datong-new / rvc
- Public
- 198 runs
Prediction
datong-new/rvc:07402961aee9e589b0d5fd05368158da3f8df772849c95c2a08c2e3d031fd19aIDkwohmxzbqtrk2ssyxusm2wf4syStatusSucceededSourceWebHardwareT4Total durationCreatedInput
- f0_up_key
- 8
- operation
- train_infer
- accompaniment
- audio_for_infer
- Video Player is loading.Current Time 00:00:000/Duration 00:00:000Loaded: 0%00:00:000Stream Type LIVERemaining Time -00:00:0001x
- Chapters
- descriptions off, selected
- captions settings, opens captions settings dialog
- captions off, selected
This is a modal window.
Beginning of dialog window. Escape will cancel and close the window.
End of dialog window.
- audio_for_train
- Video Player is loading.Current Time 00:00:000/Duration 00:00:000Loaded: 0%Stream Type LIVERemaining Time -00:00:0001x
- Chapters
- descriptions off, selected
- captions settings, opens captions settings dialog
- captions off, selected
This is a modal window.
No compatible source was found for this media.Beginning of dialog window. Escape will cancel and close the window.
End of dialog window.
{ "f0_up_key": 8, "operation": "train_infer", "accompaniment": true, "audio_for_infer": "https://replicate.delivery/pbxt/KSuE9SkdHEsXPUlZmWj4kMwRNZwJ0CR0EFTzjJAwVqKY8brY/1.wav", "audio_for_train": "https://replicate.delivery/pbxt/KSuE9M9iVBPPXjGWfkGpiwD9iZOlHSwAwVmX0vHaA2hJ41Ca/wobunanguo.flac" }
Install Replicate’s Node.js client library:npm install replicate
Import and set up the client:import Replicate from "replicate"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });
Run datong-new/rvc using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "datong-new/rvc:07402961aee9e589b0d5fd05368158da3f8df772849c95c2a08c2e3d031fd19a", { input: { f0_up_key: 8, operation: "train_infer", accompaniment: true, audio_for_infer: "https://replicate.delivery/pbxt/KSuE9SkdHEsXPUlZmWj4kMwRNZwJ0CR0EFTzjJAwVqKY8brY/1.wav", audio_for_train: "https://replicate.delivery/pbxt/KSuE9M9iVBPPXjGWfkGpiwD9iZOlHSwAwVmX0vHaA2hJ41Ca/wobunanguo.flac" } } ); console.log(output);
To learn more, take a look at the guide on getting started with Node.js.
Install Replicate’s Python client library:pip install replicate
Import the client:import replicate
Run datong-new/rvc using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "datong-new/rvc:07402961aee9e589b0d5fd05368158da3f8df772849c95c2a08c2e3d031fd19a", input={ "f0_up_key": 8, "operation": "train_infer", "accompaniment": True, "audio_for_infer": "https://replicate.delivery/pbxt/KSuE9SkdHEsXPUlZmWj4kMwRNZwJ0CR0EFTzjJAwVqKY8brY/1.wav", "audio_for_train": "https://replicate.delivery/pbxt/KSuE9M9iVBPPXjGWfkGpiwD9iZOlHSwAwVmX0vHaA2hJ41Ca/wobunanguo.flac" } ) print(output)
To learn more, take a look at the guide on getting started with Python.
Run datong-new/rvc using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
curl -s -X POST \ -H "Authorization: Bearer $REPLICATE_API_TOKEN" \ -H "Content-Type: application/json" \ -H "Prefer: wait" \ -d $'{ "version": "datong-new/rvc:07402961aee9e589b0d5fd05368158da3f8df772849c95c2a08c2e3d031fd19a", "input": { "f0_up_key": 8, "operation": "train_infer", "accompaniment": true, "audio_for_infer": "https://replicate.delivery/pbxt/KSuE9SkdHEsXPUlZmWj4kMwRNZwJ0CR0EFTzjJAwVqKY8brY/1.wav", "audio_for_train": "https://replicate.delivery/pbxt/KSuE9M9iVBPPXjGWfkGpiwD9iZOlHSwAwVmX0vHaA2hJ41Ca/wobunanguo.flac" } }' \ https://api.replicate.com/v1/predictions
To learn more, take a look at Replicate’s HTTP API reference docs.
