mirror of
https://github.com/k2-fsa/icefall.git
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1028 lines
33 KiB
Python
Executable File
1028 lines
33 KiB
Python
Executable File
#!/usr/bin/env python3
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# Copyright 2022 Johns Hopkins (authors: Amir Hussein)
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#
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# See ../../../../LICENSE for clarification regarding multiple authors
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""
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Usage:
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(1) greedy search
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./pruned_transducer_stateless5/decode.py \
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--epoch 28 \
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--avg 15 \
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--exp-dir ./pruned_transducer_stateless5/exp \
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--max-duration 600 \
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--decoding-method greedy_search
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(2) beam search (not recommended)
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./pruned_transducer_stateless5/decode.py \
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--epoch 28 \
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--avg 15 \
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--exp-dir ./pruned_transducer_stateless5/exp \
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--max-duration 600 \
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--decoding-method beam_search \
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--beam-size 4
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(3) modified beam search
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./pruned_transducer_stateless5/decode.py \
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--epoch 28 \
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--avg 15 \
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--exp-dir ./pruned_transducer_stateless5/exp \
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--max-duration 600 \
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--decoding-method modified_beam_search \
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--beam-size 4
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(4) fast beam search (one best)
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./pruned_transducer_stateless5/decode.py \
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--epoch 28 \
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--avg 15 \
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--exp-dir ./pruned_transducer_stateless5/exp \
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--max-duration 600 \
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--decoding-method fast_beam_search \
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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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(5) fast beam search (nbest)
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./pruned_transducer_stateless5/decode.py \
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--epoch 28 \
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--avg 15 \
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--exp-dir ./pruned_transducer_stateless5/exp \
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--max-duration 600 \
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--decoding-method fast_beam_search_nbest \
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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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--num-paths 200 \
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--nbest-scale 0.5
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(6) fast beam search (nbest oracle WER)
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./pruned_transducer_stateless5/decode.py \
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--epoch 28 \
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--avg 15 \
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--exp-dir ./pruned_transducer_stateless5/exp \
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--max-duration 600 \
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--decoding-method fast_beam_search_nbest_oracle \
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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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--num-paths 200 \
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--nbest-scale 0.5
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(7) fast beam search (with LG)
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./pruned_transducer_stateless5/decode.py \
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--epoch 28 \
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--avg 15 \
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--exp-dir ./pruned_transducer_stateless5/exp \
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--max-duration 600 \
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--decoding-method fast_beam_search_nbest_LG \
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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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(8) modified beam search with RNNLM shallow fusion (with LG)
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./pruned_transducer_stateless5/decode.py \
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--epoch 35 \
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--avg 15 \
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--exp-dir ./pruned_transducer_stateless5/exp \
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--max-duration 600 \
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--decoding-method fast_beam_search_nbest_LG \
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--beam 4 \
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--max-contexts 4 \
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--rnn-lm-scale 0.4 \
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--rnn-lm-exp-dir /path/to/RNNLM/exp \
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--rnn-lm-epoch 99 \
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--rnn-lm-avg 1 \
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--rnn-lm-num-layers 3 \
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--rnn-lm-tie-weights 1
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"""
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import argparse
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import logging
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import math
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import pdb
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from collections import defaultdict
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from pathlib import Path
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from typing import Dict, List, Optional, Tuple
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from lhotse.qa import validate_cut
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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 IWSLTDialectSTDataModule
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from beam_search import (
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beam_search,
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fast_beam_search_nbest,
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fast_beam_search_nbest_LG,
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fast_beam_search_nbest_oracle,
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fast_beam_search_one_best,
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greedy_search,
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greedy_search_batch,
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modified_beam_search,
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modified_beam_search_rnnlm_shallow_fusion,
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)
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from train import add_model_arguments, get_params, get_transducer_model
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from icefall.checkpoint import (
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average_checkpoints,
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average_checkpoints_with_averaged_model,
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find_checkpoints,
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load_checkpoint,
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)
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from icefall.lexicon import Lexicon
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from icefall.rnn_lm.model import RnnLmModel
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from icefall.utils import (
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AttributeDict,
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setup_logger,
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store_transcripts,
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str2bool,
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write_error_stats,
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)
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LOG_EPS = math.log(1e-10)
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def get_parser():
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parser = argparse.ArgumentParser(
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formatter_class=argparse.ArgumentDefaultsHelpFormatter
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)
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parser.add_argument(
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"--epoch",
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type=int,
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default=30,
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help="""It specifies the checkpoint to use for decoding.
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Note: Epoch counts from 1.
