End of training
Browse files- README.md +42 -182
- preprocessor_config.json +14 -0
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- runs/Jan16_11-56-55_jupyter-abhinav-2eswami-40telusinternational-2ecom/events.out.tfevents.1705406216.jupyter-abhinav-2eswami-40telusinternational-2ecom.6817.0 +3 -0
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- runs/Jan16_15-01-51_jupyter-abhinav-2eswami-40telusinternational-2ecom/events.out.tfevents.1705418399.jupyter-abhinav-2eswami-40telusinternational-2ecom.1702.1 +3 -0
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README.md
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library_name: peft
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base_model: openai/whisper-large-v3
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- **Funded by [optional]:** [More Information Needed]
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- **Model type:** [More Information Needed]
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###
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### Direct Use
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### Downstream Use [optional]
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[More Information Needed]
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### Out-of-Scope Use
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## Bias, Risks, and Limitations
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### Recommendations
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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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[More Information Needed]
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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
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#### Factors
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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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## 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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- **Hours used:** [More Information Needed]
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## Technical Specifications [optional]
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## Citation [optional]
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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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### Framework versions
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language:
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- hi
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license: apache-2.0
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library_name: peft
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tags:
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- hf-asr-leaderboard
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- generated_from_trainer
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datasets:
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- mozilla-foundation/common_voice_11_0
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base_model: openai/whisper-large-v3
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model-index:
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- name: Abhinav28/large-v3-hi-test-2
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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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# Abhinav28/large-v3-hi-test-2
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This model is a fine-tuned version of [openai/whisper-large-v3](https://huggingface.co/openai/whisper-large-v3) on the Common Voice 11.0 dataset.
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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: 0.001
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- train_batch_size: 8
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- eval_batch_size: 8
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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_steps: 5
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- training_steps: 10
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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 | 0.77 | 10 | 0.4546 |
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### Framework versions
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- PEFT 0.7.2.dev0
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- Transformers 4.36.2
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- Pytorch 2.0.0
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- Datasets 2.16.2.dev0
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- Tokenizers 0.15.0
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preprocessor_config.json
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"feature_extractor_type": "WhisperFeatureExtractor",
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"feature_size": 128,
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"hop_length": 160,
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"n_fft": 400,
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"n_samples": 480000,
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"padding_side": "right",
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"processor_class": "WhisperProcessor",
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"return_attention_mask": false,
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"sampling_rate": 16000
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}
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