Remove part of the non-inference-related modules

infer-web.py

@@ -6,13 +6,6 @@ now_dir = os.getcwd()
 sys.path.append(now_dir)
 load_dotenv()
 from infer.modules.vc.modules import VC
-from infer.modules.uvr5.modules import uvr
-from infer.lib.train.process_ckpt import (
-    change_info,
-    extract_small_model,
-    merge,
-    show_info,
-)
 from i18n.i18n import I18nAuto
 from configs.config import Config
 from sklearn.cluster import MiniBatchKMeans
@@ -451,8 +444,8 @@ def change_version19(sr2, if_f0_3, version19):
         to_return_sr2,
     )
 
-
+# f0method8,pretrained_G14,pretrained_D15
-def change_f0(if_f0_3, sr2, version19):  # f0method8,pretrained_G14,pretrained_D15
+def change_f0(if_f0_3, sr2, version19):
     path_str = "" if version19 == "v1" else "_v2"
     return (
         {"visible": if_f0_3, "__type__": "update"},
@@ -461,324 +454,7 @@ def change_f0(if_f0_3, sr2, version19):  # f0method8,pretrained_G14,pretrained_D
     )
 
 
-# but3.click(click_train,[exp_dir1,sr2,if_f0_3,save_epoch10,total_epoch11,batch_size12,if_save_latest13,pretrained_G14,pretrained_D15,gpus16])
+# ckpt_path2.change(change_info_,[ckpt_path2],[sr__,if_f0__])
-def click_train(
-    exp_dir1,
-    sr2,
-    if_f0_3,
-    spk_id5,
-    save_epoch10,
-    total_epoch11,
-    batch_size12,
-    if_save_latest13,
-    pretrained_G14,
-    pretrained_D15,
-    gpus16,
-    if_cache_gpu17,
-    if_save_every_weights18,
-    version19,
-):
-    # 生成filelist
-    exp_dir = "%s/logs/%s" % (now_dir, exp_dir1)
-    os.makedirs(exp_dir, exist_ok=True)
-    gt_wavs_dir = "%s/0_gt_wavs" % (exp_dir)
-    feature_dir = (
-        "%s/3_feature256" % (exp_dir)
-        if version19 == "v1"
-        else "%s/3_feature768" % (exp_dir)
-    )
-    if if_f0_3:
-        f0_dir = "%s/2a_f0" % (exp_dir)
-        f0nsf_dir = "%s/2b-f0nsf" % (exp_dir)
-        names = (
-            set([name.split(".")[0] for name in os.listdir(gt_wavs_dir)])
-            & set([name.split(".")[0] for name in os.listdir(feature_dir)])
-            & set([name.split(".")[0] for name in os.listdir(f0_dir)])
-            & set([name.split(".")[0] for name in os.listdir(f0nsf_dir)])
-        )
-    else:
-        names = set([name.split(".")[0] for name in os.listdir(gt_wavs_dir)]) & set(
-            [name.split(".")[0] for name in os.listdir(feature_dir)]
-        )
-    opt = []
-    for name in names:
-        if if_f0_3:
-            opt.append(
-                "%s/%s.wav|%s/%s.npy|%s/%s.wav.npy|%s/%s.wav.npy|%s"
-                % (
-                    gt_wavs_dir.replace("\\", "\\\\"),
-                    name,
-                    feature_dir.replace("\\", "\\\\"),
-                    name,
-                    f0_dir.replace("\\", "\\\\"),
-                    name,
-                    f0nsf_dir.replace("\\", "\\\\"),
-                    name,
-                    spk_id5,
-                )
-            )
-        else:
-            opt.append(
-                "%s/%s.wav|%s/%s.npy|%s"
-                % (
-                    gt_wavs_dir.replace("\\", "\\\\"),
-                    name,
-                    feature_dir.replace("\\", "\\\\"),
-                    name,
-                    spk_id5,
-                )
-            )
-    fea_dim = 256 if version19 == "v1" else 768
-    if if_f0_3:
-        for _ in range(2):
-            opt.append(
-                "%s/logs/mute/0_gt_wavs/mute%s.wav|%s/logs/mute/3_feature%s/mute.npy|%s/logs/mute/2a_f0/mute.wav.npy|%s/logs/mute/2b-f0nsf/mute.wav.npy|%s"
-                % (now_dir, sr2, now_dir, fea_dim, now_dir, now_dir, spk_id5)
-            )
-    else:
-        for _ in range(2):
-            opt.append(
-                "%s/logs/mute/0_gt_wavs/mute%s.wav|%s/logs/mute/3_feature%s/mute.npy|%s"
-                % (now_dir, sr2, now_dir, fea_dim, spk_id5)
-            )
-    shuffle(opt)
-    with open("%s/filelist.txt" % exp_dir, "w") as f:
-        f.write("\n".join(opt))
-    logger.debug("Write filelist done")
-    # 生成config#无需生成config
-    # cmd = python_cmd + " train_nsf_sim_cache_sid_load_pretrain.py -e mi-test -sr 40k -f0 1 -bs 4 -g 0 -te 10 -se 5 -pg pretrained/f0G40k.pth -pd pretrained/f0D40k.pth -l 1 -c 0"
-    logger.info("Use gpus: %s", str(gpus16))
-    if pretrained_G14 == "":
-        logger.info("No pretrained Generator")
-    if pretrained_D15 == "":
-        logger.info("No pretrained Discriminator")
-    if version19 == "v1" or sr2 == "40k":
-        config_path = "v1/%s.json" % sr2
-    else:
-        config_path = "v2/%s.json" % sr2
-    config_save_path = os.path.join(exp_dir, "config.json")
-    if not pathlib.Path(config_save_path).exists():
-        with open(config_save_path, "w", encoding="utf-8") as f:
-            json.dump(
-                config.json_config[config_path],
-                f,
-                ensure_ascii=False,
-                indent=4,
-                sort_keys=True,
-            )
-            f.write("\n")
-    if gpus16:
-        cmd = (
-            '"%s" infer/modules/train/train.py -e "%s" -sr %s -f0 %s -bs %s -g %s -te %s -se %s %s %s -l %s -c %s -sw %s -v %s'
-            % (
-                config.python_cmd,
-                exp_dir1,
-                sr2,
-                1 if if_f0_3 else 0,
-                batch_size12,
-                gpus16,
-                total_epoch11,
-                save_epoch10,
-                "-pg %s" % pretrained_G14 if pretrained_G14 != "" else "",
-                "-pd %s" % pretrained_D15 if pretrained_D15 != "" else "",
-                1 if if_save_latest13 == i18n("是") else 0,
-                1 if if_cache_gpu17 == i18n("是") else 0,
-                1 if if_save_every_weights18 == i18n("是") else 0,
-                version19,
-            )
-        )
-    else:
-        cmd = (
-            '"%s" infer/modules/train/train.py -e "%s" -sr %s -f0 %s -bs %s -te %s -se %s %s %s -l %s -c %s -sw %s -v %s'
-            % (
-                config.python_cmd,
-                exp_dir1,
-                sr2,
-                1 if if_f0_3 else 0,
-                batch_size12,
-                total_epoch11,
-                save_epoch10,
-                "-pg %s" % pretrained_G14 if pretrained_G14 != "" else "",
-                "-pd %s" % pretrained_D15 if pretrained_D15 != "" else "",
-                1 if if_save_latest13 == i18n("是") else 0,
-                1 if if_cache_gpu17 == i18n("是") else 0,
-                1 if if_save_every_weights18 == i18n("是") else 0,
-                version19,
-            )
-        )
-    logger.info("Execute: " + cmd)
-    p = Popen(cmd, shell=True, cwd=now_dir)
-    p.wait()
-    return "训练结束, 您可查看控制台训练日志或实验文件夹下的train.log"
-
-
-# but4.click(train_index, [exp_dir1], info3)
-def train_index(exp_dir1, version19):
-    # exp_dir = "%s/logs/%s" % (now_dir, exp_dir1)
-    exp_dir = "logs/%s" % (exp_dir1)
-    os.makedirs(exp_dir, exist_ok=True)
-    feature_dir = (
-        "%s/3_feature256" % (exp_dir)
-        if version19 == "v1"
-        else "%s/3_feature768" % (exp_dir)
-    )
-    if not os.path.exists(feature_dir):
-        return "请先进行特征提取!"
-    listdir_res = list(os.listdir(feature_dir))
-    if len(listdir_res) == 0:
-        return "请先进行特征提取！"
-    infos = []
-    npys = []
-    for name in sorted(listdir_res):
-        phone = np.load("%s/%s" % (feature_dir, name))
-        npys.append(phone)
-    big_npy = np.concatenate(npys, 0)
-    big_npy_idx = np.arange(big_npy.shape[0])
-    np.random.shuffle(big_npy_idx)
-    big_npy = big_npy[big_npy_idx]
-    if big_npy.shape[0] > 2e5:
-        infos.append("Trying doing kmeans %s shape to 10k centers." % big_npy.shape[0])
-        yield "\n".join(infos)
-        try:
-            big_npy = (
-                MiniBatchKMeans(
-                    n_clusters=10000,
-                    verbose=True,
-                    batch_size=256 * config.n_cpu,
-                    compute_labels=False,
-                    init="random",
-                )
-                .fit(big_npy)
-                .cluster_centers_
-            )
-        except:
-            info = traceback.format_exc()
-            logger.info(info)
-            infos.append(info)
-            yield "\n".join(infos)
-
-    np.save("%s/total_fea.npy" % exp_dir, big_npy)
-    n_ivf = min(int(16 * np.sqrt(big_npy.shape[0])), big_npy.shape[0] // 39)
-    infos.append("%s,%s" % (big_npy.shape, n_ivf))
-    yield "\n".join(infos)
-    index = faiss.index_factory(256 if version19 == "v1" else 768, "IVF%s,Flat" % n_ivf)
-    # index = faiss.index_factory(256if version19=="v1"else 768, "IVF%s,PQ128x4fs,RFlat"%n_ivf)
-    infos.append("training")
-    yield "\n".join(infos)
-    index_ivf = faiss.extract_index_ivf(index)  #
-    index_ivf.nprobe = 1
-    index.train(big_npy)
-    faiss.write_index(
-        index,
-        "%s/trained_IVF%s_Flat_nprobe_%s_%s_%s.index"
-        % (exp_dir, n_ivf, index_ivf.nprobe, exp_dir1, version19),
-    )
-    infos.append("adding")
-    yield "\n".join(infos)
-    batch_size_add = 8192
-    for i in range(0, big_npy.shape[0], batch_size_add):
-        index.add(big_npy[i : i + batch_size_add])
-    faiss.write_index(
-        index,
-        "%s/added_IVF%s_Flat_nprobe_%s_%s_%s.index"
-        % (exp_dir, n_ivf, index_ivf.nprobe, exp_dir1, version19),
-    )
-    infos.append(
-        "成功构建索引 added_IVF%s_Flat_nprobe_%s_%s_%s.index"
-        % (n_ivf, index_ivf.nprobe, exp_dir1, version19)
-    )
-    try:
-        link = os.link if platform.system() == "Windows" else os.symlink
-        link(
-            "%s/added_IVF%s_Flat_nprobe_%s_%s_%s.index"
-            % (exp_dir, n_ivf, index_ivf.nprobe, exp_dir1, version19),
-            "%s/%s_IVF%s_Flat_nprobe_%s_%s_%s.index"
-            % (
-                outside_index_root,
-                exp_dir1,
-                n_ivf,
-                index_ivf.nprobe,
-                exp_dir1,
-                version19,
-            ),
-        )
-        infos.append("链接索引到外部-%s" % (outside_index_root))
-    except:
-        infos.append("链接索引到外部-%s失败" % (outside_index_root))
-
-    # faiss.write_index(index, '%s/added_IVF%s_Flat_FastScan_%s.index'%(exp_dir,n_ivf,version19))
-    # infos.append("成功构建索引，added_IVF%s_Flat_FastScan_%s.index"%(n_ivf,version19))
-    yield "\n".join(infos)
-
-
-# but5.click(train1key, [exp_dir1, sr2, if_f0_3, trainset_dir4, spk_id5, gpus6, np7, f0method8, save_epoch10, total_epoch11, batch_size12, if_save_latest13, pretrained_G14, pretrained_D15, gpus16, if_cache_gpu17], info3)
-def train1key(
-    exp_dir1,
-    sr2,
-    if_f0_3,
-    trainset_dir4,
-    spk_id5,
-    np7,
-    f0method8,
-    save_epoch10,
-    total_epoch11,
-    batch_size12,
-    if_save_latest13,
-    pretrained_G14,
-    pretrained_D15,
-    gpus16,
-    if_cache_gpu17,
-    if_save_every_weights18,
-    version19,
-    gpus_rmvpe,
-):
-    infos = []
-
-    def get_info_str(strr):
-        infos.append(strr)
-        return "\n".join(infos)
-
-    # step1:处理数据
-    yield get_info_str(i18n("step1:正在处理数据"))
-    [get_info_str(_) for _ in preprocess_dataset(trainset_dir4, exp_dir1, sr2, np7)]
-
-    # step2a:提取音高
-    yield get_info_str(i18n("step2:正在提取音高&正在提取特征"))
-    [
-        get_info_str(_)
-        for _ in extract_f0_feature(
-            gpus16, np7, f0method8, if_f0_3, exp_dir1, version19, gpus_rmvpe
-        )
-    ]
-
-    # step3a:训练模型
-    yield get_info_str(i18n("step3a:正在训练模型"))
-    click_train(
-        exp_dir1,
-        sr2,
-        if_f0_3,
-        spk_id5,
-        save_epoch10,
-        total_epoch11,
-        batch_size12,
-        if_save_latest13,
-        pretrained_G14,
-        pretrained_D15,
-        gpus16,
-        if_cache_gpu17,
-        if_save_every_weights18,
-        version19,
-    )
-    yield get_info_str(
-        i18n("训练结束, 您可查看控制台训练日志或实验文件夹下的train.log")
-    )
-
-    # step3b:训练索引
-    [get_info_str(_) for _ in train_index(exp_dir1, version19)]
-    yield get_info_str(i18n("全流程结束！"))
-
-
-#                    ckpt_path2.change(change_info_,[ckpt_path2],[sr__,if_f0__])
 def change_info_(ckpt_path):
     if not os.path.exists(ckpt_path.replace(os.path.basename(ckpt_path), "train.log")):
         return {"__type__": "update"}, {"__type__": "update"}, {"__type__": "update"}
@@ -1015,11 +691,6 @@ with gr.Blocks(title="RVC WebUI") as app:
                             outputs=file_index4,
                             api_name="infer_refresh_batch",
                         )
-                        # file_big_npy2 = gr.Textbox(
-                        #     label=i18n("特征文件路径"),
-                        #     value="E:\\codes\\py39\\vits_vc_gpu_train\\logs\\mi-test-1key\\total_fea.npy",
-                        #     interactive=True,
-                        # )
 
