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181 lines
8.2 KiB
ReStructuredText
181 lines
8.2 KiB
ReStructuredText
.. _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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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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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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**data/lm**:
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This directory contains language models.
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