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| import csv | |
| import string | |
| import json | |
| import gensim.downloader as api | |
| import matplotlib.pyplot as plt | |
| import nltk | |
| import numpy as np | |
| import pandas as pd | |
| import gradio as gr | |
| import readability | |
| import seaborn as sns | |
| import torch | |
| from fuzzywuzzy import fuzz | |
| from nltk.corpus import stopwords | |
| from nltk.corpus import wordnet as wn | |
| from nltk.tokenize import word_tokenize | |
| from sklearn.metrics.pairwise import cosine_similarity | |
| from transformers import DistilBertTokenizer | |
| from transformers import pipeline | |
| nltk.download('wordnet') | |
| nltk.download('omw-1.4') | |
| nltk.download('cmudict') | |
| nltk.download('stopwords') | |
| nltk.download('punkt') | |
| glove_vectors = api.load('glove-wiki-gigaword-100') | |
| tokenizer = DistilBertTokenizer.from_pretrained('distilbert-base-uncased') | |
| device = torch.device('cuda' if torch.cuda.is_available else 'cpu') | |
| # loading model | |
| PATH = 'pytorchBERTmodel' | |
| model = torch.load(PATH, map_location=torch.device('cpu')) | |
| model.eval() | |
| model.to('cpu') | |
| p = pipeline("automatic-speech-recognition") | |
| def wn_syns(word): | |
| synonyms = [] | |
| for syn in wn.synsets(word): | |
| for lm in syn.lemmas(): | |
| synonyms.append(lm.name()) | |
| return set(synonyms) | |
| w2v = dict({}) | |
| for idx, key in enumerate(glove_vectors.key_to_index.keys()): | |
| w2v[key] = glove_vectors.get_vector(key) | |
| def calculate_diversity(text): | |
| stop_words = set(stopwords.words('english')) | |
| for i in string.punctuation: | |
| stop_words.add(i) | |
| tokenized_text = word_tokenize(text) | |
| tokenized_text = list(map(lambda word: word.lower(), tokenized_text)) | |
| global sim_words | |
| sim_words = {} | |
| if len(tokenized_text) <= 1: | |
| return 1, "More Text Required" | |
| for idx, anc in enumerate(tokenized_text): | |
| if anc in stop_words or not anc in w2v or anc.isdigit(): | |
| sim_words[idx] = '@' | |
| continue | |
| vocab = [anc] | |
| for pos, comp in enumerate(tokenized_text): | |
| if pos == idx: | |
| continue | |
| if comp in stop_words: | |
| continue | |
| if not comp.isalpha(): | |
| continue | |
| try: | |
| if cosine_similarity(w2v[anc].reshape(1, -1), w2v[comp].reshape(1, -1)) > .75 or comp in wn_syns(anc): | |
| vocab.append(comp) | |
| except KeyError: | |
| continue | |
| sim_words[idx] = vocab | |
| print(sim_words) | |
| scores = {} | |
| for key, value in sim_words.items(): | |
| if len(value) == 1: | |
| scores[key] = -1 | |
| continue | |
| t_sim = len(value) | |
| t_rep = (len(value)) - (len(set(value))) | |
| score = (t_sim - t_rep) / t_sim | |
| scores[key] = score | |
| mean_score = 0 | |
| total = 0 | |
| for value in scores.values(): | |
| if value == -1: | |
| continue | |
| mean_score += value | |
| total += 1 | |
| words = word_tokenize(text) | |
| interpret_values = [('', 0.0)] | |
| for key, value in scores.items(): | |
| interpret_values.append((words[key], value)) | |
| interpret_values.append(('', 0.0)) | |
| print(interpret_values) | |
| int_vals = {'original': text, 'interpretation': interpret_values} | |
| try: | |
| return int_vals, {"Diversity Score": mean_score / total} | |
| except ZeroDivisionError: | |
| return int_vals, {"Dviersity Score": "Not Enough Data"} | |
| def get_sim_words(text, word): | |
| word = word.strip() | |
| index = 0 | |
| text = word_tokenize(text) | |
| print(sim_words) | |
| for idx, i in enumerate(text): | |
| if word == i: | |
| index = idx | |
| break | |
| return ', '.join(sim_words[index]) | |
| def dict_to_list(dictionary, max_size=10): | |
| outer_list = [] | |
| inner_list = [] | |
| for key, value in dictionary.items(): | |
| inner_list.append(value) | |
| if len(inner_list) == max_size: | |
| outer_list.append(inner_list) | |
| inner_list = [] | |
| if len(inner_list) > 0: | |
| outer_list.append(inner_list) | |
| return outer_list | |
| def heatmap(scores, df): | |
| total = 0 | |
| loops = 0 | |
| for ratio in scores.values(): | |
| # conditional to visualize the difference between no ratio and a 0 ratio score | |
