkpoint ./tdnn/exp/pretrained.pt \
|
||||
--HLG ./data/lang_phone/HLG.pt \
|
||||
--words-file ./data/lang_phone/words.txt \
|
||||
download/waves_yesno/0_0_0_1_0_0_0_1.wav \
|
||||
download/waves_yesno/0_0_1_0_0_0_1_0.wav
|
||||
|
||||
The output is given below:
|
||||
|
||||
.. code-block:: bash
|
||||
|
||||
2023-08-16 20:53:19,208 INFO [pretrained.py:136] {'feature_dim': 23, 'num_classes': 4, 'sample_rate': 8000, 'search_beam': 20, 'output_beam': 8, 'min_active_states': 30, 'max_active_states': 10000, 'use_double_scores': True, 'checkpoint': './tdnn/exp/pretrained.pt', 'words_file': './data/lang_phone/words.txt', 'HLG': './data/lang_phone/HLG.pt', 'sound_files': ['download/waves_yesno/0_0_0_1_0_0_0_1.wav', 'download/waves_yesno/0_0_1_0_0_0_1_0.wav']}
|
||||
2023-08-16 20:53:19,208 INFO [pretrained.py:142] device: cpu
|
||||
2023-08-16 20:53:19,208 INFO [pretrained.py:144] Creating model
|
||||
2023-08-16 20:53:19,212 INFO [pretrained.py:156] Loading HLG from ./data/lang_phone/HLG.pt
|
||||
2023-08-16 20:53:19,213 INFO [pretrained.py:160] Constructing Fbank computer
|
||||
2023-08-16 20:53:19,213 INFO [pretrained.py:170] Reading sound files: ['download/waves_yesno/0_0_0_1_0_0_0_1.wav', 'download/waves_yesno/0_0_1_0_0_0_1_0.wav']
|
||||
2023-08-16 20:53:19,224 INFO [pretrained.py:176] Decoding started
|
||||
2023-08-16 20:53:19,304 INFO [pretrained.py:212]
|
||||
download/waves_yesno/0_0_0_1_0_0_0_1.wav:
|
||||
NO NO NO YES NO NO NO YES
|
||||
|
||||
download/waves_yesno/0_0_1_0_0_0_1_0.wav:
|
||||
NO NO YES NO NO NO YES NO
|
||||
|
||||
|
||||
2023-08-16 20:53:19,304 INFO [pretrained.py:214] Decoding Done
|
||||
|
||||
|
||||
Export via torch.jit.script()
|
||||
-----------------------------
|
||||
|
||||
The command for this kind of export is
|
||||
|
||||
.. code-block:: bash
|
||||
|
||||
cd /tmp/icefall
|
||||
export PYTHONPATH=/tmp/icefall:$PYTHONPATH
|
||||
cd egs/yesno/ASR
|
||||
|
||||
# assume that "--epoch 14 --avg 2" produces the lowest WER.
|
||||
|
||||
./tdnn/export.py --epoch 14 --avg 2 --jit true
|
||||
|
||||
The output logs are given below:
|
||||
|
||||
.. code-block:: bash
|
||||
|
||||
2023-08-16 20:47:44,666 INFO [export.py:76] {'exp_dir': PosixPath('tdnn/exp'), 'lang_dir': PosixPath('data/lang_phone'), 'lr': 0.01, 'feature_dim': 23, 'weight_decay': 1e-06, 'start_epoch': 0, 'best_train_loss': inf, 'best_valid_loss': inf, 'best_train_epoch': -1, 'best_valid_epoch': -1, 'batch_idx_train': 0, 'log_interval': 10, 'reset_interval': 20, 'valid_interval': 10, 'beam_size': 10, 'reduction': 'sum', 'use_double_scores': True, 'epoch': 14, 'avg': 2, 'jit': True}
|
||||
2023-08-16 20:47:44,667 INFO [lexicon.py:168] Loading pre-compiled data/lang_phone/Linv.pt
|
||||
2023-08-16 20:47:44,670 INFO [export.py:93] averaging ['tdnn/exp/epoch-13.pt', 'tdnn/exp/epoch-14.pt']
|
||||
2023-08-16 20:47:44,677 INFO [export.py:100] Using torch.jit.script
|
||||
2023-08-16 20:47:44,843 INFO [export.py:104] Saved to tdnn/exp/cpu_jit.pt
|
||||
|
||||
From the output logs we can see that the generated file is saved to ``tdnn/exp/cpu_jit.pt``.
|
||||
|
||||
Don't be confused by the name ``cpu_jit.pt``. The ``cpu`` part means the model is moved to
|
||||
CPU before exporting. That means, when you load it with:
|
||||
|
||||
.. code-block:: bash
|
||||
|
||||
torch.jit.load()
|
||||
|
||||
you don't need to specify the argument `map_location <https://pytorch.org/docs/stable/generated/torch.jit.load.html#torch.jit.load>`_
|
||||
and it resides on CPU by default.
|
||||
|
||||
To use ``tdnn/exp/cpu_jit.pt`` with `icefall`_ to decode files, we can use:
|
||||
|
||||
.. code-block:: bash
|
||||
|
||||
# ./tdnn/jit_pretrained.py requires kaldifeat
|
||||
#
|
||||
# Please refer to https://csukuangfj.github.io/kaldifeat/installation/from_wheels.html
|
||||
# for how to install kaldifeat
|
||||
|
||||
pip install kaldifeat==1.25.0.dev20230726+cpu.torch2.0.0 -f https://csukuangfj.github.io/kaldifeat/cpu.html
|
||||
|
||||
|
||||
./tdnn/jit_pretrained.py \
|
||||
--nn-model ./tdnn/exp/cpu_jit.pt \
|
||||
--HLG ./data/lang_phone/HLG.pt \
|
||||
--words-file ./data/lang_phone/words.txt \
|
||||
download/waves_yesno/0_0_0_1_0_0_0_1.wav \
|
||||
download/waves_yesno/0_0_1_0_0_0_1_0.wav
|
||||
|
||||
The output is given below:
|
||||
|
||||
.. code-block:: bash
|
||||
|
||||
2023-08-16 20:56:00,603 INFO [jit_pretrained.py:121] {'feature_dim': 23, 'num_classes': 4, 'sample_rate': 8000, 'search_beam': 20, 'output_beam': 8, 'min_active_states': 30, 'max_active_states': 10000, 'use_double_scores': True, 'nn_model': './tdnn/exp/cpu_jit.pt', 'words_file': './data/lang_phone/words.txt', 'HLG': './data/lang_phone/HLG.pt', 'sound_files': ['download/waves_yesno/0_0_0_1_0_0_0_1.wav', 'download/waves_yesno/0_0_1_0_0_0_1_0.wav']}
|
||||
2023-08-16 20:56:00,603 INFO [jit_pretrained.py:127] device: cpu
|
||||
2023-08-16 20:56:00,603 INFO [jit_pretrained.py:129] Loading torchscript model
|
||||
2023-08-16 20:56:00,640 INFO [jit_pretrained.py:134] Loading HLG from ./data/lang_phone/HLG.pt
|
||||
2023-08-16 20:56:00,641 INFO [jit_pretrained.py:138] Constructing Fbank computer
|
||||
2023-08-16 20:56:00,641 INFO [jit_pretrained.py:148] Reading sound files: ['download/waves_yesno/0_0_0_1_0_0_0_1.wav', 'download/waves_yesno/0_0_1_0_0_0_1_0.wav']
|
||||
2023-08-16 20:56:00,642 INFO [jit_pretrained.py:154] Decoding started
|
||||
2023-08-16 20:56:00,727 INFO [jit_pretrained.py:190]
|
||||
download/waves_yesno/0_0_0_1_0_0_0_1.wav:
|
||||
NO NO NO YES NO NO NO YES
|
||||
|
||||
download/waves_yesno/0_0_1_0_0_0_1_0.wav:
|
||||
NO NO YES NO NO NO YES NO
|
||||
|
||||
|
||||
2023-08-16 20:56:00,727 INFO [jit_pretrained.py:192] Decoding Done
|
||||
|
||||
.. hint::
|
||||
|
||||
We provide only code for ``torch.jit.script()``. You can try ``torch.jit.trace()``
|
||||
if you want.
|
||||
|
||||
Export via torch.onnx.export()