                     with gr.Column():
                         resample_sr1 = gr.Slider(
@@ -1110,504 +781,6 @@ with gr.Blocks(title="RVC WebUI") as app:
                     outputs=[spk_item, protect0, protect1, file_index2, file_index4],
                     api_name="infer_change_voice",
                 )
-        with gr.TabItem(i18n("伴奏人声分离&去混响&去回声")):
-            with gr.Group():
-                gr.Markdown(
-                    value=i18n(
-                        "人声伴奏分离批量处理， 使用UVR5模型。 <br>合格的文件夹路径格式举例： E:\\codes\\py39\\vits_vc_gpu\\白鹭霜华测试样例(去文件管理器地址栏拷就行了)。 <br>模型分为三类： <br>1、保留人声：不带和声的音频选这个，对主人声保留比HP5更好。内置HP2和HP3两个模型，HP3可能轻微漏伴奏但对主人声保留比HP2稍微好一丁点； <br>2、仅保留主人声：带和声的音频选这个，对主人声可能有削弱。内置HP5一个模型； <br> 3、去混响、去延迟模型（by FoxJoy）：<br>  (1)MDX-Net(onnx_dereverb):对于双通道混响是最好的选择，不能去除单通道混响；<br>&emsp;(234)DeEcho:去除延迟效果。Aggressive比Normal去除得更彻底，DeReverb额外去除混响，可去除单声道混响，但是对高频重的板式混响去不干净。<br>去混响/去延迟，附：<br>1、DeEcho-DeReverb模型的耗时是另外2个DeEcho模型的接近2倍；<br>2、MDX-Net-Dereverb模型挺慢的；<br>3、个人推荐的最干净的配置是先MDX-Net再DeEcho-Aggressive。"
-                    )
-                )
-                with gr.Row():
-                    with gr.Column():
-                        dir_wav_input = gr.Textbox(
-                            label=i18n("输入待处理音频文件夹路径"),
-                            placeholder="C:\\Users\\Desktop\\todo-songs",
-                        )
-                        wav_inputs = gr.File(
-                            file_count="multiple",
-                            label=i18n("也可批量输入音频文件, 二选一, 优先读文件夹"),
-                        )
-                    with gr.Column():
-                        model_choose = gr.Dropdown(
-                            label=i18n("模型"), choices=uvr5_names
-                        )
-                        agg = gr.Slider(
-                            minimum=0,
-                            maximum=20,
-                            step=1,
-                            label="人声提取激进程度",
-                            value=10,
-                            interactive=True,
-                            visible=False,  # 先不开放调整
-                        )
-                        opt_vocal_root = gr.Textbox(
-                            label=i18n("指定输出主人声文件夹"), value="opt"
-                        )
-                        opt_ins_root = gr.Textbox(
-                            label=i18n("指定输出非主人声文件夹"), value="opt"
-                        )
-                        format0 = gr.Radio(
-                            label=i18n("导出文件格式"),
-                            choices=["wav", "flac", "mp3", "m4a"],
-                            value="flac",
-                            interactive=True,
-                        )
-                    but2 = gr.Button(i18n("转换"), variant="primary")
-                    vc_output4 = gr.Textbox(label=i18n("输出信息"))
-                    but2.click(
-                        uvr,
-                        [
-                            model_choose,
-                            dir_wav_input,
-                            opt_vocal_root,
-                            wav_inputs,
-                            opt_ins_root,
-                            agg,
-                            format0,
-                        ],
-                        [vc_output4],
-                        api_name="uvr_convert",
-                    )
-        with gr.TabItem(i18n("训练")):
-            gr.Markdown(
-                value=i18n(
-                    "step1: 填写实验配置. 实验数据放在logs下, 每个实验一个文件夹, 需手工输入实验名路径, 内含实验配置, 日志, 训练得到的模型文件. "
-                )
-            )
-            with gr.Row():
-                exp_dir1 = gr.Textbox(label=i18n("输入实验名"), value="mi-test")
-                sr2 = gr.Radio(
-                    label=i18n("目标采样率"),
-                    choices=["40k", "48k"],
-                    value="40k",
-                    interactive=True,
-                )
-                if_f0_3 = gr.Radio(
-                    label=i18n("模型是否带音高指导(唱歌一定要, 语音可以不要)"),
-                    choices=[True, False],
-                    value=True,
-                    interactive=True,
-                )
-                version19 = gr.Radio(
-                    label=i18n("版本"),
-                    choices=["v1", "v2"],
-                    value="v2",
-                    interactive=True,
-                    visible=True,
-                )
-                np7 = gr.Slider(
-                    minimum=0,
-                    maximum=config.n_cpu,
-                    step=1,
-                    label=i18n("提取音高和处理数据使用的CPU进程数"),
-                    value=int(np.ceil(config.n_cpu / 1.5)),
-                    interactive=True,
-                )
-            with gr.Group():  # 暂时单人的, 后面支持最多4人的#数据处理
-                gr.Markdown(
-                    value=i18n(
-                        "step2a: 自动遍历训练文件夹下所有可解码成音频的文件并进行切片归一化, 在实验目录下生成2个wav文件夹; 暂时只支持单人训练. "
-                    )
-                )
-                with gr.Row():
-                    trainset_dir4 = gr.Textbox(
-                        label=i18n("输入训练文件夹路径"),
-                        value=i18n("E:\\语音音频+标注\\米津玄师\\src"),
-                    )
-                    spk_id5 = gr.Slider(
-                        minimum=0,
-                        maximum=4,
-                        step=1,
-                        label=i18n("请指定说话人id"),
-                        value=0,
-                        interactive=True,
-                    )
-                    but1 = gr.Button(i18n("处理数据"), variant="primary")
-                    info1 = gr.Textbox(label=i18n("输出信息"), value="")
-                    but1.click(
-                        preprocess_dataset,
-                        [trainset_dir4, exp_dir1, sr2, np7],
-                        [info1],
-                        api_name="train_preprocess",
-                    )
-            with gr.Group():
-                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)"
-                    )
-                )
-                with gr.Row():
-                    with gr.Column():
-                        gpus6 = gr.Textbox(
-                            label=i18n(
-                                "以-分隔输入使用的卡号, 例如   0-1-2   使用卡0和卡1和卡2"
-                            ),
-                            value=gpus,
-                            interactive=True,
-                            visible=F0GPUVisible,
-                        )
-                        gpu_info9 = gr.Textbox(
-                            label=i18n("显卡信息"), value=gpu_info, visible=F0GPUVisible
-                        )
-                    with gr.Column():
-                        f0method8 = gr.Radio(
-                            label=i18n(
-                                "选择音高提取算法:输入歌声可用pm提速,高质量语音但CPU差可用dio提速,harvest质量更好但慢,rmvpe效果最好且微吃CPU/GPU"
-                            ),
-                            choices=["pm", "harvest", "dio", "rmvpe", "rmvpe_gpu"],
-                            value="rmvpe_gpu",
-                            interactive=True,
-                        )
-                        gpus_rmvpe = gr.Textbox(
-                            label=i18n(
-                                "rmvpe卡号配置：以-分隔输入使用的不同进程卡号,例如0-0-1使用在卡0上跑2个进程并在卡1上跑1个进程"
-                            ),
-                            value="%s-%s" % (gpus, gpus),
-                            interactive=True,
-                            visible=F0GPUVisible,
-                        )
-                    but2 = gr.Button(i18n("特征提取"), variant="primary")
-                    info2 = gr.Textbox(label=i18n("输出信息"), value="", max_lines=8)
-                    f0method8.change(
-                        fn=change_f0_method,
-                        inputs=[f0method8],
-                        outputs=[gpus_rmvpe],
-                    )
-                    but2.click(
-                        extract_f0_feature,
-                        [
-                            gpus6,
-                            np7,
-                            f0method8,
-                            if_f0_3,
-                            exp_dir1,
-                            version19,
-                            gpus_rmvpe,
-                        ],
-                        [info2],
-                        api_name="train_extract_f0_feature",
-                    )
-            with gr.Group():
-                gr.Markdown(value=i18n("step3: 填写训练设置, 开始训练模型和索引"))
-                with gr.Row():
-                    save_epoch10 = gr.Slider(
-                        minimum=1,
-                        maximum=50,
-                        step=1,
-                        label=i18n("保存频率save_every_epoch"),
-                        value=5,
-                        interactive=True,
-                    )
-                    total_epoch11 = gr.Slider(
-                        minimum=2,
-                        maximum=1000,
-                        step=1,
-                        label=i18n("总训练轮数total_epoch"),
-                        value=20,
-                        interactive=True,
-                    )
-                    batch_size12 = gr.Slider(
-                        minimum=1,
-                        maximum=40,
-                        step=1,
-                        label=i18n("每张显卡的batch_size"),
-                        value=default_batch_size,
-                        interactive=True,
-                    )
-                    if_save_latest13 = gr.Radio(
-                        label=i18n("是否仅保存最新的ckpt文件以节省硬盘空间"),
-                        choices=[i18n("是"), i18n("否")],
-                        value=i18n("否"),
-                        interactive=True,
-                    )
-                    if_cache_gpu17 = gr.Radio(
-                        label=i18n(
-                            "是否缓存所有训练集至显存. 10min以下小数据可缓存以加速训练, 大数据缓存会炸显存也加不了多少速"
-                        ),
-                        choices=[i18n("是"), i18n("否")],
-                        value=i18n("否"),
-                        interactive=True,
-                    )
-                    if_save_every_weights18 = gr.Radio(
-                        label=i18n(
-                            "是否在每次保存时间点将最终小模型保存至weights文件夹"
-                        ),
-                        choices=[i18n("是"), i18n("否")],
-                        value=i18n("否"),
-                        interactive=True,
-                    )
-                with gr.Row():
-                    pretrained_G14 = gr.Textbox(
-                        label=i18n("加载预训练底模G路径"),
-                        value="assets/pretrained_v2/f0G40k.pth",
-                        interactive=True,
-                    )
-                    pretrained_D15 = gr.Textbox(
-                        label=i18n("加载预训练底模D路径"),
-                        value="assets/pretrained_v2/f0D40k.pth",
-                        interactive=True,
-                    )
-                    sr2.change(
