app.py

import gradio as gr
import torch
from transformers import pipeline
import spaces


# Initialize the speech-to-text pipeline
pipe = pipeline(
    "automatic-speech-recognition",
    model="ivrit-ai/whisper-large-v3",
    torch_dtype=torch.float16,
    device="cuda",
)

@spaces.GPU
def transcribe(audio):
    """
    Transcribes the given audio file to Hebrew text.
    """
    if audio is None:
        return "Please upload or record an audio file."

    # Transcribe the audio file to Hebrew
    result = pipe(audio, generate_kwargs={"language": "hebrew"})
    return result['text']

# Create the Gradio interface
demo = gr.Interface(
    fn=transcribe,
    inputs=gr.Audio(sources=["microphone", "upload"], type="filepath", label="Hebrew Audio"),
    outputs="text",
    title="Hebrew Speech-to-Text Transcription",
    description=(
        "Use this app to transcribe Hebrew speech to text. "
        "Upload your own audio file or record audio directly through your microphone. "
        "The model used is `ivrit-ai/whisper-large-v3`."
    ),
    allow_flagging="never"
)

# Launch the app
demo.launch()