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| from gradio_client import Client, handle_file | |
| import pandas as pd | |
| import gradio as gr | |
| from vosk import Model, KaldiRecognizer | |
| import json | |
| import wave | |
| clientEngText = Client("dj-dawgs-ipd/IPD-Text-English-Finetune") | |
| clientHingText = Client("dj-dawgs-ipd/IPD-Text-Hinglish") | |
| clientAud = Client("dj-dawgs-ipd/IPD_Audio_HuBERT") | |
| profanity_df = pd.read_csv('Hinglish_Profanity_List.csv', encoding='utf-8') | |
| profanity_hn = profanity_df['profanity_hn'] | |
| vosk_model = Model(lang="en-us") | |
| # import whisper | |
| # def stt_whisper(file_path): | |
| # model = whisper.load_model("base") | |
| # try: | |
| # result = model.transcribe(file_path) | |
| # return result["text"] | |
| # except Exception as e: | |
| # print(e) | |
| # return "" | |
| def stt_vosk(file_path): | |
| try: | |
| wf = wave.open(file_path, "rb") | |
| rec = KaldiRecognizer(vosk_model, wf.getframerate()) | |
| rec.SetWords(True) | |
| rec.SetPartialWords(True) | |
| while True: | |
| data = wf.readframes(4000) | |
| if len(data) == 0: | |
| break | |
| rec.AcceptWaveform(data) | |
| data = json.loads(rec.FinalResult()) | |
| return data["text"] | |
| except: | |
| return "" | |
| def extract_text(audio_path): | |
| return stt_vosk(audio_path).lower() | |
| def predict_hate_speech(audio_path): | |
| audResult = clientAud.predict( | |
| audio_path=handle_file(audio_path), | |
| api_name="/predict" | |
| ) | |
| audResult = json.loads(audResult.replace("'", '"')) | |
| stt_text = extract_text(audio_path) | |
| engResult = clientEngText.predict( | |
| text=stt_text[:200], | |
| api_name="/predict" | |
| ) | |
| hingResult = clientHingText.predict( | |
| text=stt_text[:200], | |
| api_name="/predict" | |
| ) | |
| profanityFound = any(word in stt_text.split() for word in profanity_hn) | |
| threshold = 0.6 | |
| isHate = (engResult[0] != "NEITHER" and engResult[1] > threshold) or ( | |
| hingResult[0] != "NAG" and hingResult[1] > threshold) or ( | |
| audResult['Classification'] == 'Hate Speech\n' and audResult['Confidence'] > threshold) | |
| engConf = engResult[1] if engResult[0] != "NEITHER" else (1 - engResult[1]) | |
| hingConf = hingResult[1] if hingResult[0] != "NEITHER" else (1 - hingResult[1]) | |
| audConf = audResult['Confidence'] if audResult['Classification'] == 'Hate Speech\n' else (1 - audResult['Confidence']) | |
| confidence = (engConf + hingConf + audConf) / 3 | |
| # print(profanityFound, engResult, hingResult, audResult) | |
| if profanityFound: | |
| return ["hate", f"Result: Profanity Found", f"Text: {stt_text}"] | |
| elif isHate: | |
| return ["hate", f"Confidence: {confidence}", f"Text: {stt_text}"] | |
| return ["not_hate", "No hate found, yay!"] | |
| iface = gr.Interface( | |
| fn=predict_hate_speech, | |
| inputs=gr.Audio(type="filepath", label="Upload Audio"), | |
| outputs=gr.Textbox(label="Hate Speech Analysis"), | |
| title="Hate Speech Audio Pipeline", | |
| description="Upload an audio file to detect potential hate speech content.", | |
| examples=[ | |
| ["hate_video_3_3_snippet2.wav"] | |
| ], | |
| allow_flagging="manual" | |
| ) | |
| if __name__ == "__main__": | |
| iface.launch() | |