W2V2-BERT-withLM-Malayalam – AI Model by vrclc | AlphaNeural AI

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vrclc/
W2V2-BERT-withLM-Malayalam

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automatic-speech-recognition

vrclc/festvox-iiith-ml

vrclc/openslr63

vrclc/imasc_slr

mozilla-foundation/common_voice_17_0

kavyamanohar/ml-sentences

facebook/w2v-bert-2.0

endpoints_compatible

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W2V2-BERT-withLM-Malayalam

This model is a fine-tuned version of facebook/w2v-bert-2.0 on the IMASC, MSC, OpenSLR Malayalam Train split, Festvox Malayalam, CV16 .

It achieves the following results on the validation set : OpenSLR-Test:

Loss: 0.1722
Wer: 0.1299

Trigram Language Model Trained using KENLM Library on kavyamanohar/ml-sentences dataset

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: 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: 10
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Wer
1.1416	0.46	600	0.3393	0.4616
0.1734	0.92	1200	0.2414	0.3493
0.1254	1.38	1800	0.2205	0.2963
0.1097	1.84	2400	0.2157	0.3133
0.0923	2.3	3000	0.1854	0.2473
0.0792	2.76	3600	0.1939	0.2471
0.0696	3.22	4200	0.1720	0.2282
0.0589	3.68	4800	0.1768	0.2013
0.0552	4.14	5400	0.1635	0.1864
0.0437	4.6	6000	0.1501	0.1826
0.0408	5.06	6600	0.1500	0.1645
0.0314	5.52	7200	0.1559	0.1655
0.0317	5.98	7800	0.1448	0.1553
0.022	6.44	8400	0.1592	0.1590
0.0218	6.9	9000	0.1431	0.1458
0.0154	7.36	9600	0.1514	0.1366
0.0141	7.82	10200	0.1540	0.1383
0.0113	8.28	10800	0.1558	0.1391
0.0085	8.74	11400	0.1612	0.1356
0.0072	9.2	12000	0.1697	0.1289
0.0046	9.66	12600	0.1722	0.1299

Framework versions

Transformers 4.39.3
Pytorch 2.1.1+cu121
Datasets 2.16.1
Tokenizers 0.15.1