README.md

metadata

language:
  - tr
license: apache-2.0
tags:
  - automatic-speech-recognition
  - common_voice
  - generated_from_trainer
datasets:
  - common_voice
metrics:
  - wer
model-index:
  - name: wav2vec2-common_voice-tr-demo
    results:
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: COMMON_VOICE - TR
          type: common_voice
          config: tr
          split: test
          args: 'Config: tr, Training split: train+validation, Eval split: test'
        metrics:
          - name: Wer
            type: wer
            value: 0.33398018588499645

wav2vec2-common_voice-tr-demo

This model is a fine-tuned version of facebook/wav2vec2-large-xlsr-53 on the COMMON_VOICE - TR dataset. It achieves the following results on the evaluation set:

• Loss: 0.3943
• Wer: 0.3340

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: 0.0003
• train_batch_size: 4
• eval_batch_size: 8
• seed: 42
• gradient_accumulation_steps: 2
• total_train_batch_size: 8
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• lr_scheduler_warmup_steps: 500
• num_epochs: 15.0
• mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Wer
No log	0.23	100	3.6296	1.0
No log	0.46	200	3.1588	0.9999
No log	0.69	300	2.3111	1.0083
No log	0.92	400	0.9852	0.7981
3.6643	1.15	500	0.7056	0.7363
3.6643	1.38	600	0.6146	0.6287
3.6643	1.61	700	0.5583	0.6195
3.6643	1.84	800	0.5529	0.5678
3.6643	2.07	900	0.5280	0.5373
0.5896	2.3	1000	0.5253	0.5349
0.5896	2.53	1100	0.4803	0.5057
0.5896	2.76	1200	0.4562	0.5132
0.5896	2.99	1300	0.4252	0.4873
0.5896	3.22	1400	0.4428	0.4831
0.368	3.45	1500	0.4510	0.4779
0.368	3.68	1600	0.4404	0.4946
0.368	3.91	1700	0.4330	0.4785
0.368	4.14	1800	0.4358	0.4558
0.368	4.37	1900	0.4126	0.4643
0.2629	4.6	2000	0.4197	0.4529
0.2629	4.83	2100	0.4064	0.4409
0.2629	5.06	2200	0.4285	0.4514
0.2629	5.29	2300	0.4193	0.4204
0.2629	5.52	2400	0.4301	0.4219
0.2072	5.75	2500	0.4222	0.4335
0.2072	5.98	2600	0.4077	0.4231
0.2072	6.21	2700	0.4132	0.4121
0.2072	6.44	2800	0.4113	0.4220
0.2072	6.67	2900	0.4101	0.4175
0.1731	6.9	3000	0.4240	0.4122
0.1731	7.13	3100	0.4309	0.4023
0.1731	7.36	3200	0.4275	0.3987
0.1731	7.59	3300	0.4289	0.4063
0.1731	7.82	3400	0.4181	0.4025
0.1397	8.05	3500	0.4490	0.3885
0.1397	8.28	3600	0.4198	0.3872
0.1397	8.51	3700	0.3980	0.3842
0.1397	8.74	3800	0.4051	0.3876
0.1397	8.97	3900	0.4080	0.3912
0.1224	9.2	4000	0.4180	0.3774
0.1224	9.43	4100	0.4102	0.3820
0.1224	9.66	4200	0.3978	0.3880
0.1224	9.89	4300	0.4157	0.3731
0.1224	10.11	4400	0.4175	0.3741
0.1012	10.34	4500	0.3887	0.3705
0.1012	10.57	4600	0.4064	0.3774
0.1012	10.8	4700	0.3961	0.3622
0.1012	11.03	4800	0.3912	0.3574
0.1012	11.26	4900	0.4020	0.3638
0.088	11.49	5000	0.4117	0.3560
0.088	11.72	5100	0.3916	0.3524
0.088	11.95	5200	0.4012	0.3533
0.088	12.18	5300	0.4085	0.3584
0.088	12.41	5400	0.4000	0.3547
0.0775	12.64	5500	0.4137	0.3525
0.0775	12.87	5600	0.4005	0.3466
0.0775	13.1	5700	0.3986	0.3479
0.0775	13.33	5800	0.3983	0.3470
0.0775	13.56	5900	0.3940	0.3429
0.0716	13.79	6000	0.3872	0.3383
0.0716	14.02	6100	0.4005	0.3384
0.0716	14.25	6200	0.4005	0.3363
0.0716	14.48	6300	0.3973	0.3357
0.0716	14.71	6400	0.3957	0.3347
0.0639	14.94	6500	0.3942	0.3340

Framework versions

• Transformers 4.29.2
• Pytorch 2.0.1+cu117
• Datasets 2.12.0
• Tokenizers 0.11.0