-
Notifications
You must be signed in to change notification settings - Fork 590
New issue
Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.
By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.
Already on GitHub? Sign in to your account
Add VC Noro model #247
Add VC Noro model #247
Conversation
There was a problem hiding this comment.
Choose a reason for hiding this comment
The reason will be displayed to describe this comment to others. Learn more.
Thanks for your efforts! Great job! This is our first time to introduce VC. So let us set high criteria for future developers!
egs/vc/README.md
Outdated
@@ -0,0 +1,20 @@ | |||
# Amphion Singing Voice Cloning (VC) Recipe |
There was a problem hiding this comment.
Choose a reason for hiding this comment
The reason will be displayed to describe this comment to others. Learn more.
Voice Conversion Recipe
There was a problem hiding this comment.
Choose a reason for hiding this comment
The reason will be displayed to describe this comment to others. Learn more.
fixed
egs/vc/README.md
Outdated
|
||
## Quick Start | ||
|
||
We provide a **[beginner recipe](Noro)** to demonstrate how to train a cutting edge SVC model. Specifically, it is an official implementation of the paper "NORO: A Noise-Robust One-Shot Voice Conversion System with Hidden Speaker Representation Capabilities". |
There was a problem hiding this comment.
Choose a reason for hiding this comment
The reason will be displayed to describe this comment to others. Learn more.
Typo: "SVC model"
There was a problem hiding this comment.
Choose a reason for hiding this comment
The reason will be displayed to describe this comment to others. Learn more.
fixed
There was a problem hiding this comment.
Choose a reason for hiding this comment
The reason will be displayed to describe this comment to others. Learn more.
Will this change effect the ns2, TTS model?
There was a problem hiding this comment.
Choose a reason for hiding this comment
The reason will be displayed to describe this comment to others. Learn more.
I have reverted the previous changes.
BTW, use black to format the code to pass the format check |
models/vc/Noro/noro_dataset.py
Outdated
There was a problem hiding this comment.
Choose a reason for hiding this comment
The reason will be displayed to describe this comment to others. Learn more.
The code needs to be cleaned up.
There was a problem hiding this comment.
Choose a reason for hiding this comment
The reason will be displayed to describe this comment to others. Learn more.
Code has been cleaned.
There was a problem hiding this comment.
Choose a reason for hiding this comment
The reason will be displayed to describe this comment to others. Learn more.
LGTM
✨ Description
In this PR, we release an unofficial PyTorch implementation of Noro, a Noise-Robust One-shot Voice Conversion (VC) system. This model is designed to convert the timbre of speech from a source speaker to a target speaker using only a single reference speech sample while preserving the semantic content of the original speech. Noro introduces innovative components tailored for VC using noisy reference speeches, including a dual-branch reference encoding module and a noise-agnostic contrastive speaker loss.
The main purpose of this PR is to provide a noise-robust VC solution that performs effectively even with noisy reference speeches, making it suitable for real-world applications. Additionally, we explore the hidden speaker representation capabilities of the VC system by repurposing its reference encoder as a speaker encoder, demonstrating competitive performance with advanced self-supervised learning models.
To test this PR, follow the instructions in the updated README.md to set up the environment, train the model, and evaluate its performance under different acoustic environments.
🚧 Related Issues
None
👨💻 Changes Proposed
🧑🤝🧑 Who Can Review?
@RMSnow @HarryHe11 @Adorable-Qin
✅ Checklist