test.py

import argparse

import torch

from deepspeech_pytorch.loader.data_loader import SpectrogramDataset, AudioDataLoader
from deepspeech_pytorch.decoder import GreedyDecoder
from deepspeech_pytorch.opts import add_decoder_args, add_inference_args
from deepspeech_pytorch.testing import evaluate
from deepspeech_pytorch.utils import load_model, load_decoder

parser = argparse.ArgumentParser(description='DeepSpeech transcription')
parser = add_inference_args(parser)
parser.add_argument('--test-manifest', metavar='DIR',
                    help='path to validation manifest csv', default='data/test_manifest.csv')
parser.add_argument('--batch-size', default=20, type=int, help='Batch size for testing')
parser.add_argument('--num-workers', default=4, type=int, help='Number of workers used in dataloading')
parser.add_argument('--verbose', action="store_true", help="print out decoded output and error of each sample")
parser.add_argument('--save-output', default=None, help="Saves output of model from test to this file_path")
parser = add_decoder_args(parser)

if __name__ == '__main__':
    args = parser.parse_args()
    torch.set_grad_enabled(False)
    device = torch.device("cuda" if args.cuda else "cpu")
    model = load_model(device, args.model_path, args.half)

    decoder = load_decoder(decoder_type=args.decoder,
                           labels=model.labels,
                           lm_path=args.lm_path,
                           alpha=args.alpha,
                           beta=args.beta,
                           cutoff_top_n=args.cutoff_top_n,
                           cutoff_prob=args.cutoff_prob,
                           beam_width=args.beam_width,
                           lm_workers=args.lm_workers)
    target_decoder = GreedyDecoder(model.labels,
                                   blank_index=model.labels.index('_'))
    test_dataset = SpectrogramDataset(audio_conf=model.audio_conf,
                                      manifest_filepath=args.test_manifest,
                                      labels=model.labels,
                                      normalize=True)
    test_loader = AudioDataLoader(test_dataset,
                                  batch_size=args.batch_size,
                                  num_workers=args.num_workers)
    wer, cer, output_data = evaluate(test_loader=test_loader,
                                     device=device,
                                     model=model,
                                     decoder=decoder,
                                     target_decoder=target_decoder,
                                     save_output=args.save_output,
                                     verbose=args.verbose,
                                     half=args.half)

    print('Test Summary \t'
          'Average WER {wer:.3f}\t'
          'Average CER {cer:.3f}\t'.format(wer=wer, cer=cer))
    if args.save_output is not None:
        torch.save(output_data, args.save_output)