Create app.py

app.py

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+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()