File size: 2,734 Bytes
d728879 c2de558 d728879 c2de558 d728879 c2de558 d728879 c2de558 d728879 |
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 |
---
library_name: transformers
license: apache-2.0
base_model: openai/whisper-medium
tags:
- generated_from_trainer
datasets:
- bigcgen
metrics:
- wer
model-index:
- name: whisper-medium-bigcgen-combined-20hrs-model
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: bigcgen
type: bigcgen
metrics:
- name: Wer
type: wer
value: 0.4210649229332088
---
<!-- 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-medium-bigcgen-combined-20hrs-model
This model is a fine-tuned version of [openai/whisper-medium](https://huggingface.co./openai/whisper-medium) on the bigcgen dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5368
- Wer: 0.4211
## 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: 1.75e-05
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 8
- 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: 500
- training_steps: 5000
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:------:|:----:|:---------------:|:------:|
| 3.7679 | 0.1521 | 200 | 0.8942 | 0.6455 |
| 3.4733 | 0.3042 | 400 | 0.7609 | 0.5650 |
| 2.6698 | 0.4564 | 600 | 0.6958 | 0.5375 |
| 2.5647 | 0.6085 | 800 | 0.6685 | 0.5496 |
| 2.1636 | 0.7606 | 1000 | 0.6228 | 0.5103 |
| 2.7265 | 0.9127 | 1200 | 0.5869 | 0.4706 |
| 1.5404 | 1.0654 | 1400 | 0.5990 | 0.4542 |
| 1.9844 | 1.2175 | 1600 | 0.5893 | 0.4643 |
| 1.6926 | 1.3697 | 1800 | 0.5730 | 0.4413 |
| 1.8654 | 1.5218 | 2000 | 0.5550 | 0.4599 |
| 1.8045 | 1.6739 | 2200 | 0.5445 | 0.4178 |
| 1.8258 | 1.8260 | 2400 | 0.5368 | 0.4211 |
| 1.543 | 1.9781 | 2600 | 0.5371 | 0.4245 |
| 0.9667 | 2.1308 | 2800 | 0.5587 | 0.4545 |
| 1.0216 | 2.2829 | 3000 | 0.5617 | 0.4096 |
### Framework versions
- Transformers 4.47.1
- Pytorch 2.5.1+cu124
- Datasets 3.2.0
- Tokenizers 0.21.0
|