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import argparse
import json
import os
from pathlib import Path
import torch
from tqdm import tqdm
import hw_asr.model as module_model
from hw_asr.trainer import Trainer
from hw_asr.utils import ROOT_PATH
from hw_asr.utils.object_loading import get_dataloaders
from hw_asr.utils.parse_config import ConfigParser
from hw_asr.metric.utils import calc_wer
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")
# text_encoder
text_encoder = config.get_text_encoder()
# setup data_loader instances
dataloaders = get_dataloaders(config, text_encoder)
# build model architecture
model = config.init_obj(config["arch"], module_model, n_class=len(text_encoder))
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()
results = []
argmax_wer_sum = 0
beam_search_wer_sum = 0
lm_wer_sum = 0
with torch.no_grad():
for batch_num, batch in enumerate(tqdm(dataloaders["test"])):
batch = Trainer.move_batch_to_device(batch, device)
output = model(**batch)
if type(output) is dict:
batch.update(output)
else:
batch["logits"] = output
batch["log_probs"] = torch.log_softmax(batch["logits"], dim=-1)
batch["log_probs_length"] = model.transform_input_lengths(batch["spectrogram_length"])
batch["probs"] = batch["log_probs"].exp().cpu()
batch["argmax"] = batch["probs"].argmax(-1)
for i in range(len(batch["text"])):
length = int(batch["log_probs_length"][i])
ground_truth = batch["text"][i]
argmax = batch["argmax"][i][:length].cpu().numpy()
text_argmax = text_encoder.ctc_decode(argmax)
probs = batch["probs"][i][:length].detach().cpu().numpy()
text_beam_search = text_encoder.ctc_beam_search(probs, beam_size=4)
logits = batch["logits"][i][:length].detach().cpu().numpy()
text_lm = text_encoder.ctc_lm_beam_search(logits)
argmax_wer = calc_wer(ground_truth, text_argmax) * 100
beam_search_wer = calc_wer(ground_truth, text_beam_search) * 100
lm_wer = calc_wer(ground_truth, text_lm) * 100
argmax_wer_sum += argmax_wer
beam_search_wer_sum += beam_search_wer
lm_wer_sum += lm_wer
results.append(
{
"ground_truth": ground_truth,
"pred_text_argmax": text_argmax,
"pred_text_beam_search": text_beam_search,
"pred_text_lm": text_lm,
"argmax_wer": argmax_wer,
"beam_search_wer": beam_search_wer,
"lm_wer": lm_wer,
}
)
n = len(results)
logger.info("argmax_wer_mean:")
logger.info(argmax_wer_sum / n)
logger.info("beam_search_wer_mean:")
logger.info(beam_search_wer_sum / n)
logger.info("lm_wer_mean:")
logger.info(lm_wer_sum / n)
with Path(out_file).open("w") as f:
json.dump(results, f, indent=2)
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 = 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))
# if `--test-data-folder` was provided, set it as a default test set
if args.test_data_folder is not None:
test_data_folder = Path(args.test_data_folder).absolute().resolve()
assert test_data_folder.exists()
config.config["data"] = {
"test": {
"batch_size": args.batch_size,
"num_workers": args.jobs,
"datasets": [
{
"type": "CustomDirAudioDataset",
"args": {
"audio_dir": str(test_data_folder / "audio"),
"transcription_dir": str(test_data_folder / "transcriptions"),
},
}
],
}
}
assert config.config.get("data", {}).get("test", None) is not None
config["data"]["test"]["batch_size"] = args.batch_size
config["data"]["test"]["n_jobs"] = args.jobs
main(config, args.output)
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