|
||||
------------------------------
|
||||
|
||||
The command for this kind of export is
|
||||
|
||||
.. code-block:: bash
|
||||
|
||||
cd /tmp/icefall
|
||||
export PYTHONPATH=/tmp/icefall:$PYTHONPATH
|
||||
cd egs/yesno/ASR
|
||||
|
||||
# tdnn/export_onnx.py requires onnx and onnxruntime
|
||||
pip install onnx onnxruntime
|
||||
|
||||
# assume that "--epoch 14 --avg 2" produces the lowest WER.
|
||||
|
||||
./tdnn/export_onnx.py \
|
||||
--epoch 14 \
|
||||
--avg 2
|
||||
|
||||
The output logs are given below:
|
||||
|
||||
.. code-block:: bash
|
||||
|
||||
2023-08-16 20:59:20,888 INFO [export_onnx.py:83] {'exp_dir': PosixPath('tdnn/exp'), 'lang_dir': PosixPath('data/lang_phone'), 'lr': 0.01, 'feature_dim': 23, 'weight_decay': 1e-06, 'start_epoch': 0, 'best_train_loss': inf, 'best_valid_loss': inf, 'best_train_epoch': -1, 'best_valid_epoch': -1, 'batch_idx_train': 0, 'log_interval': 10, 'reset_interval': 20, 'valid_interval': 10, 'beam_size': 10, 'reduction': 'sum', 'use_double_scores': True, 'epoch': 14, 'avg': 2}
|
||||
2023-08-16 20:59:20,888 INFO [lexicon.py:168] Loading pre-compiled data/lang_phone/Linv.pt
|
||||
2023-08-16 20:59:20,892 INFO [export_onnx.py:100] averaging ['tdnn/exp/epoch-13.pt', 'tdnn/exp/epoch-14.pt']
|
||||
================ Diagnostic Run torch.onnx.export version 2.0.0 ================
|
||||
verbose: False, log level: Level.ERROR
|
||||
======================= 0 NONE 0 NOTE 0 WARNING 0 ERROR ========================
|
||||
|
||||
2023-08-16 20:59:21,047 INFO [export_onnx.py:127] Saved to tdnn/exp/model-epoch-14-avg-2.onnx
|
||||
2023-08-16 20:59:21,047 INFO [export_onnx.py:136] meta_data: {'model_type': 'tdnn', 'version': '1', 'model_author': 'k2-fsa', 'comment': 'non-streaming tdnn for the yesno recipe', 'vocab_size': 4}
|
||||
2023-08-16 20:59:21,049 INFO [export_onnx.py:140] Generate int8 quantization models
|
||||
2023-08-16 20:59:21,075 INFO [onnx_quantizer.py:538] Quantization parameters for tensor:"/Transpose_1_output_0" not specified
|
||||
2023-08-16 20:59:21,081 INFO [export_onnx.py:151] Saved to tdnn/exp/model-epoch-14-avg-2.int8.onnx
|
||||
|
||||
We can see from the logs that it generates two files:
|
||||
|
||||
- ``tdnn/exp/model-epoch-14-avg-2.onnx`` (ONNX model with ``float32`` weights)
|
||||
- ``tdnn/exp/model-epoch-14-avg-2.int8.onnx`` (ONNX model with ``int8`` weights)
|
||||
|
||||
To use the generated ONNX model files for decoding with `onnxruntime`_, we can use
|
||||
|
||||
.. code-block:: bash
|
||||
|
||||
# ./tdnn/onnx_pretrained.py requires kaldifeat
|
||||
#
|
||||
# Please refer to https://csukuangfj.github.io/kaldifeat/installation/from_wheels.html
|
||||
# for how to install kaldifeat
|
||||
|
||||
pip install kaldifeat==1.25.0.dev20230726+cpu.torch2.0.0 -f https://csukuangfj.github.io/kaldifeat/cpu.html
|
||||
|
||||
./tdnn/onnx_pretrained.py \
|
||||
--nn-model ./tdnn/exp/model-epoch-14-avg-2.onnx \
|
||||
--HLG ./data/lang_phone/HLG.pt \
|
||||
--words-file ./data/lang_phone/words.txt \
|
||||
download/waves_yesno/0_0_0_1_0_0_0_1.wav \
|
||||
download/waves_yesno/0_0_1_0_0_0_1_0.wav
|
||||
|
||||
The output is given below:
|
||||
|
||||
.. code-block:: bash
|
||||
|
||||
2023-08-16 21:03:24,260 INFO [onnx_pretrained.py:166] {'feature_dim': 23, 'sample_rate': 8000, 'search_beam': 20, 'output_beam': 8, 'min_active_states': 30, 'max_active_states': 10000, 'use_double_scores': True, 'nn_model': './tdnn/exp/model-epoch-14-avg-2.onnx', 'words_file': './data/lang_phone/words.txt', 'HLG': './data/lang_phone/HLG.pt', 'sound_files': ['download/waves_yesno/0_0_0_1_0_0_0_1.wav', 'download/waves_yesno/0_0_1_0_0_0_1_0.wav']}
|
||||
2023-08-16 21:03:24,260 INFO [onnx_pretrained.py:171] device: cpu
|
||||
2023-08-16 21:03:24,260 INFO [onnx_pretrained.py:173] Loading onnx model ./tdnn/exp/model-epoch-14-avg-2.onnx
|
||||
2023-08-16 21:03:24,267 INFO [onnx_pretrained.py:176] Loading HLG from ./data/lang_phone/HLG.pt
|
||||
2023-08-16 21:03:24,270 INFO [onnx_pretrained.py:180] Constructing Fbank computer
|
||||
2023-08-16 21:03:24,273 INFO [onnx_pretrained.py:190] Reading sound files: ['download/waves_yesno/0_0_0_1_0_0_0_1.wav', 'download/waves_yesno/0_0_1_0_0_0_1_0.wav']
|
||||
2023-08-16 21:03:24,279 INFO [onnx_pretrained.py:196] Decoding started
|
||||
2023-08-16 21:03:24,318 INFO [onnx_pretrained.py:232]
|
||||
download/waves_yesno/0_0_0_1_0_0_0_1.wav:
|
||||
NO NO NO YES NO NO NO YES
|
||||
|
||||
download/waves_yesno/0_0_1_0_0_0_1_0.wav:
|
||||
NO NO YES NO NO NO YES NO
|
||||
|
||||
|
||||
2023-08-16 21:03:24,318 INFO [onnx_pretrained.py:234] Decoding Done
|
||||
|
||||
.. note::
|
||||
|
||||
To use the ``int8`` ONNX model for decoding, please use:
|
||||
|
||||
.. code-block:: bash
|
||||
|
||||
./tdnn/onnx_pretrained.py \
|
||||
--nn-model ./tdnn/exp/model-epoch-14-avg-2.onnx \
|
||||
--HLG ./data/lang_phone/HLG.pt \
|
||||
--words-file ./data/lang_phone/words.txt \
|
||||
download/waves_yesno/0_0_0_1_0_0_0_1.wav \
|
||||
download/waves_yesno/0_0_1_0_0_0_1_0.wav
|
||||
|
||||
For the more curious
|
||||
--------------------
|
||||
|
||||
If you are wondering how to deploy the model without ``torch``, please
|
||||
continue reading. We will show how to use `sherpa-onnx`_ to run the
|
||||
exported ONNX models, which depends only on `onnxruntime`_ and does not
|
||||
depend on ``torch``.
|
||||
|
||||
In this tutorial, we will only demonstrate the usage of `sherpa-onnx`_ with the
|
||||
pre-trained model of the `yesno`_ recipe. There are also other two frameworks
|
||||
available:
|
||||
|
||||
- `sherpa`_. It works with torchscript models.
|
||||
- `sherpa-ncnn`_. It works with models exported using :ref:`icefall_export_to_ncnn` with `ncnn`_
|
||||
|
||||
Please see `<https://k2-fsa.github.io/sherpa/>`_ for further details.
|
||||
39
_sources/for-dummies/training.rst.txt
Normal file
39
_sources/for-dummies/training.rst.txt
Normal file
@ -0,0 +1,39 @@
|
||||
.. _dummies_tutorial_training:
|
||||
|
||||
Training
|
||||
========
|
||||
|
||||
After :ref:`dummies_tutorial_data_preparation`, we can start training.
|
||||
|
||||
The command to start the training is quite simple:
|
||||
|
||||
.. code-block:: bash
|
||||
|
||||
cd /tmp/icefall
|
||||
export PYTHONPATH=/tmp/icefall:$PYTHONPATH
|
||||
cd egs/yesno/ASR
|
||||
|
||||
# We use CPU for training by setting the following environment variable
|
||||
export CUDA_VISIBLE_DEVICES=""
|
||||
|
||||
./tdnn/train.py
|
||||
|
||||
That's it!
|
||||
|
||||
You can find the training logs below:
|
||||
|
||||
.. literalinclude:: ./code/train-yesno.txt
|
||||
|
||||
For the more curious
|
||||
--------------------
|
||||
|
||||
.. code-block:: bash
|
||||
|
||||
./tdnn/train.py --help
|
||||
|
||||
will print the usage information about ``./tdnn/train.py``. For instance, you
|
||||
can specify the number of epochs to train and the location to save the training
|
||||
results.
|
||||
|
||||
The training text logs are saved in ``tdnn/exp/log`` while the tensorboard
|
||||
logs are in ``tdnn/exp/tensorboard``.
|
||||
@ -20,6 +20,7 @@ speech recognition recipes using `k2 <https://github.com/k2-fsa/k2>`_.
