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app.py

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+import gradio as gr
+from transformers import pipeline
+
+# Load the Whisper model
+model_name = "AventIQ-AI/whisper_small_Automatic_speech_recognition"
+asr_pipeline = pipeline("automatic-speech-recognition", model=model_name)
+
+def transcribe_audio(audio):
+    if audio is None:
+        return "⚠️ Please upload or record an audio file."
+    
+    transcript = asr_pipeline(audio)["text"]
+    return transcript if transcript else "⚠️ No speech detected."
+
+# Create Gradio UI
+with gr.Blocks() as demo:
+    gr.Markdown("## 🎤 Whisper Small - Speech to Text")
+    gr.Markdown("Upload an audio file or record your voice to get a transcript.")
+
+    audio_input = gr.Audio(type="filepath", interactive=True, label="🎙️ Upload or Record Audio")
+    transcribe_button = gr.Button("🔍 Transcribe")
+    output_text = gr.Textbox(label="📝 Transcription Output")
+
+    transcribe_button.click(transcribe_audio, inputs=audio_input, outputs=output_text)
+
+# Launch the app
+demo.launch()

requirements.txt

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+torch
+transformers
+gradio
+sentencepiece
+torchvision
+huggingface_hub
+pillow
+numpy