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--- |
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language: |
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- ru |
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license: apache-2.0 |
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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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- mozilla-foundation/common_voice_13_0 |
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metrics: |
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- wer |
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model-index: |
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- name: whisper-small-fine_tuned-ru |
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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_13_0 |
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type: mozilla-foundation/common_voice_13_0 |
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args: 'config: ru, split: test' |
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metrics: |
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- name: Wer |
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type: wer |
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value: 17.724332 |
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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-fine_tuned-ru |
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This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the [common_voice_13_0](https://huggingface.co/datasets/mozilla-foundation/common_voice_13_0) dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.22031 |
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- Wer: 17.724332 |
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## Model description |
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Same as original model (see [whisper-small](https://huggingface.co/openai/whisper-small)). ***But! This model has been fine-tuned for the task of transcribing the Russian language.*** |
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## Intended uses & limitations |
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Same as original model (see [whisper-small](https://huggingface.co/openai/whisper-small)). |
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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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The model is fine-tuned using the following notebook (available only in the Russian version): https://github.com/blademoon/Whisper_Train |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 1e-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: Pytorch 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: 250 |
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- training_steps: 50000 |
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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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| 0.344 | 0.22 | 500 | 0.3936 | 58.4474 | |
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| 0.1948 | 0.44 | 1000 | 0.2391 | 57.0232 | |
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| 0.1853 | 0.66 | 1500 | 0.2255 | 66.1826 | |
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| 0.186 | 0.88 | 2000 | 0.2180 | 65.3833 | |
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| 0.1532 | 1.1 | 2500 | 0.2135 | 50.6050 | |
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| 0.1374 | 1.32 | 3000 | 0.2107 | 47.9428 | |
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| 0.1359 | 1.54 | 3500 | 0.2082 | 60.0693 | |
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| 0.1387 | 1.76 | 4000 | 0.2052 | 58.8674 | |
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| 0.1212 | 1.97 | 4500 | 0.2027 | 51.9571 | |
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| 0.111 | 2.19 | 5000 | 0.2027 | 50.0780 | |
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| 0.1108 | 2.41 | 5500 | 0.2013 | 42.9664 | |
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| 0.1148 | 2.63 | 6000 | 0.2000 | 40.7882 | |
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| 0.114 | 2.85 | 6500 | 0.2002 | 32.6050 | |
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| 0.092 | 3.07 | 7000 | 0.2000 | 32.9307 | |
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| 0.0783 | 3.29 | 7500 | 0.2001 | 33.1413 | |
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| 0.0989 | 3.51 | 8000 | 0.1986 | 32.0313 | |
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| 0.0919 | 3.73 | 8500 | 0.1991 | 28.7199 | |
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| 0.0928 | 3.95 | 9000 | 0.1982 | 26.1798 | |
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| 0.0721 | 4.17 | 9500 | 0.2007 | 22.4960 | |
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| 0.078 | 4.39 | 10000 | 0.2012 | 26.0774 | |
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| 0.0764 | 4.61 | 10500 | 0.2004 | 24.7906 | |
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| 0.0812 | 4.83 | 11000 | 0.2003 | 24.8022 | |
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| 0.0531 | 5.05 | 11500 | 0.2022 | 21.3837 | |
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| 0.0587 | 5.27 | 12000 | 0.2038 | 21.1638 | |
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| 0.0553 | 5.48 | 12500 | 0.2039 | 21.9224 | |
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| 0.0537 | 5.7 | 13000 | 0.2042 | 20.9671 | |
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| 0.0608 | 5.92 | 13500 | 0.2049 | 21.1068 | |
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| 0.0467 | 6.14 | 14000 | 0.2073 | 18.6528 | |
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| 0.0533 | 6.36 | 14500 | 0.2088 | 18.7843 | |
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| 0.048 | 6.58 | 15000 | 0.2092 | 18.5609 | |
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| 0.0479 | 6.8 | 15500 | 0.2101 | 19.1648 | |
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| 0.0383 | 7.02 | 16000 | 0.2105 | 18.9379 | |
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| 0.0384 | 7.24 | 16500 | 0.2147 | 18.8018 | |
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| 0.0451 | 7.46 | 17000 | 0.2156 | 18.9170 | |
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| 0.0399 | 7.68 | 17500 | 0.2163 | 18.3806 | |
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| 0.0387 | 7.9 | 18000 | 0.2159 | 17.9605 | |
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| ***0.0347*** | ***8.12*** | ***18500*** | ***0.2203*** | ***17.7243*** | |
