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import gradio as gr
import os
from transformers import pipeline
from langdetect import detect
def process_audio(audio_file):
try:
# audio_file is a tuple (file_obj, file_path)
audio_path = audio_file if isinstance(audio_file, str) else audio_file.name
# Transcribe
asr = pipeline("automatic-speech-recognition", model="openai/whisper-large")
result = asr(audio_path)
transcript = result["text"]
except Exception as e:
return "Error in transcription: " + str(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:
asr_translate = pipeline(
"automatic-speech-recognition",
model="openai/whisper-large",
task="translate"
)
result_translate = asr_translate(audio_path)
transcript_en = result_translate["text"]
except Exception as e:
transcript_en = f"Error translating: {e}"
try:
summarizer = pipeline("summarization", model="facebook/bart-large-cnn")
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}"
# Optionally, remove uploaded file if it's saved on disk
return lang_text, transcript, transcript_en, summary_text
with gr.Blocks() as demo:
gr.Markdown("## Audio Transcript, Translation & Summary (Powered by Whisper + Hugging Face)")
audio_input = gr.Audio(source="upload", type="filepath", label="Upload MP3/WAV Audio")
btn = gr
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