auspicious3000
HuggingFace Version
Thanks to the author for his/her fantastic model that nearly eliminated the timbre leaking problem.
For someone who doesn't want to use the FairSeq, I made a HuggingFace version of content vec best legacy: Link.
@leng-yue Thank you for your contribution. Based on the model definition in our paper, it appears that removing the final projection is necessary to achieve the desired outcome.
Appreciate your quick update ⚡️
Although I recommended removing the final_proj function call in the sample code, I also decided to keep it in the model for the sake of backward compatibility. This is because there are several existing models, such as so-vits-svc, ddsp-svc, and fish-diffusion, that have been using the final_proj since 1 or 2 months ago. 😂
Surprisingly, adding the final projection did not cause any problems. First, the final projection is trained on the output of the final layer instead of the 9th layer. Applying the final projection on the 9th layer may cause mismatches. Second, the final projection is injected with speaker information. While the purpose of contentvec is to remove speaker information, adding the final projection may defeat the purpose of contentvec.
To my mind, this is probably because they use the final projection with the 9th layer, which may not be in the same latent space. In this case, the final projection may not be able to inject speaker information...
I believe removing the final projection may improve the performance of those projects.
Anyway, for those who would like to use this Huggingface interface, please remove the final projection to get the correct output.
Currently, the final_proj is just left there, and it's not called by default (in the forward).
Very very great discussion!
I converted huggingface model to onnx. Size is very small (280MB) ! And it work fine in my app that is realtime voice changer based on so-vits-svc.
Converter repo is here.
@leng-yue how did you convert to huggingface? I trained my own version of contentvec and using the "transformers/src/transformers/models/hubert/convert_hubert_original_pytorch_checkpoint_to_pytorch.py" script to convert it, however I am getting this error message:
2023-08-31 09:05:07 | INFO | main | hubert.encoder.layers.10.attention.out_proj.weight was initialized from encoder.layers.10.self_attn.out_proj.weight. 2023-08-31 09:05:07 | INFO | main | hubert.encoder.layers.10.attention.out_proj.bias was initialized from encoder.layers.10.self_attn.out_proj.bias. 2023-08-31 09:05:07 | INFO | main | hubert.encoder.layers.10.layer_norm.weight was initialized from encoder.layers.10.self_attn_layer_norm.weight. 2023-08-31 09:05:07 | INFO | main | hubert.encoder.layers.10.layer_norm.bias was initialized from encoder.layers.10.self_attn_layer_norm.bias. 2023-08-31 09:05:07 | INFO | main | hubert.encoder.layers.10.feed_forward.intermediate_dense.weight was initialized from encoder.layers.10.fc1.weight. 2023-08-31 09:05:07 | INFO | main | hubert.encoder.layers.10.feed_forward.intermediate_dense.bias was initialized from encoder.layers.10.fc1.bias. 2023-08-31 09:05:07 | INFO | main | hubert.encoder.layers.10.feed_forward.output_dense.weight was initialized from encoder.layers.10.fc2.weight. 2023-08-31 09:05:07 | INFO | main | hubert.encoder.layers.10.feed_forward.output_dense.bias was initialized from encoder.layers.10.fc2.bias. 2023-08-31 09:05:07 | INFO | main | hubert.encoder.layers.10.final_layer_norm.weight was initialized from encoder.layers.10.final_layer_norm.weight. 2023-08-31 09:05:07 | INFO | main | hubert.encoder.layers.10.final_layer_norm.bias was initialized from encoder.layers.10.final_layer_norm.bias. 2023-08-31 09:05:07 | INFO | main | hubert.encoder.layers.11.attention.k_proj.weight was initialized from encoder.layers.11.self_attn.k_proj.weight. 2023-08-31 09:05:07 | INFO | main | hubert.encoder.layers.11.attention.k_proj.bias was initialized from encoder.layers.11.self_attn.k_proj.bias. 2023-08-31 09:05:07 | INFO | main | hubert.encoder.layers.11.attention.v_proj.weight was initialized from encoder.layers.11.self_attn.v_proj.weight. 