Text-to-Speech
Transformers
Safetensors
Kabyle
agbalu_speech
feature-extraction
kabyle
taqbaylit
berber
amazigh
speech-synthesis
voice-cloning
cross-lingual-voice-cloning
expressive-speech
low-resource
custom_code
Instructions to use agbalu/Matoub-4B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use agbalu/Matoub-4B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-to-speech", model="agbalu/Matoub-4B", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("agbalu/Matoub-4B", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from agbalu/Matoub-4B: direct link, hf CLI and curl.
- Browser
- Download file 11.4 MB
-
https://huggingface.co/agbalu/Matoub-4B/resolve/main/tokenizer.json
- Command line
-
hf download hf://agbalu/Matoub-4B/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/agbalu/Matoub-4B/resolve/main/tokenizer.json
11.4 MB
- Xet hash:
- 5fa490d0d5572d5f93cd8b77f0fe84e87d9525a7331056ce4fdb0b0dbc791718
- Size of remote file:
- 11.4 MB
- SHA256:
- eb883de2de5adc5113f1f02b54830a0ea7cd6ef191cde65c41aceb3737d4d1c1
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.