Automatic Speech Recognition
Transformers
TensorBoard
Safetensors
English
whisper
Generated from Trainer
Eval Results (legacy)
Instructions to use xbilek25/wme_30s_Static_atWall with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use xbilek25/wme_30s_Static_atWall with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="xbilek25/wme_30s_Static_atWall")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("xbilek25/wme_30s_Static_atWall") model = AutoModelForSpeechSeq2Seq.from_pretrained("xbilek25/wme_30s_Static_atWall", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from xbilek25/wme_30s_Static_atWall: direct link, hf CLI and curl.
- Browser
- Download file 2.24 kB
-
https://huggingface.co/xbilek25/wme_30s_Static_atWall/resolve/main/README.md
- Command line
-
hf download hf://xbilek25/wme_30s_Static_atWall/README.md
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curl -L -o README.md https://huggingface.co/xbilek25/wme_30s_Static_atWall/resolve/main/README.md
2.24 kB
| library_name: transformers | |
| language: | |
| - en | |
| license: apache-2.0 | |
| base_model: openai/whisper-medium.en | |
| tags: | |
| - generated_from_trainer | |
| datasets: | |
| - mozilla-foundation/common_voice_17_0 | |
| metrics: | |
| - wer | |
| model-index: | |
| - name: wme_30s_Static_atWall | |
| results: | |
| - task: | |
| name: Automatic Speech Recognition | |
| type: automatic-speech-recognition | |
| dataset: | |
| name: Common Voice 17.0 | |
| type: mozilla-foundation/common_voice_17_0 | |
| args: 'config: en, split: test' | |
| metrics: | |
| - name: Wer | |
| type: wer | |
| value: 32.088181261481935 | |
| <!-- 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. --> | |
| # wme_30s_Static_atWall | |
| This model is a fine-tuned version of [openai/whisper-medium.en](https://huggingface.co/openai/whisper-medium.en) on the Common Voice 17.0 dataset. | |
| It achieves the following results on the evaluation set: | |
| - Loss: 0.9135 | |
| - Wer: 32.0882 | |
| ## 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: 48 | |
| - eval_batch_size: 32 | |
| - seed: 42 | |
| - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments | |
| - lr_scheduler_type: linear | |
| - lr_scheduler_warmup_steps: 42 | |
| - training_steps: 420 | |
| - mixed_precision_training: Native AMP | |
| ### Training results | |
| | Training Loss | Epoch | Step | Validation Loss | Wer | | |
| |:-------------:|:------:|:----:|:---------------:|:-------:| | |
| | No log | 0 | 0 | 1.5148 | 34.2009 | | |
| | 0.565 | 0.2 | 84 | 0.9564 | 29.4856 | | |
| | 0.4559 | 0.4 | 168 | 0.9294 | 29.7918 | | |
| | 0.2937 | 1.1833 | 252 | 0.9136 | 28.1384 | | |
| | 0.262 | 1.3833 | 336 | 0.9206 | 28.3527 | | |
| | 0.2024 | 2.1667 | 420 | 0.9135 | 32.0882 | | |
| ### Framework versions | |
| - Transformers 4.51.3 | |
| - Pytorch 2.6.0+cu124 | |
| - Datasets 3.6.0 | |
| - Tokenizers 0.21.1 | |