Instructions to use Splend1dchan/wav2vec2-large-lv60_mt5-base_textdecoderonly_bs64 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Splend1dchan/wav2vec2-large-lv60_mt5-base_textdecoderonly_bs64 with Transformers:
# Load model directly from transformers import SpeechMixEEDT5 model = SpeechMixEEDT5.from_pretrained("Splend1dchan/wav2vec2-large-lv60_mt5-base_textdecoderonly_bs64", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- ed0dadc098a3e4313eccf9377ae5db8aacea95e20c6ed570be2e8a7957a073a7
- Size of remote file:
- 3.31 kB
- SHA256:
- faa7881a31006fcddb857801319e109dd2867ad8fbe0888dadb3fdacd5d2900c
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.