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https://github.com/k2-fsa/icefall.git
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Add decoding script.
This commit is contained in:
Fangjun Kuang
parent
73ba843d0a
commit
e38f04e70f
@ -428,8 +428,6 @@ def decode_dataset(
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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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@ -20,31 +20,22 @@ import argparse
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import logging
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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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from typing import Dict, List, 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 conformer import Conformer
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from transducer.beam_search import greedy_search
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from transducer.conformer import Conformer
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from transducer.decoder import Decoder
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from transducer.joiner import Joiner
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from transducer.model import Transducer
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from icefall.bpe_graph_compiler import BpeCtcTrainingGraphCompiler
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from icefall.checkpoint import average_checkpoints, load_checkpoint
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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_attention_decoder,
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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.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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write_error_stats,
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@ -72,76 +63,18 @@ def get_parser():
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"'--epoch'. ",
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)
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parser.add_argument(
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"--method",
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type=str,
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default="attention-decoder",
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help="""Decoding method.
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Supported values are:
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- (0) 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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- (1) 1best. Extract the best path from the decoding lattice as the
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decoding result.
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- (2) 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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- (3) 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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- (4) 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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- (5) attention-decoder. Extract n paths from the LM rescored
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lattice, the path with the highest score is the decoding result.
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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, attention-decoder, 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=0.5,
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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, attention-decoder, 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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"--exp-dir",
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type=str,
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default="conformer_ctc/exp",
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default="transducer/exp",
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help="The experiment dir",
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)
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parser.add_argument(
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"--lang-dir",
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"--bpe-model",
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type=str,
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default="data/lang_bpe_500",
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help="The lang dir",
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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 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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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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return parser
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@ -151,250 +84,138 @@ def get_params() -> AttributeDict:
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params = AttributeDict(
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{
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# parameters for conformer
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"feature_dim": 80,
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"encoder_out_dim": 512,
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"subsampling_factor": 4,
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"attention_dim": 512,
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"nhead": 8,
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"dim_feedforward": 2048,
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"num_encoder_layers": 12,
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"vgg_frontend": False,
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"use_feat_batchnorm": True,
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"feature_dim": 80,
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"nhead": 8,
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"attention_dim": 512,
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"num_decoder_layers": 6,
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# parameters for decoding
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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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# decoder params
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"decoder_embedding_dim": 1024,
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"num_decoder_layers": 4,
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"decoder_hidden_dim": 512,
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"env_info": get_env_info(),
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}
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)
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return params
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def get_encoder_model(params: AttributeDict):
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# TODO: We can add an option to switch between Conformer and Transformer
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encoder = Conformer(
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num_features=params.feature_dim,
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output_dim=params.encoder_out_dim,
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subsampling_factor=params.subsampling_factor,
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d_model=params.attention_dim,
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nhead=params.nhead,
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dim_feedforward=params.dim_feedforward,
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num_encoder_layers=params.num_encoder_layers,
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vgg_frontend=params.vgg_frontend,
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use_feat_batchnorm=params.use_feat_batchnorm,
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)
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return encoder
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def get_decoder_model(params: AttributeDict):
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decoder = Decoder(
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vocab_size=params.vocab_size,
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embedding_dim=params.decoder_embedding_dim,
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blank_id=params.blank_id,
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sos_id=params.sos_id,
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num_layers=params.num_decoder_layers,
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hidden_dim=params.decoder_hidden_dim,
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output_dim=params.encoder_out_dim,
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)
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return decoder
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def get_joiner_model(params: AttributeDict):
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joiner = Joiner(
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input_dim=params.encoder_out_dim,
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output_dim=params.vocab_size,
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)
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return joiner
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def get_transducer_model(params: AttributeDict):
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encoder = get_encoder_model(params)
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decoder = get_decoder_model(params)
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joiner = get_joiner_model(params)
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model = Transducer(
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encoder=encoder,
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decoder=decoder,
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joiner=joiner,
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)
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return model
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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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sp: spm.SentencePieceProcessor,
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batch: dict,
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word_table: k2.SymbolTable,
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sos_id: int,
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eos_id: int,
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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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if greedy_search is used, it would be "greedy_search"
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If beam search with a beam size of 7 is used, it would be
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"beam_7"
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- value: It contains the decoding result. `len(value)` equals to
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batch size. `value[i]` is the decoding result for the i-th
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utterance in the given batch.
