import os import whisper import evaluate from evaluate.utils import launch_gradio_widget import gradio as gr import torch import pandas as pd import random import classify import replace_explitives from whisper.model import Whisper from whisper.tokenizer import get_tokenizer from speechbrain.pretrained.interfaces import foreign_class from transformers import AutoModelForSequenceClassification, pipeline, WhisperTokenizer, RobertaForSequenceClassification, RobertaTokenizer, AutoTokenizer # pull in emotion detection # --- Add element for specification # pull in text classification # --- Add custom labels # --- Associate labels with radio elements # add logic to initiate mock notificaiton when detected # pull in misophonia-specific model model_cache = {} # Building prediction function for gradio emo_dict = { 'sad': 'Sad', 'hap': 'Happy', 'ang': 'Anger', 'neu': 'Neutral' } # static classes for now, but it would be best ot have the user select from multiple, and to enter their own class_options = { "Racism": ["racism", "hate speech", "bigotry", "racially targeted", "racial slur", "ethnic slur", "ethnic hate", "pro-white nationalism"], "LGBTQ+ Hate": ["gay slur", "trans slur", "homophobic slur", "transphobia", "anti-LBGTQ+"], "Sexually Explicit": ["sexually explicit", "sexually coercive", "sexual exploitation", "vulgar", "raunchy", "sexist", "sexually demeaning", "sexual violence", "victim blaming"], "Pregnancy Complications": ["miscarriage", "child loss", "child death", "abortion", "pregnancy", "childbirth", "baby shower", "postpartum"], } pipe = pipeline("automatic-speech-recognition", model="openai/whisper-large") toxicity_module = evaluate.load("toxicity", "facebook/roberta-hate-speech-dynabench-r4-target") emotion_classifier = foreign_class(source="speechbrain/emotion-recognition-wav2vec2-IEMOCAP", pymodule_file="custom_interface.py", classname="CustomEncoderWav2vec2Classifier") device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu") text_classifier = pipeline("zero-shot-classification", model="facebook/bart-large-mnli") def classify_emotion(audio): #### Emotion classification #### # EMO MODEL LINE emotion_classifier = foreign_class(source="speechbrain/emotion-recognition-wav2vec2-IEMOCAP", pymodule_file="custom_interface.py", classname="CustomEncoderWav2vec2Classifier") out_prob, score, index, text_lab = emotion_classifier.classify_file(audio) return emo_dict[text_lab[0]] def slider_logic(slider): threshold = 0 if slider == 1: threshold = .90 elif slider == 2: threshold = .80 elif slider == 3: threshold = .60 elif slider == 4: threshold = .50 elif slider == 5: threshold = .40 else: threshold = [] return threshold # Create a Gradio interface with audio file and text inputs def classify_toxicity(audio_file, classify_anxiety, emo_class, explitive_selection, slider): # Transcribe the audio file using Whisper ASR transcribed_text = pipe(audio_file)["text"] ## SLIDER ## threshold = slider_logic(slider) #------- explitive call --------------- if replace_explitives != None and emo_class == None: transcribed_text = replace_explitives.sub_explitives(transcribed_text, explitive_selection) #### Toxicity Classifier #### # TOX MODEL LINE toxicity_module = evaluate.load("toxicity", "facebook/roberta-hate-speech-dynabench-r4-target") #toxicity_module = evaluate.load("toxicity", 'DaNLP/da-electra-hatespeech-detection', module_type="measurement") toxicity_results = toxicity_module.compute(predictions=[transcribed_text]) toxicity_score = toxicity_results["toxicity"][0] print(toxicity_score) # emo call if emo_class != None: classify_emotion(audio_file) #### Text classification ##### if classify_anxiety != None: # DEVICE LINE device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu") # CLASSIFICATION LINE text_classifier = pipeline("zero-shot-classification", model="facebook/bart-large-mnli") sequence_to_classify = transcribed_text print(classify_anxiety, class_options) candidate_labels = class_options.get(classify_anxiety, []) # classification_output = classifier(sequence_to_classify, candidate_labels, multi_label=False) classification_output = text_classifier(sequence_to_classify, candidate_labels, multi_label=True) print("class output ", type(classification_output)) # classification_df = pd.DataFrame.from_dict(classification_output) print("keys ", classification_output.keys()) # formatted_classification_output = "\n".join([f"{key}: {value}" for key, value in classification_output.items()]) # label_score_pairs = [(label, score) for label, score in zip(classification_output['labels'], classification_output['scores'])] label_score_dict = {label: score for label, score in zip(classification_output['labels'], classification_output['scores'])} k = max(label_score_dict, key=label_score_dict.get) print("k keys: ", k) maxval = label_score_dict[k] print("max value: ", maxval) topScore = "" affirm = "" if maxval > threshold: print("Toxic") affirm = positive_affirmations() topScore = maxval else: print("Not Toxic") affirm = "" topScore = maxval else: topScore = "" affirm = "" if toxicity_score > threshold: affirm = positive_affirmations() topScore = toxicity_score else: affirm = "" topScore = toxicity_score label_score_dict = {"toxicity" : toxicity_score} return transcribed_text, topScore, label_score_dict, affirm # return f"Toxicity Score ({available_models[selected_model]}): {toxicity_score:.4f}" def positive_affirmations(): affirmations = [ "I have survived my anxiety before and I will survive again now", "I am not in danger; I am just uncomfortable; this too will pass", "I forgive and release the past and look forward to the future", "I can't control what other people say but I can control my breathing and my response" ] selected_affirm = random.choice(affirmations) return selected_affirm with gr.Blocks() as iface: show_state = gr.State([]) with gr.Column(): anxiety_class = gr.Radio(label="Specify Subclass", choices=["Racism", "LGBTQ+ Hate", "Sexually Explicit", "Pregnancy Complications"]) explit_preference = gr.Radio(choices=["N-Word", "B-Word", "All Explitives"], label="Words to omit from general anxiety classes", info="certain words may be acceptible within certain contects for given groups of people, and some people may be unbothered by explitives broadly speaking.") emo_class = gr.Radio(choices=["negaitve emotionality"], label="Negative Emotionality", info="Select if you would like explitives to be considered anxiety-indiucing in the case of anger/ negative emotionality.") sense_slider = gr.Slider(minimum=1, maximum=5, step=1.0, label="How readily do you want the tool to intervene? 1 = in extreme cases and 5 = at every opportunity") with gr.Column(): aud_input = gr.Audio(source="upload", type="filepath", label="Upload Audio File") submit_btn = gr.Button(label="Run") with gr.Column(): out_text = gr.Textbox(label="Transcribed Audio") out_val = gr.Textbox(label="Overall Toxicity") out_affirm = gr.Textbox(label="Intervention") out_class = gr.Label(label="Toxicity Class Breakdown") submit_btn.click(fn=classify_toxicity, inputs=[aud_input, anxiety_class, emo_class, explit_preference, sense_slider], outputs=[out_text, out_val, out_class, out_affirm]) iface.launch()