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You can specify --avg to use more checkpoints for model averaging.""",
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)
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parser.add_argument(
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"--iter",
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type=int,
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default=0,
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help="""If positive, --epoch is ignored and it
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will use the checkpoint exp_dir/checkpoint-iter.pt.
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You can specify --avg to use more checkpoints for model averaging.
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""",
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)
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parser.add_argument(
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"--avg",
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type=int,
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default=15,
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help="Number of checkpoints to average. Automatically select "
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"consecutive checkpoints before the checkpoint specified by "
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"'--epoch' and '--iter'",
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)
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parser.add_argument(
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"--use-averaged-model",
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type=str2bool,
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default=True,
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help="Whether to load averaged model. Currently it only supports "
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"using --epoch. If True, it would decode with the averaged model "
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"over the epoch range from `epoch-avg` (excluded) to `epoch`."
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"Actually only the models with epoch number of `epoch-avg` and "
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"`epoch` are loaded for averaging. ",
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)
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parser.add_argument(
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"--exp-dir",
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type=str,
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default="pruned_transducer_stateless5/exp",
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help="The experiment dir",
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)
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parser.add_argument(
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"--bpe-model",
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type=str,
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default="data/lang_bpe_ta_1000/bpe.model",
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help="Path to source data BPE model",
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)
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parser.add_argument(
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"--bpe-tgt-model",
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type=str,
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default="data/lang_bpe_en_1000/bpe.model",
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help="Path to target data BPE model",
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)
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parser.add_argument(
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"--lang-dir",
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type=Path,
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default="data/ang_bpe_ta_1000",
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help="The lang dir containing word table and LG graph",
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)
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parser.add_argument(
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"--lang-tgt-dir",
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type=Path,
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default="data/lang_bpe_en_1000",
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help="The lang dir containing word table and LG graph",
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)
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parser.add_argument(
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"--decoding-method",
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type=str,
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default="greedy_search",
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help="""Possible values are:
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- greedy_search
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- beam_search
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- modified_beam_search
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- fast_beam_search
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- fast_beam_search_LG
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- fast_beam_search_nbest
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- fast_beam_search_nbest_oracle
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- fast_beam_search_nbest_LG
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- modified_beam_search_rnnlm_shallow_fusion # for rnn lm shallow fusion
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If you use fast_beam_search_nbest_LG, you have to specify
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`--lang-dir`, which should contain `LG.pt`.
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""",
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)
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parser.add_argument(
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"--beam-size",
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type=int,
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default=4,
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help="""An integer indicating how many candidates we will keep for each
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frame. Used only when --decoding-method is beam_search or
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modified_beam_search.""",
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)
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parser.add_argument(
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"--beam",
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type=float,
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default=20.0,
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help="""A floating point value to calculate the cutoff score during beam
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search (i.e., `cutoff = max-score - beam`), which is the same as the
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`beam` in Kaldi.
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Used only when --decoding-method is fast_beam_search, fast_beam_search_LG,
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fast_beam_search_nbest, fast_beam_search_nbest_LG,
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and fast_beam_search_nbest_oracle
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""",
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)
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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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help="""
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Used only when --decoding_method is fast_beam_search_nbest_LG and fast_beam_search_LG.
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It specifies the scale for n-gram LM scores.
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""",
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)
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parser.add_argument(
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"--decode-chunk-size",
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type=int,
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default=16,
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help="The chunk size for decoding (in frames after subsampling)",
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)
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parser.add_argument(
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"--left-context",
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type=int,
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default=64,
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help="left context can be seen during decoding (in frames after subsampling)",
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)
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parser.add_argument(
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"--max-contexts",
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type=int,
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default=8,
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help="""Used only when --decoding-method is fast_beam_search_LG,
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fast_beam_search, fast_beam_search_nbest, fast_beam_search_nbest_LG,
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and fast_beam_search_nbest_oracle""",
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)
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parser.add_argument(
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"--max-states",
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type=int,
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default=64,
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help="""Used only when --decoding-method is fast_beam_search_LG,
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fast_beam_search, fast_beam_search_nbest, fast_beam_search_nbest_LG,
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and fast_beam_search_nbest_oracle""",
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)
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parser.add_argument(
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"--context-size",
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type=int,
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default=2,
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help="The context size in the decoder. 1 means bigram; 2 means tri-gram",
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)
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parser.add_argument(
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"--max-sym-per-frame",
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type=int,
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default=1,
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help="""Maximum number of symbols per frame.
