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Add attention rescoring
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@ -21,7 +21,16 @@
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"""
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Usage:
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(1) ctc-decoding
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(1) ctc-greedy-search
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./zipformer/ctc_decode.py \
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--epoch 30 \
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--avg 15 \
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--exp-dir ./zipformer/exp \
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--use-ctc 1 \
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--max-duration 600 \
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--decoding-method ctc-greedy-search
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(2) ctc-decoding
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./zipformer/ctc_decode.py \
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--epoch 30 \
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--avg 15 \
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@ -30,7 +39,7 @@ Usage:
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--max-duration 600 \
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--decoding-method ctc-decoding
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(2) 1best
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(3) 1best
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./zipformer/ctc_decode.py \
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--epoch 30 \
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--avg 15 \
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@ -40,7 +49,7 @@ Usage:
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--hlg-scale 0.6 \
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--decoding-method 1best
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(3) nbest
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(4) nbest
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./zipformer/ctc_decode.py \
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--epoch 30 \
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--avg 15 \
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@ -50,7 +59,7 @@ Usage:
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--hlg-scale 0.6 \
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--decoding-method nbest
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(4) nbest-rescoring
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(5) nbest-rescoring
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./zipformer/ctc_decode.py \
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--epoch 30 \
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--avg 15 \
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@ -62,7 +71,7 @@ Usage:
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--lm-dir data/lm \
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--decoding-method nbest-rescoring
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(5) whole-lattice-rescoring
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(6) whole-lattice-rescoring
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./zipformer/ctc_decode.py \
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--epoch 30 \
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--avg 15 \
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@ -73,6 +82,29 @@ Usage:
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--nbest-scale 1.0 \
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--lm-dir data/lm \
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--decoding-method whole-lattice-rescoring
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(7) attention-decoder-rescoring-no-ngram
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./zipformer/ctc_decode.py \
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--epoch 30 \
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--avg 15 \
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--exp-dir ./zipformer/exp \
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--use-ctc 1 \
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--use-attention-decoder 1 \
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--max-duration 100 \
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--decoding-method attention-decoder-rescoring-no-ngram
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(8) attention-decoder-rescoring-with-ngram
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./zipformer/ctc_decode.py \
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--epoch 30 \
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--avg 15 \
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--exp-dir ./zipformer/exp \
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--use-ctc 1 \
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--use-attention-decoder 1 \
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--max-duration 100 \
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--hlg-scale 0.6 \
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--nbest-scale 1.0 \
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--lm-dir data/lm \
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--decoding-method attention-decoder-rescoring-with-ngram
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"""
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@ -87,9 +119,11 @@ import k2
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import sentencepiece as spm
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import torch
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import torch.nn as nn
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from asr_datamodule import GigaSpeechAsrDataModule
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from asr_datamodule import GigaSpeechAsrDataModule
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from gigaspeech_scoring import asr_text_post_processing
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from lhotse import set_caching_enabled
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from train import add_model_arguments, get_model, get_params
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from icefall.checkpoint import (
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@ -99,10 +133,13 @@ from icefall.checkpoint import (
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load_checkpoint,
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)
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from icefall.decode import (
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ctc_greedy_search,
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get_lattice,
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nbest_decoding,
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nbest_oracle,
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one_best_decoding,
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rescore_with_attention_decoder_no_ngram,
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rescore_with_attention_decoder_with_ngram,
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rescore_with_n_best_list,
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rescore_with_whole_lattice,
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)
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@ -197,23 +234,30 @@ def get_parser():
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default="ctc-decoding",
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help="""Decoding method.
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Supported values are:
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- (1) ctc-decoding. Use CTC decoding. It uses a sentence piece
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- (1) ctc-greedy-search. Use CTC greedy search. It uses a sentence piece
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model, i.e., lang_dir/bpe.model, to convert word pieces to words.
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It needs neither a lexicon nor an n-gram LM.
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- (2) 1best. Extract the best path from the decoding lattice as the
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- (2) ctc-decoding. Use CTC decoding. It uses a sentence piece
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model, i.e., lang_dir/bpe.model, to convert word pieces to words.
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It needs neither a lexicon nor an n-gram LM.
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- (3) 1best. Extract the best path from the decoding lattice as the
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decoding result.
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- (3) nbest. Extract n paths from the decoding lattice; the path
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- (4) nbest. Extract n paths from the decoding lattice; the path
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with the highest score is the decoding result.
