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"""Recipe for training a BPE tokenizer for Fluent Speech Commands. |
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The tokenizer coverts semantics into sub-word units that can |
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be used to train a language (LM) or an acoustic model (AM). |
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To run this recipe, do the following: |
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> python train.py hparams/tokenizer_bpe51.yaml |
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Authors |
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* Abdel Heba 2021 |
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* Mirco Ravanelli 2021 |
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* Loren Lugosch 2021 |
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""" |
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import sys |
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import speechbrain as sb |
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from hyperpyyaml import load_hyperpyyaml |
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from speechbrain.utils.distributed import run_on_main |
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if __name__ == "__main__": |
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hparams_file, run_opts, overrides = sb.parse_arguments(sys.argv[1:]) |
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with open(hparams_file) as fin: |
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hparams = load_hyperpyyaml(fin, overrides) |
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sb.utils.distributed.ddp_init_group(run_opts) |
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sb.create_experiment_directory( |
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experiment_directory=hparams["output_folder"], |
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hyperparams_to_save=hparams_file, |
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overrides=overrides, |
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) |
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from prepare import prepare_FSC |
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run_on_main( |
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prepare_FSC, |
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kwargs={ |
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"data_folder": hparams["data_folder"], |
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"save_folder": hparams["output_folder"], |
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"skip_prep": hparams["skip_prep"], |
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}, |
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) |
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hparams["tokenizer"]() |
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