Automatic Speech Recognition
Transformers
PyTorch
German
wav2vec2
audio
speech
xlsr-fine-tuning-week
Eval Results (legacy)
Instructions to use sourabharsh/wav2vec2_10july with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use sourabharsh/wav2vec2_10july with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="sourabharsh/wav2vec2_10july")# Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("sourabharsh/wav2vec2_10july") model = AutoModelForCTC.from_pretrained("sourabharsh/wav2vec2_10july") - Notebooks
- Google Colab
- Kaggle
Commit ·
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Parent(s): 1e0745c
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README.md
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language:
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datasets:
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tags:
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- audio
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- automatic-speech-recognition
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- speech
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license: apache-2.0
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language: de
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datasets:
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- common_voice
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metrics:
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- wer
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- cer
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tags:
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- audio
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- automatic-speech-recognition
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- speech
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- xlsr-fine-tuning-week
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license: apache-2.0
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model-index:
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- name: XLSR Wav2Vec2 German by Jonatas Grosman
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results:
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- task:
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name: Speech Recognition
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type: automatic-speech-recognition
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dataset:
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name: Common Voice de
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type: common_voice
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args: de
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metrics:
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- name: Test WER
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type: wer
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value: 10.55
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- name: Test CER
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type: cer
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value: 2.81
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