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import torch
from transformers import pipeline
import numpy as np
import gradio as gr
def _grab_best_device(use_gpu=True):
if torch.cuda.device_count() > 0 and use_gpu:
device = "cuda"
else:
device = "cpu"
return device
device = _grab_best_device()
HUB_PATH = "ylacombe/vits_vctk_welsh_male"
pipe = pipeline("text-to-speech", model=HUB_PATH, device=0)
title = "# 🐶 VITS"
description = """
"""
num_speakers = pipe.model.config.num_speakers
# Inference
def generate_audio(text):
out = []
for i in range(num_speakers):
forward_params = {"speaker_id": i}
output = pipe(text, forward_params=forward_params)
out.append((output["sampling_rate"], output["audio"].squeeze()))
return out
# Gradio blocks demo
with gr.Blocks() as demo_blocks:
gr.Markdown(title)
gr.Markdown(description)
with gr.Row():
with gr.Column():
inp_text = gr.Textbox(label="Input Text", info="What would you like bark to synthesise?")
btn = gr.Button("Generate Audio!")
with gr.Column():
outputs = []
for i in range(num_speakers):
out_audio = gr.Audio(type="numpy", autoplay=False, label=f"Generated Audio {i}", show_label=True)
outputs.append(out_audio)
btn.click(generate_audio, [inp_text], [outputs])
demo_blocks.launch() |