-                        change_sr2,
-                        [sr2, if_f0_3, version19],
-                        [pretrained_G14, pretrained_D15],
-                    )
-                    version19.change(
-                        change_version19,
-                        [sr2, if_f0_3, version19],
-                        [pretrained_G14, pretrained_D15, sr2],
-                    )
-                    if_f0_3.change(
-                        change_f0,
-                        [if_f0_3, sr2, version19],
-                        [f0method8, gpus_rmvpe, pretrained_G14, pretrained_D15],
-                    )
-                    gpus16 = gr.Textbox(
-                        label=i18n(
-                            "以-分隔输入使用的卡号, 例如   0-1-2   使用卡0和卡1和卡2"
-                        ),
-                        value=gpus,
-                        interactive=True,
-                    )
-                    but3 = gr.Button(i18n("训练模型"), variant="primary")
-                    but4 = gr.Button(i18n("训练特征索引"), variant="primary")
-                    but5 = gr.Button(i18n("一键训练"), variant="primary")
-                    info3 = gr.Textbox(label=i18n("输出信息"), value="", max_lines=10)
-                    but3.click(
-                        click_train,
-                        [
-                            exp_dir1,
-                            sr2,
-                            if_f0_3,
-                            spk_id5,
-                            save_epoch10,
-                            total_epoch11,
-                            batch_size12,
-                            if_save_latest13,
-                            pretrained_G14,
-                            pretrained_D15,
-                            gpus16,
-                            if_cache_gpu17,
-                            if_save_every_weights18,
-                            version19,
-                        ],
-                        info3,
-                        api_name="train_start",
-                    )
-                    but4.click(train_index, [exp_dir1, version19], info3)
-                    but5.click(
-                        train1key,
-                        [
-                            exp_dir1,
-                            sr2,
-                            if_f0_3,
-                            trainset_dir4,
-                            spk_id5,
-                            np7,
-                            f0method8,
-                            save_epoch10,
-                            total_epoch11,
-                            batch_size12,
-                            if_save_latest13,
-                            pretrained_G14,
-                            pretrained_D15,
-                            gpus16,
-                            if_cache_gpu17,
-                            if_save_every_weights18,
-                            version19,
-                            gpus_rmvpe,
-                        ],
-                        info3,
-                        api_name="train_start_all",
-                    )
-
-        with gr.TabItem(i18n("ckpt处理")):
-            with gr.Group():
-                gr.Markdown(value=i18n("模型融合, 可用于测试音色融合"))
-                with gr.Row():
-                    ckpt_a = gr.Textbox(
-                        label=i18n("A模型路径"), value="", interactive=True
-                    )
-                    ckpt_b = gr.Textbox(
-                        label=i18n("B模型路径"), value="", interactive=True
-                    )
-                    alpha_a = gr.Slider(
-                        minimum=0,
-                        maximum=1,
-                        label=i18n("A模型权重"),
-                        value=0.5,
-                        interactive=True,
-                    )
-                with gr.Row():
-                    sr_ = gr.Radio(
-                        label=i18n("目标采样率"),
-                        choices=["40k", "48k"],
-                        value="40k",
-                        interactive=True,
-                    )
-                    if_f0_ = gr.Radio(
-                        label=i18n("模型是否带音高指导"),
-                        choices=[i18n("是"), i18n("否")],
-                        value=i18n("是"),
-                        interactive=True,
-                    )
-                    info__ = gr.Textbox(
-                        label=i18n("要置入的模型信息"),
-                        value="",
-                        max_lines=8,
-                        interactive=True,
-                    )
-                    name_to_save0 = gr.Textbox(
-                        label=i18n("保存的模型名不带后缀"),
-                        value="",
-                        max_lines=1,
-                        interactive=True,
-                    )
-                    version_2 = gr.Radio(
-                        label=i18n("模型版本型号"),
-                        choices=["v1", "v2"],
-                        value="v1",
-                        interactive=True,
-                    )
-                with gr.Row():
-                    but6 = gr.Button(i18n("融合"), variant="primary")
-                    info4 = gr.Textbox(label=i18n("输出信息"), value="", max_lines=8)
-                but6.click(
-                    merge,
-                    [
-                        ckpt_a,
-                        ckpt_b,
-                        alpha_a,
-                        sr_,
-                        if_f0_,
-                        info__,
-                        name_to_save0,
-                        version_2,
-                    ],
-                    info4,
-                    api_name="ckpt_merge",
-                )  # def merge(path1,path2,alpha1,sr,f0,info):
-            with gr.Group():
-                gr.Markdown(
-                    value=i18n("修改模型信息(仅支持weights文件夹下提取的小模型文件)")
-                )
-                with gr.Row():
-                    ckpt_path0 = gr.Textbox(
-                        label=i18n("模型路径"), value="", interactive=True
-                    )
-                    info_ = gr.Textbox(
-                        label=i18n("要改的模型信息"),
-                        value="",
-                        max_lines=8,
-                        interactive=True,
-                    )
-                    name_to_save1 = gr.Textbox(
-                        label=i18n("保存的文件名, 默认空为和源文件同名"),
-                        value="",
-                        max_lines=8,
-                        interactive=True,
-                    )
-                with gr.Row():
-                    but7 = gr.Button(i18n("修改"), variant="primary")
-                    info5 = gr.Textbox(label=i18n("输出信息"), value="", max_lines=8)
-                but7.click(
-                    change_info,
-                    [ckpt_path0, info_, name_to_save1],
-                    info5,
-                    api_name="ckpt_modify",
-                )
-            with gr.Group():
-                gr.Markdown(
-                    value=i18n("查看模型信息(仅支持weights文件夹下提取的小模型文件)")
-                )
-                with gr.Row():
-                    ckpt_path1 = gr.Textbox(
-                        label=i18n("模型路径"), value="", interactive=True
-                    )
-                    but8 = gr.Button(i18n("查看"), variant="primary")
-                    info6 = gr.Textbox(label=i18n("输出信息"), value="", max_lines=8)
-                but8.click(show_info, [ckpt_path1], info6, api_name="ckpt_show")
-            with gr.Group():
-                gr.Markdown(
-                    value=i18n(
-                        "模型提取(输入logs文件夹下大文件模型路径),适用于训一半不想训了模型没有自动提取保存小文件模型,或者想测试中间模型的情况"
-                    )
-                )
-                with gr.Row():
-                    ckpt_path2 = gr.Textbox(
-                        label=i18n("模型路径"),
-                        value="E:\\codes\\py39\\logs\\mi-test_f0_48k\\G_23333.pth",
-                        interactive=True,
-                    )
-                    save_name = gr.Textbox(
-                        label=i18n("保存名"), value="", interactive=True
-                    )
-                    sr__ = gr.Radio(
-                        label=i18n("目标采样率"),
-                        choices=["32k", "40k", "48k"],
-                        value="40k",
-                        interactive=True,
-                    )
-                    if_f0__ = gr.Radio(
-                        label=i18n("模型是否带音高指导,1是0否"),
-                        choices=["1", "0"],
-                        value="1",
-                        interactive=True,
-                    )
-                    version_1 = gr.Radio(
-                        label=i18n("模型版本型号"),
-                        choices=["v1", "v2"],
-                        value="v2",
-                        interactive=True,
-                    )
-                    info___ = gr.Textbox(
-                        label=i18n("要置入的模型信息"),
-                        value="",
-                        max_lines=8,
-                        interactive=True,
-                    )
-                    but9 = gr.Button(i18n("提取"), variant="primary")
-                    info7 = gr.Textbox(label=i18n("输出信息"), value="", max_lines=8)
-                    ckpt_path2.change(
-                        change_info_, [ckpt_path2], [sr__, if_f0__, version_1]
-                    )
-                but9.click(
-                    extract_small_model,
-                    [ckpt_path2, save_name, sr__, if_f0__, info___, version_1],
-                    info7,
-                    api_name="ckpt_extract",
-                )
-
-        with gr.TabItem(i18n("Onnx导出")):
-            with gr.Row():
-                ckpt_dir = gr.Textbox(
-                    label=i18n("RVC模型路径"), value="", interactive=True
-                )
-            with gr.Row():
-                onnx_dir = gr.Textbox(
-                    label=i18n("Onnx输出路径"), value="", interactive=True
-                )
-            with gr.Row():
-                infoOnnx = gr.Label(label="info")
-            with gr.Row():
-                butOnnx = gr.Button(i18n("导出Onnx模型"), variant="primary")
-            butOnnx.click(
-                export_onnx, [ckpt_dir, onnx_dir], infoOnnx, api_name="export_onnx"
-            )
-
-        tab_faq = i18n("常见问题解答")
-        with gr.TabItem(tab_faq):
-            try:
-                if tab_faq == "常见问题解答":
-                    with open("docs/cn/faq.md", "r", encoding="utf8") as f:
-                        info = f.read()
-                else:
-                    with open("docs/en/faq_en.md", "r", encoding="utf8") as f:
-                        info = f.read()
-                gr.Markdown(value=info)
-            except:
-                gr.Markdown(traceback.format_exc())
-
     if config.iscolab:
         app.queue(concurrency_count=511, max_size=1022).launch(share=True)
     else:

