def main(_):
with open(FLAGS.wav_files) as f:
files_list = [line.replace('\n', '') for line in f]
n_files = len(files_list)
output_emedding = np.zeros((n_files, 128))
pproc = vggish_postprocess.Postprocessor(FLAGS.pca_params)
processed_fnames = []
with tf.Graph().as_default(), tf.Session() as sess:
# Define the model in inference mode, load the checkpoint, and
# locate input and output tensors.
vggish_slim.define_vggish_slim(training=False)
vggish_slim.load_vggish_slim_checkpoint(sess, FLAGS.checkpoint)
features_tensor = sess.graph.get_tensor_by_name(
vggish_params.INPUT_TENSOR_NAME)
embedding_tensor = sess.graph.get_tensor_by_name(
vggish_params.OUTPUT_TENSOR_NAME)
for n_file, wav_file in enumerate(files_list):
examples_batch = vggish_input.wavfile_to_examples(wav_file)
print(n_file, '/', n_files)
if examples_batch.shape[0] == 0:
with open('bad_files.log', 'a') as logf:
logf.write(wav_file + '\n')
else:
processed_fnames.append(wav_file)
[embedding_batch] = sess.run([embedding_tensor],
feed_dict={features_tensor: examples_batch})
postprocessed_batch = pproc.postprocess(embedding_batch)
postprocessed_batch_mean = np.mean(postprocessed_batch, axis=0)
output_emedding[n_file, :] = postprocessed_batch_mean
np.save(FLAGS.npy_file, output_emedding)
def extract_vggish_features(wav_path):
# Produce a batch of log mel spectrogram examples.
input_batch = vggish_input.wavfile_to_examples(wav_path)
if input_batch.shape[0] < 1:
print('{}: Audio sample shorter than 1 second. Ignoring ...',
os.path.basename(wav_path))
return None
# print('Log Mel Spectrogram example: ', input_batch[0])
# Define VGGish, load the checkpoint, and run the batch through the model to
# produce embeddings.
with tf.Graph().as_default(), tf.Session() as sess:
vggish_slim.define_vggish_slim()
vggish_slim.load_vggish_slim_checkpoint(sess, checkpoint_path)
features_tensor = sess.graph.get_tensor_by_name(
vggish_params.INPUT_TENSOR_NAME)
embedding_tensor = sess.graph.get_tensor_by_name(
vggish_params.OUTPUT_TENSOR_NAME)
[embedding_batch] = sess.run([embedding_tensor],
feed_dict={features_tensor: input_batch})
# Postprocess the results to produce whitened quantized embeddings.
pproc = vggish_postprocess.Postprocessor(pca_params_path)
postprocessed_batch = pproc.postprocess(embedding_batch)
return postprocessed_batch
def main(_):
# In this simple example, we run the examples from a single audio file through
# the model. If none is provided, we generate a synthetic input.
wav_file = FLAGS.wav_file
examples_batch = vggish_input.wavfile_to_examples(wav_file)
# Prepare a postprocessor to munge the model embeddings.
pproc = vggish_postprocess.Postprocessor(FLAGS.pca_params)
with tf.Graph().as_default(), tf.Session() as sess:
# Define the model in inference mode, load the checkpoint, and
# locate input and output tensors.
vggish_slim.define_vggish_slim(training=False)
vggish_slim.load_vggish_slim_checkpoint(sess, FLAGS.checkpoint)
features_tensor = sess.graph.get_tensor_by_name(vggish_params.INPUT_TENSOR_NAME)
embedding_tensor = sess.graph.get_tensor_by_name(vggish_params.OUTPUT_TENSOR_NAME)
# Run inference and postprocessing.
[embedding_batch] = sess.run([embedding_tensor], feed_dict={features_tensor: examples_batch})
postprocessed_batch = pproc.postprocess(embedding_batch)
print(postprocessed_batch)
np.save("/postprocessed_batch.npy", postprocessed_batch)
def extract_n_predict(input_wav_file, pca_params, checkpoint, checkpoint_file, train_dir, output_file):
print("Input file: " +input_wav_file)
if (os.path.isfile(input_wav_file)):
examples_batch = vggish_input.wavfile_to_examples(input_wav_file)
#print(examples_batch)
pproc = vggish_postprocess.Postprocessor(pca_params)
with tf.Graph().as_default(), tf.Session() as sess:
# Define the model in inference mode, load the checkpoint, and
# locate input and output tensors.
vggish_slim.define_vggish_slim(training=False)
vggish_slim.load_vggish_slim_checkpoint(sess, checkpoint)
features_tensor = sess.graph.get_tensor_by_name(
vggish_params.INPUT_TENSOR_NAME)
embedding_tensor = sess.graph.get_tensor_by_name(
vggish_params.OUTPUT_TENSOR_NAME)
# Run inference and postprocessing.
[embedding_batch] = sess.run([embedding_tensor],
feed_dict={features_tensor: examples_batch})
#print(embedding_batch)
postprocessed_batch = pproc.postprocess(embedding_batch)
#print(postprocessed_batch)
num_frames_batch_val = np.array([postprocessed_batch.shape[0]],dtype=np.int32)
video_batch_val = np.zeros((1, 300, 128), dtype=np.float32)
video_batch_val[0,0:postprocessed_batch.shape[0],:] = utils.Dequantize(postprocessed_batch.astype(float),2,-2)
# extract_n_predict()
predicted_class = inference(video_batch_val ,num_frames_batch_val, checkpoint_file, train_dir, output_file)
return(predicted_class)
tf.reset_default_graph()
def setup(self):