|
||||
:maxdepth: 2
|
||||
:caption: Contents:
|
||||
|
||||
for-dummies/index.rst
|
||||
installation/index
|
||||
docker/index
|
||||
faqs
|
||||
|
||||
@ -44,6 +44,7 @@
|
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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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|
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299
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<!DOCTYPE html>
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<html class="writer-html5" lang="en" >
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<li class="toctree-l3"><a class="reference internal" href="#for-the-more-curious">For the more curious</a></li>
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<li class="toctree-l3"><a class="reference internal" href="#a-quick-look-to-the-generated-files">A quick look to the generated files</a><ul>
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<li class="toctree-l4"><a class="reference internal" href="#download">download</a></li>
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|
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<li class="toctree-l1"><a class="reference internal" href="../recipes/index.html">Recipes</a></li>
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<section id="data-preparation">
|
||||
<span id="dummies-tutorial-data-preparation"></span><h1>Data Preparation<a class="headerlink" href="#data-preparation" title="Permalink to this heading"></a></h1>
|
||||
<p>After <a class="reference internal" href="environment-setup.html#dummies-tutorial-environment-setup"><span class="std std-ref">Environment setup</span></a>, we can start preparing the
|
||||
data for training and decoding.</p>
|
||||
<p>The first step is to prepare the data for training. We have already provided
|
||||
<a class="reference external" href="https://github.com/k2-fsa/icefall/blob/master/egs/yesno/ASR/prepare.sh">prepare.sh</a>
|
||||
that would prepare everything required for training.</p>
|
||||
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span>cd /tmp/icefall
|
||||
export PYTHONPATH=/tmp/icefall:$PYTHONPATH
|
||||
cd egs/yesno/ASR
|
||||
|
||||
./prepare.sh
|
||||
</pre></div>
|
||||
</div>
|
||||
<p>Note that in each recipe from <a class="reference external" href="https://github.com/k2-fsa/icefall">icefall</a>, there exists a file <code class="docutils literal notranslate"><span class="pre">prepare.sh</span></code>,
|
||||
which you should run before you run anything else.</p>
|
||||
<p>That is all you need for data preparation.</p>
|
||||
<section id="for-the-more-curious">
|
||||
<h2>For the more curious<a class="headerlink" href="#for-the-more-curious" title="Permalink to this heading"></a></h2>
|
||||
<p>If you are wondering how to prepare your own dataset, please refer to the following
|
||||
URLs for more details:</p>
|
||||
<blockquote>
|
||||
<div><ul>
|
||||
<li><p><a class="reference external" href="https://github.com/lhotse-speech/lhotse/tree/master/lhotse/recipes">https://github.com/lhotse-speech/lhotse/tree/master/lhotse/recipes</a></p>
|
||||
<p>It contains recipes for a variety of dataset. If you want to add your own
|
||||
dataset, please read recipes in this folder first.</p>
|
||||
</li>
|
||||
<li><p><a class="reference external" href="https://github.com/lhotse-speech/lhotse/blob/master/lhotse/recipes/yesno.py">https://github.com/lhotse-speech/lhotse/blob/master/lhotse/recipes/yesno.py</a></p>
|
||||
<p>The <a class="reference external" href="https://www.openslr.org/1/">yesno</a> recipe in <a class="reference external" href="https://github.com/lhotse-speech/lhotse">lhotse</a>.</p>
|
||||
</li>
|
||||
</ul>
|
||||
</div></blockquote>
|
||||
<p>If you already have a <a class="reference external" href="https://github.com/kaldi-asr/kaldi">Kaldi</a> dataset directory, which contains files like
|
||||
<code class="docutils literal notranslate"><span class="pre">wav.scp</span></code>, <code class="docutils literal notranslate"><span class="pre">feats.scp</span></code>, then you can refer to <a class="reference external" href="https://lhotse.readthedocs.io/en/latest/kaldi.html#example">https://lhotse.readthedocs.io/en/latest/kaldi.html#example</a>.</p>
|
||||
</section>
|
||||
<section id="a-quick-look-to-the-generated-files">
|
||||
<h2>A quick look to the generated files<a class="headerlink" href="#a-quick-look-to-the-generated-files" title="Permalink to this heading"></a></h2>
|
||||
<p><code class="docutils literal notranslate"><span class="pre">./prepare.sh</span></code> puts generated files into two directories:</p>
|
||||
<blockquote>
|
||||
<div><ul class="simple">
|
||||
<li><p><code class="docutils literal notranslate"><span class="pre">download</span></code></p></li>
|
||||
<li><p><code class="docutils literal notranslate"><span class="pre">data</span></code></p></li>
|
||||
</ul>
|
||||
</div></blockquote>
|
||||
<section id="download">
|
||||
<h3>download<a class="headerlink" href="#download" title="Permalink to this heading"></a></h3>
|
||||
<p>The <code class="docutils literal notranslate"><span class="pre">download</span></code> directory contains downloaded dataset files:</p>
|
||||
<div class="highlight-bas notranslate"><div class="highlight"><pre><span></span>tree -L 1 ./download/
|
||||
|
||||
./download/
|
||||
|-- waves_yesno
|
||||
`-- waves_yesno.tar.gz
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="admonition hint">
|
||||
<p class="admonition-title">Hint</p>
|
||||
<p>Please refer to <a class="reference external" href="https://github.com/lhotse-speech/lhotse/blob/master/lhotse/recipes/yesno.py#L41">https://github.com/lhotse-speech/lhotse/blob/master/lhotse/recipes/yesno.py#L41</a>
|
||||
for how the data is downloaded and extracted.</p>
|
||||
</div>
|
||||
</section>
|
||||
<section id="data">
|
||||
<h3>data<a class="headerlink" href="#data" title="Permalink to this heading"></a></h3>
|
||||
<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>tree<span class="w"> </span>./data/
|
||||
|
||||
./data/
|
||||
<span class="p">|</span>--<span class="w"> </span>fbank
|
||||
<span class="p">|</span><span class="w"> </span><span class="p">|</span>--<span class="w"> </span>yesno_cuts_test.jsonl.gz
|
||||
<span class="p">|</span><span class="w"> </span><span class="p">|</span>--<span class="w"> </span>yesno_cuts_train.jsonl.gz
|
||||
<span class="p">|</span><span class="w"> </span><span class="p">|</span>--<span class="w"> </span>yesno_feats_test.lca
|
||||
<span class="p">|</span><span class="w"> </span><span class="sb">`</span>--<span class="w"> </span>yesno_feats_train.lca
|
||||
<span class="p">|</span>--<span class="w"> </span>lang_phone
|
||||
<span class="p">|</span><span class="w"> </span><span class="p">|</span>--<span class="w"> </span>HLG.pt
|
||||
<span class="p">|</span><span class="w"> </span><span class="p">|</span>--<span class="w"> </span>L.pt
|
||||
<span class="p">|</span><span class="w"> </span><span class="p">|</span>--<span class="w"> </span>L_disambig.pt
|
||||
<span class="p">|</span><span class="w"> </span><span class="p">|</span>--<span class="w"> </span>Linv.pt
|
||||
<span class="p">|</span><span class="w"> </span><span class="p">|</span>--<span class="w"> </span>lexicon.txt
|
||||
<span class="p">|</span><span class="w"> </span><span class="p">|</span>--<span class="w"> </span>lexicon_disambig.txt
|
||||
<span class="p">|</span><span class="w"> </span><span class="p">|</span>--<span class="w"> </span>tokens.txt
|
||||
<span class="p">|</span><span class="w"> </span><span class="sb">`</span>--<span class="w"> </span>words.txt
|
||||
<span class="p">|</span>--<span class="w"> </span>lm
|
||||
<span class="p">|</span><span class="w"> </span><span class="p">|</span>--<span class="w"> </span>G.arpa
|
||||
<span class="p">|</span><span class="w"> </span><span class="sb">`</span>--<span class="w"> </span>G.fst.txt
|
||||
<span class="sb">`</span>--<span class="w"> </span>manifests
|
||||
<span class="w"> </span><span class="p">|</span>--<span class="w"> </span>yesno_recordings_test.jsonl.gz
|
||||
<span class="w"> </span><span class="p">|</span>--<span class="w"> </span>yesno_recordings_train.jsonl.gz
|
||||
<span class="w"> </span><span class="p">|</span>--<span class="w"> </span>yesno_supervisions_test.jsonl.gz
|
||||
<span class="w"> </span><span class="sb">`</span>--<span class="w"> </span>yesno_supervisions_train.jsonl.gz
|
||||
|
||||
<span class="m">4</span><span class="w"> </span>directories,<span class="w"> </span><span class="m">18</span><span class="w"> </span>files
|
||||
</pre></div>
|
||||
</div>
|
||||
<p><strong>data/manifests</strong>:</p>
|
||||
<blockquote>
|
||||
<div><p>This directory contains manifests. They are used to generate files in
|
||||
<code class="docutils literal notranslate"><span class="pre">data/fbank</span></code>.</p>
|
||||
<p>To give you an idea of what it contains, we examine the first few lines of
|
||||
the manifests related to the <code class="docutils literal notranslate"><span class="pre">train</span></code> dataset.</p>
|
||||
<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span><span class="nb">cd</span><span class="w"> </span>data/manifests
|
||||
gunzip<span class="w"> </span>-c<span class="w"> </span>yesno_recordings_train.jsonl.gz<span class="w"> </span><span class="p">|</span><span class="w"> </span>head<span class="w"> </span>-n<span class="w"> </span><span class="m">3</span>
|
||||
</pre></div>
|
||||
</div>
|
||||
<p>The output is given below:</p>
|
||||
<blockquote>
|
||||
<div><div class="highlight-bash notranslate"><div class="highlight"><pre><span></span><span class="o">{</span><span class="s2">"id"</span>:<span class="w"> </span><span class="s2">"0_0_0_0_1_1_1_1"</span>,<span class="w"> </span><span class="s2">"sources"</span>:<span class="w"> </span><span class="o">[{</span><span class="s2">"type"</span>:<span class="w"> </span><span class="s2">"file"</span>,<span class="w"> </span><span class="s2">"channels"</span>:<span class="w"> </span><span class="o">[</span><span class="m">0</span><span class="o">]</span>,<span class="w"> </span><span class="s2">"source"</span>:<span class="w"> </span><span class="s2">"/tmp/icefall/egs/yesno/ASR/download/waves_yesno/0_0_0_0_1_1_1_1.wav"</span><span class="o">}]</span>,<span class="w"> </span><span class="s2">"sampling_rate"</span>:<span class="w"> </span><span class="m">8000</span>,<span class="w"> </span><span class="s2">"num_samples"</span>:<span class="w"> </span><span class="m">50800</span>,<span class="w"> </span><span class="s2">"duration"</span>:<span class="w"> </span><span class="m">6</span>.35,<span class="w"> </span><span class="s2">"channel_ids"</span>:<span class="w"> </span><span class="o">[</span><span class="m">0</span><span class="o">]}</span>