infer/lib/train/data_utils.py

@@ -1,517 +0,0 @@
-import os
-import traceback
-import logging
-
-logger = logging.getLogger(__name__)
-
-import numpy as np
-import torch
-import torch.utils.data
-
-from infer.lib.train.mel_processing import spectrogram_torch
-from infer.lib.train.utils import load_filepaths_and_text, load_wav_to_torch
-
-
-class TextAudioLoaderMultiNSFsid(torch.utils.data.Dataset):
-    """
-    1) loads audio, text pairs
-    2) normalizes text and converts them to sequences of integers
-    3) computes spectrograms from audio files.
-    """
-
-    def __init__(self, audiopaths_and_text, hparams):
-        self.audiopaths_and_text = load_filepaths_and_text(audiopaths_and_text)
-        self.max_wav_value = hparams.max_wav_value
-        self.sampling_rate = hparams.sampling_rate
-        self.filter_length = hparams.filter_length
-        self.hop_length = hparams.hop_length
-        self.win_length = hparams.win_length
-        self.sampling_rate = hparams.sampling_rate
-        self.min_text_len = getattr(hparams, "min_text_len", 1)
-        self.max_text_len = getattr(hparams, "max_text_len", 5000)
-        self._filter()
-
-    def _filter(self):
-        """
-        Filter text & store spec lengths
-        """
-        # Store spectrogram lengths for Bucketing
-        # wav_length ~= file_size / (wav_channels * Bytes per dim) = file_size / (1 * 2)
-        # spec_length = wav_length // hop_length
-        audiopaths_and_text_new = []
-        lengths = []
-        for audiopath, text, pitch, pitchf, dv in self.audiopaths_and_text:
-            if self.min_text_len <= len(text) and len(text) <= self.max_text_len:
-                audiopaths_and_text_new.append([audiopath, text, pitch, pitchf, dv])
-                lengths.append(os.path.getsize(audiopath) // (3 * self.hop_length))
-        self.audiopaths_and_text = audiopaths_and_text_new
-        self.lengths = lengths
-
-    def get_sid(self, sid):
-        sid = torch.LongTensor([int(sid)])
-        return sid
-
-    def get_audio_text_pair(self, audiopath_and_text):
-        # separate filename and text
-        file = audiopath_and_text[0]
-        phone = audiopath_and_text[1]
-        pitch = audiopath_and_text[2]
-        pitchf = audiopath_and_text[3]
-        dv = audiopath_and_text[4]
-
-        phone, pitch, pitchf = self.get_labels(phone, pitch, pitchf)
-        spec, wav = self.get_audio(file)
-        dv = self.get_sid(dv)
-
-        len_phone = phone.size()[0]
-        len_spec = spec.size()[-1]
-        # print(123,phone.shape,pitch.shape,spec.shape)
-        if len_phone != len_spec:
-            len_min = min(len_phone, len_spec)
-            # amor
-            len_wav = len_min * self.hop_length
-
-            spec = spec[:, :len_min]
-            wav = wav[:, :len_wav]
-
-            phone = phone[:len_min, :]
-            pitch = pitch[:len_min]
-            pitchf = pitchf[:len_min]
-
-        return (spec, wav, phone, pitch, pitchf, dv)
-
-    def get_labels(self, phone, pitch, pitchf):
-        phone = np.load(phone)
-        phone = np.repeat(phone, 2, axis=0)
-        pitch = np.load(pitch)
-        pitchf = np.load(pitchf)
-        n_num = min(phone.shape[0], 900)  # DistributedBucketSampler
-        # print(234,phone.shape,pitch.shape)
-        phone = phone[:n_num, :]
-        pitch = pitch[:n_num]
-        pitchf = pitchf[:n_num]
-        phone = torch.FloatTensor(phone)
-        pitch = torch.LongTensor(pitch)
-        pitchf = torch.FloatTensor(pitchf)
-        return phone, pitch, pitchf
-
-    def get_audio(self, filename):
-        audio, sampling_rate = load_wav_to_torch(filename)
-        if sampling_rate != self.sampling_rate:
-            raise ValueError(
-                "{} SR doesn't match target {} SR".format(
-                    sampling_rate, self.sampling_rate
-                )
-            )
-        audio_norm = audio
-        #        audio_norm = audio / self.max_wav_value
-        #        audio_norm = audio / np.abs(audio).max()
-
-        audio_norm = audio_norm.unsqueeze(0)
-        spec_filename = filename.replace(".wav", ".spec.pt")
-        if os.path.exists(spec_filename):
-            try:
-                spec = torch.load(spec_filename)
-            except:
-                logger.warning("%s %s", spec_filename, traceback.format_exc())
-                spec = spectrogram_torch(
-                    audio_norm,
-                    self.filter_length,
-                    self.sampling_rate,
-                    self.hop_length,
-                    self.win_length,
-                    center=False,
-                )
-                spec = torch.squeeze(spec, 0)
-                torch.save(spec, spec_filename, _use_new_zipfile_serialization=False)
-        else:
-            spec = spectrogram_torch(
-                audio_norm,
-                self.filter_length,
-                self.sampling_rate,
-                self.hop_length,
-                self.win_length,
-                center=False,
-            )
-            spec = torch.squeeze(spec, 0)
-            torch.save(spec, spec_filename, _use_new_zipfile_serialization=False)
-        return spec, audio_norm
-
-    def __getitem__(self, index):
-        return self.get_audio_text_pair(self.audiopaths_and_text[index])
-
-    def __len__(self):
-        return len(self.audiopaths_and_text)
-
-
-class TextAudioCollateMultiNSFsid:
-    """Zero-pads model inputs and targets"""
-
-    def __init__(self, return_ids=False):
-        self.return_ids = return_ids
-
-    def __call__(self, batch):
-        """Collate's training batch from normalized text and aduio
-        PARAMS
-        ------
-        batch: [text_normalized, spec_normalized, wav_normalized]
-        """
-        # Right zero-pad all one-hot text sequences to max input length
-        _, ids_sorted_decreasing = torch.sort(
-            torch.LongTensor([x[0].size(1) for x in batch]), dim=0, descending=True
-        )
-
-        max_spec_len = max([x[0].size(1) for x in batch])
-        max_wave_len = max([x[1].size(1) for x in batch])
-        spec_lengths = torch.LongTensor(len(batch))
-        wave_lengths = torch.LongTensor(len(batch))
-        spec_padded = torch.FloatTensor(len(batch), batch[0][0].size(0), max_spec_len)
-        wave_padded = torch.FloatTensor(len(batch), 1, max_wave_len)
-        spec_padded.zero_()
-        wave_padded.zero_()
-
-        max_phone_len = max([x[2].size(0) for x in batch])
-        phone_lengths = torch.LongTensor(len(batch))
-        phone_padded = torch.FloatTensor(
-            len(batch), max_phone_len, batch[0][2].shape[1]
-        )  # (spec, wav, phone, pitch)
-        pitch_padded = torch.LongTensor(len(batch), max_phone_len)
-        pitchf_padded = torch.FloatTensor(len(batch), max_phone_len)
-        phone_padded.zero_()
-        pitch_padded.zero_()
-        pitchf_padded.zero_()
-        # dv = torch.FloatTensor(len(batch), 256)#gin=256
-        sid = torch.LongTensor(len(batch))
-
-        for i in range(len(ids_sorted_decreasing)):
-            row = batch[ids_sorted_decreasing[i]]
-
-            spec = row[0]
-            spec_padded[i, :, : spec.size(1)] = spec
-            spec_lengths[i] = spec.size(1)
-
-            wave = row[1]
-            wave_padded[i, :, : wave.size(1)] = wave
-            wave_lengths[i] = wave.size(1)
-
-            phone = row[2]
-            phone_padded[i, : phone.size(0), :] = phone
-            phone_lengths[i] = phone.size(0)
-
-            pitch = row[3]
-            pitch_padded[i, : pitch.size(0)] = pitch
-            pitchf = row[4]
-            pitchf_padded[i, : pitchf.size(0)] = pitchf
-
-            # dv[i] = row[5]
-            sid[i] = row[5]
-
-        return (
-            phone_padded,
-            phone_lengths,
-            pitch_padded,
-            pitchf_padded,
-            spec_padded,
-            spec_lengths,
-            wave_padded,
-            wave_lengths,
-            # dv
-            sid,
-        )
-
-
-class TextAudioLoader(torch.utils.data.Dataset):
-    """
-    1) loads audio, text pairs
-    2) normalizes text and converts them to sequences of integers
-    3) computes spectrograms from audio files.
-    """
-
-    def __init__(self, audiopaths_and_text, hparams):
-        self.audiopaths_and_text = load_filepaths_and_text(audiopaths_and_text)
-        self.max_wav_value = hparams.max_wav_value
-        self.sampling_rate = hparams.sampling_rate
-        self.filter_length = hparams.filter_length
-        self.hop_length = hparams.hop_length
-        self.win_length = hparams.win_length
-        self.sampling_rate = hparams.sampling_rate
-        self.min_text_len = getattr(hparams, "min_text_len", 1)
-        self.max_text_len = getattr(hparams, "max_text_len", 5000)
-        self._filter()
-
-    def _filter(self):
-        """
-        Filter text & store spec lengths
-        """
-        # Store spectrogram lengths for Bucketing
-        # wav_length ~= file_size / (wav_channels * Bytes per dim) = file_size / (1 * 2)
-        # spec_length = wav_length // hop_length
-        audiopaths_and_text_new = []
-        lengths = []
-        for audiopath, text, dv in self.audiopaths_and_text:
-            if self.min_text_len <= len(text) and len(text) <= self.max_text_len:
-                audiopaths_and_text_new.append([audiopath, text, dv])
-                lengths.append(os.path.getsize(audiopath) // (3 * self.hop_length))
-        self.audiopaths_and_text = audiopaths_and_text_new
-        self.lengths = lengths
-
-    def get_sid(self, sid):
-        sid = torch.LongTensor([int(sid)])
-        return sid
-
-    def get_audio_text_pair(self, audiopath_and_text):
-        # separate filename and text
-        file = audiopath_and_text[0]
-        phone = audiopath_and_text[1]
-        dv = audiopath_and_text[2]
-
-        phone = self.get_labels(phone)
-        spec, wav = self.get_audio(file)
-        dv = self.get_sid(dv)
-
-        len_phone = phone.size()[0]
-        len_spec = spec.size()[-1]
-        if len_phone != len_spec:
-            len_min = min(len_phone, len_spec)
-            len_wav = len_min * self.hop_length
-            spec = spec[:, :len_min]
-            wav = wav[:, :len_wav]
-            phone = phone[:len_min, :]
-        return (spec, wav, phone, dv)
-
-    def get_labels(self, phone):
-        phone = np.load(phone)
-        phone = np.repeat(phone, 2, axis=0)
-        n_num = min(phone.shape[0], 900)  # DistributedBucketSampler
-        phone = phone[:n_num, :]
-        phone = torch.FloatTensor(phone)
-        return phone
-
-    def get_audio(self, filename):
-        audio, sampling_rate = load_wav_to_torch(filename)
-        if sampling_rate != self.sampling_rate:
-            raise ValueError(
-                "{} SR doesn't match target {} SR".format(
-                    sampling_rate, self.sampling_rate
-                )
-            )
-        audio_norm = audio
-        #        audio_norm = audio / self.max_wav_value
-        #        audio_norm = audio / np.abs(audio).max()
-
-        audio_norm = audio_norm.unsqueeze(0)
-        spec_filename = filename.replace(".wav", ".spec.pt")
-        if os.path.exists(spec_filename):
-            try:
-                spec = torch.load(spec_filename)
-            except:
-                logger.warning("%s %s", spec_filename, traceback.format_exc())
-                spec = spectrogram_torch(
-                    audio_norm,
-                    self.filter_length,
-                    self.sampling_rate,
-                    self.hop_length,
-                    self.win_length,
-                    center=False,
-                )
-                spec = torch.squeeze(spec, 0)
-                torch.save(spec, spec_filename, _use_new_zipfile_serialization=False)
-        else:
-            spec = spectrogram_torch(
-                audio_norm,
-                self.filter_length,
-                self.sampling_rate,
-                self.hop_length,
-                self.win_length,
-                center=False,
-            )
-            spec = torch.squeeze(spec, 0)
-            torch.save(spec, spec_filename, _use_new_zipfile_serialization=False)
-        return spec, audio_norm
-
-    def __getitem__(self, index):
-        return self.get_audio_text_pair(self.audiopaths_and_text[index])
-
-    def __len__(self):
-        return len(self.audiopaths_and_text)
-
-
-class TextAudioCollate:
-    """Zero-pads model inputs and targets"""
-
-    def __init__(self, return_ids=False):
-        self.return_ids = return_ids
-
-    def __call__(self, batch):
-        """Collate's training batch from normalized text and aduio
-        PARAMS
-        ------
-        batch: [text_normalized, spec_normalized, wav_normalized]
-        """
-        # Right zero-pad all one-hot text sequences to max input length
-        _, ids_sorted_decreasing = torch.sort(
-            torch.LongTensor([x[0].size(1) for x in batch]), dim=0, descending=True
-        )
-
-        max_spec_len = max([x[0].size(1) for x in batch])
-        max_wave_len = max([x[1].size(1) for x in batch])
-        spec_lengths = torch.LongTensor(len(batch))
-        wave_lengths = torch.LongTensor(len(batch))
-        spec_padded = torch.FloatTensor(len(batch), batch[0][0].size(0), max_spec_len)
-        wave_padded = torch.FloatTensor(len(batch), 1, max_wave_len)
-        spec_padded.zero_()
-        wave_padded.zero_()
-
-        max_phone_len = max([x[2].size(0) for x in batch])
-        phone_lengths = torch.LongTensor(len(batch))
-        phone_padded = torch.FloatTensor(
-            len(batch), max_phone_len, batch[0][2].shape[1]
-        )
-        phone_padded.zero_()
-        sid = torch.LongTensor(len(batch))
-
-        for i in range(len(ids_sorted_decreasing)):
-            row = batch[ids_sorted_decreasing[i]]
-
-            spec = row[0]
-            spec_padded[i, :, : spec.size(1)] = spec
-            spec_lengths[i] = spec.size(1)
-
-            wave = row[1]
-            wave_padded[i, :, : wave.size(1)] = wave
-            wave_lengths[i] = wave.size(1)
-
-            phone = row[2]
-            phone_padded[i, : phone.size(0), :] = phone
-            phone_lengths[i] = phone.size(0)
-
-            sid[i] = row[3]
-
-        return (
-            phone_padded,
-            phone_lengths,
-            spec_padded,
-            spec_lengths,
-            wave_padded,
-            wave_lengths,
-            sid,
-        )
-
-
-class DistributedBucketSampler(torch.utils.data.distributed.DistributedSampler):
-    """
-    Maintain similar input lengths in a batch.
-    Length groups are specified by boundaries.
-    Ex) boundaries = [b1, b2, b3] -> any batch is included either {x | b1 < length(x) <=b2} or {x | b2 < length(x) <= b3}.
-
-    It removes samples which are not included in the boundaries.
-    Ex) boundaries = [b1, b2, b3] -> any x s.t. length(x) <= b1 or length(x) > b3 are discarded.
-    """
-
-    def __init__(
-        self,
-        dataset,
-        batch_size,
-        boundaries,
-        num_replicas=None,
-        rank=None,
-        shuffle=True,
-    ):
-        super().__init__(dataset, num_replicas=num_replicas, rank=rank, shuffle=shuffle)
-        self.lengths = dataset.lengths
-        self.batch_size = batch_size
-        self.boundaries = boundaries
-
-        self.buckets, self.num_samples_per_bucket = self._create_buckets()
-        self.total_size = sum(self.num_samples_per_bucket)
-        self.num_samples = self.total_size // self.num_replicas
-
-    def _create_buckets(self):
-        buckets = [[] for _ in range(len(self.boundaries) - 1)]
-        for i in range(len(self.lengths)):
-            length = self.lengths[i]
-            idx_bucket = self._bisect(length)
-            if idx_bucket != -1:
-                buckets[idx_bucket].append(i)
-
-        for i in range(len(buckets) - 1, -1, -1):  #
-            if len(buckets[i]) == 0:
-                buckets.pop(i)
-                self.boundaries.pop(i + 1)
-
-        num_samples_per_bucket = []
-        for i in range(len(buckets)):
-            len_bucket = len(buckets[i])
-            total_batch_size = self.num_replicas * self.batch_size
-            rem = (
-                total_batch_size - (len_bucket % total_batch_size)
-            ) % total_batch_size
-            num_samples_per_bucket.append(len_bucket + rem)
-        return buckets, num_samples_per_bucket
-
-    def __iter__(self):
-        # deterministically shuffle based on epoch
-        g = torch.Generator()
-        g.manual_seed(self.epoch)
-
-        indices = []
-        if self.shuffle:
-            for bucket in self.buckets:
-                indices.append(torch.randperm(len(bucket), generator=g).tolist())
-        else:
-            for bucket in self.buckets:
-                indices.append(list(range(len(bucket))))
-
-        batches = []
-        for i in range(len(self.buckets)):
-            bucket = self.buckets[i]
-            len_bucket = len(bucket)
-            ids_bucket = indices[i]
-            num_samples_bucket = self.num_samples_per_bucket[i]
-
-            # add extra samples to make it evenly divisible
-            rem = num_samples_bucket - len_bucket
-            ids_bucket = (
-                ids_bucket
-                + ids_bucket * (rem // len_bucket)
-                + ids_bucket[: (rem % len_bucket)]
-            )
-
-            # subsample
-            ids_bucket = ids_bucket[self.rank :: self.num_replicas]
-
-            # batching
-            for j in range(len(ids_bucket) // self.batch_size):
-                batch = [
-                    bucket[idx]
-                    for idx in ids_bucket[
-                        j * self.batch_size : (j + 1) * self.batch_size
-                    ]
-                ]
-                batches.append(batch)
-
-        if self.shuffle:
-            batch_ids = torch.randperm(len(batches), generator=g).tolist()
-            batches = [batches[i] for i in batch_ids]
-        self.batches = batches
-
-        assert len(self.batches) * self.batch_size == self.num_samples
-        return iter(self.batches)
-
-    def _bisect(self, x, lo=0, hi=None):
-        if hi is None:
-            hi = len(self.boundaries) - 1
-
-        if hi > lo:
-            mid = (hi + lo) // 2
-            if self.boundaries[mid] < x and x <= self.boundaries[mid + 1]:
-                return mid
-            elif x <= self.boundaries[mid]:
-                return self._bisect(x, lo, mid)
-            else:
-                return self._bisect(x, mid + 1, hi)
-        else:
-            return -1
-
-    def __len__(self):
-        return self.num_samples // self.batch_size

