Format code (#274)
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@ -18,9 +18,12 @@ from fairseq import checkpoint_utils
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device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
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if torch.cuda.is_available():device="cuda"
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elif torch.backends.mps.is_available():device="mps"
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else:device="cpu"
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if torch.cuda.is_available():
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device = "cuda"
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elif torch.backends.mps.is_available():
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device = "mps"
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else:
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device = "cpu"
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f = open("%s/extract_f0_feature.log" % exp_dir, "a+")
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@ -64,7 +67,7 @@ models, saved_cfg, task = checkpoint_utils.load_model_ensemble_and_task(
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model = models[0]
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model = model.to(device)
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printt("move model to %s" % device)
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if device not in ["mps","cpu"]:
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if device not in ["mps", "cpu"]:
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model = model.half()
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model.eval()
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86
infer-web.py
86
infer-web.py
@ -11,8 +11,8 @@ now_dir = os.getcwd()
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sys.path.append(now_dir)
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tmp = os.path.join(now_dir, "TEMP")
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shutil.rmtree(tmp, ignore_errors=True)
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shutil.rmtree("%s/runtime/Lib/site-packages/infer_pack"%(now_dir), ignore_errors=True)
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shutil.rmtree("%s/runtime/Lib/site-packages/uvr5_pack"%(now_dir) , ignore_errors=True)
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shutil.rmtree("%s/runtime/Lib/site-packages/infer_pack" % (now_dir), ignore_errors=True)
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shutil.rmtree("%s/runtime/Lib/site-packages/uvr5_pack" % (now_dir), ignore_errors=True)
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os.makedirs(tmp, exist_ok=True)
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os.makedirs(os.path.join(now_dir, "logs"), exist_ok=True)
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os.makedirs(os.path.join(now_dir, "weights"), exist_ok=True)
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@ -121,11 +121,11 @@ names = []
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for name in os.listdir(weight_root):
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if name.endswith(".pth"):
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names.append(name)
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index_paths=[]
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index_paths = []
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for root, dirs, files in os.walk(index_root, topdown=False):
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for name in files:
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if name.endswith(".index") and "trained" not in name:
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index_paths.append("%s/%s"%(root,name))
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index_paths.append("%s/%s" % (root, name))
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uvr5_names = []
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for name in os.listdir(weight_uvr5_root):
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if name.endswith(".pth"):
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@ -156,13 +156,17 @@ def vc_single(
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load_hubert()
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if_f0 = cpt.get("f0", 1)
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file_index = (
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file_index.strip(" ")
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.strip('"')
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.strip("\n")
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.strip('"')
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.strip(" ")
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.replace("trained", "added")
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)if file_index!=""else file_index2 # 防止小白写错，自动帮他替换掉
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(
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file_index.strip(" ")
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.strip('"')
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.strip("\n")
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.strip('"')
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.strip(" ")
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.replace("trained", "added")
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)
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if file_index != ""
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else file_index2
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) # 防止小白写错，自动帮他替换掉
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# file_big_npy = (
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# file_big_npy.strip(" ").strip('"').strip("\n").strip('"').strip(" ")
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# )
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@ -184,10 +188,19 @@ def vc_single(
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resample_sr,
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f0_file=f0_file,
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)
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if(resample_sr>=16000 and tgt_sr!=resample_sr):
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tgt_sr=resample_sr
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index_info="Using index:%s."%file_index if os.path.exists(file_index)else"Index not used."
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return "Success.\n %s\nTime:\n npy:%ss, f0:%ss, infer:%ss"%(index_info,times[0],times[1],times[2]), (tgt_sr, audio_opt)
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if resample_sr >= 16000 and tgt_sr != resample_sr:
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tgt_sr = resample_sr
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index_info = (
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"Using index:%s." % file_index
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if os.path.exists(file_index)
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else "Index not used."
