metadata
base_model: facebook/w2v-bert-2.0
datasets:
- common_voice_17_0
library_name: transformers
license: mit
metrics:
- wer
tags:
- generated_from_trainer
model-index:
- name: w2v-bert-2.0-sq
results:
- task:
type: automatic-speech-recognition
name: Automatic Speech Recognition
dataset:
name: common_voice_17_0
type: common_voice_17_0
config: sq
split: test
args: sq
metrics:
- type: wer
value: 0.3543781725888325
name: Wer
w2v-bert-2.0-sq
This model is a fine-tuned version of facebook/w2v-bert-2.0 on the common_voice_17_0 dataset. It achieves the following results on the evaluation set:
- Loss: 0.4195
- Wer: 0.3544
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: 5e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 5
- num_epochs: 7
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 3.4885 | 0.6061 | 20 | 3.2689 | 1.0 |
| 3.0283 | 1.2121 | 40 | 3.0235 | 0.9949 |
| 1.7144 | 1.8182 | 60 | 1.3367 | 0.9483 |
| 0.6599 | 2.4242 | 80 | 0.7279 | 0.6317 |
| 0.6135 | 3.0303 | 100 | 0.6208 | 0.5615 |
| 0.4033 | 3.6364 | 120 | 0.5120 | 0.4730 |
| 0.2658 | 4.2424 | 140 | 0.4693 | 0.4270 |
| 0.3056 | 4.8485 | 160 | 0.4831 | 0.4327 |
| 0.2024 | 5.4545 | 180 | 0.4536 | 0.3991 |
| 0.1963 | 6.0606 | 200 | 0.4297 | 0.3747 |
| 0.1494 | 6.6667 | 220 | 0.4195 | 0.3544 |
Framework versions
- Transformers 4.45.2
- Pytorch 2.4.1+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1