import torch import gradio as gr from transformers import pipeline from pyChatGPT import ChatGPT from speechbrain.pretrained import Tacotron2 from speechbrain.pretrained import HIFIGAN import json import soundfile as sf device = 0 if torch.cuda.is_available() else "cpu" pipe = pipeline( task="automatic-speech-recognition", model="openai/whisper-tiny.en", chunk_length_s=30, device=device, ) session_token = os.environ.get("SessionToken") # Intialize TTS (tacotron2) and Vocoder (HiFIGAN) tacotron2 = Tacotron2.from_hparams(source="speechbrain/tts-tacotron2-ljspeech", savedir="tmpdir_tts", overrides={"max_decoder_steps": 2000}, run_opts={"device":device}) hifi_gan = HIFIGAN.from_hparams(source="speechbrain/tts-hifigan-ljspeech", savedir="tmpdir_vocoder") def get_response_from_chatbot(text, reset_conversation): try: if reset_conversation: api.refresh_auth() api.reset_conversation() resp = api.send_message(text) response = resp["message"] except: response = "Sorry, the chatGPT queue is full. Please try again later." return response def chat(input_audio, chat_history, reset_conversation): # speech -> text (Whisper) message = pipe(input_audio)["text"] # text -> response (chatGPT) response = get_response_from_chatbot(message, reset_conversation) # response -> speech (Tacotron 2) mel_output, mel_length, alignment = tacotron2.encode_text(response) wav = hifi_gan.decode_batch(mel_output) sf.write("out.wav", wav.squeeze().cpu().numpy(), 22050) out_chat = [] chat_history = chat_history if not reset_conversation else "" if chat_history != "": out_chat = json.loads(chat_history) out_chat.append((message, response)) chat_history = json.dumps(out_chat) return out_chat, chat_history, "out.wav" start_work= """async() => { function isMobile() { try { document.createEvent("TouchEvent"); return true; } catch(e) { return false; } } function getClientHeight() { var clientHeight=0; if(document.body.clientHeight&&document.documentElement.clientHeight) { var clientHeight = (document.body.clientHeightdocument.documentElement.clientHeight)?document.body.clientHeight:document.documentElement.clientHeight; } return clientHeight; } function setNativeValue(element, value) { const valueSetter = Object.getOwnPropertyDescriptor(element.__proto__, 'value').set; const prototype = Object.getPrototypeOf(element); const prototypeValueSetter = Object.getOwnPropertyDescriptor(prototype, 'value').set; if (valueSetter && valueSetter !== prototypeValueSetter) { prototypeValueSetter.call(element, value); } else { valueSetter.call(element, value); } } var gradioEl = document.querySelector('body > gradio-app').shadowRoot; if (!gradioEl) { gradioEl = document.querySelector('body > gradio-app'); } if (typeof window['gradioEl'] === 'undefined') { window['gradioEl'] = gradioEl; const page1 = window['gradioEl'].querySelectorAll('#page_1')[0]; const page2 = window['gradioEl'].querySelectorAll('#page_2')[0]; page1.style.display = "none"; page2.style.display = "block"; window['div_count'] = 0; window['chat_bot'] = window['gradioEl'].querySelectorAll('#chat_bot')[0]; window['chat_bot1'] = window['gradioEl'].querySelectorAll('#chat_bot1')[0]; chat_row = window['gradioEl'].querySelectorAll('#chat_row')[0]; prompt_row = window['gradioEl'].querySelectorAll('#prompt_row')[0]; window['chat_bot1'].children[1].textContent = ''; clientHeight = getClientHeight(); new_height = (clientHeight-300) + 'px'; chat_row.style.height = new_height; window['chat_bot'].style.height = new_height; window['chat_bot'].children[2].style.height = new_height; window['chat_bot1'].style.height = new_height; window['chat_bot1'].children[2].style.height = new_height; prompt_row.children[0].style.flex = 'auto'; prompt_row.children[0].style.width = '100%'; window['checkChange'] = function checkChange() { try { if (window['chat_bot'].children[2].children[0].children.length > window['div_count']) { new_len = window['chat_bot'].children[2].children[0].children.length - window['div_count']; for (var i = 0; i < new_len; i++) { new_div = window['chat_bot'].children[2].children[0].children[window['div_count'] + i].cloneNode(true); window['chat_bot1'].children[2].children[0].appendChild(new_div); } window['div_count'] = chat_bot.children[2].children[0].children.length; } if (window['chat_bot'].children[0].children.length > 1) { window['chat_bot1'].children[1].textContent = window['chat_bot'].children[0].children[1].textContent; } else { window['chat_bot1'].children[1].textContent = ''; } } catch(e) { } } window['checkChange_interval'] = window.setInterval("window.checkChange()", 500); } return false; }""" with gr.Blocks(title="Talk to chatGPT") as demo: gr.Markdown("## Talk to chatGPT ##") gr.HTML("

You can duplicate this space and use your own session token: Duplicate Space

") gr.HTML("

Instruction on how to get session token can be seen in video here. Add your session token by going to settings and add under secrets.

") with gr.Group(elem_id="page_1", visible=True) as page_1: with gr.Box(): with gr.Row(): start_button = gr.Button("Let's talk to chatGPT! 🗣", elem_id="start-btn", visible=True) start_button.click(fn=None, inputs=[], outputs=[], _js=start_work) with gr.Group(elem_id="page_2", visible=False) as page_2: with gr.Row(elem_id="chat_row"): chatbot = gr.Chatbot(elem_id="chat_bot", visible=False).style(color_map=("green", "blue")) chatbot1 = gr.Chatbot(elem_id="chat_bot1").style(color_map=("green", "blue")) with gr.Row(): prompt_input_audio = gr.Audio( source="microphone", type="filepath", label="Record Audio Input", ) prompt_output_audio = gr.Audio() reset_conversation = gr.Checkbox(label="Reset conversation?", value=False) with gr.Row(elem_id="prompt_row"): chat_history = gr.Textbox(lines=4, label="prompt", visible=False) submit_btn = gr.Button(value="Send to chatGPT", elem_id="submit-btn").style( margin=True, rounded=(True, True, True, True), width=100, ) submit_btn.click(fn=chat, inputs=[prompt_input_audio, chat_history, reset_conversation], outputs=[chatbot, chat_history, prompt_output_audio], ) demo.launch(debug=True)