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--- |
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license: apache-2.0 |
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base_model: openai/whisper-medium |
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tags: |
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- generated_from_trainer |
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datasets: |
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- fsicoli/cv17-fleurs-coraa-mls-ted-alcaim-cf-cdc-lapsbm-lapsmail-sydney-lingualibre-voxforge-tatoeba |
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metrics: |
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- wer |
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model-index: |
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- name: whisper-medium-pt-1000h |
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results: |
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- task: |
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name: Automatic Speech Recognition |
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type: automatic-speech-recognition |
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dataset: |
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name: fsicoli/cv17-fleurs-coraa-mls-ted-alcaim-cf-cdc-lapsbm-lapsmail-sydney-lingualibre-voxforge-tatoeba |
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default |
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type: fsicoli/cv17-fleurs-coraa-mls-ted-alcaim-cf-cdc-lapsbm-lapsmail-sydney-lingualibre-voxforge-tatoeba |
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args: default |
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metrics: |
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- name: Wer |
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type: wer |
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value: 0.11473958668640959 |
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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-medium-pt-1000h |
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This model is a fine-tuned version of [openai/whisper-medium](https://huggingface.co/openai/whisper-medium) on the fsicoli/cv17-fleurs-coraa-mls-ted-alcaim-cf-cdc-lapsbm-lapsmail-sydney-lingualibre-voxforge-tatoeba default dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.6491 |
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- Wer: 0.1147 |
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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: 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: 10000 |
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- training_steps: 300000 |
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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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| 0.4574 | 0.2 | 20000 | 0.5339 | 0.1631 | |
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| 0.4124 | 0.39 | 40000 | 0.4512 | 0.1517 | |
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| 0.481 | 0.59 | 60000 | 0.4628 | 0.1466 | |
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| 0.3452 | 0.79 | 80000 | 0.4677 | 0.1392 | |
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| 0.4086 | 0.98 | 100000 | 0.4551 | 0.1364 | |
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| 0.1565 | 1.18 | 120000 | 0.5060 | 0.1316 | |
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| 0.1513 | 1.38 | 140000 | 0.5330 | 0.1286 | |
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| 0.1496 | 1.57 | 160000 | 0.5519 | 0.1263 | |
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| 0.1533 | 1.77 | 180000 | 0.5528 | 0.1234 | |
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| 0.1525 | 1.97 | 200000 | 0.4857 | 0.1194 | |
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| 0.1918 | 2.16 | 220000 | 0.5915 | 0.1189 | |
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| 0.1175 | 2.36 | 240000 | 0.6099 | 0.1174 | |
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| 0.0959 | 2.56 | 260000 | 0.6183 | 0.1157 | |
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| 0.0988 | 2.75 | 280000 | 0.6423 | 0.1152 | |
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| 0.0913 | 2.95 | 300000 | 0.6491 | 0.1147 | |
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### Framework versions |
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- Transformers 4.39.0.dev0 |
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- Pytorch 2.2.1+cu121 |
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- Datasets 2.18.1.dev0 |
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- Tokenizers 0.15.0 |
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