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---
language:
- ga
- en
license: apache-2.0
base_model: openai/whisper-medium
tags:
- generated_from_trainer
datasets:
- ymoslem/IWSLT2023-GA-EN
- ymoslem/FLEURS-GA-EN
- ymoslem/BitesizeIrish-GA-EN
- ymoslem/SpokenWords-GA-EN-MTed
- ymoslem/Tatoeba-Speech-Irish
- ymoslem/Wikimedia-Speech-Irish
metrics:
- bleu
- wer
model-index:
- name: Whisper Small GA-EN Speech Translation, 1 epoch, 10k steps
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: IWSLT-2023, FLEURS, BiteSize, SpokenWords, Tatoeba, and Wikimedia
      type: ymoslem/IWSLT2023-GA-EN
    metrics:
    - name: Bleu
      type: bleu
      value: 36.12
    - name: Wer
      type: wer
      value: 58.307068887888335
---

<!-- 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 Small GA-EN Speech Translation, 1 epoch, 10k steps

This model is a fine-tuned version of [openai/whisper-medium](https://huggingface.co/openai/whisper-medium) on the IWSLT-2023, FLEURS, BiteSize, SpokenWords, Tatoeba, and Wikimedia dataset.
It achieves the following results on the evaluation set:
- Loss: 1.3134
- Bleu: 36.12
- Chrf: 53.74
- Wer: 58.3071

