Automatic Speech Recognition
Transformers
Safetensors
wav2vec2
Generated from Trainer
Eval Results (legacy)
Instructions to use misiker/trainer_output with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use misiker/trainer_output with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="misiker/trainer_output")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("misiker/trainer_output") model = AutoModelForCTC.from_pretrained("misiker/trainer_output", device_map="auto") - Notebooks
- Google Colab
- Kaggle
End of training
Browse files
README.md
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metrics:
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model-index:
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- name: misiker/
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results:
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- task:
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type: automatic-speech-recognition
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name: Automatic Speech Recognition
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dataset:
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name: PolyAI/minds14
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type: minds14
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split: train[:500]
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args: en-US
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metrics:
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value: 1.0
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name: Wer
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---
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# misiker/
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This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the PolyAI/minds14 dataset.
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It achieves the following results on the evaluation set:
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metrics:
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- wer
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model-index:
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- name: misiker/trainer_output
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results:
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- task:
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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: PolyAI/minds14
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type: minds14
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split: train[:500]
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args: en-US
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metrics:
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- name: Wer
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type: wer
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value: 1.0
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---
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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should probably proofread and complete it, then remove this comment. -->
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# misiker/trainer_output
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This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the PolyAI/minds14 dataset.
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It achieves the following results on the evaluation set:
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