app.py

import gradio as gr
from deep_translator import GoogleTranslator
#import deepl
from transformers import pipeline
import os
from gtts import gTTS

pipe = pipeline(model="freeja/lab2-whisper-sv")

def transcribe_audio(audio,language):
    transcribed = pipe(audio,"text")
    result = "Transcribed text\n"
    result += transcribed + "\n"
    result += "Translated text\n"
    trans_text = translate_audio(transcribed,language)
    result += trans_text
    text_to_speech(trans_text,language)
    result += text_to_speech
    return result


def translate_audio(text,language):
    #translate = deepl.Translator
    language_dict = {"English":"en","Spanish":"es","German":"de","French":"fr","Italian":"it"}
    lang = language_dict[language]
    translated_text = GoogleTranslator(source='sv', target=lang).translate(text)
    return translated_text

def text_to_speech(text,language):
    language_dict = {"English":"en","Spanish":"es","German":"de","French":"fr","Italian":"it"}
    lang = language_dict[language]
    gTTS(text,lang,slow=False)



face = gr.Interface(
    fn=transcribe_audio,
    inputs=[
        gr.Audio(source="microphone", type="filepath", label="Transcribe from Microphone"),
        gr.Dropdown(["English","Spanish","Dutch","French","Italian"], value="English", label="Translate to ")
    ],
    outputs="text",
    title="Whisper Small Swedish",
    description="Realtime demo for Swedish speech recognition with translation using a fine-tuned Whisper small model"
)

iface.launch()