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- (4) nbest-rescoring. Extract n paths from the decoding lattice,
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- (5) nbest-rescoring. Extract n paths from the decoding lattice,
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rescore them with an n-gram LM (e.g., a 4-gram LM), the path with
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the highest score is the decoding result.
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- (5) whole-lattice-rescoring. Rescore the decoding lattice with an
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- (6) whole-lattice-rescoring. Rescore the decoding lattice with an
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n-gram LM (e.g., a 4-gram LM), the best path of rescored lattice
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is the decoding result.
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you have trained an RNN LM using ./rnn_lm/train.py
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- (6) nbest-oracle. Its WER is the lower bound of any n-best
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- (7) nbest-oracle. Its WER is the lower bound of any n-best
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rescoring method can achieve. Useful for debugging n-best
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rescoring method.
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- (8) attention-decoder-rescoring-no-ngram. Extract n paths from the decoding
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lattice, rescore them with the attention decoder.
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- (9) attention-decoder-rescoring-with-ngram. Extract n paths from the LM
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rescored lattice, rescore them with the attention decoder.
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""",
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)
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@ -256,6 +300,13 @@ def get_parser():
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""",
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)
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parser.add_argument(
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"--skip-scoring",
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type=str2bool,
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default=False,
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help="""Skip scoring, but still save the ASR output (for eval sets).""",
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)
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add_model_arguments(parser)
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return parser
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@ -276,17 +327,6 @@ def get_decoding_params() -> AttributeDict:
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return params
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def post_processing(
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results: List[Tuple[str, List[str], List[str]]],
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) -> List[Tuple[str, List[str], List[str]]]:
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new_results = []
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for key, ref, hyp in results:
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new_ref = asr_text_post_processing(" ".join(ref)).split()
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new_hyp = asr_text_post_processing(" ".join(hyp)).split()
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new_results.append((key, new_ref, new_hyp))
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return new_results
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def decode_one_batch(
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params: AttributeDict,
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model: nn.Module,
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@ -365,6 +405,15 @@ def decode_one_batch(
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encoder_out, encoder_out_lens = model.forward_encoder(feature, feature_lens)
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ctc_output = model.ctc_output(encoder_out) # (N, T, C)
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if params.decoding_method == "ctc-greedy-search":
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hyps = ctc_greedy_search(ctc_output, encoder_out_lens)
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# hyps is a list of str, e.g., ['xxx yyy zzz', ...]
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hyps = bpe_model.decode(hyps)
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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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key = "ctc-greedy-search"
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return {key: hyps}
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supervision_segments = torch.stack(
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(
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supervisions["sequence_idx"],
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@ -417,7 +466,27 @@ def decode_one_batch(
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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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key = "ctc-decoding"
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return {key: hyps}
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return {key: hyps} # note: returns words
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if params.decoding_method == "attention-decoder-rescoring-no-ngram":
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best_path_dict = rescore_with_attention_decoder_no_ngram(
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lattice=lattice,
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num_paths=params.num_paths,
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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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nbest_scale=params.nbest_scale,
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)
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ans = dict()
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for a_scale_str, best_path in best_path_dict.items():
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# token_ids is a lit-of-list of IDs
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token_ids = get_texts(best_path)
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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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if params.decoding_method == "nbest-oracle":
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# Note: You can also pass rescored lattices to it.
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@ -434,7 +503,7 @@ def decode_one_batch(
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)
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hyps = get_texts(best_path)
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hyps = [[word_table[i] for i in ids] for ids in hyps]
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key = f"oracle_{params.num_paths}_nbest_scale_{params.nbest_scale}" # noqa
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key = f"oracle_{params.num_paths}_nbest-scale-{params.nbest_scale}" # noqa
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return {key: hyps}
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if params.decoding_method in ["1best", "nbest"]:
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@ -442,7 +511,7 @@ def decode_one_batch(
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best_path = one_best_decoding(
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lattice=lattice, use_double_scores=params.use_double_scores
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)
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key = "no_rescore"
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key = "no-rescore"
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else:
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best_path = nbest_decoding(
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lattice=lattice,
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@ -450,15 +519,16 @@ def decode_one_batch(
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use_double_scores=params.use_double_scores,
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nbest_scale=params.nbest_scale,
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)
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key = f"no_rescore-nbest-scale-{params.nbest_scale}-{params.num_paths}" # noqa
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key = f"no-rescore_nbest-scale-{params.nbest_scale}-{params.num_paths}" # noqa
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hyps = get_texts(best_path)
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hyps = [[word_table[i] for i in ids] for ids in hyps]
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return {key: hyps}
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return {key: hyps} # note: returns BPE tokens
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assert params.decoding_method in [
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"nbest-rescoring",
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"whole-lattice-rescoring",
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"attention-decoder-rescoring-with-ngram",
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]
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lm_scale_list = [0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7]
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@ -479,6 +549,21 @@ def decode_one_batch(
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G_with_epsilon_loops=G,
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lm_scale_list=lm_scale_list,
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)
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elif params.decoding_method == "attention-decoder-rescoring-with-ngram":
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# lattice uses a 3-gram Lm. We rescore it with a 4-gram LM.
