app.py

import os
from transformers import pipeline
import gradio as gr

# Fetch the token from the environment
hf_token = os.getenv("HUGGINGFACE_HUB_TOKEN")
model_id = "akadriu/whisper-medium-sq"  # update with your model id
#model_id ="./"
pipe = pipeline("automatic-speech-recognition", model=model_id, token=hf_token)

def transcribe_speech(filepath):
    # Check if the filepath is valid
    if filepath is None:
        raise ValueError("No audio file provided.")
    
    # Perform speech transcription
    output = pipe(
        filepath,
        max_new_tokens=256,
        generate_kwargs={
            "task": "transcribe",
            "language": "albanian",
        },
        chunk_length_s=30,
        batch_size=8,
    )
    return output["text"]

# Create Gradio interfaces without the 'source' argument
mic_transcribe = gr.Interface(
    fn=transcribe_speech,
    inputs=gr.Audio(type="filepath"),
    outputs="text",
)

file_transcribe = gr.Interface(
    fn=transcribe_speech,
    inputs=gr.Audio(type="filepath"),
    outputs="text",
)

demo = gr.Blocks()

with demo:
    gr.TabbedInterface(
        [mic_transcribe, file_transcribe],
        ["Transcribe Microphone", "Transcribe Audio File"],
    )

demo.launch(debug=True)