Create app.py

app.py

@@ -0,0 +1,47 @@
+import gradio as gr
+import os
+import json
+from datetime import datetime
+from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq
+import torch
+import soundfile as sf
+
+MODEL_NAME = "mistralai/Voxtral-Small-24B-2507"
+
+processor = AutoProcessor.from_pretrained(MODEL_NAME)
+model = AutoModelForSpeechSeq2Seq.from_pretrained(MODEL_NAME)
+
+def transcribe(audio_file, metadata_file):
+    # Lade Audio
+    audio_input, sample_rate = sf.read(audio_file)
+    inputs = processor(audio_input, sampling_rate=sample_rate, return_tensors="pt")
+    with torch.no_grad():
+        generated_ids = model.generate(**inputs)
+    transcription = processor.batch_decode(generated_ids, skip_special_tokens=True)[0]
+
+    # Lade Metadaten
+    metadata = None
+    if metadata_file is not None:
+        metadata = json.load(metadata_file)
+
+    # Ergebnis
+    result = {
+        "transcription": transcription,
+        "model": MODEL_NAME,
+        "timestamp": datetime.now().isoformat(),
+        "metadata": metadata
+    }
+    return json.dumps(result, ensure_ascii=False, indent=2)
+
+demo = gr.Interface(
+    fn=transcribe,
+    inputs=[
+        gr.Audio(type="filepath", label="Audio/Video-Datei (mp3, wav, mp4, etc.)"),
+        gr.File(label="Passende Metadata JSON-Datei")
+    ],
+    outputs=gr.Textbox(label="Transkriptions-JSON"),
+    title="Voxtral Transkription mit Metadaten"
+)
+
+if __name__ == "__main__":
+    demo.launch()