metadata

library_name: transformers
license: mit
base_model: facebook/w2v-bert-2.0
tags:
  - generated_from_trainer
datasets:
  - fleurs
metrics:
  - wer
model-index:
  - name: w2v-bert-2.0-lg-CV-Fleurs-filtered-100hrs-v12
    results:
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: fleurs
          type: fleurs
          config: lg_ug
          split: test
          args: lg_ug
        metrics:
          - name: Wer
            type: wer
            value: 0.43848396501457726

w2v-bert-2.0-lg-CV-Fleurs-filtered-100hrs-v12

This model is a fine-tuned version of facebook/w2v-bert-2.0 on the fleurs dataset. It achieves the following results on the evaluation set:

Loss: 0.4980
Wer: 0.4385
Cer: 0.0852

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: 4
eval_batch_size: 2
seed: 42
gradient_accumulation_steps: 2
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_ratio: 0.1
num_epochs: 70
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Wer	Cer
0.9834	1.0	7125	0.3827	0.4584	0.0921
0.1914	2.0	14250	0.3460	0.4394	0.0837
0.165	3.0	21375	0.3377	0.4375	0.0827
0.1519	4.0	28500	0.3337	0.4246	0.0805
0.1458	5.0	35625	0.3242	0.4234	0.0789
0.1413	6.0	42750	0.3294	0.4329	0.0816
0.1395	7.0	49875	0.3441	0.4431	0.0866
0.1325	8.0	57000	0.3263	0.4332	0.0867
0.1191	9.0	64125	0.3278	0.4065	0.0788
0.1075	10.0	71250	0.3203	0.4418	0.0808
0.0974	11.0	78375	0.3304	0.4036	0.0771
0.0892	12.0	85500	0.3307	0.4263	0.0819
0.0802	13.0	92625	0.3530	0.4107	0.0785
0.0728	14.0	99750	0.3478	0.4156	0.0795
0.0632	15.0	106875	0.3620	0.4052	0.0787
0.0567	16.0	114000	0.3620	0.4219	0.0796
0.0484	17.0	121125	0.4135	0.4114	0.0787
0.0423	18.0	128250	0.4220	0.4186	0.0814
0.0358	19.0	135375	0.4476	0.4303	0.0825
0.0311	20.0	142500	0.4913	0.4134	0.0806
0.0277	21.0	149625	0.4910	0.4411	0.0850
0.0238	22.0	156750	0.5097	0.4269	0.0821
0.0214	23.0	163875	0.4755	0.4248	0.0837
0.0194	24.0	171000	0.4839	0.4249	0.0826
0.0178	25.0	178125	0.5302	0.4294	0.0828
0.016	26.0	185250	0.4980	0.4385	0.0852

Framework versions

Transformers 4.46.2
Pytorch 2.1.0+cu118
Datasets 3.1.0
Tokenizers 0.20.3