# Paths to downloaded VGGish files.
self.checkpoint_path = 'vggish_model.ckpt'
self.pca_params_path = 'vggish_pca_params.npz'
self.batch_size = 60
# If we can't find the trained model files, download them
if not os.path.exists(self.checkpoint_path):
print(
'AudiosetAnalysis: Downloading model file {} (please wait - this may take a while)'
.format(self.checkpoint_path))
urllib.urlretrieve(
'https://storage.googleapis.com/audioset/vggish_model.ckpt',
self.checkpoint_path)
if not os.path.exists(self.pca_params_path):
print(
'AudiosetAnalysis: Downloading params file {} (please wait - this may take a while)'
.format(self.pca_params_path))
urllib.urlretrieve(
'https://storage.googleapis.com/audioset/vggish_pca_params.npz',
self.pca_params_path)
# Define VGGish
self.sess = tf.Graph().as_default()
config = tf.ConfigProto(device_count={'CPU': 4})
self.sess = tf.Session(config=config)
# Load the checkpoint
vggish_slim.define_vggish_slim()
vggish_slim.load_vggish_slim_checkpoint(self.sess,
self.checkpoint_path)
self.features_tensor = self.sess.graph.get_tensor_by_name(
vggish_params.INPUT_TENSOR_NAME)
self.embedding_tensor = self.sess.graph.get_tensor_by_name(
vggish_params.OUTPUT_TENSOR_NAME)
def input_sound(): # function to calculate the FS and time series input
files = get_file_paths(DIRNAME) # function called to get the file paths
File_names = []
full_feature_vector = np.empty([0, 128])
for file in sorted(files): # loop to access each file
# print (file)
(filepath, ext) = os.path.splitext(file) # get extension of the file
file_name = os.path.basename(file) # get the file name
if ext == '.wav':
File_names.append(file_name)
y, sr = librosa.load(file, sr=None)
print(sr)
examples_batch = waveform_to_examples(y, sr)
pproc = vggish_postprocess.Postprocessor(FLAGS.pca_params)
with tf.Graph().as_default(), tf.Session() as sess:
# Define the model in inference mode, load the checkpoint, and
# locate input and output tensors.
vggish_slim.define_vggish_slim(training=False)
vggish_slim.load_vggish_slim_checkpoint(sess, FLAGS.checkpoint)
features_tensor = sess.graph.get_tensor_by_name(
vggish_params.INPUT_TENSOR_NAME)
embedding_tensor = sess.graph.get_tensor_by_name(
vggish_params.OUTPUT_TENSOR_NAME)
# Run inference and postprocessing.
[embedding_batch
] = sess.run([embedding_tensor],
feed_dict={features_tensor: examples_batch})
postprocessed_batch = pproc.postprocess(embedding_batch)
print(np.shape(postprocessed_batch))
full_feature_vector = np.concatenate(
(full_feature_vector, postprocessed_batch), axis=0)
print(np.shape(full_feature_vector))
return full_feature_vector
def main(wav_file):
"""
#Specify the path for the downloaded or recorded audio files and
#also path for writing the embeddings or pickle files
"""
if wav_file:
pkl = wav_file[:-4] + '.pkl'
print(pkl)
examples_batch = vggish_input.wavfile_to_examples(wav_file)
# Prepare a postprocessor to munge the model embeddings.
pproc = vggish_postprocess.Postprocessor(FLAGS.pca_params)
with tf.Graph().as_default(), tf.Session() as sess:
# Define the model in inference mode, load the checkpoint, and
# locate input and output tensors.
vggish_slim.define_vggish_slim(training=False)
vggish_slim.load_vggish_slim_checkpoint(sess, FLAGS.checkpoint)
features_tensor = sess.graph.get_tensor_by_name(
vggish_params.INPUT_TENSOR_NAME)
embedding_tensor = sess.graph.get_tensor_by_name(
vggish_params.OUTPUT_TENSOR_NAME)
# Run inference and postprocessing.
[embedding_batch
] = sess.run([embedding_tensor],
feed_dict={features_tensor: examples_batch})
postprocessed_batch = pproc.postprocess(embedding_batch)
print(postprocessed_batch)
predict_prob, predictions = model_function.predictions_wavfile(
postprocessed_batch)
K.clear_session()
return predict_prob, predictions
def main(_):
audio_files = os.listdir(audio_path)
# maxi = 0
for each_file in tqdm.tqdm(audio_files):
file_nm = dest_path + each_file.split('.')[0] + '.npy'
if not (path.exists(file_nm)):
try:
wav_file = audio_path + each_file
examples_batch = vggish_input.wavfile_to_examples(wav_file)
with tf.Graph().as_default(), tf.Session() as sess:
vggish_slim.define_vggish_slim(training=False)
vggish_slim.load_vggish_slim_checkpoint(
sess, FLAGS.checkpoint)
features_tensor = sess.graph.get_tensor_by_name(
vggish_params.INPUT_TENSOR_NAME)
embedding_tensor = sess.graph.get_tensor_by_name(
vggish_params.OUTPUT_TENSOR_NAME)
[embedding_batch] = sess.run(
[embedding_tensor],
feed_dict={features_tensor: examples_batch})
postprocessed_batch = embedding_batch
#indices = np.linspace(0, len(postprocessed_batch), max_frames, endpoint=False, dtype=int)
#postprocessed_batch = postprocessed_batch[indices]
np.save(dest_path + each_file.split('.')[0] + '.npy',
postprocessed_batch)
except:
print("here")
continue
def extract(wav_file):
examples_batch = vggish_input.wavfile_to_examples(wav_file)
pproc = vggish_postprocess.Postprocessor(
'/storage/haibn/yt8m/code/video_classification/feature_extractor/vggish/vggish_pca_params.npz'
)
with tf.Graph().as_default(), tf.Session() as sess:
# Define the model in inference mode, load the checkpoint, and
# locate input and output tensors.
vggish_slim.define_vggish_slim(training=False)
vggish_slim.load_vggish_slim_checkpoint(
sess,
'/storage/haibn/yt8m/code/video_classification/feature_extractor/vggish/vggish_model.ckpt'
)
features_tensor = sess.graph.get_tensor_by_name(
vggish_params.INPUT_TENSOR_NAME)
embedding_tensor = sess.graph.get_tensor_by_name(
vggish_params.OUTPUT_TENSOR_NAME)
# Run inference and postprocessing.
[embedding_batch
] = sess.run([embedding_tensor],
feed_dict={features_tensor: examples_batch})
print(embedding_batch)
postprocessed_batch = pproc.postprocess(embedding_batch)
print(postprocessed_batch)
return postprocessed_batch
def extract_audioset_features(ids, id2audio_path, id2label):
first_audio = True
for i in ids:
if first_audio:
input_data = vggish_input.wavfile_to_examples(id2audio_path[i])
ground_truth = np.repeat(id2label[i], input_data.shape[0], axis=0)
identifiers = np.repeat(i, input_data.shape[0], axis=0)
first_audio = False
else:
tmp_in = vggish_input.wavfile_to_examples(id2audio_path[i])
input_data = np.concatenate((input_data, tmp_in), axis=0)
tmp_gt = np.repeat(id2label[i], tmp_in.shape[0], axis=0)
ground_truth = np.concatenate((ground_truth, tmp_gt), axis=0)
tmp_id = np.repeat(i, tmp_in.shape[0], axis=0)
identifiers = np.concatenate((identifiers, tmp_id), axis=0)
with tf.Graph().as_default(), tf.Session() as sess:
vggish_slim.define_vggish_slim(training=False)
vggish_slim.load_vggish_slim_checkpoint(sess, 'vggish_model.ckpt')
features_tensor = sess.graph.get_tensor_by_name(
vggish_params.INPUT_TENSOR_NAME)
embedding_tensor = sess.graph.get_tensor_by_name(
vggish_params.OUTPUT_TENSOR_NAME)
extracted_feat = sess.run([embedding_tensor],
feed_dict={features_tensor: input_data})
feature = np.squeeze(np.asarray(extracted_feat))
return [feature, ground_truth, identifiers]
def inference(file_path,checkpoint_dir,checkpoint_path):
'''
Inference loop for prediction the audio file.
