load model(s) from hubert/checkpoint_best_legacy_500.pt
INFO:fairseq.tasks.hubert_pretraining:current directory is /content/so-vits-svc
INFO:fairseq.tasks.hubert_pretraining:HubertPretrainingTask Config {'_name': 'hubert_pretraining', 'data': 'metadata', 'fine_tuning': False, 'labels': ['km'], 'label_dir': 'label', 'label_rate': 50.0, 'sample_rate': 16000, 'normalize': False, 'enable_padding': False, 'max_keep_size': None, 'max_sample_size': 250000, 'min_sample_size': 32000, 'single_target': False, 'random_crop': True, 'pad_audio': False}
INFO:fairseq.models.hubert.hubert:HubertModel Config: {'_name': 'hubert', 'label_rate': 50.0, 'extractor_mode': default, 'encoder_layers': 12, 'encoder_embed_dim': 768, 'encoder_ffn_embed_dim': 3072, 'encoder_attention_heads': 12, 'activation_fn': gelu, 'layer_type': transformer, 'dropout': 0.1, 'attention_dropout': 0.1, 'activation_dropout': 0.0, 'encoder_layerdrop': 0.05, 'dropout_input': 0.1, 'dropout_features': 0.1, 'final_dim': 256, 'untie_final_proj': True, 'layer_norm_first': False, 'conv_feature_layers': '[(512,10,5)] + [(512,3,2)] * 4 + [(512,2,2)] * 2', 'conv_bias': False, 'logit_temp': 0.1, 'target_glu': False, 'feature_grad_mult': 0.1, 'mask_length': 10, 'mask_prob': 0.8, 'mask_selection': static, 'mask_other': 0.0, 'no_mask_overlap': False, 'mask_min_space': 1, 'mask_channel_length': 10, 'mask_channel_prob': 0.0, 'mask_channel_selection': static, 'mask_channel_other': 0.0, 'no_mask_channel_overlap': False, 'mask_channel_min_space': 1, 'conv_pos': 128, 'conv_pos_groups': 16, 'latent_temp': [2.0, 0.5, 0.999995], 'skip_masked': False, 'skip_nomask': False, 'checkpoint_activations': False, 'required_seq_len_multiple': 2, 'depthwise_conv_kernel_size': 31, 'attn_type': '', 'pos_enc_type': 'abs', 'fp16': False}
load
INFO:root:Loaded checkpoint '/content/so-vits-svc/logs/44k/G_31000.pth' (iteration 1938)
#=====segment start, 8.34s======
Traceback (most recent call last):
File "/content/so-vits-svc/inference_main.py", line 101, in
main()
File "/content/so-vits-svc/inference_main.py", line 85, in main
out_audio, out_sr = svc_model.infer(spk, tran, raw_path,
File "/content/so-vits-svc/inference/infer_tool.py", line 169, in infer
speaker_id = self.spk2id[speaker]
File "/content/so-vits-svc/utils.py", line 507, in getitem
return getattr(self, key)
AttributeError: 'HParams' object has no attribute 'lola'

FileNotFoundError Traceback (most recent call last)
in <cell line: 32>()
32 if download_after_inference:
33 from google.colab import files
---> 34 files.download(wav_output)

/usr/local/lib/python3.9/dist-packages/google/colab/files.py in download(filename)
220 if not _os.path.exists(filename):
221 msg = 'Cannot find file: {}'.format(filename)
--> 222 raise FileNotFoundError(msg) # pylint: disable=undefined-variable
223
224 comm_manager = _IPython.get_ipython().kernel.comm_manager

FileNotFoundError: Cannot find file: /content/so-vits-svc/results/Suegravocals.wav_0key_lola.flac