fix runtime.py and server.py

deepspeech/exps/deepspeech2/bin/deploy/runtime.py

@@ -81,15 +81,15 @@ def inference(config, args):
 def start_server(config, args):
     """Start the ASR server"""
     config.defrost()
-    config.data.manfiest = config.data.test_manifest
+    config.data.manifest = config.data.test_manifest
-    config.data.augmentation_config = ""
-    config.data.keep_transcription_text = True
     dataset = ManifestDataset.from_config(config)
-
+    
+    config.collator.augmentation_config = ""
+    config.collator.keep_transcription_text = True
     config.collator.batch_size=1
     config.collator.num_workers=0
     collate_fn = SpeechCollator.from_config(config)
-    test_loader = DataLoader(dataset_dataset, collate_fn=collate_fn, num_workers=0)
+    test_loader = DataLoader(dataset, collate_fn=collate_fn, num_workers=0)
 
     model = DeepSpeech2Model.from_pretrained(test_loader, config,
                                              args.checkpoint_path)
@@ -97,15 +97,15 @@ def start_server(config, args):
 
     # prepare ASR inference handler
     def file_to_transcript(filename):
-        feature = dataset.process_utterance(filename, "")
+        feature = collate_fn.process_utterance(filename, "")
-        audio = np.array([feature[0]]).astype('float32')  #[1, D, T]
+        audio = np.array([feature[0]]).astype('float32')  #[1, T, D]
-        audio_len = feature[0].shape[1]
+        audio_len = feature[0].shape[0]
         audio_len = np.array([audio_len]).astype('int64')  # [1]
 
         result_transcript = model.decode(
             paddle.to_tensor(audio),
             paddle.to_tensor(audio_len),
-            vocab_list=dataset.vocab_list,
+            vocab_list=test_loader.collate_fn.vocab_list,
             decoding_method=config.decoding.decoding_method,
             lang_model_path=config.decoding.lang_model_path,
             beam_alpha=config.decoding.alpha,
@@ -146,7 +146,7 @@ if __name__ == "__main__":
     add_arg('host_ip',          str,
             'localhost',
             "Server's IP address.")
-    add_arg('host_port',        int,    8086,    "Server's IP port.")
+    add_arg('host_port',        int,    8089,    "Server's IP port.")
     add_arg('speech_save_dir',  str,
             'demo_cache',
             "Directory to save demo audios.")

deepspeech/exps/deepspeech2/bin/deploy/server.py

@@ -34,15 +34,15 @@ from deepspeech.io.collator import SpeechCollator
 def start_server(config, args):
     """Start the ASR server"""
     config.defrost()
-    config.data.manfiest = config.data.test_manifest
+    config.data.manifest = config.data.test_manifest
-    config.data.augmentation_config = ""
-    config.data.keep_transcription_text = True
     dataset = ManifestDataset.from_config(config)
 
+    config.collator.augmentation_config = ""
+    config.collator.keep_transcription_text = True
     config.collator.batch_size=1
     config.collator.num_workers=0
     collate_fn = SpeechCollator.from_config(config)
-    test_loader = DataLoader(dataset_dataset, collate_fn=collate_fn, num_workers=0)
+    test_loader = DataLoader(dataset, collate_fn=collate_fn, num_workers=0)
 
     model = DeepSpeech2Model.from_pretrained(test_loader, config,
                                              args.checkpoint_path)
@@ -50,15 +50,19 @@ def start_server(config, args):
 
     # prepare ASR inference handler
     def file_to_transcript(filename):
-        feature = dataset.process_utterance(filename, "")
+        feature = test_loader.collate_fn.process_utterance(filename, "")
-        audio = np.array([feature[0]]).astype('float32')  #[1, D, T]
+        audio = np.array([feature[0]]).astype('float32')  #[1, T, D]
-        audio_len = feature[0].shape[1]
+        # audio = audio.swapaxes(1,2)
+        print('---file_to_transcript feature----')
+        print(audio.shape)
+        audio_len = feature[0].shape[0]
+        print(audio_len)
         audio_len = np.array([audio_len]).astype('int64')  # [1]
 
         result_transcript = model.decode(
             paddle.to_tensor(audio),
             paddle.to_tensor(audio_len),
-            vocab_list=dataset.vocab_list,
+            vocab_list=test_loader.collate_fn.vocab_list,
             decoding_method=config.decoding.decoding_method,
             lang_model_path=config.decoding.lang_model_path,
             beam_alpha=config.decoding.alpha,
@@ -99,7 +103,7 @@ if __name__ == "__main__":
     add_arg('host_ip',          str,
             'localhost',
             "Server's IP address.")
-    add_arg('host_port',        int,    8086,    "Server's IP port.")
+    add_arg('host_port',        int,    8088,    "Server's IP port.")
     add_arg('speech_save_dir',  str,
             'demo_cache',
             "Directory to save demo audios.")

deepspeech/io/collator.py

@@ -242,6 +242,7 @@ class SpeechCollator():
 
         # specgram augment
         specgram = self._augmentation_pipeline.transform_feature(specgram)
+        specgram=specgram.transpose([1,0])
         return specgram, transcript_part
 
     def __call__(self, batch):
@@ -269,7 +270,7 @@ class SpeechCollator():
             #utt
             utts.append(utt)
             # audio
-            audios.append(audio.T)  # [T, D]
+            audios.append(audio)  # [T, D]
             audio_lens.append(audio.shape[1])
             # text
             # for training, text is token ids

deepspeech/utils/socket_server.py

@@ -48,9 +48,9 @@ def warm_up_test(audio_process_handler,
     rng = random.Random(random_seed)
     samples = rng.sample(manifest, num_test_cases)
     for idx, sample in enumerate(samples):
-        print("Warm-up Test Case %d: %s", idx, sample['audio_filepath'])
+        print("Warm-up Test Case %d: %s"%(idx, sample['feat']))
         start_time = time.time()
-        transcript = audio_process_handler(sample['audio_filepath'])
+        transcript = audio_process_handler(sample['feat'])
         finish_time = time.time()
         print("Response Time: %f, Transcript: %s" %
               (finish_time - start_time, transcript))