Instructions to use espnet/Dan_Berrebbi_aishell4_asr with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- ESPnet
How to use espnet/Dan_Berrebbi_aishell4_asr with ESPnet:
from espnet2.bin.asr_inference import Speech2Text model = Speech2Text.from_pretrained( "espnet/Dan_Berrebbi_aishell4_asr" ) speech, rate = soundfile.read("speech.wav") text, *_ = model(speech)[0] - Notebooks
- Google Colab
- Kaggle
Download meta.yaml from espnet/Dan_Berrebbi_aishell4_asr: direct link, hf CLI and curl.
- Browser
- Download file 359 Bytes
-
https://huggingface.co/espnet/Dan_Berrebbi_aishell4_asr/resolve/main/meta.yaml
- Command line
-
hf download hf://espnet/Dan_Berrebbi_aishell4_asr/meta.yaml
-
curl -L -o meta.yaml https://huggingface.co/espnet/Dan_Berrebbi_aishell4_asr/resolve/main/meta.yaml
359 Bytes
| espnet: 0.10.3a1 | |
| files: | |
| asr_model_file: exp/asr_fine_tune5_100ep/valid.acc.ave_10best.pth | |
| lm_file: exp/lm_nuit/valid.loss.ave_10best.pth | |
| python: "3.7.11 (default, Jul 27 2021, 14:32:16) \n[GCC 7.5.0]" | |
| timestamp: 1632350504.575834 | |
| torch: 1.9.0 | |
| yaml_files: | |
| asr_train_config: exp/asr_fine_tune5_100ep/config.yaml | |
| lm_train_config: exp/lm_nuit/config.yaml | |