训练后的train.log为空

[bendchen]

我训练了一个声音，发现训练后的日志内容为空，哪位帮看看这是什么问题，谢谢! 这是模型日志目录中的文件： 2-name2text.txt 5-wav32k eval events.out.tfevents.1727580550.t630.1180145.0 train.log 3-bert 6-name2semantic.tsv events.out.tfevents.1727580226.t630.1145688.0 logs_s1 4-cnhubert config.json events.out.tfevents.1727580386.t630.1175751.0 logs_s2 这是train.log的信息： $ ll train.log -rw-r--r--. 1 abc abc 0 Sep 29 11:23 train.log

[SapphireLab]

应提供有效的终端信息以定位问题.

[bendchen]

抱歉，是我没有粘相应的log信息。 "/home/usera/miniconda3/envs/GPTSoVits/bin/python" GPT_SoVITS/s2_train.py --config "/home/usera/gptsovits/GPT-SoVITS-20240821v2/TEMP/tmp_s2.json" INFO:yidao:{'train': {'log_interval': 100, 'eval_interval': 500, 'seed': 1234, 'epochs': 8, 'learning_rate': 0.0001, 'betas': [0.8, 0.99], 'eps': 1e-09, 'batch_size': 6, '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/gsv-v2final-pretrained/s2G2333k.pth', 'pretrained_s2D': 'GPT_SoVITS/pretrained_models/gsv-v2final-pretrained/s2D2333k.pth', 'if_save_latest': True, 'if_save_every_weights': True, 'save_every_epoch': 4, 'gpu_numbers': '0-1'}, '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/yidao'}, '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, 'version': 'v2'}, 's2_ckpt_dir': 'logs/yidao', 'content_module': 'cnhubert', 'save_weight_dir': 'SoVITS_weights_v2', 'name': 'yidao', 'version': 'v2', 'pretrain': None, 'resume_step': None} phoneme_data_len: 3 wav_data_len: 99 100%|████████████| 99/99 [00:00<00:00, 82078.69it/s] skipped_phone: 0 , skipped_dur: 0 total left: 99 phoneme_data_len: 3 wav_data_len: 99 100%|████████████| 99/99 [00:00<00:00, 80895.40it/s] skipped_phone: 0 , skipped_dur: 0 total left: 99 INFO:yidao:loaded pretrained GPT_SoVITS/pretrained_models/gsv-v2final-pretrained/s2G2333k.pth

INFO:yidao:loaded pretrained GPT_SoVITS/pretrained_models/gsv-v2final-pretrained/s2D2333k.pth [00:00 [00:00 Epoch: 4 INFO:yidao:====> Epoch: 5 INFO:yidao:====> Epoch: 6 INFO:yidao:====> Epoch: 7 INFO:yidao:Saving model and optimizer state at iteration 8 to logs/yidao/logs_s2/G_233333333333.pth INFO:yidao:Saving model and optimizer state at iteration 8 to logs/yidao/logs_s2/D_233333333333.pth INFO:yidao:saving ckpt yidao_e8:Success. INFO:yidao:====> Epoch: 8 "/home/usera/miniconda3/envs/GPTSoVits/bin/python" GPT_SoVITS/s1_train.py --config_file "/home/usera/gptsovits/GPT-SoVITS-20240821v2/TEMP/tmp_s1.yaml" Seed set to 1234 Using 16bit Automatic Mixed Precision (AMP) GPU available: True (cuda), used: True TPU available: False, using: 0 TPU cores HPU available: False, using: 0 HPUs ckpt_path: None Initializing distributed: GLOBAL_RANK: 0, MEMBER: 1/2 [rank: 1] Seed set to 1234 ckpt_path: None Initializing distributed: GLOBAL_RANK: 1, MEMBER: 2/2 distributed_backend=nccl All distributed processes registered. Starting with 2 processes semantic_data_len: 3 phoneme_data_len: 3 semantic_data_len: 3 phoneme_data_len: 3 item_name semantic_audio 0 vo_YMLQ002_7_kazumichi_01.wav 1012 298 93 12 857 567 316 851 497 869 692 513... 1 vo_YMLQ002_7_kazumichi_02.wav 582 239 239 247 997 571 966 894 408 112 769 47... 2 vo_YMLQ002_7_kazumichi_03.wav 1012 403 868 414 266 740 980 733 995 570 162 9... dataset.__len__(): 99 item_name semantic_audio 0 vo_YMLQ002_7_kazumichi_01.wav 1012 298 93 12 857 567 316 851 497 869 692 513... 1 vo_YMLQ002_7_kazumichi_02.wav 582 239 239 247 997 571 966 894 408 112 769 47... 2 vo_YMLQ002_7_kazumichi_03.wav 1012 403 868 414 266 740 980 733 995 570 162 9... dataset.__len__(): 99 LOCAL_RANK: 1 - CUDA_VISIBLE_DEVICES: [0,1] LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0,1] | Name | Type | Params | Mode ------------------------------------------------------- 0 | model | Text2SemanticDecoder | 77.6 M | train ------------------------------------------------------- 77.6 M Trainable params 0 Non-trainable params 77.6 M Total params 310.426 Total estimated model params size (MB) 257 Modules in train mode 0 Modules in eval mode /home/usera/miniconda3/envs/GPTSoVits/lib/python3.9/site-packages/pytorch_lightning/loops/fit_loop.py:298: The number of training batches (9) is smaller than the logging interval Trainer(log_every_n_steps=50). Set a lower value for log_every_n_steps if you want to see logs for the training epoch. Epoch 14: 100%|█| 9/9 [00:01<00:00, 7.80it/s, v_num=0, total_loss_step=130.0, lr_step=0.002, top_3_acc_step=1.000, total_loss_epoch=234.0, lr_ep`Trainer.fit` stopped: `max_epochs=15` reached. Epoch 14: 100%|█| 9/9 [00:04<00:00, 1.83it/s, v_num=0, total_loss_step=130.0, lr_step=0.002, top_3_acc_step=1.000, total_loss_epoch=234.0, lr_ep 从日志中看到可能有关的是：fit_loop.py:298: The number of training batches (9) is smaller than the logging interval Trainer(log_every_n_steps=50) 但不知道怎么调整程序