Automatic Speech Recognition
Transformers
TensorBoard
Safetensors
whisper
Generated from Trainer
Eval Results (legacy)
Instructions to use mmcgovern574/whisper-tiny-dv with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mmcgovern574/whisper-tiny-dv with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="mmcgovern574/whisper-tiny-dv")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("mmcgovern574/whisper-tiny-dv") model = AutoModelForSpeechSeq2Seq.from_pretrained("mmcgovern574/whisper-tiny-dv", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from mmcgovern574/whisper-tiny-dv: direct link, hf CLI and curl.
- Browser
- Download file 1.76 kB
-
https://huggingface.co/mmcgovern574/whisper-tiny-dv/resolve/1b184a47c0453616aa8a8fa25f96a933b42f6105/README.md
- Command line
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hf download hf://mmcgovern574/whisper-tiny-dv@1b184a47c0453616aa8a8fa25f96a933b42f6105/README.md
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curl -L -o README.md https://huggingface.co/mmcgovern574/whisper-tiny-dv/resolve/1b184a47c0453616aa8a8fa25f96a933b42f6105/README.md
1.76 kB
| license: apache-2.0 | |
| base_model: openai/whisper-tiny | |
| tags: | |
| - generated_from_trainer | |
| datasets: | |
| - PolyAI/minds14 | |
| metrics: | |
| - wer | |
| model-index: | |
| - name: whisper-tiny-dv | |
| results: | |
| - task: | |
| name: Automatic Speech Recognition | |
| type: automatic-speech-recognition | |
| dataset: | |
| name: PolyAI/minds14 | |
| type: PolyAI/minds14 | |
| metrics: | |
| - name: Wer | |
| type: wer | |
| value: 35.714285714285715 | |
| <!-- This model card has been generated automatically according to the information the Trainer had access to. You | |
| should probably proofread and complete it, then remove this comment. --> | |
| # whisper-tiny-dv | |
| This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) on the PolyAI/minds14 dataset. | |
| It achieves the following results on the evaluation set: | |
| - Loss: 0.6947 | |
| - Wer Ortho: 35.8421 | |
| - Wer: 35.7143 | |
| ## Model description | |
| More information needed | |
| ## Intended uses & limitations | |
| More information needed | |
| ## Training and evaluation data | |
| More information needed | |
| ## Training procedure | |
| ### Training hyperparameters | |
| The following hyperparameters were used during training: | |
| - learning_rate: 1e-05 | |
| - train_batch_size: 16 | |
| - eval_batch_size: 16 | |
| - seed: 42 | |
| - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 | |
| - lr_scheduler_type: constant_with_warmup | |
| - lr_scheduler_warmup_steps: 50 | |
| - training_steps: 500 | |
| - mixed_precision_training: Native AMP | |
| ### Training results | |
| | Training Loss | Epoch | Step | Validation Loss | Wer Ortho | Wer | | |
| |:-------------:|:-----:|:----:|:---------------:|:---------:|:-------:| | |
| | 0.001 | 17.86 | 500 | 0.6947 | 35.8421 | 35.7143 | | |
| ### Framework versions | |
| - Transformers 4.36.2 | |
| - Pytorch 2.1.0+cu121 | |
| - Datasets 2.16.0 | |
| - Tokenizers 0.15.0 | |