def run(inputs, output_dir=None, suffix=None, input_repr="mel256", content_type="music",
embedding_size=6144, center=True, hop_size=0.1, verbose=False):
"""
Computes and saves L3 embedding for given inputs.
Parameters
----------
inputs : list of str, or str
File/directory path or list of file/directory paths to be processed
output_dir : str or None
Path to directory for saving output files. If None, output files will
be saved to the directory containing the input file.
suffix : str or None
String to be appended to the output filename, i.e. <base filename>_<suffix>.npy.
If None, then no suffix will be added, i.e. <base filename>.npy.
input_repr : "linear", "mel128", or "mel256"
Spectrogram representation used for model.
content_type : "music" or "env"
Type of content used to train embedding.
embedding_size : 6144 or 512
Embedding dimensionality.
center : boolean
If True, pads beginning of signal so timestamps correspond
to center of window.
hop_size : float
Hop size in seconds.
quiet : boolean
If True, suppress all non-error output to stdout
Returns
-------
"""
if isinstance(inputs, string_types):
file_list = [inputs]
elif isinstance(inputs, Iterable):
file_list = get_file_list(inputs)
else:
raise OpenL3Error('Invalid input: {}'.format(str(inputs)))
if len(file_list) == 0:
print('openl3: No WAV files found in {}. Aborting.'.format(str(inputs)))
sys.exit(-1)
# Load model
model = load_embedding_model(input_repr, content_type, embedding_size)
# Process all files in the arguments
for filepath in file_list:
if verbose:
print('openl3: Processing: {}'.format(filepath))
process_file(filepath,
output_dir=output_dir,
suffix=suffix,
model=model,
center=center,
hop_size=hop_size,
verbose=verbose)
if verbose:
print('openl3: Done!')
def get_file_list(input_list):
"""Get list of files from the list of inputs"""
if not isinstance(input_list, Iterable) or isinstance(input_list, str):
raise ArgumentTypeError('input_list must be iterable (and not string)')
file_list = []
for item in input_list:
if os.path.isfile(item):
file_list.append(os.path.abspath(item))
elif os.path.isdir(item):
for fname in os.listdir(item):
path = os.path.join(item, fname)
if os.path.isfile(path):
file_list.append(path)
else:
raise OpenL3Error('Could not find {}'.format(item))
return file_list
def run(modality,
inputs,
output_dir=None,
suffix=None,
input_repr="mel256",
content_type="music",
audio_embedding_size=6144,
audio_center=True,
audio_hop_size=0.1,
audio_batch_size=32,
image_embedding_size=8192,
image_batch_size=32,
overwrite=False,
verbose=False):
"""
Computes and saves L3 embedding for given inputs.
Parameters
----------
modality : str
String to specify the modalities to be processed: audio, image, or video
inputs : list of str, or str
File/directory path or list of file/directory paths to be processed
output_dir : str or None
Path to directory for saving output files. If None, output files will
be saved to the directory containing the input file.
suffix : str or None
String to be appended to the output filename, i.e. <base filename>_<suffix>.npy.
If None, then no suffix will be added, i.e. <base filename>.npy.
input_repr : "linear", "mel128", or "mel256"
Spectrogram representation used for model.
content_type : "music" or "env"
Type of content used to train embedding.
audio_embedding_size : 6144 or 512
Audio embedding dimensionality.
audio_center : boolean
If True, pads beginning of signal so timestamps correspond
to center of window.
audio_hop_size : float
Hop size in seconds.
audio_batch_size : int
Batch size used for input to audio embedding model
image_embedding_size : 8192 or 512
Image embedding dimensionality.
image_batch_size : int
Batch size used for input to image embedding model
overwrite : bool
If True, overwrites existing output files
verbose : boolean
If True, print verbose messages.
Returns
-------
"""
if isinstance(inputs, str):
file_list = [inputs]
elif isinstance(inputs, Iterable):
file_list = get_file_list(inputs)
else:
raise OpenL3Error('Invalid input: {}'.format(str(inputs)))
if len(file_list) == 0:
print('openl3: No files found in {}. Aborting.'.format(str(inputs)))
sys.exit(-1)
# Load model
if modality == 'audio':
model = load_audio_embedding_model(input_repr, content_type,
audio_embedding_size)
# Process all files in the arguments
process_audio_file(file_list,
output_dir=output_dir,
suffix=suffix,
model=model,
center=audio_center,
hop_size=audio_hop_size,
batch_size=audio_batch_size,
overwrite=overwrite,
verbose=verbose)
elif modality == 'image':
model = load_image_embedding_model(input_repr, content_type,
image_embedding_size)
# Process all files in the arguments
process_image_file(file_list,
output_dir=output_dir,
suffix=suffix,
model=model,
batch_size=image_batch_size,
overwrite=overwrite,
verbose=verbose)
elif modality == 'video':
audio_model = load_audio_embedding_model(input_repr, content_type,
audio_embedding_size)
image_model = load_image_embedding_model(input_repr, content_type,
image_embedding_size)
# Process all files in the arguments
process_video_file(file_list,
output_dir=output_dir,
suffix=suffix,
audio_model=audio_model,
image_model=image_model,
audio_embedding_size=audio_embedding_size,
audio_center=audio_center,
audio_hop_size=audio_hop_size,
audio_batch_size=audio_batch_size,
image_batch_size=image_batch_size,
image_embedding_size=image_embedding_size,
overwrite=overwrite,
verbose=verbose)
else:
raise OpenL3Error('Invalid modality: {}'.format(modality))
if verbose:
print('openl3: Done!')