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# flake8: noqa: E402
import sys, os
import logging
logging.getLogger("numba").setLevel(logging.WARNING)
logging.getLogger("markdown_it").setLevel(logging.WARNING)
logging.getLogger("urllib3").setLevel(logging.WARNING)
logging.getLogger("matplotlib").setLevel(logging.WARNING)
logging.basicConfig(
level=logging.INFO, format="| %(name)s | %(levelname)s | %(message)s"
)
logger = logging.getLogger(__name__)
import torch
import argparse
import commons
import utils
from models import SynthesizerTrn
from text.symbols import symbols
from text import cleaned_text_to_sequence, get_bert
from text.cleaner import clean_text
import gradio as gr
import webbrowser
import numpy as np
net_g = None
if sys.platform == "darwin" and torch.backends.mps.is_available():
device = "mps"
os.environ["PYTORCH_ENABLE_MPS_FALLBACK"] = "1"
else:
device = "cuda"
def get_text(text, language_str, hps):
norm_text, phone, tone, word2ph = clean_text(text, language_str)
phone, tone, language = cleaned_text_to_sequence(phone, tone, language_str)
if hps.data.add_blank:
phone = commons.intersperse(phone, 0)
tone = commons.intersperse(tone, 0)
language = commons.intersperse(language, 0)
for i in range(len(word2ph)):
word2ph[i] = word2ph[i] * 2
word2ph[0] += 1
bert = get_bert(norm_text, word2ph, language_str, device)
del word2ph
assert bert.shape[-1] == len(phone), phone
if language_str == "ZH":
bert = bert
ja_bert = torch.zeros(768, len(phone))
elif language_str == "JP":
ja_bert = bert
bert = torch.zeros(1024, len(phone))
else:
bert = torch.zeros(1024, len(phone))
ja_bert = torch.zeros(768, len(phone))
assert bert.shape[-1] == len(
phone
), f"Bert seq len {bert.shape[-1]} != {len(phone)}"
phone = torch.LongTensor(phone)
tone = torch.LongTensor(tone)
language = torch.LongTensor(language)
return bert, ja_bert, phone, tone, language
def infer(text, sdp_ratio, noise_scale, noise_scale_w, length_scale, sid, language):
global net_g
bert, ja_bert, phones, tones, lang_ids = get_text(text, language, hps)
with torch.no_grad():
x_tst = phones.to(device).unsqueeze(0)
tones = tones.to(device).unsqueeze(0)
lang_ids = lang_ids.to(device).unsqueeze(0)
bert = bert.to(device).unsqueeze(0)
ja_bert = ja_bert.to(device).unsqueeze(0)
x_tst_lengths = torch.LongTensor([phones.size(0)]).to(device)
del phones
speakers = torch.LongTensor([hps.data.spk2id[sid]]).to(device)
audio = (
net_g.infer(
x_tst,
x_tst_lengths,
speakers,
tones,
lang_ids,
bert,
ja_bert,
sdp_ratio=sdp_ratio,
noise_scale=noise_scale,
noise_scale_w=noise_scale_w,
length_scale=length_scale,
)[0][0, 0]
.data.cpu()
.float()
.numpy()
)
del x_tst, tones, lang_ids, bert, x_tst_lengths, speakers
torch.cuda.empty_cache()
return audio
def tts_fn(text, speaker, sdp_ratio, noise_scale, noise_scale_w, length_scale, language):
slices = text.split("|")
audio_list = []
with torch.no_grad():
for slice in slices:
audio = infer(slice, sdp_ratio=sdp_ratio, noise_scale=noise_scale, noise_scale_w=noise_scale_w, length_scale=length_scale, sid=speaker, language=language)
