import numpy as np
import speech_recognition as sr

# TASK = "transcribe"
# BATCH_SIZE = 8
# LIMIT = 60
# SAMPLING_RATE = 16000

# class A2T:
#     def __init__(self, mic):
#         self.mic = mic

#     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

#     def __preprocces(self, raw: np.ndarray, sampling_rate: int):
        
#         chunk = raw.astype(np.float32,  order='C') / 32768.0

#         print(f"Chunk : {chunk} max chunk : {np.max(chunk)}")

#         if len(chunk.shape) > 1:
#             chunk = librosa.to_mono(chunk.T)

#         chunk = chunk[:SAMPLING_RATE*LIMIT]
                
#         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")
                
#         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(source)
            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))