README.md

metadata

license: mit
base_model: facebook/w2v-bert-2.0
tags:
  - generated_from_trainer
datasets:
  - common_voice_16_0
metrics:
  - wer
model-index:
  - name: w2v-bert-2.0-bangala-gpu-CV16.0_v2
    results:
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: common_voice_16_0
          type: common_voice_16_0
          config: bn
          split: test
          args: bn
        metrics:
          - name: Wer
            type: wer
            value: 0.4811011116993118

w2v-bert-2.0-bangala-gpu-CV16.0_v2

This model is a fine-tuned version of facebook/w2v-bert-2.0 on the common_voice_16_0 dataset. It achieves the following results on the evaluation set:

• Loss: 0.4490
• Wer: 0.4811

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: 4.42184e-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
3.5221	0.31	300	0.5900	0.6271
1.2024	0.63	600	0.4088	0.4071
0.9149	0.94	900	0.3200	0.3270
0.8124	1.26	1200	0.2965	0.3080
0.7028	1.57	1500	0.2759	0.2884
0.6301	1.89	1800	0.2435	0.2671
0.6147	2.2	2100	0.2335	0.2477
0.6304	2.52	2400	0.2248	0.2458
0.5921	2.83	2700	0.2326	0.2441
0.495	3.15	3000	0.2180	0.2378
0.4987	3.46	3300	0.2139	0.2227
0.5669	3.78	3600	0.2097	0.2236
0.5904	4.09	3900	0.2038	0.2178
0.6016	4.41	4200	0.2091	0.2131
0.5325	4.72	4500	0.2064	0.2147
0.5271	5.04	4800	0.2002	0.2159
0.5229	5.35	5100	0.2069	0.2209
0.5843	5.67	5400	0.2090	0.2202
0.5477	5.98	5700	0.2085	0.2175
0.508	6.3	6000	0.2046	0.2158
0.5226	6.61	6300	0.2515	0.3250
0.7576	6.93	6600	0.2343	0.2364
1.0089	7.24	6900	0.2731	0.2713
0.9462	7.56	7200	0.2588	0.2648
0.8648	7.87	7500	0.2916	0.3393
1.1282	8.19	7800	0.3830	0.4583
1.3279	8.5	8100	0.3910	0.4117
1.2722	8.82	8400	0.4424	0.3442
1.2886	9.13	8700	0.4421	0.4011
1.3274	9.45	9000	0.4483	0.4769
1.3235	9.76	9300	0.4490	0.4811

Framework versions

• Transformers 4.39.3
• Pytorch 2.1.2+cu121
• Datasets 2.18.0
• Tokenizers 0.15.0