Update app.py

app.py

@@ -1,5 +1,61 @@
+# import gradio as gr
+# import os
+# gr.load("models/vrclc/Whisper-medium-Malayalam", examples = [
+#     [os.path.join(os.path.abspath(''),"./sample1.wav")]    
+# ]).launch()
+
 import gradio as gr
-import os
-gr.load("models/vrclc/Whisper-medium-Malayalam", examples = [
-    [os.path.join(os.path.abspath(''),"./sample1.wav")]    
-]).launch()
+import torch
+import soundfile as sf
+from transformers import pipeline
+
+device = "cuda:0" if torch.cuda.is_available() else "cpu"
+pipe = pipeline(
+    "automatic-speech-recognition",
+    model="vrclc/Whisper-medium-Malayalam",
+    chunk_length_s=10,
+    device=device,
+)
+
+def transcribe(audio):
+    """Transcribes Malayalam speech from an audio file."""
+    try:
+        if audio is None:
+            return "Please record or upload an audio file."
+
+        print(f"[DEBUG] Received audio: {audio}")
+
+        # Handle filepath case from Gradio
+        audio_path = audio if isinstance(audio, str) else audio.get("name", None)
+        if audio_path is None:
+            return "Could not read audio file."
+
+        print(f"[DEBUG] Reading audio file: {audio_path}")
+        audio_data, sample_rate = sf.read(audio_path)
+
+        print(f"[DEBUG] Audio sample rate: {sample_rate}, shape: {audio_data.shape}")
+
+        transcription = pipe(
+            {"array": audio_data, "sampling_rate": sample_rate},
+            chunk_length_s=30,
+            batch_size=8,
+        )["text"]
+
+        print(f"[DEBUG] Transcription: {transcription}")
+        return transcription
+
+    except Exception as e:
+        import traceback
+        print("[ERROR] Exception during transcription:")
+        traceback.print_exc()
+        return f"Error: {str(e)}"
+
+iface = gr.Interface(
+    fn=transcribe,
+    inputs=gr.Audio(sources=["microphone", "upload"], type="filepath"),
+    outputs="text",
+    title="Malayalam Speech Recognition",
+    description="Record or upload Malayalam speech and submit to get the transcribed text.",
+)
+
+iface.launch()