metadata
language:
- sr
license: apache-2.0
base_model: openai/whisper-small
tags:
- generated_from_trainer
datasets:
- espnet/yodas
- google/fleurs
- Sagicc/audio-lmb-ds
- mozilla-foundation/common_voice_16_1
metrics:
- wer
model-index:
- name: Whisper Small Sr Yodas
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice 16_1
type: mozilla-foundation/common_voice_16_1
config: sr
split: test
args: sr
metrics:
- name: Wer
type: wer
value: 0.12195981670778992
Whisper Small Sr Yodas
This model is a fine-tuned version of openai/whisper-small on merged datasets Common Voice 16 + Fleurs + Juzne vesti (South news) + LBM + (Yodas)[https://huggingface.co/datasets/espnet/yodas] dataset and
Rupnik, Peter and Ljubešić, Nikola, 2022,
ASR training dataset for Serbian JuzneVesti-SR v1.0, Slovenian language resource repository CLARIN.SI, ISSN 2820-4042,
http://hdl.handle.net/11356/1679.
It achieves the following results on the evaluation set:
- Loss: 0.3584
- Wer Ortho: 0.2328
- Wer: 0.1220
Model description
Added new dataset Yodas as test and experiment to improve results.
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: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 50
- num_epochs: 10
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer Ortho | Wer |
|---|---|---|---|---|---|
| 0.6958 | 0.49 | 1000 | 0.2114 | 0.2528 | 0.1563 |
| 0.5941 | 0.98 | 2000 | 0.1857 | 0.2214 | 0.1269 |
| 0.3985 | 1.46 | 3000 | 0.1729 | 0.2106 | 0.1167 |
| 0.4187 | 1.95 | 4000 | 0.1745 | 0.2120 | 0.1147 |
| 0.3446 | 2.44 | 5000 | 0.1770 | 0.2074 | 0.1139 |
| 0.2992 | 2.93 | 6000 | 0.1710 | 0.2048 | 0.1061 |
| 0.2074 | 3.42 | 7000 | 0.1887 | 0.2090 | 0.1123 |
| 0.1958 | 3.91 | 8000 | 0.1871 | 0.2136 | 0.1131 |
| 0.1707 | 4.39 | 9000 | 0.2069 | 0.2230 | 0.1126 |
| 0.1403 | 4.88 | 10000 | 0.2092 | 0.2138 | 0.1110 |
| 0.0871 | 5.37 | 11000 | 0.2345 | 0.2216 | 0.1161 |
| 0.0856 | 5.86 | 12000 | 0.2384 | 0.2281 | 0.1161 |
| 0.0496 | 6.35 | 13000 | 0.2657 | 0.2327 | 0.1211 |
| 0.0542 | 6.84 | 14000 | 0.2760 | 0.2346 | 0.1198 |
| 0.0274 | 7.32 | 15000 | 0.3024 | 0.2304 | 0.1218 |
| 0.0281 | 7.81 | 16000 | 0.3134 | 0.2357 | 0.1216 |
| 0.0151 | 8.3 | 17000 | 0.3328 | 0.2276 | 0.1188 |
| 0.0165 | 8.79 | 18000 | 0.3417 | 0.2348 | 0.1220 |
| 0.0094 | 9.28 | 19000 | 0.3545 | 0.2318 | 0.1221 |
| 0.0125 | 9.77 | 20000 | 0.3584 | 0.2328 | 0.1220 |
Framework versions
- Transformers 4.39.3
- Pytorch 2.0.1+cu117
- Datasets 2.18.0
- Tokenizers 0.15.1