mirror of
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872 lines
28 KiB
Python
Executable File
872 lines
28 KiB
Python
Executable File
#!/usr/bin/env python3
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#
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# Copyright 2021-2022 Xiaomi Corporation (Author: Fangjun Kuang,
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# Zengwei Yao)
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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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./dprnn_zipformer/decode.py \
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--epoch 30 \
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--avg 9 \
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--use-averaged-model true \
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--exp-dir ./dprnn_zipformer/exp \
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--max-duration 600 \
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--decoding-method greedy_search
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(2) modified beam search
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./dprnn_zipformer/decode.py \
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--epoch 30 \
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--avg 9 \
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--use-averaged-model true \
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--exp-dir ./dprnn_zipformer/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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"""
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import argparse
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import logging
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from collections import defaultdict
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from itertools import chain, groupby, repeat
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from pathlib import Path
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from typing import Dict, List, Optional, Tuple
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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 LibriCssAsrDataModule
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from beam_search import (
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beam_search,
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fast_beam_search_nbest_LG,
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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_LODR,
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)
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from lhotse.utils import EPSILON
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from train import add_model_arguments, get_params, get_surt_model
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from icefall import LmScorer, NgramLm
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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.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_surt_error_stats,
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)
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OVERLAP_RATIOS = ["0L", "0S", "OV10", "OV20", "OV30", "OV40"]
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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=9,
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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="dprnn_zipformer/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_500/bpe.model",
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help="Path to the 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/lang_bpe_500",
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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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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,
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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.
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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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"--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
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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
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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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"--save-masks",
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type=str2bool,
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default=False,
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help="""If true, save masks generated by unmixing module.""",
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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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ngram_lm: Optional[NgramLm] = None,
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ngram_lm_scale: float = 1.0,
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LM: Optional[LmScorer] = None,
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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 HLG, Used
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only when --decoding_method is fast_beam_search, 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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feature_lens = batch["input_lens"].to(device)
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# Apply the mask encoder
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B, T, F = feature.shape
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processed = model.mask_encoder(feature) # B,T,F*num_channels
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masks = processed.view(B, T, F, params.num_channels).unbind(dim=-1)
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x_masked = [feature * m for m in masks]
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# To save the masks, we split them by batch and trim each mask to the length of
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# the corresponding feature. We save them in a dict, where the key is the
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# cut ID and the value is the mask.
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masks_dict = {}
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for i in range(B):
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mask = torch.cat(
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[x_masked[j][i, : feature_lens[i]] for j in range(params.num_channels)],
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dim=-1,
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)
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mask = mask.cpu().numpy()
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masks_dict[batch["cuts"][i].id] = mask
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# Recognition
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# Stack the inputs along the batch axis
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h = torch.cat(x_masked, dim=0)
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h_lens = torch.cat([feature_lens for _ in range(params.num_channels)], dim=0)
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encoder_out, encoder_out_lens = model.encoder(x=h, x_lens=h_lens)
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if model.joint_encoder_layer is not None:
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encoder_out = model.joint_encoder_layer(encoder_out)
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def _group_channels(hyps: List[str]) -> List[List[str]]:
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"""
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Currently we have a batch of size M*B, where M is the number of
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channels and B is the batch size. We need to group the hypotheses
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into B groups, each of which contains M hypotheses.
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Example:
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hyps = ['a1', 'b1', 'c1', 'a2', 'b2', 'c2']
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_group_channels(hyps) = [['a1', 'a2'], ['b1', 'b2'], ['c1', 'c2']]
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"""
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assert len(hyps) == B * params.num_channels
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out_hyps = []
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for i in range(B):
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out_hyps.append(hyps[i::B])
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return out_hyps
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hyps = []
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if params.decoding_method == "fast_beam_search":
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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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for hyp in sp.decode(hyp_tokens):
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hyps.append(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 sp.decode(hyp_tokens):
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hyps.append(hyp)
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elif params.decoding_method == "greedy_search" and params.max_sym_per_frame == 1:
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hyp_tokens = greedy_search_batch(
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model=model,
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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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for hyp in sp.decode(hyp_tokens):
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hyps.append(hyp)
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elif params.decoding_method == "modified_beam_search":
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hyp_tokens = modified_beam_search(
