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Runtime error
| 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) |