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c39fa45
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Parent(s):
c15645d
Create app.py
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app.py
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import torch
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import gradio as gr
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from transformers import pipeline
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from pyChatGPT import ChatGPT
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from speechbrain.pretrained import Tacotron2
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from speechbrain.pretrained import HIFIGAN
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import json
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import soundfile as sf
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device = 0 if torch.cuda.is_available() else "cpu"
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pipe = pipeline(
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task="automatic-speech-recognition",
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model="openai/whisper-tiny.en",
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chunk_length_s=30,
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device=device,
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)
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session_token = os.environ.get("SessionToken")
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# Intialize TTS (tacotron2) and Vocoder (HiFIGAN)
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tacotron2 = Tacotron2.from_hparams(source="speechbrain/tts-tacotron2-ljspeech", savedir="tmpdir_tts", overrides={"max_decoder_steps": 2000}, run_opts={"device":device})
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hifi_gan = HIFIGAN.from_hparams(source="speechbrain/tts-hifigan-ljspeech", savedir="tmpdir_vocoder")
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def get_response_from_chatbot(text, reset_conversation):
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try:
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if reset_conversation:
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api.refresh_auth()
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api.reset_conversation()
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resp = api.send_message(text)
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response = resp["message"]
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except:
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response = "Sorry, the chatGPT queue is full. Please try again later."
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return response
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def chat(input_audio, chat_history, reset_conversation):
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# speech -> text (Whisper)
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message = pipe(input_audio)["text"]
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# text -> response (chatGPT)
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response = get_response_from_chatbot(message, reset_conversation)
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# response -> speech (Tacotron 2)
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mel_output, mel_length, alignment = tacotron2.encode_text(response)
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wav = hifi_gan.decode_batch(mel_output)
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sf.write("out.wav", wav.squeeze().cpu().numpy(), 22050)
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out_chat = []
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chat_history = chat_history if not reset_conversation else ""
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if chat_history != "":
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out_chat = json.loads(chat_history)
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out_chat.append((message, response))
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chat_history = json.dumps(out_chat)
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return out_chat, chat_history, "out.wav"
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start_work= """async() => {
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function isMobile() {
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try {
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document.createEvent("TouchEvent"); return true;
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} catch(e) {
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return false;
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}
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}
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function getClientHeight()
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{
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var clientHeight=0;
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if(document.body.clientHeight&&document.documentElement.clientHeight) {
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var clientHeight = (document.body.clientHeight<document.documentElement.clientHeight)?document.body.clientHeight:document.documentElement.clientHeight;
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} else {
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var clientHeight = (document.body.clientHeight>document.documentElement.clientHeight)?document.body.clientHeight:document.documentElement.clientHeight;
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}
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return clientHeight;
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}
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function setNativeValue(element, value) {
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const valueSetter = Object.getOwnPropertyDescriptor(element.__proto__, 'value').set;
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const prototype = Object.getPrototypeOf(element);
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const prototypeValueSetter = Object.getOwnPropertyDescriptor(prototype, 'value').set;
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if (valueSetter && valueSetter !== prototypeValueSetter) {
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prototypeValueSetter.call(element, value);
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} else {
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valueSetter.call(element, value);
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}
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}
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var gradioEl = document.querySelector('body > gradio-app').shadowRoot;
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if (!gradioEl) {
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gradioEl = document.querySelector('body > gradio-app');
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}
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if (typeof window['gradioEl'] === 'undefined') {
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window['gradioEl'] = gradioEl;
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const page1 = window['gradioEl'].querySelectorAll('#page_1')[0];
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const page2 = window['gradioEl'].querySelectorAll('#page_2')[0];
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page1.style.display = "none";
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page2.style.display = "block";
