Create app.py

app.py

@@ -0,0 +1,35 @@
+import gradio as gr
+from transformers import pipeline
+from pydub import AudioSegment
+import os
+
+# Load Whisper pipeline
+asr=pipeline("audio-classification", model="firdhokk/speech-emotion-recognition-with-openai-whisper-large-v3")
+
+def convert_audio_to_wav(audio_path):
+    """Convert audio to WAV format"""
+    audio = AudioSegment.from_file(audio_path)
+    wav_path = audio_path + ".wav"
+    audio.export(wav_path, format="wav")
+    return wav_path
+
+def transcribe(audio_path):
+    wav_path = convert_audio_to_wav(audio_path)
+    result = asr(wav_path)
+    os.remove(wav_path)
+    return result[0]
+
+# Gradio interface (DO NOT use share=True)
+demo = gr.Interface(
+    fn=transcribe,
+    inputs=gr.Audio(type="filepath", label="Upload Audio (.m4a, .mp3, .wav...)"),
+    outputs=gr.Textbox(label="Transcription"),
+    title="Whisper Speech emotion Recognition",
+    description="Transcribes most audio formats using Whisper."
+)
+
+# Just launch it — no share=True!
+demo.launch()
+
+
+