infer/lib/train/losses.py

@@ -1,58 +0,0 @@
-import torch
-
-
-def feature_loss(fmap_r, fmap_g):
-    loss = 0
-    for dr, dg in zip(fmap_r, fmap_g):
-        for rl, gl in zip(dr, dg):
-            rl = rl.float().detach()
-            gl = gl.float()
-            loss += torch.mean(torch.abs(rl - gl))
-
-    return loss * 2
-
-
-def discriminator_loss(disc_real_outputs, disc_generated_outputs):
-    loss = 0
-    r_losses = []
-    g_losses = []
-    for dr, dg in zip(disc_real_outputs, disc_generated_outputs):
-        dr = dr.float()
-        dg = dg.float()
-        r_loss = torch.mean((1 - dr) ** 2)
-        g_loss = torch.mean(dg**2)
-        loss += r_loss + g_loss
-        r_losses.append(r_loss.item())
-        g_losses.append(g_loss.item())
-
-    return loss, r_losses, g_losses
-
-
-def generator_loss(disc_outputs):
-    loss = 0
-    gen_losses = []
-    for dg in disc_outputs:
-        dg = dg.float()
-        l = torch.mean((1 - dg) ** 2)
-        gen_losses.append(l)
-        loss += l
-
-    return loss, gen_losses
-
-
-def kl_loss(z_p, logs_q, m_p, logs_p, z_mask):
-    """
-    z_p, logs_q: [b, h, t_t]
-    m_p, logs_p: [b, h, t_t]
-    """
-    z_p = z_p.float()
-    logs_q = logs_q.float()
-    m_p = m_p.float()
-    logs_p = logs_p.float()
-    z_mask = z_mask.float()
-
-    kl = logs_p - logs_q - 0.5
-    kl += 0.5 * ((z_p - m_p) ** 2) * torch.exp(-2.0 * logs_p)
-    kl = torch.sum(kl * z_mask)
-    l = kl / torch.sum(z_mask)
-    return l

