fix: MacOS 纯 CPU 推理时 Segmentation fault: 11
see: facebookresearch/faiss#2317 facebookresearch#2410
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源文雨
parent
6c7c1d933f
commit
ff1a711cad
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@ -129,10 +129,10 @@
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"#@title 从谷歌云盘加载打包好的数据集到/content/dataset\n",
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"\n",
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"#@markdown 数据集位置\n",
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"DATASET = \"/content/drive/MyDrive/dataset/lulucall_48k.zip\" #@param {type:\"string\"}\n",
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"DATASET = \"/content/drive/MyDrive/dataset/lulu20230327_32k.zip\" #@param {type:\"string\"}\n",
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"\n",
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"!mkdir -p /content/dataset\n",
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"!unzip -d /content/dataset {DATASET}"
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"!unzip -d /content/dataset -B {DATASET}"
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],
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"metadata": {
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"id": "Mwk7Q0Loqzjx"
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@ -140,13 +140,26 @@
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"execution_count": null,
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"outputs": []
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},
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{
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"cell_type": "code",
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"source": [
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"#@title 重命名数据集中的重名文件\n",
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"!ls -a /content/dataset/\n",
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"!rename 's/(\\w+)\\.(\\w+)~(\\d*)/$1_$3.$2/' /content/dataset/*.*~*"
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],
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"metadata": {
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"id": "PDlFxWHWEynD"
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},
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"execution_count": null,
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"outputs": []
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},
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{
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"cell_type": "code",
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"source": [
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"#@title 启动web\n",
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"%cd /content/Retrieval-based-Voice-Conversion-WebUI\n",
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"%load_ext tensorboard\n",
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"%tensorboard --logdir /content/Retrieval-based-Voice-Conversion-WebUI/logs\n",
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"# %load_ext tensorboard\n",
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"# %tensorboard --logdir /content/Retrieval-based-Voice-Conversion-WebUI/logs\n",
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"!python3 infer-web.py --colab --pycmd python3"
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],
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"metadata": {
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@ -164,7 +177,7 @@
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"#@markdown 模型名\n",
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"MODELNAME = \"lulu\" #@param {type:\"string\"}\n",
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"#@markdown 模型epoch\n",
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"MODELEPOCH = 7500 #@param {type:\"integer\"}\n",
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"MODELEPOCH = 9600 #@param {type:\"integer\"}\n",
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"\n",
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"!cp /content/Retrieval-based-Voice-Conversion-WebUI/logs/{MODELNAME}/G_{MODELEPOCH}.pth /content/drive/MyDrive/{MODELNAME}_D_{MODELEPOCH}.pth\n",
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"!cp /content/Retrieval-based-Voice-Conversion-WebUI/logs/{MODELNAME}/D_{MODELEPOCH}.pth /content/drive/MyDrive/{MODELNAME}_G_{MODELEPOCH}.pth\n",
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@ -188,7 +201,7 @@
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"#@markdown 模型名\n",
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"MODELNAME = \"lulu\" #@param {type:\"string\"}\n",
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"#@markdown 模型epoch\n",
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"MODELEPOCH = 6000 #@param {type:\"integer\"}\n",
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"MODELEPOCH = 7500 #@param {type:\"integer\"}\n",
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"\n",
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"!mkdir -p /content/Retrieval-based-Voice-Conversion-WebUI/logs/{MODELNAME}\n",
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"\n",
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@ -241,7 +254,7 @@
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"#@markdown 模型名\n",
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"MODELNAME = \"lulu\" #@param {type:\"string\"}\n",
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"#@markdown 停止的epoch\n",
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"MODELEPOCH = 2500 #@param {type:\"integer\"}\n",
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"MODELEPOCH = 3200 #@param {type:\"integer\"}\n",
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"#@markdown 保存epoch间隔\n",
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"EPOCHSAVE = 100 #@param {type:\"integer\"}\n",
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"#@markdown 采样率\n",
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@ -262,7 +275,7 @@
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"#@markdown 模型名\n",
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"MODELNAME = \"lulu\" #@param {type:\"string\"}\n",
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"#@markdown 选中模型epoch\n",
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"MODELEPOCH = 7700 #@param {type:\"integer\"}\n",