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Used only when --decoding_method is greedy_search""",
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)
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parser.add_argument(
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"--num-paths",
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type=int,
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default=200,
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help="""Number of paths for nbest decoding.
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Used only when the decoding method is fast_beam_search_nbest,
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fast_beam_search_nbest_LG, and fast_beam_search_nbest_oracle""",
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)
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parser.add_argument(
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"--nbest-scale",
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type=float,
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default=0.5,
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help="""Scale applied to lattice scores when computing nbest paths.
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Used only when the decoding method is fast_beam_search_nbest,
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fast_beam_search_nbest_LG, and fast_beam_search_nbest_oracle""",
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)
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parser.add_argument(
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"--simulate-streaming",
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type=str2bool,
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default=False,
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help="""Whether to simulate streaming in decoding, this is a good way to
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test a streaming model.
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""",
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)
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parser.add_argument(
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"--rnn-lm-scale",
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type=float,
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default=0.0,
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help="""Used only when --method is modified_beam_search_rnnlm_shallow_fusion.
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It specifies the path to RNN LM exp dir.
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""",
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)
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parser.add_argument(
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"--rnn-lm-exp-dir",
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type=str,
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default="rnn_lm/exp",
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help="""Used only when --method is modified_beam_search_rnnlm_shallow_fusion.
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It specifies the path to RNN LM exp dir.
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""",
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)
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parser.add_argument(
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"--rnn-lm-epoch",
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type=int,
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default=7,
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help="""Used only when --method is modified_beam_search_rnnlm_shallow_fusion.
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It specifies the checkpoint to use.
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""",
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)
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parser.add_argument(
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"--rnn-lm-avg",
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type=int,
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default=2,
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help="""Used only when --method is modified_beam_search_rnnlm_shallow_fusion.
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It specifies the number of checkpoints to average.
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""",
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)
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parser.add_argument(
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"--rnn-lm-embedding-dim",
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type=int,
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default=2048,
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help="Embedding dim of the model",
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)
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parser.add_argument(
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"--rnn-lm-hidden-dim",
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type=int,
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default=2048,
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help="Hidden dim of the model",
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)
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parser.add_argument(
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"--rnn-lm-num-layers",
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type=int,
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default=4,
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help="Number of RNN layers the model",
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)
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parser.add_argument(
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"--rnn-lm-tie-weights",
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type=str2bool,
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default=False,
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help="""True to share the weights between the input embedding layer and the
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last output linear layer
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""",
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)
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add_model_arguments(parser)
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return parser
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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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sp: spm.SentencePieceProcessor,
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batch: dict,
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word_table: Optional[k2.SymbolTable] = None,
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decoding_graph: Optional[k2.Fsa] = None,
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rnnlm: Optional[RnnLmModel] = None,
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rnnlm_scale: float = 1.0,
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) -> Dict[str, List[List[str]]]:
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"""Decode one batch and return the result in a dict. The dict has the
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following format:
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- key: It indicates the setting used for decoding. For example,
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if greedy_search is used, it would be "greedy_search"
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If beam search with a beam size of 7 is used, it would be
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"beam_7"
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- value: It contains the decoding result. `len(value)` equals to
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batch size. `value[i]` is the decoding result for the i-th
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utterance in the given batch.
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Args:
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params:
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It's the return value of :func:`get_params`.
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model:
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The neural model.
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sp:
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The BPE model.
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batch:
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It is the return value from iterating
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`lhotse.dataset.K2SpeechRecognitionDataset`. See its documentation
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for the format of the `batch`.
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word_table:
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The word symbol table.
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decoding_graph:
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The decoding graph. Can be either a `k2.trivial_graph` or LG, Used
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only when --decoding_method is fast_beam_search, fast_beam_search_LG, fast_beam_search_nbest,
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fast_beam_search_nbest_oracle, and fast_beam_search_nbest_LG.
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Returns:
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Return the decoding result. See above description for the format of
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the returned dict.
