def get_model_output_10_classes(filename):
with tf.gfile.GFile(filename, 'rb') as f:
graph_def = tf.GraphDef()
graph_def.ParseFromString(f.read())
with tf.Graph().as_default() as graph:
tensors_and_ops = tf.import_graph_def(graph_def, name='')
with tf.Session(graph=graph) as sess:
output_op = graph.get_operation_by_name('Minimum_3').outputs[0]
xs = []
ys = []
filename_list = []
for idx, filename in enumerate(glob.glob(os.path.join(Constants.AUDIO_DATA_FOLDER, '*wav*', '*.wav'))):
name = filename.split('/')[-2:]
if int(name[-1].strip('.wav')) < 1000:
continue
y = name[-1].strip('.wav')
y = str(int(y) - 1000)
name = '/'.join(name)
name = name.replace('.wav', '')
filename_list.append(name)
if idx % 50 == 0:
print(name)
fs, audio = wav.read(filename)
x = audioToInputVector(audio, fs, N_FEATURES, N_CONTEXT)
out = sess.run(output_op, {'input_node:0': [
x], 'input_lengths:0': [len(x)]})
xs.append(out)
ys.append(y)
xs = fix_seq_length(xs, length=20)
xs = apply_pca(xs, n_components=25)
xs = np.array([np.ravel(x) for x in xs])
to_csv(xs, ys, os.path.join(Constants.DATA_FOLDER, 'audio10classes.csv'),
filename_list=filename_list)
def audiofile_to_input_vector(audio_filename, numcep, numcontext):
r"""
Given a WAV audio file at ``audio_filename``, calculates ``numcep`` MFCC features
at every 0.01s time step with a window length of 0.025s. Appends ``numcontext``
context frames to the left and right of each time step, and returns this data
in a numpy array.
"""
# Load wav files
fs, audio = wav.read(audio_filename)
return audioToInputVector(audio, fs, numcep, numcontext)
def audiofile_to_input_vector(audio_filename, numcep, numcontext):
r"""
Given a WAV audio file at ``audio_filename``, calculates ``numcep`` MFCC features
at every 0.01s time step with a window length of 0.025s. Appends ``numcontext``
context frames to the left and right of each time step, and returns this data
in a numpy array.
"""
# Load wav files
fs, audio = wav.read(audio_filename)
return audioToInputVector(audio, fs, numcep, numcontext)
def infer_audio(input_file_path):
initialize_globals()
n_input = 26
n_context = 9
graph = load_graph(model_path)
# We access the input and output nodes
inp = graph.get_tensor_by_name('prefix/input_node:0')
inp_len = graph.get_tensor_by_name('prefix/input_lengths:0')
logits = graph.get_tensor_by_name('prefix/logits:0')
op = graph.get_tensor_by_name('prefix/output_node:0')
with tf.Session(graph=graph) as session:
fs, audio = wav.read(input_file_path)
mfcc = audioToInputVector(audio, fs, n_input, n_context)
output = session.run(logits,
feed_dict={
inp: [mfcc],
inp_len: [len(mfcc)],
})
return output