Automatic Speech Recognition
Transformers
TensorBoard
Safetensors
whisper
Generated from Trainer
Eval Results (legacy)
Instructions to use akhooli/whisper-small-ar2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use akhooli/whisper-small-ar2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="akhooli/whisper-small-ar2")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("akhooli/whisper-small-ar2") model = AutoModelForSpeechSeq2Seq.from_pretrained("akhooli/whisper-small-ar2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
End of training
Browse files- README.md +4 -4
- all_results.json +15 -0
- eval_results.json +9 -0
- runs/Dec26_11-34-23_2969512e7c3f/events.out.tfevents.1735215754.2969512e7c3f.118.1 +3 -0
- train_results.json +9 -0
- trainer_state.json +74 -0
README.md
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tags:
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- generated_from_trainer
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datasets:
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- common_voice_17_0
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metrics:
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- wer
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model-index:
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name: Automatic Speech Recognition
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type: automatic-speech-recognition
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dataset:
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name: common_voice_17_0
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type: common_voice_17_0
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config: ar
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split: None
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args: ar
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# whisper-small-ar2
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-
This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the common_voice_17_0 dataset.
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It achieves the following results on the evaluation set:
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- Loss: 1.2796
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- Wer: 0.7637
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tags:
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- generated_from_trainer
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datasets:
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- mozilla-foundation/common_voice_17_0
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metrics:
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- wer
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model-index:
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name: Automatic Speech Recognition
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type: automatic-speech-recognition
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dataset:
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name: mozilla-foundation/common_voice_17_0 ar
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type: mozilla-foundation/common_voice_17_0
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config: ar
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split: None
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args: ar
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# whisper-small-ar2
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+
This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the mozilla-foundation/common_voice_17_0 ar dataset.
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It achieves the following results on the evaluation set:
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- Loss: 1.2796
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- Wer: 0.7637
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all_results.json
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{
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"eval_loss": 1.2795981168746948,
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"eval_wer": 0.763668430335097,
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"total_flos": 4.617366645252293e+18,
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"train_loss": 0.16236594557762146,
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"train_runtime": 2555.1718,
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"train_samples": 360,
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"train_samples_per_second": 6.262,
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"train_steps_per_second": 0.196
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}
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eval_results.json
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{
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"epoch": 41.666666666666664,
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"eval_loss": 1.2795981168746948,
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"eval_runtime": 20.697,
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"eval_samples": 85,
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"eval_samples_per_second": 4.107,
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"eval_steps_per_second": 0.145,
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"eval_wer": 0.763668430335097
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}
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runs/Dec26_11-34-23_2969512e7c3f/events.out.tfevents.1735215754.2969512e7c3f.118.1
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version https://git-lfs.github.com/spec/v1
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oid sha256:4c3f2ed977aa0efad3f444d21a9e3dae57133c92c3acafdb8718f0d847d35a15
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size 406
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train_results.json
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{
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"epoch": 41.666666666666664,
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"total_flos": 4.617366645252293e+18,
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"train_loss": 0.16236594557762146,
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"train_runtime": 2555.1718,
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"train_samples": 360,
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"train_samples_per_second": 6.262,
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"train_steps_per_second": 0.196
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}
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trainer_state.json
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"log_history": [
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