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whisper-small-sv

Automatic Speech Recognition
Transformers
PyTorch
whisper
Generated from Trainer
Eval Results
Inference Endpoints
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metadata
license: apache-2.0
tags:
  - generated_from_trainer
datasets:
  - dataset/riksdagen
metrics:
  - wer
model-index:
  - name: whisper-small-sv
    results:
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: dataset/riksdagen audiofolder
          type: dataset/riksdagen
          config: test
          split: test
          args: audiofolder
        metrics:
          - name: WER
            type: wer
            value: 0.22405586116204554
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: Common Voice 11.0
          type: mozilla-foundation/common_voice_11_0
          config: sv-SE
          split: test
          args:
            language: sv-SE
        metrics:
          - name: WER
            type: wer
            value: 26.69

whisper-small-sv

This model is a fine-tuned version of openai/whisper-small on the dataset/riksdagen audiofolder dataset. It achieves the following results on the evaluation set:

  • Loss: 0.2917
  • Wer: 0.2241

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

  • learning_rate: 1e-05
  • train_batch_size: 64
  • eval_batch_size: 64
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • lr_scheduler_warmup_steps: 100
  • training_steps: 20000

Training results

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

Framework versions

  • Transformers 4.26.0.dev0
  • Pytorch 1.12.0a0+8a1a93a
  • Datasets 2.7.1
  • Tokenizers 0.13.2