Merge pull request #296 from chriamue/tensorflow

Tensorflow

examples/tensorflow_commands.py

@@ -0,0 +1,26 @@
+#!/usr/bin/env python3
+import time
+import speech_recognition as sr
+from tensorflow.contrib.framework.python.ops import audio_ops as contrib_audio # noqa
+
+# obtain audio from the microphone
+r = sr.Recognizer()
+m = sr.Microphone()
+
+with m as source:
+    r.adjust_for_ambient_noise(source)
+
+
+def callback(recognizer, audio):
+    try:
+        # You can download the data here: http://download.tensorflow.org/models/speech_commands_v0.01.zip
+        spoken = recognizer.recognize_tensorflow(audio, tensor_graph='speech_recognition/tensorflow-data/conv_actions_frozen.pb', tensor_label='speech_recognition/tensorflow-data/conv_actions_labels.txt')
+        print(spoken)
+    except sr.UnknownValueError:
+        print("Tensorflow could not understand audio")
+    except sr.RequestError as e:
+        print("Could not request results from Tensorflow service; {0}".format(e))
+
+
+stop_listening = r.listen_in_background(m, callback, phrase_time_limit=0.6)
+time.sleep(100)

speech_recognition/__init__.py

@@ -1214,6 +1214,53 @@ def recognize_ibm(self, audio_data, username, password, language="en-US", show_a
                     transcription.append(hypothesis["transcript"])
         return "\n".join(transcription)
 
+    lasttfgraph = ''
+    tflabels = None
+
+    def recognize_tensorflow(self, audio_data, tensor_graph='tensorflow-data/conv_actions_frozen.pb', tensor_label='tensorflow-data/conv_actions_labels.txt'):
+        """
+        Performs speech recognition on ``audio_data`` (an ``AudioData`` instance).
+
+        Path to Tensor loaded from ``tensor_graph``. You can download a model here: http://download.tensorflow.org/models/speech_commands_v0.01.zip
+
+        Path to Tensor Labels file loaded from ``tensor_label``.
+        """
+        assert isinstance(audio_data, AudioData), "Data must be audio data"
+        assert isinstance(tensor_graph, str), "``tensor_graph`` must be a string"
+        assert isinstance(tensor_label, str), "``tensor_label`` must be a string"
+
+        try:
+            import tensorflow as tf
+        except ImportError:
+            raise RequestError("missing tensorflow module: ensure that tensorflow is set up correctly.")
+
+        if not (tensor_graph == self.lasttfgraph):
+            self.lasttfgraph = tensor_graph
+
+            # load graph
+            with tf.gfile.FastGFile(tensor_graph, 'rb') as f:
+                graph_def = tf.GraphDef()
+                graph_def.ParseFromString(f.read())
+                tf.import_graph_def(graph_def, name='')
+            # load labels
+            self.tflabels = [line.rstrip() for line in tf.gfile.GFile(tensor_label)]
+
+        wav_data = audio_data.get_wav_data(
+            convert_rate=16000, convert_width=2
+        )
+
+        with tf.Session() as sess:
+            input_layer_name = 'wav_data:0'
+            output_layer_name = 'labels_softmax:0'
+            softmax_tensor = sess.graph.get_tensor_by_name(output_layer_name)
+            predictions, = sess.run(softmax_tensor, {input_layer_name: wav_data})
+
+            # Sort labels in order of confidence
+            top_k = predictions.argsort()[-1:][::-1]
+            for node_id in top_k:
+                human_string = self.tflabels[node_id]
+                return human_string
+
 
 def get_flac_converter():
     """Returns the absolute path of a FLAC converter executable, or raises an OSError if none can be found."""