Update app.py

app.py

@@ -1,24 +1,15 @@
 import gradio as gr
-from transformers import pipeline
+from faster_whisper import WhisperModel
 
-# Specify the correct model name and adjust the loading configuration
-model_name = "Subhaka/whisper-small-Sinhala-Fine_Tune"
-
-# Define the pipeline with `use_safetensors=True`
-def load_pipeline():
-    return pipeline(
-        "automatic-speech-recognition",
-        model=model_name,
-        use_safetensors=True,  # Ensure compatibility with safetensors
-    )
-
-# Load the model pipeline
-transcriber = load_pipeline()
+# Load the Faster Whisper model
+model_name = "Systran/faster-whisper-large-v3"
+model = WhisperModel(model_name, device="cpu")  # Use "cuda" if you have a GPU
 
 # Define a transcription function
 def transcribe_audio(audio_file):
     try:
-        transcription = transcriber(audio_file)["text"]
+        segments, info = model.transcribe(audio_file, beam_size=5)  # Customize parameters as needed
+        transcription = " ".join([segment.text for segment in segments])
         return transcription
     except Exception as e:
         return f"Error: {str(e)}"
@@ -29,7 +20,7 @@ interface = gr.Interface(
     inputs=gr.Audio(source="upload", type="filepath", label="Upload Audio"),
     outputs=gr.Textbox(label="Transcription"),
     title="Sinhala Audio-to-Text Transcription",
-    description="Upload an audio file and get the transcription in Sinhala using the Whisper model fine-tuned by Subhaka.",
+    description="Upload an audio file and get the transcription in Sinhala using the Faster Whisper model.",
     allow_flagging="never"
 )