mirror of
https://github.com/k2-fsa/icefall.git
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688 lines
22 KiB
Python
Executable File
688 lines
22 KiB
Python
Executable File
#!/usr/bin/env python3
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#
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# Copyright 2021-2023 Xiaomi Corporation (Author: Fangjun Kuang,
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# Zengwei Yao,
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# Wei Kang)
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#
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# See ../../../../LICENSE for clarification regarding multiple authors
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#
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# 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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./zipformer/decode.py \
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--epoch 28 \
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--avg 15 \
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--exp-dir ./zipformer/exp \
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--max-duration 600 \
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--keywords-file keywords.txt \
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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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import math
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import os
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from collections import defaultdict
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from dataclasses import dataclass, field
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from pathlib import Path
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from typing import Dict, List, Optional, Set, 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 GigaSpeechAsrDataModule
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from beam_search import keywords_search
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from lhotse.cut import Cut
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from train import add_model_arguments, get_model, get_params
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from icefall import ContextGraph
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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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make_pad_mask,
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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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@dataclass
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class KwMetric:
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TP: int = 0 # True positive
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FN: int = 0 # False negative
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FP: int = 0 # False positive
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TN: int = 0 # True negative
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FN_list: List[str] = field(default_factory=list)
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FP_list: List[str] = field(default_factory=list)
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TP_list: List[str] = field(default_factory=list)
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def __str__(self) -> str:
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return f"(TP:{self.TP}, FN:{self.FN}, FP:{self.FP}, TN:{self.TN})"
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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="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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"--beam",
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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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"--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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"--keywords-file",
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type=str,
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help="File contains keywords.",
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)
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parser.add_argument(
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"--test-set",
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type=str,
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default="small",
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help="small or large",
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)
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parser.add_argument(
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"--keywords-score",
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type=float,
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default=1.5,
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help="""
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The default boosting score (token level) for keywords. it will boost the
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paths that match keywords to make them survive beam search.
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""",
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)
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parser.add_argument(
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"--keywords-threshold",
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type=float,
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default=0.35,
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help="The default threshold (probability) to trigger the keyword.",
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)
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parser.add_argument(
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"--num-tailing-blanks",
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type=int,
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default=1,
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help="The number of tailing blanks should have after hitting one keyword.",
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)
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parser.add_argument(
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"--blank-penalty",
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type=float,
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default=0.0,
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help="""
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The penalty applied on blank symbol during decoding.
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Note: It is a positive value that would be applied to logits like
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this `logits[:, 0] -= blank_penalty` (suppose logits.shape is
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[batch_size, vocab] and blank id is 0).
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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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keywords_graph: Optional[ContextGraph] = None,
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) -> List[List[Tuple[str, Tuple[int, int]]]]:
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"""Decode one batch and return the result in a list.
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The length of the list equals to batch size, the i-th element contains the
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triggered keywords for the i-th utterance in the given batch. The triggered
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keywords are also a list, each of it contains a tuple of hitting keyword and
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the corresponding start timestamps and end timestamps of the hitting keyword.
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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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keywords_graph:
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The graph containing keywords.
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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 list.
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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.causal:
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# this seems to cause insertions at the end of the utterance if used with zipformer.
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pad_len = 30
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feature_lens += pad_len
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feature = torch.nn.functional.pad(
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feature,
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pad=(0, 0, 0, pad_len),
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value=LOG_EPS,
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)
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encoder_out, encoder_out_lens = model.forward_encoder(feature, feature_lens)
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ans_dict = keywords_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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keywords_graph=keywords_graph,
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beam=params.beam,
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num_tailing_blanks=params.num_tailing_blanks,
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blank_penalty=params.blank_penalty,
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)
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hyps = []
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for ans in ans_dict:
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hyp = []
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for hit in ans:
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hyp.append((hit.phrase, (hit.timestamps[0], hit.timestamps[-1])))
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hyps.append(hyp)
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return hyps
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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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keywords_graph: ContextGraph,
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keywords: Set[str],
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test_only_keywords: bool,
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) -> Tuple[List[Tuple[str, List[str], List[str]]], KwMetric]:
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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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keywords_graph:
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The graph containing keywords.
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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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log_interval = 50
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results = []
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metric = {"all": KwMetric()}
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for k in keywords:
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metric[k] = KwMetric()
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for batch_idx, batch in enumerate(dl):
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texts = batch["supervisions"]["text"]
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cut_ids = [cut.id for cut in batch["supervisions"]["cut"]]
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hyps = 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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keywords_graph=keywords_graph,
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batch=batch,
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)
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this_batch = []
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assert len(hyps) == len(texts)
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for cut_id, hyp_words, ref_text in zip(cut_ids, hyps, texts):
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ref_text = ref_text.upper()
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ref_words = ref_text.split()
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hyp_words = [x[0] for x in hyp_words]
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# for computing WER
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this_batch.append((cut_id, ref_words, " ".join(hyp_words).split()))
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hyp_set = set(hyp_words) # each item is a keyword phrase
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if len(hyp_words) > 1:
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logging.warning(
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f"Cut {cut_id} triggers more than one keywords : {hyp_words},"
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f"please check the transcript to see if it really has more "
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f"than one keywords, if so consider splitting this audio and"
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f"keep only one keyword for each audio."