## 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: 0.0001
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.02
- training_steps: 10000
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch  | Step  | Bleu  | Chrf  | Validation Loss | Wer      |
|:-------------:|:------:|:-----:|:-----:|:-----:|:---------------:|:--------:|
| 2.6291        | 0.0109 | 100   | 2.33  | 16.34 | 2.1971          | 175.5516 |
| 2.6591        | 0.0219 | 200   | 5.57  | 22.49 | 2.0357          | 122.2873 |
| 2.5637        | 0.0328 | 300   | 7.67  | 26.29 | 1.8690          | 133.0032 |
| 2.2954        | 0.0438 | 400   | 11.2  | 30.03 | 1.8062          | 114.2278 |
| 2.3292        | 0.0547 | 500   | 9.85  | 29.28 | 1.7421          | 117.2895 |
| 2.1223        | 0.0657 | 600   | 14.56 | 32.56 | 1.6739          | 84.2864  |
| 2.2398        | 0.0766 | 700   | 13.86 | 34.74 | 1.7187          | 98.9644  |
| 2.002         | 0.0876 | 800   | 15.53 | 36.64 | 1.6392          | 96.7582  |
| 1.8611        | 0.0985 | 900   | 15.8  | 36.32 | 1.6283          | 94.3719  |
| 1.8498        | 0.1095 | 1000  | 17.58 | 36.0  | 1.6102          | 85.5921  |
| 1.7585        | 0.1204 | 1100  | 15.91 | 36.61 | 1.6337          | 100.2251 |
| 1.6115        | 0.1314 | 1200  | 22.21 | 39.94 | 1.5381          | 76.8122  |
| 1.4415        | 0.1423 | 1300  | 20.36 | 37.87 | 1.5864          | 79.1986  |
| 1.5103        | 0.1533 | 1400  | 23.2  | 41.26 | 1.4925          | 75.2364  |
| 1.6576        | 0.1642 | 1500  | 18.12 | 40.49 | 1.4508          | 102.9266 |
| 1.3429        | 0.1752 | 1600  | 27.88 | 43.74 | 1.4399          | 69.7884  |
| 1.2522        | 0.1861 | 1700  | 23.04 | 43.31 | 1.4256          | 77.1724  |
| 1.2018        | 0.1970 | 1800  | 21.06 | 40.39 | 1.4072          | 78.6583  |
| 1.1945        | 0.2080 | 1900  | 23.0  | 42.71 | 1.4222          | 76.7222  |
| 1.1869        | 0.2189 | 2000  | 22.54 | 42.02 | 1.3992          | 75.8667  |
| 1.1752        | 0.2299 | 2100  | 20.81 | 41.07 | 1.3926          | 79.5137  |
| 1.0281        | 0.2408 | 2200  | 27.24 | 45.55 | 1.3633          | 69.6083  |
| 0.894         | 0.2518 | 2300  | 28.6  | 45.58 | 1.3287          | 65.8712  |
| 0.9788        | 0.2627 | 2400  | 27.75 | 46.21 | 1.3138          | 69.2931  |
| 0.8418        | 0.2737 | 2500  | 27.85 | 46.17 | 1.3064          | 68.3026  |
| 0.7559        | 0.2846 | 2600  | 28.44 | 48.52 | 1.2903          | 68.3476  |
| 0.8632        | 0.2956 | 2700  | 27.87 | 46.86 | 1.2834          | 68.3476  |
| 0.7501        | 0.3065 | 2800  | 28.63 | 49.25 | 1.2669          | 68.5277  |
| 0.6953        | 0.3175 | 2900  | 30.46 | 48.83 | 1.2615          | 64.4304  |
| 0.7195        | 0.3284 | 3000  | 27.49 | 47.94 | 1.2514          | 71.0941  |
| 0.6155        | 0.3394 | 3100  | 30.06 | 49.64 | 1.2428          | 66.5916  |
| 0.605         | 0.3503 | 3200  | 31.64 | 50.27 | 1.2040          | 63.8451  |
| 0.6349        | 0.3612 | 3300  | 28.96 | 49.35 | 1.2077          | 65.3760  |
| 0.4669        | 0.3722 | 3400  | 31.17 | 48.95 | 1.2219          | 64.2503  |
| 0.5196        | 0.3831 | 3500  | 30.97 | 50.13 | 1.2124          | 63.8001  |
| 0.5141        | 0.3941 | 3600  | 31.97 | 50.8  | 1.2026          | 63.0347  |
| 0.4221        | 0.4050 | 3700  | 31.76 | 51.35 | 1.1893          | 63.4399  |
| 0.2951        | 0.4160 | 3800  | 32.4  | 51.08 | 1.2049          | 63.1247  |
| 0.3898        | 0.4269 | 3900  | 32.15 | 51.09 | 1.1906          | 63.5299  |
| 0.4071        | 0.4379 | 4000  | 33.1  | 51.85 | 1.1873          | 62.4043  |
| 0.3975        | 0.4488 | 4100  | 29.58 | 49.33 | 1.2117          | 70.3287  |
| 0.4206        | 0.4598 | 4200  | 31.69 | 50.8  | 1.2150          | 65.0158  |
| 0.2935        | 0.4707 | 4300  | 32.9  | 50.01 | 1.2484          | 62.8546  |
| 0.3718        | 0.4817 | 4400  | 31.64 | 50.55 | 1.2055          | 63.8451  |
| 0.3722        | 0.4926 | 4500  | 28.16 | 49.28 | 1.2200          | 70.4638  |
| 0.2986        | 0.5036 | 4600  | 28.76 | 49.9  | 1.2240          | 68.7528  |
| 0.3327        | 0.5145 | 4700  | 29.34 | 49.67 | 1.2052          | 67.5822  |
| 0.2489        | 0.5255 | 4800  | 32.52 | 51.77 | 1.2083          | 62.4493  |
| 0.3653        | 0.5364 | 4900  | 31.48 | 51.16 | 1.2166          | 63.8451  |
