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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_dict = {
"current_model": "ylacombe/vits_vctk_welsh_male",
"pipe": pipeline("text-to-speech", model=HUB_PATH, device=0),
}
title = "# 🐶 VITS"
description = """
"""
max_speakers = 15
# Inference
def generate_audio(text, model_id):
if pipe_dict["current_model"] != model_id:
gr.Warning("Model has changed - loading new model")
pipe_dict["pipe"] = pipeline("text-to-speech", model=model_id, device=0)
pipe_dict["current_model"] = model_id
num_speakers = pipe_dict["pipe"].model.config.num_speakers
out = []
for i in range(min(num_speakers, max_speakers)):
forward_params = {"speaker_id": i}
output = pipe_dict["pipe"](text, forward_params=forward_params)
output = gr.Audio(value = (output["sampling_rate"], output["audio"].squeeze()), type="numpy", autoplay=False, label=f"Generated Audio - speaker {i}", show_label=True,
visible=True)
out.append(output)
out.extend([gr.Audio(visible=False)]*(max_speakers-num_speakers))
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!")
model_id = gr.Dropdown(
[
"ylacombe/vits_vctk_welsh_male",
"ylacombe/vits_vctk_welsh_female",
"ylacombe/vits_ljs_welsh_male",
"ylacombe/vits_ljs_welsh_female",
"ylacombe/vits_vctk_irish_male",
"ylacombe/vits_vctk_scottish_female",
"ylacombe/vits_ljs_irish_male",
"ylacombe/vits_ljs_scottish_female",
"ylacombe/mms-tam-finetuned-multispeaker",
"ylacombe/mms-spa-finetuned-chilean-multispeaker",
],
value="ylacombe/vits_vctk_welsh_male",
label="Model",
info="Model you want to test",
)
with gr.Column():
outputs = []
for i in range(max_speakers):
out_audio = gr.Audio(type="numpy", autoplay=False, label=f"Generated Audio - speaker {i}", show_label=True, visible=False)
outputs.append(out_audio)
btn.click(generate_audio, [inp_text, model_id], outputs)
demo_blocks.queue().launch() |