Instructions to use voidful/tts_hubert_m2m100 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use voidful/tts_hubert_m2m100 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="voidful/tts_hubert_m2m100")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("voidful/tts_hubert_m2m100") model = AutoModel.from_pretrained("voidful/tts_hubert_m2m100") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 168cd33151d82888090dd26bbe421ea4cd03850058815c8ee2865533c91d871c
- Size of remote file:
- 1.95 GB
- SHA256:
- 46760d7b6b3f311f4eb04e8672b59fc4e7dbe4f2c95cb1b5cdd32ffc1d3b6449
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