Instructions to use amin-oj/wav2vec2-base-960h-finetuned-asr-PolyAI_minds14-en-US with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use amin-oj/wav2vec2-base-960h-finetuned-asr-PolyAI_minds14-en-US with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="amin-oj/wav2vec2-base-960h-finetuned-asr-PolyAI_minds14-en-US")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("amin-oj/wav2vec2-base-960h-finetuned-asr-PolyAI_minds14-en-US") model = AutoModelForCTC.from_pretrained("amin-oj/wav2vec2-base-960h-finetuned-asr-PolyAI_minds14-en-US", device_map="auto") - Notebooks
- Google Colab
- Kaggle
End of training
Browse files- README.md +72 -0
- model.safetensors +1 -1
README.md
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---
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library_name: transformers
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license: apache-2.0
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base_model: facebook/wav2vec2-base-960h
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tags:
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- generated_from_trainer
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metrics:
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- wer
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model-index:
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- name: wav2vec2-base-960h-finetuned-asr-PolyAI_minds14-en-US
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results: []
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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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# wav2vec2-base-960h-finetuned-asr-PolyAI_minds14-en-US
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This model is a fine-tuned version of [facebook/wav2vec2-base-960h](https://huggingface.co/facebook/wav2vec2-base-960h) on an unknown dataset.
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It achieves the following results on the evaluation set:
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- Loss: 1.6485
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- Wer: 0.3319
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## Model description
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More information needed
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## Intended uses & limitations
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More information needed
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## Training and evaluation data
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More information needed
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## Training procedure
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### Training hyperparameters
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The following hyperparameters were used during training:
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- learning_rate: 1e-05
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- train_batch_size: 8
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- eval_batch_size: 8
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- seed: 42
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- gradient_accumulation_steps: 2
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- total_train_batch_size: 16
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- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
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- lr_scheduler_type: linear
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- lr_scheduler_warmup_steps: 125
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- training_steps: 500
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- mixed_precision_training: Native AMP
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### Training results
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| Training Loss | Epoch | Step | Validation Loss | Wer |
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|:-------------:|:-----:|:----:|:---------------:|:------:|
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| 1.5518 | 12.4 | 62 | 1.5976 | 0.4723 |
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| 1.0477 | 24.8 | 124 | 1.1343 | 0.3617 |
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| 0.7908 | 37.2 | 186 | 1.5092 | 0.3447 |
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| 0.6269 | 49.6 | 248 | 1.5146 | 0.3617 |
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| 0.5075 | 62.0 | 310 | 1.4677 | 0.3574 |
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| 0.4534 | 74.4 | 372 | 1.5825 | 0.3191 |
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| 0.4621 | 86.8 | 434 | 1.6253 | 0.3191 |
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| 0.4314 | 99.2 | 496 | 1.6485 | 0.3319 |
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### Framework versions
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- Transformers 4.57.1
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- Pytorch 2.8.0+cu126
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- Datasets 4.4.2
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- Tokenizers 0.22.1
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model.safetensors
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version https://git-lfs.github.com/spec/v1
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size 377611120
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version https://git-lfs.github.com/spec/v1
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size 377611120
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