from transformers import pipeline
import gradio as gr
import time

p = pipeline("automatic-speech-recognition",model="jonatasgrosman/wav2vec2-large-xlsr-53-spanish")
pc = pipeline("automatic-speech-recognition",model="softcatala/wav2vec2-large-xlsr-catala")
pe = pipeline("automatic-speech-recognition",model="jonatasgrosman/wav2vec2-large-xlsr-53-english")
pj = pipeline("automatic-speech-recognition",model="jonatasgrosman/wav2vec2-large-xlsr-53-japanese")
pf = pipeline("automatic-speech-recognition",model="jonatasgrosman/wav2vec2-large-xlsr-53-french")




def transcribe(language,audio, state=""):#language="Spanish",
    time.sleep(1)
    if language=="Spanish":
        state=""
        text = p(audio)["text"]
    if language=="Catalan":
        state=""
        text = pc(audio)["text"]
    if language=="English":
        state=""
        text = pe(audio)["text"]
    if language=="French":
        state=""
        text = pf(audio)["text"]
    if language=="Japanese":
        state=""
        text = pj(audio)["text"]
    state += text + " "
    #text2="Esto es loq ue te he entendido"
    return state, state

demo=gr.Interface(
    fn=transcribe, 
    
    title="TEDCAS Offline Speech recognition",
    description="1)Select language 2)Click on 'record from microphone' and talk 3)Click on 'stop recording' 4)Click on submit 5)Before starting again, click on 'clear'",

    inputs=[
        gr.Dropdown(["Spanish","Catalan","English", "French", "Japanese"],value="Spanish"),
        #gr.Audio(source="microphone", type="filepath", streaming=True), 
        gr.inputs.Audio(source="microphone", type="filepath"), 
        "state"#,"language"
    ],
    outputs=[
        "textbox",
        "state"
    ],
    #live=True).launch()
)
demo.launch()
#demo.launch(auth=("TedCas", "Kike1234"))