Automatic Speech Recognition
Transformers
PyTorch
English
whisper
hf-asr-leaderboard
Generated from Trainer
Eval Results (legacy)
Instructions to use Jakaria/atcosim_fn_tiny with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Jakaria/atcosim_fn_tiny with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="Jakaria/atcosim_fn_tiny")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("Jakaria/atcosim_fn_tiny") model = AutoModelForSpeechSeq2Seq.from_pretrained("Jakaria/atcosim_fn_tiny") - Notebooks
- Google Colab
- Kaggle
Update README.md
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README.md
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# atcosim_corpus
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This model is a fine-tuned version of [openai/whisper-
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It achieves the following results on the evaluation set:
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- Loss: 0.0623
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- Wer: 2.4909
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# atcosim_corpus
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This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) on the atcosim_corpus dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.0623
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- Wer: 2.4909
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