test.py

import argparse
import json
import os
from pathlib import Path

import torch
from tqdm import tqdm

import hw_hifi.model as module_model
from hw_hifi.utils import ROOT_PATH
from hw_hifi.utils.parse_config import ConfigParser
import torchaudio
from hw_hifi.utils.mel import MelSpectrogram

DEFAULT_CHECKPOINT_PATH = ROOT_PATH / "default_test_model" / "checkpoint.pth"


def main(config, out_file):
    logger = config.get_logger("test")

    # define cpu or gpu if possible
    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")


    # setup data_loader instances

    # build model architecture
    model = config.init_obj(config["arch"], module_model)
    #logger.info(model)

    logger.info("Loading checkpoint: {} ...".format(config.resume))
    checkpoint = torch.load(config.resume, map_location=device)
    state_dict = checkpoint["state_dict"]
    if config["n_gpu"] > 1:
        model = torch.nn.DataParallel(model)
    model.load_state_dict(state_dict)

    # prepare model for testing
    model = model.to(device)
    model.eval()

    test_dir = ROOT_PATH / 'test_audios'
    gen_dir = ROOT_PATH / 'generated_audios'
    wave2spec = MelSpectrogram().to(device)
    with torch.no_grad():
        for audio_file in test_dir.iterdir():
            audio_tensor, sr = torchaudio.load(audio_file)
            target_sr = config["preprocessing"]["sr"]
            if sr != target_sr:
                print("Sample rate mismatch!")
                audio_tensor = torchaudio.functional.resample(audio_tensor, sr, target_sr)
            audio_tensor = audio_tensor.to(device)
            spectrogram = wave2spec(audio_tensor)
            fake = model.generator(spectrogram).squeeze(0).cpu()
            torchaudio.save(gen_dir / (audio_file.stem + '_generated.wav'), fake, sample_rate=target_sr, format='wav')

        print("Generated audios saved in generated_audios")


if __name__ == "__main__":
    args = argparse.ArgumentParser(description="PyTorch Template")
    args.add_argument(
        "-c",
        "--config",
        default=None,
        type=str,
        help="config file path (default: None)",
    )
    args.add_argument(
        "-r",
        "--resume",
        default=str(DEFAULT_CHECKPOINT_PATH.absolute().resolve()),
        type=str,
        help="path to latest checkpoint (default: None)",
    )
    args.add_argument(
        "-d",
        "--device",
        default=None,
        type=str,
        help="indices of GPUs to enable (default: all)",
    )
    args.add_argument(
        "-o",
        "--output",
        default="output.json",
        type=str,
        help="File to write results (.json)",
    )
    args.add_argument(
        "-t",
        "--test-data-folder",
        default=None,
        type=str,
        help="Path to dataset",
    )
    args.add_argument(
        "-b",
        "--batch-size",
        default=20,
        type=int,
        help="Test dataset batch size",
    )
    args.add_argument(
        "-j",
        "--jobs",
        default=1,
        type=int,
        help="Number of workers for test dataloader",
    )

    args = args.parse_args()

    # set GPUs
    if args.device is not None:
        os.environ["CUDA_VISIBLE_DEVICES"] = args.device

    # first, we need to obtain config with model parameters
    # we assume it is located with checkpoint in the same folder
    #model_config = ROOT_PATH / Path(args.config)
    model_config = Path(args.resume).parent / "config.json"
    
    with model_config.open() as f:
        config = ConfigParser(json.load(f), resume=args.resume)

    # update with addition configs from `args.config` if provided
    if args.config is not None:
        with Path(args.config).open() as f:
            config.config.update(json.load(f))


    main(config, args.output)