Delete app.py

app.py

@@ -1,61 +0,0 @@
-from transformers import pipeline
-import os
-import gradio as gr
-import torch
-from IPython.display import Audio as IPythonAudio
-from gtts import gTTS
-import IPython.display as ipd
-
-#Audio to text
-asr = pipeline(task="automatic-speech-recognition",
-               model="distil-whisper/distil-small.en")
-#Text to text
-translator = pipeline(task="translation",
-                      model="facebook/nllb-200-distilled-600M",
-                      torch_dtype=torch.bfloat16) 
-#Text to audio
-pipe = pipeline("text-to-speech", model="suno/bark-small",
-                      torch_dtype=torch.bfloat16)
-
-                
-demo = gr.Blocks()
-def transcribe_speech(filepath):
-    if filepath is None:
-        gr.Warning("No audio found, please retry.")
-        return ""
-    output = translator(asr(filepath)["text"],
-                             src_lang="eng_Latn",
-                             tgt_lang="hin_Deva")
-    narrated_text=pipe(output[0]['translation_text'])
-    #tts = gTTS(text=narrated_text, lang='hi', slow=False)
-    #tts.save("translated_audio.mp3")
-
-    #return ipd.Audio("translated_audio.mp3", autoplay=True)
-    return narrated_text
-    
-mic_transcribe = gr.Interface(
-    fn=transcribe_speech,
-    inputs=gr.Audio(sources="microphone",
-                    type="filepath"),
-    #outputs="audio",
-    outputs=gr.Audio(label="Translated Message"),
-    allow_flagging="never")
-
-file_transcribe = gr.Interface(
-    fn=transcribe_speech,
-    inputs=gr.Audio(sources="upload",
-                    type="filepath"),
-    #outputs="audio",
-    outputs=gr.Audio(label="Translated Message"),
-    allow_flagging="never"
-)
-with demo:
-    gr.TabbedInterface(
-        [mic_transcribe,
-         file_transcribe],
-        ["Transcribe Microphone",
-         "Transcribe Audio File"],
-    )
-
-demo.launch(share=True)
-demo.close()