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"""
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device = next(model.parameters()).device
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feature = batch["inputs"]
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assert feature.ndim == 3
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feature = feature.to(device)
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# at entry, feature is (N, T, C)
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supervisions = batch["supervisions"]
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feature_lens = supervisions["num_frames"].to(device)
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if params.simulate_streaming:
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feature_lens += params.left_context
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feature = torch.nn.functional.pad(
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feature,
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pad=(0, 0, 0, params.left_context),
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value=LOG_EPS,
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)
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encoder_out, encoder_out_lens, _ = model.encoder.streaming_forward(
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x=feature,
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x_lens=feature_lens,
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chunk_size=params.decode_chunk_size,
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left_context=params.left_context,
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simulate_streaming=True,
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)
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else:
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encoder_out, encoder_out_lens = model.encoder(
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x=feature, x_lens=feature_lens)
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hyps = []
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if (
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params.decoding_method == "fast_beam_search"
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or params.decoding_method == "fast_beam_search_LG"
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):
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hyp_tokens = fast_beam_search_one_best(
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model=model,
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decoding_graph=decoding_graph,
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encoder_out=encoder_out,
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encoder_out_lens=encoder_out_lens,
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beam=params.beam,
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max_contexts=params.max_contexts,
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max_states=params.max_states,
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)
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if params.decoding_method == "fast_beam_search":
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for hyp in sp.decode(hyp_tokens):
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hyps.append(hyp.split())
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else:
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for hyp in hyp_tokens:
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hyps.append([word_table[i] for i in hyp])
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elif params.decoding_method == "fast_beam_search_nbest_LG":
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hyp_tokens = fast_beam_search_nbest_LG(
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model=model,
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decoding_graph=decoding_graph,
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encoder_out=encoder_out,
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encoder_out_lens=encoder_out_lens,
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beam=params.beam,
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max_contexts=params.max_contexts,
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max_states=params.max_states,
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num_paths=params.num_paths,
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nbest_scale=params.nbest_scale,
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)
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for hyp in hyp_tokens:
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hyps.append([word_table[i] for i in hyp])
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elif params.decoding_method == "fast_beam_search_nbest":
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hyp_tokens = fast_beam_search_nbest(
|
|
model=model,
|
|
decoding_graph=decoding_graph,
|
|
encoder_out=encoder_out,
|
|
encoder_out_lens=encoder_out_lens,
|
|
beam=params.beam,
|
|
max_contexts=params.max_contexts,
|
|
max_states=params.max_states,
