#import whisper
import time
from google.cloud import speech_v1
from google.cloud.speech_v1 import types
import json
from fastapi import FastAPI
from pydantic import BaseModel
app = FastAPI()

@app.get("/")
def read_main():
    return {"message": "Ok"}
class Response(BaseModel):
    path: str
    ratehertz:int
    encoding:str


#model = whisper.load_model("medium")
#@app.post("/voice2txt/")
#def calculate_api(response: Response):
    #path = response.path
    #t=time.time()
    #result = model.transcribe(path)["text"]
    #return {"message": result,"time":time.time()-t}


@app.post("/voice2txtGoogle/")
def calculate_api_g(response: Response):
    path = response.path
    ratehertz = response.ratehertz
    encoding = response.encoding
    t=time.time()
    result = transcribe_audio(audio_file_path=path,ratehertz=ratehertz,encoding=encoding)
    return {"message": result,"time":time.time()-t}


#import argparse

import os

os.environ['GOOGLE_APPLICATION_CREDENTIALS'] = "token.json"

def transcribe_audio(audio_file_path, language_code='es-US',ratehertz=48000,encoding="OGG"):
    client = speech_v1.SpeechClient()
    if encoding=="OGG":
        confEncoding=types.RecognitionConfig.AudioEncoding.OGG_OPUS
    elif encoding=="WEBM":
        confEncoding=types.RecognitionConfig.AudioEncoding.WEBM_OPUS
    else:
        confEncoding=types.RecognitionConfig.AudioEncoding.OGG_OPUS
    with open(audio_file_path, 'rb') as audio_file:
        content = audio_file.read()
    audio = speech_v1.RecognitionAudio(content=content)
    config = speech_v1.RecognitionConfig(
        encoding=confEncoding,#.FLAC,  # Use OGG encoding
        sample_rate_hertz=ratehertz,
        language_code=language_code,
    )
   
    response = client.recognize(config=config, audio=audio)

    return list(response.results[0].alternatives)[0].transcript
#results.alternatives[0].transcript