|
||||
<span class="o">{</span><span class="s2">"id"</span>:<span class="w"> </span><span class="s2">"0_0_0_1_0_1_1_0"</span>,<span class="w"> </span><span class="s2">"sources"</span>:<span class="w"> </span><span class="o">[{</span><span class="s2">"type"</span>:<span class="w"> </span><span class="s2">"file"</span>,<span class="w"> </span><span class="s2">"channels"</span>:<span class="w"> </span><span class="o">[</span><span class="m">0</span><span class="o">]</span>,<span class="w"> </span><span class="s2">"source"</span>:<span class="w"> </span><span class="s2">"/tmp/icefall/egs/yesno/ASR/download/waves_yesno/0_0_0_1_0_1_1_0.wav"</span><span class="o">}]</span>,<span class="w"> </span><span class="s2">"sampling_rate"</span>:<span class="w"> </span><span class="m">8000</span>,<span class="w"> </span><span class="s2">"num_samples"</span>:<span class="w"> </span><span class="m">48880</span>,<span class="w"> </span><span class="s2">"duration"</span>:<span class="w"> </span><span class="m">6</span>.11,<span class="w"> </span><span class="s2">"channel_ids"</span>:<span class="w"> </span><span class="o">[</span><span class="m">0</span><span class="o">]}</span>
|
||||
<span class="o">{</span><span class="s2">"id"</span>:<span class="w"> </span><span class="s2">"0_0_1_0_0_1_1_0"</span>,<span class="w"> </span><span class="s2">"sources"</span>:<span class="w"> </span><span class="o">[{</span><span class="s2">"type"</span>:<span class="w"> </span><span class="s2">"file"</span>,<span class="w"> </span><span class="s2">"channels"</span>:<span class="w"> </span><span class="o">[</span><span class="m">0</span><span class="o">]</span>,<span class="w"> </span><span class="s2">"source"</span>:<span class="w"> </span><span class="s2">"/tmp/icefall/egs/yesno/ASR/download/waves_yesno/0_0_1_0_0_1_1_0.wav"</span><span class="o">}]</span>,<span class="w"> </span><span class="s2">"sampling_rate"</span>:<span class="w"> </span><span class="m">8000</span>,<span class="w"> </span><span class="s2">"num_samples"</span>:<span class="w"> </span><span class="m">48160</span>,<span class="w"> </span><span class="s2">"duration"</span>:<span class="w"> </span><span class="m">6</span>.02,<span class="w"> </span><span class="s2">"channel_ids"</span>:<span class="w"> </span><span class="o">[</span><span class="m">0</span><span class="o">]}</span>
|
||||
</pre></div>
|
||||
</div>
|
||||
</div></blockquote>
|
||||
<p>Please refer to <a class="reference external" href="https://github.com/lhotse-speech/lhotse/blob/master/lhotse/audio.py#L300">https://github.com/lhotse-speech/lhotse/blob/master/lhotse/audio.py#L300</a>
|
||||
for the meaning of each field per line.</p>
|
||||
<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>gunzip<span class="w"> </span>-c<span class="w"> </span>yesno_supervisions_train.jsonl.gz<span class="w"> </span><span class="p">|</span><span class="w"> </span>head<span class="w"> </span>-n<span class="w"> </span><span class="m">3</span>
|
||||
</pre></div>
|
||||
</div>
|
||||
<p>The output is given below:</p>
|
||||
<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span><span class="o">{</span><span class="s2">"id"</span>:<span class="w"> </span><span class="s2">"0_0_0_0_1_1_1_1"</span>,<span class="w"> </span><span class="s2">"recording_id"</span>:<span class="w"> </span><span class="s2">"0_0_0_0_1_1_1_1"</span>,<span class="w"> </span><span class="s2">"start"</span>:<span class="w"> </span><span class="m">0</span>.0,<span class="w"> </span><span class="s2">"duration"</span>:<span class="w"> </span><span class="m">6</span>.35,<span class="w"> </span><span class="s2">"channel"</span>:<span class="w"> </span><span class="m">0</span>,<span class="w"> </span><span class="s2">"text"</span>:<span class="w"> </span><span class="s2">"NO NO NO NO YES YES YES YES"</span>,<span class="w"> </span><span class="s2">"language"</span>:<span class="w"> </span><span class="s2">"Hebrew"</span><span class="o">}</span>
|
||||
<span class="o">{</span><span class="s2">"id"</span>:<span class="w"> </span><span class="s2">"0_0_0_1_0_1_1_0"</span>,<span class="w"> </span><span class="s2">"recording_id"</span>:<span class="w"> </span><span class="s2">"0_0_0_1_0_1_1_0"</span>,<span class="w"> </span><span class="s2">"start"</span>:<span class="w"> </span><span class="m">0</span>.0,<span class="w"> </span><span class="s2">"duration"</span>:<span class="w"> </span><span class="m">6</span>.11,<span class="w"> </span><span class="s2">"channel"</span>:<span class="w"> </span><span class="m">0</span>,<span class="w"> </span><span class="s2">"text"</span>:<span class="w"> </span><span class="s2">"NO NO NO YES NO YES YES NO"</span>,<span class="w"> </span><span class="s2">"language"</span>:<span class="w"> </span><span class="s2">"Hebrew"</span><span class="o">}</span>
|
||||
<span class="o">{</span><span class="s2">"id"</span>:<span class="w"> </span><span class="s2">"0_0_1_0_0_1_1_0"</span>,<span class="w"> </span><span class="s2">"recording_id"</span>:<span class="w"> </span><span class="s2">"0_0_1_0_0_1_1_0"</span>,<span class="w"> </span><span class="s2">"start"</span>:<span class="w"> </span><span class="m">0</span>.0,<span class="w"> </span><span class="s2">"duration"</span>:<span class="w"> </span><span class="m">6</span>.02,<span class="w"> </span><span class="s2">"channel"</span>:<span class="w"> </span><span class="m">0</span>,<span class="w"> </span><span class="s2">"text"</span>:<span class="w"> </span><span class="s2">"NO NO YES NO NO YES YES NO"</span>,<span class="w"> </span><span class="s2">"language"</span>:<span class="w"> </span><span class="s2">"Hebrew"</span><span class="o">}</span>
|
||||
</pre></div>
|
||||
</div>
|
||||
<p>Please refer to <a class="reference external" href="https://github.com/lhotse-speech/lhotse/blob/master/lhotse/supervision.py#L510">https://github.com/lhotse-speech/lhotse/blob/master/lhotse/supervision.py#L510</a>
|
||||
for the meaning of each field per line.</p>
|
||||
</div></blockquote>
|
||||
<p><strong>data/fbank</strong>:</p>
|
||||
<blockquote>
|
||||
<div><p>This directory contains everything from <code class="docutils literal notranslate"><span class="pre">data/manifests</span></code>. Furthermore, it also contains features
|
||||
for training.</p>
|
||||
<p><code class="docutils literal notranslate"><span class="pre">data/fbank/yesno_feats_train.lca</span></code> contains the features for the train dataset.
|
||||
Features are compressed using <a class="reference external" href="https://github.com/danpovey/lilcom">lilcom</a>.</p>
|
||||
<p><code class="docutils literal notranslate"><span class="pre">data/fbank/yesno_cuts_train.jsonl.gz</span></code> stores the <a class="reference external" href="https://github.com/lhotse-speech/lhotse/blob/master/lhotse/cut/set.py#L72">CutSet</a>,
|
||||
which stores <a class="reference external" href="https://github.com/lhotse-speech/lhotse/blob/master/lhotse/audio.py#L928">RecordingSet</a>,
|
||||
<a class="reference external" href="https://github.com/lhotse-speech/lhotse/blob/master/lhotse/supervision.py#L510">SupervisionSet</a>,
|
||||
and <a class="reference external" href="https://github.com/lhotse-speech/lhotse/blob/master/lhotse/features/base.py#L593">FeatureSet</a>.</p>
|
||||
<p>To give you an idea about what it looks like, we can run the following command:</p>
|
||||
<blockquote>
|
||||
<div><div class="highlight-bash notranslate"><div class="highlight"><pre><span></span><span class="nb">cd</span><span class="w"> </span>data/fbank
|
||||
|
||||
gunzip<span class="w"> </span>-c<span class="w"> </span>yesno_cuts_train.jsonl.gz<span class="w"> </span><span class="p">|</span><span class="w"> </span>head<span class="w"> </span>-n<span class="w"> </span><span class="m">3</span>
|
||||
</pre></div>
|
||||
</div>
|
||||
</div></blockquote>
|
||||
<p>The output is given below:</p>
|
||||
<blockquote>
|
||||
<div><div class="highlight-bash notranslate"><div class="highlight"><pre><span></span><span class="o">{</span><span class="s2">"id"</span>:<span class="w"> </span><span class="s2">"0_0_0_0_1_1_1_1-0"</span>,<span class="w"> </span><span class="s2">"start"</span>:<span class="w"> </span><span class="m">0</span>,<span class="w"> </span><span class="s2">"duration"</span>:<span class="w"> </span><span class="m">6</span>.35,<span class="w"> </span><span class="s2">"channel"</span>:<span class="w"> </span><span class="m">0</span>,<span class="w"> </span><span class="s2">"supervisions"</span>:<span class="w"> </span><span class="o">[{</span><span class="s2">"id"</span>:<span class="w"> </span><span class="s2">"0_0_0_0_1_1_1_1"</span>,<span class="w"> </span><span class="s2">"recording_id"</span>:<span class="w"> </span><span class="s2">"0_0_0_0_1_1_1_1"</span>,<span class="w"> </span><span class="s2">"start"</span>:<span class="w"> </span><span class="m">0</span>.0,<span class="w"> </span><span class="s2">"duration"</span>:<span class="w"> </span><span class="m">6</span>.35,<span class="w"> </span><span class="s2">"channel"</span>:<span class="w"> </span><span class="m">0</span>,<span class="w"> </span><span class="s2">"text"</span>:<span class="w"> </span><span class="s2">"NO NO NO NO YES YES YES YES"</span>,<span class="w"> </span><span class="s2">"language"</span>:<span class="w"> </span><span class="s2">"Hebrew"</span><span class="o">}]</span>,<span class="w"> </span><span class="s2">"features"</span>:<span class="w"> </span><span class="o">{</span><span class="s2">"type"</span>:<span class="w"> </span><span class="s2">"kaldi-fbank"</span>,<span class="w"> </span><span class="s2">"num_frames"</span>:<span class="w"> </span><span class="m">635</span>,<span class="w"> </span><span class="s2">"num_features"</span>:<span class="w"> </span><span class="m">23</span>,<span class="w"> </span><span class="s2">"frame_shift"</span>:<span class="w"> </span><span class="m">0</span>.01,<span class="w"> </span><span class="s2">"sampling_rate"</span>:<span class="w"> </span><span class="m">8000</span>,<span class="w"> </span><span class="s2">"start"</span>:<span class="w"> </span><span class="m">0</span>,<span class="w"> </span><span class="s2">"duration"</span>:<span class="w"> </span><span