| if ratio != -.3: | |
| total += ratio | |
| loops += 1 | |
| diversity_average = total / loops | |
| return sns.heatmap(df, cmap='gist_gray_r', vmin=-.3).set( | |
| title='Word Diversity Score Heatmap (Average Score: ' + str(diversity_average) + ')') | |
| def stats(text): | |
| results = readability.getmeasures(text, lang='en') | |
| return results | |
| def predict(text, tokenizer=tokenizer): | |
| model.eval() | |
| model.to('cpu') | |
| def prepare_data(text, tokenizer): | |
| input_ids = [] | |
| attention_masks = [] | |
| encoded_text = tokenizer.encode_plus( | |
| text, | |
| truncation=True, | |
| add_special_tokens=True, | |
| max_length=315, | |
| pad_to_max_length=True, | |
| return_attention_mask=True, | |
| return_tensors='pt' | |
| ) | |
| input_ids.append(encoded_text['input_ids']) | |
| attention_masks.append(encoded_text['attention_mask']) | |
| input_ids = torch.cat(input_ids, dim=0) | |
| attention_masks = torch.cat(attention_masks, dim=0) | |
| return {'input_ids': input_ids, 'attention_masks': attention_masks} | |
| tokenized_example_text = prepare_data(text, tokenizer) | |
| with torch.no_grad(): | |
| result = model( | |
| tokenized_example_text['input_ids'].to('cpu'), | |
| attention_mask=tokenized_example_text['attention_masks'].to('cpu'), | |
| return_dict=True | |
| ).logits | |
| return result | |
| def level(score): | |
| if score <= 3: | |
| return "n Elementary School" | |
| elif 3 <= score <= 6: | |
| return " Middle School" | |
| elif 6 <= score <= 8: | |
| return " High School" | |
| else: | |
| return " College" | |
| def reading_difficulty(excerpt): | |
| if len(excerpt) == 0: | |
| return "No Text Provided" | |
| windows = [] | |
| words = tokenizer.tokenize(excerpt) | |
| if len(words) > 301: | |
| for idx, text in enumerate(words): | |
| if idx % 300 == 0: | |
| if idx <= len(words) - 301: | |
| x = ' '.join(words[idx: idx + 299]) | |
| windows.append(x) | |
| win_preds = [] | |
| for text in windows: | |
| win_preds.append(predict(text, tokenizer).item()) | |
| result = np.mean(win_preds) | |
| score = -(result * 1.786 + 6.4) + 10 | |
| return "Difficulty Level: " + str(round(score, 2)) + '/10' + ' | A' + str( | |
| level(score)) + " student could understand this" | |
| else: | |
| result = predict(excerpt).item() | |
| score = -(result * 1.786 + 6.4) + 10 | |
| return 'Difficulty Level: ' + str(round(score, 2)) + '/10' + ' | A' + str( | |
| level(score)) + " student could understand this" | |
| def calculate_stats(file_name, data_index): | |
| # unicode escape only for essays | |
| with open(file_name, encoding='unicode_escape') as f: | |
| information = {'lines': 0, 'words_per_sentence': 0, 'words': 0, 'syll_per_word': 0, 'characters_per_word': 0, | |
| 'reading_difficulty': 0} | |
| reader = csv.reader(f) | |
| for line in reader: | |
| if len(line[data_index]) < 100: | |
| continue | |
| # if detect(line[data_index][len(line[data_index]) -400: len(line[data_index])-1]) == 'en': | |
| try: | |
| stat = stats(line[data_index]) | |
| except ValueError: | |
| continue | |
| information['lines'] += 1 | |
| information['words_per_sentence'] += stat['sentence info']['words_per_sentence'] | |
| information['words'] += stat['sentence info']['words'] | |
| information['syll_per_word'] += stat['sentence info']['syll_per_word'] | |
| information['characters_per_word'] += stat['sentence info']['characters_per_word'] | |
| information['reading_difficulty'] += reading_difficulty(line[data_index]) | |
| for i in information: | |
| if i != 'lines' and i != 'words': | |
| information[i] /= information['lines'] | |
| return information | |
| def transcribe(audio): | |
| # speech to text using pipeline | |
| text = p(audio)["text"] | |
| return text | |
| def compute_score(target, actual): | |
| print(target) | |
| target = target.lower() | |
| actual = actual.lower() | |
| return fuzz.ratio(target, actual) | |
| def phon(text): | |
| alph = nltk.corpus.cmudict.dict() | |
| text = word_tokenize(text) | |
| pronun = [] | |
| for word in text: | |
| try: | |
| pronun.append(alph[word][0]) | |
| except Exception as e: | |
| pronun.append(word) | |
| def remove_digits(lists): | |
