Replace predictor by estimator

spleeter/separator.py

@@ -27,7 +27,7 @@ from . import SpleeterError
 from .audio.adapter import get_default_audio_adapter
 from .audio.convertor import to_stereo
 from .utils.configuration import load_configuration
-from .utils.estimator import create_estimator, to_predictor, get_default_model_dir
+from .utils.estimator import create_estimator, get_default_model_dir
 from .model import EstimatorSpecBuilder, InputProviderFactory
 
 
@@ -40,8 +40,34 @@ logger = logging.getLogger("spleeter")
 
 
 
+class DataGenerator():
+    """
+        generator object that store a sample and generate it once while called.
+        Used to feed a tensorflow estimator without knowing the whole data at build time.
+
+    """
+
+    def __init__(self):
+        self._current_data = None
+
+    def update_data(self, data):
+        """
+            replace data
+        """
+        self._current_data = data
+
+    def __call__(self):
+        res = self._current_data
+        while res is not None:
+            yield res
+            res = self._current_data
+
+
+
 def get_backend(backend):
     assert backend in ["auto", "tensorflow", "librosa"]
+    # print("USING TENSORFLOW BACKEND !!!!!!")
+    # return "tensorflow"
     if backend == "auto":
         return "tensorflow" if tf.test.is_gpu_available() else "librosa"
     return backend
@@ -61,7 +87,7 @@ class Separator(object):
         self._sample_rate = self._params['sample_rate']
         self._MWF = MWF
         self._tf_graph = tf.Graph()
-        self._predictor = None
+        self._prediction_generator = None
         self._input_provider = None
         self._builder = None
         self._features = None
@@ -69,20 +95,30 @@ class Separator(object):
         self._pool = Pool() if multiprocess else None
         self._tasks = []
         self._params["stft_backend"] = get_backend(stft_backend)
+        self._data_generator = DataGenerator()
+
 
     def __del__(self):
+
         if self._session:
             self._session.close()
 
-    def _get_predictor(self):
+    def _get_prediction_generator(self):
-        """ Lazy loading access method for internal predictor instance.
-
-        :returns: Predictor to use for source separation.
         """
-        if self._predictor is None:
+            Lazy loading access method for internal prediction generator returned by the predict method of a tensorflow estimator.
+
+            :returns: generator of prediction.
+        """
+
+        if self._prediction_generator is None:
             estimator = create_estimator(self._params, self._MWF)
-            self._predictor = to_predictor(estimator)
+            def get_dataset():
-        return self._predictor
+                return tf.data.Dataset.from_generator(self._data_generator, output_types={"waveform":tf.float32, "audio_id":tf.string}, output_shapes={"waveform":(None,2),"audio_id":()})
+            self._prediction_generator = estimator.predict(get_dataset,
+                                                            yield_single_examples=False)
+
+
+        return self._prediction_generator
 
     def join(self, timeout=200):
         """ Wait for all pending tasks to be finished.
@@ -103,10 +139,14 @@ class Separator(object):
         """
         if not waveform.shape[-1] == 2:
             waveform = to_stereo(waveform)
-        predictor = self._get_predictor()
+        prediction_generator = self._get_prediction_generator()
-        prediction = predictor({
+
-            'waveform': waveform,
+        # update data in generator before performing separation
-            'audio_id': audio_descriptor})
+        self._data_generator.update_data({"waveform": waveform,
+                                          'audio_id': np.array(audio_descriptor)})
+
+        # perform separation
+        prediction = next(prediction_generator)
         prediction.pop('audio_id')
         return prediction
 
@@ -155,9 +195,9 @@ class Separator(object):
 
     def _get_session(self):
         if self._session is None:
-            saver = tf.train.Saver()
+            saver = tf.compat.v1.train.Saver()
             latest_checkpoint = tf.train.latest_checkpoint(get_default_model_dir(self._params['model_dir']))
-            self._session = tf.Session()
+            self._session = tf.compat.v1.Session()
             saver.restore(self._session, latest_checkpoint)
         return self._session