Hugging Face's logo

csikasote
/
xls-r-1b-bigcgen-combined-30hrs-model

Automatic Speech Recognition
Transformers
TensorBoard
Safetensors
wav2vec2
bigcgen
Generated from Trainer
Inference Endpoints
Model card Files Metrics Community
xls-r-1b-bigcgen-combined-30hrs-model / README.md
csikasote's picture
csikasote
End of training
46f4645 verified
preview code
|
raw
history blame contribute delete
3.47 kB
metadata
library_name: transformers
license: apache-2.0
base_model: facebook/wav2vec2-xls-r-1b
tags:
  - automatic-speech-recognition
  - bigcgen
  - generated_from_trainer
metrics:
  - wer
model-index:
  - name: xls-r-1b-bigcgen-combined-30hrs-model
    results: []

xls-r-1b-bigcgen-combined-30hrs-model

This model is a fine-tuned version of facebook/wav2vec2-xls-r-1b on the BIGCGEN - NA dataset. It achieves the following results on the evaluation set:

  • Loss: 0.6444
  • Wer: 0.6496

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: 3e-05
  • train_batch_size: 4
  • eval_batch_size: 4
  • 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_steps: 500
  • num_epochs: 30.0

Training results

Training Loss Epoch Step Validation Loss Wer
No log 0.0703 100 3.5624 1.0
No log 0.1406 200 2.7976 1.0
No log 0.2110 300 1.7477 1.0
No log 0.2813 400 0.9556 0.9632
5.9145 0.3516 500 0.8887 0.8956
5.9145 0.4219 600 0.8703 0.9244
5.9145 0.4923 700 0.6985 0.7486
5.9145 0.5626 800 0.8053 0.8899
5.9145 0.6329 900 0.6571 0.6398
1.3395 0.7032 1000 0.6413 0.6394
1.3395 0.7736 1100 0.6008 0.5699
1.3395 0.8439 1200 0.6438 0.6576
1.3395 0.9142 1300 0.6003 0.6022
1.3395 0.9845 1400 0.7025 0.7080
1.1915 1.0549 1500 0.8043 0.8568
1.1915 1.1252 1600 0.6106 0.6685
1.1915 1.1955 1700 0.6003 0.6195
1.1915 1.2658 1800 0.6331 0.6743
1.1915 1.3361 1900 0.7777 0.8180
1.0769 1.4065 2000 0.5906 0.6095
1.0769 1.4768 2100 0.6689 0.7242
1.0769 1.5471 2200 0.6466 0.6489
1.0769 1.6174 2300 0.5825 0.6113
1.0769 1.6878 2400 0.7114 0.7517
1.0964 1.7581 2500 0.8009 0.7965
1.0964 1.8284 2600 0.6260 0.6364
1.0964 1.8987 2700 0.5530 0.5519
1.0964 1.9691 2800 0.6974 0.7149
1.0964 2.0394 2900 0.5647 0.5955
1.0268 2.1097 3000 0.5556 0.5488
1.0268 2.1800 3100 0.6444 0.6502

Framework versions

  • Transformers 4.47.1
  • Pytorch 2.5.1+cu124
  • Datasets 3.2.0
  • Tokenizers 0.21.0