Instructions to use espnet/roshansh_how2_asr_raw_ft_sum_valid.acc with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- ESPnet
How to use espnet/roshansh_how2_asr_raw_ft_sum_valid.acc with ESPnet:
import soundfile from espnet2.bin.asr_inference import Speech2Text model = Speech2Text.from_pretrained( "espnet/roshansh_how2_asr_raw_ft_sum_valid.acc" ) speech, rate = soundfile.read("speech.wav") text, *_ = model(speech)[0] - Notebooks
- Google Colab
- Kaggle
Download meta.yaml from espnet/roshansh_how2_asr_raw_ft_sum_valid.acc: direct link, hf CLI and curl.
- Browser
- Download file 284 Bytes
-
https://huggingface.co/espnet/roshansh_how2_asr_raw_ft_sum_valid.acc/resolve/main/meta.yaml
- Command line
-
hf download hf://espnet/roshansh_how2_asr_raw_ft_sum_valid.acc/meta.yaml
-
curl -L -o meta.yaml https://huggingface.co/espnet/roshansh_how2_asr_raw_ft_sum_valid.acc/resolve/main/meta.yaml
284 Bytes
| espnet: 0.10.7a1 | |
| files: | |
| asr_model_file: exp/roshansh_how2_asr_raw_ft_sum/valid.acc.ave_10best.pth | |
| python: "3.8.12 (default, Oct 12 2021, 13:49:34) \n[GCC 7.5.0]" | |
| timestamp: 1644953744.151672 | |
| torch: 1.10.1 | |
| yaml_files: | |
| asr_train_config: exp/roshansh_how2_asr_raw_ft_sum/config.yaml | |