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"MODELEPOCH = 9600 #@param {type:\"integer\"}\n",
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"\n",
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"!echo \"备份选中的模型。。。\"\n",
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"!cp /content/Retrieval-based-Voice-Conversion-WebUI/logs/{MODELNAME}/G_{MODELEPOCH}.pth /content/{MODELNAME}_D_{MODELEPOCH}.pth\n",
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@ -292,7 +305,7 @@
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"#@markdown 模型名\n",
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"MODELNAME = \"lulu\" #@param {type:\"string\"}\n",
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"#@markdown 选中模型epoch\n",
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"MODELEPOCH = 7700 #@param {type:\"integer\"}\n",
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"MODELEPOCH = 9600 #@param {type:\"integer\"}\n",
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"\n",
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"!echo \"备份选中的模型。。。\"\n",
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"!cp /content/Retrieval-based-Voice-Conversion-WebUI/logs/{MODELNAME}/G_{MODELEPOCH}.pth /content/{MODELNAME}_D_{MODELEPOCH}.pth\n",
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@ -9,7 +9,7 @@ import faiss
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ncpu=cpu_count()
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ngpu=torch.cuda.device_count()
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gpu_infos=[]
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if(torch.cuda.is_available()==False or ngpu==0):if_gpu_ok=False
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if((not torch.cuda.is_available()) or ngpu==0):if_gpu_ok=False
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else:
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if_gpu_ok = False
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for i in range(ngpu):
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@ -140,7 +140,7 @@ def uvr(model_name,inp_root,save_root_vocal,paths,save_root_ins):
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except:
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traceback.print_exc()
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print("clean_empty_cache")
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torch.cuda.empty_cache()
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if torch.cuda.is_available(): torch.cuda.empty_cache()
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yield "\n".join(infos)
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#一个选项卡全局只能有一个音色
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@ -152,7 +152,7 @@ def get_vc(sid):
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print("clean_empty_cache")
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del net_g, n_spk, vc, hubert_model,tgt_sr#,cpt
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hubert_model = net_g=n_spk=vc=hubert_model=tgt_sr=None
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torch.cuda.empty_cache()
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if torch.cuda.is_available(): torch.cuda.empty_cache()
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###楼下不这么折腾清理不干净
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if_f0 = cpt.get("f0", 1)
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if (if_f0 == 1):
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@ -160,7 +160,7 @@ def get_vc(sid):
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else:
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net_g = SynthesizerTrnMs256NSFsid_nono(*cpt["config"])
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del net_g,cpt
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torch.cuda.empty_cache()
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if torch.cuda.is_available(): torch.cuda.empty_cache()
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cpt=None
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return {"visible": False, "__type__": "update"}
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person = "%s/%s" % (weight_root, sid)
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@ -104,7 +104,7 @@ for idx,name in enumerate(["冬之花clip1.wav",]):##
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"padding_mask": padding_mask.to(device),
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"output_layer": 9, # layer 9
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}
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torch.cuda.synchronize()
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if torch.cuda.is_available(): torch.cuda.synchronize()
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t0=ttime()
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with torch.no_grad():
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logits = model.extract_features(**inputs)
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@ -116,13 +116,13 @@ for idx,name in enumerate(["冬之花clip1.wav",]):##
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feats = torch.from_numpy(big_npy[I.squeeze()].astype("float16")).unsqueeze(0).to(device)
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feats=F.interpolate(feats.permute(0,2,1),scale_factor=2).permute(0,2,1)
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torch.cuda.synchronize()
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if torch.cuda.is_available(): torch.cuda.synchronize()
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t1=ttime()
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# p_len = min(feats.shape[1],10000,pitch.shape[0])#太大了爆显存
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p_len = min(feats.shape[1],10000)#
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pitch, pitchf = get_f0(audio, p_len,f0_up_key)
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p_len = min(feats.shape[1],10000,pitch.shape[0])#太大了爆显存
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torch.cuda.synchronize()
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if torch.cuda.is_available(): torch.cuda.synchronize()
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t2=ttime()
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feats = feats[:,:p_len, :]
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pitch = pitch[:p_len]
