mirror of
https://github.com/k2-fsa/icefall.git
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fba5e67d5e
- Introduce unified AMP helpers (create_grad_scaler, torch_autocast) to handle deprecations in PyTorch ≥2.3.0 - Replace direct uses of torch.cuda.amp.GradScaler and torch.cuda.amp.autocast with the new utilities across all training and inference scripts - Update all torch.load calls to include weights_only=False for compatibility with newer PyTorch versions
776 lines
26 KiB
Python
776 lines
26 KiB
Python
#!/usr/bin/env python3
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#
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# Copyright 2021-2022 Xiaomi Corporation (Author: Fangjun Kuang,
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# Liyong Guo,
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# Quandong Wang,
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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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import argparse
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import logging
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import math
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import pprint
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from collections import defaultdict
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from pathlib import Path
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from typing import Dict, List, Optional, Tuple
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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 LibriSpeechAsrDataModule
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from train import add_model_arguments, get_params, get_transducer_model
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from icefall.checkpoint import (
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average_checkpoints,
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average_checkpoints_with_averaged_model,
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find_checkpoints,
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load_checkpoint,
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)
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from icefall.decode import (
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get_lattice,
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nbest_decoding,
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nbest_oracle,
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one_best_decoding,
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rescore_with_n_best_list,
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rescore_with_whole_lattice,
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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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get_texts,
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setup_logger,
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store_transcripts,
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str2bool,
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write_error_stats,
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)
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LOG_EPS = math.log(1e-10)
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def get_parser():
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parser = argparse.ArgumentParser(
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formatter_class=argparse.ArgumentDefaultsHelpFormatter
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)
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parser.add_argument(
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"--exp-dir",
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type=str,
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default="tiny_transducer_ctc/exp",
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help="The experiment dir",
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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=1,
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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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"--lang-dir",
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type=Path,
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default="data/lang_phone",
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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="1best",
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help="""Decoding method.
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Supported values are:
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- (1) ctc-decoding. Use CTC decoding. It uses a sentence piece
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model, i.e., lang_dir/bpe.model, to convert word pieces to words.
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It needs neither a lexicon nor an n-gram LM.
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- (2) 1best. Extract the best path from the decoding lattice as the
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decoding result.
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- (3) nbest. Extract n paths from the decoding lattice; the path
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with the highest score is the decoding result.
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- (4) nbest-rescoring. Extract n paths from the decoding lattice,
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rescore them with an n-gram LM (e.g., a 4-gram LM), the path with
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the highest score is the decoding result.
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- (5) whole-lattice-rescoring. Rescore the decoding lattice with an
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n-gram LM (e.g., a 4-gram LM), the best path of rescored lattice
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is the decoding result.
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you have trained an RNN LM using ./rnn_lm/train.py
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- (6) nbest-oracle. Its WER is the lower bound of any n-best
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rescoring method can achieve. Useful for debugging n-best
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rescoring method.
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""",
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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=100,
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help="""Number of paths for n-best based decoding method.
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Used only when "method" is one of the following values:
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nbest, nbest-rescoring, and nbest-oracle
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""",
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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=1.0,
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help="""The scale to be applied to `lattice.scores`.
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It's needed if you use any kinds of n-best based rescoring.
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Used only when "method" is one of the following values:
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nbest, nbest-rescoring, and nbest-oracle
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A smaller value results in more unique paths.
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""",
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)
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parser.add_argument(
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"--hlg-scale",
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type=float,
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default=0.7,
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help="""The scale to be applied to `hlg.scores`.
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""",
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)
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parser.add_argument(
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"--lm-dir",
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type=str,
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default="data/lm",
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help="""The n-gram LM dir.
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It should contain either G_4_gram.pt or G_4_gram.fst.txt
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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 get_decoding_params() -> AttributeDict:
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"""Parameters for decoding."""
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params = AttributeDict(
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{
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"search_beam": 20,
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"output_beam": 8,
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"min_active_states": 30,
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"max_active_states": 10000,
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"use_double_scores": True,
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"context_size": 2,
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}
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)
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return params
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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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HLG: Optional[k2.Fsa],
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H: Optional[k2.Fsa],
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bpe_model: Optional[spm.SentencePieceProcessor],
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batch: dict,
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word_table: k2.SymbolTable,
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G: Optional[k2.Fsa] = 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 no rescoring is used, the key is the string `no_rescore`.
