Instructions to use alakxender/whisper-small-dv-full with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use alakxender/whisper-small-dv-full with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="alakxender/whisper-small-dv-full")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("alakxender/whisper-small-dv-full") model = AutoModelForSpeechSeq2Seq.from_pretrained("alakxender/whisper-small-dv-full") - Notebooks
- Google Colab
- Kaggle
Commit ·
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Parent(s): 2b36ece
Training in progress, step 3500
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README.md
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This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the arrow 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_ortho:
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- eval_wer: 2.
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- eval_runtime:
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- eval_samples_per_second: 3.
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- eval_steps_per_second: 0.
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- epoch: 0.
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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-small](https://huggingface.co/openai/whisper-small) on the arrow dataset.
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It achieves the following results on the evaluation set:
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- eval_loss: 0.0287
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- eval_wer_ortho: 16.5967
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- eval_wer: 2.4205
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- eval_runtime: 11555.3152
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- eval_samples_per_second: 3.797
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- eval_steps_per_second: 0.079
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- epoch: 0.5748
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- step: 3500
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## Model description
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