Merge pull request #319 from deezer/fix_multiple_call_to_separate

Fix multiple call to Separator.separate by instantiating the tensorflow graph only once in the methods.

spleeter/separator.py

@@ -61,6 +61,9 @@ class Separator(object):
         self._sample_rate = self._params['sample_rate']
         self._MWF = MWF
         self._predictor = None
+        self._input_provider = None
+        self._builder = None
+        self._features = None
         self._pool = Pool() if multiprocess else None
         self._tasks = []
         self._params["stft_backend"] = get_backend(stft_backend)
@@ -128,19 +131,33 @@ class Separator(object):
             return out[0]
         return np.concatenate(out, axis=2-inverse)
 
+
+    def _get_input_provider(self):
+        if self._input_provider is None:
+            self._input_provider = InputProviderFactory.get(self._params)
+        return self._input_provider
+
+    def _get_features(self):
+        if self._features is None:
+            self._features = self._get_input_provider().get_input_dict_placeholders()
+        return self._features
+
+    def _get_builder(self):
+        if self._builder is None:
+            self._builder = EstimatorSpecBuilder(self._get_features(), self._params)
+        return self._builder
+
     def _separate_librosa(self, waveform, audio_id):
         """
         Performs separation with librosa backend for STFT.
         """
         out = {}
-        input_provider = InputProviderFactory.get(self._params)
-        features = input_provider.get_input_dict_placeholders()
+        features = self._get_features()
 
-        builder = EstimatorSpecBuilder(features, self._params)
         latest_checkpoint = tf.train.latest_checkpoint(get_default_model_dir(self._params['model_dir']))
 
         # TODO: fix the logic, build sometimes return, sometimes set attribute
-        outputs = builder.outputs
+        outputs = self._get_builder().outputs
         stft = self._stft(waveform)
         if stft.shape[-1] == 1:
             stft = np.concatenate([stft, stft], axis=-1)
@@ -150,8 +167,8 @@ class Separator(object):
         saver = tf.train.Saver()
         with tf.Session() as sess:
             saver.restore(sess, latest_checkpoint)
-            outputs = sess.run(outputs, feed_dict=input_provider.get_feed_dict(features, stft, audio_id))
-            for inst in builder.instruments:
+            outputs = sess.run(outputs, feed_dict=self._get_input_provider().get_feed_dict(features, stft, audio_id))
+            for inst in self._get_builder().instruments:
                 out[inst] = self._stft(outputs[inst], inverse=True, length=waveform.shape[0])
         return out