Automatic Speech Recognition
Transformers
PyTorch
wav2vec2
Generated from Trainer
Eval Results (legacy)
Instructions to use golesheed/wav2vec2-large-xls-r-1b-cv-13-elderly-frisian with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use golesheed/wav2vec2-large-xls-r-1b-cv-13-elderly-frisian with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="golesheed/wav2vec2-large-xls-r-1b-cv-13-elderly-frisian")# Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("golesheed/wav2vec2-large-xls-r-1b-cv-13-elderly-frisian") model = AutoModelForCTC.from_pretrained("golesheed/wav2vec2-large-xls-r-1b-cv-13-elderly-frisian", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Update README.md
Browse files
README.md
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- wer
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model-index:
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- name: wav2vec2-large-xls-r-1b-cv-13-elderly-frisian
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results:
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---
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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- wer
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model-index:
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- name: wav2vec2-large-xls-r-1b-cv-13-elderly-frisian
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results:
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- task:
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name: Automatic Speech Recognition
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type: automatic-speech-recognition
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dataset:
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name: common_voice_13_0
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type: common_voice_13_0
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config: fy-NL
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split: test
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args: fy-NL
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metrics:
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- name: Wer
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type: wer
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value: 0.3621700879765396
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- task:
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name: Automatic Speech Recognition
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type: automatic-speech-recognition
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dataset:
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name: common_voice_13_0
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type: common_voice_13_0
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config: fy-NL
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split: validation
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args: fy-NL
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metrics:
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- name: Wer
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type: wer
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value: 0.4703
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---
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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