src/run_config.py

import ast
import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Dict, List, Optional, Tuple
from transformers import (
    HfArgumentParser,
    Wav2Vec2Config,
    Wav2Vec2FeatureExtractor
)

logger = logging.getLogger(__name__)


@dataclass
class ConfigArguments:
    """
    Arguments to which config we are going to set up.
    """
    output_dir: str = field(
        default=".",
        metadata={"help": "The output directory where the config will be written."},
    )
    name_or_path: Optional[str] = field(
        default=None,
        metadata={
            "help": "The model checkpoint for weights initialization."
                    "Don't set if you want to train a model from scratch."
        },
    )
    config_params: Optional[str] = field(
        default=None,
        metadata={"help": "Custom configuration for the specific `name_or_path`"}
    )
    feature_extractor_params: Optional[str] = field(
        default=None,
        metadata={"help": "Custom feature extractor configuration for the specific `name_or_path`"}
    )

    def __post_init__(self):
        if self.config_params:
            try:
                self.config_params = ast.literal_eval(self.config_params)
            except Exception as e:
                print(f"Your custom `config` parameters do not acceptable due to {e}")

        if self.feature_extractor_params:
            try:
                self.feature_extractor_params = ast.literal_eval(self.feature_extractor_params)
            except Exception as e:
                print(f"Your custom `feature_extractor` parameters do not acceptable due to {e}")


def main():
    parser = HfArgumentParser([ConfigArguments])
    if len(sys.argv) == 2 and sys.argv[1].endswith(".json"):
        # If we pass only one argument to the script and it's the path to a json file,
        # let's parse it to get our arguments.
        config_args = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]))[0]
    else:
        config_args = parser.parse_args_into_dataclasses()[0]
    # Setup logging
    logging.basicConfig(
        format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
        datefmt="%m/%d/%Y %H:%M:%S",
        handlers=[logging.StreamHandler(sys.stdout)],
    )
    logger.setLevel(logging.INFO)
    logger.info(f"Setting up configuration {config_args.name_or_path} with extra params {config_args.config_params}")
    if config_args.config_params and isinstance(config_args.config_params, dict):
        config = Wav2Vec2Config.from_pretrained(
            config_args.name_or_path,
            **config_args.config_params
        )
    else:
        config = Wav2Vec2Config.from_pretrained(
            config_args.name_or_path,
            mask_time_length=10,
            mask_time_prob=0.05,
            diversity_loss_weight=0.1,
            num_negatives=100,
            do_stable_layer_norm=True,
            feat_extract_norm="layer",
            vocab_size=40
        )

    logger.info(f"Setting up feature_extractor {config_args.name_or_path} with extra params "
                f"{config_args.feature_extractor_params}")
    if config_args.feature_extractor_params and isinstance(config_args.feature_extractor_params, dict):
        feature_extractor = Wav2Vec2FeatureExtractor.from_pretrained(
            config_args.name_or_path,
            **config_args.feature_extractor_params
        )
    else:
        feature_extractor = Wav2Vec2FeatureExtractor.from_pretrained(
            config_args.name_or_path,
            return_attention_mask=True
        )
    logger.info(f"Your `config` saved here {config_args.output_dir}/config.json")
    config.save_pretrained(config_args.output_dir)

    logger.info(f"Your `feature_extractor` saved here {config_args.output_dir}/preprocessor_config.json")
    feature_extractor.save_pretrained(config_args.output_dir)


if __name__ == '__main__':
    main()