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import torch
from transformers import pipeline
import numpy as np
import gradio as gr
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
pipe = pipeline("text-to-speech", model="kakao-enterprise/vits-ljs", device=device)
# Inference
def generate_audio(text):
output = pipe(text)
output = gr.Audio(value = (output["sampling_rate"], output["audio"].squeeze()), type="numpy", autoplay=True, label="Response Voice Player", show_label=True,
visible=True)
###############language = "english"
return output
css = """
#container{
margin: 0 auto;
max-width: 80rem;
}
#intro{
max-width: 100%;
text-align: center;
margin: 0 auto;
}
"""
# Gradio blocks demo
with gr.Blocks(css=css) as demo_blocks:
with gr.Row():
with gr.Column():
inp_text = gr.Textbox(label="Input Text", info="What sentence would you like to synthesise?")
btn = gr.Button("Generate Audio!")
#"Enter the text you would like to synthesise into speech. Amazing! One plus one is equal to two. \n The quick brown fox jumps over the lazy dog. \n 1. Mangoes \n 2. Fruits"
with gr.Column():
out_audio = gr.Audio(value = None, label="Response Voice Player", show_label=True, visible=False)
btn.click(generate_audio, [inp_text], out_audio)
demo_blocks.queue().launch() |