metadata

license: apache-2.0
base_model: facebook/wav2vec2-large-xlsr-53
tags:
  - generated_from_trainer
datasets:
  - common_voice_16_1
metrics:
  - wer
model-index:
  - name: wav2vec2-large-xls-r-300m-upper-sorbian-cz-frozen-2-colab
    results:
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: common_voice_16_1
          type: common_voice_16_1
          config: hsb
          split: test
          args: hsb
        metrics:
          - name: Wer
            type: wer
            value: 0.4301780693533271

wav2vec2-large-xls-r-300m-upper-sorbian-cz-frozen-2-colab

This model is a fine-tuned version of facebook/wav2vec2-large-xlsr-53 on the common_voice_16_1 dataset. It achieves the following results on the evaluation set:

Loss: 0.7163
Wer: 0.4302
Cer: 0.1003

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: 0.0003
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: 500
num_epochs: 60
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Wer	Cer
0.6172	3.23	100	0.6599	0.6999	0.1787
0.4414	6.45	200	0.6030	0.6251	0.1524
0.289	9.68	300	0.5899	0.5670	0.1336
0.1953	12.9	400	0.6095	0.5457	0.1308
0.1388	16.13	500	0.6628	0.5159	0.1224
0.1187	19.35	600	0.7075	0.4932	0.1180
0.0994	22.58	700	0.7131	0.4780	0.1143
0.0816	25.81	800	0.6959	0.4752	0.1101
0.0727	29.03	900	0.7201	0.4644	0.1104
0.0637	32.26	1000	0.7288	0.4630	0.1080
0.0592	35.48	1100	0.7219	0.4524	0.1056
0.0549	38.71	1200	0.7204	0.4480	0.1041
0.0473	41.94	1300	0.7238	0.4470	0.1048
0.0412	45.16	1400	0.7109	0.4278	0.1011
0.0423	48.39	1500	0.7252	0.4407	0.1045
0.0419	51.61	1600	0.7193	0.4393	0.1028
0.0365	54.84	1700	0.7231	0.4318	0.1010
0.0347	58.06	1800	0.7163	0.4302	0.1003

Framework versions

Transformers 4.38.2
Pytorch 2.2.1+cu121
Datasets 2.18.0
Tokenizers 0.15.2