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model=model,
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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_size,
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)
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for hyp in sp.decode(hyp_tokens):
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hyps.append(hyp)
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elif params.decoding_method == "modified_beam_search_LODR":
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hyp_tokens = modified_beam_search_LODR(
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model=model,
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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_size,
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LODR_lm=ngram_lm,
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LODR_lm_scale=ngram_lm_scale,
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LM=LM,
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)
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for hyp in sp.decode(hyp_tokens):
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hyps.append(hyp)
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else:
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batch_size = encoder_out.size(0)
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for i in range(batch_size):
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# fmt: off
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encoder_out_i = encoder_out[i:i+1, :encoder_out_lens[i]]
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# fmt: on
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if params.decoding_method == "greedy_search":
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hyp = greedy_search(
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model=model,
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encoder_out=encoder_out_i,
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max_sym_per_frame=params.max_sym_per_frame,
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)
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elif params.decoding_method == "beam_search":
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hyp = beam_search(
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model=model,
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encoder_out=encoder_out_i,
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beam=params.beam_size,
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)
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else:
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raise ValueError(
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f"Unsupported decoding method: {params.decoding_method}"
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)
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hyps.append(sp.decode(hyp))
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if params.decoding_method == "greedy_search":
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return {"greedy_search": _group_channels(hyps)}, masks_dict
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elif "fast_beam_search" in params.decoding_method:
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key = f"beam_{params.beam}_"
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key += f"max_contexts_{params.max_contexts}_"
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key += f"max_states_{params.max_states}"
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if "nbest" in params.decoding_method:
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key += f"_num_paths_{params.num_paths}_"
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key += f"nbest_scale_{params.nbest_scale}"
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if "LG" in params.decoding_method:
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key += f"_ngram_lm_scale_{params.ngram_lm_scale}"
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return {key: _group_channels(hyps)}, masks_dict
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else:
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return {f"beam_size_{params.beam_size}": _group_channels(hyps)}, masks_dict
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def decode_dataset(
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dl: torch.utils.data.DataLoader,
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params: AttributeDict,
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model: nn.Module,
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sp: spm.SentencePieceProcessor,
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word_table: Optional[k2.SymbolTable] = None,
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decoding_graph: Optional[k2.Fsa] = None,
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ngram_lm: Optional[NgramLm] = None,
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ngram_lm_scale: float = 1.0,
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LM: Optional[LmScorer] = None,
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) -> Dict[str, List[Tuple[str, List[str], List[str]]]]:
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"""Decode dataset.
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Args:
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dl:
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PyTorch's dataloader containing the dataset to decode.
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params:
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It is returned by :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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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 HLG, Used
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only when --decoding_method is fast_beam_search, 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 a dict, whose key may be "greedy_search" if greedy search
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is used, or it may be "beam_7" if beam size of 7 is used.
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Its value is a list of tuples. Each tuple contains two elements:
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The first is the reference transcript, and the second is the
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predicted result.
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"""
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num_cuts = 0
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try:
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num_batches = len(dl)
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except TypeError:
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num_batches = "?"
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if params.decoding_method == "greedy_search":
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log_interval = 50
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else:
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log_interval = 20
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results = defaultdict(list)
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masks = {}
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for batch_idx, batch in enumerate(dl):
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cut_ids = [cut.id for cut in batch["cuts"]]
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cuts_batch = batch["cuts"]
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hyps_dict, masks_dict = decode_one_batch(
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params=params,
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model=model,
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sp=sp,
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decoding_graph=decoding_graph,
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word_table=word_table,
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batch=batch,
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ngram_lm=ngram_lm,
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ngram_lm_scale=ngram_lm_scale,
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LM=LM,
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)
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masks.update(masks_dict)
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for name, hyps in hyps_dict.items():
|
|
this_batch = []
|
|
for cut_id, hyp_words in zip(cut_ids, hyps):
|
|
# Reference is a list of supervision texts sorted by start time.
|
|
ref_words = [
|
|
s.text.strip()
|
|
for s in sorted(
|
|
cuts_batch[cut_id].supervisions, key=lambda s: s.start
|
|
)
|
|
]
|
|
this_batch.append((cut_id, ref_words, hyp_words))
|
|
|
|
results[name].extend(this_batch)
|
|
|
|
num_cuts += len(cut_ids)
|
|
|
|
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, masks_dict
|
|
|
|
|
|
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_surt_error_stats(
|
|
f,
|
|
f"{test_set_name}-{key}",
|
|
results,
|
|
enable_log=True,
|
|
num_channels=params.num_channels,
|
|
)
|
|
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)
|
|
|
|
|
|
def save_masks(
|
|
params: AttributeDict,
|
|
test_set_name: str,
|
|
masks: List[torch.Tensor],
|
|
):
|
|
masks_path = params.res_dir / f"masks-{test_set_name}.txt"
|
|
torch.save(masks, masks_path)
|
|
logging.info(f"The masks are stored in {masks_path}")
|
|
|
|
|
|
@torch.no_grad()
|
|
def main():
|
|
parser = get_parser()
|
|
LmScorer.add_arguments(parser)
|
|
LibriCssAsrDataModule.add_arguments(parser)
|
|
args = parser.parse_args()
|
|
args.exp_dir = Path(args.exp_dir)
|
|
args.lang_dir = Path(args.lang_dir)
|
|
|
|
params = get_params()
|
|
params.update(vars(args))
|
|
|
|
assert params.decoding_method in (
|
|
"greedy_search",
|
|
"beam_search",
|
|
"fast_beam_search",
|
|
"fast_beam_search_nbest",
|
|
"fast_beam_search_nbest_LG",
|
|
"fast_beam_search_nbest_oracle",
|
|
"modified_beam_search",
|
|
"modified_beam_search_LODR",
|
|
), f"Decoding method {params.decoding_method} is not supported."
