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---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- ncc_s
metrics:
- wer
model-index:
- name: whisper-tiny-nob
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: ncc_s
      type: ncc_s
      config: 'no'
      split: validation
      args: 'no'
    metrics:
    - name: Wer
      type: wer
      value: 24.96954933008526
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# whisper-tiny-nob

This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) on the ncc_s dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5128
- Wer: 24.9695

## 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: 3e-06
- train_batch_size: 256
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant_with_warmup
- lr_scheduler_warmup_steps: 1000
- training_steps: 100000
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step   | Validation Loss | Wer     |
|:-------------:|:-----:|:------:|:---------------:|:-------:|
| 1.8819        | 0.01  | 1000   | 1.1869          | 61.9671 |
| 1.6425        | 0.02  | 2000   | 0.9991          | 53.6541 |
| 1.548         | 0.03  | 3000   | 0.9147          | 50.2132 |
| 1.4636        | 0.04  | 4000   | 0.8605          | 47.0767 |
| 1.4113        | 0.05  | 5000   | 0.8253          | 45.7369 |
| 1.3484        | 0.01  | 6000   | 0.7946          | 43.4531 |
| 1.3127        | 0.02  | 7000   | 0.7740          | 42.2655 |
| 1.2994        | 0.03  | 8000   | 0.7551          | 40.8952 |
| 1.265         | 0.04  | 9000   | 0.7378          | 39.8599 |
| 1.2458        | 0.05  | 10000  | 0.7257          | 39.8904 |
| 1.2257        | 0.06  | 11000  | 0.7114          | 39.7990 |
| 1.2126        | 0.07  | 12000  | 0.6972          | 37.8806 |
| 1.1971        | 0.08  | 13000  | 0.6871          | 37.3021 |
| 1.1786        | 1.01  | 14000  | 0.6786          | 37.4239 |
| 1.1486        | 1.02  | 15000  | 0.6703          | 36.9976 |
| 1.1505        | 1.03  | 16000  | 0.6647          | 36.3581 |
| 1.1238        | 1.04  | 17000  | 0.6559          | 36.3886 |
| 1.1184        | 1.05  | 18000  | 0.6509          | 36.5104 |
| 1.115         | 1.06  | 19000  | 0.6452          | 35.9927 |
| 1.1013        | 1.07  | 20000  | 0.6382          | 34.5006 |
| 1.0969        | 1.08  | 21000  | 0.6331          | 34.3484 |
| 1.0784        | 2.0   | 22000  | 0.6304          | 34.2875 |
| 1.0774        | 2.01  | 23000  | 0.6249          | 34.1048 |
| 1.0719        | 2.02  | 24000  | 0.6194          | 33.8307 |
| 1.0638        | 2.03  | 25000  | 0.6158          | 32.9781 |
| 1.0592        | 2.04  | 26000  | 0.6105          | 32.6431 |
| 1.0493        | 2.05  | 27000  | 0.6041          | 32.7345 |
| 1.047         | 2.06  | 28000  | 0.6040          | 32.7649 |
| 1.0323        | 2.07  | 29000  | 0.5984          | 31.6078 |
| 1.0189        | 3.0   | 30000  | 0.5957          | 31.3033 |
| 1.0078        | 3.01  | 31000  | 0.5924          | 31.4251 |
| 1.0146        | 3.02  | 32000  | 0.5940          | 31.3033 |
| 1.0128        | 3.03  | 33000  | 0.5892          | 31.0292 |
| 1.0025        | 3.04  | 34000  | 0.5873          | 31.1815 |
| 0.999         | 3.05  | 35000  | 0.5838          | 30.6334 |
| 1.0045        | 3.06  | 36000  | 0.5799          | 30.4202 |
| 1.0005        | 3.07  | 37000  | 0.5770          | 30.1766 |
| 1.0017        | 3.08  | 38000  | 0.5733          | 29.6590 |
| 0.9878        | 4.01  | 39000  | 0.5745          | 30.2680 |
| 0.9854        | 4.02  | 40000  | 0.5720          | 30.0548 |
| 0.9624        | 4.03  | 41000  | 0.5703          | 29.5981 |
| 0.9639        | 4.04  | 42000  | 0.5681          | 29.5067 |
| 0.9569        | 4.05  | 43000  | 0.5679          | 29.6285 |
