train_meta,
verbose=True)
# save some memory
del train_x
del train_meta
# test serialization
with tempfile.TemporaryDirectory() as tmp_dir:
tmp_file = os.path.join(tmp_dir, 'model.bin')
print('testing serialization in temp file', tmp_file)
model.save(trained_model, tmp_file)
trained_model = model.load(tmp_file)
print('Validating...')
predicted_score = model.predict(trained_model, test_x,
test_meta.file_number)
result_meta = test_meta.assign(predicted_score=predicted_score)
result_meta = result_meta.assign(
predicted_label=np.round(predicted_score))
result_meta = result_meta.assign(label=np.round(result_meta.label))
result_meta = result_meta.assign(
correct=result_meta.predicted_label == result_meta.label)
bias = trained_model[1]
r = validate_speech_detection(result_meta)
sync_r = test_correct_sync(result_meta, bias)
sync_results.append(
sync_r.assign(speech_detection_accuracy=list(r.correct)))
sync_results = pd.concat(sync_results)
print(sync_results)
def synchronize(video_file, subtitle_file, output_file, verbose=False, \
parallelism=3, fixed_skew=None, model_file=None, return_parameters=False, \
**kwargs):
"""
Automatically synchronize subtitles with audio in a video file.
Uses FFMPEG to extract the audio from the video file and the command line
tool "ffmpeg" must be available. Uses temporary files which are deleted
automatically.
Args:
video_file (string): Input video file name
subtitle_file (string): Input SRT subtitle file name
output_file (string): Output (synchronized) SRT subtitle file name
verbose (boolean): If True, print progress information to stdout
return_parameters (boolean): If True, returns the syncrhonization
parameters instead of just the success flag
other arguments: Search parameters, see ``autosubsync --help``
Returns:
If return_parameters is False (default), returns
True on success (quality of fit test passed), False if failed.
If return_parameters is True, returns a tuple of four values
success (boolean) success flag as above
quality (float) metric used to determine the value of "success"
skew (float) best fit skew/speed (unitless)
shift (float) best fit shift in seconds
"""
# these are here to enable running as python3 autosubsync/main.py
from autosubsync import features
from autosubsync import find_transform
from autosubsync import model
from autosubsync import preprocessing
from autosubsync import quality_of_fit
# argument parsing
if model_file is None:
from pkg_resources import resource_filename
model_file = resource_filename(__name__, '../trained-model.bin')
fixed_skew = parse_skew(fixed_skew)
# load model
trained_model = model.load(model_file)
if verbose: print('Extracting audio using ffmpeg and reading subtitles...')
sound_data, subvec = preprocessing.import_target_files(
video_file, subtitle_file)
if verbose: print(('computing features for %d audio samples ' + \
'using %d parallel process(es)') % (len(subvec), parallelism))
features_x, shifted_y = features.compute(sound_data,
subvec,
parallelism=parallelism)
if verbose: print('extracted features of size %s, performing speech detection' % \
str(features_x.shape))
y_scores = model.predict(trained_model, features_x)
# save some memory before parallelization fork so we look less bad
del features_x, sound_data, subvec
gc.collect()
if verbose:
print('computing best fit with %d frames' % len(y_scores))
skew, shift, quality = find_transform.find_transform_parameters(\
shifted_y, y_scores, \
parallelism=parallelism, fixed_skew=fixed_skew, bias=trained_model[1], \
verbose=verbose, **kwargs)
success = quality > quality_of_fit.threshold
if verbose:
print('quality of fit: %g, threshold %g' %
(quality, quality_of_fit.threshold))
print('Fit complete. Performing resync, writing to ' + output_file)
transform_func = find_transform.parameters_to_transform(skew, shift)
preprocessing.transform_srt(subtitle_file, output_file, transform_func)
if verbose and success: print('success!')
if return_parameters:
return success, quality, skew, shift
else:
return success
print('Training...', X_train.shape)
trained_model = model.train_with_spleeter_output(X_train, y_train)
# save some memory
del X_train
del y_train
# test serialization
with tempfile.TemporaryDirectory() as tmp_dir:
tmp_file = os.path.join(tmp_dir, 'model.bin')
print('testing serialization in temp file', tmp_file)
model.save(trained_model, tmp_file)
trained_model = model.load(tmp_file)
print('Validating...')
predicted_score = model.predict(trained_model, X_test)
predicted_label = np.round(predicted_score)
correct = predicted_label == y_test
result_meta = pd.DataFrame(
np.array([y_test, predicted_score, predicted_label, correct]).T,
columns=['label', 'predicted_score', 'predicted_label', 'correct'])
result_meta['label'] = result_meta['label'].astype(float)
from sklearn.metrics import roc_auc_score
print('AUC-ROC:', roc_auc_score(y_test, predicted_label))
bias = trained_model[1]
r = result_meta.groupby('label').agg('mean')
sync_r = test_correct_sync(result_meta, bias)
sync_results.append(