huggingface-hub-rvc

huggingface-hub-rvc provides a small Hugging Face style API for RVC voice conversion artifacts:

• RVCPipeline.from_pretrained(...)
• RVCPipeline.save_pretrained(...)
• RVCPipeline.push_to_hub(...)
• RVCPipeline.train(...)
• RVCPipeline.convert_file(...)
• RVCPipeline.convert_directory(...)

The package uses the working RVC v2 F0 48 kHz architecture and stores new model weights as safetensors.

Artifact Layout

config.json
voice_transform/
  manifest.json
  model.safetensors
  features.safetensors
  index.index

model.safetensors contains the RVC generator weights plus string metadata for the RVC config and training summary. features.safetensors contains retrieval vectors as tensor payload. index.index is the FAISS index and remains a separate binary file. config.json includes model_name, which is also mirrored into voice_transform/manifest.json so the artifact remains identifiable even if it is downloaded into a generic cache or renamed folder. README.md is generated as a Hub model card when save_pretrained writes the artifact.

Legacy model.pth and features.npy artifacts can still be loaded.

Load

from huggingface_hub_rvc import RVCPipeline

pipe = RVCPipeline.from_pretrained("org/rvc-model")
pipe.convert_directory("input_audio", "converted_audio")

Use local_files_only=True to avoid network lookup:

pipe = RVCPipeline.from_pretrained("./my-rvc-model", local_files_only=True)

Train

from huggingface_hub_rvc import RVCPipeline

pipe = RVCPipeline.train(
    identity_dir="identity_audio",
    output_dir="rvc_artifact",
    model_name="Example Voice",
    training_steps=1000,
    identity_audio_mode="separate",
)
pipe.convert_directory("source_audio", "converted_audio")

Save And Push

pipe.save_pretrained("rvc_artifact", model_name="Example Voice")
pipe.push_to_hub("org/rvc-model", folder_path="rvc_artifact")

or:

pipe.save_pretrained("rvc_artifact", push_to_hub=True, repo_id="org/rvc-model")