def get_logpriors():
logpriors = 0
if FLAGS.post2like:
with kaldi_data(os.path.join(FLAGS.data_dir,
FLAGS.occupances)) as kd:
logpriors = kd.read_counts()
return logpriors
def produce_likelihoods():
tf_model = import_tf_model()
logpriors = get_logpriors()
with tf.Graph().as_default():
features = tf.placeholder(tf.float32, shape=(None, None))
logits = tf_model.inference(features)
ckpt_path = os.path.join(FLAGS.data_dir,
FLAGS.tf_ckpt_path)
saver = tf.train.Saver()
sess = tf.Session()
saver.restore(sess, ckpt_path)
with kaldi_data(FLAGS.features_rspec) as kd_reader:
with kaldi_data(FLAGS.prob_wspec, 'w') as kd_writer:
for d in kd_reader.read_utterance(FLAGS.batch_size):
utterance_id = d[0]
batch = tf_model.process_data(d[1], FLAGS.transpose_input)
r = sess.run([logits], feed_dict={features: batch})
kd_writer.write_batches([utterance_id, r[0] - logpriors])
sess.close()
return
def produce_likelihoods():
with kaldi_helpers.kaldi_data('./t.ark') as kd:
batch1 = kd.read_utterance(-1)
u1 = batch1.next()
u2 = batch1.next()
print(np.shape(u1[1]))
with kaldi_helpers.kaldi_data(FLAGS.occupances) as kd:
logprioirs = kd.read_counts()
with tf.Graph().as_default():
val_images, val_labels = eval_inputs()
images = tf.placeholder(tf.float32, shape=(None, 1320))
labels = tf.placeholder(tf.int32, shape=(None))
logits = tf_model.inference(images,
2048,
2048,
2048)
loss = tf_model.loss(logits, labels)
saver = tf.train.Saver()
sess = tf.Session()
coord = tf.train.Coordinator()
threads = tf.train.start_queue_runners(sess=sess, coord=coord)
saver.restore(sess, '../../../data/tf_fbank_deltas_nocmvn/cnnmodel-31530')
vi, vl = sess.run([val_images, val_labels])
r = sess.run([logits], feed_dict={images: vi})
l = r[0] - logprioirs
with kaldi_helpers.kaldi_data('./t_like_u1.ark', 'w') as kd:
kd.write_utterance([[u1[0], l]])
r = sess.run([loss], feed_dict={images: vi, labels: vl})
print(r)
r = sess.run([loss], feed_dict={images: u1[1], labels: vl})
print(r)
l = vi
# with kaldi_helpers.kaldi_data('./t_feats_tfr.ark', 'w') as kd:
# kd.write_utterance([[u1[0], l]])
coord.request_stop()
# Wait for threads to finish.
coord.join(threads)
sess.close()
