Instructions to use espnet/owls_9B_180K with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- ESPnet
How to use espnet/owls_9B_180K with ESPnet:
import soundfile from espnet2.bin.asr_inference import Speech2Text model = Speech2Text.from_pretrained( "espnet/owls_9B_180K" ) speech, rate = soundfile.read("speech.wav") text, *_ = model(speech)[0] - Notebooks
- Google Colab
- Kaggle
Download meta.yaml from espnet/owls_9B_180K: direct link, hf CLI and curl.
- Browser
- Download file 329 Bytes
-
https://huggingface.co/espnet/owls_9B_180K/resolve/main/meta.yaml
- Command line
-
hf download hf://espnet/owls_9B_180K/meta.yaml
-
curl -L -o meta.yaml https://huggingface.co/espnet/owls_9B_180K/resolve/main/meta.yaml
329 Bytes
| espnet: '202409' | |
| files: | |
| s2t_model_file: exp_owsm/s2t_train_9b_ds_raw_bpe50000/valid.total_count.ave_5best.pth | |
| python: 3.10.14 | packaged by conda-forge | (main, Mar 20 2024, 12:45:18) [GCC 12.3.0] | |
| timestamp: 1740457539.986012 | |
| torch: 2.4.0+cu121 | |
| yaml_files: | |
| s2t_train_config: exp_owsm/s2t_train_9b_ds_raw_bpe50000/config.yaml | |