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# -*- coding: utf-8 -*- | |
import traceback | |
import torch | |
from scipy.io import wavfile | |
import edge_tts | |
import subprocess | |
import gradio as gr | |
import gradio.processing_utils as gr_pu | |
import io | |
import os | |
import logging | |
import time | |
from pathlib import Path | |
import re | |
import json | |
import argparse | |
import librosa | |
import matplotlib.pyplot as plt | |
import numpy as np | |
import soundfile | |
from inference import infer_tool | |
from inference import slicer | |
from inference.infer_tool import Svc | |
logging.getLogger('numba').setLevel(logging.WARNING) | |
chunks_dict = infer_tool.read_temp("inference/chunks_temp.json") | |
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.getLogger('multipart').setLevel(logging.WARNING) | |
model = None | |
spk = None | |
debug = False | |
class HParams(): | |
def __init__(self, **kwargs): | |
for k, v in kwargs.items(): | |
if type(v) == dict: | |
v = HParams(**v) | |
self[k] = v | |
def keys(self): | |
return self.__dict__.keys() | |
def items(self): | |
return self.__dict__.items() | |
def values(self): | |
return self.__dict__.values() | |
def __len__(self): | |
return len(self.__dict__) | |
def __getitem__(self, key): | |
return getattr(self, key) | |
def __setitem__(self, key, value): | |
return setattr(self, key, value) | |
def __contains__(self, key): | |
return key in self.__dict__ | |
def __repr__(self): | |
return self.__dict__.__repr__() | |
def get_hparams_from_file(config_path): | |
with open(config_path, "r", encoding="utf-8") as f: | |
data = f.read() | |
config = json.loads(data) | |
hparams = HParams(**config) | |
return hparams | |
def vc_fn(sid, input_audio, vc_transform, auto_f0, cluster_ratio, slice_db, noise_scale, pad_seconds, cl_num, lg_num, lgr_num, f0_predictor, enhancer_adaptive_key, cr_threshold): | |
try: | |
if input_audio is None: | |
raise gr.Error("你需要上传音频") | |
if model is None: | |
raise gr.Error("你需要指定模型") | |
sampling_rate, audio = input_audio | |
# print(audio.shape,sampling_rate) | |
audio = (audio / np.iinfo(audio.dtype).max).astype(np.float32) | |
if len(audio.shape) > 1: | |
audio = librosa.to_mono(audio.transpose(1, 0)) | |
temp_path = "temp.wav" | |
soundfile.write(temp_path, audio, sampling_rate, format="wav") | |
_audio = model.slice_inference(temp_path, sid, vc_transform, slice_db, cluster_ratio, auto_f0, noise_scale, | |
pad_seconds, cl_num, lg_num, lgr_num, f0_predictor, enhancer_adaptive_key, cr_threshold) | |
model.clear_empty() | |
os.remove(temp_path) | |
# 构建保存文件的路径,并保存到results文件夹内 | |
try: | |
timestamp = str(int(time.time())) | |
filename = sid + "_" + timestamp + ".wav" | |
# output_file = os.path.join("./results", filename) | |
# soundfile.write(output_file, _audio, model.target_sample, format="wav") | |
soundfile.write('/tmp/'+filename, _audio, | |
model.target_sample, format="wav") | |
# return f"推理成功,音频文件保存为results/{filename}", (model.target_sample, _audio) | |
return f"推理成功,音频文件保存为{filename}", (model.target_sample, _audio) | |
except Exception as e: | |
if debug: | |
traceback.print_exc() | |
return f"文件保存失败,请手动保存", (model.target_sample, _audio) | |
except Exception as e: | |
if debug: | |
traceback.print_exc() | |
raise gr.Error(e) | |
def tts_func(_text, _rate, _voice): | |
# 使用edge-tts把文字转成音频 | |
# voice = "zh-CN-XiaoyiNeural"#女性,较高音 | |
# voice = "zh-CN-YunxiNeural"#男性 | |
voice = "zh-CN-YunxiNeural" # 男性 | |
