import time
import torch
import string
from espnet_model_zoo.downloader import ModelDownloader
from espnet2.bin.asr_inference import Speech2Text


import soundfile
import librosa.display
import matplotlib.pyplot as plt
import gradio as gr


d = ModelDownloader()
speech2text = Speech2Text(
    **d.download_and_unpack(tag),
    device="cpu",
    minlenratio=0.0,
    maxlenratio=0.0,
    ctc_weight=0.3,
    beam_size=10,
    batch_size=0,
    nbest=1
)

def text_normalizer(text):
    text = text.upper()
    return text.translate(str.maketrans('', '', string.punctuation))
    
lang = 'multilingual'
fs = 16000 
tag = 'ftshijt/open_li52_asr_train_asr_raw_bpe7000_valid.acc.ave_10best' 


def inference(audio):
  speech, rate = soundfile.read(audio.name)
  assert rate == fs, "mismatch in sampling rate"
  nbests = speech2text(speech)
  text, *_ = nbests[0]

  print(f"Input Speech: {file_name}")
  display(Audio(speech, rate=rate))
  librosa.display.waveplot(speech, sr=rate)
  plt.show()
  print(f"ASR hypothesis: {text_normalizer(text)}")
  print("*" * 50)