'''
with tf.Graph().as_default(), tf.compat.v1.Session() as sess:
logits_inf = vggish_slim.define_audio_slim(training=False,is_reuse=None)
with tf.compat.v1.variable_scope('mymodel'):
predict_inf = tf.sigmoid(logits_inf, name='prediction_inf')
# Add inference ops.
with tf.variable_scope('train'):
global_step = tf.Variable(
0, name='global_step', trainable=False,
collections=[tf.compat.v1.GraphKeys.GLOBAL_VARIABLES,
tf.compat.v1.GraphKeys.GLOBAL_STEP])
# Initialize all variables in the model
sess.run(tf.compat.v1.global_variables_initializer())
# Restore the model
saver = tf.compat.v1.train.Saver()
saver.restore(sess, checkpoint_path)
# Locate all the tensors and ops we need for the inference loop.
features_tensor_inf = sess.graph.get_tensor_by_name(
'audio/audio_input_features:0')
prediction_tensor_inf = sess.graph.get_tensor_by_name('mymodel/prediction_inf:0')
graph = tf.Graph()
with graph.as_default():
vggish_slim.define_vggish_slim(training=False)
sess_ext = tf.compat.v1.Session(graph=graph)
vggish_slim.load_vggish_slim_checkpoint(sess_ext, checkpoint_dir + "vggish_model.ckpt")
input_tensor = graph.get_tensor_by_name('vggish/input_features:0')
output_tensor = graph.get_tensor_by_name('vggish/embedding:0')
pproc = Postprocessor.Postprocessor(checkpoint_dir + "vggish_pca_params.npz")
print('\n###################')
print('# Inference loop #')
print('###################')
try:
data, sampleratde = sf.read(Path(file_path))
wave_array_example_pre = data_transformation.waveform_to_examples(data,sampleratde,display=0)
[embedding_batch] = sess_ext.run([output_tensor],
feed_dict={input_tensor: wave_array_example_pre})
wave_arrays = pproc.postprocess(embedding_batch)
pred_inf_restore = sess.run(prediction_tensor_inf, feed_dict={features_tensor_inf: wave_arrays})
return pred_inf_restore
except:
print('This program does not support the input file format or file does not found ')
def extract_and_predict(wav):
wav_file = wav
examples_batch = vggish_input.wavfile_to_examples(wav_file)
# Prepare a postprocessor to munge the model embeddings.
# pproc = vggish_postprocess.Postprocessor()
with tf.Graph().as_default(), tf.Session() as sess:
# Define the model in inference mode, load the checkpoint, and
# locate input and output tensors.
vggish_slim.define_vggish_slim(training=False)
vggish_slim.load_vggish_slim_checkpoint(sess, FLAGS.checkpoint)
features_tensor = sess.graph.get_tensor_by_name(
vggish_params.INPUT_TENSOR_NAME)
embedding_tensor = sess.graph.get_tensor_by_name(
vggish_params.OUTPUT_TENSOR_NAME)
# Run inference and postprocessing.
[embedding_batch
] = sess.run([embedding_tensor],
feed_dict={features_tensor: examples_batch})
postprocessed_batch = vggish_postprocess.postprocess(embedding_batch)
postprocessed_batch = [
postprocessed_batch[i] for i in range(len(postprocessed_batch))
]
pred_each_n_seconds = predict_with_saved_model(postprocessed_batch)
print(str(pred_each_n_seconds))
def main(_):
if FLAGS.wav_file:
wav_file = FLAGS.wav_file
else:
num_secs = 5
freq = 1000
sr = 44100
t = np.linspace(0, num_secs, int(num_secs * sr))
x = np.sin(2 * np.pi * freq * t)
samples = np.clip(x * 32768, -32768, 32767).astype(np.int16)
wav_file = six.BytesIO()
wavfile.write(wav_file, sr, samples)
wav_file.seek(0)
examples_batch = vggish_input.wavfile_to_examples(wav_file)
# pproc = vggish_postprocess.Postprocessor(FLAGS.pca_params)
# print(FLAGS.checkpoint)
# print(os.getcwd())
# print(path.exists(FLAGS.checkpoint))
with tf.Graph().as_default(), tf.Session() as sess:
vggish_slim.define_vggish_slim(training=False)
vggish_slim.load_vggish_slim_checkpoint(sess, FLAGS.checkpoint)
features_tensor = sess.graph.get_tensor_by_name(
vggish_params.INPUT_TENSOR_NAME)
embedding_tensor = sess.graph.get_tensor_by_name(
vggish_params.OUTPUT_TENSOR_NAME)
[embedding_batch
] = sess.run([embedding_tensor],
feed_dict={features_tensor: examples_batch})
# embedding_batch为提取结果
with open(path.splitext(wav_file)[0] + '.pk', "wb") as f:
pickle.dump(embedding_batch, f)
def create_vggish_frozen_graph():
"""Create the VGGish frozen graph."""
os.system('git clone https://github.com/tensorflow/models.git')
sys.path.append('models/research/audioset/vggish/')
import vggish_slim
os.system(
'curl -O https://storage.googleapis.com/audioset/vggish_model.ckpt')
ckpt_path = 'vggish_model.ckpt'
with tf.Graph().as_default(), tf.Session() as sess:
vggish_slim.define_vggish_slim(training=False)
vggish_slim.load_vggish_slim_checkpoint(sess, ckpt_path)
saver = tf.train.Saver(tf.all_variables())
freeze_graph.freeze_graph_with_def_protos(
sess.graph_def,
saver.as_saver_def(),
ckpt_path,
'vggish/fc2/BiasAdd',
restore_op_name=None,
filename_tensor_name=None,
output_graph='/tmp/mediapipe/vggish_new.pb',
clear_devices=True,
initializer_nodes=None)
os.system('rm -rf models/')
os.system('rm %s' % ckpt_path)
def extract_and_predict(wav):
print("Boom from PYTHON!!!")
