Update app.py

app.py

@@ -1,55 +1,21 @@
-import torch
-from transformers import AutoModelForSpeechSeq2Seq, AutoProcessor, pipeline
-import gradio as gr
 from pydub import AudioSegment
-import os
-
-# 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(
-    model_id, torch_dtype=torch_dtype
-).to(device)
-
-processor = AutoProcessor.from_pretrained(model_id)
-
-pipe = pipeline(
-    "automatic-speech-recognition",
-    model=model,
-    tokenizer=processor.tokenizer,
-    feature_extractor=processor.feature_extractor,
-    device=device,
-    torch_dtype=torch_dtype,
-)
+import mimetypes
 
 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:
+        # Detect file type using MIME or extension
+        mime_type, _ = mimetypes.guess_type(audio_path)
+        ext = os.path.splitext(audio_path)[1].lower()
+
+        if mime_type == "audio/mp4" or ext == ".m4a":
+            print("Converting .m4a to .wav...")
+            sound = AudioSegment.from_file(audio_path, format="m4a")
+            converted_path = audio_path.replace(".m4a", ".converted.wav")
+            sound.export(converted_path, format="wav")
+            audio_path = converted_path
+
         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="Upload .m4a, .mp3, or .wav files. Transcribes Swedish speech using KBLab's Whisper Large model.",
-).launch()
+        return f"Error during transcription: {str(e)}"