Update audio2text/a2t.py

audio2text/a2t.py

@@ -1,53 +1,69 @@
-import librosa
 import numpy as np
-from .init import pipe
+import speech_recognition as sr
 
-TASK = "transcribe"
-BATCH_SIZE = 8
-LIMIT = 60
-SAMPLING_RATE = 16000
+# TASK = "transcribe"
+# BATCH_SIZE = 8
+# LIMIT = 60
+# SAMPLING_RATE = 16000
 
-class A2T:
-    def __init__(self, mic):
-        self.mic = mic
+# class A2T:
+#     def __init__(self, mic):
+#         self.mic = mic
 
-    def __transcribe(self, inputs, task: str = None):
-        if inputs is  None:
-            print("Inputs None")
+#     def __transcribe(self, inputs, task: str = None):
+#         if inputs is  None:
+#             print("Inputs None")
 
-        transcribed_text = pipe(inputs, batch_size=BATCH_SIZE, generate_kwargs={"task": task}, return_timestamps=True)["text"]
-        return transcribed_text
+#         transcribed_text = pipe(inputs, batch_size=BATCH_SIZE, generate_kwargs={"task": task}, return_timestamps=True)["text"]
+#         return transcribed_text
 
-    def __preprocces(self, raw: np.ndarray, sampling_rate: int):
+#     def __preprocces(self, raw: np.ndarray, sampling_rate: int):
         
-        chunk = raw.astype(np.float32,  order='C') / 32768.0
+#         chunk = raw.astype(np.float32,  order='C') / 32768.0
 
-        print(f"Chunk : {chunk} max chunk : {np.max(chunk)}")
+#         print(f"Chunk : {chunk} max chunk : {np.max(chunk)}")
 
-        if len(chunk.shape) > 1:
-            chunk = librosa.to_mono(chunk.T)
+#         if len(chunk.shape) > 1:
+#             chunk = librosa.to_mono(chunk.T)
 
-        chunk = chunk[:SAMPLING_RATE*LIMIT]
+#         chunk = chunk[:SAMPLING_RATE*LIMIT]
                 
-        return chunk
+#         return chunk
         
-    def predict(self):
-        try:
-            if self.mic is not None:
-                raw = self.mic.get_array_of_samples()
-                chunk = np.array(raw, dtype=np.int16)
-                sampling_rate = self.mic.frame_rate
-                audio = self.__preprocces(raw=chunk, sampling_rate=sampling_rate)
-                print(f"audio : {audio} \n shape : {audio.shape} \n max : {np.max(audio)} \n shape of chunk : {chunk.shape} \n sampling rate : {sampling_rate} \n max chunk : {np.max(chunk)} \n chunk : {chunk}")
-            else:
-                raise Exception("please provide audio")
-
-            if isinstance(audio , np.ndarray):
-                inputs = {"array": inputs, "sampling_rate": pipe.feature_extractor.sampling_rate}
-                return self.__transcribe(inputs=inputs, task=TASK)
-            else:
-                raise Exception("Audio is not np array")
+#     def predict(self):
+#         try:
+#             if self.mic is not None:
+#                 raw = self.mic.get_array_of_samples()
+#                 chunk = np.array(raw, dtype=np.int16)
+#                 sampling_rate = self.mic.frame_rate
+#                 audio = self.__preprocces(raw=chunk, sampling_rate=sampling_rate)
+#                 print(f"audio : {audio} \n shape : {audio.shape} \n max : {np.max(audio)} \n shape of chunk : {chunk.shape} \n sampling rate : {sampling_rate} \n max chunk : {np.max(chunk)} \n chunk : {chunk}")
+#             else:
+#                 raise Exception("please provide audio")
+
+#             if isinstance(audio , np.ndarray):
+#                 inputs = {"array": inputs, "sampling_rate": pipe.feature_extractor.sampling_rate}
+#                 return self.__transcribe(inputs=inputs, task=TASK)
+#             else:
+#                 raise Exception("Audio is not np array")
                 
-        except Exception as e:
-            return f"Oops some kinda error : {e}"
+#         except Exception as e:
+#             return f"Oops some kinda error : {e}"
+
+
+class A2T:
+    def get_text(self):
+        # obtain audio from the microphone
+        r = sr.Recognizer()
+        with sr.Microphone() as source:
+            print("Say something!")
+            audio = r.listen(source)
+        
+        # recognize speech using Sphinx
+        try:
+            return r.recognize_sphinx(audio)
+        except sr.UnknownValueError:
+            raise Exception("Sphinx could not understand audio")
+        except sr.RequestError as e:
+            raise Exception("Sphinx error; {0}".format(e))