if (_voice == "女"): | |
voice = "zh-CN-XiaoyiNeural" | |
output_file = "/tmp/"+_text[0:10]+".wav" | |
# communicate = edge_tts.Communicate(_text, voice) | |
# await communicate.save(output_file) | |
if _rate >= 0: | |
ratestr = "+{:.0%}".format(_rate) | |
elif _rate < 0: | |
ratestr = "{:.0%}".format(_rate) # 减号自带 | |
p = subprocess.Popen("edge-tts " + | |
" --text "+_text + | |
" --write-media "+output_file + | |
" --voice "+voice + | |
" --rate="+ratestr, shell=True, | |
stdout=subprocess.PIPE, | |
stdin=subprocess.PIPE) | |
p.wait() | |
return output_file | |
def text_clear(text): | |
return re.sub(r"[\n\,\(\) ]", "", text) | |
def vc_fn2(sid, input_audio, vc_transform, auto_f0, cluster_ratio, slice_db, noise_scale, pad_seconds, cl_num, lg_num, lgr_num, text2tts, tts_rate, tts_voice, f0_predictor, enhancer_adaptive_key, cr_threshold): | |
# 使用edge-tts把文字转成音频 | |
text2tts = text_clear(text2tts) | |
output_file = tts_func(text2tts, tts_rate, tts_voice) | |
# 调整采样率 | |
sr2 = 44100 | |
wav, sr = librosa.load(output_file) | |
wav2 = librosa.resample(wav, orig_sr=sr, target_sr=sr2) | |
save_path2 = text2tts[0:10]+"_44k"+".wav" | |
wavfile.write(save_path2, sr2, | |
(wav2 * np.iinfo(np.int16).max).astype(np.int16) | |
) | |
# 读取音频 | |
sample_rate, data = gr_pu.audio_from_file(save_path2) | |
vc_input = (sample_rate, data) | |
a, b = vc_fn(sid, vc_input, vc_transform, auto_f0, cluster_ratio, slice_db, noise_scale, | |
pad_seconds, cl_num, lg_num, lgr_num, f0_predictor, enhancer_adaptive_key, cr_threshold) | |
os.remove(output_file) | |
os.remove(save_path2) | |
return a, b | |
models_info = [ | |
{ | |
"description": """ | |
这个模型包含碧蓝档案的141名角色。\n\n | |
Space采用CPU推理,速度极慢,建议下载模型本地GPU推理。\n\n | |
""", | |
"model_path": "./G_387200.pth", | |
"config_path": "./config.json", | |
} | |
] | |
model_inferall = [] | |
if __name__ == "__main__": | |
parser = argparse.ArgumentParser() | |
parser.add_argument("--share", action="store_true", | |
default=False, help="share gradio app") | |
# 一定要设置的部分 | |
parser.add_argument('-cl', '--clip', type=float, | |
default=0, help='音频强制切片,默认0为自动切片,单位为秒/s') | |
parser.add_argument('-n', '--clean_names', type=str, nargs='+', | |
default=["君の知らない物語-src.wav"], help='wav文件名列表,放在raw文件夹下') | |
parser.add_argument('-t', '--trans', type=int, nargs='+', | |
default=[0], help='音高调整,支持正负(半音)') | |
parser.add_argument('-s', '--spk_list', type=str, | |
nargs='+', default=['nen'], help='合成目标说话人名称') | |
# 可选项部分 | |
parser.add_argument('-a', '--auto_predict_f0', action='store_true', | |
default=False, help='语音转换自动预测音高,转换歌声时不要打开这个会严重跑调') | |
parser.add_argument('-cm', '--cluster_model_path', type=str, | |
default="logs/44k/kmeans_10000.pt", help='聚类模型路径,如果没有训练聚类则随便填') | |
parser.add_argument('-cr', '--cluster_infer_ratio', type=float, | |
default=0, help='聚类方案占比,范围0-1,若没有训练聚类模型则默认0即可') | |
parser.add_argument('-lg', '--linear_gradient', type=float, default=0, | |
help='两段音频切片的交叉淡入长度,如果强制切片后出现人声不连贯可调整该数值,如果连贯建议采用默认值0,单位为秒') | |
parser.add_argument('-f0p', '--f0_predictor', type=str, default="pm", | |
help='选择F0预测器,可选择crepe,pm,dio,harvest,默认为pm(注意:crepe为原F0使用均值滤波器)') | |
parser.add_argument('-eh', '--enhance', action='store_true', default=False, | |
help='是否使用NSF_HIFIGAN增强器,该选项对部分训练集少的模型有一定的音质增强效果,但是对训练好的模型有反面效果,默认关闭') | |
parser.add_argument('-shd', '--shallow_diffusion', action='store_true', | |
