Instructions to use alakxender/whisper-large-v3-cv17-dv with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use alakxender/whisper-large-v3-cv17-dv with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="alakxender/whisper-large-v3-cv17-dv")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("alakxender/whisper-large-v3-cv17-dv") model = AutoModelForSpeechSeq2Seq.from_pretrained("alakxender/whisper-large-v3-cv17-dv") - Notebooks
- Google Colab
- Kaggle
Commit ·
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Parent(s): 0c33168
Training in progress, step 3900
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README.md
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This model is a fine-tuned version of [openai/whisper-large-v3](https://huggingface.co/openai/whisper-large-v3) on the None dataset.
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It achieves the following results on the evaluation set:
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- eval_loss: 0.
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- eval_wer: 71.
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- eval_runtime:
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- eval_samples_per_second: 0.
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- eval_steps_per_second: 0.
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- epoch:
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- step:
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## Model description
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This model is a fine-tuned version of [openai/whisper-large-v3](https://huggingface.co/openai/whisper-large-v3) on the None dataset.
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It achieves the following results on the evaluation set:
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- eval_loss: 0.4610
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- eval_wer: 71.0345
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- eval_runtime: 143.56
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- eval_samples_per_second: 0.697
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- eval_steps_per_second: 0.021
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- epoch: 11.8632
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- step: 3900
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## Model description
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