Automatic Speech Recognition
Transformers
PyTorch
wav2vec2
mozilla-foundation/common_voice_8_0
Generated from Trainer
rm-vallader
robust-speech-event
model_for_talk
hf-asr-leaderboard
Eval Results (legacy)
Instructions to use DrishtiSharma/wav2vec2-xls-r-300m-rm-vallader-d1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use DrishtiSharma/wav2vec2-xls-r-300m-rm-vallader-d1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="DrishtiSharma/wav2vec2-xls-r-300m-rm-vallader-d1")# Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("DrishtiSharma/wav2vec2-xls-r-300m-rm-vallader-d1") model = AutoModelForCTC.from_pretrained("DrishtiSharma/wav2vec2-xls-r-300m-rm-vallader-d1") - Notebooks
- Google Colab
- Kaggle
Commit ·
ad0cb1f
1
Parent(s): d2f1f94
Update README.md
Browse files
README.md
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- name: Test CER
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type: cer
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value: 0.05860608074430969
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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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should probably proofread and complete it, then remove this comment. -->
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- Loss: 0.2754
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- Wer: 0.2831
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## Model description
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More information needed
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## Intended uses & limitations
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More information needed
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## Training and evaluation data
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More information needed
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## Training procedure
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### Training hyperparameters
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- name: Test CER
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type: cer
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value: 0.05860608074430969
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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: Robust Speech Event - Dev Data
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type: speech-recognition-community-v2/dev_data
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args: vot
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metrics:
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- name: Test WER
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type: wer
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value: NA
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- name: Test CER
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type: cer
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value: NA
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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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should probably proofread and complete it, then remove this comment. -->
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- Loss: 0.2754
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- Wer: 0.2831
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### Training hyperparameters
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