def write_stats(self, filestream):
"""Write all relevant info (e.g., error rate alignments) to file.
* See MetricStats.write_stats()
"""
if not self.summary:
self.summarize()
print_wer_summary(self.summary, filestream)
print_alignments(self.scores, filestream)
parser.add_argument(
"--utt2spk",
help="Provide a mapping from utterance ids to speaker ids."
"If provided, print a list of speakers with highest WER.",
)
args = parser.parse_args()
details_by_utterance = edit_distance.wer_details_by_utterance(
_plain_text_keydict(args.ref),
_plain_text_keydict(args.hyp),
compute_alignments=args.print_alignments,
scoring_mode=args.mode,
)
summary_details = edit_distance.wer_summary(details_by_utterance)
wer_io.print_wer_summary(summary_details)
if args.print_top_wer:
top_non_empty, top_empty = edit_distance.top_wer_utts(
details_by_utterance)
wer_io._print_top_wer_utts(top_non_empty, top_empty)
if args.utt2spk:
utt2spk = _utt2spk_keydict(args.utt2spk)
details_by_speaker = edit_distance.wer_details_by_speaker(
details_by_utterance, utt2spk)
top_spks = edit_distance.top_wer_spks(details_by_speaker)
wer_io._print_top_wer_spks(top_spks)
if args.print_alignments:
wer_io.print_alignments(
details_by_utterance,
empty_symbol=args.align_empty,
separator=args.align_separator,
)
)
train_set = params.train_loader()
valid_set = params.valid_loader()
first_x, first_y = next(iter(train_set))
if hasattr(params, "augmentation"):
modules.append(params.augmentation)
asr_brain = ASR(
modules=modules,
optimizer=params.optimizer,
first_inputs=[first_x, first_y],
)
# Load latest checkpoint to resume training
checkpointer.recover_if_possible()
asr_brain.fit(params.epoch_counter, train_set, valid_set)
# Load best checkpoint for evaluation
checkpointer.recover_if_possible(lambda c: -c.meta["PER"])
test_stats = asr_brain.evaluate(params.test_loader())
params.train_logger.log_stats(
stats_meta={"Epoch loaded": params.epoch_counter.current},
test_stats=test_stats,
)
# Write alignments to file
per_summary = edit_distance.wer_summary(test_stats["PER"])
with open(params.wer_file, "w") as fo:
wer_io.print_wer_summary(per_summary, fo)
wer_io.print_alignments(test_stats["PER"], fo)