audio_list.append(audio)
silence = np.zeros(hps.data.sampling_rate) # 生成1秒的静音
audio_list.append(silence) # 将静音添加到列表中
audio_concat = np.concatenate(audio_list)
return "Success", (hps.data.sampling_rate, audio_concat)
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument(
"-m", "--model", default="./logs/illi/G_25000.pth", help="path of your model"
)
parser.add_argument(
"-c",
"--config",
default="./configs/config.json",
help="path of your config file",
)
parser.add_argument(
"--share", default=False, help="make link public", action="store_true"
)
parser.add_argument(
"-d", "--debug", action="store_true", help="enable DEBUG-LEVEL log"
)
args = parser.parse_args()
if args.debug:
logger.info("Enable DEBUG-LEVEL log")
logging.basicConfig(level=logging.DEBUG)
hps = utils.get_hparams_from_file(args.config)
device = (
"cuda:0"
if torch.cuda.is_available()
else (
"mps"
if sys.platform == "darwin" and torch.backends.mps.is_available()
else "cpu"
)
)
net_g = SynthesizerTrn(
len(symbols),
hps.data.filter_length // 2 + 1,
hps.train.segment_size // hps.data.hop_length,
n_speakers=hps.data.n_speakers,
**hps.model,
).to(device)
_ = net_g.eval()
_ = utils.load_checkpoint(args.model, net_g, None, skip_optimizer=True)
speaker_ids = hps.data.spk2id
speakers = list(speaker_ids.keys())
languages = ["ZH", "JP"]
with gr.Blocks() as app:
with gr.Row():
with gr.Column():
gr.Markdown(value="""
🤖 【AI以里illi】在线语音合成 Bert-Vits2 🤖\n
📝 作者:Rayzggz 📰博客 https://roi.moe 📺B站 https://space.bilibili.com/10501326 📝\n
🎤 声音来源:以里illi https://space.bilibili.com/3035038 🎤\n
🔗 Bert-VITS2:https://github.com/fishaudio/Bert-VITS2 🔗\n
✅ 使用本模型请遵守中华人民共和国和美利坚合众国法律 ✅\n
🏷️ 使用基于本模型的所有生成内容均需标注「使用Bert-VITS2 AI生成」、「本项目地址」、「作者名称」和「声音来源」 🏷️\n
""")
text = gr.TextArea(
label="Text",
placeholder="Input Text Here",
value="野生的门卫室魔法师出现了!",
)
speaker = gr.Dropdown(
choices=speakers, value=speakers[0], label="Speaker"
)
sdp_ratio = gr.Slider(
minimum=0, maximum=1, value=0.2, step=0.1, label="SDP Ratio"
)
noise_scale = gr.Slider(
minimum=0.1, maximum=2, value=0.6, step=0.1, label="Noise Scale"
)
noise_scale_w = gr.Slider(
minimum=0.1, maximum=2, value=0.8, step=0.1, label="Noise Scale W"
)
length_scale = gr.Slider(
minimum=0.1, maximum=2, value=1, step=0.1, label="Length Scale"
)
language = gr.Dropdown(
choices=languages, value=languages[0], label="Language"
)
btn = gr.Button("Generate 生成!", variant="primary")
with gr.Column():
text_output = gr.Textbox(label="Message")
audio_output = gr.Audio(label="Output Audio")
gr.Markdown(value="""
👏 鸣谢: 👏\n
👤 团子是咸鱼 https://space.bilibili.com/10685437 👤\n
👤 领航员未鸟 https://space.bilibili.com/2403955 👤\n
👤 Xz乔希 https://space.bilibili.com/5859321 👤\n
👤 怎么好就怎么来 https://space.bilibili.com/259582714 👤\n
🧠 Google Colab https://colab.research.google.com/ 🧠\n
📧 如果你是“以里illi”,并且希望对此模型主张权利,请通过上方“作者”部分的联系方式联系,我将积极配合处理。📧 \n
""")
btn.click(
tts_fn,
inputs=[
text,
speaker,
sdp_ratio,
noise_scale,
noise_scale_w,
length_scale,
language,
],
outputs=[text_output, audio_output],
)
app.launch(show_error=True)
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