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, AutoModelForTextToWaveform tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-SeamlessM4Tv2ForTextToSpeech") model = AutoModelForTextToWaveform.from_pretrained("hf-internal-testing/tiny-random-SeamlessM4Tv2ForTextToSpeech") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- f015372e6ffab25f04ce353f7992ff5d11c4662cdefb26a967ac24196f6af185
- Size of remote file:
- 309 kB
- SHA256:
- 5d6de29dd0fd244ada34738fbd2e92a8d90ef2777c3cdc51675191192f436610
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.