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)
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return "Success.\n %s\nTime:\n npy:%ss, f0:%ss, infer:%ss" % (
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index_info,
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times[0],
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times[1],
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times[2],
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), (tgt_sr, audio_opt)
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except:
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info = traceback.format_exc()
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print(info)
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@ -237,7 +250,7 @@ def vc_multi(
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filter_radius,
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resample_sr,
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)
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if "Success"in info:
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if "Success" in info:
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try:
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tgt_sr, audio_opt = opt
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wavfile.write(
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@ -323,7 +336,7 @@ def uvr(model_name, inp_root, save_root_vocal, paths, save_root_ins, agg):
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# 一个选项卡全局只能有一个音色
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def get_vc(sid):
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global n_spk, tgt_sr, net_g, vc, cpt
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if sid == ""or sid==[]:
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if sid == "" or sid == []:
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global hubert_model
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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
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print("clean_empty_cache")
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@ -371,12 +384,15 @@ def change_choices():
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for name in os.listdir(weight_root):
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if name.endswith(".pth"):
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names.append(name)
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index_paths=[]
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index_paths = []
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for root, dirs, files in os.walk(index_root, topdown=False):
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for name in files:
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if name.endswith(".index") and "trained" not in name:
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index_paths.append("%s/%s" % (root, name))
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return {"choices": sorted(names), "__type__": "update"},{"choices": sorted(index_paths), "__type__": "update"}
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return {"choices": sorted(names), "__type__": "update"}, {
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"choices": sorted(index_paths),
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"__type__": "update",
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}
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def clean():
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@ -1096,7 +1112,7 @@ with gr.Blocks() as app:
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value="pm",
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interactive=True,
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)
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filter_radius0=gr.Slider(
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filter_radius0 = gr.Slider(
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minimum=0,
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maximum=7,
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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"),
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@ -1115,7 +1131,9 @@ with gr.Blocks() as app:
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choices=sorted(index_paths),
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interactive=True,
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)
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refresh_button.click(fn=change_choices, inputs=[], outputs=[sid0, file_index2])
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refresh_button.click(
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fn=change_choices, inputs=[], outputs=[sid0, file_index2]
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)
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# file_big_npy1 = gr.Textbox(
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# label=i18n("特征文件路径"),
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# value="E:\\codes\py39\\vits_vc_gpu_train\\logs\\mi-test-1key\\total_fea.npy",
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@ -1128,7 +1146,7 @@ with gr.Blocks() as app:
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value=0.76,
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interactive=True,
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)
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resample_sr0=gr.Slider(
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resample_sr0 = gr.Slider(
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minimum=0,
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maximum=48000,
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label=i18n("后处理重采样至最终采样率，0为不进行重采样"),
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@ -1154,7 +1172,7 @@ with gr.Blocks() as app:
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# file_big_npy1,
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index_rate1,
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filter_radius0,
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resample_sr0
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resample_sr0,
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],
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[vc_output1, vc_output2],
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)
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@ -1174,7 +1192,7 @@ with gr.Blocks() as app:
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value="pm",
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interactive=True,
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)
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filter_radius1=gr.Slider(
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filter_radius1 = gr.Slider(
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minimum=0,
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maximum=7,
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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"),
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@ -1205,7 +1223,7 @@ with gr.Blocks() as app:
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value=1,
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interactive=True,
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)
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resample_sr1=gr.Slider(
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resample_sr1 = gr.Slider(
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minimum=0,
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maximum=48000,
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label=i18n("后处理重采样至最终采样率，0为不进行重采样"),
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@ -1237,7 +1255,7 @@ with gr.Blocks() as app:
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# file_big_npy2,
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index_rate2,
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filter_radius1,
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resample_sr1
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resample_sr1,
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],
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[vc_output3],
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)
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@ -1335,7 +1353,7 @@ with gr.Blocks() as app:
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but1 = gr.Button(i18n("处理数据"), variant="primary")
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info1 = gr.Textbox(label=i18n("输出信息"), value="")
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but1.click(
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preprocess_dataset, [trainset_dir4, exp_dir1, sr2,np7], [info1]
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preprocess_dataset, [trainset_dir4, exp_dir1, sr2, np7], [info1]
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)
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with gr.Group():
