Automatic Speech Recognition
Transformers
TensorBoard
Safetensors
English
whisper
Generated from Trainer
Eval Results (legacy)
Instructions to use Dev372/Finetuned_whisper_tiny with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Dev372/Finetuned_whisper_tiny with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="Dev372/Finetuned_whisper_tiny")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("Dev372/Finetuned_whisper_tiny") model = AutoModelForSpeechSeq2Seq.from_pretrained("Dev372/Finetuned_whisper_tiny", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| language: | |
| - en | |
| license: apache-2.0 | |
| base_model: openai/whisper-tiny | |
| tags: | |
| - generated_from_trainer | |
| datasets: | |
| - custom_dataset | |
| metrics: | |
| - wer | |
| model-index: | |
| - name: Finetuned_whisper_tiny | |
| results: | |
| - task: | |
| name: Automatic Speech Recognition | |
| type: automatic-speech-recognition | |
| dataset: | |
| name: Dev372/Cardiology_Medical_STT_Dataset_split | |
| type: custom_dataset | |
| args: 'split: test' | |
| metrics: | |
| - name: Wer | |
| type: wer | |
| value: 2.4311183144246353 | |
| <!-- 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. --> | |
| # Finetuned_whisper_tiny | |
| This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) on the Dev372/Cardiology_Medical_STT_Dataset_split dataset. | |
| It achieves the following results on the evaluation set: | |
| - Loss: 0.0460 | |
| - Wer: 2.4311 | |
| ## 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: 15 | |
| - 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: 1500 | |
| - mixed_precision_training: Native AMP | |
| ### Training results | |
| | Training Loss | Epoch | Step | Validation Loss | Wer | | |
| |:-------------:|:-------:|:----:|:---------------:|:------:| | |
| | 0.0045 | 6.0976 | 500 | 0.0424 | 2.4311 | | |
| | 0.0008 | 12.1951 | 1000 | 0.0446 | 2.4311 | | |
| | 0.0006 | 18.2927 | 1500 | 0.0460 | 2.4311 | | |
| ### Framework versions | |
| - Transformers 4.42.4 | |
| - Pytorch 2.1.2 | |
| - Datasets 2.20.0 | |
| - Tokenizers 0.19.1 | |