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--- |
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license: apache-2.0 |
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tags: |
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- generated_from_trainer |
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datasets: |
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- dataset/riksdagen |
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metrics: |
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- wer |
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model-index: |
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- name: whisper-small-sv |
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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: dataset/riksdagen audiofolder |
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type: dataset/riksdagen |
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config: test |
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split: test |
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args: audiofolder |
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metrics: |
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- name: WER |
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type: wer |
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value: 0.22405586116204554 |
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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 11.0 |
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type: mozilla-foundation/common_voice_11_0 |
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config: sv-SE |
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split: test |
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args: |
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language: sv-SE |
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metrics: |
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- name: WER |
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type: wer |
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value: 26.69 |
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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-sv |
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This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co./openai/whisper-small) on the dataset/riksdagen audiofolder dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.2917 |
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- Wer: 0.2241 |
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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: 64 |
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- eval_batch_size: 64 |
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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: 100 |
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- training_steps: 20000 |
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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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| 0.5023 | 0.04 | 250 | 0.5072 | 0.2949 | |
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| 0.4678 | 0.08 | 500 | 0.4632 | 0.2780 | |
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| 0.4233 | 0.12 | 750 | 0.4384 | 0.2749 | |
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| 0.4113 | 0.17 | 1000 | 0.4205 | 0.2673 | |
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| 0.3994 | 0.21 | 1250 | 0.4079 | 0.2649 | |
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| 0.3841 | 0.25 | 1500 | 0.3947 | 0.2609 | |
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| 0.3775 | 0.29 | 1750 | 0.3854 | 0.2564 | |
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| 0.383 | 0.33 | 2000 | 0.3781 | 0.2540 | |
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| 0.3651 | 0.37 | 2250 | 0.3721 | 0.2532 | |
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| 0.3456 | 0.42 | 2500 | 0.3651 | 0.2517 | |
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| 0.3719 | 0.46 | 2750 | 0.3612 | 0.2481 | |
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| 0.3399 | 0.5 | 3000 | 0.3561 | 0.2437 | |
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| 0.3428 | 0.54 | 3250 | 0.3522 | 0.2465 | |
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| 0.3442 | 0.58 | 3500 | 0.3451 | 0.2399 | |
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| 0.3315 | 0.62 | 3750 | 0.3431 | 0.2417 | |
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| 0.3299 | 0.66 | 4000 | 0.3404 | 0.2428 | |
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| 0.3417 | 0.71 | 4250 | 0.3373 | 0.2395 | |
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| 0.3399 | 0.75 | 4500 | 0.3332 | 0.2390 | |
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| 0.3222 | 0.79 | 4750 | 0.3310 | 0.2385 | |
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| 0.3319 | 0.83 | 5000 | 0.3291 | 0.2372 | |
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| 0.3188 | 0.87 | 5250 | 0.3265 | 0.2359 | |
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| 0.3197 | 0.91 | 5500 | 0.3240 | 0.2378 | |
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| 0.3099 | 0.96 | 5750 | 0.3215 | 0.2342 | |
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| 0.3132 | 1.0 | 6000 | 0.3195 | 0.2374 | |
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| 0.286 | 1.04 | 6250 | 0.3179 | 0.2348 | |
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| 0.2765 | 1.08 | 6500 | 0.3166 | 0.2354 | |
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| 0.2795 | 1.12 | 6750 | 0.3153 | 0.2324 | |
