Create app.py

app.py

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+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"
+
+)