Output
ckpt_path
default.pthcloned_audio
Video Player is loading.Current Time 00:00:000/Duration 00:00:000Loaded: 0%00:00:000Stream Type LIVERemaining Time -00:00:0001x- Chapters
- descriptions off, selected
- captions settings, opens captions settings dialog
- captions off, selected
This is a modal window.
Beginning of dialog window. Escape will cancel and close the window.
End of dialog window.
{ "completed_at": "2024-02-25T12:51:58.322834Z", "created_at": "2024-02-25T12:44:50.386033Z", "data_removed": false, "error": null, "id": "kwohmxzbqtrk2ssyxusm2wf4sy", "input": { "f0_up_key": 8, "operation": "train_infer", "accompaniment": true, "audio_for_infer": "https://replicate.delivery/pbxt/KSuE9SkdHEsXPUlZmWj4kMwRNZwJ0CR0EFTzjJAwVqKY8brY/1.wav", "audio_for_train": "https://replicate.delivery/pbxt/KSuE9M9iVBPPXjGWfkGpiwD9iZOlHSwAwVmX0vHaA2hJ41Ca/wobunanguo.flac" }, "logs": "0%| | 0/116 [00:00<?, ?it/s]\n 1%| | 1/116 [00:01<02:31, 1.32s/it]\n 3%|▎ | 3/116 [00:01<00:46, 2.44it/s]\n 4%|▍ | 5/116 [00:01<00:26, 4.19it/s]\n 6%|▌ | 7/116 [00:01<00:17, 6.09it/s]\n 8%|▊ | 9/116 [00:01<00:13, 7.95it/s]\n 9%|▉ | 11/116 [00:02<00:10, 9.62it/s]\n 11%|█ | 13/116 [00:02<00:09, 11.13it/s]\n 13%|█▎ | 15/116 [00:02<00:08, 12.23it/s]\n 15%|█▍ | 17/116 [00:02<00:07, 13.11it/s]\n 16%|█▋ | 19/116 [00:02<00:06, 13.90it/s]\n 18%|█▊ | 21/116 [00:02<00:06, 14.54it/s]\n 20%|█▉ | 23/116 [00:02<00:06, 14.98it/s]\n 22%|██▏ | 25/116 [00:02<00:05, 15.21it/s]\n 23%|██▎ | 27/116 [00:03<00:05, 15.31it/s]\n 25%|██▌ | 29/116 [00:03<00:05, 15.47it/s]\n 27%|██▋ | 31/116 [00:03<00:05, 15.77it/s]\n 28%|██▊ | 33/116 [00:03<00:05, 15.90it/s]\n 30%|███ | 35/116 [00:03<00:05, 16.05it/s]\n 32%|███▏ | 37/116 [00:03<00:04, 16.16it/s]\n 34%|███▎ | 39/116 [00:03<00:04, 16.15it/s]\n 35%|███▌ | 41/116 [00:03<00:04, 15.93it/s]\n 37%|███▋ | 43/116 [00:04<00:04, 15.79it/s]\n 39%|███▉ | 45/116 [00:04<00:04, 15.85it/s]\n 41%|████ | 47/116 [00:04<00:04, 15.82it/s]\n 42%|████▏ | 49/116 [00:04<00:04, 15.92it/s]\n 44%|████▍ | 51/116 [00:04<00:04, 15.89it/s]\n 46%|████▌ | 53/116 [00:04<00:03, 15.96it/s]\n 47%|████▋ | 55/116 [00:04<00:03, 15.96it/s]\n 49%|████▉ | 57/116 [00:04<00:03, 15.92it/s]\n 51%|█████ | 59/116 [00:05<00:03, 15.87it/s]\n 53%|█████▎ | 61/116 [00:05<00:03, 15.94it/s]\n 54%|█████▍ | 63/116 [00:05<00:03, 16.00it/s]\n 56%|█████▌ | 65/116 [00:05<00:03, 16.06it/s]\n 58%|█████▊ | 67/116 [00:05<00:03, 16.01it/s]\n 59%|█████▉ | 69/116 [00:05<00:02, 16.05it/s]\n 61%|██████ | 71/116 [00:05<00:02, 16.06it/s]\n 63%|██████▎ | 73/116 [00:05<00:02, 16.09it/s]\n 65%|██████▍ | 75/116 [00:06<00:02, 15.72it/s]\n 66%|██████▋ | 77/116 [00:06<00:02, 15.75it/s]\n 68%|██████▊ | 79/116 [00:06<00:02, 15.66it/s]\n 70%|██████▉ | 81/116 [00:06<00:02, 15.75it/s]\n 72%|███████▏ | 83/116 [00:06<00:02, 15.84it/s]\n 73%|███████▎ | 85/116 [00:06<00:01, 15.84it/s]\n 75%|███████▌ | 87/116 [00:06<00:01, 15.81it/s]\n 77%|███████▋ | 89/116 [00:06<00:01, 15.84it/s]\n 78%|███████▊ | 91/116 [00:07<00:01, 15.84it/s]\n 80%|████████ | 93/116 [00:07<00:01, 15.91it/s]\n 82%|████████▏ | 95/116 [00:07<00:01, 15.86it/s]\n 84%|████████▎ | 97/116 [00:07<00:01, 15.89it/s]\n 