import gradio as gr from transformers import pipeline import librosa ########################LLama model############################### # from transformers import AutoModelForCausalLM, AutoTokenizer # model_name_or_path = "TheBloke/llama2_7b_chat_uncensored-GPTQ" # # To use a different branch, change revision # # For example: revision="main" # model = AutoModelForCausalLM.from_pretrained(model_name_or_path, # device_map="auto", # trust_remote_code=True, # revision="main", # #quantization_config=QuantizationConfig(disable_exllama=True) # ) # tokenizer = AutoTokenizer.from_pretrained(model_name_or_path, use_fast=True) # Llama_pipe = pipeline( # "text-generation", # model=model, # tokenizer=tokenizer, # max_new_tokens=40, # do_sample=True, # temperature=0.7, # top_p=0.95, # top_k=40, # repetition_penalty=1.1 # ) # history="""User: Hello, Rally? # Rally: I'm happy to see you again. What you want to talk to day? # User: Let's talk about food # Rally: Sure. # User: I'm hungry right now. Do you know any Vietnamese food?""" # prompt_template = f"""<|im_start|>system # Write one sentence to continue the conversation<|im_end|> # {history} # Rally:""" # print(Llama_pipe(prompt_template)[0]['generated_text']) # def RallyRespone(chat_history, message): # chat_history += "User: " + message + "\n" # t_chat = Llama_pipe(prompt_template)[0]['generated_text'] # res = t_chat[t_chat.rfind("Rally: "):] # return res ########################ASR model############################### from transformers import WhisperProcessor, WhisperForConditionalGeneration # load model and processor processor = WhisperProcessor.from_pretrained("openai/whisper-base") model = WhisperForConditionalGeneration.from_pretrained("openai/whisper-base") model.config.forced_decoder_ids = None sample_rate = 16000 def ASR_model(audio, sr=16000): DB_audio = audio input_features = processor(audio, sampling_rate=sr, return_tensors="pt").input_features # generate token ids predicted_ids = model.generate(input_features) # decode token ids to text transcription = processor.batch_decode(predicted_ids, skip_special_tokens=False) transcription = processor.batch_decode(predicted_ids, skip_special_tokens=True) return transcription ########################Gradio UI############################### # Chatbot demo with multimodal input (text, markdown, LaTeX, code blocks, image, audio, & video). Plus shows support for streaming text. def add_file(files): return files.name def print_like_dislike(x: gr.LikeData): print(x.index, x.value, x.liked) def upfile(files): x = librosa.load(files, sr=16000) print(x[0]) text = ASR_model(x[0]) return [text[0], text[0]] def transcribe(audio): sr, y = audio y = y.astype(np.float32) y /= np.max(np.abs(y)) return transcriber({"sampling_rate": sr, "raw": y})["text"], transcriber({"sampling_rate": sr, "raw": y})["text"] # def recommand(text): # ret = "answer for" # return ret + text def add_text(history, text): history = history + [(text, None)] return history, gr.Textbox(value="", interactive=False) # def bot(history): # response = "**That's cool!**" # history[-1][1] = "" # for character in response: # history[-1][1] += character # time.sleep(0.05) # yield history with gr.Blocks() as demo: chatbot = gr.Chatbot( [], elem_id="chatbot", bubble_full_width=False, ) file_output = gr.File() def respond(message, chat_history): bot_message = RallyRespone(chat_history, message) chat_history.append((message, bot_message)) time.sleep(2) print (chat_history[-1]) return chat_history[-1][-1], chat_history with gr.Row(): with gr.Column(): audio_speech = gr.Audio(sources=["microphone"]) submit = gr.Button("Submit") send = gr.Button("Send") btn = gr.UploadButton("📁", file_types=["audio"]) with gr.Column(): opt1 = gr.Button("1: ") opt2 = gr.Button("2: ") #submit.click(translate, inputs=audio_speech, outputs=[opt1, opt2]) # output is opt1 value, opt2 value [ , ] file_msg = btn.upload(add_file, btn, file_output) submit.click(upfile, inputs=file_output, outputs=[opt1, opt2]) send.click(transcribe, inputs=audio_speech, outputs=[opt1, opt2]) opt1.click(respond, [opt1, chatbot], [opt1, chatbot]) opt2.click(respond, [opt2, chatbot], [opt2, chatbot]) #opt2.click(recommand, inputs=opt2) #click event maybe BOT . generate history = optx.value, chatbot.like(print_like_dislike, None, None) if __name__ == "__main__": demo.queue() demo.launch(debug=True)