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franziskaM/
b25-wav2vec2-large-xls-r-romansh-colab

Name: b25-wav2vec2-large-xls-r-romansh-colab
Availability: InStock

automatic-speech-recognition

generated_from_trainer

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b25-wav2vec2-large-xls-r-romansh-colab

This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the common_voice_13_0 dataset. It achieves the following results on the evaluation set:

Loss: 0.3303
Wer: 0.2415

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: 4
eval_batch_size: 8
seed: 42
gradient_accumulation_steps: 2
total_train_batch_size: 8
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_steps: 500
num_epochs: 30
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Wer
6.1605	3.05	400	2.9535	1.0
2.9451	6.11	800	2.9092	1.0
1.7795	9.16	1200	0.4982	0.4951
0.4094	12.21	1600	0.3883	0.3575
0.2374	15.27	2000	0.3151	0.2876
0.1674	18.32	2400	0.3284	0.2783
0.1385	21.37	2800	0.3408	0.2641
0.1133	24.43	3200	0.3355	0.2538
0.1015	27.48	3600	0.3303	0.2415

Framework versions

Transformers 4.26.0
Pytorch 2.0.1+cu118
Datasets 2.14.4
Tokenizers 0.13.3

franziskaM/b25-wav2vec2-large-xls-r-romansh-colab

b25-wav2vec2-large-xls-r-romansh-colab

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

franziskaM/
b25-wav2vec2-large-xls-r-romansh-colab