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--- |
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language: |
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- ro |
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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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- hf-asr-leaderboard |
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- generated_from_trainer |
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datasets: |
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- VladS159/common_voice_romanian_speech_synthesis |
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metrics: |
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- wer |
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model-index: |
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- name: Whisper Medium Ro - Sarbu Vlad - multi gpu |
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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: Common Voice 16.1 + Romanian speech synthesis |
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type: VladS159/common_voice_romanian_speech_synthesis |
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args: 'config: ro, split: test' |
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metrics: |
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- name: Wer |
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type: wer |
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value: 11.726235741444867 |
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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 Ro - Sarbu Vlad - multi gpu |
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This model is a fine-tuned version of [openai/whisper-medium](https://huggingface.co/openai/whisper-medium) on the Common Voice 16.1 + Romanian speech synthesis dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.1247 |
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- Wer: 11.7262 |
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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: 10 |
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- eval_batch_size: 10 |
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- seed: 42 |
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- distributed_type: multi-GPU |
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- num_devices: 3 |
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- total_train_batch_size: 30 |
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- total_eval_batch_size: 30 |
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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: 100 |
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- training_steps: 1000 |
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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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| 0.1447 | 0.61 | 250 | 0.1532 | 13.8768 | |
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| 0.0599 | 1.23 | 500 | 0.1305 | 12.5141 | |
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| 0.0595 | 1.84 | 750 | 0.1256 | 12.3255 | |
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| 0.032 | 2.46 | 1000 | 0.1247 | 11.7262 | |
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### Framework versions |
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- Transformers 4.37.2 |
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- Pytorch 2.2.0 |
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- Datasets 2.17.0 |
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- Tokenizers 0.15.1 |
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