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| import streamlit as st | |
| import torch | |
| from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor | |
| from speechbrain.pretrained import GraphemeToPhoneme | |
| import os | |
| import torchaudio | |
| from wav2vecasr.MispronounciationDetector import MispronounciationDetector | |
| def load_model(): | |
| device = "cuda" if torch.cuda.is_available() else "cpu" | |
| path = os.path.join(os.getcwd(), "wav2vecasr", "model", "checkpoint-1200") | |
| model = Wav2Vec2ForCTC.from_pretrained(path).to(device) | |
| processor = Wav2Vec2Processor.from_pretrained(path) | |
| g2p = GraphemeToPhoneme.from_hparams("speechbrain/soundchoice-g2p") | |
| mispronounciation_detector = MispronounciationDetector(model, processor, g2p, "cpu") | |
| return mispronounciation_detector | |
| def save_file(sound_file): | |
| # save your sound file in the right folder by following the path | |
| with open(os.path.join(os.getcwd(), 'audio_files', sound_file.name), 'wb') as f: | |
| f.write(sound_file.getbuffer()) | |
| return sound_file.name | |
| def get_audio(saved_sound_filename): | |
| audio_path = f'audio_files/{saved_sound_filename}' | |
| audio, org_sr = torchaudio.load(audio_path) | |
| audio = torchaudio.functional.resample(audio, orig_freq=org_sr, new_freq=16000) | |
| audio = audio.view(audio.shape[1]) | |
| return audio | |
| def mispronounciation_detection_section(): | |
| st.write('# Prediction') | |
| st.write('1. Upload a recording of you saying the text in .wav format') | |
| uploaded_file = st.file_uploader(' ', type='wav') | |
| st.write('2. Input the text you are saying in your recording') | |
| text = st.text_input( | |
| "Enter the text you want to read 👇", | |
| label_visibility='collapsed' | |
| ) | |
| if st.button('Predict'): | |
| if uploaded_file is not None and len(text) > 0: | |
| # get audio from loaded file | |
| save_file(uploaded_file) | |
| audio = get_audio(uploaded_file.name) | |
| # load model | |
| mispronunciation_detector = load_model() | |
| # start prediction | |
| st.write('# Detection Results') | |
| with st.spinner('Predicting...'): | |
| raw_info = mispronunciation_detector.detect(audio, text) | |
| st.write('#### Phoneme Level Analysis') | |
| st.markdown(f"Phoneme Error Rate: ___{round(raw_info['per'],2)}___") | |
| # enable horizontal scrolling for phoneme output | |
| #st.text_area(label="Aligned phoneme outputs", value=raw_info['phoneme_output'],height=150) | |
| st.markdown( | |
| f""" | |
| <style> | |
| textarea {{ | |
| white-space: nowrap; | |
| }} | |
| </style> | |
| ``` | |
| {" ".join(raw_info['ref'])} | |
| {" ".join(raw_info['hyp'])} | |
| {" ".join(raw_info['phoneme_errors'])} | |
| ``` | |
| """, | |
| unsafe_allow_html=True, | |
| ) | |
| st.divider() | |
| md = [] | |
| for word, has_error in zip(raw_info["words"], raw_info["word_errors"]): | |
| if has_error: | |
| md.append(f"**{word}**") | |
| else: | |
| md.append(word) | |
| st.write('#### Word Level Analysis') | |
| st.write(f"Word Error Rate: ___{round(raw_info['wer'], 2)}___ and the following words in bold have errors:") | |
| st.markdown(" ".join(md)) | |
| else: | |
| st.error('The audio or text has not been properly input', icon="🚨") | |
| return | |
| if __name__ == '__main__': | |
| st.write('___') | |
| # create a sidebar | |
| st.sidebar.title('Pronounciation Evaluation') | |
| select = st.sidebar.selectbox('', ['Main Page', 'Mispronounciation Detection'], key='1', label_visibility='collapsed') | |
| st.sidebar.write(select) | |
| if select=='Mispronounciation Detection': | |
| mispronounciation_detection_section() | |
| # else: stay on the home page | |
| else: | |
| st.write('# Pronounciation Evaluation') | |
| st.write('This app is designed to detect mispronounciation of English words for English learners from Asian countries like Korean, Mandarin and Vietnameses.') | |
| st.write('Wav2Vec2.0 was used to detect the phonemes from the learner and this output is compared with the correct phoneme sequence generated from input text') | |