infer/lib/train/mel_processing.py

@@ -1,127 +0,0 @@
-import torch
-import torch.utils.data
-from librosa.filters import mel as librosa_mel_fn
-import logging
-
-logger = logging.getLogger(__name__)
-
-MAX_WAV_VALUE = 32768.0
-
-
-def dynamic_range_compression_torch(x, C=1, clip_val=1e-5):
-    """
-    PARAMS
-    ------
-    C: compression factor
-    """
-    return torch.log(torch.clamp(x, min=clip_val) * C)
-
-
-def dynamic_range_decompression_torch(x, C=1):
-    """
-    PARAMS
-    ------
-    C: compression factor used to compress
-    """
-    return torch.exp(x) / C
-
-
-def spectral_normalize_torch(magnitudes):
-    return dynamic_range_compression_torch(magnitudes)
-
-
-def spectral_de_normalize_torch(magnitudes):
-    return dynamic_range_decompression_torch(magnitudes)
-
-
-# Reusable banks
-mel_basis = {}
-hann_window = {}
-
-
-def spectrogram_torch(y, n_fft, sampling_rate, hop_size, win_size, center=False):
-    """Convert waveform into Linear-frequency Linear-amplitude spectrogram.
-
-    Args:
-        y             :: (B, T) - Audio waveforms
-        n_fft
-        sampling_rate
-        hop_size
-        win_size
-        center
-    Returns:
-        :: (B, Freq, Frame) - Linear-frequency Linear-amplitude spectrogram
-    """
-
-    # Window - Cache if needed
-    global hann_window
-    dtype_device = str(y.dtype) + "_" + str(y.device)
-    wnsize_dtype_device = str(win_size) + "_" + dtype_device
-    if wnsize_dtype_device not in hann_window:
-        hann_window[wnsize_dtype_device] = torch.hann_window(win_size).to(
-            dtype=y.dtype, device=y.device
-        )
-
-    # Padding
-    y = torch.nn.functional.pad(
-        y.unsqueeze(1),
-        (int((n_fft - hop_size) / 2), int((n_fft - hop_size) / 2)),
-        mode="reflect",
-    )
-    y = y.squeeze(1)
-
-    # Complex Spectrogram :: (B, T) -> (B, Freq, Frame, RealComplex=2)
-    spec = torch.stft(
-        y,
-        n_fft,
-        hop_length=hop_size,
-        win_length=win_size,
-        window=hann_window[wnsize_dtype_device],
-        center=center,
-        pad_mode="reflect",
-        normalized=False,
-        onesided=True,
-        return_complex=True,
-    )
-
-    # Linear-frequency Linear-amplitude spectrogram :: (B, Freq, Frame, RealComplex=2) -> (B, Freq, Frame)
-    spec = torch.sqrt(spec.real.pow(2) + spec.imag.pow(2) + 1e-6)
-    return spec
-
-
-def spec_to_mel_torch(spec, n_fft, num_mels, sampling_rate, fmin, fmax):
-    # MelBasis - Cache if needed
-    global mel_basis
-    dtype_device = str(spec.dtype) + "_" + str(spec.device)
-    fmax_dtype_device = str(fmax) + "_" + dtype_device
-    if fmax_dtype_device not in mel_basis:
-        mel = librosa_mel_fn(
-            sr=sampling_rate, n_fft=n_fft, n_mels=num_mels, fmin=fmin, fmax=fmax
-        )
-        mel_basis[fmax_dtype_device] = torch.from_numpy(mel).to(
-            dtype=spec.dtype, device=spec.device
-        )
-
-    # Mel-frequency Log-amplitude spectrogram :: (B, Freq=num_mels, Frame)
-    melspec = torch.matmul(mel_basis[fmax_dtype_device], spec)
-    melspec = spectral_normalize_torch(melspec)
-    return melspec
-
-
-def mel_spectrogram_torch(
-    y, n_fft, num_mels, sampling_rate, hop_size, win_size, fmin, fmax, center=False
-):
-    """Convert waveform into Mel-frequency Log-amplitude spectrogram.
-
-    Args:
-        y       :: (B, T)           - Waveforms
-    Returns:
-        melspec :: (B, Freq, Frame) - Mel-frequency Log-amplitude spectrogram
-    """
-    # Linear-frequency Linear-amplitude spectrogram :: (B, T) -> (B, Freq, Frame)
-    spec = spectrogram_torch(y, n_fft, sampling_rate, hop_size, win_size, center)
-
-    # Mel-frequency Log-amplitude spectrogram :: (B, Freq, Frame) -> (B, Freq=num_mels, Frame)
-    melspec = spec_to_mel_torch(spec, n_fft, num_mels, sampling_rate, fmin, fmax)
-
-    return melspec

infer/lib/train/process_ckpt.py

@@ -1,261 +0,0 @@
-import os
-import sys
-import traceback
-from collections import OrderedDict
-
-import torch
-
-from i18n.i18n import I18nAuto
-
-i18n = I18nAuto()
-
-
-def savee(ckpt, sr, if_f0, name, epoch, version, hps):
-    try:
-        opt = OrderedDict()
-        opt["weight"] = {}
-        for key in ckpt.keys():
-            if "enc_q" in key:
-                continue
-            opt["weight"][key] = ckpt[key].half()
-        opt["config"] = [
-            hps.data.filter_length // 2 + 1,
-            32,
-            hps.model.inter_channels,
-            hps.model.hidden_channels,
-            hps.model.filter_channels,
-            hps.model.n_heads,
-            hps.model.n_layers,
-            hps.model.kernel_size,
-            hps.model.p_dropout,
-            hps.model.resblock,
-            hps.model.resblock_kernel_sizes,
-            hps.model.resblock_dilation_sizes,
-            hps.model.upsample_rates,
-            hps.model.upsample_initial_channel,
-            hps.model.upsample_kernel_sizes,
-            hps.model.spk_embed_dim,
-            hps.model.gin_channels,
-            hps.data.sampling_rate,
-        ]
-        opt["info"] = "%sepoch" % epoch
-        opt["sr"] = sr
-        opt["f0"] = if_f0
-        opt["version"] = version
-        torch.save(opt, "assets/weights/%s.pth" % name)
-        return "Success."
-    except:
-        return traceback.format_exc()
-
-
-def show_info(path):
-    try:
-        a = torch.load(path, map_location="cpu")
-        return "模型信息:%s\n采样率:%s\n模型是否输入音高引导:%s\n版本:%s" % (
-            a.get("info", "None"),
-            a.get("sr", "None"),
-            a.get("f0", "None"),
-            a.get("version", "None"),
-        )
-    except:
-        return traceback.format_exc()
-
-
-def extract_small_model(path, name, sr, if_f0, info, version):
-    try:
-        ckpt = torch.load(path, map_location="cpu")
-        if "model" in ckpt:
-            ckpt = ckpt["model"]
-        opt = OrderedDict()
-        opt["weight"] = {}
-        for key in ckpt.keys():
-            if "enc_q" in key:
-                continue
-            opt["weight"][key] = ckpt[key].half()
-        if sr == "40k":
-            opt["config"] = [
-                1025,
-                32,
-                192,
-                192,
-                768,
-                2,
-                6,
-                3,
-                0,
-                "1",
-                [3, 7, 11],
-                [[1, 3, 5], [1, 3, 5], [1, 3, 5]],
-                [10, 10, 2, 2],
-                512,
-                [16, 16, 4, 4],
-                109,
-                256,
-                40000,
-            ]
-        elif sr == "48k":
-            if version == "v1":
-                opt["config"] = [
-                    1025,
-                    32,
-                    192,
-                    192,
-                    768,
-                    2,
-                    6,
-                    3,
-                    0,
-                    "1",
-                    [3, 7, 11],
-                    [[1, 3, 5], [1, 3, 5], [1, 3, 5]],
-                    [10, 6, 2, 2, 2],
-                    512,
-                    [16, 16, 4, 4, 4],
-                    109,
-                    256,
-                    48000,
-                ]
-            else:
-                opt["config"] = [
-                    1025,
-                    32,
-                    192,
-                    192,
-                    768,
-                    2,
-                    6,
-                    3,
-                    0,
-                    "1",
-                    [3, 7, 11],
-                    [[1, 3, 5], [1, 3, 5], [1, 3, 5]],
-                    [12, 10, 2, 2],
-                    512,
-                    [24, 20, 4, 4],
-                    109,
-                    256,
-                    48000,
-                ]
-        elif sr == "32k":
-            if version == "v1":
-                opt["config"] = [
-                    513,
-                    32,
-                    192,
-                    192,
-                    768,
-                    2,
-                    6,
-                    3,
-                    0,
-                    "1",
-                    [3, 7, 11],
-                    [[1, 3, 5], [1, 3, 5], [1, 3, 5]],
-                    [10, 4, 2, 2, 2],
-                    512,
-                    [16, 16, 4, 4, 4],
-                    109,
-                    256,
-                    32000,
-                ]
-            else:
-                opt["config"] = [
-                    513,
-                    32,
-                    192,
-                    192,
-                    768,
-                    2,
-                    6,
-                    3,
-                    0,
-                    "1",
-                    [3, 7, 11],
-                    [[1, 3, 5], [1, 3, 5], [1, 3, 5]],
-                    [10, 8, 2, 2],
-                    512,
-                    [20, 16, 4, 4],
-                    109,
-                    256,
-                    32000,
-                ]
-        if info == "":
-            info = "Extracted model."
-        opt["info"] = info
-        opt["version"] = version
-        opt["sr"] = sr
-        opt["f0"] = int(if_f0)
-        torch.save(opt, "assets/weights/%s.pth" % name)
-        return "Success."
-    except:
-        return traceback.format_exc()
-
-
-def change_info(path, info, name):
-    try:
-        ckpt = torch.load(path, map_location="cpu")
-        ckpt["info"] = info
-        if name == "":
-            name = os.path.basename(path)
-        torch.save(ckpt, "assets/weights/%s" % name)
-        return "Success."
-    except:
-        return traceback.format_exc()
-
-
-def merge(path1, path2, alpha1, sr, f0, info, name, version):
-    try:
-
-        def extract(ckpt):
-            a = ckpt["model"]
-            opt = OrderedDict()
-            opt["weight"] = {}
-            for key in a.keys():
-                if "enc_q" in key:
-                    continue
-                opt["weight"][key] = a[key]
-            return opt
-
-        ckpt1 = torch.load(path1, map_location="cpu")
-        ckpt2 = torch.load(path2, map_location="cpu")
-        cfg = ckpt1["config"]
-        if "model" in ckpt1:
-            ckpt1 = extract(ckpt1)
-        else:
-            ckpt1 = ckpt1["weight"]
-        if "model" in ckpt2:
-            ckpt2 = extract(ckpt2)
-        else:
-            ckpt2 = ckpt2["weight"]
-        if sorted(list(ckpt1.keys())) != sorted(list(ckpt2.keys())):
-            return "Fail to merge the models. The model architectures are not the same."
-        opt = OrderedDict()
-        opt["weight"] = {}
-        for key in ckpt1.keys():
-            # try:
-            if key == "emb_g.weight" and ckpt1[key].shape != ckpt2[key].shape:
-                min_shape0 = min(ckpt1[key].shape[0], ckpt2[key].shape[0])
-                opt["weight"][key] = (
-                    alpha1 * (ckpt1[key][:min_shape0].float())
-                    + (1 - alpha1) * (ckpt2[key][:min_shape0].float())
-                ).half()
-            else:
-                opt["weight"][key] = (
-                    alpha1 * (ckpt1[key].float()) + (1 - alpha1) * (ckpt2[key].float())
-                ).half()
-        # except:
-        #     pdb.set_trace()
-        opt["config"] = cfg
-        """
-        if(sr=="40k"):opt["config"] = [1025, 32, 192, 192, 768, 2, 6, 3, 0, "1", [3, 7, 11], [[1, 3, 5], [1, 3, 5], [1, 3, 5]], [10, 10, 2, 2], 512, [16, 16, 4, 4,4], 109, 256, 40000]
-        elif(sr=="48k"):opt["config"] = [1025, 32, 192, 192, 768, 2, 6, 3, 0, "1", [3, 7, 11], [[1, 3, 5], [1, 3, 5], [1, 3, 5]], [10,6,2,2,2], 512, [16, 16, 4, 4], 109, 256, 48000]
-        elif(sr=="32k"):opt["config"] = [513, 32, 192, 192, 768, 2, 6, 3, 0, "1", [3, 7, 11], [[1, 3, 5], [1, 3, 5], [1, 3, 5]], [10, 4, 2, 2, 2], 512, [16, 16, 4, 4,4], 109, 256, 32000]
-        """
-        opt["sr"] = sr
-        opt["f0"] = 1 if f0 == i18n("是") else 0
-        opt["version"] = version
-        opt["info"] = info
-        torch.save(opt, "assets/weights/%s.pth" % name)
-        return "Success."
-    except:
-        return traceback.format_exc()

