Compatible with Tensorflow 1.1.0rc1 + Python 3.5.2

README.md

@@ -15,11 +15,16 @@ This model deals with raw speech waveforms on many noise conditions at different
 * [improved-gan](https://github.com/openai/improved-gan): implementing improvements to train GANs in a more stable way
 *  [DCGAN-tensorflow](https://github.com/carpedm20/DCGAN-tensorflow): implementation of the DCGAN in tensorflow
 
-### Dependencies
+### Dependencies-Original
 
 * Python 2.7
 * TensorFlow 0.12
 
+### Dependencies-Forked by kmjeon, 2017-04-23
+
+* Python 3.5.2
+* TensorFlow 1.1.0rc1
+
 You can install the requirements either to your virtualenv or the system via pip with:
 
 ```

clean_wav.sh

@@ -20,5 +20,5 @@ echo "SAVE PATH: $SAVE_PATH"
 mkdir -p $SAVE_PATH
 
 CUDA_VISIBLE_DEVICES="" python main.py --init_noise_std 0. --save_path segan_v1 \
-                        --batch_size 100 --g_nl prelu --weights SEGAN-41800 \
+                        --batch_size 100 --g_nl prelu --weights SEGAN-72900 \
                         --test_wav $NOISY_WAVNAME --save_clean_path $SAVE_PATH

generator.py

@@ -50,7 +50,7 @@ def make_z(shape, mean=0., std=1., name='z'):
         kwidth = 3
         z = make_z([segan.batch_size, h_i.get_shape().as_list()[1],
                     segan.g_enc_depths[-1]])
-        h_i = tf.concat(2, [h_i, z])
+        h_i = tf.concat([h_i, z], 2)
         skip_out = True
         skips = []
         for block_idx, dilation in enumerate(segan.g_dilated_blocks):
@@ -182,7 +182,7 @@ def make_z(shape, mean=0., std=1., name='z'):
                 # random code is fused with intermediate representation
                 z = make_z([segan.batch_size, h_i.get_shape().as_list()[1],
                             segan.g_enc_depths[-1]])
-                h_i = tf.concat(2, [z, h_i])
+                h_i = tf.concat([z, h_i], 2)
 
             #SECOND DECODER (reverse order)
             g_dec_depths = segan.g_enc_depths[:-1][::-1] + [1]
@@ -219,7 +219,7 @@ def make_z(shape, mean=0., std=1., name='z'):
                     if is_ref:
                         print('Fusing skip connection of '
                               'shape {}'.format(skip_.get_shape()))
-                    h_i = tf.concat(2, [h_i, skip_])
+                    h_i = tf.concat([h_i, skip_], 2)
 
                 else:
                     if is_ref:

main.py

@@ -94,7 +94,7 @@ def main(_):
             print('test wave min:{}  max:{}'.format(np.min(wave), np.max(wave)))
             c_wave = se_model.clean(wave)
             print('c wave min:{}  max:{}'.format(np.min(c_wave), np.max(c_wave)))
-            wavfile.write(os.path.join(FLAGS.save_clean_path, wavname), 16e3, c_wave)
+            wavfile.write(os.path.join(FLAGS.save_clean_path, wavname), 16000, c_wave)
             print('Done cleaning {} and saved '
                   'to {}'.format(FLAGS.test_wav,
                                  os.path.join(FLAGS.save_clean_path, wavname)))

make_tfrecords.py

@@ -89,7 +89,7 @@ def main(opts):
         beg_enc_t = timeit.default_timer()
         out_file = tf.python_io.TFRecordWriter(out_filepath)
         # process the acoustic and textual data now
-        for dset_i, (dset, dset_desc) in enumerate(cfg_desc.iteritems()):
+        for dset_i, (dset, dset_desc) in enumerate(cfg_desc.items()):
             print('-' * 50)
             wav_dir = dset_desc['clean']
             wav_files = [os.path.join(wav_dir, wav) for wav in