Instructions to use facebook/mms-tts-bnp with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use facebook/mms-tts-bnp with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-to-speech", model="facebook/mms-tts-bnp")# Load model directly from transformers import AutoTokenizer, AutoModelForTextToWaveform tokenizer = AutoTokenizer.from_pretrained("facebook/mms-tts-bnp") model = AutoModelForTextToWaveform.from_pretrained("facebook/mms-tts-bnp", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from facebook/mms-tts-bnp: direct link, hf CLI and curl.
- Browser
- Download file 145 MB
-
https://huggingface.co/facebook/mms-tts-bnp/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://facebook/mms-tts-bnp/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/facebook/mms-tts-bnp/resolve/main/pytorch_model.bin
145 MB
- Xet hash:
- 87c69cd26f6949d1aef19e9b6189fa3ebc83908dd893bc7d95fc6644e21de6a1
- Size of remote file:
- 145 MB
- SHA256:
- e85bb7ec7f86c015882d74121b890a6920f2cc76cd80c33c082b5b8f409082ee
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.