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If LM rescoring is used, the key is the string `lm_scale_xxx`,
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where `xxx` is the value of `lm_scale`. An example key is
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`lm_scale_0.7`
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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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- params.decoding_method is "1best", it uses 1best decoding without LM rescoring.
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- params.decoding_method is "nbest", it uses nbest decoding without LM rescoring.
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- params.decoding_method is "nbest-rescoring", it uses nbest LM rescoring.
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- params.decoding_method is "whole-lattice-rescoring", it uses whole lattice LM
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rescoring.
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model:
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The neural model.
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HLG:
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The decoding graph. Used only when params.decoding_method is NOT ctc-decoding.
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H:
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The ctc topo. Used only when params.decoding_method is ctc-decoding.
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bpe_model:
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The BPE model. Used only when params.decoding_method is ctc-decoding.
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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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G:
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An LM. It is not None when params.decoding_method is "nbest-rescoring"
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or "whole-lattice-rescoring". In general, the G in HLG
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is a 3-gram LM, while this G is a 4-gram LM.
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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. Note: If it decodes to nothing, then return None.
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"""
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if HLG is not None:
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device = HLG.device
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else:
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device = H.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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encoder_out, _ = model.encoder(feature, feature_lens)
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nnet_output = model.ctc_output(encoder_out)
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# nnet_output is (N, T, C)
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supervision_segments = torch.stack(
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(
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supervisions["sequence_idx"],
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torch.div(
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supervisions["start_frame"],
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params.subsampling_factor,
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rounding_mode="trunc",
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),
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torch.div(
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supervisions["num_frames"],
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params.subsampling_factor,
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rounding_mode="trunc",
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),
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),
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1,
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).to(torch.int32)
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if H is None:
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assert HLG is not None
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decoding_graph = HLG
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else:
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assert HLG is None
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assert bpe_model is not None
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decoding_graph = H
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lattice = get_lattice(
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nnet_output=nnet_output,
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decoding_graph=decoding_graph,
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supervision_segments=supervision_segments,
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search_beam=params.search_beam,
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output_beam=params.output_beam,
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min_active_states=params.min_active_states,
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max_active_states=params.max_active_states,
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subsampling_factor=params.subsampling_factor,
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)
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if params.decoding_method == "ctc-decoding":
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best_path = one_best_decoding(
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lattice=lattice, use_double_scores=params.use_double_scores
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)
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# Note: `best_path.aux_labels` contains token IDs, not word IDs
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# since we are using H, not HLG here.
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#
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# token_ids is a lit-of-list of IDs
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token_ids = get_texts(best_path)
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# hyps is a list of str, e.g., ['xxx yyy zzz', ...]
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hyps = bpe_model.decode(token_ids)
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# hyps is a list of list of str, e.g., [['xxx', 'yyy', 'zzz'], ... ]
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hyps = [s.split() for s in hyps]
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key = "ctc-decoding"
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return {key: hyps}
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if params.decoding_method == "nbest-oracle":
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# Note: You can also pass rescored lattices to it.
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# We choose the HLG decoded lattice for speed reasons
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# as HLG decoding is faster and the oracle WER
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# is only slightly worse than that of rescored lattices.
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best_path = nbest_oracle(
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lattice=lattice,
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num_paths=params.num_paths,
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ref_texts=supervisions["text"],
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word_table=word_table,
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nbest_scale=params.nbest_scale,
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oov="<UNK>",
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)
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hyps = get_texts(best_path)
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hyps = [[word_table[i] for i in ids] for ids in hyps]
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key = f"oracle_{params.num_paths}_nbest_scale_{params.nbest_scale}" # noqa
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return {key: hyps}
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if params.decoding_method in ["1best", "nbest"]:
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if params.decoding_method == "1best":
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best_path = one_best_decoding(
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lattice=lattice, use_double_scores=params.use_double_scores
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)
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key = "no_rescore"
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else:
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best_path = nbest_decoding(
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lattice=lattice,
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num_paths=params.num_paths,
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use_double_scores=params.use_double_scores,
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nbest_scale=params.nbest_scale,