2023-08-31 09:05:07 | INFO | main | hubert.encoder.layers.11.attention.v_proj.bias was initialized from encoder.layers.11.self_attn.v_proj.bias. 2023-08-31 09:05:07 | INFO | main | hubert.encoder.layers.11.attention.q_proj.weight was initialized from encoder.layers.11.self_attn.q_proj.weight. 2023-08-31 09:05:07 | INFO | main | hubert.encoder.layers.11.attention.q_proj.bias was initialized from encoder.layers.11.self_attn.q_proj.bias. 2023-08-31 09:05:07 | INFO | main | hubert.encoder.layers.11.attention.out_proj.weight was initialized from encoder.layers.11.self_attn.out_proj.weight. 2023-08-31 09:05:07 | INFO | main | hubert.encoder.layers.11.attention.out_proj.bias was initialized from encoder.layers.11.self_attn.out_proj.bias. 2023-08-31 09:05:07 | INFO | main | hubert.encoder.layers.11.layer_norm.weight was initialized from encoder.layers.11.self_attn_layer_norm.weight. 2023-08-31 09:05:07 | INFO | main | hubert.encoder.layers.11.layer_norm.bias was initialized from encoder.layers.11.self_attn_layer_norm.bias. 2023-08-31 09:05:07 | INFO | main | hubert.encoder.layers.11.feed_forward.intermediate_dense.weight was initialized from encoder.layers.11.fc1.weight. 2023-08-31 09:05:07 | INFO | main | hubert.encoder.layers.11.feed_forward.intermediate_dense.bias was initialized from encoder.layers.11.fc1.bias. 2023-08-31 09:05:07 | INFO | main | hubert.encoder.layers.11.feed_forward.output_dense.weight was initialized from encoder.layers.11.fc2.weight. 2023-08-31 09:05:07 | INFO | main | hubert.encoder.layers.11.feed_forward.output_dense.bias was initialized from encoder.layers.11.fc2.bias. 2023-08-31 09:05:07 | INFO | main | hubert.encoder.layers.11.final_layer_norm.weight was initialized from encoder.layers.11.final_layer_norm.weight. 2023-08-31 09:05:07 | INFO | main | hubert.encoder.layers.11.final_layer_norm.bias was initialized from encoder.layers.11.final_layer_norm.bias. Traceback (most recent call last): File "transformers/src/transformers/models/hubert/convert_hubert_original_pytorch_checkpoint_to_pytorch.py", line 248, in
args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned File "/root/anaconda/envs/contentvec_hf/lib/python3.7/site-packages/torch/autograd/grad_mode.py", line 27, in decorate_context return func(*args, **kwargs) File "transformers/src/transformers/models/hubert/convert_hubert_original_pytorch_checkpoint_to_pytorch.py", line 232, in convert_hubert_checkpoint recursively_load_weights(model, hf_wav2vec, is_finetuned) File "transformers/src/transformers/models/hubert/convert_hubert_original_pytorch_checkpoint_to_pytorch.py", line 122, in recursively_load_weights set_recursively(hf_model, mapped_key, value, name, weight_type) File "transformers/src/transformers/models/hubert/convert_hubert_original_pytorch_checkpoint_to_pytorch.py", line 60, in set_recursively hf_pointer = getattr(hf_pointer, attribute) File "/root/anaconda/envs/contentvec_hf/lib/python3.7/site-packages/torch/nn/modules/module.py", line 1270, in getattr type(self).name, name)) AttributeError: 'ModuleList' object has no attribute '12'
You can use this file: https://huggingface.co/lengyue233/content-vec-best/blob/main/convert.py
You can use this file: https://huggingface.co/lengyue233/content-vec-best/blob/main/convert.py
Hi, lengyue, how can i convert this pth model of content to onnx?
dear friend, could I ask the detailed steps of training a better contentvec model applied on svc task, based on the open source model? Is there any other methods except changing a bigger dataset? Thanks a lot @leng-yue
Very very great discussion!
I converted huggingface model to onnx. Size is very small (280MB) ! And it work fine in my app that is realtime voice changer based on so-vits-svc.
Converter repo is here.
Hello, can you share your way of converting to onnx with me? I also need it