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@ -133,7 +133,7 @@ for idx,name in enumerate(["冬之花clip1.wav",]):##
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pitchf = torch.FloatTensor(pitchf).unsqueeze(0).to(device)
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with torch.no_grad():
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audio = net_g.infer(feats, p_len,pitch,pitchf,sid)[0][0, 0].data.cpu().float().numpy()#nsf
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torch.cuda.synchronize()
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if torch.cuda.is_available(): torch.cuda.synchronize()
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t3=ttime()
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ta0+=(t1-t0)
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ta1+=(t2-t1)
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@ -4,7 +4,7 @@ scipy==1.9.3
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librosa==0.9.2
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llvmlite==0.39.0
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fairseq==0.12.2
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faiss-cpu==1.7.2
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faiss-cpu==1.7.0
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gradio
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Cython
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future>=0.18.3
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@ -72,6 +72,7 @@ class VC(object):
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"output_layer": 9, # layer 9
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}
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t0 = ttime()
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print("vc npy start time:", t0)
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with torch.no_grad():
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logits = model.extract_features(**inputs)
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feats = model.final_proj(logits[0])
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@ -79,13 +80,14 @@ class VC(object):
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if(isinstance(index,type(None))==False and isinstance(big_npy,type(None))==False and index_rate!=0):
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npy = feats[0].cpu().numpy()
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if(self.is_half==True):npy=npy.astype("float32")
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D, I = index.search(npy, 1)
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_, I = index.search(npy, 1)
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npy=big_npy[I.squeeze()]
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if(self.is_half==True):npy=npy.astype("float16")
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feats = torch.from_numpy(npy).unsqueeze(0).to(self.device)*index_rate + (1-index_rate)*feats
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feats = F.interpolate(feats.permute(0, 2, 1), scale_factor=2).permute(0, 2, 1)
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t1 = ttime()
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print("vc infer start time:", t1)
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p_len = audio0.shape[0]//self.window
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if(feats.shape[1]<p_len):
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p_len=feats.shape[1]
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@ -99,8 +101,9 @@ class VC(object):
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else:
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audio1 = (net_g.infer(feats, p_len, sid)[0][0, 0] * 32768).data.cpu().float().numpy().astype(np.int16)
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del feats,p_len,padding_mask
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torch.cuda.empty_cache()
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if torch.cuda.is_available(): torch.cuda.empty_cache()
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t2 = ttime()
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print("vc infer end time:", t2)
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times[0] += (t1 - t0)
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times[2] += (t2 - t1)
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return audio1
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@ -125,6 +128,7 @@ class VC(object):
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audio_opt=[]
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t=None
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t1=ttime()
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print("f0 start time:", t1)
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audio_pad = np.pad(audio, (self.t_pad, self.t_pad), mode='reflect')
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p_len=audio_pad.shape[0]//self.window
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inp_f0=None
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@ -146,6 +150,7 @@ class VC(object):
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pitch = torch.tensor(pitch,device=self.device).unsqueeze(0).long()
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pitchf = torch.tensor(pitchf,device=self.device).unsqueeze(0).float()
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t2=ttime()
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print("f0 end time:", t2)
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times[1] += (t2 - t1)
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for t in opt_ts:
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t=t//self.window*self.window
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@ -160,5 +165,5 @@ class VC(object):
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audio_opt.append(self.vc(model,net_g,sid,audio_pad[t:],None,None,times,index,big_npy,index_rate)[self.t_pad_tgt:-self.t_pad_tgt])
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audio_opt=np.concatenate(audio_opt)
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del pitch,pitchf,sid
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torch.cuda.empty_cache()
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if torch.cuda.is_available(): torch.cuda.empty_cache()
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return audio_opt
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