---
library_name: transformers
language:
- lv
license: apache-2.0
base_model: FelixK7/whisper-medium-lv
tags:
- hf-asr-leaderboard
- generated_from_trainer
datasets:
- mozilla-foundation/common_voice_16_1
metrics:
- wer
model-index:
- name: Whisper medium LV - Felikss Kleins
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: Common Voice 16.1
      type: mozilla-foundation/common_voice_16_1
      config: lv
      split: None
      args: 'config: lv, split: test'
    metrics:
    - name: Wer
      type: wer
      value: 9.459716154242761
---

<!-- 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 medium LV - Felikss Kleins

This model is a fine-tuned version of [FelixK7/whisper-medium-lv](https://huggingface.co/FelixK7/whisper-medium-lv) on the Common Voice 16.1 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2053
- Wer: 9.4597

## 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-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 10000
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch   | Step  | Validation Loss | Wer     |
|:-------------:|:-------:|:-----:|:---------------:|:-------:|
| No log        | 0.02    | 200   | 0.1318          | 7.6741  |
| 0.0445        | 1.0199  | 400   | 0.1527          | 8.4475  |
| 0.0338        | 2.0199  | 600   | 0.1703          | 9.7148  |
| 0.0345        | 3.0198  | 800   | 0.1725          | 9.7392  |
| 0.0311        | 4.0198  | 1000  | 0.1789          | 9.8830  |
| 0.0311        | 5.0198  | 1200  | 0.1792          | 10.0187 |
| 0.0288        | 6.0197  | 1400  | 0.1858          | 9.6063  |
| 0.0237        | 7.0197  | 1600  | 0.1839          | 9.8803  |
| 0.022         | 8.0196  | 1800  | 0.1847          | 10.2955 |
| 0.0198        | 9.0196  | 2000  | 0.1878          | 9.8885  |
| 0.0198        | 10.0195 | 2200  | 0.1909          | 9.9237  |
| 0.0183        | 11.0195 | 2400  | 0.1948          | 10.1924 |
| 0.0161        | 12.0194 | 2600  | 0.1951          | 10.4122 |
| 0.0154        | 13.0193 | 2800  | 0.1952          | 9.9997  |
| 0.0141        | 14.0193 | 3000  | 0.1972          | 10.1001 |
| 0.0141        | 15.0192 | 3200  | 0.1976          | 10.1544 |
| 0.0118        | 16.0192 | 3400  | 0.2014          | 10.4258 |
| 0.0115        | 17.0191 | 3600  | 0.2021          | 10.6890 |
| 0.0106        | 18.0191 | 3800  | 0.2005          | 10.1951 |
| 0.0092        | 19.0191 | 4000  | 0.2022          | 10.4638 |
| 0.0092        | 20.019  | 4200  | 0.2003          | 10.0947 |
| 0.0089        | 21.0190 | 4400  | 0.2043          | 9.8776  |
| 0.0085        | 22.0189 | 4600  | 0.2063          | 10.4719 |
| 0.0083        | 23.0189 | 4800  | 0.2067          | 10.0540 |
| 0.0069        | 24.0188 | 5000  | 0.2058          | 9.7908  |
| 0.0069        | 25.0188 | 5200  | 0.2056          | 10.4583 |
| 0.0078        | 26.0187 | 5400  | 0.2090          | 10.1843 |
| 0.0063        | 27.0187 | 5600  | 0.2096          | 10.2250 |
| 0.0058        | 28.0186 | 5800  | 0.2047          | 10.2602 |
| 0.0052        | 29.0186 | 6000  | 0.2087          | 9.9319  |
| 0.0052        | 30.0185 | 6200  | 0.2040          | 10.0811 |
| 0.0054        | 31.0185 | 6400  | 0.2081          | 9.9482  |
| 0.0045        | 32.0184 | 6600  | 0.2063          | 9.6849  |
| 0.004         | 33.0183 | 6800  | 0.2077          | 10.0052 |
| 0.0035        | 34.0183 | 7000  | 0.2105          | 10.1056 |
| 0.0035        | 35.0183 | 7200  | 0.2075          | 9.6985  |
| 0.0035        | 36.0182 | 7400  | 0.2075          | 9.6063  |
| 0.003         | 37.0181 | 7600  | 0.2115          | 9.8396  |
| 0.0027        | 38.0181 | 7800  | 0.2061          | 9.5601  |
| 0.0025        | 39.0181 | 8000  | 0.2082          | 9.6252  |
| 0.0025        | 40.018  | 8200  | 0.2052          | 9.5520  |
| 0.0023        | 41.0179 | 8400  | 0.2060          | 9.7826  |
| 0.0024        | 42.0179 | 8600  | 0.2083          | 9.6361  |
| 0.002         | 43.0179 | 8800  | 0.2069          | 9.5981  |
| 0.0021        | 44.0178 | 9000  | 0.2051          | 9.3892  |
| 0.0021        | 45.0177 | 9200  | 0.2054          | 9.3756  |
| 0.0019        | 46.0177 | 9400  | 0.2049          | 9.5167  |
| 0.0017        | 47.0177 | 9600  | 0.2051          | 9.4733  |
| 0.0017        | 48.0176 | 9800  | 0.2050          | 9.4923  |
| 0.0014        | 49.0175 | 10000 | 0.2053          | 9.4597  |


### Framework versions

- Transformers 4.45.0.dev0
- Pytorch 2.0.1
- Datasets 3.0.0
- Tokenizers 0.19.1