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--- |
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language: |
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- sr |
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license: apache-2.0 |
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base_model: openai/whisper-small |
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tags: |
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- generated_from_trainer |
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datasets: |
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- espnet/yodas |
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- google/fleurs |
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- Sagicc/audio-lmb-ds |
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- mozilla-foundation/common_voice_16_1 |
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metrics: |
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- wer |
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model-index: |
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- name: Whisper Small Sr Yodas |
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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 |
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type: mozilla-foundation/common_voice_16_1 |
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config: sr |
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split: test |
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args: sr |
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metrics: |
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- name: Wer |
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type: wer |
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value: 0.12195981670778992 |
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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 Small Sr Yodas |
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This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on merged datasets Common Voice 16 + Fleurs + [Juzne vesti (South news)](http://hdl.handle.net/11356/1679) + [LBM](https://huggingface.co/datasets/Sagicc/audio-lmb-ds) + (Yodas)[https://huggingface.co/datasets/espnet/yodas] dataset and |
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Rupnik, Peter and Ljubešić, Nikola, 2022,\ |
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ASR training dataset for Serbian JuzneVesti-SR v1.0, Slovenian language resource repository CLARIN.SI, ISSN 2820-4042,\ |
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http://hdl.handle.net/11356/1679. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.3584 |
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- Wer Ortho: 0.2328 |
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- Wer: 0.1220 |
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## Model description |
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Added new dataset Yodas as test and experiment to improve results. |
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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: 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: 50 |
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- num_epochs: 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 | Wer Ortho | Wer | |
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|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:| |
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| 0.6958 | 0.49 | 1000 | 0.2114 | 0.2528 | 0.1563 | |
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| 0.5941 | 0.98 | 2000 | 0.1857 | 0.2214 | 0.1269 | |
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| 0.3985 | 1.46 | 3000 | 0.1729 | 0.2106 | 0.1167 | |
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| 0.4187 | 1.95 | 4000 | 0.1745 | 0.2120 | 0.1147 | |
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| 0.3446 | 2.44 | 5000 | 0.1770 | 0.2074 | 0.1139 | |
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| 0.2992 | 2.93 | 6000 | 0.1710 | 0.2048 | 0.1061 | |
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| 0.2074 | 3.42 | 7000 | 0.1887 | 0.2090 | 0.1123 | |
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| 0.1958 | 3.91 | 8000 | 0.1871 | 0.2136 | 0.1131 | |
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| 0.1707 | 4.39 | 9000 | 0.2069 | 0.2230 | 0.1126 | |
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| 0.1403 | 4.88 | 10000 | 0.2092 | 0.2138 | 0.1110 | |
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| 0.0871 | 5.37 | 11000 | 0.2345 | 0.2216 | 0.1161 | |
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| 0.0856 | 5.86 | 12000 | 0.2384 | 0.2281 | 0.1161 | |
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| 0.0496 | 6.35 | 13000 | 0.2657 | 0.2327 | 0.1211 | |
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| 0.0542 | 6.84 | 14000 | 0.2760 | 0.2346 | 0.1198 | |
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| 0.0274 | 7.32 | 15000 | 0.3024 | 0.2304 | 0.1218 | |
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| 0.0281 | 7.81 | 16000 | 0.3134 | 0.2357 | 0.1216 | |
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| 0.0151 | 8.3 | 17000 | 0.3328 | 0.2276 | 0.1188 | |
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| 0.0165 | 8.79 | 18000 | 0.3417 | 0.2348 | 0.1220 | |
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| 0.0094 | 9.28 | 19000 | 0.3545 | 0.2318 | 0.1221 | |
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| 0.0125 | 9.77 | 20000 | 0.3584 | 0.2328 | 0.1220 | |
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### Framework versions |
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- Transformers 4.39.3 |
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- Pytorch 2.0.1+cu117 |
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- Datasets 2.18.0 |
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- Tokenizers 0.15.1 |