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gr.Markdown(value=i18n("step2b: 使用CPU提取音高(如果模型带音高), 使用GPU提取特征(选择卡号)"))
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@ -1597,16 +1615,16 @@ with gr.Blocks() as app:
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butOnnx = gr.Button(i18n("导出Onnx模型"), variant="primary")
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butOnnx.click(export_onnx, [ckpt_dir, onnx_dir, moevs], infoOnnx)
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tab_faq=i18n("常见问题解答")
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tab_faq = i18n("常见问题解答")
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with gr.TabItem(tab_faq):
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try:
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if(tab_faq=="常见问题解答"):
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with open("docs/faq.md","r",encoding="utf8")as f:info=f.read()
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if tab_faq == "常见问题解答":
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with open("docs/faq.md", "r", encoding="utf8") as f:
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info = f.read()
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else:
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with open("docs/faq_en.md", "r")as f:info = f.read()
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gr.Markdown(
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value=info
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)
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with open("docs/faq_en.md", "r") as f:
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info = f.read()
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gr.Markdown(value=info)
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except:
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gr.Markdown(traceback.format_exc())
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@ -2,16 +2,18 @@ import numpy as np, parselmouth, torch, pdb
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from time import time as ttime
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import torch.nn.functional as F
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import scipy.signal as signal
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import pyworld, os, traceback, faiss,librosa
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import pyworld, os, traceback, faiss, librosa
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from scipy import signal
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from functools import lru_cache
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bh, ah = signal.butter(N=5, Wn=48, btype="high", fs=16000)
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input_audio_path2wav={}
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input_audio_path2wav = {}
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@lru_cache
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def cache_harvest_f0(input_audio_path,fs,f0max,f0min,frame_period):
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audio=input_audio_path2wav[input_audio_path]
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def cache_harvest_f0(input_audio_path, fs, f0max, f0min, frame_period):
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audio = input_audio_path2wav[input_audio_path]
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f0, t = pyworld.harvest(
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audio,
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fs=fs,
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@ -22,6 +24,7 @@ def cache_harvest_f0(input_audio_path,fs,f0max,f0min,frame_period):
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f0 = pyworld.stonemask(audio, f0, t, fs)
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return f0
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class VC(object):
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def __init__(self, tgt_sr, config):
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self.x_pad, self.x_query, self.x_center, self.x_max, self.is_half = (
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@ -41,7 +44,16 @@ class VC(object):
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self.t_max = self.sr * self.x_max # 免查询时长阈值
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self.device = config.device
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def get_f0(self, input_audio_path,x, p_len, f0_up_key, f0_method,filter_radius, inp_f0=None):
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def get_f0(
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self,
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input_audio_path,
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x,
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p_len,
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f0_up_key,
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f0_method,
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filter_radius,
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inp_f0=None,
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):
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global input_audio_path2wav
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time_step = self.window / self.sr * 1000
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f0_min = 50
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@ -65,9 +77,9 @@ class VC(object):
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f0, [[pad_size, p_len - len(f0) - pad_size]], mode="constant"
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)
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elif f0_method == "harvest":
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input_audio_path2wav[input_audio_path]=x.astype(np.double)
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f0=cache_harvest_f0(input_audio_path,self.sr,f0_max,f0_min,10)
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if(filter_radius>2):
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input_audio_path2wav[input_audio_path] = x.astype(np.double)
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f0 = cache_harvest_f0(input_audio_path, self.sr, f0_max, f0_min, 10)
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if filter_radius > 2:
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f0 = signal.medfilt(f0, 3)
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f0 *= pow(2, f0_up_key / 12)
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# with open("test.txt","w")as f:f.write("\n".join([str(i)for i in f0.tolist()]))
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@ -255,7 +267,15 @@ class VC(object):
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sid = torch.tensor(sid, device=self.device).unsqueeze(0).long()
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pitch, pitchf = None, None
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if if_f0 == 1:
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pitch, pitchf = self.get_f0(input_audio_path,audio_pad, p_len, f0_up_key, f0_method,filter_radius, inp_f0)
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pitch, pitchf = self.get_f0(
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input_audio_path,
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audio_pad,
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p_len,
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f0_up_key,
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f0_method,
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filter_radius,
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inp_f0,
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)
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pitch = pitch[:p_len]
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pitchf = pitchf[:p_len]
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if self.device == "mps":
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@ -328,11 +348,11 @@ class VC(object):
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)[self.t_pad_tgt : -self.t_pad_tgt]
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)
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audio_opt = np.concatenate(audio_opt)
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if(resample_sr>=16000 and tgt_sr!=resample_sr):
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if resample_sr >= 16000 and tgt_sr != resample_sr:
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audio_opt = librosa.resample(
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audio_opt, orig_sr=tgt_sr, target_sr=resample_sr
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)
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audio_opt=audio_opt.astype(np.int16)
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audio_opt = audio_opt.astype(np.int16)
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del pitch, pitchf, sid
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if torch.cuda.is_available():
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torch.cuda.empty_cache()
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