|
|
num_paths=params.num_paths,
|
|
nbest_scale=params.nbest_scale,
|
|
)
|
|
for hyp in sp.decode(hyp_tokens):
|
|
hyps.append(hyp.split())
|
|
elif params.decoding_method == "fast_beam_search_nbest_oracle":
|
|
hyp_tokens = fast_beam_search_nbest_oracle(
|
|
model=model,
|
|
decoding_graph=decoding_graph,
|
|
encoder_out=encoder_out,
|
|
encoder_out_lens=encoder_out_lens,
|
|
beam=params.beam,
|
|
max_contexts=params.max_contexts,
|
|
max_states=params.max_states,
|
|
num_paths=params.num_paths,
|
|
ref_texts=sp.encode(supervisions["text"]),
|
|
nbest_scale=params.nbest_scale,
|
|
)
|
|
for hyp in sp.decode(hyp_tokens):
|
|
hyps.append(hyp.split())
|
|
elif params.decoding_method == "greedy_search" and params.max_sym_per_frame == 1:
|
|
hyp_tokens = greedy_search_batch(
|
|
model=model,
|
|
encoder_out=encoder_out,
|
|
encoder_out_lens=encoder_out_lens,
|
|
)
|
|
for hyp in sp.decode(hyp_tokens):
|
|
hyps.append(hyp.split())
|
|
elif params.decoding_method == "modified_beam_search":
|
|
hyp_tokens = modified_beam_search(
|
|
model=model,
|
|
encoder_out=encoder_out,
|
|
encoder_out_lens=encoder_out_lens,
|
|
beam=params.beam_size,
|
|
)
|
|
for hyp in sp.decode(hyp_tokens):
|
|
hyps.append(hyp.split())
|
|
elif params.decoding_method == "modified_beam_search_rnnlm_shallow_fusion":
|
|
hyp_tokens = modified_beam_search_rnnlm_shallow_fusion(
|
|
model=model,
|
|
encoder_out=encoder_out,
|
|
encoder_out_lens=encoder_out_lens,
|
|
beam=params.beam_size,
|
|
sp=sp,
|
|
rnnlm=rnnlm,
|
|
rnnlm_scale=rnnlm_scale,
|
|
)
|
|
for hyp in sp.decode(hyp_tokens):
|
|
hyps.append(hyp.split())
|
|
else:
|
|
batch_size = encoder_out.size(0)
|
|
|
|
for i in range(batch_size):
|
|
# fmt: off
|
|
encoder_out_i = encoder_out[i:i+1, :encoder_out_lens[i]]
|
|
# fmt: on
|
|
if params.decoding_method == "greedy_search":
|
|
hyp = greedy_search(
|
|
model=model,
|
|
encoder_out=encoder_out_i,
|
|
max_sym_per_frame=params.max_sym_per_frame,
|
|
)
|
|
elif params.decoding_method == "beam_search":
|
|
hyp = beam_search(
|
|
model=model,
|
|
encoder_out=encoder_out_i,
|
|
beam=params.beam_size,
|
|
)
|
|
else:
|
|
raise ValueError(
|
|
f"Unsupported decoding method: {params.decoding_method}"
|
|
)
|
|
hyps.append(sp.decode(hyp).split())
|
|
|
|
if params.decoding_method == "greedy_search":
|
|
return {"greedy_search": hyps}
|
|
elif "fast_beam_search" in params.decoding_method:
|
|
key = f"beam_{params.beam}_"
|
|
key += f"max_contexts_{params.max_contexts}_"
|
|
key += f"max_states_{params.max_states}"
|
|
if "nbest" in params.decoding_method:
|
|
key += f"_num_paths_{params.num_paths}_"
|
|
key += f"nbest_scale_{params.nbest_scale}"
|
|
if "LG" in params.decoding_method:
|
|
key += f"_ngram_lm_scale_{params.ngram_lm_scale}"
|
|
|
|
return {key: hyps}
|
|
else:
|
|
return {f"beam_size_{params.beam_size}": hyps}
|
|
def remove_short_and_long_utt(c):
|
|
# Keep only utterances with duration between 1 second and 20 seconds
|
|
#
|
|
# Caution: There is a reason to select 20.0 here. Please see
|
|
# ../local/display_manifest_statistics.py
|
|
#
|
|
# You should use ../local/display_manifest_statistics.py to get
|
|
# an utterance duration distribution for your dataset to select
|
|
# the threshold
|
|
if c.duration < 0.5 or c.duration > 30.0:
|
|
#logging.warning(
|
|
# f"Exclude cut with ID {c.id} from training. Duration: {c.duration}"
|
|
#)
|
|
return False
|
|
if c.supervisions == []:
|
|
return False
|
|
# In pruned RNN-T, we require that T >= S
|
|
# where T is the number of feature frames after subsampling
|
|
# and S is the number of tokens in the utterance
|
|
|
|
# In ./conformer.py, the conv module uses the following expression
|
|
# for subsamplin
|
|
|
|
return True
|
|
|
|
# def remove_seg(c):
|
|
# if c.supervisions[0].id != 'fla_0102_1_0B_00107':
|
|
# return True
|
|
# else:
|
|
# return False
|
|
|
|
def decode_dataset(
|
|
dl: torch.utils.data.DataLoader,
|
|
params: AttributeDict,
|
|
model: nn.Module,
|
|
sp: spm.SentencePieceProcessor,
|
|
word_table: Optional[k2.SymbolTable] = None,
|
|
decoding_graph: Optional[k2.Fsa] = None,
|
|
rnnlm: Optional[RnnLmModel] = None,
|
|
rnnlm_scale: float = 1.0,
|
|
) -> Dict[str, List[Tuple[str, List[str], List[str]]]]:
|
|
"""Decode dataset.
|
|
|
|
Args:
|
|
dl:
|
|
PyTorch's dataloader containing the dataset to decode.
|
|
params:
|
|
It is returned by :func:`get_params`.
|
|
model:
|
|
The neural model.
|
|
sp:
|
|
The BPE model.
|
|
word_table:
|
|
The word symbol table.
|
|
decoding_graph:
|
|
The decoding graph. Can be either a `k2.trivial_graph` or LG, Used
|
|
only when --decoding_method is fast_beam_search, fast_beam_search_LG, fast_beam_search_nbest,
|
|
fast_beam_search_nbest_oracle, and fast_beam_search_nbest_LG.
|
|
Returns:
|
|
Return a dict, whose key may be "greedy_search" if greedy search
|
|
is used, or it may be "beam_7" if beam size of 7 is used.
|
|
Its value is a list of tuples. Each tuple contains two elements:
|
|
The first is the reference transcript, and the second is the
|
|
predicted result.
|
|
"""
|
|
num_cuts = 0
|
|
|
|
try:
|
|
num_batches = len(dl)
|
|
except TypeError:
|
|
num_batches = "?"
