免费GPT-SoVITS模型|五星上将麦克阿瑟配音模型

GPT-SoVITS模型|五星上将麦克阿瑟配音模型，一键离线生成，本地化部署，永久使用！

炼丹师永久会员299，全站资源免费下载，模型持续更新

高级炼丹师免费训练1个配音模型

配音介绍

五星上将麦克阿瑟梗文案套话主要体现在其独特的评论风格和金句上，‌这些言论不仅在网络上广为传播，‌还成为了文化现象的一部分。‌麦克阿瑟以其自信、‌好大喜功、‌野心勃勃的性格特点，‌以及其在历史上的言行，‌被网友以幽默的方式重新解读和演绎，‌形成了一种独特的文化符号。‌

给他一个xxxx，他能xxxx，不是xxxx用不起，而是xxxx更有性价比·······对此，远在大洋彼岸的五星评论家麦克阿瑟表示，如果xxxxxxxxxx·······大型纪录片《xxxxxxx》持续播出~

1. 自信与自大：‌麦克阿瑟的自信在其言论中体现得淋漓尽致，‌他曾经说过：‌“如果上帝让我去写暑假作业的话，‌那么我宁愿去对抗上帝。‌”这种自信的态度，‌加上其历史上的某些言论和行为，‌如保证战争很快结束等，‌都成为了网友调侃的对象。‌

2. 金句频出：‌麦克阿瑟不仅在军事上有其建树，‌他的言论也经常成为经典。‌例如，‌“老兵永远不死，‌只会慢慢凋零”这一金句，‌出自他在国会大厦发表的演讲，‌之后被广泛引用。‌这种金句的输出，‌使得麦克阿瑟在网络文化中成为了一个造梗王。‌

3. 文化现象：‌麦克阿瑟梗不仅在网络上广泛传播，‌还渗透到了现实生活中。‌例如，‌有人在结婚典礼上播放以麦克阿瑟形象制作的评论助兴视频，‌为将军开拓了婚庆事业版图。‌

4. 舆论宣传：‌麦克阿瑟热衷于舆论宣传，‌1944年他率军打回菲律宾时所拍摄的涉水登陆照片，‌就是为了获得完美的宣传效果而反复拍摄的。‌这种重视形象管理和自我宣传的行为，‌也符合了一个网红的自我修养。‌

GPT-SoVITS模型配音效果

测试文本

五星上将麦克阿瑟梗文案套话主要体现在其独特的评论风格和金句上，‌这些言论不仅在网络上广为传播，‌还成为了文化现象的一部分。‌麦克阿瑟以其自信、‌好大喜功、‌野心勃勃的性格特点，‌以及其在历史上的言行，‌被网友以幽默的方式重新解读和演绎，‌形成了一种独特的文化符号。‌The five-star general MacArthur terrier copywriting trope is mainly reflected in its unique comment style and golden phrases, which are not only widely spread on the Internet, but also become part of the cultural phenomenon. MacArthur, with his self-confident, good-humored and ambitious character traits, as well as his words and deeds in history, have been reinterpreted and interpreted by netizens in a humorous way that forming a unique cultural symbol.

Translated with DeepL.com (free version)

