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import io | |
import os | |
import torch | |
# os.system("wget -P cvec/ https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/hubert_base.pt") | |
import gradio as gr | |
import librosa | |
import numpy as np | |
import soundfile | |
import logging | |
from fairseq import checkpoint_utils | |
from my_utils import load_audio | |
from vc_infer_pipeline import VC | |
import traceback | |
from config import Config | |
from infer_pack.models import ( | |
SynthesizerTrnMs256NSFsid, | |
SynthesizerTrnMs256NSFsid_nono, | |
SynthesizerTrnMs768NSFsid, | |
SynthesizerTrnMs768NSFsid_nono, | |
) | |
from i18n import I18nAuto | |
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) | |
i18n = I18nAuto() | |
i18n.print() | |
config = Config() | |
weight_root = "weights" | |
weight_uvr5_root = "uvr5_weights" | |
index_root = "logs" | |
names = [] | |
hubert_model = None | |
for name in os.listdir(weight_root): | |
if name.endswith(".pth"): | |
names.append(name) | |
index_paths = [] | |
for root, dirs, files in os.walk(index_root, topdown=False): | |
for name in files: | |
if name.endswith(".index") and "trained" not in name: | |
index_paths.append("%s/%s" % (root, name)) | |
def get_vc(sid): | |
global n_spk, tgt_sr, net_g, vc, cpt, version | |
if sid == "" or sid == []: | |
global hubert_model | |
if hubert_model != None: # 考虑到轮询, 需要加个判断看是否 sid 是由有模型切换到无模型的 | |
print("clean_empty_cache") | |
del net_g, n_spk, vc, hubert_model, tgt_sr # ,cpt | |
hubert_model = net_g = n_spk = vc = hubert_model = tgt_sr = None | |
if torch.cuda.is_available(): | |
torch.cuda.empty_cache() | |
###楼下不这么折腾清理不干净 | |
if_f0 = cpt.get("f0", 1) | |
version = cpt.get("version", "v1") | |
if version == "v1": | |
if if_f0 == 1: | |
net_g = SynthesizerTrnMs256NSFsid( | |
*cpt["config"], is_half=config.is_half | |
) | |
else: | |
net_g = SynthesizerTrnMs256NSFsid_nono(*cpt["config"]) | |
elif version == "v2": | |
if if_f0 == 1: | |
net_g = SynthesizerTrnMs768NSFsid( | |
*cpt["config"], is_half=config.is_half | |
) | |
else: | |
net_g = SynthesizerTrnMs768NSFsid_nono(*cpt["config"]) | |
del net_g, cpt | |
if torch.cuda.is_available(): | |
torch.cuda.empty_cache() | |
cpt = None | |
return {"visible": False, "__type__": "update"} | |
person = "%s/%s" % (weight_root, sid) | |
print("loading %s" % person) | |
cpt = torch.load(person, map_location="cpu") | |
tgt_sr = cpt["config"][-1] | |
cpt["config"][-3] = cpt["weight"]["emb_g.weight"].shape[0] # n_spk | |
if_f0 = cpt.get("f0", 1) | |
version = cpt.get("version", "v1") | |
if version == "v1": | |
if if_f0 == 1: | |
net_g = SynthesizerTrnMs256NSFsid(*cpt["config"], is_half=config.is_half) | |
else: | |
net_g = SynthesizerTrnMs256NSFsid_nono(*cpt["config"]) | |
elif version == "v2": | |
if if_f0 == 1: | |
net_g = SynthesizerTrnMs768NSFsid(*cpt["config"], is_half=config.is_half) | |
else: | |
net_g = SynthesizerTrnMs768NSFsid_nono(*cpt["config"]) | |
del net_g.enc_q | |
print(net_g.load_state_dict(cpt["weight"], strict=False)) | |
net_g.eval().to(config.device) | |
if config.is_half: | |
net_g = net_g.half() | |
else: | |
net_g = net_g.float() | |
vc = VC(tgt_sr, config) | |
n_spk = cpt["config"][-3] | |
return {"visible": True, "maximum": n_spk, "__type__": "update"} | |
def load_hubert(): | |
global hubert_model | |
models, _, _ = checkpoint_utils.load_model_ensemble_and_task( | |
["hubert_base.pt"], | |
suffix="", | |
) | |
hubert_model = models[0] | |
hubert_model = hubert_model.to(config.device) | |
if config.is_half: | |
hubert_model = hubert_model.half() | |
else: | |
hubert_model = hubert_model.float() | |
hubert_model.eval() | |
def vc_single( | |
sid, | |
input_audio_path, | |
f0_up_key, | |
f0_file, | |
f0_method, | |
file_index, | |
file_index2, | |
# file_big_npy, | |
index_rate, | |
filter_radius, | |
resample_sr, | |
rms_mix_rate, | |
protect, | |
): # spk_item, input_audio0, vc_transform0,f0_file,f0method0 | |
global tgt_sr, net_g, vc, hubert_model, version | |
if input_audio_path is None: | |