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Args:
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params:
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It's the return value of :func:`get_params`.
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- params.method is "1best", it uses 1best decoding without LM rescoring.
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- params.method is "nbest", it uses nbest decoding without LM rescoring.
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- params.method is "nbest-rescoring", it uses nbest LM rescoring.
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- params.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.method is NOT ctc-decoding.
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H:
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The ctc topo. Used only when params.method is ctc-decoding.
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bpe_model:
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The BPE model. Used only when params.method is ctc-decoding.
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sp:
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The BPE model.
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batch:
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It is the return value from iterating
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`lhotse.dataset.K2SpeechRecognitionDataset`. See its documentation
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for the format of the `batch`.
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word_table:
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The word symbol table.
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sos_id:
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The token ID of the SOS.
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eos_id:
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The token ID of the EOS.
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G:
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An LM. It is not None when params.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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the returned dict.
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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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device = model.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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nnet_output, memory, memory_key_padding_mask = model(feature, supervisions)
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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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supervisions["start_frame"] // params.subsampling_factor,
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supervisions["num_frames"] // params.subsampling_factor,
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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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encoder_out, encoder_out_lens = model.encoder(
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x=feature, x_lens=feature_lens
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)
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hyps = []
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batch_size = encoder_out.size(0)
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if params.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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for i in range(batch_size):
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# fmt: off
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encoder_out_i = encoder_out[i:i+1, :encoder_out_lens[i]]
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# fmt: on
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hyp = greedy_search(model=model, encoder_out=encoder_out_i)
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hyps.append(sp.decode(hyp).split())
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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.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.method in ["1best", "nbest"]:
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if params.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.method in [
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"nbest-rescoring",
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"whole-lattice-rescoring",
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"attention-decoder",
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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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if params.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.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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elif params.method == "attention-decoder":
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# lattice uses a 3-gram Lm. We rescore it with a 4-gram LM.
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rescored_lattice = 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=None,
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)
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# TODO: pass `lattice` instead of `rescored_lattice` to
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# `rescore_with_attention_decoder`
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best_path_dict = rescore_with_attention_decoder(
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lattice=rescored_lattice,
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num_paths=params.num_paths,
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model=model,
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memory=memory,
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memory_key_padding_mask=memory_key_padding_mask,
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sos_id=sos_id,
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eos_id=eos_id,
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nbest_scale=params.nbest_scale,
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)
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else:
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assert False, f"Unsupported decoding method: {params.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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return {"greedy_search": hyps}
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# TODO: Implement beam search
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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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sos_id: int,
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eos_id: int,
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G: Optional[k2.Fsa] = None,
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sp: spm.SentencePieceProcessor,
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) -> Dict[str, List[Tuple[List[str], List[str]]]]:
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"""Decode dataset.
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@ -405,31 +226,15 @@ def decode_dataset(
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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.method is NOT ctc-decoding.
|
||||
H:
|
||||
The ctc topo. Used only when params.method is ctc-decoding.
|
||||
bpe_model:
|
||||
The BPE model. Used only when params.method is ctc-decoding.
|
||||
word_table:
|
||||
It is the word symbol table.
|
||||
sos_id:
|
||||
The token ID for SOS.
|
||||
eos_id:
|
||||
The token ID for EOS.
|
||||
G:
|
||||
An LM. It is not None when params.method is "nbest-rescoring"
|
||||
or "whole-lattice-rescoring". In general, the G in HLG
|
||||
is a 3-gram LM, while this G is a 4-gram LM.
|
||||
sp:
|
||||
The BPE model.
