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whisper-small-LIDmetric-4060-2lang-properLORAsettings

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hf-asr-leaderboard
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metadata
base_model: openai/whisper-small
datasets:
  - mozilla-foundation/common_voice_13_0
language:
  - multilingual
library_name: peft
license: apache-2.0
tags:
  - hf-asr-leaderboard
  - generated_from_trainer
model-index:
  - name: Whisper Small 2 lang LORA 2nd Settings
    results: []

Whisper Small 2 lang LORA 2nd Settings

This model is a fine-tuned version of openai/whisper-small on the Common Voice 13.0 dataset. It achieves the following results on the evaluation set:

  • Loss: 0.2569

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: 4
  • eval_batch_size: 1
  • seed: 42
  • gradient_accumulation_steps: 2
  • total_train_batch_size: 8
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • lr_scheduler_warmup_steps: 500
  • training_steps: 20000
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss
1.9564 0.1002 250 2.1385
1.2046 0.2004 500 1.1713
0.8719 0.3006 750 0.9414
0.7799 0.4008 1000 0.8261
0.6956 0.5010 1250 0.6536
0.3536 0.6012 1500 0.2990
0.3167 0.7014 1750 0.2902
0.3252 0.8016 2000 0.2850
0.3133 0.9018 2250 0.2821
0.3764 1.0020 2500 0.2800
0.2942 1.1022 2750 0.2775
0.2937 1.2024 3000 0.2770
0.3478 1.3026 3250 0.2745
0.3776 1.4028 3500 0.2729
0.3099 1.5030 3750 0.2713
0.3087 1.6032 4000 0.2705
0.2998 1.7034 4250 0.2699
0.3226 1.8036 4500 0.2683
0.3589 1.9038 4750 0.2676
0.3411 2.0040 5000 0.2673
0.3084 2.1042 5250 0.2674
0.31 2.2044 5500 0.2663
0.3388 2.3046 5750 0.2657
0.2716 2.4048 6000 0.2652
0.3059 2.5050 6250 0.2652
0.27 2.6052 6500 0.2648
0.2954 2.7054 6750 0.2639
0.336 2.8056 7000 0.2641
0.2833 2.9058 7250 0.2631
0.2777 3.0060 7500 0.2624
0.2418 3.1062 7750 0.2618
0.3194 3.2064 8000 0.2623
0.3319 3.3066 8250 0.2623
0.3551 3.4068 8500 0.2615
0.3421 3.5070 8750 0.2619
0.3862 3.6072 9000 0.2616
0.2437 3.7074 9250 0.2609
0.2995 3.8076 9500 0.2604
0.3535 3.9078 9750 0.2603
0.2871 4.0080 10000 0.2601
0.2908 4.1082 10250 0.2604
0.3203 4.2084 10500 0.2599
0.2598 4.3086 10750 0.2594
0.2942 4.4088 11000 0.2593
0.3302 4.5090 11250 0.2590
0.3615 4.6092 11500 0.2584
0.3291 4.7094 11750 0.2582
0.2781 4.8096 12000 0.2588
0.3106 4.9098 12250 0.2585
0.2484 5.0100 12500 0.2583
0.2645 5.1102 12750 0.2583
0.3034 5.2104 13000 0.2581
0.2865 5.3106 13250 0.2576
0.3301 5.4108 13500 0.2580
0.3759 5.5110 13750 0.2579
0.3318 5.6112 14000 0.2581
0.2825 5.7114 14250 0.2579
0.2976 5.8116 14500 0.2578
0.2976 5.9118 14750 0.2577
0.3681 6.0120 15000 0.2575
0.3274 6.1122 15250 0.2575
0.2948 6.2124 15500 0.2577
0.2932 6.3126 15750 0.2576
0.2587 6.4128 16000 0.2578
0.2564 6.5130 16250 0.2573
0.2776 6.6132 16500 0.2569
0.2954 6.7134 16750 0.2569
0.2891 6.8136 17000 0.2568
0.2373 6.9138 17250 0.2569
0.3532 7.0140 17500 0.2569
0.2676 7.1142 17750 0.2569
0.2763 7.2144 18000 0.2569
0.2692 7.3146 18250 0.2571
0.3198 7.4148 18500 0.2570
0.2158 7.5150 18750 0.2571
0.277 7.6152 19000 0.2572
0.2308 7.7154 19250 0.2572
0.3166 7.8156 19500 0.2569
0.3064 7.9158 19750 0.2570
0.2743 8.0160 20000 0.2569

Framework versions

  • PEFT 0.11.2.dev0
  • Transformers 4.43.0.dev0
  • Pytorch 2.3.0+cu121
  • Datasets 2.20.0
  • Tokenizers 0.19.1