Zengyi-Qin
commented
10 months ago Hi @yihong1120 - Very much appreciated for your advice. Could you first test on the languages you mentioned to help us identify what exactly the problems are. We already used the IPA (international phonetic alphabet) phonemes to train the networks to minimize the potential issues with underrepresented languages
Dear OpenVoice Contributors,
First and foremost, I would like to extend my sincerest commendations for the remarkable work you have accomplished with OpenVoice. The technology's ability to clone voice tones accurately and facilitate flexible voice style control is nothing short of revolutionary. Moreover, the zero-shot cross-lingual voice cloning feature is a testament to the innovative strides you are making in the field of speech synthesis.
Having perused your paper and explored the OpenVoice demos, I am thoroughly impressed by the system's capabilities. However, I would like to propose an enhancement that could potentially augment the versatility of OpenVoice, particularly in handling diverse linguistic contexts.
Issue: Expanding Linguistic Adaptability for Underrepresented Languages
While OpenVoice performs admirably with languages and accents present in the massive-speaker multi-lingual training dataset, there is an opportunity to extend its adaptability to underrepresented languages that are often not included in global datasets. These languages, which may have unique phonetic and prosodic characteristics, present a challenge for any voice cloning technology.
Proposed Enhancement:
Incorporating a Broader Range of Phonetic and Prosodic Features: By expanding the dataset to include a wider array of phonetic and prosodic features from underrepresented languages, OpenVoice could potentially improve its cloning accuracy for these languages.
Developing a Framework for Community-Driven Dataset Expansion: Establishing a platform where native speakers of underrepresented languages can contribute voice samples could enrich the training dataset and enhance the model's performance across a broader linguistic spectrum.
Integrating Adaptive Algorithms for Phonetic Variation: Implementing machine learning algorithms that can adapt to the phonetic variations of new languages could make OpenVoice more robust in handling the nuances of different linguistic contexts.
I believe these enhancements could not only refine the performance of OpenVoice but also contribute to the preservation and representation of linguistic diversity in the digital realm.
Thank you for considering my proposal. I eagerly await your thoughts on this matter and am keen to contribute further to this discussion.
Best regards, yihong1120