|
||||
Returns:
|
||||
Return a dict, whose key may be "no-rescore" if no LM rescoring
|
||||
is used, or it may be "lm_scale_0.7" if LM rescoring is used.
|
||||
Return a dict, whose key may be "greedy_search" if greedy search
|
||||
is used, or it may be "beam_7" if beam size of 7 is used.
|
||||
Its value is a list of tuples. Each tuple contains two elements:
|
||||
The first is the reference transcript, and the second is the
|
||||
predicted result.
|
||||
"""
|
||||
results = []
|
||||
|
||||
num_cuts = 0
|
||||
|
||||
try:
|
||||
@ -444,37 +249,18 @@ def decode_dataset(
|
||||
hyps_dict = decode_one_batch(
|
||||
params=params,
|
||||
model=model,
|
||||
HLG=HLG,
|
||||
H=H,
|
||||
bpe_model=bpe_model,
|
||||
sp=sp,
|
||||
batch=batch,
|
||||
word_table=word_table,
|
||||
G=G,
|
||||
sos_id=sos_id,
|
||||
eos_id=eos_id,
|
||||
)
|
||||
|
||||
if hyps_dict is not None:
|
||||
for lm_scale, hyps in hyps_dict.items():
|
||||
this_batch = []
|
||||
assert len(hyps) == len(texts)
|
||||
for hyp_words, ref_text in zip(hyps, texts):
|
||||
ref_words = ref_text.split()
|
||||
this_batch.append((ref_words, hyp_words))
|
||||
|
||||
results[lm_scale].extend(this_batch)
|
||||
else:
|
||||
assert (
|
||||
len(results) > 0
|
||||
), "It should not decode to empty in the first batch!"
|
||||
for name, hyps in hyps_dict.items():
|
||||
this_batch = []
|
||||
hyp_words = []
|
||||
for ref_text in texts:
|
||||
assert len(hyps) == len(texts)
|
||||
for hyp_words, ref_text in zip(hyps, texts):
|
||||
ref_words = ref_text.split()
|
||||
this_batch.append((ref_words, hyp_words))
|
||||
|
||||
for lm_scale in results.keys():
|
||||
results[lm_scale].extend(this_batch)
|
||||
results[name].extend(this_batch)
|
||||
|
||||
num_cuts += len(texts)
|
||||
|
||||
@ -492,31 +278,22 @@ def save_results(
|
||||
test_set_name: str,
|
||||
results_dict: Dict[str, List[Tuple[List[int], List[int]]]],
|
||||
):
|
||||
if params.method == "attention-decoder":
|
||||
# Set it to False since there are too many logs.
|
||||
enable_log = False
|
||||
else:
|
||||
enable_log = True
|
||||
test_set_wers = dict()
|
||||
for key, results in results_dict.items():
|
||||
recog_path = params.exp_dir / f"recogs-{test_set_name}-{key}.txt"
|
||||
store_transcripts(filename=recog_path, texts=results)
|
||||
if enable_log:
|
||||
logging.info(f"The transcripts are stored in {recog_path}")
|
||||
logging.info(f"The transcripts are stored in {recog_path}")