# tf.enable_v2_behavior()
# loaded_model = tf2.saved_model.load(saved_model_path)
# print("I can load model now!!!")
wav_file = wav
examples_batch = vggish_input.wavfile_to_examples(wav_file)
print("Jerry audio_to_prediction.py: after wavfile_to_examples")
# Prepare a postprocessor to munge the model embeddings.
pproc = vggish_postprocess.Postprocessor()
print("Jerry audio_to_prediction.py: after pproc")
with tf.Graph().as_default(), tf.Session() as sess:
# Define the model in inference mode, load the checkpoint, and
# locate input and output tensors.
vggish_slim.define_vggish_slim(training=False)
vggish_slim.load_vggish_slim_checkpoint(sess, FLAGS.checkpoint)
print("Jerry audio_to_prediction.py: after load vggish_slim")
features_tensor = sess.graph.get_tensor_by_name(
vggish_params.INPUT_TENSOR_NAME)
embedding_tensor = sess.graph.get_tensor_by_name(
vggish_params.OUTPUT_TENSOR_NAME)
# Run inference and postprocessing.
[embedding_batch] = sess.run([embedding_tensor],
feed_dict={features_tensor: examples_batch})
postprocessed_batch = pproc.postprocess(embedding_batch)
postprocessed_batch = [postprocessed_batch[i] for i in range(len(postprocessed_batch))]
pred_each_n_seconds = predict_with_saved_model(postprocessed_batch)
return str(pred_each_n_seconds)
def main(_):
with tf.Graph().as_default(), tf.Session() as sess:
embeddings = vggish_slim.define_vggish_slim(FLAGS.train_vggish)
with tf.variable_scope('mymodel'):
num_units = 100
fc = slim.fully_connected(embeddings, num_units)
logits = slim.fully_connected(fc,
_NUM_CLASSES,
activation_fn=None,
scope='logits')
tf.sigmoid(logits, name='prediction')
with tf.variable_scope('train'):
global_step = tf.Variable(0,
name='global_step',
trainable=False,
collections=[
tf.GraphKeys.GLOBAL_VARIABLES,
tf.GraphKeys.GLOBAL_STEP
])
labels = tf.placeholder(tf.float32,
shape=(None, _NUM_CLASSES),
name='labels')
xent = tf.nn.sigmoid_cross_entropy_with_logits(logits=logits,
labels=labels,
name='xent')
loss = tf.reduce_mean(xent, name='loss_op')
tf.summary.scalar('loss', loss)
optimizer = tf.train.AdamOptimizer(
learning_rate=vggish_params.LEARNING_RATE,
epsilon=vggish_params.ADAM_EPSILON)
optimizer.minimize(loss,
global_step=global_step,
name='train_op')
sess.run(tf.global_variables_initializer())
vggish_slim.load_vggish_slim_checkpoint(sess, FLAGS.checkpoint)
features_tensor = sess.graph.get_tensor_by_name(
vggish_params.INPUT_TENSOR_NAME)
labels_tensor = sess.graph.get_tensor_by_name('mymodel/train/labels:0')
global_step_tensor = sess.graph.get_tensor_by_name(
'mymodel/train/global_step:0')
loss_tensor = sess.graph.get_tensor_by_name('mymodel/train/loss_op:0')
train_op = sess.graph.get_operation_by_name('mymodel/train/train_op')
for _ in range(FLAGS.num_batches):
(features, labels) = _get_examples_batch()
[num_steps, loss,
_] = sess.run([global_step_tensor, loss_tensor, train_op],
feed_dict={
features_tensor: features,
labels_tensor: labels
})
print('Step %d: loss %g' % (num_steps, loss))
def main(_):
opt = parse_opt()
with tf.Graph().as_default(), tf.Session() as sess:
vggish_slim.define_vggish_slim(training=False)
vggish_slim.load_vggish_slim_checkpoint(sess, FLAGS.checkpoint)
features_tensor = sess.graph.get_tensor_by_name(vggish_params.INPUT_TENSOR_NAME)
embedding_tensor = sess.graph.get_tensor_by_name(vggish_params.OUTPUT_TENSOR_NAME)
keys, values = ['train', 'val', 'test'], [opt.train_range, opt.val_range, opt.test_range]
for i in range(3):
h5_path = opt.feat_h5 + '2016' + '_' + keys[i] + '_' + opt.type + '.h5'
if os.path.exists(h5_path): os.remove(h5_path)
h5 = h5py.File(h5_path, 'w')
dataset_feats = h5.create_dataset('feats', ((values[i][1] - values[i][0] + 1), opt.feat_size), dtype='float32')
# print(values[i])
for audio_id in range(values[i][0], values[i][1] + 1):
wav_file = opt.video_root + 'video' + str(audio_id) + '.mp4.wav'
#print(wav_file)
# id = int(audio_id[5:-9])
#print(audio_id)
if os.path.isfile(wav_file):
examples_batch = vggish_input.wavfile_to_examples(wav_file)
pproc = vggish_postprocess.Postprocessor(FLAGS.pca_params)
writer = tf.python_io.TFRecordWriter(FLAGS.tfrecord_file) if FLAGS.tfrecord_file else None
[embedding_batch] = sess.run([embedding_tensor], feed_dict={features_tensor: examples_batch})
#print(len(embedding_batch), len(embedding_batch[0]))
embedding_batch = embedding_batch.mean(0)
dataset_feats[audio_id - values[i][0]] = embedding_batch
#print(embedding_batch)
if writer:
writer.close()
def embed(wavform_slice, rate):
norm_wavform_slice = preprocessing.normalize(wavform_slice)
examples_batch = vggish_input.waveform_to_examples(norm_wavform_slice,rate)
#print('examples_batch:')
#print(examples_batch)
print('examples_batch len: ' + str(len(examples_batch)))
# Prepare a postprocessor to munge the model embeddings.
pproc = vggish_postprocess.Postprocessor(FLAGS.pca_params)
with tf.Graph().as_default(), tf.Session() as sess:
# Define the model in inference mode, load the checkpoint, and
# locate input and output tensors.
vggish_slim.define_vggish_slim(training=False)
vggish_slim.load_vggish_slim_checkpoint(sess, FLAGS.checkpoint)
features_tensor = sess.graph.get_tensor_by_name(
vggish_params.INPUT_TENSOR_NAME)
embedding_tensor = sess.graph.get_tensor_by_name(
vggish_params.OUTPUT_TENSOR_NAME)
