Update app.py

app.py

@@ -1,3 +1,4 @@
+# Gradio for Multi ASR
 import gradio as gr
 import torch
 import soundfile as sf
@@ -34,6 +35,7 @@ def get_pipeline(language):
     )
     return pipelines[language]
 
+# Transcription code with error debugging
 def transcribe(audio, language):
     """Transcribes speech from an audio file based on selected language."""
     try:
@@ -78,59 +80,3 @@ iface = gr.Interface(
 
 iface.launch()
 
-
-# import gradio as gr
-# 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="leenag/Tamil_ASR",
-#     chunk_length_s=10,
-#     device=device,
-# )
-
-# def transcribe(audio):
-#     """Transcribes Tamil 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=10,
-#             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="Tamil Speech Recognition",
-#     description="Record or upload Tamil speech and submit to get the transcribed text.",
-# )
-
-# iface.launch()