import os 

from huggingface_hub.hf_api import HfFolder
HfFolder.save_token(os.environ.get("auth_token"))

from huggingface_hub import Repository
import gradio as gr
from faster_whisper import WhisperModel
import numpy as np
from scipy.io.wavfile import write
repo = Repository(local_dir="huggingface-hub", clone_from="https://huggingface.co/nadsoft/faster-hamsa")

file_name = "recording0.wav"
# check if the file exists
if os.path.exists(file_name):
    os.remove(file_name)

transcriber = WhisperModel(repo.local_dir,device="cuda", compute_type="float16")
model = transcriber

def transcribe(stream, new_chunk):
    sr, y = new_chunk
    y = y.astype(np.float32)
    y /= np.max(np.abs(y))

    if stream is not None:
        stream = np.concatenate([stream, y])
    else:
        stream = y

    write("recording0.wav", sr, stream)
    segments, _ = model.transcribe("recording0.wav", language="ar")
    segments = list(segments)  # The transcription will actually run here
    return stream, str(segments[0][2])


demo = gr.Interface(
    transcribe,
    ["state", gr.Audio(sources=["microphone"], streaming=True)],
    ["state", "text"],
    live=True,
)

demo.launch()