File size: 1,928 Bytes
5c8ca39 3554ec3 5c8ca39 3554ec3 5c8ca39 3554ec3 5c8ca39 3554ec3 5c8ca39 3554ec3 5c8ca39 |
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 |
---
base_model: wav2vec2-pretrained-base-gumbelVQ
tags:
- automatic-speech-recognition
- timit_asr
- generated_from_trainer
datasets:
- timit_asr
metrics:
- wer
model-index:
- name: wav2vec2-base-gumbelVQ-timit-fine-tuned
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: TIMIT_ASR - NA
type: timit_asr
config: clean
split: test
args: 'Config: na, Training split: train, Eval split: test'
metrics:
- name: Wer
type: wer
value: 0.4901798635517883
---
<!-- 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. -->
# wav2vec2-base-gumbelVQ-timit-fine-tuned
This model is a fine-tuned version of [wav2vec2-pretrained-base-gumbelVQ](https://huggingface.co/wav2vec2-pretrained-base-gumbelVQ) on the TIMIT_ASR - NA dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7549
- Wer: 0.4902
## 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: 0.0001
- train_batch_size: 32
- eval_batch_size: 1
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 20.0
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.4628 | 10.0 | 1450 | 0.6779 | 0.5171 |
| 0.3036 | 20.0 | 2900 | 0.7549 | 0.4902 |
### Framework versions
- Transformers 4.36.2
- Pytorch 2.3.0.dev20231229+cu118
- Datasets 2.16.0
- Tokenizers 0.15.0
|