85%|████████▌ | 99/116 [00:07<00:01, 15.87it/s]\n 87%|████████▋ | 101/116 [00:07<00:00, 15.92it/s]\n 89%|████████▉ | 103/116 [00:07<00:00, 15.79it/s]\n 91%|█████████ | 105/116 [00:07<00:00, 15.36it/s]\n 92%|█████████▏| 107/116 [00:08<00:00, 15.20it/s]\n 94%|█████████▍| 109/116 [00:08<00:00, 14.89it/s]\n 96%|█████████▌| 111/116 [00:08<00:00, 14.91it/s]\n 97%|█████████▋| 113/116 [00:08<00:00, 14.87it/s]\n 99%|█████████▉| 115/116 [00:08<00:00, 14.91it/s]\n100%|██████████| 116/116 [00:08<00:00, 13.33it/s]\n2024-02-25 12:49:19 | INFO | fairseq.tasks.hubert_pretraining | current directory is /src\n2024-02-25 12:49:19 | 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}\n2024-02-25 12:49:19 | 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}\n/root/.pyenv/versions/3.8.10/lib/python3.8/site-packages/torch/nn/utils/weight_norm.py:28: UserWarning: torch.nn.utils.weight_norm is deprecated in favor of torch.nn.utils.parametrizations.weight_norm.\nwarnings.warn(\"torch.nn.utils.weight_norm is deprecated in favor of torch.nn.utils.parametrizations.weight_norm.\")\n/root/.pyenv/versions/3.8.10/lib/python3.8/site-packages/torch/nn/utils/weight_norm.py:28: UserWarning: torch.nn.utils.weight_norm is deprecated in favor of torch.nn.utils.parametrizations.weight_norm.\nwarnings.warn(\"torch.nn.utils.weight_norm is deprecated in favor of torch.nn.utils.parametrizations.weight_norm.\")\n/root/.pyenv/versions/3.8.10/lib/python3.8/site-packages/torch/functional.py:660: UserWarning: stft with return_complex=False is deprecated. In a future pytorch release, stft will return complex tensors for all inputs, and return_complex=False will raise an error.\nNote: you can still call torch.view_as_real on the complex output to recover the old return format. (Triggered internally at ../aten/src/ATen/native/SpectralOps.cpp:874.)\nreturn _VF.stft(input, n_fft, hop_length, win_length, window, # type: ignore[attr-defined]\n/root/.pyenv/versions/3.8.10/lib/python3.8/site-packages/torch/functional.py:660: UserWarning: stft with return_complex=False is deprecated. In a future pytorch release, stft will return complex tensors for all inputs, and return_complex=False will raise an error.\nNote: you can still call torch.view_as_real on the complex output to recover the old return format. (Triggered internally at ../aten/src/ATen/native/SpectralOps.cpp:874.)\nreturn _VF.stft(input, n_fft, hop_length, win_length, window, # type: ignore[attr-defined]\n/root/.pyenv/versions/3.8.10/lib/python3.8/site-packages/torch/functional.py:660: UserWarning: stft with return_complex=False is deprecated. In a future pytorch release, stft will return complex tensors for all inputs, and return_complex=False will raise an error.\nNote: you can still call torch.view_as_real on the complex output to recover the old return format. (Triggered internally at ../aten/src/ATen/native/SpectralOps.cpp:874.)\nreturn _VF.stft(input, n_fft, hop_length, win_length, window, # type: ignore[attr-defined]\n/root/.pyenv/versions/3.8.10/lib/python3.8/site-packages/torch/functional.py:660: UserWarning: stft with return_complex=False is deprecated. In a future pytorch release, stft will return complex tensors for all inputs, and return_complex=False will raise an error.