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import speech_recognition as sr |
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import gradio as gr |
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import numpy as np |
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from transformers import AutoTokenizer, AutoModelForCausalLM, GenerationConfig,BitsAndBytesConfig |
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import torch |
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import os |
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from openai import OpenAI |
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key = os.environ.get('OPENAI_API_KEY') |
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client = OpenAI(api_key=key) |
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Medical_finetunned_model = "truongghieu/deci-finetuned_Prj2" |
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answer_text = "This is an answer" |
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device = torch.device("cuda" if torch.cuda.is_available() else "cpu") |
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bnb_config = BitsAndBytesConfig( |
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load_in_4bit=True, bnb_4bit_quant_type="nf4", bnb_4bit_compute_dtype="float16", bnb_4bit_use_double_quant=True |
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) |
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tokenizer = AutoTokenizer.from_pretrained(Medical_finetunned_model, trust_remote_code=True) |
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if torch.cuda.is_available(): |
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model = AutoModelForCausalLM.from_pretrained(Medical_finetunned_model, trust_remote_code=True, quantization_config=bnb_config) |
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else: |
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model = AutoModelForCausalLM.from_pretrained("truongghieu/deci-finetuned", trust_remote_code=True) |
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def generate_text(*args): |
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if args[0] == "": |
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return "Please input text" |
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generation_config = GenerationConfig( |
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penalty_alpha=args[1], |
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do_sample=args[2], |
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top_k=args[3], |
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temperature=args[4], |
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repetition_penalty=args[5], |
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max_new_tokens=args[6], |
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pad_token_id=tokenizer.eos_token_id |
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) |
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input_text = f'###Human : {args[0]}' |
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input_ids = tokenizer.encode(input_text, return_tensors="pt").to(device) |
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output_ids = model.generate(input_ids, generation_config=generation_config) |
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output_text = tokenizer.decode(output_ids[0]) |
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return output_text |
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def gpt_generate(*args): |
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response = client.chat.completions.create( |
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model="gpt-3.5-turbo", |
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messages=[{"role": "user", "content": args[0]}], |
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temperature = args[4], |
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max_tokens = args[6], |
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) |
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return response.choices[0].message.content |
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def recognize_speech(audio_data): |
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audio_data = sr.AudioData(np.array(audio_data[1]), sample_rate=audio_data[0] , sample_width=2) |
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recognizer = sr.Recognizer() |
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try: |
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text = recognizer.recognize_google(audio_data) |
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return text |
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except sr.UnknownValueError: |
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return "Speech Recognition could not understand audio." |
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except sr.RequestError as e: |
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return f"Could not request results from Google Speech Recognition service; {e}" |
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with gr.Blocks() as demo: |
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with gr.Row(): |
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inp = gr.Audio(type="numpy") |
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out_text_predict = gr.Textbox(label="Recognized Speech") |
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button = gr.Button("Recognize Speech" , size="lg") |
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button.click(recognize_speech, inp, out_text_predict) |
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with gr.Row(): |
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with gr.Row(): |
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penalty_alpha_slider = gr.Slider(minimum=0, maximum=1, step=0.1, label="penalty alpha",value=0.6) |
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do_sample_checkbox = gr.Checkbox(label="do sample",value=True) |
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top_k_slider = gr.Slider(minimum=0, maximum=10, step=1, label="top k", value=5) |
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with gr.Row(): |
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temperature_slider = gr.Slider(minimum=0, maximum=1, step=0.1, label="temperature",value=0.5) |
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repetition_penalty_slider = gr.Slider(minimum=0, maximum=2, step=0.1, label="repetition penalty",value=1.0) |
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max_new_tokens_slider = gr.Slider(minimum=0, maximum=200, step=1, label="max new tokens",value=30) |
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with gr.Row(): |
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out_answer = gr.Textbox(label="Answer") |
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button_answer = gr.Button("Answer") |
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button_answer.click(generate_text, [out_text_predict, penalty_alpha_slider, do_sample_checkbox, top_k_slider, temperature_slider, repetition_penalty_slider, max_new_tokens_slider], out_answer) |
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with gr.Row(): |
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gpt_output = gr.Textbox(label="GPT-3.5 Turbo Output") |
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button_gpt = gr.Button("GPT-3.5 Answer") |
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button_gpt.click(gpt_generate,[out_text_predict, penalty_alpha_slider, do_sample_checkbox, top_k_slider, temperature_slider, repetition_penalty_slider, max_new_tokens_slider],gpt_output) |
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demo.launch() |