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rescored_lattice = rescore_with_whole_lattice(
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lattice=lattice,
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G_with_epsilon_loops=G,
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lm_scale_list=None,
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)
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best_path_dict = rescore_with_attention_decoder_with_ngram(
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lattice=rescored_lattice,
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num_paths=params.num_paths,
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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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nbest_scale=params.nbest_scale,
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)
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else:
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assert False, f"Unsupported decoding method: {params.decoding_method}"
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@ -572,39 +657,64 @@ def decode_dataset(
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return results
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def save_results(
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def save_asr_output(
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params: AttributeDict,
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test_set_name: str,
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results_dict: Dict[str, List[Tuple[str, List[str], List[str]]]],
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):
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test_set_wers = dict()
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"""
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Save text produced by ASR.
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"""
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for key, results in results_dict.items():
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recog_path = params.res_dir / f"recogs-{test_set_name}-{params.suffix}.txt"
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recogs_filename = params.res_dir / f"recogs-{test_set_name}-{params.suffix}.txt"
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results = post_processing(results)
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results = sorted(results)
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store_transcripts(filename=recog_path, texts=results)
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logging.info(f"The transcripts are stored in {recog_path}")
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store_transcripts(filename=recogs_filename, texts=results)
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logging.info(f"The transcripts are stored in {recogs_filename}")
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def save_wer_results(
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params: AttributeDict,
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test_set_name: str,
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results_dict: Dict[str, List[Tuple[str, List[str], List[str]]]],
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):
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if params.decoding_method in (
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"attention-decoder-rescoring-with-ngram",
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"whole-lattice-rescoring",
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):
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# Set it to False since there are too many logs.
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enable_log = False
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else:
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enable_log = True
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test_set_wers = dict()
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for key, results in results_dict.items():
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results = post_processing(results)
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# The following prints out WERs, per-word error statistics and aligned
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# ref/hyp pairs.
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errs_filename = params.res_dir / f"errs-{test_set_name}-{params.suffix}.txt"
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with open(errs_filename, "w") as f:
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wer = write_error_stats(f, f"{test_set_name}-{key}", results)
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with open(errs_filename, "w", encoding="utf8") as fd:
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wer = write_error_stats(
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fd, f"{test_set_name}_{key}", results, enable_log=enable_log
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)
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test_set_wers[key] = wer
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logging.info("Wrote detailed error stats to {}".format(errs_filename))
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logging.info(f"Wrote detailed error stats to {errs_filename}")
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test_set_wers = sorted(test_set_wers.items(), key=lambda x: x[1])
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errs_info = params.res_dir / f"wer-summary-{test_set_name}-{params.suffix}.txt"
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with open(errs_info, "w") as f:
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print("settings\tWER", file=f)
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for key, val in test_set_wers:
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print("{}\t{}".format(key, val), file=f)
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s = "\nFor {}, WER of different settings are:\n".format(test_set_name)
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note = "\tbest for {}".format(test_set_name)
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wer_filename = params.res_dir / f"wer-summary-{test_set_name}-{params.suffix}.txt"
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with open(wer_filename, "w", encoding="utf8") as fd:
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print("settings\tWER", file=fd)
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for key, val in test_set_wers:
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s += "{}\t{}{}\n".format(key, val, note)
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print(f"{key}\t{val}", file=fd)
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s = f"\nFor {test_set_name}, WER of different settings are:\n"
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note = f"\tbest for {test_set_name}"
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for key, val in test_set_wers:
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s += f"{key}\t{val}{note}\n"
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note = ""
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logging.info(s)
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@ -623,20 +733,26 @@ def main():
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params.update(get_decoding_params())
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params.update(vars(args))
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# enable AudioCache
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set_caching_enabled(True) # lhotse
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assert params.decoding_method in (
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"ctc-greedy-search",
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"ctc-decoding",
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"1best",
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"nbest",
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"nbest-rescoring",
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"whole-lattice-rescoring",
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"nbest-oracle",
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"attention-decoder-rescoring-no-ngram",
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"attention-decoder-rescoring-with-ngram",
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)
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params.res_dir = params.exp_dir / params.decoding_method
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if params.iter > 0:
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params.suffix = f"iter-{params.iter}-avg-{params.avg}"
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params.suffix = f"iter-{params.iter}_avg-{params.avg}"
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else:
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params.suffix = f"epoch-{params.epoch}-avg-{params.avg}"
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params.suffix = f"epoch-{params.epoch}_avg-{params.avg}"
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if params.causal:
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assert (
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@ -645,11 +761,11 @@ def main():
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assert (
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"," not in params.left_context_frames
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), "left_context_frames should be one value in decoding."
