File size: 2,980 Bytes
fa32092 b2c7929 fa32092 06821f0 fa32092 b2c7929 fa32092 b2c7929 06821f0 fa32092 |
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 91 92 93 94 95 96 97 98 99 |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- dataset/riksdagen
metrics:
- wer
model-index:
- name: whisper-tiny-sv
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: dataset/riksdagen audiofolder
type: dataset/riksdagen
config: audiofolder
split: train
args: audiofolder
metrics:
- name: Wer
type: wer
value: 0.3700987201570632
---
<!-- 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-tiny-sv
This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) on the dataset/riksdagen audiofolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6435
- Wer: 0.3701
## 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: 32
- eval_batch_size: 32
- seed: 42
- distributed_type: multi-GPU
- num_devices: 4
- total_train_batch_size: 128
- total_eval_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 100
- training_steps: 5000
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 1.0032 | 0.08 | 250 | 1.0075 | 0.5063 |
| 0.8983 | 0.17 | 500 | 0.8945 | 0.4649 |
| 0.8227 | 0.25 | 750 | 0.8336 | 0.4491 |
| 0.777 | 0.33 | 1000 | 0.7931 | 0.4314 |
| 0.7728 | 0.42 | 1250 | 0.7640 | 0.4217 |
| 0.7141 | 0.5 | 1500 | 0.7407 | 0.4134 |
| 0.7208 | 0.58 | 1750 | 0.7225 | 0.4023 |
| 0.6911 | 0.66 | 2000 | 0.7083 | 0.3942 |
| 0.6924 | 0.75 | 2250 | 0.6948 | 0.3911 |
| 0.6702 | 0.83 | 2500 | 0.6849 | 0.3884 |
| 0.663 | 0.91 | 2750 | 0.6766 | 0.3769 |
| 0.6548 | 1.0 | 3000 | 0.6686 | 0.3759 |
| 0.638 | 1.08 | 3250 | 0.6627 | 0.3728 |
| 0.6222 | 1.16 | 3500 | 0.6574 | 0.3733 |
| 0.6323 | 1.25 | 3750 | 0.6528 | 0.3691 |
| 0.6192 | 1.33 | 4000 | 0.6498 | 0.3688 |
| 0.633 | 1.41 | 4250 | 0.6469 | 0.3677 |
| 0.6229 | 1.5 | 4500 | 0.6451 | 0.3681 |
| 0.6246 | 1.58 | 4750 | 0.6439 | 0.3706 |
| 0.6214 | 1.66 | 5000 | 0.6435 | 0.3701 |
### Framework versions
- Transformers 4.26.0.dev0
- Pytorch 1.12.0a0+8a1a93a
- Datasets 2.7.1
- Tokenizers 0.13.2
|