WAXAL-wal_asr-w2v-bert-2.0 – AI Model by dhasmana | AlphaNeural AI

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dhasmana/
WAXAL-wal_asr-w2v-bert-2.0

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automatic-speech-recognition

generated_from_trainer

facebook/w2v-bert-2.0

endpoints_compatible

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WAXAL-wal_asr-w2v-bert-2.0

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

Loss: 1.1595
Cer: 0.2217

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: 8
seed: 42
gradient_accumulation_steps: 2
total_train_batch_size: 8
optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED 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: 10

Training results

Training Loss	Epoch	Step	Validation Loss	Cer
3.7991	1.6322	300	1.0322	0.2493
1.1458	3.2616	600	1.0429	0.2345
0.9316	4.8937	900	0.9821	0.2269
0.7695	6.5232	1200	1.1250	0.2231
0.6165	8.1526	1500	1.1479	0.2221
0.4559	9.7847	1800	1.1580	0.2219
0.4559	10.0	1840	1.1595	0.2217

Framework versions

Transformers 5.12.1
Pytorch 2.11.0+cu128
Datasets 4.0.0
Tokenizers 0.22.2