infer/lib/train/utils.py

@@ -1,483 +0,0 @@
-import argparse
-import glob
-import json
-import logging
-import os
-import subprocess
-import sys
-import shutil
-
-import numpy as np
-import torch
-from scipy.io.wavfile import read
-
-MATPLOTLIB_FLAG = False
-
-logging.basicConfig(stream=sys.stdout, level=logging.DEBUG)
-logger = logging
-
-
-def load_checkpoint_d(checkpoint_path, combd, sbd, optimizer=None, load_opt=1):
-    assert os.path.isfile(checkpoint_path)
-    checkpoint_dict = torch.load(checkpoint_path, map_location="cpu")
-
-    ##################
-    def go(model, bkey):
-        saved_state_dict = checkpoint_dict[bkey]
-        if hasattr(model, "module"):
-            state_dict = model.module.state_dict()
-        else:
-            state_dict = model.state_dict()
-        new_state_dict = {}
-        for k, v in state_dict.items():  # 模型需要的shape
-            try:
-                new_state_dict[k] = saved_state_dict[k]
-                if saved_state_dict[k].shape != state_dict[k].shape:
-                    logger.warning(
-                        "shape-%s-mismatch. need: %s, get: %s",
-                        k,
-                        state_dict[k].shape,
-                        saved_state_dict[k].shape,
-                    )  #
-                    raise KeyError
-            except:
-                # logger.info(traceback.format_exc())
-                logger.info("%s is not in the checkpoint", k)  # pretrain缺失的
-                new_state_dict[k] = v  # 模型自带的随机值
-        if hasattr(model, "module"):
-            model.module.load_state_dict(new_state_dict, strict=False)
-        else:
-            model.load_state_dict(new_state_dict, strict=False)
-        return model
-
-    go(combd, "combd")
-    model = go(sbd, "sbd")
-    #############
-    logger.info("Loaded model weights")
-
-    iteration = checkpoint_dict["iteration"]
-    learning_rate = checkpoint_dict["learning_rate"]
-    if (
-        optimizer is not None and load_opt == 1
-    ):  ###加载不了，如果是空的的话，重新初始化，可能还会影响lr时间表的更新，因此在train文件最外围catch
-        #   try:
-        optimizer.load_state_dict(checkpoint_dict["optimizer"])
-    #   except:
-    #     traceback.print_exc()
-    logger.info("Loaded checkpoint '{}' (epoch {})".format(checkpoint_path, iteration))
-    return model, optimizer, learning_rate, iteration
-
-
-# def load_checkpoint(checkpoint_path, model, optimizer=None):
-#   assert os.path.isfile(checkpoint_path)
-#   checkpoint_dict = torch.load(checkpoint_path, map_location='cpu')
-#   iteration = checkpoint_dict['iteration']
-#   learning_rate = checkpoint_dict['learning_rate']
-#   if optimizer is not None:
-#     optimizer.load_state_dict(checkpoint_dict['optimizer'])
-#   # print(1111)
-#   saved_state_dict = checkpoint_dict['model']
-#   # print(1111)
-#
-#   if hasattr(model, 'module'):
-#     state_dict = model.module.state_dict()
-#   else:
-#     state_dict = model.state_dict()
-#   new_state_dict= {}
-#   for k, v in state_dict.items():
-#     try:
-#       new_state_dict[k] = saved_state_dict[k]
-#     except:
-#       logger.info("%s is not in the checkpoint" % k)
-#       new_state_dict[k] = v
-#   if hasattr(model, 'module'):
-#     model.module.load_state_dict(new_state_dict)
-#   else:
-#     model.load_state_dict(new_state_dict)
-#   logger.info("Loaded checkpoint '{}' (epoch {})" .format(
-#     checkpoint_path, iteration))
-#   return model, optimizer, learning_rate, iteration
-def load_checkpoint(checkpoint_path, model, optimizer=None, load_opt=1):
-    assert os.path.isfile(checkpoint_path)
-    checkpoint_dict = torch.load(checkpoint_path, map_location="cpu")
-
-    saved_state_dict = checkpoint_dict["model"]
-    if hasattr(model, "module"):
-        state_dict = model.module.state_dict()
-    else:
-        state_dict = model.state_dict()
-    new_state_dict = {}
-    for k, v in state_dict.items():  # 模型需要的shape
-        try:
-            new_state_dict[k] = saved_state_dict[k]
-            if saved_state_dict[k].shape != state_dict[k].shape:
-                logger.warning(
-                    "shape-%s-mismatch|need-%s|get-%s",
-                    k,
-                    state_dict[k].shape,
-                    saved_state_dict[k].shape,
-                )  #
-                raise KeyError
-        except:
-            # logger.info(traceback.format_exc())
-            logger.info("%s is not in the checkpoint", k)  # pretrain缺失的
-            new_state_dict[k] = v  # 模型自带的随机值
-    if hasattr(model, "module"):
-        model.module.load_state_dict(new_state_dict, strict=False)
-    else:
-        model.load_state_dict(new_state_dict, strict=False)
-    logger.info("Loaded model weights")
-
-    iteration = checkpoint_dict["iteration"]
-    learning_rate = checkpoint_dict["learning_rate"]
-    if (
-        optimizer is not None and load_opt == 1
-    ):  ###加载不了，如果是空的的话，重新初始化，可能还会影响lr时间表的更新，因此在train文件最外围catch
-        #   try:
-        optimizer.load_state_dict(checkpoint_dict["optimizer"])
-    #   except:
-    #     traceback.print_exc()
-    logger.info("Loaded checkpoint '{}' (epoch {})".format(checkpoint_path, iteration))
-    return model, optimizer, learning_rate, iteration
-
-
-def save_checkpoint(model, optimizer, learning_rate, iteration, checkpoint_path):
-    logger.info(
-        "Saving model and optimizer state at epoch {} to {}".format(
-            iteration, checkpoint_path
-        )
-    )
-    if hasattr(model, "module"):
-        state_dict = model.module.state_dict()
-    else:
-        state_dict = model.state_dict()
-    torch.save(
-        {
-            "model": state_dict,
-            "iteration": iteration,
-            "optimizer": optimizer.state_dict(),
-            "learning_rate": learning_rate,
-        },
-        checkpoint_path,
-    )
-
-
-def save_checkpoint_d(combd, sbd, optimizer, learning_rate, iteration, checkpoint_path):
-    logger.info(
-        "Saving model and optimizer state at epoch {} to {}".format(
-            iteration, checkpoint_path
-        )
-    )
-    if hasattr(combd, "module"):
-        state_dict_combd = combd.module.state_dict()
-    else:
-        state_dict_combd = combd.state_dict()
-    if hasattr(sbd, "module"):
-        state_dict_sbd = sbd.module.state_dict()
-    else:
-        state_dict_sbd = sbd.state_dict()
-    torch.save(
-        {
-            "combd": state_dict_combd,
-            "sbd": state_dict_sbd,
-            "iteration": iteration,
-            "optimizer": optimizer.state_dict(),
-            "learning_rate": learning_rate,
-        },
-        checkpoint_path,
-    )
-
-
-def summarize(
-    writer,
-    global_step,
-    scalars={},
-    histograms={},
-    images={},
-    audios={},
-    audio_sampling_rate=22050,
-):
-    for k, v in scalars.items():
-        writer.add_scalar(k, v, global_step)
-    for k, v in histograms.items():
-        writer.add_histogram(k, v, global_step)
-    for k, v in images.items():
-        writer.add_image(k, v, global_step, dataformats="HWC")
-    for k, v in audios.items():
-        writer.add_audio(k, v, global_step, audio_sampling_rate)
-
-
-def latest_checkpoint_path(dir_path, regex="G_*.pth"):
-    f_list = glob.glob(os.path.join(dir_path, regex))
-    f_list.sort(key=lambda f: int("".join(filter(str.isdigit, f))))
-    x = f_list[-1]
-    logger.debug(x)
-    return x
-
-
-def plot_spectrogram_to_numpy(spectrogram):
-    global MATPLOTLIB_FLAG
-    if not MATPLOTLIB_FLAG:
-        import matplotlib
-
-        matplotlib.use("Agg")
-        MATPLOTLIB_FLAG = True
-        mpl_logger = logging.getLogger("matplotlib")
-        mpl_logger.setLevel(logging.WARNING)
-    import matplotlib.pylab as plt
-    import numpy as np
-
-    fig, ax = plt.subplots(figsize=(10, 2))
-    im = ax.imshow(spectrogram, aspect="auto", origin="lower", interpolation="none")
-    plt.colorbar(im, ax=ax)
-    plt.xlabel("Frames")
-    plt.ylabel("Channels")
-    plt.tight_layout()
-
-    fig.canvas.draw()
-    data = np.fromstring(fig.canvas.tostring_rgb(), dtype=np.uint8, sep="")
-    data = data.reshape(fig.canvas.get_width_height()[::-1] + (3,))
-    plt.close()
-    return data
-
-
-def plot_alignment_to_numpy(alignment, info=None):
-    global MATPLOTLIB_FLAG
-    if not MATPLOTLIB_FLAG:
-        import matplotlib
-
-        matplotlib.use("Agg")
-        MATPLOTLIB_FLAG = True
-        mpl_logger = logging.getLogger("matplotlib")
-        mpl_logger.setLevel(logging.WARNING)
-    import matplotlib.pylab as plt
-    import numpy as np
-
-    fig, ax = plt.subplots(figsize=(6, 4))
-    im = ax.imshow(
-        alignment.transpose(), aspect="auto", origin="lower", interpolation="none"
-    )
-    fig.colorbar(im, ax=ax)
-    xlabel = "Decoder timestep"
-    if info is not None:
-        xlabel += "\n\n" + info
-    plt.xlabel(xlabel)
-    plt.ylabel("Encoder timestep")
-    plt.tight_layout()
-
-    fig.canvas.draw()
-    data = np.fromstring(fig.canvas.tostring_rgb(), dtype=np.uint8, sep="")
-    data = data.reshape(fig.canvas.get_width_height()[::-1] + (3,))
-    plt.close()
-    return data
-
-
-def load_wav_to_torch(full_path):
-    sampling_rate, data = read(full_path)
-    return torch.FloatTensor(data.astype(np.float32)), sampling_rate
-
-
-def load_filepaths_and_text(filename, split="|"):
-    try:
-        with open(filename, encoding="utf-8") as f:
-            filepaths_and_text = [line.strip().split(split) for line in f]
-    except UnicodeDecodeError:
-        with open(filename) as f:
-            filepaths_and_text = [line.strip().split(split) for line in f]
-    
-    return filepaths_and_text
-
-
-def get_hparams(init=True):
-    """
-    todo:
-      结尾七人组：
-        保存频率、总epoch                     done
-        bs                                    done
-        pretrainG、pretrainD                  done
-        卡号：os.en["CUDA_VISIBLE_DEVICES"]   done
-        if_latest                             done
-      模型：if_f0                             done
-      采样率：自动选择config                  done
-      是否缓存数据集进GPU:if_cache_data_in_gpu done
-
-      -m:
-        自动决定training_files路径,改掉train_nsf_load_pretrain.py里的hps.data.training_files    done
-      -c不要了
-    """
-    parser = argparse.ArgumentParser()
-    parser.add_argument(
-        "-se",
-        "--save_every_epoch",
-        type=int,
-        required=True,
-        help="checkpoint save frequency (epoch)",
-    )
-    parser.add_argument(
-        "-te", "--total_epoch", type=int, required=True, help="total_epoch"
-    )
-    parser.add_argument(
-        "-pg", "--pretrainG", type=str, default="", help="Pretrained Generator path"
-    )
-    parser.add_argument(
-        "-pd", "--pretrainD", type=str, default="", help="Pretrained Discriminator path"
-    )
-    parser.add_argument("-g", "--gpus", type=str, default="0", help="split by -")
-    parser.add_argument(
-        "-bs", "--batch_size", type=int, required=True, help="batch size"
-    )
-    parser.add_argument(
-        "-e", "--experiment_dir", type=str, required=True, help="experiment dir"
-    )  # -m
-    parser.add_argument(
-        "-sr", "--sample_rate", type=str, required=True, help="sample rate, 32k/40k/48k"
-    )
-    parser.add_argument(
-        "-sw",
-        "--save_every_weights",
-        type=str,
-        default="0",
-        help="save the extracted model in weights directory when saving checkpoints",
-    )
-    parser.add_argument(
-        "-v", "--version", type=str, required=True, help="model version"
-    )
-    parser.add_argument(
-        "-f0",
-        "--if_f0",
-        type=int,
-        required=True,
-        help="use f0 as one of the inputs of the model, 1 or 0",
-    )
-    parser.add_argument(
-        "-l",
-        "--if_latest",
-        type=int,
-        required=True,
-        help="if only save the latest G/D pth file, 1 or 0",
-    )
-    parser.add_argument(
-        "-c",
-        "--if_cache_data_in_gpu",
-        type=int,
-        required=True,
-        help="if caching the dataset in GPU memory, 1 or 0",
-    )
-
-    args = parser.parse_args()
-    name = args.experiment_dir
-    experiment_dir = os.path.join("./logs", args.experiment_dir)
-
-    config_save_path = os.path.join(experiment_dir, "config.json")
-    with open(config_save_path, "r") as f:
-        config = json.load(f)
-
-    hparams = HParams(**config)
-    hparams.model_dir = hparams.experiment_dir = experiment_dir
-    hparams.save_every_epoch = args.save_every_epoch
-    hparams.name = name
-    hparams.total_epoch = args.total_epoch
-    hparams.pretrainG = args.pretrainG
-    hparams.pretrainD = args.pretrainD
-    hparams.version = args.version
-    hparams.gpus = args.gpus
-    hparams.train.batch_size = args.batch_size
-    hparams.sample_rate = args.sample_rate
-    hparams.if_f0 = args.if_f0
-    hparams.if_latest = args.if_latest
-    hparams.save_every_weights = args.save_every_weights
-    hparams.if_cache_data_in_gpu = args.if_cache_data_in_gpu
-    hparams.data.training_files = "%s/filelist.txt" % experiment_dir
-    return hparams
-
-
-def get_hparams_from_dir(model_dir):
-    config_save_path = os.path.join(model_dir, "config.json")
-    with open(config_save_path, "r") as f:
-        data = f.read()
-    config = json.loads(data)
-
-    hparams = HParams(**config)
-    hparams.model_dir = model_dir
-    return hparams
-
-
-def get_hparams_from_file(config_path):
-    with open(config_path, "r") as f:
-        data = f.read()
-    config = json.loads(data)
-
-    hparams = HParams(**config)
-    return hparams
-
-
-def check_git_hash(model_dir):
-    source_dir = os.path.dirname(os.path.realpath(__file__))
-    if not os.path.exists(os.path.join(source_dir, ".git")):
-        logger.warning(
-            "{} is not a git repository, therefore hash value comparison will be ignored.".format(
-                source_dir
-            )
-        )
-        return
-
-    cur_hash = subprocess.getoutput("git rev-parse HEAD")
-
-    path = os.path.join(model_dir, "githash")
-    if os.path.exists(path):
-        saved_hash = open(path).read()
-        if saved_hash != cur_hash:
-            logger.warning(
-                "git hash values are different. {}(saved) != {}(current)".format(
-                    saved_hash[:8], cur_hash[:8]
-                )
-            )
-    else:
-        open(path, "w").write(cur_hash)
-
-
-def get_logger(model_dir, filename="train.log"):
-    global logger
-    logger = logging.getLogger(os.path.basename(model_dir))
-    logger.setLevel(logging.DEBUG)
-
-    formatter = logging.Formatter("%(asctime)s\t%(name)s\t%(levelname)s\t%(message)s")
-    if not os.path.exists(model_dir):
-        os.makedirs(model_dir)
-    h = logging.FileHandler(os.path.join(model_dir, filename))
-    h.setLevel(logging.DEBUG)
-    h.setFormatter(formatter)
-    logger.addHandler(h)
-    return logger
-
-
-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__()