| 0.3326        | 0.5473 | 5000  | 33.04 | 51.71 | 1.2169          | 62.4493  |
| 0.3045        | 0.5583 | 5100  | 27.45 | 48.22 | 1.2460          | 68.9779  |
| 0.3444        | 0.5692 | 5200  | 33.14 | 50.76 | 1.2829          | 62.2692  |
| 0.3236        | 0.5802 | 5300  | 28.89 | 49.37 | 1.2499          | 70.3737  |
| 0.3004        | 0.5911 | 5400  | 29.89 | 49.29 | 1.3165          | 68.7078  |
| 0.3019        | 0.6021 | 5500  | 32.8  | 49.78 | 1.2782          | 62.8095  |
| 0.2923        | 0.6130 | 5600  | 31.75 | 50.26 | 1.2468          | 63.3498  |
| 0.3237        | 0.6240 | 5700  | 34.4  | 52.59 | 1.2511          | 61.0986  |
| 0.2226        | 0.6349 | 5800  | 30.51 | 50.38 | 1.2479          | 63.3498  |
| 0.2207        | 0.6459 | 5900  | 32.68 | 51.97 | 1.2641          | 62.1342  |
| 0.2017        | 0.6568 | 6000  | 32.47 | 51.36 | 1.2640          | 62.6745  |
| 0.201         | 0.6678 | 6100  | 33.6  | 52.29 | 1.2774          | 61.4588  |
| 0.203         | 0.6787 | 6200  | 30.27 | 50.84 | 1.2670          | 65.6461  |
| 0.1456        | 0.6897 | 6300  | 31.2  | 51.05 | 1.2656          | 63.3048  |
| 0.1607        | 0.7006 | 6400  | 30.39 | 51.04 | 1.2611          | 65.8262  |
| 0.1933        | 0.7115 | 6500  | 31.78 | 50.92 | 1.2545          | 63.0797  |
| 0.1537        | 0.7225 | 6600  | 30.18 | 50.18 | 1.2500          | 64.7006  |
| 0.1279        | 0.7334 | 6700  | 33.23 | 51.0  | 1.2548          | 59.8379  |
| 0.1189        | 0.7444 | 6800  | 33.51 | 50.67 | 1.2594          | 61.1887  |
| 0.1056        | 0.7553 | 6900  | 32.97 | 51.02 | 1.2578          | 61.9991  |
| 0.1105        | 0.7663 | 7000  | 32.74 | 50.83 | 1.2569          | 62.0441  |
| 0.1183        | 0.7772 | 7100  | 34.07 | 52.2  | 1.2590          | 60.4232  |
| 0.1373        | 0.7882 | 7200  | 33.55 | 50.6  | 1.2430          | 61.2787  |
| 0.1325        | 0.7991 | 7300  | 32.36 | 50.39 | 1.2548          | 62.3143  |
| 0.0907        | 0.8101 | 7400  | 32.28 | 50.99 | 1.2578          | 61.2787  |
| 0.0919        | 0.8210 | 7500  | 33.01 | 51.81 | 1.2791          | 60.4683  |
| 0.0852        | 0.8320 | 7600  | 32.97 | 51.56 | 1.2782          | 61.5489  |
| 0.1223        | 0.8429 | 7700  | 33.57 | 52.33 | 1.2638          | 59.9280  |
| 0.0826        | 0.8539 | 7800  | 33.83 | 52.7  | 1.2634          | 60.1531  |
| 0.0783        | 0.8648 | 7900  | 33.79 | 52.31 | 1.2595          | 60.1081  |
| 0.0986        | 0.8758 | 8000  | 34.33 | 52.54 | 1.2608          | 59.4327  |
| 0.1148        | 0.8867 | 8100  | 1.2736| 34.03 | 52.52           | 59.8829  |
| 0.1134        | 0.8976 | 8200  | 1.3073| 34.14 | 51.64           | 61.5038  |
| 0.1166        | 0.9086 | 8300  | 1.3385| 30.51 | 49.26           | 65.5561  |
| 0.0871        | 0.9195 | 8400  | 1.3313| 32.31 | 51.06           | 62.5394  |
| 0.0927        | 0.9305 | 8500  | 1.3898| 28.64 | 48.43           | 69.3832  |
| 0.1012        | 0.9414 | 8600  | 1.3144| 33.12 | 52.02           | 61.4138  |
| 0.0742        | 0.9524 | 8700  | 1.3284| 33.68 | 51.38           | 61.7740  |
| 0.0802        | 0.9633 | 8800  | 1.3300| 34.33 | 51.38           | 61.4138  |
| 0.0799        | 0.9743 | 8900  | 1.3328| 33.72 | 50.77           | 60.1981  |
| 0.0936        | 0.9852 | 9000  | 1.3181| 34.76 | 51.4            | 60.0630  |
| 0.1091        | 0.9962 | 9100  | 1.3096| 35.13 | 52.6            | 59.9730  |
| 0.0427        | 1.0071 | 9200  | 1.2905| 35.49 | 53.12           | 59.8379  |
| 0.0338        | 1.0181 | 9300  | 1.3097| 35.33 | 52.62           | 60.5133  |
| 0.0363        | 1.0290 | 9400  | 1.3172| 35.51 | 53.06           | 59.6128  |
| 0.0319        | 1.0400 | 9500  | 1.3166| 36.82 | 53.6            | 58.3971  |
| 0.0434        | 1.0509 | 9600  | 1.3050| 35.62 | 53.28           | 59.6578  |
| 0.0218        | 1.0619 | 9700  | 1.3096| 35.57 | 53.28           | 59.5227  |
| 0.0316        | 1.0728 | 9800  | 1.3162| 36.14 | 53.87           | 58.3971  |
| 0.0315        | 1.0837 | 9900  | 1.3121| 36.26 | 54.16           | 58.3521  |
| 0.0229        | 1.0947 | 10000 | 1.3134| 36.12 | 53.74           | 58.3071  |


### Framework versions

- Transformers 4.41.2
- Pytorch 2.2.0+cu121
- Datasets 2.19.2
- Tokenizers 0.19.1