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)
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hyp_str = " | ".join(
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hyp_words
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) # The triggered keywords for this utterance.
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TP = False
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FP = False
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for x in hyp_set:
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assert x in keywords, x # can only trigger keywords
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if (test_only_keywords and x == ref_text) or (
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not test_only_keywords and x in ref_text
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):
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TP = True
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metric[x].TP += 1
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metric[x].TP_list.append(f"({ref_text} -> {x})")
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if (test_only_keywords and x != ref_text) or (
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not test_only_keywords and x not in ref_text
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):
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FP = True
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metric[x].FP += 1
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metric[x].FP_list.append(f"({ref_text} -> {x})")
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if TP:
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metric["all"].TP += 1
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if FP:
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metric["all"].FP += 1
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TN = True # all keywords are true negative then the summery is true negative.
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FN = False
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for x in keywords:
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if x not in ref_text and x not in hyp_set:
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metric[x].TN += 1
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continue
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TN = False
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if (test_only_keywords and x == ref_text) or (
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not test_only_keywords and x in ref_text
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):
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fn = True
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for y in hyp_set:
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if (test_only_keywords and y == ref_text) or (
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not test_only_keywords and y in ref_text
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):
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fn = False
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break
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if fn:
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FN = True
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metric[x].FN += 1
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metric[x].FN_list.append(f"({ref_text} -> {hyp_str})")
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if TN:
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metric["all"].TN += 1
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if FN:
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metric["all"].FN += 1
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results.extend(this_batch)
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num_cuts += len(texts)
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if batch_idx % log_interval == 0:
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batch_str = f"{batch_idx}/{num_batches}"
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logging.info(f"batch {batch_str}, cuts processed until now is {num_cuts}")
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return results, metric
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def save_results(
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params: AttributeDict,
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test_set_name: str,
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results: List[Tuple[str, List[str], List[str]]],
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metric: KwMetric,
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):
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recog_path = params.res_dir / f"recogs-{test_set_name}-{params.suffix}.txt"
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results = sorted(results)
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store_transcripts(filename=recog_path, texts=results)
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logging.info(f"The transcripts are stored in {recog_path}")
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# The following prints out WERs, per-word error statistics and aligned
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# ref/hyp pairs.
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errs_filename = params.res_dir / f"errs-{test_set_name}-{params.suffix}.txt"
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with open(errs_filename, "w") as f:
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wer = write_error_stats(f, f"{test_set_name}", results, enable_log=True)
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logging.info("Wrote detailed error stats to {}".format(errs_filename))
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metric_filename = params.res_dir / f"metric-{test_set_name}-{params.suffix}.txt"
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with open(metric_filename, "w") as of:
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width = 10
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for key, item in sorted(
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metric.items(), key=lambda x: (x[1].FP, x[1].FN), reverse=True
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):
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acc = (item.TP + item.TN) / (item.TP + item.TN + item.FP + item.FN)
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precision = (
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0.0 if (item.TP + item.FP) == 0 else item.TP / (item.TP + item.FP)
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)
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recall = 0.0 if (item.TP + item.FN) == 0 else item.TP / (item.TP + item.FN)
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fpr = 0.0 if (item.FP + item.TN) == 0 else item.FP / (item.FP + item.TN)
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s = f"{key}:\n"
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s += f"\t{'TP':{width}}{'FP':{width}}{'FN':{width}}{'TN':{width}}\n"
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s += f"\t{str(item.TP):{width}}{str(item.FP):{width}}{str(item.FN):{width}}{str(item.TN):{width}}\n"
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s += f"\tAccuracy: {acc:.3f}\n"
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s += f"\tPrecision: {precision:.3f}\n"
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s += f"\tRecall(PPR): {recall:.3f}\n"
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s += f"\tFPR: {fpr:.3f}\n"
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s += f"\tF1: {0.0 if precision * recall == 0 else 2 * precision * recall / (precision + recall):.3f}\n"
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if key != "all":
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s += f"\tTP list: {' # '.join(item.TP_list)}\n"
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s += f"\tFP list: {' # '.join(item.FP_list)}\n"
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s += f"\tFN list: {' # '.join(item.FN_list)}\n"
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of.write(s + "\n")
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if key == "all":
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logging.info(s)
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of.write(f"\n\n{params.keywords_config}")
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logging.info("Wrote metric stats to {}".format(metric_filename))
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@torch.no_grad()
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def main():
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parser = get_parser()
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GigaSpeechAsrDataModule.add_arguments(parser)
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args = parser.parse_args()
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args.exp_dir = Path(args.exp_dir)
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params = get_params()
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params.update(vars(args))
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params.res_dir = params.exp_dir / "kws"
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params.suffix = params.test_set
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if params.iter > 0:
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params.suffix += f"-iter-{params.iter}-avg-{params.avg}"
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else:
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params.suffix += f"-epoch-{params.epoch}-avg-{params.avg}"
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if params.causal:
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assert (
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"," not in params.chunk_size
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), "chunk_size should be one value in decoding."