default=False, help='是否使用浅层扩散,使用后可解决一部分电音问题,默认关闭,该选项打开时,NSF_HIFIGAN增强器将会被禁止') | |
# 浅扩散设置 | |
parser.add_argument('-dm', '--diffusion_model_path', type=str, | |
default="logs/44k/diffusion/model_0.pt", help='扩散模型路径') | |
parser.add_argument('-dc', '--diffusion_config_path', type=str, | |
default="logs/44k/diffusion/config.yaml", help='扩散模型配置文件路径') | |
parser.add_argument('-ks', '--k_step', type=int, | |
default=100, help='扩散步数,越大越接近扩散模型的结果,默认100') | |
parser.add_argument('-od', '--only_diffusion', action='store_true', | |
default=False, help='纯扩散模式,该模式不会加载sovits模型,以扩散模型推理') | |
# 不用动的部分 | |
parser.add_argument('-sd', '--slice_db', type=int, | |
default=-40, help='默认-40,嘈杂的音频可以-30,干声保留呼吸可以-50') | |
parser.add_argument('-d', '--device', type=str, | |
default=None, help='推理设备,None则为自动选择cpu和gpu') | |
parser.add_argument('-ns', '--noice_scale', type=float, | |
default=0.4, help='噪音级别,会影响咬字和音质,较为玄学') | |
parser.add_argument('-p', '--pad_seconds', type=float, default=0.5, | |
help='推理音频pad秒数,由于未知原因开头结尾会有异响,pad一小段静音段后就不会出现') | |
parser.add_argument('-wf', '--wav_format', type=str, | |
default='flac', help='音频输出格式') | |
parser.add_argument('-lgr', '--linear_gradient_retain', type=float, | |
default=0.75, help='自动音频切片后,需要舍弃每段切片的头尾。该参数设置交叉长度保留的比例,范围0-1,左开右闭') | |
parser.add_argument('-eak', '--enhancer_adaptive_key', | |
type=int, default=0, help='使增强器适应更高的音域(单位为半音数)|默认为0') | |
parser.add_argument('-ft', '--f0_filter_threshold', type=float, default=0.05, | |
help='F0过滤阈值,只有使用crepe时有效. 数值范围从0-1. 降低该值可减少跑调概率,但会增加哑音') | |
args = parser.parse_args() | |
categories = ["Blue Archive"] | |
others = { | |
"PCR vits-fast-fineturning": "https://huggingface.co/spaces/FrankZxShen/vits-fast-finetuning-pcr", | |
"Blue Archive vits-fast-fineturning": "https://huggingface.co/spaces/FrankZxShen/vits-fast-fineturning-models-ba", | |
} | |
for info in models_info: | |
config_path = info['config_path'] | |
model_path = info['model_path'] | |
description = info['description'] | |
clean_names = args.clean_names | |
trans = args.trans | |
spk_list = list(get_hparams_from_file(config_path).spk.keys()) | |
slice_db = args.slice_db | |
wav_format = args.wav_format | |
auto_predict_f0 = args.auto_predict_f0 | |
cluster_infer_ratio = args.cluster_infer_ratio | |
noice_scale = args.noice_scale | |
pad_seconds = args.pad_seconds | |
clip = args.clip | |
lg = args.linear_gradient | |
lgr = args.linear_gradient_retain | |
f0p = args.f0_predictor | |
enhance = args.enhance | |
enhancer_adaptive_key = args.enhancer_adaptive_key | |
cr_threshold = args.f0_filter_threshold | |
diffusion_model_path = args.diffusion_model_path | |
diffusion_config_path = args.diffusion_config_path | |
k_step = args.k_step | |
only_diffusion = args.only_diffusion | |
shallow_diffusion = args.shallow_diffusion | |
model = Svc(model_path, config_path, args.device, args.cluster_model_path, enhance, | |
diffusion_model_path, diffusion_config_path, shallow_diffusion, only_diffusion) | |
model_inferall.append((description, spk_list, model)) | |
app = gr.Blocks() | |
with app: | |
gr.Markdown( | |
"# <center> so-vits-svc-models-ba\n" | |
"# <center> Pay attention!!! Space uses CPU inferencing, which is extremely slow. It is recommended to download models.\n" | |
"# <center> 注意!!!Space采用CPU推理,速度极慢,建议下载模型使用本地GPU推理。\n" | |
"## <center> Please do not generate content that could infringe upon the rights or cause harm to individuals or organizations.\n" | |
"## <center> 请不要生成会对个人以及组织造成侵害的内容\n\n" | |
