83 lines
2.6 KiB
Markdown
83 lines
2.6 KiB
Markdown
---
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library_name: transformers
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license: mit
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base_model: Sunbird/asr-whisper-large-v3-salt
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tags:
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- generated_from_trainer
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metrics:
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- wer
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model-index:
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- name: cdli-whisper-ml-eng-lug-full-a40
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results: []
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datasets:
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- cdli/ugandan_luganda_nonstandard_speech_v1.0
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- cdli/ugandan_english_nonstandard_speech_v1.0
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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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# cdli-whisper-ml-eng-lug-full-a40
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This model is a fine-tuned version of [Sunbird/asr-whisper-large-v3-salt](https://huggingface.co/Sunbird/asr-whisper-large-v3-salt) on the None dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.7960
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- Wer: 0.4932
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- Cer: 0.3124
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- test_cer = 0.1888
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- test_loss = 0.5511
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- test_runtime = 0:30:54.57
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- test_samples_per_second = 1.1
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- test_steps_per_second = 0.275
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- test_wer = 0.3478
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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: 1e-05
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- train_batch_size: 2
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- eval_batch_size: 4
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- seed: 42
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- gradient_accumulation_steps: 8
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- total_train_batch_size: 16
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- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
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- lr_scheduler_type: linear
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- lr_scheduler_warmup_steps: 150
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- training_steps: 2500
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### Training results
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| Training Loss | Epoch | Step | Validation Loss | Wer | Cer |
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|:-------------:|:------:|:----:|:---------------:|:------:|:------:|
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| 0.7765 | 0.3615 | 250 | 0.8623 | 0.5111 | 0.3216 |
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| 0.6632 | 0.7229 | 500 | 0.8226 | 0.5051 | 0.3236 |
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| 0.5672 | 1.0839 | 750 | 0.8067 | 0.4862 | 0.3044 |
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| 0.6058 | 1.4453 | 1000 | 0.7991 | 0.4949 | 0.3142 |
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| 0.6589 | 1.8068 | 1250 | 0.7972 | 0.4901 | 0.3106 |
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| 0.5959 | 2.1677 | 1500 | 0.7977 | 0.4926 | 0.3118 |
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| 0.5402 | 2.5292 | 1750 | 0.7964 | 0.4926 | 0.3114 |
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| 0.5934 | 2.8907 | 2000 | 0.7964 | 0.4921 | 0.3118 |
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| 0.5464 | 3.2516 | 2250 | 0.7960 | 0.4931 | 0.3113 |
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| 0.5497 | 3.6130 | 2500 | 0.7960 | 0.4932 | 0.3124 |
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### Framework versions
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- Transformers 4.52.0
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- Pytorch 2.7.1+cu118
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- Datasets 3.6.0
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- Tokenizers 0.21.4 |