mirror of
https://github.com/k2-fsa/icefall.git
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320 lines
8.9 KiB
Python
Executable File
320 lines
8.9 KiB
Python
Executable File
#!/usr/bin/env python3
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import argparse
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import logging
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from pathlib import Path
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from typing import List, Tuple
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import k2
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import torch
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import torch.nn as nn
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from asr_datamodule import YesNoAsrDataModule
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from model import Tdnn
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from icefall.checkpoint import average_checkpoints, load_checkpoint
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from icefall.decode import get_lattice, one_best_decoding
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from icefall.env import get_env_info
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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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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=14,
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help="It specifies the checkpoint to use for decoding."
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"Note: Epoch counts from 0.",
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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=2,
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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'. ",
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)
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parser.add_argument(
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"--export",
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type=str2bool,
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default=False,
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help="""When enabled, the averaged model is saved to
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tdnn/exp/pretrained.pt. Note: only model.state_dict() is saved.
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pretrained.pt contains a dict {"model": model.state_dict()},
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which can be loaded by `icefall.checkpoint.load_checkpoint()`.
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""",
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)
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return parser
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def get_params() -> AttributeDict:
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params = AttributeDict(
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{
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"exp_dir": Path("tdnn/exp/"),
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"lang_dir": Path("data/lang_phone"),
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"feature_dim": 23,
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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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}
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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: k2.Fsa,
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batch: dict,
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word_table: k2.SymbolTable,
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) -> List[List[int]]:
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"""Decode one batch and return the result in a list-of-list.
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Each sub list contains the word IDs for an utterance in the 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.method is "1best", it uses 1best decoding.
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- params.method is "nbest", it uses nbest decoding.
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model:
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The neural model.
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HLG:
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The decoding graph.
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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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(https://github.com/lhotse-speech/lhotse/blob/master/lhotse/dataset/speech_recognition.py)
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word_table:
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It is the word symbol table.
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Returns:
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Return the decoding result. `len(ans)` == batch size.
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"""
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device = HLG.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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nnet_output = model(feature)
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# nnet_output is (N, T, C)
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batch_size = nnet_output.shape[0]
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supervision_segments = torch.tensor(
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[[i, 0, nnet_output.shape[1]] for i in range(batch_size)],
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dtype=torch.int32,
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)
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lattice = get_lattice(
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nnet_output=nnet_output,
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decoding_graph=HLG,
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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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)
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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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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 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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HLG: k2.Fsa,
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word_table: k2.SymbolTable,
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) -> 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.
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word_table:
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It is word symbol table.
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Returns:
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Return a tuple contains two elements (ref_text, hyp_text):
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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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results = []
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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 = []
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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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HLG=HLG,
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batch=batch,
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word_table=word_table,
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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_words = ref_text.split()
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this_batch.append((cut_id, ref_words, hyp_words))
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results.extend(this_batch)
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num_cuts += len(batch["supervisions"]["text"])
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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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exp_dir: Path,
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test_set_name: str,
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results: List[Tuple[str, List[str], List[str]]],
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) -> None:
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"""Save results to `exp_dir`.
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Args:
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exp_dir:
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The output directory. This function create the following files inside
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this directory:
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- recogs-{test_set_name}.text
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It contains the reference and hypothesis results, like below::
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ref=['NO', 'NO', 'NO', 'YES', 'NO', 'NO', 'NO', 'YES']
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hyp=['NO', 'NO', 'NO', 'YES', 'NO', 'NO', 'NO', 'YES']
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ref=['NO', 'NO', 'YES', 'NO', 'YES', 'NO', 'NO', 'YES']
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hyp=['NO', 'NO', 'YES', 'NO', 'YES', 'NO', 'NO', 'YES']
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- errs-{test_set_name}.txt
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It contains the detailed WER.
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test_set_name:
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The name of the test set, which will be part of the result filename.
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results:
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A list of tuples, each of which contains (ref_words, hyp_words).
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Returns:
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Return None.
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"""
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recog_path = exp_dir / f"recogs-{test_set_name}.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 = exp_dir / f"errs-{test_set_name}.txt"
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with open(errs_filename, "w") as f:
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write_error_stats(f, f"{test_set_name}", results)
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logging.info("Wrote detailed error stats to {}".format(errs_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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YesNoAsrDataModule.add_arguments(parser)
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args = parser.parse_args()
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params = get_params()
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params.update(vars(args))
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params["env_info"] = get_env_info()
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setup_logger(f"{params.exp_dir}/log/log-decode")
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logging.info("Decoding started")
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logging.info(params)
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lexicon = Lexicon(params.lang_dir)
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max_token_id = max(lexicon.tokens)
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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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HLG = k2.Fsa.from_dict(torch.load(f"{params.lang_dir}/HLG.pt", map_location="cpu"))
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HLG = HLG.to(device)
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assert HLG.requires_grad is False
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model = Tdnn(
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num_features=params.feature_dim,
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num_classes=max_token_id + 1, # +1 for the blank symbol
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)
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if params.avg == 1:
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load_checkpoint(f"{params.exp_dir}/epoch-{params.epoch}.pt", model)
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else:
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start = params.epoch - params.avg + 1
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filenames = []
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for i in range(start, params.epoch + 1):
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if start >= 0:
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filenames.append(f"{params.exp_dir}/epoch-{i}.pt")
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logging.info(f"averaging {filenames}")
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model.load_state_dict(average_checkpoints(filenames))
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if params.export:
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logging.info(f"Export averaged model to {params.exp_dir}/pretrained.pt")
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torch.save({"model": model.state_dict()}, f"{params.exp_dir}/pretrained.pt")
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return
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model.to(device)
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model.eval()
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# we need cut ids to display recognition results.
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args.return_cuts = True
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yes_no = YesNoAsrDataModule(args)
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test_dl = yes_no.test_dataloaders()
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results = decode_dataset(
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dl=test_dl,
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params=params,
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model=model,
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HLG=HLG,
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word_table=lexicon.word_table,
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)
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save_results(exp_dir=params.exp_dir, test_set_name="test_set", results=results)
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logging.info("Done!")
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if __name__ == "__main__":
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main()
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