app.py

import gradio as gr
from transformers import pipeline

# 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()

# Define a transcription function
def transcribe_audio(audio_file):
    try:
        transcription = transcriber(audio_file)["text"]
        return transcription
    except Exception as e:
        return f"Error: {str(e)}"

# Create Gradio interface
interface = gr.Interface(
    fn=transcribe_audio,
    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.",
    allow_flagging="never"
)

# Launch the Gradio app
if __name__ == "__main__":
    interface.launch(server_name="0.0.0.0", server_port=7860, share=True)