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add ngram-lm-scale=0.35 results
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@ -15,7 +15,7 @@ When training with the L subset, the WERs are
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| fast beam search (1best) | 7.94 | 8.74 | 13.80 | --epoch 10, --avg 2, --max-duration 1500 |
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| fast beam search (nbest) | 9.82 | 10.98 | 16.37 | --epoch 10, --avg 2, --max-duration 600 |
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| fast beam search (nbest oracle) | 6.88 | 7.18 | 11.77 | --epoch 10, --avg 2, --max-duration 600 |
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| fast beam search (nbest LG) | 14.94 | 16.14 | 22.93 | --epoch 10, --avg 2, --max-duration 600 |
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| fast beam search (nbest LG, ngram_lm_scale=0.35) | 8.83 | 9.88 | 15.47 | --epoch 10, --avg 2, --max-duration 600 |
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The training command for reproducing is given below:
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@ -110,6 +110,7 @@ avg=2
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--lang-dir data/lang_char \
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--max-duration 600 \
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--decoding-method fast_beam_search_nbest_LG \
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--ngram-lm-scale 0.35 \
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--beam 20.0 \
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--max-contexts 8 \
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--max-states 64
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@ -225,7 +225,7 @@ def get_parser():
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parser.add_argument(
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"--ngram-lm-scale",
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type=float,
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default=0.01,
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default=0.07,
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help="""
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Used only when --decoding_method is fast_beam_search_nbest_LG.
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It specifies the scale for n-gram LM scores.
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@ -590,6 +590,8 @@ def main():
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params.suffix += f"-beam-{params.beam}"
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params.suffix += f"-max-contexts-{params.max_contexts}"
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params.suffix += f"-max-states-{params.max_states}"
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if params.decoding_method == "fast_beam_search_nbest_LG":
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params.suffix += f"-ngram-lm-scale-{params.ngram_lm_scale}"
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elif "beam_search" in params.decoding_method:
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params.suffix += f"-beam-{params.beam_size}"
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else:
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