模型下载

1，百度网盘

2，夸克网盘

3，123网盘

炼丹师永久会员299，全站资源免费下载，模型持续更新
高级炼丹师免费训练1个配音模型

训练日志

2024-07-19 15:26:41,402	aiaf.cc_mkas	INFO	{'train': {'log_interval': 100, 'eval_interval': 500, 'seed': 1234, 'epochs': 20, 'learning_rate': 0.0001, 'betas': [0.8, 0.99], 'eps': 1e-09, 'batch_size': 15, 'fp16_run': True, 'lr_decay': 0.999875, 'segment_size': 20480, 'init_lr_ratio': 1, 'warmup_epochs': 0, 'c_mel': 45, 'c_kl': 1.0, 'text_low_lr_rate': 0.4, 'pretrained_s2G': 'GPT_SoVITS/pretrained_models/s2G488k.pth', 'pretrained_s2D': 'GPT_SoVITS/pretrained_models/s2D488k.pth', 'if_save_latest': True, 'if_save_every_weights': True, 'save_every_epoch': 20, 'gpu_numbers': '0'}, 'data': {'max_wav_value': 32768.0, 'sampling_rate': 32000, 'filter_length': 2048, 'hop_length': 640, 'win_length': 2048, 'n_mel_channels': 128, 'mel_fmin': 0.0, 'mel_fmax': None, 'add_blank': True, 'n_speakers': 300, 'cleaned_text': True, 'exp_dir': 'logs/aiaf.cc_mkas'}, 'model': {'inter_channels': 192, 'hidden_channels': 192, 'filter_channels': 768, 'n_heads': 2, 'n_layers': 6, 'kernel_size': 3, 'p_dropout': 0.1, 'resblock': '1', 'resblock_kernel_sizes': [3, 7, 11], 'resblock_dilation_sizes': [[1, 3, 5], [1, 3, 5], [1, 3, 5]], 'upsample_rates': [10, 8, 2, 2, 2], 'upsample_initial_channel': 512, 'upsample_kernel_sizes': [16, 16, 8, 2, 2], 'n_layers_q': 3, 'use_spectral_norm': False, 'gin_channels': 512, 'semantic_frame_rate': '25hz', 'freeze_quantizer': True}, 's2_ckpt_dir': 'logs/aiaf.cc_mkas', 'content_module': 'cnhubert', 'save_weight_dir': 'SoVITS_weights', 'name': 'aiaf.cc_mkas', 'pretrain': None, 'resume_step': None}
2024-07-19 15:26:42,713	aiaf.cc_mkas	INFO	loaded pretrained GPT_SoVITS/pretrained_models/s2G488k.pth
2024-07-19 15:26:42,927	aiaf.cc_mkas	INFO	loaded pretrained GPT_SoVITS/pretrained_models/s2D488k.pth
2024-07-19 15:27:10,147	aiaf.cc_mkas	INFO	Train Epoch: 1 [0%]
2024-07-19 15:27:10,148	aiaf.cc_mkas	INFO	[2.703822612762451, 2.0623793601989746, 7.478734016418457, 19.774877548217773, 0.0, 1.2794076204299927, 0, 9.99875e-05]
2024-07-19 15:27:23,279	aiaf.cc_mkas	INFO	====> Epoch: 1
2024-07-19 15:27:35,797	aiaf.cc_mkas	INFO	====> Epoch: 2
2024-07-19 15:27:48,155	aiaf.cc_mkas	INFO	====> Epoch: 3
2024-07-19 15:28:00,504	aiaf.cc_mkas	INFO	====> Epoch: 4
2024-07-19 15:28:12,740	aiaf.cc_mkas	INFO	====> Epoch: 5
2024-07-19 15:28:24,954	aiaf.cc_mkas	INFO	====> Epoch: 6
2024-07-19 15:28:30,032	aiaf.cc_mkas	INFO	Train Epoch: 7 [25%]
2024-07-19 15:28:30,032	aiaf.cc_mkas	INFO	[2.5224015712738037, 2.51821231842041, 9.130533218383789, 19.872297286987305, 0.0, 1.0485355854034424, 100, 9.991253280566489e-05]
2024-07-19 15:28:37,712	aiaf.cc_mkas	INFO	====> Epoch: 7
2024-07-19 15:28:49,827	aiaf.cc_mkas	INFO	====> Epoch: 8
2024-07-19 15:29:01,954	aiaf.cc_mkas	INFO	====> Epoch: 9
2024-07-19 15:29:14,080	aiaf.cc_mkas	INFO	====> Epoch: 10
2024-07-19 15:29:26,290	aiaf.cc_mkas	INFO	====> Epoch: 11
2024-07-19 15:29:38,368	aiaf.cc_mkas	INFO	====> Epoch: 12
2024-07-19 15:29:45,994	aiaf.cc_mkas	INFO	Train Epoch: 13 [50%]
2024-07-19 15:29:45,995	aiaf.cc_mkas	INFO	[2.417562484741211, 2.4924750328063965, 10.34283447265625, 21.760183334350586, 0.0, 1.0759611129760742, 200, 9.983762181915804e-05]
2024-07-19 15:29:51,052	aiaf.cc_mkas	INFO	====> Epoch: 13
2024-07-19 15:30:03,370	aiaf.cc_mkas	INFO	====> Epoch: 14
2024-07-19 15:30:15,519	aiaf.cc_mkas	INFO	====> Epoch: 15
2024-07-19 15:30:27,652	aiaf.cc_mkas	INFO	====> Epoch: 16
2024-07-19 15:30:39,842	aiaf.cc_mkas	INFO	====> Epoch: 17
2024-07-19 15:30:52,169	aiaf.cc_mkas	INFO	====> Epoch: 18
2024-07-19 15:31:02,302	aiaf.cc_mkas	INFO	Train Epoch: 19 [75%]
2024-07-19 15:31:02,302	aiaf.cc_mkas	INFO	[2.4361085891723633, 2.6323657035827637, 10.106511116027832, 21.3712158203125, 0.0, 1.1513057947158813, 300, 9.976276699833672e-05]
2024-07-19 15:31:04,773	aiaf.cc_mkas	INFO	====> Epoch: 19
2024-07-19 15:31:16,906	aiaf.cc_mkas	INFO	Saving model and optimizer state at iteration 20 to logs/aiaf.cc_mkas/logs_s2\G_233333333333.pth
2024-07-19 15:31:17,667	aiaf.cc_mkas	INFO	Saving model and optimizer state at iteration 20 to logs/aiaf.cc_mkas/logs_s2\D_233333333333.pth
2024-07-19 15:31:19,325	aiaf.cc_mkas	INFO	saving ckpt aiaf.cc_mkas_e20:Success.
2024-07-19 15:31:19,325	aiaf.cc_mkas	INFO	====> Epoch: 20

如何使用配音模型

1，GPT-SoVITS模型本地部署（适合有显卡的用户）

https://aiaf.cc/69/.html

2，GPT-SoVITS模型云端部署

https://aiaf.cc/99/.html

如果您想一对一远程教学模型安装、模型训练，请联系微信 xiaoming1870

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