Automatic Speech Recognition
Transformers
PyTorch
Slovenian
wav2vec2
Generated from Trainer
hf-asr-leaderboard
model_for_talk
mozilla-foundation/common_voice_8_0
robust-speech-event
Eval Results (legacy)
Instructions to use DrishtiSharma/wav2vec2-large-xls-r-300m-sl-with-LM-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use DrishtiSharma/wav2vec2-large-xls-r-300m-sl-with-LM-v2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="DrishtiSharma/wav2vec2-large-xls-r-300m-sl-with-LM-v2")# Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("DrishtiSharma/wav2vec2-large-xls-r-300m-sl-with-LM-v2") model = AutoModelForCTC.from_pretrained("DrishtiSharma/wav2vec2-large-xls-r-300m-sl-with-LM-v2") - Notebooks
- Google Colab
- Kaggle
Commit ·
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Parent(s): d2f7926
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README.md
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- Loss: 0.2855
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- Wer: 0.2401
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##
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More information needed
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## Training procedure
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### Training hyperparameters
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- Loss: 0.2855
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- Wer: 0.2401
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### Evaluation Commands
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1. To evaluate on mozilla-foundation/common_voice_8_0 with test split
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python eval.py --model_id DrishtiSharma/wav2vec2-large-xls-r-300m-sl-with-LM-v2 --dataset mozilla-foundation/common_voice_8_0 --config sl --split test --log_outputs
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2. To evaluate on speech-recognition-community-v2/dev_data
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python eval.py --model_id DrishtiSharma/wav2vec2-large-xls-r-300m-sl-with-LM-v2 --dataset speech-recognition-community-v2/dev_data --config sl --split validation --chunk_length_s 10 --stride_length_s 1
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### Training hyperparameters
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