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)
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key = f"no_rescore-nbest-scale-{params.nbest_scale}-{params.num_paths}" # noqa
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hyps = get_texts(best_path)
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hyps = [[word_table[i] for i in ids] for ids in hyps]
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return {key: hyps}
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assert params.decoding_method in [
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"nbest-rescoring",
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"whole-lattice-rescoring",
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]
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# lm_scale_list = [0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7]
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# lm_scale_list += [0.8, 0.9, 1.0, 1.1, 1.2, 1.3]
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# lm_scale_list += [1.4, 1.5, 1.6, 1.7, 1.8, 1.9, 2.0]
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lm_scale_list = [0.6, 0.7, 0.8, 0.9]
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if params.decoding_method == "nbest-rescoring":
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best_path_dict = rescore_with_n_best_list(
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lattice=lattice,
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G=G,
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num_paths=params.num_paths,
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lm_scale_list=lm_scale_list,
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nbest_scale=params.nbest_scale,
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)
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elif params.decoding_method == "whole-lattice-rescoring":
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best_path_dict = rescore_with_whole_lattice(
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lattice=lattice,
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G_with_epsilon_loops=G,
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lm_scale_list=lm_scale_list,
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)
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else:
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assert False, f"Unsupported decoding method: {params.decoding_method}"
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ans = dict()
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if best_path_dict is not None:
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for lm_scale_str, best_path in best_path_dict.items():
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hyps = get_texts(best_path)
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hyps = [[word_table[i] for i in ids] for ids in hyps]
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ans[lm_scale_str] = hyps
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else:
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ans = None
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return ans
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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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HLG: Optional[k2.Fsa],
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H: Optional[k2.Fsa],
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bpe_model: Optional[spm.SentencePieceProcessor],
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word_table: k2.SymbolTable,
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G: 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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HLG:
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The decoding graph. Used only when params.decoding_method is NOT ctc-decoding.
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H:
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The ctc topo. Used only when params.decoding_method is ctc-decoding.
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bpe_model:
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The BPE model. Used only when params.decoding_method is ctc-decoding.
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word_table:
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It is the word symbol table.
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G:
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An LM. It is not None when params.decoding_method is "nbest-rescoring"
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or "whole-lattice-rescoring". In general, the G in HLG
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is a 3-gram LM, while this G is a 4-gram LM.
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Returns:
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Return a dict, whose key may be "no-rescore" if no LM rescoring
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is used, or it may be "lm_scale_0.7" if LM rescoring 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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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 = decode_one_batch(
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params=params,
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model=model,
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HLG=HLG,
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H=H,
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bpe_model=bpe_model,
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batch=batch,
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word_table=word_table,
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G=G,
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)
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for name, hyps in hyps_dict.items():
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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_words = ref_text.split()
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this_batch.append((cut_id, ref_words, hyp_words))
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results[name].extend(this_batch)
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num_cuts += len(texts)
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if batch_idx % 100 == 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_results(
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params: AttributeDict,
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test_set_name: str,
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results_dict: Dict[str, List[Tuple[str, List[str], List[str]]]],
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):
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test_set_wers = dict()
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for key, results in results_dict.items():
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recog_path = (
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params.res_dir / f"recogs-{test_set_name}-{key}-{params.suffix}.txt"
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)
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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}-{key}-{params.suffix}.txt"
|
|
)
|
|
with open(errs_filename, "w") as f:
|
|
wer = write_error_stats(f, f"{test_set_name}-{key}", results)
|
|
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}-{test_set_name}-{key}-{params.suffix}.txt"
|
|
with open(errs_info, "w") as f:
|
|
print("settings\tWER", file=f)
|
|
for key, val in test_set_wers:
|
|
print("{}\t{}".format(key, val), file=f)
|
|
|
|
s = "\nFor {}, WER of different settings are:\n".format(test_set_name)
|
|
note = "\tbest for {}".format(test_set_name)
|
|
for key, val in test_set_wers:
|
|
s += "{}\t{}{}\n".format(key, val, note)
|
|
note = ""
|
|
logging.info(s)
|
|
|
|
|
|
@torch.no_grad()
|
|
def main():
|
|
parser = get_parser()
|
|
LibriSpeechAsrDataModule.add_arguments(parser)
|
|
args = parser.parse_args()
|
|
args.exp_dir = Path(args.exp_dir)
|
|
args.lang_dir = Path(args.lang_dir)
|
|
args.lm_dir = Path(args.lm_dir)
|
|
|
|
params = get_params()
|
|
# add decoding params
|
|
params.update(get_decoding_params())
|
|
params.update(vars(args))
|
|
|
|
assert params.decoding_method in (
|
|
"ctc-decoding",
|
|
"1best",
|
|
"nbest",
|
|
"nbest-rescoring",
|
|
"whole-lattice-rescoring",
|
|
"nbest-oracle",
|
|
)
|
|
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}"
|
|
|
|
params.suffix += f"-hlg-scale-{params.hlg_scale}"
|
|
|
|
if params.use_averaged_model:
|
|
params.suffix += "-uam"
|
|
|
|
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}")
|
|
logging.info(pprint.pformat(params, indent=2))
|
|
|
|
lexicon = Lexicon(params.lang_dir)
|
|
max_token_id = max(lexicon.tokens)
|
|
num_classes = max_token_id + 1 # +1 for the blank
|
|
|
|
params.vocab_size = num_classes
|
|
# <blk> and <unk> are defined in local/train_bpe_model.py
|
|
params.blank_id = 0
|
|
|
|
if params.decoding_method == "ctc-decoding":
|
|
assert "lang_bpe" in str(
|
|
params.lang_dir
|
|
), "ctc-decoding only supports BPE lexicons."
