Update app.py

app.py

@@ -1,10 +1,14 @@
 import torch
 from transformers import AutoModelForSpeechSeq2Seq, AutoProcessor, pipeline
 import gradio as gr
+from pydub import AudioSegment
+import os
 
-device = "cpu"  # Free CPU only
+# Set device and precision for CPU
+device = "cpu"
 torch_dtype = torch.float32
 
+# Load KB-Whisper model (Large variant)
 model_id = "KBLab/kb-whisper-large"
 
 model = AutoModelForSpeechSeq2Seq.from_pretrained(
@@ -22,14 +26,30 @@ pipe = pipeline(
     torch_dtype=torch_dtype,
 )
 
-def transcribe(audio):
-    result = pipe(audio, chunk_length_s=30, generate_kwargs={"task": "transcribe", "language": "sv"})
-    return result["text"]
+def transcribe(audio_path):
+    # Handle m4a or other formats by converting to wav
+    base, ext = os.path.splitext(audio_path)
+    if ext.lower() != ".wav":
+        try:
+            sound = AudioSegment.from_file(audio_path)
+            audio_converted_path = base + ".converted.wav"
+            sound.export(audio_converted_path, format="wav")
+            audio_path = audio_converted_path
+        except Exception as e:
+            return f"Error converting audio: {str(e)}"
 
+    # Transcribe
+    try:
+        result = pipe(audio_path, chunk_length_s=30, generate_kwargs={"task": "transcribe", "language": "sv"})
+        return result["text"]
+    except Exception as e:
+        return f"Transcription failed: {str(e)}"
+
+# Build Gradio interface
 gr.Interface(
     fn=transcribe,
     inputs=gr.Audio(type="filepath", label="Upload Swedish Audio"),
     outputs=gr.Textbox(label="Transcribed Text"),
     title="KB-Whisper Transcriber (Swedish, Free CPU)",
-    description="Transcribes Swedish audio using KBLab's Whisper Large model. Running on free CPU — may be slow."
+    description="Upload .m4a, .mp3, or .wav files. Transcribes Swedish speech using KBLab's Whisper Large model.",
 ).launch(share=True)