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| import gradio as gr | |
| import time | |
| import openai | |
| import json | |
| import os | |
| from transformers import pipeline | |
| from transformers import AutoProcessor, AutoModelForCTC | |
| processor = AutoProcessor.from_pretrained("facebook/wav2vec2-large-robust-ft-libri-960h") | |
| model = AutoModelForCTC.from_pretrained("facebook/wav2vec2-large-robust-ft-libri-960h") | |
| # asr_pipeline = pipeline("automatic-speech-recognition", model="facebook/wav2vec2-large-robust-ft-libri-960h") | |
| openai.api_key = os.environ.get('OPENAI_KEY') | |
| def classify_audio(audio): | |
| # Transcribe the audio to text | |
| # audio_transcript = asr_pipeline(audio)["text"] | |
| # audio_transcript = audio_transcript.lower() | |
| input_values = processor(audio, return_tensors="pt", padding="longest").input_values | |
| # retrieve logits | |
| logits = model(input_values).logits | |
| # take argmax and decode | |
| predicted_ids = torch.argmax(logits, dim=-1) | |
| transcription = processor.batch_decode(predicted_ids) | |
| messages = [ | |
| {"role": "system", "content": "Is this chat a scam, spam or is safe? Only answer in JSON format with 'classification': '' as string and 'reasons': '' as the most plausible reasons why. The reason should be explaning to the potential victim why the conversation is probably a scam"}, | |
| {"role": "user", "content": transcription}, | |
| ] | |
| # Call the OpenAI API to generate a response | |
| response = openai.ChatCompletion.create( | |
| model="gpt-4", # Replace with the actual GPT-4 model ID | |
| messages=messages | |
| ) | |
| # Extract the generated text | |
| text = response.choices[0].message['content'] | |
| text = json.loads(text) | |
| # Get the decision and reasons from the JSON dictionary | |
| decision = text["classification"] | |
| reasons = text["reasons"] | |
| # Return the transcription and the prediction as a dictionary | |
| return transcription, decision, reasons | |
| gr.Interface( | |
| fn=classify_audio, | |
| inputs=gr.inputs.Audio(source="upload", type="numpy"), | |
| outputs=[ | |
| gr.outputs.Textbox(label="Transcription"), | |
| gr.outputs.Textbox(label="Classification"), | |
| gr.outputs.Textbox(label="Reason"), | |
| ], | |
| live=True | |
| ).launch() | |