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#!/usr/bin/env python3
"""Evaluate the lightning module by loading the checkpoint, the SentencePiece model, and the global_stats.json.
Example:
python eval.py --model-type tedlium3 --checkpoint-path ./experiments/checkpoints/epoch=119-step=254999.ckpt
--dataset-path ./datasets/tedlium --sp-model-path ./spm_bpe_500.model
"""
import logging
import pathlib
from argparse import ArgumentParser, RawTextHelpFormatter
import torch
import torchaudio
from common import MODEL_TYPE_LIBRISPEECH, MODEL_TYPE_MUSTC, MODEL_TYPE_TEDLIUM3
from librispeech.lightning import LibriSpeechRNNTModule
from mustc.lightning import MuSTCRNNTModule
from tedlium3.lightning import TEDLIUM3RNNTModule
logger = logging.getLogger(__name__)
def compute_word_level_distance(seq1, seq2):
return torchaudio.functional.edit_distance(seq1.lower().split(), seq2.lower().split())
def run_eval_subset(model, dataloader, subset):
total_edit_distance = 0
total_length = 0
with torch.no_grad():
for idx, (batch, transcripts) in enumerate(dataloader):
actual = transcripts[0]
predicted = model(batch)
total_edit_distance += compute_word_level_distance(actual, predicted)
total_length += len(actual.split())
if idx % 100 == 0:
logger.info(f"Processed elem {idx}; WER: {total_edit_distance / total_length}")
logger.info(f"Final WER for {subset} set: {total_edit_distance / total_length}")
def run_eval(model, model_type):
if model_type == MODEL_TYPE_LIBRISPEECH:
dataloader = model.test_dataloader()
run_eval_subset(model, dataloader, "test")
elif model_type == MODEL_TYPE_TEDLIUM3:
dev_loader = model.dev_dataloader()
test_loader = model.test_dataloader()
run_eval_subset(model, dev_loader, "dev")
run_eval_subset(model, test_loader, "test")
elif model_type == MODEL_TYPE_MUSTC:
dev_loader = model.dev_dataloader()
test_common_loader = model.test_common_dataloader()
test_he_loader = model.test_he_dataloader()
run_eval_subset(model, dev_loader, "dev")
run_eval_subset(model, test_common_loader, "tst-COMMON")
run_eval_subset(model, test_he_loader, "tst-HE")
else:
raise ValueError(f"Encountered unsupported model type {model_type}.")
def get_lightning_module(args):
if args.model_type == MODEL_TYPE_LIBRISPEECH:
return LibriSpeechRNNTModule.load_from_checkpoint(
args.checkpoint_path,
librispeech_path=str(args.dataset_path),
sp_model_path=str(args.sp_model_path),
global_stats_path=str(args.global_stats_path),
)
elif args.model_type == MODEL_TYPE_TEDLIUM3:
return TEDLIUM3RNNTModule.load_from_checkpoint(
args.checkpoint_path,
tedlium_path=str(args.dataset_path),
sp_model_path=str(args.sp_model_path),
global_stats_path=str(args.global_stats_path),
)
elif args.model_type == MODEL_TYPE_MUSTC:
return MuSTCRNNTModule.load_from_checkpoint(
args.checkpoint_path,
mustc_path=str(args.dataset_path),
sp_model_path=str(args.sp_model_path),
global_stats_path=str(args.global_stats_path),
)
else:
raise ValueError(f"Encountered unsupported model type {args.model_type}.")
def parse_args():
parser = ArgumentParser(description=__doc__, formatter_class=RawTextHelpFormatter)
parser.add_argument(
"--model-type", type=str, choices=[MODEL_TYPE_LIBRISPEECH, MODEL_TYPE_TEDLIUM3, MODEL_TYPE_MUSTC], required=True
)
parser.add_argument(
"--checkpoint-path",
type=pathlib.Path,
help="Path to checkpoint to use for evaluation.",
)
parser.add_argument(
"--global-stats-path",
default=pathlib.Path("global_stats.json"),
type=pathlib.Path,
help="Path to JSON file containing feature means and stddevs.",
)
parser.add_argument(
"--dataset-path",
type=pathlib.Path,
help="Path to dataset.",
)
parser.add_argument(
"--sp-model-path",
type=pathlib.Path,
help="Path to SentencePiece model.",
)
parser.add_argument(
"--use-cuda",
action="store_true",
default=False,
help="Run using CUDA.",
)
parser.add_argument("--debug", action="store_true", help="whether to use debug level for logging")
return parser.parse_args()
def init_logger(debug):
fmt = "%(asctime)s %(message)s" if debug else "%(message)s"
level = logging.DEBUG if debug else logging.INFO
logging.basicConfig(format=fmt, level=level, datefmt="%Y-%m-%d %H:%M:%S")
def cli_main():
args = parse_args()
init_logger(args.debug)
model = get_lightning_module(args)
if args.use_cuda:
model = model.to(device="cuda")
run_eval(model, args.model_type)
if __name__ == "__main__":
cli_main()
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