\nNote: you can still call torch.view_as_real on the complex output to recover the old return format. (Triggered internally at ../aten/src/ATen/native/SpectralOps.cpp:874.)\nreturn _VF.stft(input, n_fft, hop_length, win_length, window, # type: ignore[attr-defined]\n/root/.pyenv/versions/3.8.10/lib/python3.8/site-packages/torch/functional.py:660: UserWarning: stft with return_complex=False is deprecated. In a future pytorch release, stft will return complex tensors for all inputs, and return_complex=False will raise an error.\nNote: you can still call torch.view_as_real on the complex output to recover the old return format. (Triggered internally at ../aten/src/ATen/native/SpectralOps.cpp:874.)\nreturn _VF.stft(input, n_fft, hop_length, win_length, window, # type: ignore[attr-defined]\n/root/.pyenv/versions/3.8.10/lib/python3.8/site-packages/torch/autograd/__init__.py:266: UserWarning: Grad strides do not match bucket view strides. This may indicate grad was not created according to the gradient layout contract, or that the param's strides changed since DDP was constructed. This is not an error, but may impair performance.\ngrad.sizes() = [64, 1, 4], strides() = [4, 1, 1]\nbucket_view.sizes() = [64, 1, 4], strides() = [4, 4, 1] (Triggered internally at ../torch/csrc/distributed/c10d/reducer.cpp:322.)\nVariable._execution_engine.run_backward( # Calls into the C++ engine to run the backward pass\n/root/.pyenv/versions/3.8.10/lib/python3.8/multiprocessing/resource_tracker.py:216: UserWarning: resource_tracker: There appear to be 20 leaked semaphore objects to clean up at shutdown\nwarnings.warn('resource_tracker: There appear to be %d '\n 0%| | 0/65 [00:00<?, ?it/s]\n 2%|▏ | 1/65 [00:00<00:06, 9.82it/s]\n 5%|▍ | 3/65 [00:00<00:05, 11.97it/s]\n 8%|▊ | 5/65 [00:00<00:04, 13.09it/s]\n 11%|█ | 7/65 [00:00<00:04, 13.78it/s]\n 14%|█▍ | 9/65 [00:00<00:03, 14.08it/s]\n 17%|█▋ | 11/65 [00:00<00:03, 14.28it/s]\n 20%|██ | 13/65 [00:00<00:03, 14.47it/s]\n 23%|██▎ | 15/65 [00:01<00:03, 14.56it/s]\n 26%|██▌ | 17/65 [00:01<00:03, 14.66it/s]\n 29%|██▉ | 19/65 [00:01<00:03, 14.64it/s]\n 32%|███▏ | 21/65 [00:01<00:02, 14.76it/s]\n 35%|███▌ | 23/65 [00:01<00:02, 14.84it/s]\n 38%|███▊ | 25/65 [00:01<00:02, 14.95it/s]\n 42%|████▏ | 27/65 [00:01<00:02, 15.00it/s]\n 45%|████▍ | 29/65 [00:02<00:02, 15.02it/s]\n 48%|████▊ | 31/65 [00:02<00:02, 14.98it/s]\n 51%|█████ | 33/65 [00:02<00:02, 14.79it/s]\n 54%|█████▍ | 35/65 [00:02<00:02, 14.89it/s]\n 57%|█████▋ | 37/65 [00:02<00:01, 14.84it/s]\n 60%|██████ | 39/65 [00:02<00:01, 14.79it/s]\n 63%|██████▎ | 41/65 [00:02<00:01, 14.79it/s]\n 66%|██████▌ | 43/65 [00:02<00:01, 14.84it/s]\n 69%|██████▉ | 45/65 [00:03<00:01, 14.84it/s]\n 72%|███████▏ | 47/65 [00:03<00:01, 14.78it/s]\n 75%|███████▌ | 49/65 [00:03<00:01, 14.78it/s]\n 78%|███████▊ | 51/65 [00:03<00:00, 14.82it/s]\n 82%|████████▏ | 53/65 [00:03<00:00, 14.93it/s]\n 85%|████████▍ | 55/65 [00:03<00:00, 14.89it/s]\n 88%|████████▊ | 57/65 [00:03<00:00, 14.88it/s]\n 91%|█████████ | 59/65 [00:04<00:00, 14.90it/s]\n 94%|█████████▍| 61/65 [00:04<00:00, 14.90it/s]\n 97%|█████████▋| 63/65 [00:04<00:00, 14.98it/s]\n100%|██████████| 65/65 [00:04<00:00, 14.97it/s]\n100%|██████████| 65/65 [00:04<00:00, 14.67it/s]", "metrics": { "predict_time": 262.949361, "total_time": 427.936801 }, "output": { "ckpt_path": "https://replicate.delivery/pbxt/V3fTVi4SctXuQSxtwkw57z1fJdHeSzJeUXJl1DvlmG02e0RTC/default.pth", "cloned_audio": "https://replicate.delivery/pbxt/dIl0M30PMzaTLpGgF2v4BpZAqhBOMESzTcVTylAFpmW7pjmE/audio_cloned.wav" }, "started_at": "2024-02-25T12:47:35.373473Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/kwohmxzbqtrk2ssyxusm2wf4sy", "cancel": "https://api.replicate.com/v1/predictions/kwohmxzbqtrk2ssyxusm2wf4sy/cancel" }, "version": "07402961aee9e589b0d5fd05368158da3f8df772849c95c2a08c2e3d031fd19a" }
Generated in0%| | 0/116 [00:00<?, ?it/s] 1%| | 1/116 [00:01<02:31, 1.32s/it] 3%|▎ | 3/116 [00:01<00:46, 2.44it/s] 4%|▍ | 5/116 [00:01<00:26, 4.19it/s] 6%|▌ | 7/116 [00:01<00:17, 6.09it/s] 8%|▊ | 9/116 [00:01<00:13, 7.95it/s] 9%|▉ | 11/116 [00:02<00:10, 9.62it/s] 11%|█ | 13/116 [00:02<00:09, 11.13it/s] 13%|█▎ | 15/116 [00:02<00:08, 12.23it/s] 15%|█▍ | 17/116 [00:02<00:07, 13.11it/s] 16%|█▋ | 19/116 [00:02<00:06, 13.90it/s] 18%|█▊ | 21/116 [00:02<00:06, 14.54it/s] 20%|█▉ | 23/116 [00:02<00:06, 14.98it/s] 22%|██▏ | 25/116 [00:02<00:05, 15.21it/s] 23%|██▎ | 27/116 [00:03<00:05, 15.31it/s] 25%|██▌ | 29/116 [00:03<00:05, 15.47it/s] 27%|██▋ | 31/116 [00:03<00:05, 15.77it/s] 28%|██▊ | 33/116 [00:03<00:05, 15.90it/s] 30%|███ | 35/116 [00:03<00:05, 16.05it/s] 32%|███▏ | 37/116 [00:03<00:04, 16.16it/s] 34%|███▎ | 39/116 [00:03<00:04, 16.15it/s] 35%|███▌ | 41/116 [00:03<00:04, 15.93it/s] 37%|███▋ | 43/116 [00:04<00:04, 15.79it/s] 39%|███▉ | 45/116 [00:04<00:04, 15.85it/s] 41%|████ | 47/116 [00:04<00:04, 15.82it/s] 42%|████▏ | 49/116 [00:04<00:04, 15.92it/s] 44%|████▍ | 51/116 [00:04<00:04, 15.89it/s] 46%|████▌ | 53/116 [00:04<00:03, 15.96it/s] 47%|████▋ | 55/116 [00:04<00:03, 15.96it/s] 49%|████▉ | 57/116 [00:04<00:03, 15.92it/s] 51%|█████ | 59/116 [00:05<00:03, 15.87it/s] 53%|█████▎ | 61/116 [00:05<00:03, 15.94it/s] 54%|█████▍ | 63/116 [00:05<00:03, 16.00it/s] 56%|█████▌ | 65/116 [00:05<00:03, 16.06it/s] 58%|█████▊ | 67/116 [00:05<00:03, 16.01it/s] 59%|█████▉ | 69/116 [00:05<00:02, 16.05it/s] 61%|██████ | 71/116 [00:05<00:02, 16.06it/s] 63%|██████▎ | 73/116 [00:05<00:02, 16.09it/s] 65%|██████▍ | 75/116 [00:06<00:02, 15.72it/s] 66%|██████▋ | 77/116 [00:06<00:02, 15.75it/s] 68%|██████▊ | 79/116 [00:06<00:02, 15.66it/s] 70%|██████▉ | 81/116 [00:06<00:02, 15.75it/s] 72%|███████▏ | 83/116 [00:06<00:02, 15.84it/s] 73%|███████▎ | 85/116 [00:06<00:01, 15.84it/s] 75%|███████▌ | 87/116 [00:06<00:01, 15.81it/s] 77%|███████▋ | 89/116 [00:06<00:01, 15.84it/s] 78%|███████▊ | 91/116 [00:07<00:01, 15.84it/s] 80%|████████ | 93/116 [00:07<00:01, 15.91it/s] 82%|████████▏ | 95/116 [00:07<00:01, 15.86it/s] 84%|████████▎ | 97/116 [00:07<00:01, 15.89it/s] 85%|████████▌ | 99/116 [00:07<00:01, 15.87it/s] 87%|████████▋ | 101/116 [00:07<00:00, 15.92it/s] 89%|████████▉ | 103/116 [00:07<00:00, 15.79it/s] 