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assert (
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"," not in params.left_context_frames
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), "left_context_frames should be one value in decoding."
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params.suffix += f"-chunk-{params.chunk_size}"
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params.suffix += f"-left-context-{params.left_context_frames}"
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params.suffix += f"-score-{params.keywords_score}"
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params.suffix += f"-threshold-{params.keywords_threshold}"
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params.suffix += f"-tailing-blanks-{params.num_tailing_blanks}"
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if params.blank_penalty != 0:
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params.suffix += f"-blank-penalty-{params.blank_penalty}"
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params.suffix += f"-keywords-{params.keywords_file.split('/')[-1]}"
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setup_logger(f"{params.res_dir}/log-decode-{params.suffix}")
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logging.info("Decoding started")
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device = torch.device("cpu")
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if torch.cuda.is_available():
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device = torch.device("cuda", 0)
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logging.info(f"Device: {device}")
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sp = spm.SentencePieceProcessor()
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sp.load(params.bpe_model)
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# <blk> and <unk> are defined in local/train_bpe_model.py
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params.blank_id = sp.piece_to_id("<blk>")
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params.unk_id = sp.piece_to_id("<unk>")
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params.vocab_size = sp.get_piece_size()
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logging.info(params)
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phrases = []
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token_ids = []
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keywords_scores = []
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keywords_thresholds = []
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keywords_config = []
|
|
with open(params.keywords_file, "r") as f:
|
|
for line in f.readlines():
|
|
keywords_config.append(line)
|
|
score = 0
|
|
threshold = 0
|
|
keyword = []
|
|
words = line.strip().upper().split()
|
|
for word in words:
|
|
word = word.strip()
|
|
if word[0] == ":":
|
|
score = float(word[1:])
|
|
continue
|
|
if word[0] == "#":
|
|
threshold = float(word[1:])
|
|
continue
|
|
keyword.append(word)
|
|
keyword = " ".join(keyword)
|
|
phrases.append(keyword)
|
|
token_ids.append(sp.encode(keyword))
|
|
keywords_scores.append(score)
|
|
keywords_thresholds.append(threshold)
|
|
|
|
params.keywords_config = "".join(keywords_config)
|
|
|
|
keywords_graph = ContextGraph(
|
|
context_score=params.keywords_score, ac_threshold=params.keywords_threshold
|
|
)
|
|
keywords_graph.build(
|
|
token_ids=token_ids,
|
|
phrases=phrases,
|
|
scores=keywords_scores,
|
|
ac_thresholds=keywords_thresholds,
|
|
)
|
|
keywords = set(phrases)
|
|
|
|
logging.info("About to create model")
|
|
model = get_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()
|
|
|
|
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
|
|
gigaspeech = GigaSpeechAsrDataModule(args)
|
|
|
|
test_cuts = gigaspeech.test_cuts()
|
|
test_dl = gigaspeech.test_dataloaders(test_cuts)
|
|
|
|
if params.test_set == "small":
|
|
test_fsc_small_cuts = gigaspeech.fsc_test_small_cuts()
|
|
test_fsc_small_dl = gigaspeech.test_dataloaders(test_fsc_small_cuts)
|
|
test_sets = ["small-fsc", "test"]
|
|
test_dls = [test_fsc_small_dl, test_dl]
|
|
else:
|
|
assert params.test_set == "large", params.test_set
|
|
test_fsc_large_cuts = gigaspeech.fsc_test_large_cuts()
|
|
test_fsc_large_dl = gigaspeech.test_dataloaders(test_fsc_large_cuts)
|
|
test_sets = ["large-fsc", "test"]
|
|
test_dls = [test_fsc_large_dl, test_dl]
|
|
|
|
for test_set, test_dl in zip(test_sets, test_dls):
|
|
results, metric = decode_dataset(
|
|
dl=test_dl,
|
|
params=params,
|
|
model=model,
|
|
sp=sp,
|
|
keywords_graph=keywords_graph,
|
|
keywords=keywords,
|
|
test_only_keywords="fsc" in test_set,
|
|
)
|
|
|
|
save_results(
|
|
params=params,
|
|
test_set_name=test_set,
|
|
results=results,
|
|
metric=metric,
|
|
)
|
|
|
|
logging.info("Done!")
|
|
|
|
|
|
if __name__ == "__main__":
|
|
main()
|