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import gradio as gr
from transformers import pipeline
from langdetect import detect
asr = pipeline("automatic-speech-recognition", model="openai/whisper-small")
summarizer = pipeline("summarization", model="facebook/bart-large-cnn")
def process_audio(audio_path):
if not audio_path or isinstance(audio_path, bool):
return "No audio file provided.", "", "", ""
try:
result = asr(audio_path)
transcript = result["text"]
except Exception as e:
return f"Error in transcription: {e}", "", "", ""
try:
detected_lang = detect(transcript)
except Exception:
detected_lang = "unknown"
lang_map = {'en': 'English', 'hi': 'Hindi', 'ta': 'Tamil'}
lang_text = lang_map.get(detected_lang, detected_lang)
transcript_en = transcript
if detected_lang != "en":
try:
result_translate = asr(audio_path, generate_kwargs={"task": "translate"})
transcript_en = result_translate["text"]
except Exception as e:
transcript_en = f"Error translating: {e}"
try:
summary = summarizer(transcript_en, max_length=100, min_length=30, do_sample=False)
summary_text = summary[0]["summary_text"]
except Exception as e:
summary_text = f"Error summarizing: {e}"
return lang_text, transcript, transcript_en, summary_text
iface = gr.Interface(
fn=process_audio,
inputs=gr.Audio(source="upload", type="filepath", label="Upload MP3/WAV Audio"),
outputs=[
gr.Textbox(label="Detected Language"),
gr.Textbox(label="Original Transcript"),
gr.Textbox(label="English Transcript (if translated)"),
gr.Textbox(label="Summary"),
],
title="Audio Transcript, Translation & Summary",
description="Upload your audio file (MP3/WAV). This app transcribes, detects language, translates to English if needed, and summarizes."
)
iface.launch()
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