| for lst in lists: | |
| for idx, word in enumerate(lst): | |
| lst[idx] = ''.join([letter for letter in word if not letter.isdigit()]) | |
| return lists | |
| output = [] | |
| for i in remove_digits(pronun): | |
| output.append('-'.join(i).lower()) | |
| return ' '.join(output) | |
| def plot(): | |
| diversity = calculate_diversity(text)[0] | |
| print(diversity) | |
| df = pd.DataFrame(dict_to_list(diversity)) | |
| return heatmap(diversity, df) | |
| def sliding_window(text): | |
| words = word_tokenize(text) | |
| improved_window = [] | |
| improved_wind_preds = [] | |
| for idx, text in enumerate(words): | |
| if idx <= len(words) - 26: | |
| x = ' '.join(words[idx: idx + 25]) | |
| throw_away = [] | |
| score = 0 | |
| for idx, i in enumerate(range(idx, idx + 25)): | |
| if idx == 0: | |
| better_prediction = -(predict(x).item() * 1.786 + 6.4) + 10 | |
| score = better_prediction | |
| throw_away.append((better_prediction, i)) | |
| else: | |
| throw_away.append((score, i)) | |
| improved_window.append(throw_away) | |
| average_scores = {k: 0 for k in range(len(words) - 1)} | |
| total_windows = {k: 0 for k in range(len(words) - 1)} | |
| for idx, i in enumerate(improved_window): | |
| for score, idx in i: | |
| average_scores[idx] += score | |
| total_windows[idx] += 1 | |
| for k, v in total_windows.items(): | |
| if v != 0: | |
| average_scores[k] /= v | |
| inter_scores = [v for v in average_scores.values()] | |
| copy_list = inter_scores.copy() | |
| print(inter_scores) | |
| while len(inter_scores) <= len(words) - 1: | |
| inter_scores.append(copy_list[-1]) | |
| x = list(range(len(inter_scores))) | |
| y = inter_scores | |
| fig, ax = plt.subplots() | |
| ax.plot(x, y, color='orange', linewidth=2) | |
| ax.grid(False) | |
| plt.xlabel('Word Number', fontweight='bold') | |
| plt.ylabel('Difficulty Score', fontweight='bold') | |
| plt.suptitle('Difficulty Score Across Text', fontsize=14, fontweight='bold') | |
| plt.style.use('ggplot') | |
| ax.set_facecolor('w') | |
| fig = plt.gcf() | |
| mapd = [('', 0)] | |
| maxy = max(inter_scores) | |
| miny = min(inter_scores) | |
| spread = maxy - miny | |
| for idx, i in enumerate(words): | |
| mapd.append((i, (inter_scores[idx] - miny) / spread)) | |
| mapd.append(('', 0)) | |
| return fig, {'original': text, 'interpretation': mapd} | |
| def speech_to_text(speech, target): | |
| text = p(speech)["text"] | |
| return text.lower(), {'Pronunciation Score': compute_score(text, target) / 100}, phon(target) | |
| def speech_to_score(speech): | |
| text = p(speech)["text"] | |
| return reading_difficulty(text), text | |
| def my_i_func(text): | |
| return {"original": "", "interpretation": [('', 0.0), ('what', -0.2), ('great', 0.3), ('day', 0.5), ('', 0.0)]} | |
| def gen_syns(word, level): | |
| with open('balanced_synonym_data.json') as f: | |
| word = word.strip(" ") | |
| data = json.loads(f.read()) | |
| school_to_level = {"Elementary Level":'1', "Middle School Level":'2', "High School Level":'3', "College Level":'4'} | |
| pins = wn_syns(word) | |
| reko = [] | |
| for i in pins: | |
| if i in data[school_to_level[level]]: | |
| reko.append(i) | |
| str_reko = "" | |
| for idx, i in enumerate(reko): | |
| if idx != len(reko) -1: | |
| str_reko+= i + ' | ' | |
| else: | |
| str_reko+= i | |
| return str_reko | |
| with gr.Blocks(title="Automatic Literacy and Speech Assesmen") as demo: | |
| gr.HTML("""<center><h7 style="font-size: 35px">Automatic Literacy and Speech Assesment</h7></center>""") | |
| with gr.Column(): | |
| with gr.Row(): | |
| with gr.Box(): | |
| with gr.Column(): | |
| with gr.Group(): | |
| with gr.Tabs(): | |
| with gr.TabItem("Text"): | |
| in_text = gr.Textbox(label="Input Text Or Speech For Analysis") | |
| grade = gr.Button("Grade Your Text") | |
| with gr.TabItem("Speech"): | |
| audio_file = gr.Audio(source="microphone",type="filepath") | |
| grade1 = gr.Button("Grade Your Speech") | |
| with gr.Group(): | |
| gr.Markdown("Reading Level Based Synonyms | Enter only one word at a time") | |
| words = gr.Textbox(label="Word For Synonyms") | |
| lvl = gr.Dropdown(choices=["Elementary Level", "Middle School Level", "High School Level", "College Level" ], label="Intended Reading Level For Synonym") | |