) | |
gr.Markdown("# Blue Archive\n\n" | |
) | |
with gr.Tabs(): | |
for category in categories: | |
with gr.TabItem(category): | |
for i, (description, speakers, model) in enumerate( | |
model_inferall): | |
gr.Markdown(description) | |
with gr.Row(): | |
with gr.Column(): | |
# textbox = gr.TextArea(label="Text", | |
# placeholder="Type your sentence here ", | |
# value="新たなキャラを解放できるようになったようですね。", elem_id=f"tts-input") | |
gr.Markdown(value=""" | |
<font size=2> 推理设置</font> | |
""") | |
sid = gr.Dropdown( | |
choices=speakers, value=speakers[0], label='角色选择') | |
auto_f0 = gr.Checkbox( | |
label="自动f0预测,配合聚类模型f0预测效果更好,会导致变调功能失效(仅限转换语音,歌声勾选此项会究极跑调)", value=False) | |
f0_predictor = gr.Dropdown(label="选择F0预测器,可选择crepe,pm,dio,harvest,默认为pm(注意:crepe为原F0使用均值滤波器)", choices=[ | |
"pm", "dio", "harvest", "crepe"], value="pm") | |
vc_transform = gr.Number( | |
label="变调(整数,可以正负,半音数量,升高八度就是12)", value=0) | |
cluster_ratio = gr.Number( | |
label="聚类模型混合比例,0-1之间,0即不启用聚类。使用聚类模型能提升音色相似度,但会导致咬字下降(如果使用建议0.5左右)", value=0) | |
slice_db = gr.Number(label="切片阈值", value=-40) | |
noise_scale = gr.Number( | |
label="noise_scale 建议不要动,会影响音质,玄学参数", value=0.4) | |
with gr.Column(): | |
pad_seconds = gr.Number( | |
label="推理音频pad秒数,由于未知原因开头结尾会有异响,pad一小段静音段后就不会出现", value=0.5) | |
cl_num = gr.Number( | |
label="音频自动切片,0为不切片,单位为秒(s)", value=0) | |
lg_num = gr.Number( | |
label="两端音频切片的交叉淡入长度,如果自动切片后出现人声不连贯可调整该数值,如果连贯建议采用默认值0,注意,该设置会影响推理速度,单位为秒/s", value=0) | |
lgr_num = gr.Number( | |
label="自动音频切片后,需要舍弃每段切片的头尾。该参数设置交叉长度保留的比例,范围0-1,左开右闭", value=0.75) | |
enhancer_adaptive_key = gr.Number( | |
label="使增强器适应更高的音域(单位为半音数)|默认为0", value=0) | |
cr_threshold = gr.Number( | |
label="F0过滤阈值,只有启动crepe时有效. 数值范围从0-1. 降低该值可减少跑调概率,但会增加哑音", value=0.05) | |
with gr.Tabs(): | |
with gr.TabItem("音频转音频"): | |
vc_input3 = gr.Audio(label="选择音频") | |
vc_submit = gr.Button( | |
"音频转换", variant="primary") | |
with gr.TabItem("文字转音频"): | |
text2tts = gr.Textbox( | |
label="在此输入要转译的文字。注意,使用该功能建议打开F0预测,不然会很怪") | |
tts_rate = gr.Number(label="tts语速", value=0) | |
tts_voice = gr.Radio(label="性别", choices=[ | |
"男", "女"], value="男") | |
vc_submit2 = gr.Button( | |
"文字转换", variant="primary") | |
with gr.Row(): | |
with gr.Column(): | |
vc_output1 = gr.Textbox(label="Output Message") | |
with gr.Column(): | |
vc_output2 = gr.Audio( | |
label="Output Audio", interactive=False) | |
vc_submit.click(vc_fn, [sid, vc_input3, vc_transform, auto_f0, cluster_ratio, slice_db, noise_scale, pad_seconds, | |
cl_num, lg_num, lgr_num, f0_predictor, enhancer_adaptive_key, cr_threshold], [vc_output1, vc_output2]) | |
vc_submit2.click(vc_fn2, [sid, vc_input3, vc_transform, auto_f0, cluster_ratio, slice_db, noise_scale, pad_seconds, cl_num, | |
lg_num, lgr_num, text2tts, tts_rate, tts_voice, f0_predictor, enhancer_adaptive_key, cr_threshold], [vc_output1, vc_output2]) | |
# gr.Examples( | |
# examples=example, | |
# inputs=[textbox, char_dropdown, language_dropdown, | |
# duration_slider, symbol_input], | |
# outputs=[text_output, audio_output], | |
# fn=tts_fn | |
# ) | |
for category, link in others.items(): | |
with gr.TabItem(category): | |
gr.Markdown( | |
f''' | |
<center> | |
<h2>Click to Go</h2> | |
<a href="{link}"> | |
<img src="https://huggingface.co/datasets/huggingface/badges/raw/main/open-in-hf-spaces-xl-dark.svg" | |
</a> | |
</center> | |
''' | |
) | |
app.queue(concurrency_count=3).launch(show_api=False, share=args.share) | |