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()