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| 0.2825 | 1.16 | 7000 | 0.3145 | 0.2316 | |
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| 0.2865 | 1.21 | 7250 | 0.3144 | 0.2329 | |
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| 0.2703 | 1.25 | 7500 | 0.3126 | 0.2326 | |
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| 0.2792 | 1.29 | 7750 | 0.3121 | 0.2324 | |
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| 0.2749 | 1.33 | 8000 | 0.3106 | 0.2325 | |
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| 0.2762 | 1.37 | 8250 | 0.3093 | 0.2315 | |
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| 0.2813 | 1.41 | 8500 | 0.3080 | 0.2302 | |
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| 0.2755 | 1.45 | 8750 | 0.3078 | 0.2321 | |
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| 0.2779 | 1.5 | 9000 | 0.3062 | 0.2305 | |
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| 0.2764 | 1.54 | 9250 | 0.3059 | 0.2336 | |
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| 0.2763 | 1.58 | 9500 | 0.3041 | 0.2310 | |
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| 0.2723 | 1.62 | 9750 | 0.3027 | 0.2292 | |
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| 0.2756 | 1.66 | 10000 | 0.3026 | 0.2301 | |
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| 0.2663 | 1.7 | 10250 | 0.3008 | 0.2262 | |
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| 0.269 | 1.75 | 10500 | 0.3006 | 0.2280 | |
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| 0.2682 | 1.79 | 10750 | 0.3002 | 0.2291 | |
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| 0.2721 | 1.83 | 11000 | 0.2994 | 0.2267 | |
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| 0.2681 | 1.87 | 11250 | 0.2987 | 0.2288 | |
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| 0.278 | 1.91 | 11500 | 0.2978 | 0.2296 | |
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| 0.2625 | 1.95 | 11750 | 0.2978 | 0.2278 | |
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| 0.2583 | 1.99 | 12000 | 0.2967 | 0.2259 | |
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| 0.2403 | 2.04 | 12250 | 0.2976 | 0.2276 | |
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| 0.2414 | 2.08 | 12500 | 0.2972 | 0.2264 | |
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| 0.251 | 2.12 | 12750 | 0.2969 | 0.2256 | |
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| 0.2404 | 2.16 | 13000 | 0.2968 | 0.2253 | |
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| 0.2473 | 2.2 | 13250 | 0.2966 | 0.2253 | |
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| 0.2444 | 2.24 | 13500 | 0.2965 | 0.2262 | |
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| 0.2512 | 2.29 | 13750 | 0.2962 | 0.2253 | |
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| 0.2417 | 2.33 | 14000 | 0.2950 | 0.2280 | |
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| 0.2445 | 2.37 | 14250 | 0.2950 | 0.2256 | |
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| 0.2461 | 2.41 | 14500 | 0.2949 | 0.2262 | |
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| 0.2496 | 2.45 | 14750 | 0.2944 | 0.2261 | |
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| 0.2422 | 2.49 | 15000 | 0.2942 | 0.2248 | |
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| 0.2415 | 2.53 | 15250 | 0.2940 | 0.2252 | |
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| 0.2465 | 2.58 | 15500 | 0.2932 | 0.2269 | |
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| 0.2508 | 2.62 | 15750 | 0.2931 | 0.2245 | |
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| 0.2339 | 2.66 | 16000 | 0.2930 | 0.2257 | |
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| 0.2441 | 2.7 | 16250 | 0.2923 | 0.2247 | |
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| 0.2444 | 2.74 | 16500 | 0.2921 | 0.2246 | |
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| 0.2416 | 2.78 | 16750 | 0.2918 | 0.2264 | |
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| 0.2425 | 2.83 | 17000 | 0.2916 | 0.2251 | |
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| 0.2404 | 2.87 | 17250 | 0.2916 | 0.2234 | |
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| 0.2456 | 2.91 | 17500 | 0.2911 | 0.2238 | |
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| 0.2384 | 2.95 | 17750 | 0.2908 | 0.2252 | |
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| 0.244 | 2.99 | 18000 | 0.2905 | 0.2251 | |
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| 0.2197 | 3.03 | 18250 | 0.2919 | 0.2239 | |
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| 0.2194 | 3.08 | 18500 | 0.2919 | 0.2237 | |
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| 0.2294 | 3.12 | 18750 | 0.2919 | 0.2243 | |
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| 0.2225 | 3.16 | 19000 | 0.2918 | 0.2252 | |
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| 0.2229 | 3.2 | 19250 | 0.2919 | 0.2242 | |
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| 0.2153 | 3.24 | 19500 | 0.2917 | 0.2241 | |
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| 0.2137 | 3.28 | 19750 | 0.2917 | 0.2239 | |
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| 0.2194 | 3.32 | 20000 | 0.2917 | 0.2241 | |
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### Framework versions |
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- Transformers 4.26.0.dev0 |
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- Pytorch 1.12.0a0+8a1a93a |
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- Datasets 2.7.1 |
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- Tokenizers 0.13.2 |
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