|
||||
|
||||
# The following prints out WERs, per-word error statistics and aligned
|
||||
# ref/hyp pairs.
|
||||
errs_filename = params.exp_dir / f"errs-{test_set_name}-{key}.txt"
|
||||
with open(errs_filename, "w") as f:
|
||||
wer = write_error_stats(
|
||||
f, f"{test_set_name}-{key}", results, enable_log=enable_log
|
||||
f, f"{test_set_name}-{key}", results, enable_log=True
|
||||
)
|
||||
test_set_wers[key] = wer
|
||||
|
||||
if enable_log:
|
||||
logging.info(
|
||||
"Wrote detailed error stats to {}".format(errs_filename)
|
||||
)
|
||||
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.exp_dir / f"wer-summary-{test_set_name}.txt"
|
||||
@ -539,113 +316,31 @@ def main():
|
||||
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()
|
||||
params.update(vars(args))
|
||||
|
||||
setup_logger(f"{params.exp_dir}/log-{params.method}/log-decode")
|
||||
setup_logger(f"{params.exp_dir}/log-decode")
|
||||
logging.info("Decoding started")
|
||||
logging.info(params)
|
||||
|
||||
lexicon = Lexicon(params.lang_dir)
|
||||
max_token_id = max(lexicon.tokens)
|
||||
num_classes = max_token_id + 1 # +1 for the blank
|
||||
|
||||
device = torch.device("cpu")
|
||||
if torch.cuda.is_available():
|
||||
device = torch.device("cuda", 0)
|
||||
|
||||
logging.info(f"device: {device}")
|
||||
logging.info(f"Device: {device}")
|
||||
|
||||
graph_compiler = BpeCtcTrainingGraphCompiler(
|
||||
params.lang_dir,
|
||||
device=device,
|
||||
sos_token="<sos/eos>",
|
||||
eos_token="<sos/eos>",
|
||||
)
|
||||
sos_id = graph_compiler.sos_id
|
||||
eos_id = graph_compiler.eos_id
|
||||
sp = spm.SentencePieceProcessor()
|
||||
sp.load(params.bpe_model)
|
||||
|
||||
if params.method == "ctc-decoding":
|
||||
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)
|
||||
)
|
||||
assert HLG.requires_grad is False
|
||||
# <blk> and <sos/eos> are defined in local/train_bpe_model.py
|
||||
params.blank_id = sp.piece_to_id("<blk>")
|
||||
params.sos_id = sp.piece_to_id("<sos/eos>")
|
||||
params.vocab_size = sp.get_piece_size()
|
||||
|
||||
if not hasattr(HLG, "lm_scores"):
|
||||
HLG.lm_scores = HLG.scores.clone()
|
||||
logging.info(params)
|
||||
|
||||
if params.method in (
|
||||
"nbest-rescoring",
|
||||
"whole-lattice-rescoring",
|
||||
"attention-decoder",
|
||||
):
|
||||
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)
|
||||
G = k2.Fsa.from_dict(d)
|
||||
|
||||
if params.method in ["whole-lattice-rescoring", "attention-decoder"]:
|
||||
# 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
|
||||
|
||||
model = Conformer(
|
||||
num_features=params.feature_dim,
|
||||
nhead=params.nhead,
|
||||
d_model=params.attention_dim,
|
||||
num_classes=num_classes,
|
||||
subsampling_factor=params.subsampling_factor,
|
||||
num_decoder_layers=params.num_decoder_layers,
|
||||
vgg_frontend=params.vgg_frontend,
|
||||
use_feat_batchnorm=params.use_feat_batchnorm,
|
||||
)
|
||||
logging.info("About to create model")
|
||||
model = get_transducer_model(params)
|
||||
|
||||
if params.avg == 1:
|
||||
load_checkpoint(f"{params.exp_dir}/epoch-{params.epoch}.pt", model)
|
||||
@ -661,6 +356,8 @@ def main():
|
||||
|
||||
model.to(device)
|
||||
model.eval()
|
||||
model.device = device
|
||||
|
||||
num_param = sum([p.numel() for p in model.parameters()])
|
||||
logging.info(f"Number of model parameters: {num_param}")
|
||||
|
||||
@ -680,17 +377,13 @@ def main():
|
||||
dl=test_dl,
|
||||
params=params,
|
||||
model=model,
|
||||
HLG=HLG,
|
||||
H=H,
|
||||
bpe_model=bpe_model,
|
||||
word_table=lexicon.word_table,
|
||||
G=G,
|
||||
sos_id=sos_id,
|
||||
eos_id=eos_id,
|
||||
sp=sp,
|
||||
)
|
||||
|
||||
save_results(
|
||||
params=params, test_set_name=test_set, results_dict=results_dict
|
||||
params=params,
|
||||
test_set_name=test_set,
|
||||
results_dict=results_dict,
|
||||
)
|
||||
|
||||
logging.info("Done!")
|
||||
|
||||
Loading…
x
Reference in New Issue
Block a user