|
import gradio as gr |
|
|
|
from PIL import Image |
|
import os |
|
token = os.environ.get('HF_TOKEN') |
|
whisper_to_gpt = gr.Blocks.load(name="spaces/fffiloni/whisper-to-chatGPT") |
|
tts = gr.Interface.load(name="spaces/Flux9665/IMS-Toucan") |
|
talking_face = gr.Blocks.load(name="spaces/fffiloni/one-shot-talking-face", api_key=token) |
|
|
|
def infer(audio): |
|
gpt_response = whisper_to_gpt(audio, "translate", fn_index=0) |
|
|
|
audio_response = tts(gpt_response[1], "English Text", "English Accent", "English Speaker's Voice", fn_index=0) |
|
|
|
portrait_link = talking_face("wise_woman_portrait.png", audio_response, fn_index=0) |
|
|
|
|
|
|
|
return portrait_link |
|
|
|
title = """ |
|
<div style="text-align: center; max-width: 500px; margin: 0 auto;"> |
|
<div |
|
style=" |
|
display: inline-flex; |
|
align-items: center; |
|
gap: 0.8rem; |
|
font-size: 1.75rem; |
|
margin-bottom: 10px; |
|
" |
|
> |
|
<h1 style="font-weight: 600; margin-bottom: 7px;"> |
|
GPT Talking Portrait |
|
</h1> |
|
</div> |
|
<p style="margin-bottom: 10px;font-size: 94%;font-weight: 100;line-height: 1.5em;"> |
|
Use Whisper to ask, alive portrait responds ! |
|
</p> |
|
</div> |
|
""" |
|
|
|
css = ''' |
|
#col-container, #col-container-2 {max-width: 510px; margin-left: auto; margin-right: auto;} |
|
a {text-decoration-line: underline; font-weight: 600;} |
|
div#record_btn > .mt-6 { |
|
margin-top: 0!important; |
|
} |
|
div#record_btn > .mt-6 button { |
|
width: 100%; |
|
height: 40px; |
|
} |
|
.footer { |
|
margin-bottom: 45px; |
|
margin-top: 10px; |
|
text-align: center; |
|
border-bottom: 1px solid #e5e5e5; |
|
} |
|
.footer>p { |
|
font-size: .8rem; |
|
display: inline-block; |
|
padding: 0 10px; |
|
transform: translateY(10px); |
|
background: white; |
|
} |
|
.dark .footer { |
|
border-color: #303030; |
|
} |
|
.dark .footer>p { |
|
background: #0b0f19; |
|
} |
|
''' |
|
|
|
with gr.Blocks(css=css) as demo: |
|
|
|
with gr.Column(elem_id="col-container"): |
|
|
|
gr.HTML(title) |
|
|
|
with gr.Row(): |
|
record_input = gr.Audio(source="microphone",type="filepath", show_label=False,elem_id="record_btn") |
|
|
|
with gr.Row(): |
|
|
|
send_btn = gr.Button("Send my request !") |
|
|
|
|
|
with gr.Column(elem_id="col-container-2"): |
|
|
|
gpt_response = gr.Video() |
|
|
|
|
|
|
|
send_btn.click(infer, inputs=[record_input], outputs=[gpt_response]) |
|
|
|
demo.queue(max_size=32, concurrency_count=20).launch(debug=True) |
|
|
|
|
|
|