|
|
|
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 |
|
|
|
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) |
|
|
|
try: |
|
timestamp = str(int(time.time())) |
|
filename = sid + "_" + timestamp + ".wav" |
|
|
|
|
|
soundfile.write('/tmp/'+filename, _audio, |
|
model.target_sample, format="wav") |
|
|
|
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): |
|
|
|
|
|
|
|
voice = "zh-CN-YunxiNeural" |
|
if (_voice == "女"): |
|
voice = "zh-CN-XiaoyiNeural" |
|
output_file = "/tmp/"+_text[0:10]+".wav" |
|
|
|
|
|
if _rate >= 0: |
|
ratestr = "+{:.0%}".format(_rate) |
|
elif _rate < 0: |
|
ratestr = "{:.0%}".format(_rate) |
|
|
|
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): |
|
|
|
text2tts = text_clear(text2tts) |
|
output_file = tts_func(text2tts, tts_rate, tts_voice) |
|
|
|
|
|
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) |
|
) |
|
|
|
|
|
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") |
|
|
|
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') |
|
|
|
|
|
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') |
|
|
|
|
|
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') |
|
|
|
|
|
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(): |
|
|
|
|
|
|
|
|
|
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]) |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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) |
|
|
