Automatic Speech Recognition
Transformers
PyTorch
TensorBoard
speech-encoder-decoder
Generated from Trainer
Instructions to use Rolv-Arild/xls-r-300m-npsc-seq2seq with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Rolv-Arild/xls-r-300m-npsc-seq2seq with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="Rolv-Arild/xls-r-300m-npsc-seq2seq")# Load model directly from transformers import AutoTokenizer, AutoModelForSpeechSeq2Seq tokenizer = AutoTokenizer.from_pretrained("Rolv-Arild/xls-r-300m-npsc-seq2seq") model = AutoModelForSpeechSeq2Seq.from_pretrained("Rolv-Arild/xls-r-300m-npsc-seq2seq", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from Rolv-Arild/xls-r-300m-npsc-seq2seq: direct link, hf CLI and curl.
- Browser
- Download file 2.35 GB
-
https://huggingface.co/Rolv-Arild/xls-r-300m-npsc-seq2seq/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://Rolv-Arild/xls-r-300m-npsc-seq2seq/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/Rolv-Arild/xls-r-300m-npsc-seq2seq/resolve/main/pytorch_model.bin
2.35 GB
- Xet hash:
- 73335d30d2db5fbe504d7e8426271bd77e85f84cc642c34a27bd23a3a03bae5f
- Size of remote file:
- 2.35 GB
- SHA256:
- 72833d7f498e65d280052fede15a72fe199410b45d2cccc101a47ceebc73b08a
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