|
|
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 "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}"
|
|
|
|
if params.use_averaged_model:
|
|
params.suffix += "-use-averaged-model"
|
|
|
|
if "LODR" in params.decoding_method:
|
|
params.suffix += (
|
|
f"-LODR-{params.tokens_ngram}gram-scale-{params.ngram_lm_scale}"
|
|
)
|
|
|
|
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()
|
|
|
|
logging.info(params)
|
|
|
|
logging.info("About to create model")
|
|
model = get_surt_model(params)
|
|
assert model.encoder.decode_chunk_size == params.decode_chunk_len // 2, (
|
|
model.encoder.decode_chunk_size,
|
|
params.decode_chunk_len,
|
|
)
|
|
|
|
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()
|
|
|
|
if "fast_beam_search" in params.decoding_method:
|
|
if params.decoding_method == "fast_beam_search_nbest_LG":
|
|
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
|
|
|
|
# only load N-gram LM when needed
|
|
if "LODR" in params.decoding_method:
|
|
lm_filename = params.lang_dir / f"{params.tokens_ngram}gram.fst.txt"
|
|
logging.info(f"lm filename: {lm_filename}")
|
|
ngram_lm = NgramLm(
|
|
lm_filename,
|
|
backoff_id=params.backoff_id,
|
|
is_binary=False,
|
|
)
|
|
logging.info(f"num states: {ngram_lm.lm.num_states}")
|
|
ngram_lm_scale = params.ngram_lm_scale
|
|
else:
|
|
ngram_lm = None
|
|
ngram_lm_scale = None
|
|
|
|
# only load the neural network LM if doing shallow fusion
|
|
if params.use_shallow_fusion:
|
|
LM = LmScorer(
|
|
lm_type=params.lm_type,
|
|
params=params,
|
|
device=device,
|
|
lm_scale=params.lm_scale,
|
|
)
|
|
LM.to(device)
|
|
LM.eval()
|
|
|
|
else:
|
|
LM = 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
|
|
libricss = LibriCssAsrDataModule(args)
|
|
|
|
dev_cuts = libricss.libricss_cuts(split="dev", type="ihm-mix").to_eager()
|
|
dev_cuts_grouped = [dev_cuts.filter(lambda x: ol in x.id) for ol in OVERLAP_RATIOS]
|
|
test_cuts = libricss.libricss_cuts(split="test", type="ihm-mix").to_eager()
|
|
test_cuts_grouped = [
|
|
test_cuts.filter(lambda x: ol in x.id) for ol in OVERLAP_RATIOS
|
|
]
|
|
|
|
for dev_set, ol in zip(dev_cuts_grouped, OVERLAP_RATIOS):
|
|
dev_dl = libricss.test_dataloaders(dev_set)
|
|
results_dict, masks = decode_dataset(
|
|
dl=dev_dl,
|
|
params=params,
|
|
model=model,
|
|
sp=sp,
|
|
word_table=word_table,
|
|
decoding_graph=decoding_graph,
|
|
ngram_lm=ngram_lm,
|
|
ngram_lm_scale=ngram_lm_scale,
|
|
LM=LM,
|
|
)
|
|
|
|
save_results(
|
|
params=params,
|
|
test_set_name=f"dev_{ol}",
|
|
results_dict=results_dict,
|
|
)
|
|
|
|
if params.save_masks:
|
|
save_masks(
|
|
params=params,
|
|
test_set_name=f"dev_{ol}",
|
|
masks=masks,
|
|
)
|
|
|
|
for test_set, ol in zip(test_cuts_grouped, OVERLAP_RATIOS):
|
|
test_dl = libricss.test_dataloaders(test_set)
|
|
results_dict, masks = decode_dataset(
|
|
dl=test_dl,
|
|
params=params,
|
|
model=model,
|
|
sp=sp,
|
|
word_table=word_table,
|
|
decoding_graph=decoding_graph,
|
|
ngram_lm=ngram_lm,
|
|
ngram_lm_scale=ngram_lm_scale,
|
|
LM=LM,
|
|
)
|
|
|
|
save_results(
|
|
params=params,
|
|
test_set_name=f"test_{ol}",
|
|
results_dict=results_dict,
|
|
)
|
|
|
|
if params.save_masks:
|
|
save_masks(
|
|
params=params,
|
|
test_set_name=f"test_{ol}",
|
|
masks=masks,
|
|
)
|
|
|
|
logging.info("Done!")
|
|
|
|
|
|
if __name__ == "__main__":
|
|
main()
|