|
|
HLG = None
|
|
H = k2.ctc_topo(
|
|
max_token=max_token_id,
|
|
modified=False,
|
|
device=device,
|
|
)
|
|
bpe_model = spm.SentencePieceProcessor()
|
|
bpe_model.load(str(params.lang_dir / "bpe.model"))
|
|
else:
|
|
H = None
|
|
bpe_model = None
|
|
HLG = k2.Fsa.from_dict(
|
|
torch.load(
|
|
f"{params.lang_dir}/HLG.pt", map_location=device, weights_only=False
|
|
)
|
|
)
|
|
assert HLG.requires_grad is False
|
|
|
|
HLG.scores *= params.hlg_scale
|
|
if not hasattr(HLG, "lm_scores"):
|
|
HLG.lm_scores = HLG.scores.clone()
|
|
|
|
if params.decoding_method in (
|
|
"nbest-rescoring",
|
|
"whole-lattice-rescoring",
|
|
):
|
|
if not (params.lm_dir / "G_4_gram.pt").is_file():
|
|
logging.info("Loading G_4_gram.fst.txt")
|
|
logging.warning("It may take 8 minutes.")
|
|
with open(params.lm_dir / "G_4_gram.fst.txt") as f:
|
|
first_word_disambig_id = lexicon.word_table["#0"]
|
|
|
|
G = k2.Fsa.from_openfst(f.read(), acceptor=False)
|
|
# G.aux_labels is not needed in later computations, so
|
|
# remove it here.
|
|
del G.aux_labels
|
|
# CAUTION: The following line is crucial.
|
|
# Arcs entering the back-off state have label equal to #0.
|
|
# We have to change it to 0 here.
|
|
G.labels[G.labels >= first_word_disambig_id] = 0
|
|
# See https://github.com/k2-fsa/k2/issues/874
|
|
# for why we need to set G.properties to None
|
|
G.__dict__["_properties"] = None
|
|
G = k2.Fsa.from_fsas([G]).to(device)
|
|
G = k2.arc_sort(G)
|
|
# Save a dummy value so that it can be loaded in C++.
|
|
# See https://github.com/pytorch/pytorch/issues/67902
|
|
# for why we need to do this.
|
|
G.dummy = 1
|
|
|
|
torch.save(G.as_dict(), params.lm_dir / "G_4_gram.pt")
|
|
else:
|
|
logging.info("Loading pre-compiled G_4_gram.pt")
|
|
d = torch.load(
|
|
params.lm_dir / "G_4_gram.pt", map_location=device, weights_only=False
|
|
)
|
|
G = k2.Fsa.from_dict(d)
|
|
|
|
if params.decoding_method == "whole-lattice-rescoring":
|
|
# Add epsilon self-loops to G as we will compose
|
|
# it with the whole lattice later
|
|
G = k2.add_epsilon_self_loops(G)
|
|
G = k2.arc_sort(G)
|
|
G = G.to(device)
