mirror of
https://github.com/k2-fsa/icefall.git
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77357ebb06
- tool for forced-alignment with CTC model - provides timeline, computes per-token and per-utterance acoustic confidences - based on torchaudio `forced_align()` - confidences are computed in several ways other modifications: - LibriSpeechAsrDataModel extended with `::load_manifest()` to allow passing-in cutset from CLI. - update @custom_fwd @custom_bwd in scaling.py - streaming_decode.py update errs/recogs/log filenames '-' <-> '_'
662 lines
21 KiB
Python
Executable File
662 lines
21 KiB
Python
Executable File
#!/usr/bin/env python3
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#
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# Copyright 2025 Brno University of Technology (Author: Karel Vesely)
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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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Batch aligning with CTC model (it can be Tranducer + CTC).
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It works with both causal an non-causal models.
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Streaming is disabled, or simulated by attention masks
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(see: --chunk-size --left-context-frames).
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Whole utterance processed by 1 forward() call.
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Note: model averaging is present. With `--epoch 10 --avg 3`,
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the epochs 8-10 are taken for averaging. Model averaging
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is smoothing the CTC posteriors to some extent.
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Usage:
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(1) torchaudio forced_align()
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./zipformer/ctc_align.py \
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--epoch 10 \
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--avg 3 \
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--exp-dir ./zipformer/exp \
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--max-duration 300 \
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--decoding-method ctc_align
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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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from collections import defaultdict
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from pathlib import Path, PurePath
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from typing import Dict, List, Optional, Tuple
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import k2
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import numpy as np
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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 LibriSpeechAsrDataModule as AsrDataModule
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from lhotse import set_caching_enabled
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from torchaudio.functional import (
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forced_align,
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merge_tokens,
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)
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from train import add_model_arguments, get_model, get_params
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from icefall.checkpoint import (
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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.utils import (
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AttributeDict,
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setup_logger,
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str2bool,
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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="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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"--res-dir-suffix",
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type=str,
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default="",
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help="Suffix to where alignments are stored",
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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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"--ignored-tokens",
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type=str,
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nargs="+",
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default=[],
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help="",
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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="ctc_align",
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choices=[
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"ctc_align",
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],
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help=""" Decoding method for doing the forced alignment.""",
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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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"dataset_manifests",
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type=str,
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nargs="+",
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help="""Manifests of test-sets to be evaluated""",
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)
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add_model_arguments(parser)
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return parser
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def align_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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ignored_tokens: set[int],
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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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) -> Dict[str, List[List[str]]]:
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"""Align 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 alignment.
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For now, just "ctc_alignment" is used.
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- value: It contains the alignment result: (labels, log_probs).
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`len(value)` equals to batch size. `value[i]` is the alignment
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result for the i-th 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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ignored_tokens:
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Set of int token-codes to be ignored for calculation of confidence.
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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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UNUSED_PART, CAN BE USED LATER FOR ALIGNING TO A DECODING_GRAPH:
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word_table [UNUSED]:
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The word symbol table.
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decoding_graph [UNUSED]:
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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 alignment 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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batch_size = feature.shape[0]
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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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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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ctc_output = model.ctc_output(encoder_out) # (N, T, C)
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hyps = []
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# tokenize the transcripts:
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text_encoded = sp.encode(supervisions["text"])
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# lengths
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num_tokens = [len(te) for te in text_encoded]
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max_tokens = max(num_tokens)
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# convert to padded np.array:
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targets = np.array(
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[
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np.pad(seq, (0, max_tokens - len(seq)), "constant", constant_values=-1)
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for seq in text_encoded
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]
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)
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# convert to tensor:
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targets = torch.tensor(targets, dtype=torch.int32, device=device)
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target_lengths = torch.tensor(num_tokens, dtype=torch.int32, device=device)
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# torchaudio2.4.0+
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# The batch dimension for log_probs must be 1 at the current version:
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# https://github.com/pytorch/audio/blob/main/src/libtorchaudio/forced_align/gpu/compute.cu#L277
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for ii in range(batch_size):
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labels, log_probs = forced_align(
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log_probs=ctc_output[ii, : encoder_out_lens[ii]].unsqueeze(dim=0),
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targets=targets[ii, : target_lengths[ii]].unsqueeze(dim=0),
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input_lengths=encoder_out_lens[ii].unsqueeze(dim=0),
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target_lengths=target_lengths[ii].unsqueeze(dim=0),
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blank=0,
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)
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# per-token time, score
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token_spans = merge_tokens(labels[0], log_probs[0].exp())
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# int -> token
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for s in token_spans:
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s.token = sp.id_to_piece(s.token)
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# mean conf. from the per-token scores
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mean_token_conf = np.mean([token_span.score for token_span in token_spans])
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# confidences
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ignore_mask = labels == 0
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for tok in ignored_tokens:
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ignore_mask += labels == tok
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nonblank_scores = log_probs[~ignore_mask].exp()
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num_scores = nonblank_scores.shape[0]
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if num_scores > 0:
