nvidia-riva / nvidia-riva/tutorials
Request for 'Bad List' of Noisy German Samples
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- Dominant language
- Jupyter Notebook
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Description
Hello,
I am currently working with the German speech recognition data provided by this project and came across the following line in the README:
"In addition, we also filter out samples that are considered 'noisy', that is, samples having very high WER (word error rate) or CER (character error rate) w.r.t. a previously trained German model."
Unfortunately, I do not have access to a pre-trained German model to calculate the WER or CER for my dataset. This makes it challenging for me to filter out the noisy samples effectively.
Could you please provide a list of these 'noisy' samples or the criteria used to identify them?
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First steps
- Read the whole issue, then the project's contributing guide.
- Comment on the issue to say you are picking it up — it saves two people doing the same work.
- Fork the repository and make your change on a branch.
- Open a pull request that references the issue number.
Research direction
Start with the README passage describing noisy German speech-recognition samples and trace how the provided data was filtered using WER or CER against a German model. Done means documenting the noisy-sample list or the identification criteria so users without that model can reproduce the filtering.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- jupyter-notebook, machine-learning
- Domain
- data, documentation, machine-learning
- Issue type
- Documentation
- Difficulty
- 4/5
- Estimated time
- 3-5 days
- Activity status
- Stale
- Clarity
- Mostly clear
- Newbie friendliness
- 25/100