# Run inference and postprocessing.
[embedding_batch] = sess.run([embedding_tensor],
feed_dict={features_tensor: examples_batch})
#print('embedding_batch: ')
#print(embedding_batch)
#print(embedding_batch.shape)
postprocessed_batch = pproc.postprocess(embedding_batch)
print('postprocessed_batch: ')
print(postprocessed_batch)
print(postprocessed_batch.shape)
return postprocessed_batch
def main(_):
# In this simple example, we run the examples from a single audio file through
# the model. If none is provided, we generate a synthetic input.
# Prepare a postprocessor to munge the model embeddings.
pproc = vggish_postprocess.Postprocessor(pca_params)
vggish_params.EXAMPLE_HOP_SECONDS = (
1 - args.overlap) * vggish_params.EXAMPLE_WINDOW_SECONDS
# If needed, prepare a record writer_dict to store the postprocessed embeddings.
with tf.Graph().as_default(), tf.Session(config=config) as sess:
vggish_slim.define_vggish_slim(training=False)
vggish_slim.load_vggish_slim_checkpoint(sess, checkpoint)
features_tensor = sess.graph.get_tensor_by_name(
vggish_params.INPUT_TENSOR_NAME)
embedding_tensor = sess.graph.get_tensor_by_name(
vggish_params.OUTPUT_TENSOR_NAME)
movie_id = args.wav_file[args.wav_file.rfind('/') +
1:args.wav_file.rfind('.')]
examples_batch = vggish_input.wavfile_to_examples(args.wav_file)
num_splits = min(int(examples_batch.shape[0] / 10), 100)
num_splits = max(1, num_splits)
examples_batch = np.array_split(examples_batch, num_splits)
embedding_batch = []
for i in range(num_splits):
[batch] = sess.run([embedding_tensor],
feed_dict={features_tensor: examples_batch[i]})
embedding_batch.extend(batch)
postprocessed_batch = pproc.postprocess(np.array(embedding_batch))
# Write the postprocessed embeddings as a SequenceExample, in a similar
# format as the features released in AudioSet. Each row of the batch of
# embeddings corresponds to roughly a second of audio (96 10ms frames), and
# the rows are written as a sequence of bytes-valued features, where each
# feature value contains the 128 bytes of the whitened quantized embedding.
seq_example = tf.train.SequenceExample(
context=tf.train.Features(
feature={
'movie_id':
tf.train.Feature(bytes_list=tf.train.BytesList(
value=[movie_id]))
}),
feature_lists=tf.train.FeatureLists(
feature_list={
vggish_params.AUDIO_EMBEDDING_FEATURE_NAME:
tf.train.FeatureList(feature=[
tf.train.Feature(bytes_list=tf.train.BytesList(
value=[embedding.tobytes()]))
for embedding in postprocessed_batch
])
}))
writer = tf.python_io.TFRecordWriter(
os.path.join(args.write_dir, movie_id + '.tfrecord'))
writer.write(seq_example.SerializeToString())
writer.close()
def initialize_classifier(self):
if not os.path.exists(os.path.join(os.getcwd(), self.model_dir)):
os.mkdir(os.path.join(os.getcwd(), self.model_dir))
# Load pre-trained VGGish
vggish_slim.load_vggish_slim_checkpoint(self.sess,
self.vggish_checkpoint)
# Save model checkpoint
self.save_variables()
def readDirectory(dirname, label):
pproc = vggish_postprocess.Postprocessor("vggish_pca_params.npz")
for wav_file in glob.glob(dirname + "*.wav"):
print(wav_file)
try:
examples_batch = vggish_input.wavfile_to_examples(wav_file)
except:
continue
writer = tf.python_io.TFRecordWriter(wav_file[:-3] + "tfrecord")
with tf.Graph().as_default(), tf.Session() as sess:
vggish_slim.define_vggish_slim(training=False)
vggish_slim.load_vggish_slim_checkpoint(sess, "vggish_model.ckpt")
features_tensor = sess.graph.get_tensor_by_name(
vggish_params.INPUT_TENSOR_NAME)
embedding_tensor = sess.graph.get_tensor_by_name(
vggish_params.OUTPUT_TENSOR_NAME)
try:
[embedding_batch
] = sess.run([embedding_tensor],
feed_dict={features_tensor: examples_batch})
except:
continue
postprocessed_batch = pproc.postprocess(embedding_batch)
nBatches = len(postprocessed_batch)
if nBatches < 10:
nBatches = 1
else:
nBatches = nBatches / 10
for i in range(nBatches):
seq_example = tf.train.SequenceExample(
context=tf.train.Features(
feature={
"labels":
tf.train.Feature(int64_list=tf.train.Int64List(
value=[label]))
}),
feature_lists=tf.train.FeatureLists(
feature_list={
vggish_params.AUDIO_EMBEDDING_FEATURE_NAME:
tf.train.FeatureList(feature=[
tf.train.Feature(bytes_list=tf.train.BytesList(
value=[embedding.tobytes()]))
for embedding in postprocessed_batch[i * 10:i *
10 + 10]
])
}))
if writer:
writer.write(seq_example.SerializeToString())
if writer:
writer.close()
def extract_audioset_embedding():
"""Extract log mel spectrogram features.
"""
# Arguments & parameters
mel_bins = vggish_params.NUM_BANDS
sample_rate = vggish_params.SAMPLE_RATE
input_len = vggish_params.NUM_FRAMES
embedding_size = vggish_params.EMBEDDING_SIZE
'''You may modify the EXAMPLE_HOP_SECONDS in vggish_params.py to change the
hop size. '''
# Paths
audio_path = 'appendixes/01.wav'
checkpoint_path = os.path.join('vggish_model.ckpt')
pcm_params_path = os.path.join('vggish_pca_params.npz')
if not os.path.isfile(checkpoint_path):
raise Exception(
'Please download vggish_model.ckpt from '
'https://storage.googleapis.com/audioset/vggish_model.ckpt '
'and put it in the root of this codebase. ')
if not os.path.isfile(pcm_params_path):
raise Exception(
'Please download pcm_params_path from '
'https://storage.googleapis.com/audioset/vggish_pca_params.npz '
'and put it in the root of this codebase. ')
# Load model
sess = tf.Session()
vggish_slim.define_vggish_slim(training=False)
vggish_slim.load_vggish_slim_checkpoint(sess, checkpoint_path)
features_tensor = sess.graph.get_tensor_by_name(
vggish_params.INPUT_TENSOR_NAME)
embedding_tensor = sess.graph.get_tensor_by_name(
vggish_params.OUTPUT_TENSOR_NAME)
pproc = vggish_postprocess.Postprocessor(pcm_params_path)
# Read audio
(audio, _) = read_audio(audio_path, target_fs=sample_rate)
# Extract log mel feature
logmel = vggish_input.waveform_to_examples(audio, sample_rate)
# Extract embedding feature
[embedding_batch] = sess.run([embedding_tensor],
feed_dict={features_tensor: logmel})
# PCA
postprocessed_batch = pproc.postprocess(embedding_batch)
print('Audio length: {}'.format(len(audio)))
print('Log mel shape: {}'.format(logmel.shape))
print('Embedding feature shape: {}'.format(postprocessed_batch.shape))
def main(_):
if FLAGS.wav_file:
wav_file = FLAGS.wav_file
else:
return "No wav file"
examples_batch = vggish_input.wavfile_to_examples(wav_file)
