Instructions to use AfriSpeech/afri-vits-bases with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use AfriSpeech/afri-vits-bases with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-to-speech", model="AfriSpeech/afri-vits-bases")# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("AfriSpeech/afri-vits-bases", device_map="auto") - Notebooks
- Google Colab
- Kaggle
afri-vits-bases
Starting checkpoints for fine-tuning VITS TTS in African languages, one folder per language code (361 folders). Each folder is a Meta MMS-TTS generator whose text vocabulary has been expanded with the IPA symbols produced by africa-g2p (about 190 symbols covering its 750 languages), so IPA text for any African language can be fed in.
Use them through AfriSpeech/afri-vits-finetuner, which maps any
africa-g2p language to its best base (afri-vits pick-base <lang>) and runs LoRA or full fine-tuning on your data.
Important
- These models do not speak new languages on their own. The added symbol embeddings start from the nearest existing sound or the mean embedding plus noise and are meant to be learned during fine-tuning. Without fine-tuning a base behaves like the original MMS model for its own language.
- Generator only. The discriminator needed for GAN training is fetched from
facebook/mms-ttsby the fine-tuner. - Each folder's
ipa_expansion.jsonlists the original vocab size, the symbols added and how each row was initialised. krsis absent: its upstream MMS repo has no weights.
Licence
Derivative of Meta's MMS-TTS checkpoints: CC BY-NC 4.0, non-commercial use only, with attribution to the MMS project.
Model tree for AfriSpeech/afri-vits-bases
Base model
facebook/mms-tts