Instructions to use espnet/jv_openslr35 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- ESPnet
How to use espnet/jv_openslr35 with ESPnet:
import soundfile from espnet2.bin.asr_inference import Speech2Text model = Speech2Text.from_pretrained( "espnet/jv_openslr35" ) speech, rate = soundfile.read("speech.wav") text, *_ = model(speech)[0] - Notebooks
- Google Colab
- Kaggle
Download meta.yaml from espnet/jv_openslr35: direct link, hf CLI and curl.
- Browser
- Download file 274 Bytes
-
https://huggingface.co/espnet/jv_openslr35/resolve/main/meta.yaml
- Command line
-
hf download hf://espnet/jv_openslr35/meta.yaml
-
curl -L -o meta.yaml https://huggingface.co/espnet/jv_openslr35/resolve/main/meta.yaml
274 Bytes
| espnet: 0.10.0 | |
| files: | |
| asr_model_file: exp/asr_train_asr_raw_bpe1000/valid.acc.best.pth | |
| python: "3.8.5 (default, Sep 4 2020, 07:30:14) \n[GCC 7.3.0]" | |
| timestamp: 1625878616.133883 | |
| torch: 1.8.1+cu102 | |
| yaml_files: | |
| asr_train_config: exp/asr_train_asr_raw_bpe1000/config.yaml | |