File size: 2,181 Bytes
d297f95 e74011f d297f95 e74011f d297f95 e74011f d297f95 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 |
---
license: apache-2.0
base_model: openai/whisper-large-v3
tags:
- generated_from_trainer
datasets:
- honzapucalek/hc_train_v3_independent_v2
metrics:
- wer
model-index:
- name: hc-train-v3-independent-v2
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: honzapucalek/hc_train_v3_independent_v2 cs
type: honzapucalek/hc_train_v3_independent_v2
config: cs
split: test
args: cs
metrics:
- name: Wer
type: wer
value: 0.1169068862960421
---
<!-- 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. -->
# hc-train-v3-independent-v2
This model is a fine-tuned version of [openai/whisper-large-v3](https://huggingface.co./openai/whisper-large-v3) on the honzapucalek/hc_train_v3_independent_v2 cs dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3728
- Wer: 0.1169
## 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: 1e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 5000
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.0079 | 13.51 | 1000 | 0.2854 | 0.1256 |
| 0.0037 | 27.03 | 2000 | 0.3198 | 0.1373 |
| 0.0002 | 40.54 | 3000 | 0.3459 | 0.1177 |
| 0.0001 | 54.05 | 4000 | 0.3650 | 0.1168 |
| 0.0001 | 67.57 | 5000 | 0.3728 | 0.1169 |
### Framework versions
- Transformers 4.37.2
- Pytorch 2.1.2+cu121
- Datasets 2.16.1
- Tokenizers 0.15.1
|