return "You need to upload an audio", None | |
f0_up_key = int(f0_up_key) | |
try: | |
audio = input_audio_path[1] / 32768.0 | |
if len(audio.shape) == 2: | |
audio = np.mean(audio, -1) | |
audio = librosa.resample(audio, orig_sr=input_audio_path[0], target_sr=16000) | |
audio_max = np.abs(audio).max() / 0.95 | |
if audio_max > 1: | |
audio /= audio_max | |
times = [0, 0, 0] | |
if hubert_model == None: | |
load_hubert() | |
if_f0 = cpt.get("f0", 1) | |
file_index = ( | |
( | |
file_index.strip(" ") | |
.strip('"') | |
.strip("\n") | |
.strip('"') | |
.strip(" ") | |
.replace("trained", "added") | |
) | |
if file_index != "" | |
else file_index2 | |
) # 防止小白写错,自动帮他替换掉 | |
# file_big_npy = ( | |
# file_big_npy.strip(" ").strip('"').strip("\n").strip('"').strip(" ") | |
# ) | |
audio_opt = vc.pipeline( | |
hubert_model, | |
net_g, | |
sid, | |
audio, | |
input_audio_path, | |
times, | |
f0_up_key, | |
f0_method, | |
file_index, | |
# file_big_npy, | |
index_rate, | |
if_f0, | |
filter_radius, | |
tgt_sr, | |
resample_sr, | |
rms_mix_rate, | |
version, | |
protect, | |
f0_file=f0_file, | |
) | |
if resample_sr >= 16000 and tgt_sr != resample_sr: | |
tgt_sr = resample_sr | |
index_info = ( | |
"Using index:%s." % file_index | |
if os.path.exists(file_index) | |
else "Index not used." | |
) | |
return "Success.\n %s\nTime:\n npy:%ss, f0:%ss, infer:%ss" % ( | |
index_info, | |
times[0], | |
times[1], | |
times[2], | |
), (tgt_sr, audio_opt) | |
except: | |
info = traceback.format_exc() | |
print(info) | |
return info, (None, None) | |
app = gr.Blocks() | |
with app: | |
with gr.Tabs(): | |
with gr.TabItem("在线demo"): | |
gr.Markdown( | |
value=""" | |
RVC 在线demo | |
""" | |
) | |
sid = gr.Dropdown(label=i18n("推理音色"), choices=sorted(names)) | |
with gr.Column(): | |
spk_item = gr.Slider( | |
minimum=0, | |
maximum=2333, | |
step=1, | |
label=i18n("请选择说话人id"), | |
value=0, | |
visible=False, | |
interactive=True, | |
) | |
sid.change( | |
fn=get_vc, | |
inputs=[sid], | |
outputs=[spk_item], | |
) | |
gr.Markdown( | |
value=i18n("男转女推荐+12key, 女转男推荐-12key, 如果音域爆炸导致音色失真也可以自己调整到合适音域. ") | |
) | |
vc_input3 = gr.Audio(label="上传音频(长度小于90秒)") | |
vc_transform0 = gr.Number(label=i18n("变调(整数, 半音数量, 升八度12降八度-12)"), value=0) | |
f0method0 = gr.Radio( | |
label=i18n("选择音高提取算法,输入歌声可用pm提速,harvest低音好但巨慢无比,crepe效果好但吃GPU"), | |
choices=["pm", "harvest", "crepe"], | |
value="pm", | |
interactive=True, | |
) | |
filter_radius0 = gr.Slider( | |
minimum=0, | |
maximum=7, | |
label=i18n(">=3则使用对harvest音高识别的结果使用中值滤波,数值为滤波半径,使用可以削弱哑音"), | |
value=3, | |
step=1, | |
interactive=True, | |
) | |
with gr.Column(): | |
file_index1 = gr.Textbox( | |
label=i18n("特征检索库文件路径,为空则使用下拉的选择结果"), | |
value="", | |
interactive=False, | |
visible=False, | |
) | |
file_index2 = gr.Dropdown( | |
label=i18n("自动检测index路径,下拉式选择(dropdown)"), | |
choices=sorted(index_paths), | |
interactive=True, | |
) | |
index_rate1 = gr.Slider( | |
minimum=0, | |
maximum=1, | |
label=i18n("检索特征占比"), | |
value=0.88, | |
interactive=True, | |
) | |
resample_sr0 = gr.Slider( | |
minimum=0, | |
maximum=48000, | |
label=i18n("后处理重采样至最终采样率,0为不进行重采样"), | |
value=0, | |
step=1, | |
interactive=True, | |
) | |
rms_mix_rate0 = gr.Slider( | |
minimum=0, | |
maximum=1, | |
label=i18n("输入源音量包络替换输出音量包络融合比例,越靠近1越使用输出包络"), | |
value=1, | |
interactive=True, | |
) | |
protect0 = gr.Slider( | |
minimum=0, | |
maximum=0.5, | |
label=i18n("保护清辅音和呼吸声,防止电音撕裂等artifact,拉满0.5不开启,调低加大保护力度但可能降低索引效果"), | |
value=0.33, | |
step=0.01, | |
interactive=True, | |
) | |
f0_file = gr.File(label=i18n("F0曲线文件, 可选, 一行一个音高, 代替默认F0及升降调")) | |
but0 = gr.Button(i18n("转换"), variant="primary") | |
vc_output1 = gr.Textbox(label=i18n("输出信息")) | |
vc_output2 = gr.Audio(label=i18n("输出音频(右下角三个点,点了可以下载)")) | |
but0.click( | |
vc_single, | |
[ | |
spk_item, | |
vc_input3, | |
vc_transform0, | |
f0_file, | |
f0method0, | |
file_index1, | |
file_index2, | |
# file_big_npy1, | |
index_rate1, | |
filter_radius0, | |
resample_sr0, | |
rms_mix_rate0, | |
protect0, | |
], | |
[vc_output1, vc_output2], | |
) | |
app.launch() | |