91%|█████████ | 105/116 [00:07<00:00, 15.36it/s] 92%|█████████▏| 107/116 [00:08<00:00, 15.20it/s] 94%|█████████▍| 109/116 [00:08<00:00, 14.89it/s] 96%|█████████▌| 111/116 [00:08<00:00, 14.91it/s] 97%|█████████▋| 113/116 [00:08<00:00, 14.87it/s] 99%|█████████▉| 115/116 [00:08<00:00, 14.91it/s] 100%|██████████| 116/116 [00:08<00:00, 13.33it/s] 2024-02-25 12:49:19 | INFO | fairseq.tasks.hubert_pretraining | current directory is /src 2024-02-25 12:49:19 | 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} 2024-02-25 12:49:19 | 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} /root/.pyenv/versions/3.8.10/lib/python3.8/site-packages/torch/nn/utils/weight_norm.py:28: UserWarning: torch.nn.utils.weight_norm is deprecated in favor of torch.nn.utils.parametrizations.weight_norm. warnings.warn("torch.nn.utils.weight_norm is deprecated in favor of torch.nn.utils.parametrizations.weight_norm.") /root/.pyenv/versions/3.8.10/lib/python3.8/site-packages/torch/nn/utils/weight_norm.py:28: UserWarning: torch.nn.utils.weight_norm is deprecated in favor of torch.nn.utils.parametrizations.weight_norm. warnings.warn("torch.nn.utils.weight_norm is deprecated in favor of torch.nn.utils.parametrizations.weight_norm.") /root/.pyenv/versions/3.8.10/lib/python3.8/site-packages/torch/functional.py:660: UserWarning: stft with return_complex=False is deprecated. In a future pytorch release, stft will return complex tensors for all inputs, and return_complex=False will raise an error. Note: you can still call torch.view_as_real on the complex output to recover the old return format. (Triggered internally at ../aten/src/ATen/native/SpectralOps.cpp:874.) return _VF.stft(input, n_fft, hop_length, win_length, window, # type: ignore[attr-defined] /root/.pyenv/versions/3.8.10/lib/python3.8/site-packages/torch/functional.py:660: UserWarning: stft with return_complex=False is deprecated. In a future pytorch release, stft will return complex tensors for all inputs, and return_complex=False will raise an error. Note: you can still call torch.view_as_real on the complex output to recover the old return format. (Triggered internally at ../aten/src/ATen/native/SpectralOps.cpp:874.) return _VF.stft(input, n_fft, hop_length, win_length, window, # type: ignore[attr-defined] /root/.pyenv/versions/3.8.10/lib/python3.8/site-packages/torch/functional.py:660: UserWarning: stft with return_complex=False is deprecated. In a future pytorch release, stft will return complex tensors for all inputs, and return_complex=False will raise an error. Note: you can still call torch.view_as_real on the complex output to recover the old return format. (Triggered internally at ../aten/src/ATen/native/SpectralOps.cpp:874.) return _VF.stft(input, n_fft, hop_length, win_length, window, # type: ignore[attr-defined] /root/.pyenv/versions/3.8.10/lib/python3.8/site-packages/torch/functional.py:660: UserWarning: stft with return_complex=False is deprecated. In a future pytorch release, stft will return complex tensors for all inputs, and return_complex=False will raise an error. Note: you can still call torch.view_as_real on the complex output