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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+", "hate speech"],
"sexually explicit": ["sexually explicit", "sexually coercive", "sexual exploitation", "vulgar", "raunchy", "sexist", "sexually demeaning", "sexual violence", "victim blaming"],
"alcohol use": ["alcohol", "drinking", "drinks", "under the influence", "liquor", "beer", "wine"]
}
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 = .98
elif slider == 2:
threshold = .88
elif slider == 3:
threshold = .77
elif slider == 4:
threshold = .66
elif slider == 5:
threshold = .55
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)
if maxval > toxicity_score:
if maxval > threshold:
print("Toxic")
affirm = positive_affirmations()
topScore = maxval
else:
print("Not Toxic")
affirm = ""
topScore = maxval
else:
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(["racism", "LGBTQ+ hate", "sexually explicit", "alcohol use"])
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_class = gr.Label(label="Toxicity Class Breakdown")
out_affirm = gr.Textbox(label="Automated Text Message")
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() |