| get_syns = gr.Button("Get Synonyms") | |
| reccos = gr.Label() | |
| with gr.Box(): | |
| diff_output = gr.Label(label='Difficulty Level',show_label=True) | |
| gr.Markdown("Difficulty Score Across Text") | |
| plotter = gr.Plot() | |
| with gr.Row(): | |
| with gr.Box(): | |
| div_output = gr.Label(label='Diversity Score', show_label=False) | |
| gr.Markdown("Diversity Heatmap | Blue cells are omitted from score. | Darker = More Diverse") | |
| interpretation = gr.components.Interpretation(in_text, label="Diversity Heatmap") | |
| gr.Markdown("Find Similar Words | Word must be part of analysis text box | Enter only one word at a time") | |
| words1 = gr.Textbox(label="Word For Similarity") | |
| find_sim = gr.Button("Find Similar Words") | |
| sims = gr.Label() | |
| with gr.Box(): | |
| gr.Markdown("Relative Difficulty Heatmap- How confusing the text is in that area") | |
| interpretation2 = gr.components.Interpretation(in_text, label="Difficulty Heatmap") | |
| with gr.Row(): | |
| with gr.Box(): | |
| with gr.Group(): | |
| target = gr.Textbox(label="Target Text") | |
| with gr.Group(): | |
| audio_file1 = gr.Audio(source="microphone",type="filepath") | |
| b1 = gr.Button("Grade Your Pronunciation") | |
| with gr.Box(): | |
| some_val = gr.Label() | |
| text = gr.Textbox() | |
| phones = gr.Textbox() | |
| gr.Markdown("""**Reading Difficulty**- Automatically determining how difficult something is to read is a difficult task as underlying | |
| semantics are relevant. To efficiently compute text difficulty, a Distil-Bert pre-trained model is fine-tuned for regression | |
| using The CommonLit Ease of Readability (CLEAR) Corpus. This model scores the text on how difficult it would be for a student | |
| to understand. | |
| """) | |
| gr.Markdown("""**Lexical Diversity**- The lexical diversity score is computed by taking the ratio of unique similar words to total similar words | |
| . The similarity is computed as if the cosine similarity of the word2vec embeddings is greater than .75. It is bad writing/speech | |
| practice to repeat the same words when it's possible not to. Vocabulary diversity is generally computed by taking the ratio of unique | |
| strings/ total strings. This does not give an indication if the person has a large vocabulary or if the topic does not require a diverse | |
| vocabulary to express it. This algorithm only scores the text based on how many times a unique word was chosen for a semantic idea, e.g., | |
| "Forest" and "Woods" are 2 words to represent one semantic idea, so this would receive a 100% lexical diversity score, vs using the word | |
| "Forest" twice would yield you a 25% diversity score, (1 unique word/ 2 total words) | |
| """) | |
| gr.Markdown("""**Speech Pronunciation Scoring-**- The Wave2Vec 2.0 model is utilized to convert audio into text in real-time. The model predicts words or phonemes | |
| (smallest unit of speech distinguishing one word (or word element) from another) from the input audio from the user. Due to the nature of the model, | |
| users with poor pronunciation get inaccurate results. This project attempts to score pronunciation by asking a user to read a target excerpt into the | |
| microphone. We then pass this audio through Wave2Vec to get the inferred intended words. We measure the loss as the Levenshtein distance between the | |
| target and actual transcripts- the Levenshtein distance between two words is the minimum number of single-character edits required to change one word | |
| into the other. | |
| """) | |
| grade.click(reading_difficulty, inputs=in_text, outputs=diff_output) | |
| grade.click(calculate_diversity, inputs=in_text, outputs=[interpretation, div_output]) | |
| grade.click(sliding_window, inputs=in_text, outputs=[plotter, interpretation2]) | |
| grade1.click(speech_to_score, inputs=audio_file, outputs=diff_output) | |
| b1.click(speech_to_text, inputs=[audio_file1, target], outputs=[text, some_val, phones]) | |
| get_syns.click(gen_syns, inputs=[words, lvl], outputs=reccos) | |
| find_sim.click(get_sim_words, inputs=[in_text, words1], outputs=sims) | |
| demo.launch(debug=True) |