from flask import Flask, request, jsonify
from whisper_online import *
import librosa
import gradio as gr

@lru_cache
def load_audio(fname):
    a, _ = librosa.load(fname, sr=16000, dtype=np.float32)
    return a


asr = FasterWhisperASR("en", "large-v2")  # loads and wraps Whisper model
online = OnlineASRProcessor(asr)  # create processing object with default buffer trimming option

def transcribe(audio_file)
    try:
        audio_data = load_audio(audio_file)
        
        # Insert audio chunk into the ASR processor
        online.insert_audio_chunk(audio_data)
        
        # Process the audio chunk
        o = online.process_iter()
        
        # Send current partial output
        return o
        
    except Exception as e:
        return str(e)

def finish_stream():
    try:
        # Finish audio processing
        o = online.finish()
        
        # Send last output
        return o
        
    except Exception as e:
        return str(e)

iface = gr.Interface(fn=transcribe,inputs = "text", outputs = "text")
iface.launch()