class="m">6</span>.35,<span class="w"> </span><span class="s2">"storage_type"</span>:<span class="w"> </span><span class="s2">"lilcom_chunky"</span>,<span class="w"> </span><span class="s2">"storage_path"</span>:<span class="w"> </span><span class="s2">"data/fbank/yesno_feats_train.lca"</span>,<span class="w"> </span><span class="s2">"storage_key"</span>:<span class="w"> </span><span class="s2">"0,13000,3570"</span>,<span class="w"> </span><span class="s2">"channels"</span>:<span class="w"> </span><span class="m">0</span><span class="o">}</span>,<span class="w"> </span><span class="s2">"recording"</span>:<span class="w"> </span><span class="o">{</span><span class="s2">"id"</span>:<span class="w"> </span><span class="s2">"0_0_0_0_1_1_1_1"</span>,<span class="w"> </span><span class="s2">"sources"</span>:<span class="w"> </span><span class="o">[{</span><span class="s2">"type"</span>:<span class="w"> </span><span class="s2">"file"</span>,<span class="w"> </span><span class="s2">"channels"</span>:<span class="w"> </span><span class="o">[</span><span class="m">0</span><span class="o">]</span>,<span class="w"> </span><span class="s2">"source"</span>:<span class="w"> </span><span class="s2">"/tmp/icefall/egs/yesno/ASR/download/waves_yesno/0_0_0_0_1_1_1_1.wav"</span><span class="o">}]</span>,<span class="w"> </span><span class="s2">"sampling_rate"</span>:<span class="w"> </span><span class="m">8000</span>,<span class="w"> </span><span class="s2">"num_samples"</span>:<span class="w"> </span><span class="m">50800</span>,<span class="w"> </span><span class="s2">"duration"</span>:<span class="w"> </span><span class="m">6</span>.35,<span class="w"> </span><span class="s2">"channel_ids"</span>:<span class="w"> </span><span class="o">[</span><span class="m">0</span><span class="o">]}</span>,<span class="w"> </span><span class="s2">"type"</span>:<span class="w"> </span><span class="s2">"MonoCut"</span><span class="o">}</span>
|
||||
<span class="o">{</span><span class="s2">"id"</span>:<span class="w"> </span><span class="s2">"0_0_0_1_0_1_1_0-1"</span>,<span class="w"> </span><span class="s2">"start"</span>:<span class="w"> </span><span class="m">0</span>,<span class="w"> </span><span class="s2">"duration"</span>:<span class="w"> </span><span class="m">6</span>.11,<span class="w"> </span><span class="s2">"channel"</span>:<span class="w"> </span><span class="m">0</span>,<span class="w"> </span><span class="s2">"supervisions"</span>:<span class="w"> </span><span class="o">[{</span><span class="s2">"id"</span>:<span class="w"> </span><span class="s2">"0_0_0_1_0_1_1_0"</span>,<span class="w"> </span><span class="s2">"recording_id"</span>:<span class="w"> </span><span class="s2">"0_0_0_1_0_1_1_0"</span>,<span class="w"> </span><span class="s2">"start"</span>:<span class="w"> </span><span class="m">0</span>.0,<span class="w"> </span><span class="s2">"duration"</span>:<span class="w"> </span><span class="m">6</span>.11,<span class="w"> </span><span class="s2">"channel"</span>:<span class="w"> </span><span class="m">0</span>,<span class="w"> </span><span class="s2">"text"</span>:<span class="w"> </span><span class="s2">"NO NO NO YES NO YES YES NO"</span>,<span class="w"> </span><span class="s2">"language"</span>:<span class="w"> </span><span class="s2">"Hebrew"</span><span class="o">}]</span>,<span class="w"> </span><span class="s2">"features"</span>:<span class="w"> </span><span class="o">{</span><span class="s2">"type"</span>:<span class="w"> </span><span class="s2">"kaldi-fbank"</span>,<span class="w"> </span><span class="s2">"num_frames"</span>:<span class="w"> </span><span class="m">611</span>,<span class="w"> </span><span class="s2">"num_features"</span>:<span class="w"> </span><span class="m">23</span>,<span class="w"> </span><span class="s2">"frame_shift"</span>:<span class="w"> </span><span class="m">0</span>.01,<span class="w"> </span><span class="s2">"sampling_rate"</span>:<span class="w"> </span><span class="m">8000</span>,<span class="w"> </span><span class="s2">"start"</span>:<span class="w"> </span><span class="m">0</span>,<span class="w"> </span><span class="s2">"duration"</span>:<span class="w"> </span><span class="m">6</span>.11,<span class="w"> </span><span class="s2">"storage_type"</span>:<span class="w"> </span><span class="s2">"lilcom_chunky"</span>,<span class="w"> </span><span class="s2">"storage_path"</span>:<span class="w"> </span><span class="s2">"data/fbank/yesno_feats_train.lca"</span>,<span class="w"> </span><span class="s2">"storage_key"</span>:<span class="w"> </span><span class="s2">"16570,12964,2929"</span>,<span class="w"> </span><span class="s2">"channels"</span>:<span class="w"> </span><span class="m">0</span><span class="o">}</span>,<span class="w"> </span><span class="s2">"recording"</span>:<span class="w"> </span><span class="o">{</span><span class="s2">"id"</span>:<span class="w"> </span><span class="s2">"0_0_0_1_0_1_1_0"</span>,<span class="w"> </span><span class="s2">"sources"</span>:<span class="w"> </span><span class="o">[{</span><span class="s2">"type"</span>:<span class="w"> </span><span class="s2">"file"</span>,<span class="w"> </span><span class="s2">"channels"</span>:<span class="w"> </span><span class="o">[</span><span class="m">0</span><span class="o">]</span>,<span class="w"> </span><span class="s2">"source"</span>:<span class="w"> </span><span class="s2">"/tmp/icefall/egs/yesno/ASR/download/waves_yesno/0_0_0_1_0_1_1_0.wav"</span><span class="o">}]</span>,<span class="w"> </span><span class="s2">"sampling_rate"</span>:<span class="w"> </span><span class="m">8000</span>,<span class="w"> </span><span class="s2">"num_samples"</span>:<span class="w"> </span><span class="m">48880</span>,<span class="w"> </span><span class="s2">"duration"</span>:<span class="w"> </span><span class="m">6</span>.11,<span class="w"> </span><span class="s2">"channel_ids"</span>:<span class="w"> </span><span class="o">[</span><span class="m">0</span><span class="o">]}</span>,<span class="w"> </span><span class="s2">"type"</span>:<span class="w"> </span><span class="s2">"MonoCut"</span><span class="o">}</span>
|
||||
<span class="o">{</span><span class="s2">"id"</span>:<span class="w"> </span><span class="s2">"0_0_1_0_0_1_1_0-2"</span>,<span class="w"> </span><span class="s2">"start"</span>:<span class="w"> </span><span class="m">0</span>,<span class="w"> </span><span class="s2">"duration"</span>:<span class="w"> </span><span class="m">6</span>.02,<span class="w"> </span><span class="s2">"channel"</span>:<span class="w"> </span><span class="m">0</span>,<span class="w"> </span><span class="s2">"supervisions"</span>:<span class="w"> </span><span class="o">[{</span><span class="s2">"id"</span>:<span class="w"> </span><span class="s2">"0_0_1_0_0_1_1_0"</span>,<span class="w"> </span><span class="s2">"recording_id"</span>:<span class="w"> </span><span class="s2">"0_0_1_0_0_1_1_0"</span>,<span class="w"> </span><span class="s2">"start"</span>:<span class="w"> </span><span class="m">0</span>.0,<span class="w"> </span><span class="s2">"duration"</span>:<span class="w"> </span><span class="m">6</span>.02,<span class="w"> </span><span class="s2">"channel"</span>:<span class="w"> </span><span class="m">0</span>,<span class="w"> </span><span class="s2">"text"</span>:<span class="w"> </span><span class="s2">"NO NO YES NO NO YES YES NO"</span>,<span class="w"> </span><span class="s2">"language"</span>:<span class="w"> </span><span class="s2">"Hebrew"</span><span class="o">}]</span>,<span class="w"> </span><span class="s2">"features"</span>:<span class="w"> </span><span class="o">{</span><span class="s2">"type"</span>:<span class="w"> </span><span class="s2">"kaldi-fbank"</span>,<span class="w"> </span><span class="s2">"num_frames"</span>:<span class="w"> </span><span class="m">602</span>,<span class="w"> </span><span class="s2">"num_features"</span>:<span class="w"> </span><span class="m">23</span>,<span class="w"> </span><span class="s2">"frame_shift"</span>:<span class="w"> </span><span class="m">0</span>.01,<span class="w"> </span><span class="s2">"sampling_rate"</span>:<span class="w"> </span><span class="m">8000</span>,<span class="w"> </span><span class="s2">"start"</span>:<span class="w"> </span><span class="m">0</span>,<span class="w"> </span><span class="s2">"duration"</span>:<span class="w"> </span><span class="m">6</span>.02,<span class="w"> </span><span class="s2">"storage_type"</span>:<span class="w"> </span><span class="s2">"lilcom_chunky"</span>,<span class="w"> </span><span class="s2">"storage_path"</span>:<span class="w"> </span><span class="s2">"data/fbank/yesno_feats_train.lca"</span>,<span class="w"> </span><span class="s2">"storage_key"</span>:<span class="w"> </span><span class="s2">"32463,12936,2696"</span>,<span class="w"> </span><span class="s2">"channels"</span>:<span class="w"> </span><span class="m">0</span><span class="o">}</span>,<span class="w"> </span><span class="s2">"recording"</span>:<span class="w"> </span><span class="o">{</span><span class="s2">"id"</span>:<span class="w"> </span><span class="s2">"0_0_1_0_0_1_1_0"</span>,<span class="w"> </span><span class="s2">"sources"</span>:<span class="w"> </span><span class="o">[{</span><span class="s2">"type"</span>:<span class="w"> </span><span class="s2">"file"</span>,<span class="w"> </span><span class="s2">"channels"</span>:<span class="w"> </span><span class="o">[</span><span class="m">0</span><span class="o">]</span>,<span class="w"> </span><span class="s2">"source"</span>:<span class="w"> </span><span class="s2">"/tmp/icefall/egs/yesno/ASR/download/waves_yesno/0_0_1_0_0_1_1_0.wav"</span><span class="o">}]</span>,<span class="w"> </span><span class="s2">"sampling_rate"</span>:<span class="w"> </span><span class="m">8000</span>,<span class="w"> </span><span class="s2">"num_samples"</span>:<span class="w"> </span><span class="m">48160</span>,<span class="w"> </span><span class="s2">"duration"</span>:<span class="w"> </span><span class="m">6</span>.02,<span class="w"> </span><span class="s2">"channel_ids"</span>:<span class="w"> </span><span class="o">[</span><span class="m">0</span><span class="o">]}</span>,<span class="w"> </span><span class="s2">"type"</span>:<span class="w"> </span><span class="s2">"MonoCut"</span><span class="o">}</span>
|
||||
</pre></div>
|
||||
</div>
|
||||
</div></blockquote>
|
||||
<p>Note that <code class="docutils literal notranslate"><span class="pre">yesno_cuts_train.jsonl.gz</span></code> only stores the information about how to read the features.