|
|
|
|
# G.lm_scores is used to replace HLG.lm_scores during
|
|
# LM rescoring.
|
|
G.lm_scores = G.scores.clone()
|
|
else:
|
|
G = None
|
|
|
|
logging.info("About to create model")
|
|
model = get_transducer_model(params)
|
|
|
|
if not params.use_averaged_model:
|
|
if params.iter > 0:
|
|
filenames = find_checkpoints(params.exp_dir, iteration=-params.iter)[
|
|
: params.avg
|
|
]
|
|
if len(filenames) == 0:
|
|
raise ValueError(
|
|
f"No checkpoints found for"
|
|
f" --iter {params.iter}, --avg {params.avg}"
|
|
)
|
|
elif len(filenames) < params.avg:
|
|
raise ValueError(
|
|
f"Not enough checkpoints ({len(filenames)}) found for"
|
|
f" --iter {params.iter}, --avg {params.avg}"
|
|
)
|
|
logging.info(f"averaging {filenames}")
|
|
model.to(device)
|
|
model.load_state_dict(average_checkpoints(filenames, device=device))
|
|
elif params.avg == 1:
|
|
load_checkpoint(f"{params.exp_dir}/epoch-{params.epoch}.pt", model)
|
|
else:
|
|
start = params.epoch - params.avg + 1
|
|
filenames = []
|
|
for i in range(start, params.epoch + 1):
|
|
if i >= 1:
|
|
filenames.append(f"{params.exp_dir}/epoch-{i}.pt")
|
|
logging.info(f"averaging {filenames}")
|
|
model.to(device)
|
|
model.load_state_dict(average_checkpoints(filenames, device=device))
|
|
else:
|
|
if params.iter > 0:
|
|
filenames = find_checkpoints(params.exp_dir, iteration=-params.iter)[
|
|
: params.avg + 1
|
|
]
|
|
if len(filenames) == 0:
|
|
raise ValueError(
|
|
f"No checkpoints found for"
|
|
f" --iter {params.iter}, --avg {params.avg}"
|
|
)
|
|
elif len(filenames) < params.avg + 1:
|
|
raise ValueError(
|
|
f"Not enough checkpoints ({len(filenames)}) found for"
|
|
f" --iter {params.iter}, --avg {params.avg}"
|
|
)
|
|
filename_start = filenames[-1]
|
|
filename_end = filenames[0]
|
|
logging.info(
|
|
"Calculating the averaged model over iteration checkpoints"
|
|
f" from {filename_start} (excluded) to {filename_end}"
|
|
)
|
|
model.to(device)
|
|
model.load_state_dict(
|
|
average_checkpoints_with_averaged_model(
|
|
filename_start=filename_start,
|
|
filename_end=filename_end,
|
|
device=device,
|
|
)
|
|
)
|
|
else:
|
|
assert params.avg > 0, params.avg
|
|
start = params.epoch - params.avg
|
|
assert start >= 1, start
|
|
filename_start = f"{params.exp_dir}/epoch-{start}.pt"
|
|
filename_end = f"{params.exp_dir}/epoch-{params.epoch}.pt"
|
|
logging.info(
|
|
f"Calculating the averaged model over epoch range from "
|
|
f"{start} (excluded) to {params.epoch}"
|
|
)
|
|
model.to(device)
|
|
model.load_state_dict(
|
|
average_checkpoints_with_averaged_model(
|
|
filename_start=filename_start,
|
|
filename_end=filename_end,
|
|
device=device,
|
|
)
|
|
)
|
|
|
|
model.to(device)
|
|
model.eval()
|
|
|
|
num_param = sum([p.numel() for p in model.parameters()])
|
|
enc_param = sum([p.numel() for p in model.encoder.parameters()])
|
|
ctc_param = sum([p.numel() for p in model.ctc_output.parameters()])
|
|
logging.info(f"Number of model parameters: {num_param}")
|
|
logging.info(f"Parameters for CTC decoding: {enc_param + ctc_param}")
|
|
|
|
# we need cut ids to display recognition results.
|
|
args.return_cuts = True
|
|
librispeech = LibriSpeechAsrDataModule(args)
|
|
|
|
test_clean_cuts = librispeech.test_clean_cuts()
|
|
test_other_cuts = librispeech.test_other_cuts()
|
|
|
|
test_clean_dl = librispeech.test_dataloaders(test_clean_cuts)
|
|
test_other_dl = librispeech.test_dataloaders(test_other_cuts)
|
|
|
|
test_sets = ["test-clean", "test-other"]
|
|
test_dl = [test_clean_dl, test_other_dl]
|
|
|
|
for test_set, test_dl in zip(test_sets, test_dl):
|
|
results_dict = decode_dataset(
|
|
dl=test_dl,
|
|
params=params,
|
|
model=model,
|
|
HLG=HLG,
|
|
H=H,
|
|
bpe_model=bpe_model,
|
|
word_table=lexicon.word_table,
|
|
G=G,
|
|
)
|
|
|
|
save_results(
|
|
params=params,
|
|
test_set_name=test_set,
|
|
results_dict=results_dict,
|
|
)
|
|
|
|
logging.info("Done!")
|
|
|
|
|
|
if __name__ == "__main__":
|
|
main()
|