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params.suffix += f"-chunk-{params.chunk_size}"
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params.suffix += f"-left-context-{params.left_context_frames}"
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params.suffix += f"_chunk-{params.chunk_size}"
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params.suffix += f"_left-context-{params.left_context_frames}"
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if params.use_averaged_model:
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params.suffix += "-use-averaged-model"
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params.suffix += "_use-averaged-model"
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setup_logger(f"{params.res_dir}/log-decode-{params.suffix}")
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logging.info("Decoding started")
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@ -668,8 +784,14 @@ def main():
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params.vocab_size = num_classes
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# <blk> and <unk> are defined in local/train_bpe_model.py
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params.blank_id = 0
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params.eos_id = 1
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params.sos_id = 1
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if params.decoding_method == "ctc-decoding":
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if params.decoding_method in [
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"ctc-greedy-search",
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"ctc-decoding",
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"attention-decoder-rescoring-no-ngram",
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]:
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HLG = None
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H = k2.ctc_topo(
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max_token=max_token_id,
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@ -693,6 +815,7 @@ def main():
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if params.decoding_method in (
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"nbest-rescoring",
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"whole-lattice-rescoring",
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"attention-decoder-rescoring-with-ngram",
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):
|
||||
if not (params.lm_dir / "G_4_gram.pt").is_file():
|
||||
logging.info("Loading G_4_gram.fst.txt")
|
||||
@ -724,7 +847,10 @@ def main():
|
||||
d = torch.load(params.lm_dir / "G_4_gram.pt", map_location=device)
|
||||
G = k2.Fsa.from_dict(d)
|
||||
|
||||
if params.decoding_method == "whole-lattice-rescoring":
|
||||
if params.decoding_method in [
|
||||
"whole-lattice-rescoring",
|
||||
"attention-decoder-rescoring-with-ngram",
|
||||
]:
|
||||
# Add epsilon self-loops to G as we will compose
|
||||
# it with the whole lattice later
|
||||
G = k2.add_epsilon_self_loops(G)
|
||||
@ -825,6 +951,7 @@ def main():
|
||||
|
||||
# we need cut ids to display recognition results.
|
||||
args.return_cuts = True
|
||||
|
||||
gigaspeech = GigaSpeechAsrDataModule(args)
|
||||
|
||||
test_cuts = gigaspeech.test_cuts()
|
||||
@ -832,9 +959,9 @@ def main():
|
||||
test_dl = gigaspeech.test_dataloaders(test_cuts)
|
||||
|
||||
test_sets = ["test"]
|
||||
test_dl = [test_dl]
|
||||
test_dls = [test_dl]
|
||||
|
||||
for test_set, test_dl in zip(test_sets, test_dl):
|
||||
for test_set, test_dl in zip(test_sets, test_dls):
|
||||
results_dict = decode_dataset(
|
||||
dl=test_dl,
|
||||
params=params,
|
||||
@ -846,7 +973,14 @@ def main():
|
||||
G=G,
|
||||
)
|
||||
|
||||
save_results(
|
||||
save_asr_output(
|
||||
params=params,
|
||||
test_set_name=test_set,
|
||||
results_dict=results_dict,
|
||||
)
|
||||
|
||||
if not params.skip_scoring:
|
||||
save_wer_results(
|
||||
params=params,
|
||||
test_set_name=test_set,
|
||||
results_dict=results_dict,
|
||||
|
Loading…
x
Reference in New Issue
Block a user