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| 0.0324 | 8.34 | 19000 | 0.2231 | 17.8163 | |
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| 0.035 | 8.56 | 19500 | 0.2231 | 17.8954 | |
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| 0.0338 | 8.78 | 20000 | 0.2234 | 17.7371 | |
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| 0.0305 | 9.0 | 20500 | 0.2244 | 17.8035 | |
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| 0.0244 | 9.21 | 21000 | 0.2305 | 17.8942 | |
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| 0.0249 | 9.43 | 21500 | 0.2321 | 17.9024 | |
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| 0.0242 | 9.65 | 22000 | 0.2328 | 18.2212 | |
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| 0.0269 | 9.87 | 22500 | 0.2327 | 17.8104 | |
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| 0.0198 | 10.09 | 23000 | 0.2380 | 17.7301 | |
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| 0.0191 | 10.31 | 23500 | 0.2396 | 17.8861 | |
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| 0.0218 | 10.53 | 24000 | 0.2412 | 17.7464 | |
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| 0.0219 | 10.75 | 24500 | 0.2406 | 17.7453 | |
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| 0.0206 | 10.97 | 25000 | 0.2427 | 17.9128 | |
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| 0.0182 | 11.19 | 25500 | 0.2482 | 18.0676 | |
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| 0.0143 | 11.41 | 26000 | 0.2506 | 17.9245 | |
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| 0.0162 | 11.63 | 26500 | 0.2501 | 18.1572 | |
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| 0.0172 | 11.85 | 27000 | 0.2535 | 18.1164 | |
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| 0.0148 | 12.07 | 27500 | 0.2558 | 18.1130 | |
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| 0.0123 | 12.29 | 28000 | 0.2573 | 18.4085 | |
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| 0.0129 | 12.51 | 28500 | 0.2603 | 18.0978 | |
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| 0.0136 | 12.72 | 29000 | 0.2615 | 18.1793 | |
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| 0.011 | 12.94 | 29500 | 0.2617 | 18.2247 | |
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| 0.0096 | 13.16 | 30000 | 0.2666 | 18.2712 | |
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| 0.01 | 13.38 | 30500 | 0.2667 | 18.4457 | |
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| 0.0122 | 13.6 | 31000 | 0.2690 | 18.1095 | |
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| 0.0121 | 13.82 | 31500 | 0.2700 | 18.1653 | |
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| 0.0088 | 14.04 | 32000 | 0.2720 | 18.4539 | |
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| 0.0076 | 14.26 | 32500 | 0.2746 | 18.2956 | |
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| 0.0086 | 14.48 | 33000 | 0.2764 | 18.5644 | |
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| 0.0086 | 14.7 | 33500 | 0.2771 | 18.5260 | |
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| 0.0085 | 14.92 | 34000 | 0.2788 | 18.4481 | |
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| 0.008 | 15.14 | 34500 | 0.2803 | 18.4923 | |
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| 0.0074 | 15.36 | 35000 | 0.2824 | 18.6028 | |
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| 0.0069 | 15.58 | 35500 | 0.2838 | 18.7692 | |
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| 0.008 | 15.8 | 36000 | 0.2848 | 18.6901 | |
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| 0.0065 | 16.02 | 36500 | 0.2864 | 18.7413 | |
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| 0.006 | 16.24 | 37000 | 0.2885 | 18.5458 | |
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| 0.0061 | 16.45 | 37500 | 0.2885 | 18.6470 | |
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| 0.0056 | 16.67 | 38000 | 0.2898 | 18.3736 | |
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| 0.0061 | 16.89 | 38500 | 0.2912 | 18.8064 | |
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| 0.0048 | 17.11 | 39000 | 0.2933 | 18.9018 | |
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| 0.0053 | 17.33 | 39500 | 0.2939 | 18.6168 | |
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| 0.006 | 17.55 | 40000 | 0.2954 | 18.7238 | |
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| 0.0045 | 17.77 | 40500 | 0.2952 | 18.8099 | |
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| 0.0059 | 17.99 | 41000 | 0.2964 | 18.5551 | |
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| 0.0053 | 18.21 | 41500 | 0.2980 | 18.7157 | |
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| 0.004 | 18.43 | 42000 | 0.2988 | 18.6412 | |
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| 0.0049 | 18.65 | 42500 | 0.2990 | 18.7099 | |
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| 0.0048 | 18.87 | 43000 | 0.3004 | 18.7552 | |
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| 0.0041 | 19.09 | 43500 | 0.3015 | 18.8169 | |
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| 0.0048 | 19.31 | 44000 | 0.3018 | 18.8518 | |
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| 0.0039 | 19.53 | 44500 | 0.3022 | 18.9437 | |
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| 0.0041 | 19.75 | 45000 | 0.3029 | 18.8239 | |
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| 0.0041 | 19.96 | 45500 | 0.3036 | 18.8169 | |
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| 0.004 | 20.18 | 46000 | 0.3045 | 18.8274 | |
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| 0.0044 | 20.4 | 46500 | 0.3048 | 18.8867 | |
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| 0.0042 | 20.62 | 47000 | 0.3054 | 18.8425 | |
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| 0.0044 | 20.84 | 47500 | 0.3058 | 18.8448 | |
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| 0.004 | 21.06 | 48000 | 0.3057 | 18.8425 | |
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| 0.0038 | 21.28 | 48500 | 0.3062 | 18.7029 | |
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| 0.0038 | 21.5 | 49000 | 0.3063 | 18.8413 | |
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| 0.0046 | 21.72 | 49500 | 0.3063 | 18.8227 | |
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| 0.0036 | 21.94 | 50000 | 0.3064 | 18.8483 | |
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### Framework versions |
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- Transformers 4.30.2 |
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- Pytorch 2.0.1+cu117 |
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- Datasets 2.13.1 |
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- Tokenizers 0.13.3 |
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