|
|
|
|
if params.decoding_method == "greedy_search":
|
|
log_interval = 50
|
|
else:
|
|
log_interval = 20
|
|
|
|
results = defaultdict(list)
|
|
for batch_idx, batch in enumerate(dl):
|
|
texts = batch["supervisions"]["text"]
|
|
cut_ids = [cut.id for cut in batch["supervisions"]["cut"]]
|
|
logging.info(f"Decoding {batch_idx}-th batch")
|
|
|
|
hyps_dict = decode_one_batch(
|
|
params=params,
|
|
model=model,
|
|
sp=sp,
|
|
decoding_graph=decoding_graph,
|
|
word_table=word_table,
|
|
batch=batch,
|
|
rnnlm=rnnlm,
|
|
rnnlm_scale=rnnlm_scale,
|
|
)
|
|
|
|
for name, hyps in hyps_dict.items():
|
|
this_batch = []
|
|
assert len(hyps) == len(texts)
|
|
for cut_id, hyp_words, ref_text in zip(cut_ids, hyps, texts):
|
|
ref_words = ref_text.split()
|
|
this_batch.append((cut_id, ref_words, hyp_words))
|
|
|
|
results[name].extend(this_batch)
|
|
|
|
num_cuts += len(texts)
|
|
|
|
if batch_idx % log_interval == 0:
|
|
batch_str = f"{batch_idx}/{num_batches}"
|
|
|
|
logging.info(
|
|
f"batch {batch_str}, cuts processed until now is {num_cuts}")
|
|
return results
|
|
|
|
|
|
def save_results(
|
|
params: AttributeDict,
|
|
test_set_name: str,
|
|
results_dict: Dict[str, List[Tuple[str, List[str], List[str]]]],
|
|
):
|
|
test_set_wers = dict()
|
|
for key, results in results_dict.items():
|
|
recog_path = (
|
|
params.res_dir /
|
|
f"recogs-{test_set_name}-{key}-{params.suffix}.txt"
|
|
)
|
|
results = sorted(results)
|
|
store_transcripts(filename=recog_path, texts=results)
|
|
logging.info(f"The transcripts are stored in {recog_path}")
|
|
|
|
# The following prints out WERs, per-word error statistics and aligned
|
|
# ref/hyp pairs.
|
|
errs_filename = (
|
|
params.res_dir / f"errs-{test_set_name}-{key}-{params.suffix}.txt"
|
|
)
|
|
with open(errs_filename, "w") as f:
|
|
wer = write_error_stats(
|
|
f, f"{test_set_name}-{key}", results, enable_log=True
|
|
)
|
|
test_set_wers[key] = wer
|
|
|
|
logging.info("Wrote detailed error stats to {}".format(errs_filename))
|
|
|
|
test_set_wers = sorted(test_set_wers.items(), key=lambda x: x[1])
|
|
errs_info = (
|
|
params.res_dir /
|
|
f"wer-summary-{test_set_name}-{key}-{params.suffix}.txt"
|
|
)
|
|
with open(errs_info, "w") as f:
|
|
print("settings\tWER", file=f)
|
|
for key, val in test_set_wers:
|
|
print("{}\t{}".format(key, val), file=f)
|
|
|
|
s = "\nFor {}, WER of different settings are:\n".format(test_set_name)
|
|
note = "\tbest for {}".format(test_set_name)
|
|
for key, val in test_set_wers:
|
|
s += "{}\t{}{}\n".format(key, val, note)
|
|
note = ""
|
|
logging.info(s)
|
|
|
|
|
|
@torch.no_grad()
|
|
def main():
|
|
parser = get_parser()
|
|
IWSLTDialectSTDataModule.add_arguments(parser)
|
|
args = parser.parse_args()
|
|
args.exp_dir = Path(args.exp_dir)
|
|
|
|
params = get_params()
|
|
params.update(vars(args))
|
|
|
|
assert params.decoding_method in (
|
|
"greedy_search",
|
|
"beam_search",
|
|
"fast_beam_search",
|
|
"fast_beam_search_LG",
|
|
"fast_beam_search_nbest",
|
|
"fast_beam_search_nbest_LG",
|
|
"fast_beam_search_nbest_oracle",
|
|
"modified_beam_search",
|
|
"modified_beam_search_rnnlm_shallow_fusion",
|
|
)
|
|
params.res_dir = params.exp_dir / params.decoding_method
|
|
|
|
if params.iter > 0:
|
|
params.suffix = f"iter-{params.iter}-avg-{params.avg}"
|
|
else:
|
|
params.suffix = f"epoch-{params.epoch}-avg-{params.avg}"
|
|
if params.simulate_streaming:
|
|
