Upload 2 files

app.py

@@ -0,0 +1,43 @@
+import gradio as gr
+from transformers import pipeline
+
+# Load Whisper model
+model_name = "AventIQ-AI/whisper-speech-text"
+stt_pipeline = pipeline("automatic-speech-recognition", model=model_name)
+
+def transcribe(audio_path):
+    """Transcribe speech to text using Whisper."""
+    if audio_path is None:
+        return "⚠️ Please upload an audio file."
+    
+    try:
+        # Pass the file path directly to the Whisper pipeline
+        result = stt_pipeline(audio_path)
+
+        return f"📝 **Transcription:**\n{result['text']}"
+    
+    except Exception as e:
+        return f"❌ Error processing audio: {str(e)}"
+
+# Create Enhanced Gradio Interface
+with gr.Blocks(theme="default") as demo:
+    gr.Markdown(
+        """
+        # 🎤 **Whisper Speech-to-Text**
+        **Upload an audio file** and this tool will convert your speech into text using **AventIQ-AI Whisper Model**.  
+        Supports **MP3, WAV, FLAC** formats.
+        """
+    )
+
+    with gr.Row():
+        audio_input = gr.File(label="🎙️ Upload an Audio File", type="filepath")  # Corrected `type`
+    
+    transcribed_text = gr.Textbox(label="📝 Transcription", interactive=False)
+
+    submit_btn = gr.Button("🎧 Transcribe", variant="primary")
+
+    submit_btn.click(transcribe, inputs=audio_input, outputs=transcribed_text)
+
+# Launch the app
+if __name__ == "__main__":
+    demo.launch()

requirements.txt

@@ -0,0 +1,4 @@
+transformers
+torch
+gradio
+sentencepiece