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nonblank_min = float(nonblank_scores.min())
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nonblank_q05 = float(torch.quantile(nonblank_scores, 0.05))
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nonblank_q10 = float(torch.quantile(nonblank_scores, 0.10))
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nonblank_q20 = float(torch.quantile(nonblank_scores, 0.20))
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nonblank_q30 = float(torch.quantile(nonblank_scores, 0.30))
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nonblank_mean = float(nonblank_scores.mean())
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else:
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nonblank_min = -1.0
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nonblank_q05 = -1.0
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nonblank_q10 = -1.0
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nonblank_q20 = -1.0
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nonblank_q30 = -1.0
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nonblank_mean = -1.0
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if num_scores > 0:
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confidence = (nonblank_min + nonblank_q05 + nonblank_q10 + nonblank_q20) / 4
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else:
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confidence = 1.0 # default score for short utts
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hyps.append(
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{
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"token_spans": token_spans,
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"mean_token_conf": mean_token_conf,
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"confidence": confidence,
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"num_scores": num_scores,
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"nonblank_mean": nonblank_mean,
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"nonblank_min": nonblank_min,
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"nonblank_q05": nonblank_q05,
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"nonblank_q10": nonblank_q10,
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"nonblank_q20": nonblank_q20,
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"nonblank_q30": nonblank_q30,
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}
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)
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return {"ctc_align": hyps}
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def align_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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) -> 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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ignored_tokens = params.ignored_tokens + ["<sos/eos>", "<unk>"]
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ignored_tokens_ints = [sp.piece_to_id(token) for token in ignored_tokens]
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logging.info(f"ignored tokens {ignored_tokens}")
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logging.info(f"ignored int codes {ignored_tokens_ints}")
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results = defaultdict(list)
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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_dict = align_one_batch(
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params=params,
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model=model,
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sp=sp,
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ignored_tokens=ignored_tokens_ints,
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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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)
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for name, alignments in hyps_dict.items():
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this_batch = []
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assert len(alignments) == len(texts)
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for cut_id, alignments, ref_text in zip(cut_ids, alignments, texts):
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ref_words = ref_text.split()
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this_batch.append((cut_id, ref_words, alignments))
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results[name].extend(this_batch)
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num_cuts += len(texts)
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log_interval = 100
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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
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def save_alignment_output(
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params: AttributeDict,
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test_set_name: str,
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results_dict: Dict[str, List[Tuple[str, List[str], List[str]]]],
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):
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"""
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Save the token alignments and per-utterance confidences.
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"""
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for key, results in results_dict.items():
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alignments_filename = params.res_dir / f"alignments-{test_set_name}.txt"
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time_step = 0.04
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with open(alignments_filename, "w", encoding="utf8") as fd:
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for key, ref_text, ali in results:
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for token_span in ali["token_spans"]:
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t_beg = token_span.start * time_step
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t_end = token_span.end * time_step
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t_dur = t_end - t_beg
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token = token_span.token
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score = token_span.score
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# CTM format : (wav_name, ch, t_beg, t_dur, token, score)
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print(
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f"{key} A {t_beg:.2f} {t_dur:.2f} {token} {score:.6f}", file=fd
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)
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logging.info(f"The alignments are stored in `{alignments_filename}`")
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# ---------------------------
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confidences_filename = params.res_dir / f"confidences-{test_set_name}.txt"
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with open(confidences_filename, "w", encoding="utf8") as fd:
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print(
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"utterance_key mean_token_conf mean_frame_conf q0-20_conf "
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"(nonblank_min,q05,q10,q20,q30) (num_scores,num_tokens)",
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file=fd,
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) # header
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for key, ref_text, ali in results:
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mean_token_conf = ali["mean_token_conf"]
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mean_frame_conf = ali["nonblank_mean"]
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q0_20_conf = ali["confidence"]
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min_ = ali["nonblank_min"]
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q05 = ali["nonblank_q05"]
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q10 = ali["nonblank_q10"]
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q20 = ali["nonblank_q20"]
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q30 = ali["nonblank_q30"]
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num_scores = ali[
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"num_scores"
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] # scores used to compute `mean_frame_conf`
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num_tokens = len(ali["token_spans"]) # tokens in ref transcript
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print(
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f"{key} {mean_token_conf:.4f} {mean_frame_conf:.4f} "
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f"{q0_20_conf:.4f} "
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f"({min_:.4f},{q05:.4f},{q10:.4f},{q20:.4f},{q30:.4f}) "
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f"({num_scores},{num_tokens})",
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file=fd,
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)
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logging.info(f"The confidences are stored in `{confidences_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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AsrDataModule.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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# enable AudioCache
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set_caching_enabled(True) # lhotse
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assert params.decoding_method in ("ctc_align",)
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assert params.enable_spec_aug is False
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assert params.use_ctc is True
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params.res_dir = params.exp_dir / (params.decoding_method + params.res_dir_suffix)
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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"_{params.decoding_method}"
|
|
|
|
if params.use_averaged_model:
|
|
params.suffix += "_use-averaged-model"
|
|
|
|
setup_logger(f"{params.res_dir}/log-align-{params.suffix}")
|
|
logging.info("Forced-alignment 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_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
|
|
asr_datamodule = AsrDataModule(args)
|
|
|
|
# create array of dataloaders (one per test-set)
|
|
testset_labels = []
|
|
testset_dataloaders = []
|
|
for testset_manifest in args.dataset_manifests:
|
|
label = PurePath(testset_manifest).name # basename
|
|
label = label.replace(".jsonl.gz", "")
|
|
|
|
test_cuts = asr_datamodule.load_manifest(testset_manifest)
|
|
test_dataloader = asr_datamodule.test_dataloaders(test_cuts)
|
|
|
|
testset_labels.append(label)
|
|
testset_dataloaders.append(test_dataloader)
|
|
|
|
# align
|
|
for test_set, test_dl in zip(testset_labels, testset_dataloaders):
|
|
results_dict = align_dataset(
|
|
dl=test_dl,
|
|
params=params,
|
|
model=model,
|
|
sp=sp,
|
|
word_table=None,
|
|
decoding_graph=None,
|
|
)
|
|
|
|
save_alignment_output(
|
|
params=params,
|
|
test_set_name=test_set,
|
|
results_dict=results_dict,
|
|
)
|
|
|
|
logging.info("Done!")
|
|
|
|
|
|
if __name__ == "__main__":
|
|
main()
|