params.suffix += f"-streaming-chunk-size-{params.decode_chunk_size}"
|
|
params.suffix += f"-left-context-{params.left_context}"
|
|
if "fast_beam_search" in params.decoding_method:
|
|
params.suffix += f"-beam-{params.beam}"
|
|
params.suffix += f"-max-contexts-{params.max_contexts}"
|
|
params.suffix += f"-max-states-{params.max_states}"
|
|
if "nbest" in params.decoding_method:
|
|
params.suffix += f"-nbest-scale-{params.nbest_scale}"
|
|
params.suffix += f"-num-paths-{params.num_paths}"
|
|
if "LG" in params.decoding_method:
|
|
params.suffix += f"-ngram-lm-scale-{params.ngram_lm_scale}"
|
|
elif "beam_search" in params.decoding_method:
|
|
params.suffix += f"-{params.decoding_method}-beam-size-{params.beam_size}"
|
|
else:
|
|
params.suffix += f"-context-{params.context_size}"
|
|
params.suffix += f"-max-sym-per-frame-{params.max_sym_per_frame}"
|
|
|
|
params.suffix += f"-rnnlm-lm-scale-{params.rnn_lm_scale}"
|
|
|
|
if params.use_averaged_model:
|
|
params.suffix += "-use-averaged-model"
|
|
|
|
setup_logger(f"{params.res_dir}/log-decode-{params.suffix}")
|
|
logging.info("Decoding started")
|
|
|
|
device = torch.device("cpu")
|
|
if torch.cuda.is_available():
|
|
device = torch.device("cuda", 0)
|
|
|
|
logging.info(f"Device: {device}")
|
|
|
|
sp = spm.SentencePieceProcessor()
|
|
sp.load(params.bpe_model)
|
|
|
|
# <blk> and <unk> are defined in local/train_bpe_model.py
|
|
params.blank_id = sp.piece_to_id("<blk>")
|
|
params.unk_id = sp.piece_to_id("<unk>")
|
|
params.vocab_size = sp.get_piece_size()
|
|
|
|
if params.simulate_streaming:
|
|
assert (
|
|
params.causal_convolution
|
|
), "Decoding in streaming requires causal convolution"
|
|
|
|
logging.info(params)
|
|
|
|
logging.info("About to create model")
|
|
model = get_transducer_model(params)
|
|
|
|
if not params.use_averaged_model:
|
|
if params.iter > 0:
|
|
filenames = find_checkpoints(params.exp_dir, iteration=-params.iter)[
|
|
: params.avg
|
|
]
|
|
if len(filenames) == 0:
|
|
raise ValueError(
|
|
f"No checkpoints found for"
|
|
f" --iter {params.iter}, --avg {params.avg}"
|
|
)
|
|
elif len(filenames) < params.avg:
|
|
raise ValueError(
|
|
f"Not enough checkpoints ({len(filenames)}) found for"
|
|
f" --iter {params.iter}, --avg {params.avg}"
|
|
)
|
|
logging.info(f"averaging {filenames}")
|
|
model.to(device)
|
|
model.load_state_dict(
|
|
average_checkpoints(filenames, device=device))
|
|
elif params.avg == 1:
|
|
load_checkpoint(f"{params.exp_dir}/epoch-{params.epoch}.pt", model)
|
|
else:
|
|
start = params.epoch - params.avg + 1
|
|
filenames = []
|
|
for i in range(start, params.epoch + 1):
|
|
if i >= 1:
|
|
filenames.append(f"{params.exp_dir}/epoch-{i}.pt")
|
|
logging.info(f"averaging {filenames}")
|
|
model.to(device)
|
|
model.load_state_dict(
|
|
average_checkpoints(filenames, device=device))
|
|
else:
|
|
if params.iter > 0:
|
|
filenames = find_checkpoints(params.exp_dir, iteration=-params.iter)[
|
|
: params.avg + 1
|
|
]
|
|
if len(filenames) == 0:
|
|
raise ValueError(
|
|
f"No checkpoints found for"
|
|
f" --iter {params.iter}, --avg {params.avg}"
|
|
)
|
|
elif len(filenames) < params.avg + 1:
|
|
raise ValueError(
|
|
f"Not enough checkpoints ({len(filenames)}) found for"
|
|
f" --iter {params.iter}, --avg {params.avg}"
|
|
)
|
|
filename_start = filenames[-1]
|
|
filename_end = filenames[0]
|
|
logging.info(
|
|
"Calculating the averaged model over iteration checkpoints"