# Prepare a postprocessor to munge the model embeddings.
pproc = vggish_postprocess.Postprocessor(FLAGS.pca_params)
# If needed, prepare a record writer to store the postprocessed embeddings.
writer = tf.python_io.TFRecordWriter(
FLAGS.tfrecord_file) if FLAGS.tfrecord_file else None
with tf.Graph().as_default(), tf.Session() as sess:
# Define the model in inference mode, load the checkpoint, and
# locate input and output tensors.
vggish_slim.define_vggish_slim(training=False)
vggish_slim.load_vggish_slim_checkpoint(sess, FLAGS.checkpoint)
features_tensor = sess.graph.get_tensor_by_name(
vggish_params.INPUT_TENSOR_NAME)
embedding_tensor = sess.graph.get_tensor_by_name(
vggish_params.OUTPUT_TENSOR_NAME)
# Run inference and postprocessing.
[embedding_batch] = sess.run([embedding_tensor],
feed_dict={features_tensor: examples_batch})
print(embedding_batch)
postprocessed_batch = pproc.postprocess(embedding_batch)
print(postprocessed_batch)
# Write the postprocessed embeddings as a SequenceExample, in a similar
# format as the features released in AudioSet. Each row of the batch of
# embeddings corresponds to roughly a second of audio (96 10ms frames), and
# the rows are written as a sequence of bytes-valued features, where each
# feature value contains the 128 bytes of the whitened quantized embedding.
seq_example = tf.train.SequenceExample(
feature_lists=tf.train.FeatureLists(
feature_list={
vggish_params.AUDIO_EMBEDDING_FEATURE_NAME:
tf.train.FeatureList(
feature=[
tf.train.Feature(
bytes_list=tf.train.BytesList(
value=[embedding.tobytes()]))
for embedding in postprocessed_batch
]
)
}
)
)
print(seq_example)
if writer:
writer.write(seq_example.SerializeToString())
if writer:
writer.close()
def main(_):
ontology_lookup = {}
with open(ONTROLOGY, 'r') as f:
label_json = json.load(f)
for entry in label_json:
label_id = entry['id'].replace('/', '_')
assert label_id not in ontology_lookup.keys()
ontology_lookup[label_id] = entry
wav_paths = glob.glob(os.path.join(AUDIO_CHUNKS, '*', '*.wav'))
# Prepare a postprocessor to munge the model embeddings.
pproc = vggish_postprocess.Postprocessor(FLAGS.pca_params)
audio_tsv = []
label_tsv = []
emb_tsv = []
for wavfile in tqdm(wav_paths):
label = Path(Path(wavfile).parent).stem
filename = Path(wavfile).name
examples_batch = vggish_input.wavfile_to_examples(wavfile)
with tf.Graph().as_default(), tf.Session() as sess:
# Define the model in inference mode, load the checkpoint, and
# locate input and output tensors.
vggish_slim.define_vggish_slim(training=False)
vggish_slim.load_vggish_slim_checkpoint(sess, FLAGS.checkpoint)
features_tensor = sess.graph.get_tensor_by_name(
vggish_params.INPUT_TENSOR_NAME)
embedding_tensor = sess.graph.get_tensor_by_name(
vggish_params.OUTPUT_TENSOR_NAME)
# Run inference and postprocessing.
[embedding_batch
] = sess.run([embedding_tensor],
feed_dict={features_tensor: examples_batch})
# emb = []
# for embedding in embedding_batch:
# emb.append(embedding.tolist())
emb = np.mean(embedding_batch, axis=0).tolist()
label_tsv.append([ontology_lookup[label]['name']])
audio_tsv.append([f'{label}/{filename}'])
emb_tsv.append(emb)
assert len(emb_tsv[0]) == len(emb)
with open(f'{OUTPUTDIR}/emb.tsv', 'w') as f:
for emb in emb_tsv:
csv.writer(f, delimiter='\t').writerow(emb)
with open(f'{OUTPUTDIR}/label.tsv', 'w') as f:
for label in label_tsv:
csv.writer(f, delimiter='\t').writerow(label)
with open(f'{OUTPUTDIR}/audio.tsv', 'w') as f:
for audio_path in audio_tsv:
csv.writer(f, delimiter='\t').writerow(audio_path)
def main(wav_file, npz_path):
# In this simple example, we run the examples from a single audio file through
# the model. If none is provided, we generate a synthetic input.
#if FLAGS.wav_file:
# wav_file = str(FLAGS.wav_file)
# print (FLAGS.wav_file)
if 1:
wav_file = wav_file
else:
# Write a WAV of a sine wav into an in-memory file object.
num_secs = 5
freq = 1000
sr = 44100
t = np.linspace(0, num_secs, int(num_secs * sr))
x = np.sin(2 * np.pi * freq * t)
# Convert to signed 16-bit samples.
samples = np.clip(x * 32768, -32768, 32767).astype(np.int16)
wav_file = six.BytesIO()
wavfile.write(wav_file, sr, samples)
wav_file.seek(0)
examples_batch = vggish_input.wavfile_to_examples(wav_file)
#print(examples_batch)
# Prepare a postprocessor to munge the model embeddings.
pproc = vggish_postprocess.Postprocessor(FLAGS.pca_params)
# If needed, prepare a record writer to store the postprocessed embeddings.
writer = tf.python_io.TFRecordWriter(tfrecord_file)
with tf.Graph().as_default(), tf.Session() as sess:
# Define the model in inference mode, load the checkpoint, and
# locate input and output tensors.
vggish_slim.define_vggish_slim(training=False)
vggish_slim.load_vggish_slim_checkpoint(sess, FLAGS.checkpoint)
features_tensor = sess.graph.get_tensor_by_name(
vggish_params.INPUT_TENSOR_NAME)
embedding_tensor = sess.graph.get_tensor_by_name(
vggish_params.OUTPUT_TENSOR_NAME)
