Automatic Speech Recognition
Transformers
PyTorch
JAX
Safetensors
Esperanto
wav2vec2
audio
speech
xlsr-fine-tuning-week
Eval Results (legacy)
Instructions to use cpierse/wav2vec2-large-xlsr-53-esperanto with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use cpierse/wav2vec2-large-xlsr-53-esperanto with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="cpierse/wav2vec2-large-xlsr-53-esperanto")# Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("cpierse/wav2vec2-large-xlsr-53-esperanto") model = AutoModelForCTC.from_pretrained("cpierse/wav2vec2-large-xlsr-53-esperanto", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 2159355f0dbf69943171a88cac55ed50b0876011f41f85ce6f6b92e30d390e33
- Size of remote file:
- 1.26 GB
- SHA256:
- 9f15fed167825d37bba445d9195ef414334fad03047bbf8318a9c70b3fca6461
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