https://huggingface.co/Yehor/w2v-xls-r-uk requires about 1.2 GB of GPU memory with float16.
It gives following metrics (without an external LM):
Accuracy on words: 79.76%
Accuracy on chars: 96.36%
Around 300 million of parameters.
#asr #ai #speech
It gives following metrics (without an external LM):
Accuracy on words: 79.76%
Accuracy on chars: 96.36%
Around 300 million of parameters.
#asr #ai #speech
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Published the testset for Ukrainian to HF: https://huggingface.co/datasets/Yehor/cv10-uk-testset-clean #ai #speech
doing something
What happends when you use Whisper #asr #ai #speech
openai/whisper-large-v3-turbo looks like does not recognize Ρ correctly
it's huggingface checkpoint
it's huggingface checkpoint
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Evaluation results...
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Published the testset for Ukrainian to HF: https://huggingface.co/datasets/Yehor/cv10-uk-testset-clean #ai #speech
Use the following colabs to see how you can download this dataset in Python:
datasets: https://colab.research.google.com/drive/1qqnr5-WkaJi8iqHa_Pmlx7PbbXwXiimD?usp=sharing
polars: https://colab.research.google.com/drive/1upeXw3WbLjK37b1LetpM0HxFXDdOZqSK?usp=sharing
#ai #asr #speech
datasets: https://colab.research.google.com/drive/1qqnr5-WkaJi8iqHa_Pmlx7PbbXwXiimD?usp=sharing
polars: https://colab.research.google.com/drive/1upeXw3WbLjK37b1LetpM0HxFXDdOZqSK?usp=sharing
#ai #asr #speech
Google
Load Yehor/cv10-uk-testset-clean by datasets.ipynb
Colab notebook
When your work lives own life, but then you notice wonderful things
https://github.com/egorsmkv/qirimtatar-tts-datasets/stargazers
#tts #speech
https://github.com/egorsmkv/qirimtatar-tts-datasets/stargazers
#tts #speech