Automatic Speech Recognition
Transformers
PyTorch
JAX
Maltese
wav2vec2
audio
speech
xlsr-fine-tuning-week
Eval Results (legacy)
Instructions to use Akashpb13/xlsr_maltese_wav2vec2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Akashpb13/xlsr_maltese_wav2vec2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="Akashpb13/xlsr_maltese_wav2vec2")# Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("Akashpb13/xlsr_maltese_wav2vec2") model = AutoModelForCTC.from_pretrained("Akashpb13/xlsr_maltese_wav2vec2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from Akashpb13/xlsr_maltese_wav2vec2: direct link, hf CLI and curl.
- Browser
- Download file 1.26 GB
-
https://huggingface.co/Akashpb13/xlsr_maltese_wav2vec2/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://Akashpb13/xlsr_maltese_wav2vec2/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/Akashpb13/xlsr_maltese_wav2vec2/resolve/main/pytorch_model.bin
1.26 GB
- Xet hash:
- fc256f43d6317b443cf08a9627b6434e11713a37acca665e6358761f5fe73ab0
- Size of remote file:
- 1.26 GB
- SHA256:
- b873cbca45c62120b2594415530ee01d589290c9031e18960513ad5ba06458e7
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