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add ctc prefix beam search attention decoder rescoring
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@ -1,9 +1,10 @@
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#!/usr/bin/env python3
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#
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# Copyright 2021-2022 Xiaomi Corporation (Author: Fangjun Kuang,
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# Copyright 2021-2024 Xiaomi Corporation (Author: Fangjun Kuang,
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# Liyong Guo,
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# Quandong Wang,
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# Zengwei Yao)
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# Zengwei Yao,
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# Wei Kang)
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#
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# See ../../../../LICENSE for clarification regarding multiple authors
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#
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@ -135,6 +136,7 @@ from icefall.checkpoint import (
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from icefall.decode import (
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ctc_greedy_search,
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ctc_prefix_beam_search,
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ctc_prefix_beam_search_attention_decoder_rescoring,
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get_lattice,
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nbest_decoding,
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nbest_oracle,
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@ -435,6 +437,22 @@ def decode_one_batch(
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key = "prefix-beam-search"
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return {key: hyps}
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if params.decoding_method == "ctc-prefix-beam-search-attention-decoder-rescoring":
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best_path_dict = ctc_prefix_beam_search_attention_decoder_rescoring(
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ctc_output=ctc_output,
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attention_decoder=model.attention_decoder,
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encoder_out=encoder_out,
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encoder_out_lens=encoder_out_lens,
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)
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ans = dict()
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for a_scale_str, token_ids in best_path_dict.items():
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# hyps is a list of str, e.g., ['xxx yyy zzz', ...]
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hyps = bpe_model.decode(token_ids)
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# hyps is a list of list of str, e.g., [['xxx', 'yyy', 'zzz'], ... ]
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hyps = [s.split() for s in hyps]
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ans[a_scale_str] = hyps
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return ans
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supervision_segments = torch.stack(
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(
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supervisions["sequence_idx"],
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@ -760,6 +778,7 @@ def main():
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assert params.decoding_method in (
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"ctc-greedy-search",
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"prefix-beam-search",
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"ctc-prefix-beam-search-attention-decoder-rescoring",
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"ctc-decoding",
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"1best",
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"nbest",
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@ -814,6 +833,7 @@ def main():
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"ctc-greedy-search",
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"ctc-decoding",
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"prefix-beam-search",
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"ctc-prefix-beam-search-attention-decoder-rescoring",
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"attention-decoder-rescoring-no-ngram",
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]:
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HLG = None
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@ -1,4 +1,5 @@
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# Copyright 2021 Xiaomi Corp. (authors: Fangjun Kuang)
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# Copyright 2021-2024 Xiaomi Corp. (authors: Fangjun Kuang,
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# Wei Kang)
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#
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# See ../../../../LICENSE for clarification regarding multiple authors
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#
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