|
||||
The actual features are stored separately in <code class="docutils literal notranslate"><span class="pre">data/fbank/yesno_feats_train.lca</span></code>.</p>
|
||||
</div></blockquote>
|
||||
<p><strong>data/lang</strong>:</p>
|
||||
<blockquote>
|
||||
<div><p>This directory contains the lexicon.</p>
|
||||
</div></blockquote>
|
||||
<p><strong>data/lm</strong>:</p>
|
||||
<blockquote>
|
||||
<div><p>This directory contains language models.</p>
|
||||
</div></blockquote>
|
||||
</section>
|
||||
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|
||||
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|
||||
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163
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Normal file
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Normal file
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<li class="toctree-l1 current"><a class="reference internal" href="index.html">Icefall for dummies tutorial</a><ul class="current">
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<li class="toctree-l2"><a class="reference internal" href="environment-setup.html">Environment setup</a></li>
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<li class="toctree-l2 current"><a class="current reference internal" href="#">Decoding</a><ul>
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<li class="toctree-l1"><a class="reference internal" href="../decoding-with-langugage-models/index.html">Decoding with language models</a></li>
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<div role="main" class="document" itemscope="itemscope" itemtype="http://schema.org/Article">
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<section id="decoding">
|
||||
<span id="dummies-tutorial-decoding"></span><h1>Decoding<a class="headerlink" href="#decoding" title="Permalink to this heading"></a></h1>
|
||||
<p>After <a class="reference internal" href="training.html#dummies-tutorial-training"><span class="std std-ref">Training</span></a>, we can start decoding.</p>
|
||||
<p>The command to start the decoding is quite simple:</p>
|
||||
<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span><span class="nb">cd</span><span class="w"> </span>/tmp/icefall
|
||||
<span class="nb">export</span><span class="w"> </span><span class="nv">PYTHONPATH</span><span class="o">=</span>/tmp/icefall:<span class="nv">$PYTHONPATH</span>
|
||||
<span class="nb">cd</span><span class="w"> </span>egs/yesno/ASR
|
||||
|
||||
<span class="c1"># We use CPU for decoding by setting the following environment variable</span>
|
||||
<span class="nb">export</span><span class="w"> </span><span class="nv">CUDA_VISIBLE_DEVICES</span><span class="o">=</span><span class="s2">""</span>
|
||||
|
||||
./tdnn/decode.py
|
||||
</pre></div>
|
||||
</div>
|
||||
<p>The output logs are given below:</p>
|
||||
<section id="for-the-more-curious">
|
||||
<h2>For the more curious<a class="headerlink" href="#for-the-more-curious" title="Permalink to this heading"></a></h2>
|
||||
<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>./tdnn/decode.py<span class="w"> </span>--help
|
||||
</pre></div>
|
||||
</div>
|
||||
<p>will print the usage information about <code class="docutils literal notranslate"><span class="pre">./tdnn/decode.py</span></code>. For instance, you
|
||||
can specify:</p>
|
||||
<blockquote>
|
||||
<div><ul class="simple">
|
||||
<li><p><code class="docutils literal notranslate"><span class="pre">--epoch</span></code> to use which checkpoint for decoding</p></li>
|
||||
<li><p><code class="docutils literal notranslate"><span class="pre">--avg</span></code> to select how many checkpoints to use for model averaging</p></li>
|
||||
</ul>
|
||||
</div></blockquote>
|
||||
<p>You usually try different combinations of <code class="docutils literal notranslate"><span class="pre">--epoch</span></code> and <code class="docutils literal notranslate"><span class="pre">--avg</span></code> and select
|
||||
one that leads to the lowest WER (<a class="reference external" href="https://en.wikipedia.org/wiki/Word_error_rate">Word Error Rate</a>).</p>
|
||||
</section>
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238
for-dummies/environment-setup.html
Normal file
238
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Normal file
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<section id="environment-setup">
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<span id="dummies-tutorial-environment-setup"></span><h1>Environment setup<a class="headerlink" href="#environment-setup" title="Permalink to this heading"></a></h1>
|
||||
<p>We will create an environment for <a class="reference external" href="https://github.com/k2-fsa">Next-gen Kaldi</a> that runs on <code class="docutils literal notranslate"><span class="pre">CPU</span></code>
|
||||
in this tutorial.</p>
|
||||
<div class="admonition note">
|
||||
<p class="admonition-title">Note</p>
|
||||
<p>Since the <a class="reference external" href="https://www.openslr.org/1/">yesno</a> dataset used in this tutorial is very tiny, training on
|
||||
<code class="docutils literal notranslate"><span class="pre">CPU</span></code> works very well for it.</p>
|
||||
<p>If your dataset is very large, e.g., hundreds or thousands of hours of
|
||||
training data, please follow <a class="reference internal" href="../installation/index.html#install-icefall"><span class="std std-ref">Installation</span></a> to install <a class="reference external" href="https://github.com/k2-fsa/icefall">icefall</a>
|
||||
that works with <code class="docutils literal notranslate"><span class="pre">GPU</span></code>.</p>
|
||||
</div>
|
||||
<section id="create-a-virtual-environment">
|
||||
<h2>Create a virtual environment<a class="headerlink" href="#create-a-virtual-environment" title="Permalink to this heading"></a></h2>
|
||||
<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>virtualenv<span class="w"> </span>-p<span class="w"> </span>python3<span class="w"> </span>/tmp/icefall_env
|
||||
</pre></div>
|
||||
</div>
|
||||
<p>The above command creates a virtual environment in the directory <code class="docutils literal notranslate"><span class="pre">/tmp/icefall_env</span></code>.
|
||||
You can select any directory you want.</p>
|
||||
<p>The output of the above command is given below:</p>
|
||||
<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>Already<span class="w"> </span>using<span class="w"> </span>interpreter<span class="w"> </span>/usr/bin/python3
|
||||
Using<span class="w"> </span>base<span class="w"> </span>prefix<span class="w"> </span><span class="s1">'/usr'</span>
|
||||
New<span class="w"> </span>python<span class="w"> </span>executable<span class="w"> </span><span class="k">in</span><span class="w"> </span>/tmp/icefall_env/bin/python3
|
||||
Also<span class="w"> </span>creating<span class="w"> </span>executable<span class="w"> </span><span class="k">in</span><span class="w"> </span>/tmp/icefall_env/bin/python
|
||||
Installing<span class="w"> </span>setuptools,<span class="w"> </span>pkg_resources,<span class="w"> </span>pip,<span class="w"> </span>wheel...done.
