Create app.py

app.py

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+import gradio as gr
+from pydub import AudioSegment
+from faster_whisper import WhisperModel
+import os
+
+model = WhisperModel("openai/whisper-large-v3-turbo", compute_type="int8")
+
+def convert_to_wav(file_path):
+    audio = AudioSegment.from_file(file_path)
+    audio = audio.set_frame_rate(16000).set_channels(1).set_sample_width(2)
+    out_path = file_path.replace(".", "_ready.")
+    audio.export(out_path, format="wav")
+    return out_path
+
+def detect_language(audio_file):
+    wav = convert_to_wav(audio_file.name)
+    segments, info = model.transcribe(wav)
+    transcript = "\n".join([seg.text for seg in segments])
+    return f"🌐 Language: {info.language}\n\n📝 Transcript:\n{transcript}"
+
+gr.Interface(fn=detect_language,
+             inputs=gr.Audio(type="filepath"),
+             outputs="text",
+             title="🎧 Audio Language Detector",
+             description="Drop any MP3/WAV/FLAC to identify spoken language & get transcript."
+).launch()