# Run inference and postprocessing.
[embedding_batch
] = sess.run([embedding_tensor],
feed_dict={features_tensor: examples_batch})
#print(embedding_batch)
postprocessed_batch = pproc.postprocess(embedding_batch)
#print(postprocessed_batch)
if 0 in embedding_batch.shape:
print('NO')
return 0
np.savez_compressed(npz_path, postprocessed_batch)
return 1
def post_init(self):
self.to_device()
import tensorflow as tf
tf.compat.v1.disable_eager_execution()
self.sess = tf.compat.v1.Session()
vggish_slim.define_vggish_slim()
vggish_slim.load_vggish_slim_checkpoint(self.sess, self.model_path)
self.feature_tensor = self.sess.graph.get_tensor_by_name(
vggish_params.INPUT_TENSOR_NAME)
self.embedding_tensor = self.sess.graph.get_tensor_by_name(
vggish_params.OUTPUT_TENSOR_NAME)
self.post_processor = vggish_postprocess.Postprocessor(self.pca_path)
def __init__(self):
self.graph = tf.Graph()
with self.graph.as_default():
self.sess = tf.Session(graph=self.graph)
# Define the model in inference mode, load the checkpoint, and
# locate input and output tensors.
vggish_slim.define_vggish_slim(training=False)
vggish_slim.load_vggish_slim_checkpoint(self.sess, FLAGS.checkpoint)
self.features_tensor = self.sess.graph.get_tensor_by_name(
vggish_params.INPUT_TENSOR_NAME)
self.embedding_tensor = self.sess.graph.get_tensor_by_name(
vggish_params.OUTPUT_TENSOR_NAME)
def _build_model(self):
# Restore VGGish model trained on YouTube8M dataset
# Retrieve PCA-embeddings of bottleneck features
# Define the model in inference mode, load the checkpoint, and
# locate input and output tensors.
vggish_slim.define_vggish_slim(training=False)
vggish_slim.load_vggish_slim_checkpoint(self.sess, model_checkpoint)
self.features_tensor = self.sess.graph.get_tensor_by_name(
vggish_params.INPUT_TENSOR_NAME)
self.embedding_tensor = self.sess.graph.get_tensor_by_name(
vggish_params.OUTPUT_TENSOR_NAME)
# Prepare a postprocessor to munge the model embeddings.
self.pproc = vggish_postprocess.Postprocessor(pca_params)
def processList(filelist):
# Generate a 1 kHz sine wave at 44.1 kHz (we use a high sampling rate
# to test resampling to 16 kHz during feature extraction).
out_file_list = []
input_list = open(filelist)
input_lists = input_list.readlines()
for i, infl in tqdm(enumerate(input_lists)):
infl_new = audio_root + '/' + infl.replace("\ ", " ")
y, sr = librosa.load(infl_new.strip(), sr=None)
if len(y) < sr:
y1 = np.pad(y, (0, sr - len(y)), 'wrap')
y = y1
# Produce a batch of log mel spectrogram examples.
input_batch = vggish_input.waveform_to_examples(y, sr)
print('Log Mel Spectrogram example: ', input_batch[0])
# Define VGGish, load the checkpoint, and run the batch through the model to
# produce embeddings.
gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=0.5)
with tf.Graph().as_default(), tf.Session(config=tf.ConfigProto(
gpu_options=gpu_options)) as sess:
vggish_slim.define_vggish_slim()
vggish_slim.load_vggish_slim_checkpoint(sess, checkpoint_path)
features_tensor = sess.graph.get_tensor_by_name(
vggish_params.INPUT_TENSOR_NAME)
embedding_tensor = sess.graph.get_tensor_by_name(
vggish_params.OUTPUT_TENSOR_NAME)
[embedding_batch
] = sess.run([embedding_tensor],
feed_dict={features_tensor: input_batch})
print('VGGish embedding: done ', i)
# Postprocess the results to produce whitened quantized embeddings.
pproc = vggish_postprocess.Postprocessor(pca_params_path)
postprocessed_batch = pproc.postprocess(embedding_batch)
infl_list = infl_new.strip().split("/")
file_name = infl_list[-1].strip()
out_dir = output_root + "/" + infl_list[-2]
if not os.path.exists(out_dir):
os.makedirs(out_dir)
featfile = str(out_dir) + '/' + str(file_name) + '.txt'
out_file_list.append(featfile.strip())
np.savetxt(featfile,
postprocessed_batch.astype(int),
fmt='%i',
delimiter=",")
create_file(output_file, out_file_list)
def OutputAudioEmbeddings(pathIn, row):
video_id = row['video_id']
video_path = row['video_path']
split = row['split']
full_path = os.path.join(pathIn, video_path)
full_path = full_path.replace("%(ext)s",
"wav") # output file of the downloader path
if split == 'train':
full_path_cut = full_path.replace("train", "train/cut")
elif split == 'test':
full_path_cut = full_path.replace("test", "test/cut")
# In this simple example, we run the examples from a single audio file through
# the model. If none is provided, we generate a synthetic input.
if os.path.isfile(full_path_cut):
wav_file = full_path_cut
examples_batch = vggish_input.wavfile_to_examples(wav_file)
#print(examples_batch)
# Prepare a postprocessor to munge the model embeddings.
pproc = vggish_postprocess.Postprocessor(FLAGS.pca_params)
# If needed, prepare a record writer to store the postprocessed embeddings.
writer = tf.python_io.TFRecordWriter(
FLAGS.tfrecord_file) if FLAGS.tfrecord_file else None
with tf.Graph().as_default(), tf.Session() as sess:
# Define the model in inference mode, load the checkpoint, and
# locate input and output tensors.
vggish_slim.define_vggish_slim(training=False)
vggish_slim.load_vggish_slim_checkpoint(sess, FLAGS.checkpoint)
features_tensor = sess.graph.get_tensor_by_name(
vggish_params.INPUT_TENSOR_NAME)
embedding_tensor = sess.graph.get_tensor_by_name(
vggish_params.OUTPUT_TENSOR_NAME)