|
|
f" from {filename_start} (excluded) to {filename_end}"
|
|
)
|
|
model.to(device)
|
|
model.load_state_dict(
|
|
average_checkpoints_with_averaged_model(
|
|
filename_start=filename_start,
|
|
filename_end=filename_end,
|
|
device=device,
|
|
)
|
|
)
|
|
else:
|
|
assert params.avg > 0, params.avg
|
|
start = params.epoch - params.avg
|
|
assert start >= 1, start
|
|
filename_start = f"{params.exp_dir}/epoch-{start}.pt"
|
|
filename_end = f"{params.exp_dir}/epoch-{params.epoch}.pt"
|
|
logging.info(
|
|
f"Calculating the averaged model over epoch range from "
|
|
f"{start} (excluded) to {params.epoch}"
|
|
)
|
|
model.to(device)
|
|
model.load_state_dict(
|
|
average_checkpoints_with_averaged_model(
|
|
filename_start=filename_start,
|
|
filename_end=filename_end,
|
|
device=device,
|
|
)
|
|
)
|
|
|
|
model.to(device)
|
|
model.eval()
|
|
|
|
rnn_lm_model = None
|
|
rnn_lm_scale = params.rnn_lm_scale
|
|
if params.decoding_method == "modified_beam_search_rnnlm_shallow_fusion":
|
|
rnn_lm_model = RnnLmModel(
|
|
vocab_size=params.vocab_size,
|
|
embedding_dim=params.rnn_lm_embedding_dim,
|
|
hidden_dim=params.rnn_lm_hidden_dim,
|
|
num_layers=params.rnn_lm_num_layers,
|
|
tie_weights=params.rnn_lm_tie_weights,
|
|
)
|
|
assert params.rnn_lm_avg == 1
|
|
|
|
load_checkpoint(
|
|
f"{params.rnn_lm_exp_dir}/epoch-{params.rnn_lm_epoch}.pt",
|
|
rnn_lm_model,
|
|
)
|
|
rnn_lm_model.to(device)
|
|
rnn_lm_model.eval()
|
|
|
|
if "fast_beam_search" in params.decoding_method:
|
|
if "LG" in params.decoding_method:
|
|
lexicon = Lexicon(params.lang_dir)
|
|
word_table = lexicon.word_table
|
|
lg_filename = params.lang_dir / "LG.pt"
|
|
logging.info(f"Loading {lg_filename}")
|
|
decoding_graph = k2.Fsa.from_dict(
|
|
torch.load(lg_filename, map_location=device)
|
|
)
|
|
decoding_graph.scores *= params.ngram_lm_scale
|
|
else:
|
|
word_table = None
|
|
decoding_graph = k2.trivial_graph(
|
|
params.vocab_size - 1, device=device)
|
|
else:
|
|
decoding_graph = None
|
|
word_table = None
|
|
|
|
num_param = sum([p.numel() for p in model.parameters()])
|
|
logging.info(f"Number of model parameters: {num_param}")
|
|
|
|
# we need cut ids to display recognition results.
|
|
args.return_cuts = True
|
|
iwslt_ta = IWSLTDialectSTDataModule(args)
|
|
|
|
test_cuts = iwslt_ta.test_cuts()
|
|
dev_cuts = iwslt_ta.dev_cuts()
|
|
|
|
# lev_test_cuts = lev_test_cuts.filter(remove_short_and_long_utt)
|
|
# # lev_test_cuts = lev_test_cuts.filter(remove_seg)
|
|
# gulf_test_cuts = gulf_test_cuts.filter(remove_short_and_long_utt)
|
|
# egy_test_cuts = egy_test_cuts.filter(remove_short_and_long_utt)
|
|
# egy_h5_cuts = egy_sup_cuts.filter(remove_short_and_long_utt)
|
|
# egy_sup_cuts = egy_h5_cuts.filter(remove_short_and_long_utt)
|
|
|
|
test_dl = iwslt_ta.test_dataloaders(test_cuts)
|
|
dev_dl = iwslt_ta.test_dataloaders(dev_cuts)
|
|
|
|
test_sets = ["test", "dev"]
|
|
test_all_dl = [test_dl, dev_dl]
|
|
|
|
|
|
for test_set, test_dl in zip(test_sets, test_all_dl):
|
|
results_dict = decode_dataset(
|
|
dl=test_dl,
|
|
params=params,
|
|
model=model,
|
|
sp=sp,
|
|
word_table=word_table,
|
|
decoding_graph=decoding_graph,
|
|
rnnlm=rnn_lm_model,
|
|
rnnlm_scale=rnn_lm_scale,
|
|
)
|
|
|
|
save_results(
|
|
params=params,
|
|
test_set_name=test_set,
|
|
results_dict=results_dict,
|
|
)
|
|
|
|
logging.info("Done!")
|
|
|
|
|
|
if __name__ == "__main__":
|
|
main()
|