experiments/STT
1
0
Fork 0
Code Issues Pull Requests Projects Releases Wiki Activity

Clean up evaluate.py

Browse Source
This commit is contained in:
Reuben Morais 2018-11-11 18:04:00 -02:00
parent 5cb1aff531
commit 0dcb1f87c5
1 changed files with 23 additions and 23 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

46
evaluate.py
View File

@ -159,29 +159,29 @@ def evaluate(test_data, inference_graph, alphabet):
logitses.append(logits) logitses.append(logits)
losses.extend(loss) losses.extend(loss)
ground_truths = [] ground_truths = []
predictions = [] predictions = []
distances = []
print('Decoding predictions...') print('Decoding predictions...')
bar = progressbar.ProgressBar(max_value=batch_count, bar = progressbar.ProgressBar(max_value=batch_count,
widget=progressbar.AdaptiveETA) widget=progressbar.AdaptiveETA)
# Get number of accessible CPU cores for this process # Get number of accessible CPU cores for this process
try: try:
num_processes = cpu_count() num_processes = cpu_count()
except: except:
num_processes = 1 num_processes = 1
# Second pass, decode logits and compute WER and edit distance metrics # Second pass, decode logits and compute WER and edit distance metrics
for logits, batch in bar(zip(logitses, split_data(test_data, FLAGS.test_batch_size))): for logits, batch in bar(zip(logitses, split_data(test_data, FLAGS.test_batch_size))):
seq_lengths = batch['features_len'].values.astype(np.int32) seq_lengths = batch['features_len'].values.astype(np.int32)
decoded = ctc_beam_search_decoder_batch(logits, seq_lengths, alphabet, FLAGS.beam_width, decoded = ctc_beam_search_decoder_batch(logits, seq_lengths, alphabet, FLAGS.beam_width,
num_processes=num_processes, scorer=scorer) num_processes=num_processes, scorer=scorer)
ground_truths.extend(alphabet.decode(l) for l in batch['transcript']) ground_truths.extend(alphabet.decode(l) for l in batch['transcript'])
predictions.extend(d[0][1] for d in decoded) predictions.extend(d[0][1] for d in decoded)
distances.extend(levenshtein(a, b) for a, b in zip(labels, predictions))
distances = [levenshtein(a, b) for a, b in zip(ground_truths, predictions)]
wer, samples = calculate_report(ground_truths, predictions, distances, losses) wer, samples = calculate_report(ground_truths, predictions, distances, losses)
mean_edit_distance = np.mean(distances) mean_edit_distance = np.mean(distances)
@ -190,12 +190,12 @@ def evaluate(test_data, inference_graph, alphabet):
# Take only the first report_count items # Take only the first report_count items
report_samples = itertools.islice(samples, FLAGS.report_count) report_samples = itertools.islice(samples, FLAGS.report_count)
print('Test - WER: %f, loss: %f, mean edit distance: %f' % print('Test - WER: %f, CER: %f, loss: %f' %
(wer, mean_loss, mean_edit_distance)) (wer, mean_edit_distance, mean_loss))
print('-' * 80) print('-' * 80)
for sample in report_samples: for sample in report_samples:
print('WER: %f, loss: %f, edit distance: %f' % print('WER: %f, CER: %f, loss: %f' %
(sample.wer, sample.loss, sample.distance)) (sample.wer, sample.distance, sample.loss))
print(' - src: "%s"' % sample.src) print(' - src: "%s"' % sample.src)
print(' - res: "%s"' % sample.res) print(' - res: "%s"' % sample.res)
print('-' * 80) print('-' * 80)