File size: 2,572 Bytes
b7f96ea d8eac2c b7f96ea d8eac2c b7f96ea d8eac2c b7f96ea d8eac2c b7f96ea d8eac2c b7f96ea d8eac2c b7f96ea d8eac2c b7f96ea d8eac2c b7f96ea d8eac2c b7f96ea d8eac2c b7f96ea d8eac2c b7f96ea d8eac2c b7f96ea d8eac2c b7f96ea d8eac2c b7f96ea d8eac2c b7f96ea d8eac2c b7f96ea d8eac2c |
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 85 86 87 88 89 90 |
---
library_name: transformers
language:
- en
license: apache-2.0
base_model: openai/whisper-large-v3
tags:
- wft
- whisper
- automatic-speech-recognition
- audio
- speech
- generated_from_trainer
datasets:
- ntnu-smil/sandi2025-ds
metrics:
- wer
model-index:
- name: whisper-large-v3-sandi-7k-64-448steps
results:
- task:
type: automatic-speech-recognition
name: Automatic Speech Recognition
dataset:
name: ntnu-smil/sandi2025-ds
type: ntnu-smil/sandi2025-ds
metrics:
- type: wer
value: 24.09465733000756
name: Wer
---
<!-- 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-large-v3-sandi-7k-64-448steps
This model is a fine-tuned version of [openai/whisper-large-v3](https://huggingface.co/openai/whisper-large-v3) on the ntnu-smil/sandi2025-ds dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5722
- Wer: 24.0947
- Cer: 56.2387
- Decode Runtime: 203.1841
- Wer Runtime: 0.1735
- Cer Runtime: 0.3276
## 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: 7e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 64
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.98) and epsilon=1e-06 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- training_steps: 448
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer | Cer | Decode Runtime | Wer Runtime | Cer Runtime |
|:-------------:|:------:|:----:|:---------------:|:-------:|:-------:|:--------------:|:-----------:|:-----------:|
| 0.6419 | 1.0223 | 112 | 0.6663 | 20.0083 | 24.8032 | 187.4701 | 0.1653 | 0.2986 |
| 0.6651 | 2.0446 | 224 | 0.6117 | 20.0564 | 34.0018 | 189.8527 | 0.1717 | 0.3134 |
| 0.4682 | 3.0670 | 336 | 0.5826 | 21.0683 | 32.6385 | 190.6981 | 0.1750 | 0.3082 |
| 0.8059 | 4.0893 | 448 | 0.5722 | 24.0947 | 56.2387 | 203.1841 | 0.1735 | 0.3276 |
### Framework versions
- PEFT 0.15.2
- Transformers 4.51.3
- Pytorch 2.4.1+cu124
- Datasets 3.5.1
- Tokenizers 0.21.1 |