def encode(wav_data, checkpoint_path, sample_length=64000):
"""Padded loading of a wave file.
Args:
wav_data: Numpy array [batch_size, sample_length]
checkpoint_path: Location of the pretrained model.
sample_length: The total length of the final wave file, padded with 0s.
Returns:
encoding: a [mb, 125, 16] encoding (for 64000 sample audio file).
hop_length: Pooling size of the autoencoder.
"""
if wav_data.ndim == 1:
wav_data = np.expand_dims(wav_data, 0)
batch_size = 1
elif wav_data.ndim == 2:
batch_size = wav_data.shape[0]
# Load up the model for encoding and find the encoding of "wav_data"
session_config = tf.ConfigProto(allow_soft_placement=True)
with tf.Graph().as_default(), tf.Session(config=session_config) as sess:
hop_length = Config().ae_hop_length
wav_data, sample_length = utils.trim_for_encoding(
wav_data, sample_length, hop_length)
net = load_nsynth(batch_size=batch_size, sample_length=sample_length)
saver = tf.train.Saver()
saver.restore(sess, checkpoint_path)
encoding = sess.run(net["encoding"], feed_dict={net["X"]: wav_data})
return encoding, hop_length
def encode(wav_data, checkpoint_path, sample_length=64000):
"""Generate an array of embeddings from an array of audio.
Args:
wav_data: Numpy array [batch_size, sample_length]
checkpoint_path: Location of the pretrained model.
sample_length: The total length of the final wave file, padded with 0s.
Returns:
encoding: a [mb, 125, 16] encoding (for 64000 sample audio file).
"""
if wav_data.ndim == 1:
wav_data = np.expand_dims(wav_data, 0)
batch_size = 1
elif wav_data.ndim == 2:
batch_size = wav_data.shape[0]
# Load up the model for encoding and find the encoding of "wav_data"
session_config = tf.ConfigProto(allow_soft_placement=True)
with tf.Graph().as_default(), tf.Session(config=session_config) as sess:
hop_length = Config().ae_hop_length
wav_data, sample_length = utils.trim_for_encoding(wav_data, sample_length,
hop_length)
net = load_nsynth(batch_size=batch_size, sample_length=sample_length)
saver = tf.train.Saver()
saver.restore(sess, checkpoint_path)
encodings = sess.run(net["encoding"], feed_dict={net["X"]: wav_data})
return encodings