infer/modules/uvr5/modules.py

@@ -1,108 +0,0 @@
-import os
-import traceback
-import logging
-
-logger = logging.getLogger(__name__)
-
-import ffmpeg
-import torch
-
-from configs.config import Config
-from infer.modules.uvr5.mdxnet import MDXNetDereverb
-from infer.modules.uvr5.vr import AudioPre, AudioPreDeEcho
-
-config = Config()
-
-
-def uvr(model_name, inp_root, save_root_vocal, paths, save_root_ins, agg, format0):
-    infos = []
-    try:
-        inp_root = inp_root.strip(" ").strip('"').strip("\n").strip('"').strip(" ")
-        save_root_vocal = (
-            save_root_vocal.strip(" ").strip('"').strip("\n").strip('"').strip(" ")
-        )
-        save_root_ins = (
-            save_root_ins.strip(" ").strip('"').strip("\n").strip('"').strip(" ")
-        )
-        if model_name == "onnx_dereverb_By_FoxJoy":
-            pre_fun = MDXNetDereverb(15, config.device)
-        else:
-            func = AudioPre if "DeEcho" not in model_name else AudioPreDeEcho
-            pre_fun = func(
-                agg=int(agg),
-                model_path=os.path.join(
-                    os.getenv("weight_uvr5_root"), model_name + ".pth"
-                ),
-                device=config.device,
-                is_half=config.is_half,
-            )
-        is_hp3 = "HP3" in model_name
-        if inp_root != "":
-            paths = [os.path.join(inp_root, name) for name in os.listdir(inp_root)]
-        else:
-            paths = [path.name for path in paths]
-        for path in paths:
-            inp_path = os.path.join(inp_root, path)
-            need_reformat = 1
-            done = 0
-            try:
-                info = ffmpeg.probe(inp_path, cmd="ffprobe")
-                if (
-                    info["streams"][0]["channels"] == 2
-                    and info["streams"][0]["sample_rate"] == "44100"
-                ):
-                    need_reformat = 0
-                    pre_fun._path_audio_(
-                        inp_path, save_root_ins, save_root_vocal, format0, is_hp3=is_hp3
-                    )
-                    done = 1
-            except:
-                need_reformat = 1
-                traceback.print_exc()
-            if need_reformat == 1:
-                tmp_path = "%s/%s.reformatted.wav" % (
-                    os.path.join(os.environ["TEMP"]),
-                    os.path.basename(inp_path),
-                )
-                os.system(
-                    'ffmpeg -i "%s" -vn -acodec pcm_s16le -ac 2 -ar 44100 "%s" -y'
-                    % (inp_path, tmp_path)
-                )
-                inp_path = tmp_path
-            try:
-                if done == 0:
-                    pre_fun._path_audio_(
-                        inp_path, save_root_ins, save_root_vocal, format0
-                    )
-                infos.append("%s->Success" % (os.path.basename(inp_path)))
-                yield "\n".join(infos)
-            except:
-                try:
-                    if done == 0:
-                        pre_fun._path_audio_(
-                            inp_path, save_root_ins, save_root_vocal, format0
-                        )
-                    infos.append("%s->Success" % (os.path.basename(inp_path)))
-                    yield "\n".join(infos)
-                except:
-                    infos.append(
-                        "%s->%s" % (os.path.basename(inp_path), traceback.format_exc())
-                    )
-                    yield "\n".join(infos)
-    except:
-        infos.append(traceback.format_exc())
-        yield "\n".join(infos)
-    finally:
-        try:
-            if model_name == "onnx_dereverb_By_FoxJoy":
-                del pre_fun.pred.model
-                del pre_fun.pred.model_
-            else:
-                del pre_fun.model
-                del pre_fun
-        except:
-            traceback.print_exc()
-        if torch.cuda.is_available():
-            torch.cuda.empty_cache()
-            logger.info("Executed torch.cuda.empty_cache()")
-    yield "\n".join(infos)