metadata
library_name: transformers
license: apache-2.0
base_model: openai/whisper-large-v3-turbo
tags:
- generated_from_trainer
datasets:
- google/fleurs
metrics:
- wer
model-index:
- name: whisper-large-v3-turbo-Hindi-Version2
results: []
language:
- hi
pipeline_tag: automatic-speech-recognition
whisper-large-v3-turbo-Hindi-Version2
This model is a fine-tuned version of openai/whisper-large-v3-turbo on the fleurs dataset. It achieves the following results on the evaluation set:
- Loss: 0.2175
- Wer: 23.1550
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: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- training_steps: 20000
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 0.2178 | 6.7797 | 2000 | 0.2245 | 25.6931 |
| 0.1841 | 13.5593 | 4000 | 0.2146 | 24.6095 |
| 0.1572 | 20.3390 | 6000 | 0.2121 | 23.5845 |
| 0.1489 | 27.1186 | 8000 | 0.2120 | 23.9848 |
| 0.1315 | 33.8983 | 10000 | 0.2118 | 23.6822 |
| 0.1253 | 40.6780 | 12000 | 0.2145 | 22.9793 |
| 0.1154 | 47.4576 | 14000 | 0.2154 | 23.1941 |
| 0.1151 | 54.2373 | 16000 | 0.2168 | 23.0964 |
| 0.1079 | 61.0169 | 18000 | 0.2175 | 23.1550 |
Framework versions
- PEFT 0.14.0
- Transformers 4.46.1
- Pytorch 2.5.1+cu124
- Datasets 3.2.0
- Tokenizers 0.20.1