whisper-large-v2-medical-6
Browse files- README.md +52 -180
- generation_config.json +317 -0
- model-00001-of-00002.safetensors +3 -0
- model-00002-of-00002.safetensors +3 -0
- model.safetensors.index.json +0 -0
README.md
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- **Funded by [optional]:** [More Information Needed]
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- **Shared by [optional]:** [More Information Needed]
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- **Model type:** [More Information Needed]
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- **Language(s) (NLP):** [More Information Needed]
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- **License:** [More Information Needed]
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- **Finetuned from model [optional]:** [More Information Needed]
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### Direct Use
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<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
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[More Information Needed]
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### Out-of-Scope Use
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<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
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[More Information Needed]
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## Bias, Risks, and Limitations
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[More Information Needed]
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### Recommendations
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<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
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## How to Get Started with the Model
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Use the code below to get started with the model.
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[More Information Needed]
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## Training Details
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### Training Data
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[More Information Needed]
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### Training Procedure
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#### Preprocessing [optional]
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#### Training Hyperparameters
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- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
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#### Speeds, Sizes, Times [optional]
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## Evaluation
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### Testing Data, Factors & Metrics
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#### Testing Data
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#### Factors
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<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
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#### Metrics
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### Results
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#### Summary
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## Model Examination [optional]
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[More Information Needed]
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## Environmental Impact
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<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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- **Hardware Type:** [More Information Needed]
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- **Hours used:** [More Information Needed]
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- **Cloud Provider:** [More Information Needed]
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- **Compute Region:** [More Information Needed]
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- **Carbon Emitted:** [More Information Needed]
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## Technical Specifications [optional]
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### Model Architecture and Objective
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### Compute Infrastructure
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#### Software
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## Citation [optional]
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<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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**BibTeX:**
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**APA:**
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## Glossary [optional]
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## More Information [optional]
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## Model Card Authors [optional]
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## Model Card Contact
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---
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license: apache-2.0
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base_model: openai/whisper-large-v2
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tags:
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- generated_from_trainer
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datasets:
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- audiofolder
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model-index:
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- name: whisper-large-v2-medical-6
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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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# whisper-large-v2-medical-6
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This model is a fine-tuned version of [openai/whisper-large-v2](https://huggingface.co/openai/whisper-large-v2) on the audiofolder dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.1435
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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: 5e-06
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- train_batch_size: 64
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- eval_batch_size: 64
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- seed: 42
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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- lr_scheduler_type: linear
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- lr_scheduler_warmup_ratio: 0.1
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- num_epochs: 3
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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 |
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|:-------------:|:-----:|:----:|:---------------:|
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| No log | 0.26 | 20 | 0.6876 |
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| 1.0862 | 0.51 | 40 | 0.4528 |
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| 0.4702 | 0.77 | 60 | 0.3280 |
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| 0.311 | 1.03 | 80 | 0.2105 |
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| 0.145 | 1.28 | 100 | 0.1609 |
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| 0.145 | 1.54 | 120 | 0.1526 |
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| 0.1185 | 1.79 | 140 | 0.1483 |
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| 0.1195 | 2.05 | 160 | 0.1449 |
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| 0.092 | 2.31 | 180 | 0.1449 |
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| 0.0829 | 2.56 | 200 | 0.1441 |
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| 0.0829 | 2.82 | 220 | 0.1435 |
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### Framework versions
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- Transformers 4.39.3
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- Pytorch 2.4.0+cu121
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- Datasets 2.18.0
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- Tokenizers 0.15.2
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generation_config.json
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