app.py

import pandas as pd
import gradio as gr
print(gr.__version__)
import torch
import torchaudio


df= pd.read_csv('native_words_subset.csv')

torch._C._jit_override_can_fuse_on_cpu(False)
torch._C._jit_override_can_fuse_on_gpu(False)
torch._C._jit_set_texpr_fuser_enabled(False)
torch._C._jit_set_nvfuser_enabled(False)

loader = torch.jit.load("audio_loader.pt")
model = torch.jit.load('QuartzNet_thunderspeech_3.pt').eval()

vocab = model.text_transform.vocab.itos
vocab[-1] = ''

def convert_probs(probs):
  ids = probs.argmax(1)[0]
  s = []
  if vocab[ids[0]]: s.append(vocab[ids[0]])
  for i in range(1,len(ids)):
    if ids[i-1] != ids[i]:
      new = vocab[ids[i]]
      if new: s.append(new)
  #return '.'.join(s)
  return s
 
  
def predict(path):
  audio = loader(path)
  probs = model(audio, torch.tensor(audio.shape[0] * [audio.shape[-1]], device=audio.device))[0]
  return convert_probs(probs)
 
 
from difflib import SequenceMatcher

def similar(a, b):
    return SequenceMatcher(None, a, b).ratio()

def compare(chosen_word, path):
  etalons = [list(val.split('.')) for val in df.loc[df['replica'] == chosen_word, 'transcription'].values]
  user = predict(path)
  coeff = 0.0
  idx=0
  for i in range(len(etalons)):
    new_coeff =  similar(user, etalons[i])
    if new_coeff > coeff:
      coeff = new_coeff
      idx=i
  return f'The similarity coefficient of your pronunciation and the pronunciation of a native speaker is {coeff}. The closer the coefficient is to 1, the better.' + '\nYour pronunciation: [' + ''.join(user) + ']\nClosest native pronunciation: ['  + ''.join(etalons[idx]) + ']'


word_choice = gr.inputs.Dropdown(sorted(list(df['replica'].unique())), label="Choose a word")

gr.Interface(fn=compare, inputs=[word_choice, gr.inputs.Audio(source='microphone', type='filepath', optional=True)], outputs= 'text').launch(debug=True)