Update audio2text/a2t.py

audio2text/a2t.py

@@ -4,64 +4,30 @@ import librosa
 import torch
 from .init import pipe
 
-LIMIT = 90 # limit 90 seconds
 TASK = "transcribe"
 
 class A2T:
     def __init__(self, mic):
         self.mic = mic
 
-    def __preprocces(self, audio, frame_rate):
-        try:
-            print("Audio before : ", audio)
-            audio = audio / 32678.0
-            print("Audio div : ", audio)
-
-            if len(audio.shape) > 1: 
-                audio = librosa.to_mono(audio.T)
-                
-            print("Audio mono : ", audio)
-    
-            if frame_rate != 16_000:
-                audio = librosa.resample(audio, orig_sr=frame_rate, target_sr=16000)
-
-            print("Audio resample : ", audio)
-    
-            audio = audio[:16_000*LIMIT]
-
-            print("Audio cut : ", audio)
-    
-            audio = torch.tensor(audio)
-
-            print("Audio torch : ", audio)
-            return audio
-        except Exception as e:
-            print("Preprocces error", e)
-            return None
-
     def __transcribe(self, inputs, task: str = None):
         if inputs is  None:
             print("Inputs None")
 
         transcribed_text = pipe(inputs, generate_kwargs={"task": task}, return_timestamps=True)["text"]
+        print(transcribed_text)
         return transcribed_text
-            
         
     def predict(self):
-        if self.mic is not None:
-            chunk = self.mic.get_array_of_samples()
-            audio = np.array(chunk)
-            # frame_rate = self.mic.frame_rate
-        else:
-            return "please provide audio"
-    
         try:
-            # forced_decoder_ids = processor.get_decoder_prompt_ids(language="english", task="transcribe")
-            # # audio = self.__preprocces(audio=audio, frame_rate=frame_rate)
-            # inputs = processor(audio=audio, sampling_rate=16000, return_tensors="pt")
-            # predicted_ids = model.generate(**inputs, max_length=400, forced_decoder_ids=forced_decoder_ids)
-            # transcription = processor.batch_decode(predicted_ids, skip_special_tokens=True)
-            return self.__transcribe(inputs=audio, task=TASK)
+            if self.mic is not None:
+                chunk = self.mic.get_array_of_samples()
+                audio = np.array(chunk)
+                print(audio)
+                return self.__transcribe(inputs=audio, task=TASK)
+            else:
+                return "please provide audio"
+                
         except Exception as e:
             print("Predict error", e)
             return "Oops some kinda error"