Source code for espnet2.bin.uasr_extract_feature

#!/usr/bin/env python3
import argparse
import logging
import sys
from pathlib import Path
from typing import Optional, Sequence, Tuple, Union

from torch.nn.parallel import data_parallel
from typeguard import check_argument_types

from espnet2.fileio.npy_scp import NpyScpWriter
from espnet2.tasks.uasr import UASRTask
from espnet2.torch_utils.device_funcs import to_device
from espnet2.torch_utils.forward_adaptor import ForwardAdaptor
from espnet2.utils.types import str2bool, str2triple_str, str_or_none
from espnet.utils.cli_utils import get_commandline_args

[docs]def get_parser(): parser = argparse.ArgumentParser( description="UASR Decoding", formatter_class=argparse.ArgumentDefaultsHelpFormatter, ) parser.add_argument( "--data_path_and_name_and_type", type=str2triple_str, required=True, action="append", ) parser.add_argument( "--uasr_train_config", type=str, help="uasr training configuration", ) parser.add_argument( "--uasr_model_file", type=str, help="uasr model parameter file", ) parser.add_argument( "--key_file", type=str_or_none, help="key file", ) parser.add_argument( "--allow_variable_data_keys", type=str2bool, default=False, ) parser.add_argument( "--ngpu", type=int, default=0, help="The number of gpus. 0 indicates CPU mode", ) parser.add_argument( "--num_workers", type=int, default=1, help="The number of workers used for DataLoader", ) parser.add_argument( "--batch_size", type=int, default=1, help="The batch size for feature extraction", ) parser.add_argument( "--dtype", default="float32", choices=["float16", "float32", "float64"], help="Data type", ) parser.add_argument( "--dset", type=str, help="dataset", ) parser.add_argument( "--output_dir", type=str, help="Output directory", ) parser.add_argument( "--log_level", type=lambda x: x.upper(), default="INFO", choices=("ERROR", "WARNING", "INFO", "DEBUG", "NOTSET"), help="The verbose level of logging", ) return parser
[docs]def extract_feature( uasr_train_config: Optional[str], uasr_model_file: Optional[str], data_path_and_name_and_type: Sequence[Tuple[str, str, str]], key_file: Optional[str], batch_size: int, dtype: str, num_workers: int, allow_variable_data_keys: bool, ngpu: int, output_dir: str, dset: str, log_level: Union[int, str], ): assert check_argument_types() logging.basicConfig( level=log_level, format="%(asctime)s (%(module)s:%(lineno)d) %(levelname)s: %(message)s", ) output_dir_path = Path(output_dir) if ngpu >= 1: device = "cuda" else: device = "cpu" uasr_model, uasr_train_args = UASRTask.build_model_from_file( uasr_train_config, uasr_model_file, device ) test_iter = UASRTask.build_streaming_iterator( data_path_and_name_and_type=data_path_and_name_and_type, key_file=key_file, batch_size=batch_size, dtype=dtype, num_workers=num_workers, preprocess_fn=UASRTask.build_preprocess_fn(uasr_train_args, False), collate_fn=UASRTask.build_collate_fn(uasr_train_args, False), allow_variable_data_keys=allow_variable_data_keys, inference=True, ) npy_scp_writers = {} for keys, batch in test_iter: batch = to_device(batch, "cuda" if ngpu > 0 else "cpu") if ngpu <= 1: data = uasr_model.collect_feats(**batch) else: data = data_parallel( ForwardAdaptor(uasr_model, "collect_feats"), (), range(ngpu), module_kwargs=batch, ) for key, v in data.items(): for i, (uttid, seq) in enumerate(zip(keys, v.cpu().detach().numpy())): if f"{key}_lengths" in data: length = data[f"{key}_lengths"][i] seq = seq[:length] else: seq = seq[None] if (key, dset) not in npy_scp_writers: p = output_dir_path / dset / "collect_feats" npy_scp_writers[(key, dset)] = NpyScpWriter( p / f"data_{key}", p / f"{key}.scp" ) npy_scp_writers[(key, dset)][uttid] = seq
[docs]def main(cmd=None): print(get_commandline_args(), file=sys.stderr) parser = get_parser() args = parser.parse_args(cmd) kwargs = vars(args) extract_feature(**kwargs)
if __name__ == "__main__": main()