152 lines
4.1 KiB
Python
Executable File
152 lines
4.1 KiB
Python
Executable File
#!/usr/bin/env python3
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#
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# Copyright 2022 Xiaomi Corporation (Author: Fangjun Kuang)
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#
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# See ../../../../LICENSE for clarification regarding multiple authors
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""
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This file takes a torchscript model, either quantized or not, and uses
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it for decoding.
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"""
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import argparse
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import logging
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from pathlib import Path
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import sentencepiece as spm
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import torch
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from asr_datamodule import AsrDataModule
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from decode import add_decoding_arguments, decode_dataset, save_results
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from librispeech import LibriSpeech
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from train import add_model_arguments, get_params
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from icefall.utils import setup_logger
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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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"--nn-model-filename",
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type=str,
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help="It specifies the path to load the torchscript model",
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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="pruned_transducer_stateless3/exp",
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help="Directory to save the decoding results",
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)
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parser.add_argument(
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"--bpe-model",
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type=str,
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default="data/lang_bpe_500/bpe.model",
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help="Path to the BPE model",
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)
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add_decoding_arguments(parser)
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add_model_arguments(parser)
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return parser
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@torch.no_grad()
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def main():
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parser = get_parser()
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AsrDataModule.add_arguments(parser)
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args = parser.parse_args()
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# We add only greedy_search for simplicity
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assert args.decoding_method == "greedy_search"
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params = get_params()
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params.update(vars(args))
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params.nn_model_filename = Path(args.nn_model_filename)
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assert params.nn_model_filename.is_file(), params.nn_model_filename
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params.res_dir = Path(params.exp_dir) / Path(params.nn_model_filename).stem
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params.res_dir = params.res_dir / params.decoding_method
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setup_logger(f"{params.res_dir}/log-decode")
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logging.info("Decoding started")
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model = torch.jit.load(params.nn_model_filename)
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device = torch.device("cpu")
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if torch.cuda.is_available() and hasattr(
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model.simple_lm_proj, "_packed_params"
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):
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device = torch.device("cuda", 0)
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logging.info(f"Device: {device}")
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sp = spm.SentencePieceProcessor()
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sp.load(params.bpe_model)
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# <blk> and <unk> are defined in local/train_bpe_model.py
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params.blank_id = sp.piece_to_id("<blk>")
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params.unk_id = sp.piece_to_id("<unk>")
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params.vocab_size = sp.get_piece_size()
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model.to(device)
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model.device = device
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model.unk_id = params.unk_id
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logging.info(params)
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num_param = sum([p.numel() for p in model.parameters()])
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logging.info(f"Number of model parameters: {num_param}")
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asr_datamodule = AsrDataModule(args)
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librispeech = LibriSpeech(manifest_dir=args.manifest_dir)
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test_clean_cuts = librispeech.test_clean_cuts()
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test_other_cuts = librispeech.test_other_cuts()
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test_clean_dl = asr_datamodule.test_dataloaders(test_clean_cuts)
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test_other_dl = asr_datamodule.test_dataloaders(test_other_cuts)
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test_sets = ["test-clean", "test-other"]
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test_dl = [test_clean_dl, test_other_dl]
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for test_set, test_dl in zip(test_sets, test_dl):
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results_dict = 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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sp=sp,
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word_table=None,
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decoding_graph=None,
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G=None,
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rnn_lm_model=None,
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)
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save_results(
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params=params,
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test_set_name=test_set,
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results_dict=results_dict,
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)
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logging.info("Done!")
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if __name__ == "__main__":
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main()
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