|
||||
</pre></div>
|
||||
</div>
|
||||
<p>Now we can activate the environment using:</p>
|
||||
<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span><span class="nb">source</span><span class="w"> </span>/tmp/icefall_env/bin/activate
|
||||
</pre></div>
|
||||
</div>
|
||||
</section>
|
||||
<section id="install-dependencies">
|
||||
<h2>Install dependencies<a class="headerlink" href="#install-dependencies" title="Permalink to this heading"></a></h2>
|
||||
<div class="admonition warning">
|
||||
<p class="admonition-title">Warning</p>
|
||||
<p>Remeber to activate your virtual environment before you continue!</p>
|
||||
</div>
|
||||
<p>After activating the virtual environment, we can use the following command
|
||||
to install dependencies of <a class="reference external" href="https://github.com/k2-fsa/icefall">icefall</a>:</p>
|
||||
<div class="admonition hint">
|
||||
<p class="admonition-title">Hint</p>
|
||||
<p>Remeber that we will run this tutorial on <code class="docutils literal notranslate"><span class="pre">CPU</span></code>, so we install
|
||||
dependencies required only by running on <code class="docutils literal notranslate"><span class="pre">CPU</span></code>.</p>
|
||||
</div>
|
||||
<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span><span class="c1"># Caution: Installation order matters!</span>
|
||||
|
||||
<span class="c1"># We use torch 2.0.0 and torchaduio 2.0.0 in this tutorial.</span>
|
||||
<span class="c1"># Other versions should also work.</span>
|
||||
|
||||
pip<span class="w"> </span>install<span class="w"> </span><span class="nv">torch</span><span class="o">==</span><span class="m">2</span>.0.0+cpu<span class="w"> </span><span class="nv">torchaudio</span><span class="o">==</span><span class="m">2</span>.0.0+cpu<span class="w"> </span>-f<span class="w"> </span>https://download.pytorch.org/whl/torch_stable.html
|
||||
|
||||
<span class="c1"># If you are using macOS or Windows, please use the following command to install torch and torchaudio</span>
|
||||
<span class="c1"># pip install torch==2.0.0 torchaudio==2.0.0 -f https://download.pytorch.org/whl/torch_stable.html</span>
|
||||
|
||||
<span class="c1"># Now install k2</span>
|
||||
<span class="c1"># Please refer to https://k2-fsa.github.io/k2/installation/from_wheels.html#linux-cpu-example</span>
|
||||
|
||||
pip<span class="w"> </span>install<span class="w"> </span><span class="nv">k2</span><span class="o">==</span><span class="m">1</span>.24.3.dev20230726+cpu.torch2.0.0<span class="w"> </span>-f<span class="w"> </span>https://k2-fsa.github.io/k2/cpu.html
|
||||
|
||||
<span class="c1"># Install the latest version of lhotse</span>
|
||||
|
||||
pip<span class="w"> </span>install<span class="w"> </span>git+https://github.com/lhotse-speech/lhotse
|
||||
</pre></div>
|
||||
</div>
|
||||
</section>
|
||||
<section id="install-icefall">
|
||||
<h2>Install icefall<a class="headerlink" href="#install-icefall" title="Permalink to this heading"></a></h2>
|
||||
<p>We will put the source code of <a class="reference external" href="https://github.com/k2-fsa/icefall">icefall</a> into the directory <code class="docutils literal notranslate"><span class="pre">/tmp</span></code>
|
||||
You can select any directory you want.</p>
|
||||
<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span><span class="nb">cd</span><span class="w"> </span>/tmp
|
||||
git<span class="w"> </span>clone<span class="w"> </span>https://github.com/k2-fsa/icefall
|
||||
<span class="nb">cd</span><span class="w"> </span>icefall
|
||||
pip<span class="w"> </span>install<span class="w"> </span>-r<span class="w"> </span>./requirements.txt
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span><span class="c1"># Anytime we want to use icefall, we have to set the following</span>
|
||||
<span class="c1"># environment variable</span>
|
||||
|
||||
<span class="nb">export</span><span class="w"> </span><span class="nv">PYTHONPATH</span><span class="o">=</span>/tmp/icefall:<span class="nv">$PYTHONPATH</span>
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="admonition hint">
|
||||
<p class="admonition-title">Hint</p>
|
||||
<blockquote>
|
||||
<div><p>If you get the following error during this tutorial:</p>
|
||||
<blockquote>
|
||||
<div><div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>ModuleNotFoundError:<span class="w"> </span>No<span class="w"> </span>module<span class="w"> </span>named<span class="w"> </span><span class="s1">'icefall'</span>
|
||||
</pre></div>
|
||||
</div>
|
||||
</div></blockquote>
|
||||
</div></blockquote>
|
||||
<p>please set the above environment variable to fix it.</p>
|
||||
</div>
|
||||
<p>Congratulations! You have installed <a class="reference external" href="https://github.com/k2-fsa/icefall">icefall</a> successfully.</p>
|
||||
</section>
|
||||
<section id="for-the-more-curious">
|
||||
<h2>For the more curious<a class="headerlink" href="#for-the-more-curious" title="Permalink to this heading"></a></h2>
|
||||
<p><a class="reference external" href="https://github.com/k2-fsa/icefall">icefall</a> contains a collection of Python scripts and you don’t need to
|
||||
use <code class="docutils literal notranslate"><span class="pre">python3</span> <span class="pre">setup.py</span> <span class="pre">install</span></code> or <code class="docutils literal notranslate"><span class="pre">pip</span> <span class="pre">install</span> <span class="pre">icefall</span></code> to install it.
|
||||
All you need to do is to download the code and set the environment variable
|
||||
<code class="docutils literal notranslate"><span class="pre">PYTHONPATH</span></code>.</p>
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<section id="icefall-for-dummies-tutorial">
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<h1>Icefall for dummies tutorial<a class="headerlink" href="#icefall-for-dummies-tutorial" title="Permalink to this heading"></a></h1>
|
||||
<p>This tutorial walks you step by step about how to create a simple
|
||||
ASR (<a class="reference external" href="https://en.wikipedia.org/wiki/Speech_recognition">Automatic Speech Recognition</a>)
|
||||
system with <a class="reference external" href="https://github.com/k2-fsa">Next-gen Kaldi</a>.</p>
|
||||
<p>We use the <a class="reference external" href="https://www.openslr.org/1/">yesno</a> dataset for demonstration. We select it out of two reasons:</p>
|
||||
<blockquote>
|
||||
<div><ul class="simple">
|
||||
<li><p>It is quite tiny, containing only about 12 minutes of data</p></li>
|
||||
<li><p>The training can be finished within 20 seconds on <code class="docutils literal notranslate"><span class="pre">CPU</span></code>.</p></li>
|
||||
</ul>
|
||||
</div></blockquote>
|
||||
<p>That also means you don’t need a <code class="docutils literal notranslate"><span class="pre">GPU</span></code> to run this tutorial.</p>
|
||||
<p>Let’s get started!</p>
|
||||
<p>Please follow items below <strong>sequentially</strong>.</p>
|
||||
<div class="admonition note">
|
||||
<p class="admonition-title">Note</p>
|
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<p>The <a class="reference internal" href="data-preparation.html#dummies-tutorial-data-preparation"><span class="std std-ref">Data Preparation</span></a> runs only on Linux and on macOS.
|
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All other parts run on Linux, macOS, and Windows.</p>
|
||||
<p>Help from the community is appreciated to port the <a class="reference internal" href="data-preparation.html#dummies-tutorial-data-preparation"><span class="std std-ref">Data Preparation</span></a>
|
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to Windows.</p>
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<li class="toctree-l2"><a class="reference internal" href="environment-setup.html#install-icefall">Install icefall</a></li>
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<li class="toctree-l2"><a class="reference internal" href="data-preparation.html#a-quick-look-to-the-generated-files">A quick look to the generated files</a></li>
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</ul>
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</li>
|
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<li class="toctree-l1"><a class="reference internal" href="model-export.html">Model Export</a><ul>
|
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<li class="toctree-l2"><a class="reference internal" href="model-export.html#export-the-model-parameters-via-model-state-dict">Export the model parameters via model.state_dict()</a></li>
|
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<li class="toctree-l2"><a class="reference internal" href="model-export.html#export-via-torch-jit-script">Export via torch.jit.script()</a></li>
|
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<li class="toctree-l2"><a class="reference internal" href="model-export.html#export-via-torch-onnx-export">Export via torch.onnx.export()</a></li>
|
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<li class="toctree-l2"><a class="reference internal" href="model-export.html#for-the-more-curious">For the more curious</a></li>
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<section id="training">
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<span id="dummies-tutorial-training"></span><h1>Training<a class="headerlink" href="#training" title="Permalink to this heading"></a></h1>
|
||||
<p>After <a class="reference internal" href="data-preparation.html#dummies-tutorial-data-preparation"><span class="std std-ref">Data Preparation</span></a>, we can start training.</p>
|
||||
<p>The command to start the training is quite simple:</p>
|
||||
<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span><span class="nb">cd</span><span class="w"> </span>/tmp/icefall
|
||||
<span class="nb">export</span><span class="w"> </span><span class="nv">PYTHONPATH</span><span class="o">=</span>/tmp/icefall:<span class="nv">$PYTHONPATH</span>
|
||||
<span class="nb">cd</span><span class="w"> </span>egs/yesno/ASR
|
||||
|
||||
<span class="c1"># We use CPU for training by setting the following environment variable</span>
|
||||
<span class="nb">export</span><span class="w"> </span><span class="nv">CUDA_VISIBLE_DEVICES</span><span class="o">=</span><span class="s2">""</span>
|
||||
|
||||
./tdnn/train.py
|
||||
</pre></div>
|
||||
</div>
|
||||
<p>That’s it!</p>
|
||||
<p>You can find the training logs below:</p>
|
||||
<section id="for-the-more-curious">
|
||||
<h2>For the more curious<a class="headerlink" href="#for-the-more-curious" title="Permalink to this heading"></a></h2>
|
||||
<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>./tdnn/train.py<span class="w"> </span>--help
|
||||
</pre></div>
|
||||
</div>
|
||||
<p>will print the usage information about <code class="docutils literal notranslate"><span class="pre">./tdnn/train.py</span></code>. For instance, you
|
||||
can specify the number of epochs to train and the location to save the training
|
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results.</p>
|
||||
<p>The training text logs are saved in <code class="docutils literal notranslate"><span class="pre">tdnn/exp/log</span></code> while the tensorboard
|
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logs are in <code class="docutils literal notranslate"><span class="pre">tdnn/exp/tensorboard</span></code>.</p>
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BIN
objects.inv
BIN
objects.inv
Binary file not shown.
