optimize code, add some log

app.py

@@ -47,7 +47,7 @@ def txt_to_audio(text_):
     text = text_
     t = time.time()
     asyncio.get_event_loop().run_until_complete(main(voicename,text,nerfreal))
-    print('-------tts time: ',time.time()-t)
+    print(f'-------tts time:{time.time()-t:.4f}s')
     
 @sockets.route('/humanecho')
 def echo_socket(ws):

asrreal.py

@@ -116,7 +116,7 @@ class ASR:
         self.att_feats = [torch.zeros(self.audio_dim, 16, dtype=torch.float32, device=self.device)] * 4 # 4 zero padding...
 
         # warm up steps needed: mid + right + window_size + attention_size
-        self.warm_up_steps = self.context_size + self.stride_right_size + 8 + 2 * 3
+        self.warm_up_steps = self.context_size + self.stride_right_size + self.stride_left_size   #+ 8 + 2 * 3
 
         self.listening = False
         self.playing = False
@@ -204,7 +204,6 @@ class ASR:
         else:
             self.frames.append(frame)
             # put to output
-            #if self.play:
             self.output_queue.put(frame)
             # context not enough, do not run network.
             if len(self.frames) < self.stride_left_size + self.context_size + self.stride_right_size:
@@ -217,7 +216,9 @@ class ASR:
             self.frames = self.frames[-(self.stride_left_size + self.stride_right_size):]
 
         print(f'[INFO] frame_to_text... ')
+        #t = time.time()
         logits, labels, text = self.frame_to_text(inputs)
+        #print(f'-------wav2vec time:{time.time()-t:.4f}s')
         feats = logits # better lips-sync than labels
 
         # save feats
@@ -257,6 +258,7 @@ class ASR:
                 np.save(output_path, unfold_feats.cpu().numpy())
                 print(f"[INFO] saved logits to {output_path}")
     
+    '''
     def create_file_stream(self):
     
         stream, sample_rate = sf.read(self.opt.asr_wav) # [T*sample_rate,] float64
@@ -302,7 +304,7 @@ class ASR:
                             frames_per_buffer=self.chunk)
         
         return audio, stream
-
+    '''
     
     def get_audio_frame(self):
 
@@ -351,8 +353,8 @@ class ASR:
 
         # print(frame.shape, inputs.input_values.shape, logits.shape)
     
-        predicted_ids = torch.argmax(logits, dim=-1)
+        #predicted_ids = torch.argmax(logits, dim=-1)
-        transcription = self.processor.batch_decode(predicted_ids)[0].lower()
+        #transcription = self.processor.batch_decode(predicted_ids)[0].lower()
 
         
         # for esperanto
@@ -363,7 +365,7 @@ class ASR:
         # print(predicted_ids[0])
         # print(transcription)
 
-        return logits[0], predicted_ids[0], transcription # [N,]
+        return logits[0], None,None #predicted_ids[0], transcription # [N,]
     
     def create_bytes_stream(self,byte_stream):
         #byte_stream=BytesIO(buffer)
@@ -404,8 +406,8 @@ class ASR:
                 self.queue.put(stream[idx:idx+self.chunk])
                 streamlen -= self.chunk
                 idx += self.chunk
-            if streamlen>0:
+            #if streamlen>0:  #skip last frame(not 20ms)
-                self.queue.put(stream[idx:])
+            #    self.queue.put(stream[idx:])
             self.input_stream.seek(0)
             self.input_stream.truncate()
     

echo.html

@@ -22,7 +22,7 @@
     <div id="log">
 		
 	</div>
-	<video id="video_player" width="40%" autoplay controls></video>
+	<video id="video_player" width="40%" controls autoplay muted></video>
   </div>
 </body>
 <script type="text/javascript" charset="utf-8">

nerfreal.py

@@ -144,7 +144,9 @@ class NeRFReal:
                 # use the live audio stream
                 data['auds'] = self.asr.get_next_feat()
 
+            #t = time.time()
             outputs = self.trainer.test_gui_with_data(data, self.W, self.H)
+            #print('-------ernerf time: ',time.time()-t)
             #print(f'[INFO] outputs shape ',outputs['image'].shape)
             image = (outputs['image'] * 255).astype(np.uint8)
             self.streamer.stream_frame(image)
@@ -168,7 +170,9 @@ class NeRFReal:
             
     def render(self):
         if self.opt.asr:
-            self.asr.warm_up()
+             self.asr.warm_up()
+        count=0
+        totaltime=0
         while True: #todo
             # update texture every frame
             # audio stream thread...
@@ -178,6 +182,12 @@ class NeRFReal:
                 for _ in range(2):
                     self.asr.run_step()
             self.test_step()
+            totaltime += (time.time() - t)
+            count += 1
+            if count==100:
+                print(f"------actual avg fps:{count/totaltime:.4f}")
+                count=0
+                totaltime=0
             # delay = 0.04 - (time.time() - t) #40ms
             # if delay > 0:
             #     time.sleep(delay)