Instructions to use hf-internal-testing/tiny-random-SeamlessM4Tv2ForTextToSpeech with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-SeamlessM4Tv2ForTextToSpeech with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-to-audio", model="hf-internal-testing/tiny-random-SeamlessM4Tv2ForTextToSpeech")# Load model directly from transformers import AutoTokenizer, AutoModelForMultimodalLM tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-SeamlessM4Tv2ForTextToSpeech") model = AutoModelForMultimodalLM.from_pretrained("hf-internal-testing/tiny-random-SeamlessM4Tv2ForTextToSpeech") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 9375056d8f589f91e5bf709724a078492cdb6bac457c84b863c7e07dcbf3c6ac
- Size of remote file:
- 309 kB
- SHA256:
- de61ef885c34812e82b72c60da015c61c34ae3ddc80a51a6306d5e66a840bb32
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.