Automatic Speech Recognition
Transformers
TensorBoard
Safetensors
English
whisper
Generated from Trainer
Eval Results (legacy)
Instructions to use MTWD/whisper-small-brain-hack with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use MTWD/whisper-small-brain-hack with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="MTWD/whisper-small-brain-hack")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("MTWD/whisper-small-brain-hack") model = AutoModelForSpeechSeq2Seq.from_pretrained("MTWD/whisper-small-brain-hack", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from MTWD/whisper-small-brain-hack: direct link, hf CLI and curl.
- Browser
- Download file 2.01 kB
-
https://huggingface.co/MTWD/whisper-small-brain-hack/resolve/main/README.md
- Command line
-
hf download hf://MTWD/whisper-small-brain-hack/README.md
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curl -L -o README.md https://huggingface.co/MTWD/whisper-small-brain-hack/resolve/main/README.md
2.01 kB
| language: | |
| - en | |
| license: apache-2.0 | |
| base_model: openai/whisper-small | |
| tags: | |
| - generated_from_trainer | |
| datasets: | |
| - openai/whisper-small | |
| metrics: | |
| - wer | |
| model-index: | |
| - name: Whisper Small fine tuned with comms | |
| results: | |
| - task: | |
| name: Automatic Speech Recognition | |
| type: automatic-speech-recognition | |
| dataset: | |
| name: BrainHack ASR | |
| type: openai/whisper-small | |
| metrics: | |
| - name: Wer | |
| type: wer | |
| value: 0.004561529393675362 | |
| <!-- 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 Small fine tuned with comms | |
| This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the BrainHack ASR dataset. | |
| It achieves the following results on the evaluation set: | |
| - Loss: 0.0818 | |
| - Wer: 0.0046 | |
| ## 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: 8 | |
| - seed: 42 | |
| - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 | |
| - lr_scheduler_type: linear | |
| - lr_scheduler_warmup_steps: 500 | |
| - training_steps: 5000 | |
| - mixed_precision_training: Native AMP | |
| ### Training results | |
| | Training Loss | Epoch | Step | Validation Loss | Wer | | |
| |:-------------:|:-------:|:----:|:---------------:|:------:| | |
| | 0.0022 | 6.4935 | 1000 | 0.0641 | 0.0057 | | |
| | 0.0009 | 12.9870 | 2000 | 0.0705 | 0.0050 | | |
| | 0.0 | 19.4805 | 3000 | 0.0766 | 0.0045 | | |
| | 0.0 | 25.9740 | 4000 | 0.0805 | 0.0046 | | |
| | 0.0 | 32.4675 | 5000 | 0.0818 | 0.0046 | | |
| ### Framework versions | |
| - Transformers 4.40.2 | |
| - Pytorch 2.3.0+cu121 | |
| - Datasets 2.19.1 | |
| - Tokenizers 0.19.1 | |