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window['div_count'] = 0;
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window['chat_bot'] = window['gradioEl'].querySelectorAll('#chat_bot')[0];
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window['chat_bot1'] = window['gradioEl'].querySelectorAll('#chat_bot1')[0];
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chat_row = window['gradioEl'].querySelectorAll('#chat_row')[0];
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prompt_row = window['gradioEl'].querySelectorAll('#prompt_row')[0];
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window['chat_bot1'].children[1].textContent = '';
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clientHeight = getClientHeight();
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new_height = (clientHeight-300) + 'px';
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chat_row.style.height = new_height;
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window['chat_bot'].style.height = new_height;
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window['chat_bot'].children[2].style.height = new_height;
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window['chat_bot1'].style.height = new_height;
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window['chat_bot1'].children[2].style.height = new_height;
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prompt_row.children[0].style.flex = 'auto';
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prompt_row.children[0].style.width = '100%';
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window['checkChange'] = function checkChange() {
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try {
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if (window['chat_bot'].children[2].children[0].children.length > window['div_count']) {
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new_len = window['chat_bot'].children[2].children[0].children.length - window['div_count'];
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for (var i = 0; i < new_len; i++) {
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new_div = window['chat_bot'].children[2].children[0].children[window['div_count'] + i].cloneNode(true);
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window['chat_bot1'].children[2].children[0].appendChild(new_div);
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}
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window['div_count'] = chat_bot.children[2].children[0].children.length;
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}
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if (window['chat_bot'].children[0].children.length > 1) {
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window['chat_bot1'].children[1].textContent = window['chat_bot'].children[0].children[1].textContent;
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} else {
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window['chat_bot1'].children[1].textContent = '';
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}
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} catch(e) {
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}
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}
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window['checkChange_interval'] = window.setInterval("window.checkChange()", 500);
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}
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return false;
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}"""
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with gr.Blocks(title="Talk to chatGPT") as demo:
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gr.Markdown("## Talk to chatGPT ##")
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gr.HTML("<p>You can duplicate this space and use your own session token: <a style='display:inline-block' href='https://huggingface.co/spaces/yizhangliu/chatGPT?duplicate=true'><img src='https://img.shields.io/badge/-Duplicate%20Space-blue?labelColor=white&style=flat&logo=data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAABAAAAAQCAYAAAAf8/9hAAAAAXNSR0IArs4c6QAAAP5JREFUOE+lk7FqAkEURY+ltunEgFXS2sZGIbXfEPdLlnxJyDdYB62sbbUKpLbVNhyYFzbrrA74YJlh9r079973psed0cvUD4A+4HoCjsA85X0Dfn/RBLBgBDxnQPfAEJgBY+A9gALA4tcbamSzS4xq4FOQAJgCDwV2CPKV8tZAJcAjMMkUe1vX+U+SMhfAJEHasQIWmXNN3abzDwHUrgcRGmYcgKe0bxrblHEB4E/pndMazNpSZGcsZdBlYJcEL9Afo75molJyM2FxmPgmgPqlWNLGfwZGG6UiyEvLzHYDmoPkDDiNm9JR9uboiONcBXrpY1qmgs21x1QwyZcpvxt9NS09PlsPAAAAAElFTkSuQmCC&logoWidth=10' alt='Duplicate Space'></a></p>")
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gr.HTML("<p> Instruction on how to get session token can be seen in video <a style='display:inline-block' href='https://www.youtube.com/watch?v=TdNSj_qgdFk'><font style='color:blue;weight:bold;'>here</font></a>. Add your session token by going to settings and add under secrets. </p>")
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with gr.Group(elem_id="page_1", visible=True) as page_1:
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with gr.Box():
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with gr.Row():
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start_button = gr.Button("Let's talk to chatGPT! 🗣", elem_id="start-btn", visible=True)
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start_button.click(fn=None, inputs=[], outputs=[], _js=start_work)
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with gr.Group(elem_id="page_2", visible=False) as page_2:
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with gr.Row(elem_id="chat_row"):
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chatbot = gr.Chatbot(elem_id="chat_bot", visible=False).style(color_map=("green", "blue"))
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chatbot1 = gr.Chatbot(elem_id="chat_bot1").style(color_map=("green", "blue"))
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with gr.Row():
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prompt_input_audio = gr.Audio(
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source="microphone",
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type="filepath",
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label="Record Audio Input",
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)
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prompt_output_audio = gr.Audio()
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reset_conversation = gr.Checkbox(label="Reset conversation?", value=False)
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with gr.Row(elem_id="prompt_row"):
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chat_history = gr.Textbox(lines=4, label="prompt", visible=False)
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submit_btn = gr.Button(value="Send to chatGPT", elem_id="submit-btn").style(
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margin=True,
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rounded=(True, True, True, True),
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width=100,
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)
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submit_btn.click(fn=chat,
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inputs=[prompt_input_audio, chat_history, reset_conversation],
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outputs=[chatbot, chat_history, prompt_output_audio],
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)
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demo.launch(debug=True)
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