@ -44,6 +44,7 @@
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@ -44,6 +44,7 @@
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@ -347,10 +348,10 @@ $<span class="w"> </span>tensorboard<span class="w"> </span>dev<span class="w">
|
||||
<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="id6">
|
||||
<div><figure class="align-center" id="id7">
|
||||
<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="#id6" title="Permalink to this image"></a></p>
|
||||
<p><span class="caption-number">Fig. 4 </span><span class="caption-text">TensorBoard screenshot.</span><a class="headerlink" href="#id7" title="Permalink to this image"></a></p>
|
||||
</figcaption>
|
||||
</figure>
|
||||
</div></blockquote>
|
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|
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@ -44,6 +44,7 @@
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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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<p class="caption" role="heading"><span class="caption-text">Contents:</span></p>
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@ -230,10 +231,10 @@ and prepares MVQ-augmented training manifests.</p>
|
||||
</div>
|
||||
<p>Please see the
|
||||
following screenshot for the output of an example execution.</p>
|
||||
<figure class="align-center" id="id4">
|
||||
<figure class="align-center" id="id5">
|
||||
<a class="reference internal image-reference" href="../../../_images/distillation_codebook.png"><img alt="Downloading codebook indexes and preparing training manifest." src="../../../_images/distillation_codebook.png" style="width: 800px;" /></a>
|
||||
<figcaption>
|
||||
<p><span class="caption-number">Fig. 6 </span><span class="caption-text">Downloading codebook indexes and preparing training manifest.</span><a class="headerlink" href="#id4" title="Permalink to this image"></a></p>
|
||||
<p><span class="caption-number">Fig. 6 </span><span class="caption-text">Downloading codebook indexes and preparing training manifest.</span><a class="headerlink" href="#id5" title="Permalink to this image"></a></p>
|
||||
</figcaption>
|
||||
</figure>
|
||||
<div class="admonition hint">
|
||||
@ -245,10 +246,10 @@ set <code class="docutils literal notranslate"><span class="pre">use_extracted_c
|
||||
<code class="docutils literal notranslate"><span class="pre">num_codebooks</span></code> by yourself.</p>
|
||||
</div>
|
||||
<p>Now, you should see the following files under the directory <code class="docutils literal notranslate"><span class="pre">./data/vq_fbank_layer36_cb8</span></code>.</p>
|
||||
<figure class="align-center" id="id5">
|
||||
<figure class="align-center" id="id6">
|
||||
<a class="reference internal image-reference" href="../../../_images/distillation_directory.png"><img alt="MVQ-augmented training manifests" src="../../../_images/distillation_directory.png" style="width: 800px;" /></a>
|
||||
<figcaption>
|
||||
<p><span class="caption-number">Fig. 7 </span><span class="caption-text">MVQ-augmented training manifests.</span><a class="headerlink" href="#id5" title="Permalink to this image"></a></p>
|
||||
<p><span class="caption-number">Fig. 7 </span><span class="caption-text">MVQ-augmented training manifests.</span><a class="headerlink" href="#id6" title="Permalink to this image"></a></p>
|
||||
</figcaption>
|
||||
</figure>
|
||||
<p>Whola! You are ready to perform knowledge distillation training now!</p>
|
||||
|
||||
@ -44,6 +44,7 @@
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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="../../../docker/index.html">Docker</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="../../../faqs.html">Frequently Asked Questions (FAQs)</a></li>
|
||||
@ -385,10 +386,10 @@ $<span class="w"> </span>tensorboard<span class="w"> </span>dev<span class="w">
|
||||
<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="id4">
|
||||
<div><figure class="align-center" id="id5">
|
||||
<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="#id4" title="Permalink to this image"></a></p>
|
||||
<p><span class="caption-number">Fig. 5 </span><span class="caption-text">TensorBoard screenshot.</span><a class="headerlink" href="#id5" title="Permalink to this image"></a></p>
|
||||
</figcaption>
|
||||
</figure>
|
||||
</div></blockquote>
|
||||
|
||||
@ -44,6 +44,7 @@
|
||||
</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="../../../for-dummies/index.html">Icefall for dummies tutorial</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="../../../installation/index.html">Installation</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="../../../docker/index.html">Docker</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="../../../faqs.html">Frequently Asked Questions (FAQs)</a></li>
|
||||
|
||||
@ -44,6 +44,7 @@
|
||||
</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="../../../for-dummies/index.html">Icefall for dummies tutorial</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="../../../installation/index.html">Installation</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="../../../docker/index.html">Docker</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="../../../faqs.html">Frequently Asked Questions (FAQs)</a></li>
|
||||
|
||||
@ -44,6 +44,7 @@
|
||||
</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="../../../for-dummies/index.html">Icefall for dummies tutorial</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="../../../installation/index.html">Installation</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="../../../docker/index.html">Docker</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="../../../faqs.html">Frequently Asked Questions (FAQs)</a></li>
|
||||
|
||||
@ -44,6 +44,7 @@
|
||||
</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="../../../for-dummies/index.html">Icefall for dummies tutorial</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="../../../installation/index.html">Installation</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="../../../docker/index.html">Docker</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="../../../faqs.html">Frequently Asked Questions (FAQs)</a></li>
|
||||
|
||||
@ -44,6 +44,7 @@
|
||||
</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="../../../for-dummies/index.html">Icefall for dummies tutorial</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="../../../installation/index.html">Installation</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="../../../docker/index.html">Docker</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="../../../faqs.html">Frequently Asked Questions (FAQs)</a></li>
|
||||
|
||||
@ -44,6 +44,7 @@
|
||||
</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="../../../for-dummies/index.html">Icefall for dummies tutorial</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="../../../installation/index.html">Installation</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="../../../docker/index.html">Docker</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="../../../faqs.html">Frequently Asked Questions (FAQs)</a></li>
|
||||
|
||||
@ -44,6 +44,7 @@
|
||||
</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="../../../for-dummies/index.html">Icefall for dummies tutorial</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="../../../installation/index.html">Installation</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="../../../docker/index.html">Docker</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="../../../faqs.html">Frequently Asked Questions (FAQs)</a></li>
|
||||
|
||||
@ -44,6 +44,7 @@
|
||||
</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="../../../for-dummies/index.html">Icefall for dummies tutorial</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="../../../installation/index.html">Installation</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="../../../docker/index.html">Docker</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="../../../faqs.html">Frequently Asked Questions (FAQs)</a></li>
|
||||
|
||||
@ -44,6 +44,7 @@
|
||||
</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="../../for-dummies/index.html">Icefall for dummies tutorial</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="../../installation/index.html">Installation</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="../../docker/index.html">Docker</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="../../faqs.html">Frequently Asked Questions (FAQs)</a></li>
|
||||
|
||||
@ -44,6 +44,7 @@
|
||||
</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="../../for-dummies/index.html">Icefall for dummies tutorial</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="../../installation/index.html">Installation</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="../../docker/index.html">Docker</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="../../faqs.html">Frequently Asked Questions (FAQs)</a></li>
|
||||
|
||||
@ -44,6 +44,7 @@
|
||||
</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="../../../for-dummies/index.html">Icefall for dummies tutorial</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="../../../installation/index.html">Installation</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="../../../docker/index.html">Docker</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="../../../faqs.html">Frequently Asked Questions (FAQs)</a></li>
|
||||
|
||||
@ -44,6 +44,7 @@
|
||||
</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="../../../for-dummies/index.html">Icefall for dummies tutorial</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="../../../installation/index.html">Installation</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="../../../docker/index.html">Docker</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="../../../faqs.html">Frequently Asked Questions (FAQs)</a></li>
|
||||
@ -384,10 +385,10 @@ $<span class="w"> </span>tensorboard<span class="w"> </span>dev<span class="w">
|
||||
<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="id5">
|
||||
<div><figure class="align-center" id="id6">
|
||||
<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. 10 </span><span class="caption-text">TensorBoard screenshot.</span><a class="headerlink" href="#id5" title="Permalink to this image"></a></p>
|
||||
<p><span class="caption-number">Fig. 10 </span><span class="caption-text">TensorBoard screenshot.</span><a class="headerlink" href="#id6" title="Permalink to this image"></a></p>
|
||||
</figcaption>
|
||||
</figure>
|
||||
</div></blockquote>
|
||||
|
||||
@ -44,6 +44,7 @@
|
||||
</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="../../../for-dummies/index.html">Icefall for dummies tutorial</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="../../../installation/index.html">Installation</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="../../../docker/index.html">Docker</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="../../../faqs.html">Frequently Asked Questions (FAQs)</a></li>
|
||||
@ -404,10 +405,10 @@ $<span class="w"> </span>tensorboard<span class="w"> </span>dev<span class="w">
|
||||
<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="id5">
|
||||
<div><figure class="align-center" id="id6">
|
||||
<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. 9 </span><span class="caption-text">TensorBoard screenshot.</span><a class="headerlink" href="#id5" title="Permalink to this image"></a></p>
|
||||
<p><span class="caption-number">Fig. 9 </span><span class="caption-text">TensorBoard screenshot.</span><a class="headerlink" href="#id6" title="Permalink to this image"></a></p>
|
||||
</figcaption>
|
||||
</figure>
|
||||
</div></blockquote>
|
||||
|
||||
@ -44,6 +44,7 @@
|
||||
</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="../../../for-dummies/index.html">Icefall for dummies tutorial</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="../../../installation/index.html">Installation</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="../../../docker/index.html">Docker</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="../../../faqs.html">Frequently Asked Questions (FAQs)</a></li>
|
||||
|
||||
@ -44,6 +44,7 @@
|
||||
</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="../for-dummies/index.html">Icefall for dummies tutorial</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="../installation/index.html">Installation</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="../docker/index.html">Docker</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="../faqs.html">Frequently Asked Questions (FAQs)</a></li>
|
||||
|
||||
@ -44,6 +44,7 @@
|
||||
</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="for-dummies/index.html">Icefall for dummies tutorial</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="installation/index.html">Installation</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="docker/index.html">Docker</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="faqs.html">Frequently Asked Questions (FAQs)</a></li>
|
||||
|
||||
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