| 0.9682        | 4.06  | 44000  | 0.5643          | 29.5676 |
| 0.9539        | 4.07  | 45000  | 0.5601          | 29.5676 |
| 0.946         | 4.08  | 46000  | 0.5562          | 29.7199 |
| 0.9429        | 5.01  | 47000  | 0.5592          | 29.2935 |
| 0.9462        | 5.02  | 48000  | 0.5540          | 29.0804 |
| 0.9312        | 5.03  | 49000  | 0.5535          | 29.2935 |
| 0.9462        | 5.04  | 50000  | 0.5536          | 28.6845 |
| 0.922         | 5.05  | 51000  | 0.5539          | 28.7150 |
| 0.9253        | 5.06  | 52000  | 0.5510          | 28.8368 |
| 0.9065        | 0.01  | 53000  | 0.5493          | 28.5932 |
| 0.9096        | 0.02  | 54000  | 0.5490          | 28.5018 |
| 0.9329        | 0.03  | 55000  | 0.5483          | 28.2887 |
| 0.9181        | 0.04  | 56000  | 0.5471          | 27.9842 |
| 0.914         | 0.05  | 57000  | 0.5457          | 28.4105 |
| 0.9149        | 0.06  | 58000  | 0.5449          | 27.5883 |
| 0.9092        | 0.07  | 59000  | 0.5405          | 27.8319 |
| 0.9101        | 0.08  | 60000  | 0.5402          | 27.3447 |
| 0.9046        | 1.01  | 61000  | 0.5374          | 27.5579 |
| 0.8917        | 1.02  | 62000  | 0.5390          | 27.7406 |
| 0.8993        | 1.03  | 63000  | 0.5386          | 27.4056 |
| 0.8875        | 1.04  | 64000  | 0.5361          | 26.8575 |
| 0.8892        | 1.05  | 65000  | 0.5358          | 27.3447 |
| 0.8929        | 1.06  | 66000  | 0.5346          | 26.7357 |
| 0.8703        | 0.01  | 67000  | 0.5332          | 26.8270 |
| 0.8709        | 0.02  | 68000  | 0.5336          | 26.7052 |
| 0.8917        | 0.03  | 69000  | 0.5329          | 27.0706 |
| 0.8867        | 0.04  | 70000  | 0.5323          | 26.3398 |
| 0.8778        | 0.05  | 71000  | 0.5315          | 27.2838 |
| 0.8757        | 0.06  | 72000  | 0.5317          | 26.2485 |
| 0.8726        | 0.07  | 73000  | 0.5269          | 26.6443 |
| 0.8792        | 0.08  | 74000  | 0.5268          | 26.1571 |
| 0.8706        | 1.01  | 75000  | 0.5247          | 26.1571 |
| 0.8585        | 1.02  | 76000  | 0.5265          | 26.3703 |
| 0.8659        | 1.03  | 77000  | 0.5262          | 26.7357 |
| 0.8551        | 1.04  | 78000  | 0.5249          | 26.0658 |
| 0.8572        | 1.05  | 79000  | 0.5249          | 26.2789 |
| 0.8612        | 1.06  | 80000  | 0.5235          | 25.7613 |
| 0.8598        | 1.07  | 81000  | 0.5208          | 25.7004 |
| 0.8686        | 1.08  | 82000  | 0.5214          | 25.7004 |
| 0.8503        | 2.0   | 83000  | 0.5214          | 25.7004 |
| 0.8545        | 2.01  | 84000  | 0.5215          | 28.2278 |
| 0.8594        | 2.02  | 85000  | 0.5186          | 25.6699 |
| 0.86          | 2.03  | 86000  | 0.5196          | 25.5786 |
| 0.8514        | 2.04  | 87000  | 0.5203          | 25.1827 |
| 0.8505        | 2.05  | 88000  | 0.5164          | 28.0146 |
| 0.8512        | 2.06  | 89000  | 0.5174          | 25.0914 |
| 0.8495        | 2.07  | 90000  | 0.5141          | 25.5481 |
| 0.8381        | 3.0   | 91000  | 0.5130          | 24.9695 |
| 0.8253        | 3.01  | 92000  | 0.5147          | 25.5786 |
| 0.8387        | 3.02  | 93000  | 0.5168          | 24.9086 |
| 0.8425        | 3.03  | 94000  | 0.5135          | 25.2436 |
| 0.8339        | 3.04  | 95000  | 0.5162          | 25.6699 |
| 0.8402        | 3.05  | 96000  | 0.5147          | 25.7308 |
| 0.8396        | 3.06  | 97000  | 0.5143          | 25.6699 |
| 0.8432        | 3.07  | 98000  | 0.5100          | 24.8782 |
| 0.844         | 3.08  | 99000  | 0.5100          | 25.0609 |
| 0.8333        | 4.01  | 100000 | 0.5128          | 24.9695 |


### Framework versions

- Transformers 4.26.0.dev0
- Pytorch 1.13.0+cu117
- Datasets 2.7.1.dev0
- Tokenizers 0.13.2