W2V2-BERT-Malayalam

vrclc/W2V2-BERT-Malayalam

W2V2-BERT-Malayalam is a automatic speech recognition model from vrclc. Use it when you need speech turned into text. It is set up for transformers. The card lists the license as mit.

This model is a fine-tuned version of facebook/w2v-bert-2.0 on an these datasets: IMASC, MSC, OpenSLR Malayalam Train split, Festvox Malayalam, commonvoice161

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At a glance

Task: Automatic Speech Recognition
Library: transformers
License: mit
Model type: wav2vec2-bert
Access: Public
Created: May 9, 2024
Updated: Jul 31, 2024
SHA: 6aff1fef

Base models

facebook/w2v-bert-2.0

Datasets

Spaces

vrclc/W2V2-BERT-Malayalam

Model card

From the Hugging Face model README

W2V2-BERT-Malayalam

This model is a fine-tuned version of facebook/w2v-bert-2.0 on an these datasets: IMASC, MSC, OpenSLR Malayalam Train split, Festvox Malayalam, common_voice_16_1 It achieves the following results on the evaluation set:

Loss: 0.1722
Wer: 0.1299

Training procedure

Trained on NVIDIA A100 GPU

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

Task: Automatic Speech Recognition
Library: transformers
Type: wav2vec2-bert
License: mit
Created: May 9, 2024
Updated: Jul 31, 2024