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import gradio as gr
import torch
from modules.normalization import text_normalize
from modules.utils.hf import spaces
from modules.webui import webui_utils
from modules.webui.webui_utils import get_speakers, get_styles, split_long_text
# NOTE: 因为 text_normalize 需要使用 tokenizer
@torch.inference_mode()
@spaces.GPU(duration=120)
def merge_dataframe_to_ssml(dataframe, spk, style, seed):
if style == "*auto":
style = None
if spk == "-1" or spk == -1:
spk = None
if seed == -1 or seed == "-1":
seed = None
ssml = ""
indent = " " * 2
for i, row in dataframe.iterrows():
text = row.iloc[1]
text = text_normalize(text)
if text.strip() == "":
continue
ssml += f"{indent}<voice"
if spk:
ssml += f' spk="{spk}"'
if style:
ssml += f' style="{style}"'
if seed:
ssml += f' seed="{seed}"'
ssml += ">\n"
ssml += f"{indent}{indent}{text}\n"
ssml += f"{indent}</voice>\n"
# 原封不动输出回去是为了触发 loadding 效果
return dataframe, spk, style, seed, f"<speak version='0.1'>\n{ssml}</speak>"
# 长文本处理
# 可以输入长文本，并选择切割方法，切割之后可以将拼接的SSML发送到SSML tab
# 根据 。 句号切割，切割之后显示到 data table
def create_spliter_tab(ssml_input, tabs1, tabs2):
speakers, speaker_names = webui_utils.get_speaker_names()
speaker_names = ["*random"] + speaker_names
styles = ["*auto"] + [s.get("name") for s in get_styles()]
with gr.Row():
with gr.Column(scale=1):
# 选择说话人 选择风格 选择seed
with gr.Group():
gr.Markdown("🗣️Speaker")
spk_input_text = gr.Textbox(
label="Speaker (Text or Seed)",
value="female2",
show_label=False,
)
spk_input_dropdown = gr.Dropdown(
choices=speaker_names,
interactive=True,
value="female : female2",
show_label=False,
)
spk_rand_button = gr.Button(
value="🎲",
variant="secondary",
)
with gr.Group():
gr.Markdown("🎭Style")
style_input_dropdown = gr.Dropdown(
choices=styles,
interactive=True,
show_label=False,
value="*auto",
)
with gr.Group():
gr.Markdown("💃Inference Seed")
infer_seed_input = gr.Number(
value=42,
label="Inference Seed",
show_label=False,
minimum=-1,
maximum=2**32 - 1,
)
infer_seed_rand_button = gr.Button(
value="🎲",
# tooltip="Random Seed",
variant="secondary",
)
with gr.Group():
gr.Markdown("🎛️Spliter")
eos_input = gr.Textbox(
label="eos",
value="[uv_break]",
)
spliter_thr_input = gr.Slider(
label="Spliter Threshold",
value=100,
minimum=50,
maximum=1000,
step=1,
)
with gr.Column(scale=3):
with gr.Group():
gr.Markdown("📝Long Text Input")
gr.Markdown("SSML_SPLITER_GUIDE")
long_text_input = gr.Textbox(
label="Long Text Input",
lines=10,
placeholder="输入长文本",
elem_id="long-text-input",
show_label=False,
)
long_text_split_button = gr.Button("🔪Split Text")
with gr.Group():
gr.Markdown("🎨Output")
long_text_output = gr.DataFrame(
headers=["index", "text", "length"],
datatype=["number", "str", "number"],
elem_id="long-text-output",
interactive=True,
wrap=True,
value=[],
row_count=(0, "dynamic"),
col_count=(3, "fixed"),
)
send_btn = gr.Button("📩Send to SSML", variant="primary")
spk_input_dropdown.change(
fn=lambda x: x.startswith("*") and "-1" or x.split(":")[-1].strip(),
inputs=[spk_input_dropdown],
outputs=[spk_input_text],
)
spk_rand_button.click(
lambda x: int(torch.randint(0, 2**32 - 1, (1,)).item()),
inputs=[spk_input_text],
outputs=[spk_input_text],
)
infer_seed_rand_button.click(
lambda x: int(torch.randint(0, 2**32 - 1, (1,)).item()),
inputs=[infer_seed_input],
outputs=[infer_seed_input],
)
long_text_split_button.click(
split_long_text,
inputs=[
long_text_input,
spliter_thr_input,
eos_input,
],
outputs=[
long_text_output,
],
)
infer_seed_rand_button.click(
lambda x: int(torch.randint(0, 2**32 - 1, (1,)).item()),
inputs=[infer_seed_input],
outputs=[infer_seed_input],
)
send_btn.click(
merge_dataframe_to_ssml,
inputs=[
long_text_output,
spk_input_text,
style_input_dropdown,
infer_seed_input,
],
outputs=[
long_text_output,
spk_input_text,
style_input_dropdown,
infer_seed_input,
ssml_input,
],
)
def change_tab():
return gr.Tabs(selected="ssml"), gr.Tabs(selected="ssml.editor")
send_btn.click(change_tab, inputs=[], outputs=[tabs1, tabs2])
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