metadata

base_model: facebook/w2v-bert-2.0
license: mit
metrics:
  - wer
model-index:
  - name: W2V2-BERT-withLM-Malayalam by Bajiyo Baiju, Kavya Manohar
    results:
      - task:
          type: automatic-speech-recognition
          name: Automatic Speech Recognition
        dataset:
          name: OpenSLR Malayalam -Test
          type: vrclc/openslr63
          config: ml
          split: test
          args: ml
        metrics:
          - type: wer
            value: 18.23
            name: WER
      - task:
          type: automatic-speech-recognition
          name: Automatic Speech Recognition
        dataset:
          name: Google Fleurs
          type: google/fleurs
          config: ml
          split: test
          args: ml
        metrics:
          - type: wer
            value: 31.92
            name: WER
      - task:
          type: automatic-speech-recognition
          name: Automatic Speech Recognition
        dataset:
          name: Mozilla Common Voice
          type: mozilla-foundation/common_voice_16_1
          config: ml
          split: test
          args: ml
        metrics:
          - type: wer
            value: 49.79
            name: WER
datasets:
  - vrclc/festvox-iiith-ml
  - vrclc/openslr63
  - vrclc/imasc_slr
  - mozilla-foundation/common_voice_17_0
  - smcproject/MSC
  - kavyamanohar/ml-sentences
language:
  - ml
pipeline_tag: automatic-speech-recognition

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