to recover the old return format. (Triggered internally at ../aten/src/ATen/native/SpectralOps.cpp:874.) return _VF.stft(input, n_fft, hop_length, win_length, window, # type: ignore[attr-defined] /root/.pyenv/versions/3.8.10/lib/python3.8/site-packages/torch/functional.py:660: UserWarning: stft with return_complex=False is deprecated. In a future pytorch release, stft will return complex tensors for all inputs, and return_complex=False will raise an error. Note: you can still call torch.view_as_real on the complex output to recover the old return format. (Triggered internally at ../aten/src/ATen/native/SpectralOps.cpp:874.) return _VF.stft(input, n_fft, hop_length, win_length, window, # type: ignore[attr-defined] /root/.pyenv/versions/3.8.10/lib/python3.8/site-packages/torch/autograd/__init__.py:266: UserWarning: Grad strides do not match bucket view strides. This may indicate grad was not created according to the gradient layout contract, or that the param's strides changed since DDP was constructed. This is not an error, but may impair performance. grad.sizes() = [64, 1, 4], strides() = [4, 1, 1] bucket_view.sizes() = [64, 1, 4], strides() = [4, 4, 1] (Triggered internally at ../torch/csrc/distributed/c10d/reducer.cpp:322.) Variable._execution_engine.run_backward( # Calls into the C++ engine to run the backward pass /root/.pyenv/versions/3.8.10/lib/python3.8/multiprocessing/resource_tracker.py:216: UserWarning: resource_tracker: There appear to be 20 leaked semaphore objects to clean up at shutdown warnings.warn('resource_tracker: There appear to be %d ' 0%| | 0/65 [00:00<?, ?it/s] 2%|▏ | 1/65 [00:00<00:06, 9.82it/s] 5%|▍ | 3/65 [00:00<00:05, 11.97it/s] 8%|▊ | 5/65 [00:00<00:04, 13.09it/s] 11%|█ | 7/65 [00:00<00:04, 13.78it/s] 14%|█▍ | 9/65 [00:00<00:03, 14.08it/s] 17%|█▋ | 11/65 [00:00<00:03, 14.28it/s] 20%|██ | 13/65 [00:00<00:03, 14.47it/s] 23%|██▎ | 15/65 [00:01<00:03, 14.56it/s] 26%|██▌ | 17/65 [00:01<00:03, 14.66it/s] 29%|██▉ | 19/65 [00:01<00:03, 14.64it/s] 32%|███▏ | 21/65 [00:01<00:02, 14.76it/s] 35%|███▌ | 23/65 [00:01<00:02, 14.84it/s] 38%|███▊ | 25/65 [00:01<00:02, 14.95it/s] 42%|████▏ | 27/65 [00:01<00:02, 15.00it/s] 45%|████▍ | 29/65 [00:02<00:02, 15.02it/s] 48%|████▊ | 31/65 [00:02<00:02, 14.98it/s] 51%|█████ | 33/65 [00:02<00:02, 14.79it/s] 54%|█████▍ | 35/65 [00:02<00:02, 14.89it/s] 57%|█████▋ | 37/65 [00:02<00:01, 14.84it/s] 60%|██████ | 39/65 [00:02<00:01, 14.79it/s] 63%|██████▎ | 41/65 [00:02<00:01, 14.79it/s] 66%|██████▌ | 43/65 [00:02<00:01, 14.84it/s] 69%|██████▉ | 45/65 [00:03<00:01, 14.84it/s] 72%|███████▏ | 47/65 [00:03<00:01, 14.78it/s] 75%|███████▌ | 49/65 [00:03<00:01, 14.78it/s] 78%|███████▊ | 51/65 [00:03<00:00, 14.82it/s] 82%|████████▏ | 53/65 [00:03<00:00, 14.93it/s] 85%|████████▍ | 55/65 [00:03<00:00, 14.89it/s] 88%|████████▊ | 57/65 [00:03<00:00, 14.88it/s] 91%|█████████ | 59/65 [00:04<00:00, 14.90it/s] 94%|█████████▍| 61/65 [00:04<00:00, 14.90it/s] 97%|█████████▋| 63/65 [00:04<00:00, 14.98it/s] 100%|██████████| 65/65 [00:04<00:00, 14.97it/s] 100%|██████████| 65/65 [00:04<00:00, 14.67it/s]
Want to make some of these yourself?
Run this model