# Run inference and postprocessing.
[embedding_batch
] = sess.run([embedding_tensor],
feed_dict={features_tensor: examples_batch})
#print(embedding_batch)
postprocessed_batch = pproc.postprocess(embedding_batch)
print(postprocessed_batch)
#print(postprocessed_batch.shape)
np.save(
'/lfs01/workdirs/shams010/shams010u1/code/audio_features/' +
split + '/' + video_id, postprocessed_batch)
def main(_):
with tf.Graph().as_default(), tf.Session() as sess:
# Define VGGish.
embeddings = vggish_slim.define_vggish_slim(FLAGS.train_vggish)
# Define a shallow classification model and associated training ops on top
# of VGGish.
with tf.variable_scope('mymodel'):
# Add a fully connected layer with 100 units.
num_units = 100
fc = slim.fully_connected(embeddings, num_units)
# Add a classifier layer at the end, consisting of parallel logistic
# classifiers, one per class. This allows for multi-class tasks.
logits = slim.fully_connected(
fc, _NUM_CLASSES, activation_fn=None, scope='logits')
tf.sigmoid(logits, name='prediction')
# Add training ops.
with tf.variable_scope('train'):
global_step = tf.Variable(
0, name='global_step', trainable=False,
collections=[tf.GraphKeys.GLOBAL_VARIABLES,
tf.GraphKeys.GLOBAL_STEP])
# Labels are assumed to be fed as a batch multi-hot vectors, with
# a 1 in the position of each positive class label, and 0 elsewhere.
labels = tf.placeholder(
tf.float32, shape=(None, _NUM_CLASSES), name='labels')
# Cross-entropy label loss.
xent = tf.nn.sigmoid_cross_entropy_with_logits(
logits=logits, labels=labels, name='xent')
loss = tf.reduce_mean(xent, name='loss_op')
tf.summary.scalar('loss', loss)
# We use the same optimizer and hyperparameters as used to train VGGish.
optimizer = tf.train.AdamOptimizer(
learning_rate=vggish_params.LEARNING_RATE,
epsilon=vggish_params.ADAM_EPSILON)
optimizer.minimize(loss, global_step=global_step, name='train_op')
# Initialize all variables in the model, and then load the pre-trained
# VGGish checkpoint.
sess.run(tf.global_variables_initializer())
vggish_slim.load_vggish_slim_checkpoint(sess, FLAGS.checkpoint)
# Locate all the tensors and ops we need for the training loop.
features_tensor = sess.graph.get_tensor_by_name(
vggish_params.INPUT_TENSOR_NAME)
labels_tensor = sess.graph.get_tensor_by_name('mymodel/train/labels:0')
global_step_tensor = sess.graph.get_tensor_by_name(
'mymodel/train/global_step:0')
loss_tensor = sess.graph.get_tensor_by_name('mymodel/train/loss_op:0')
train_op = sess.graph.get_operation_by_name('mymodel/train/train_op')
# The training loop.
for _ in range(FLAGS.num_batches):
(features, labels) = _get_examples_batch()
[num_steps, loss, _] = sess.run(
[global_step_tensor, loss_tensor, train_op],
feed_dict={features_tensor: features, labels_tensor: labels})
print('Step %d: loss %g' % (num_steps, loss))
def main(_):
# In this simple example, we run the examples from a single audio file through
# the model. If none is provided, we generate a synthetic input.
if FLAGS.wav_file:
wav_file = FLAGS.wav_file
else:
# Write a WAV of a sine wav into an in-memory file object.
num_secs = 5
freq = 1000
sr = 44100
t = np.linspace(0, num_secs, int(num_secs * sr))
x = np.sin(2 * np.pi * freq * t)
# Convert to signed 16-bit samples.
samples = np.clip(x * 32768, -32768, 32767).astype(np.int16)
wav_file = six.BytesIO()
wavfile.write(wav_file, sr, samples)
wav_file.seek(0)
examples_batch = vggish_input.wavfile_to_examples(wav_file)
print(examples_batch)
# Prepare a postprocessor to munge the model embeddings.
pproc = vggish_postprocess.Postprocessor(FLAGS.pca_params)
# If needed, prepare a record writer to store the postprocessed embeddings.
writer = tf.python_io.TFRecordWriter(
FLAGS.tfrecord_file) if FLAGS.tfrecord_file else None
with tf.Graph().as_default(), tf.Session() as sess:
# Define the model in inference mode, load the checkpoint, and
# locate input and output tensors.
vggish_slim.define_vggish_slim(training=False)
vggish_slim.load_vggish_slim_checkpoint(sess, FLAGS.checkpoint)
features_tensor = sess.graph.get_tensor_by_name(
vggish_params.INPUT_TENSOR_NAME)
embedding_tensor = sess.graph.get_tensor_by_name(
vggish_params.OUTPUT_TENSOR_NAME)
# Run inference and postprocessing.
[embedding_batch] = sess.run([embedding_tensor],
feed_dict={features_tensor: examples_batch})
print(embedding_batch)
postprocessed_batch = pproc.postprocess(embedding_batch)
print(postprocessed_batch)
# Write the postprocessed embeddings as a SequenceExample, in a similar
# format as the features released in AudioSet. Each row of the batch of
# embeddings corresponds to roughly a second of audio (96 10ms frames), and
# the rows are written as a sequence of bytes-valued features, where each
# feature value contains the 128 bytes of the whitened quantized embedding.
seq_example = tf.train.SequenceExample(
feature_lists=tf.train.FeatureLists(
feature_list={
vggish_params.AUDIO_EMBEDDING_FEATURE_NAME:
tf.train.FeatureList(
feature=[
tf.train.Feature(
bytes_list=tf.train.BytesList(
value=[embedding.tobytes()]))
for embedding in postprocessed_batch
]
)
}
)
)
print(seq_example)
if writer:
writer.write(seq_example.SerializeToString())
if writer:
writer.close()
sr = 44100
t = np.linspace(0, num_secs, int(num_secs * sr))
x = np.sin(2 * np.pi * freq * t)
# Produce a batch of log mel spectrogram examples.
input_batch = vggish_input.waveform_to_examples(x, sr)
print('Log Mel Spectrogram example: ', input_batch[0])
np.testing.assert_equal(
input_batch.shape,
[num_secs, vggish_params.NUM_FRAMES, vggish_params.NUM_BANDS])
# Define VGGish, load the checkpoint, and run the batch through the model to
# produce embeddings.
with tf.Graph().as_default(), tf.Session() as sess:
vggish_slim.define_vggish_slim()
vggish_slim.load_vggish_slim_checkpoint(sess, checkpoint_path)
features_tensor = sess.graph.get_tensor_by_name(
vggish_params.INPUT_TENSOR_NAME)
embedding_tensor = sess.graph.get_tensor_by_name(
vggish_params.OUTPUT_TENSOR_NAME)
[embedding_batch] = sess.run([embedding_tensor],
feed_dict={features_tensor: input_batch})
print('VGGish embedding: ', embedding_batch[0])
expected_embedding_mean = 0.131
expected_embedding_std = 0.238
np.testing.assert_allclose(
[np.mean(embedding_batch), np.std(embedding_batch)],
[expected_embedding_mean, expected_embedding_std],
rtol=rel_error)