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Browse files- README.md +21 -18
- all_results.json +10 -10
- config.json +1 -1
- eval_results.json +6 -6
- pytorch_model.bin +1 -1
- runs/Dec16_10-58-32_Farsipal/1671208272.5271125/events.out.tfevents.1671208272.Farsipal.14496.1 +3 -0
- runs/Dec16_10-58-32_Farsipal/events.out.tfevents.1671208272.Farsipal.14496.0 +3 -0
- runs/Dec16_10-58-32_Farsipal/events.out.tfevents.1671285655.Farsipal.14496.2 +3 -0
- train_results.json +5 -5
- trainer_state.json +1681 -436
- training_args.bin +1 -1
README.md
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metrics:
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- name: Wer
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type: wer
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value:
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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-sm-el-intlv-xl
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This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the mozilla-foundation/common_voice_11_0
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It achieves the following results on the evaluation set:
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- Loss: 0.
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- Wer:
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## Model description
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## Intended uses & limitations
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## Training and evaluation data
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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:
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- train_batch_size: 16
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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: 500
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- training_steps:
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- mixed_precision_training: Native AMP
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### Training results
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| Training Loss | Epoch | Step
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### Framework versions
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metrics:
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- name: Wer
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type: wer
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value: 19.48365527488856
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---
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# whisper-sm-el-intlv-xl
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This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the mozilla-foundation/common_voice_11_0 (el) and the google/fleurs (el_gr) datasets.
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It achieves the following results on the evaluation set:
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- Loss: 0.4725
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- Wer: 19.4837
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## Model description
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The model was trained over 10000 steps on translation from Greek to English.
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## Intended uses & limitations
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This model was part of the Whisper Finetuning Event (Dec 2022) and was used primarily to compare relative improvements between transcription and translation tasks.
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## Training and evaluation data
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The training datasets combined examples from both train and evaluation splits and use the train split of the mozilla-foundation/common_voice_11_0 (el) dataset for evaluation and selection of the best checkpoint.
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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: 8.5e-06
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- train_batch_size: 16
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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: 500
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- training_steps: 10000
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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 | Wer |
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|:-------------:|:-----:|:-----:|:---------------:|:-------:|
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| 0.0545 | 2.49 | 1000 | 0.2891 | 22.4926 |
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| 0.0093 | 4.98 | 2000 | 0.3927 | 20.1337 |
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| 0.0018 | 7.46 | 3000 | 0.4031 | 20.1616 |
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| 0.001 | 9.95 | 4000 | 0.4209 | 19.6880 |
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| 0.0008 | 12.44 | 5000 | 0.4498 | 20.0966 |
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| 0.0005 | 14.93 | 6000 | 0.4725 | 19.4837 |
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| 0.0002 | 17.41 | 7000 | 0.4917 | 19.5951 |
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| 0.0001 | 19.9 | 8000 | 0.5050 | 19.6230 |
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| 0.0001 | 22.39 | 9000 | 0.5146 | 19.5672 |
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| 0.0001 | 24.88 | 10000 | 0.5186 | 19.4837 |
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### Framework versions
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| 1258 |
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"
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| 1259 |
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| 1260 |
-
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| 1261 |
-
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| 1262 |
-
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