app.py

import pandas as pd
import gradio as gr
import torch
import torchaudio
import warnings
from cryptography.utils import CryptographyDeprecationWarning
with warnings.catch_warnings():
    warnings.filterwarnings('ignore', category=CryptographyDeprecationWarning)
    import paramiko


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')

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 (word_choice, path):
  etalon = df.loc[df['replica'] == word_choice, 'transcription'].values[0]
  user = predict(path)
  similar(user, etalon) 


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

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