README.md

metadata

language:
  - uk
license: apache-2.0
tags:
  - automatic-speech-recognition
  - common_voice
  - generated_from_trainer
datasets:
  - common_voice
model-index:
  - name: wav2vec2-xls-r-300m-uk
    results:
      - task:
          name: Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: Common Voice uk
          type: common_voice
          args: uk
        metrics:
          - name: Test WER
            type: wer
            value: 27.99

wav2vec2-xls-r-300m-uk

This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the common_voice dataset.
Notebook for training is located in this repository: https://github.com/robinhad/wav2vec2-xls-r-ukrainian.
It achieves the following results on the evaluation set:

• Loss: 0.4165
• Wer: 0.2799
• Cer: 0.0601

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: 8
• eval_batch_size: 8
• seed: 42
• gradient_accumulation_steps: 20
• total_train_batch_size: 160
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• lr_scheduler_warmup_steps: 500
• num_epochs: 500
• mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Cer	Validation Loss	Wer
4.3982	9.3	400	0.1437	0.5218	0.6507
0.229	18.6	800	0.0848	0.3679	0.4048
0.1054	27.9	1200	0.0778	0.3813	0.3670
0.0784	37.21	1600	0.0747	0.3839	0.3550
0.066	46.51	2000	0.0736	0.3970	0.3443
0.0603	55.8	2400	0.0722	0.3702	0.3393
0.0539	65.11	2800	0.0724	0.3762	0.3388
0.0497	74.41	3200	0.0713	0.3623	0.3414
0.0432	83.71	3600	0.0725	0.3847	0.3346
0.0438	93.02	4000	0.0750	0.4058	0.3393
0.0413	102.32	4400	0.0727	0.3957	0.3363
0.039	111.62	4800	0.0718	0.3865	0.3330
0.0356	120.92	5200	0.0711	0.3860	0.3319
0.0336	130.23	5600	0.0700	0.3902	0.3242
0.034	139.53	6000	0.0732	0.3930	0.3337
0.0273	148.83	6400	0.0748	0.3912	0.3375
0.027	158.14	6800	0.0752	0.4266	0.3434
0.028	167.44	7200	0.0708	0.3895	0.3227
0.0241	176.73	7600	0.0727	0.3967	0.3294
0.0241	186.05	8000	0.0712	0.4058	0.3255
0.0209	195.34	8400	0.0702	0.4102	0.3233
0.0206	204.64	8800	0.0699	0.4075	0.3194
0.0172	213.94	9200	0.0695	0.4222	0.3191
0.0166	223.25	9600	0.0678	0.3860	0.3135
0.0156	232.55	10000	0.0677	0.4035	0.3117
0.0149	241.85	10400	0.0677	0.3951	0.3087
0.0142	251.16	10800	0.0674	0.3972	0.3097
0.0134	260.46	11200	0.0675	0.4069	0.3111
0.0116	269.76	11600	0.0697	0.4189	0.3161
0.0119	279.07	12000	0.0648	0.3902	0.3008
0.0098	288.37	12400	0.0652	0.4095	0.3002
0.0091	297.67	12800	0.0644	0.3892	0.2990
0.0094	306.96	13200	0.0647	0.4026	0.2983
0.0081	316.28	13600	0.0646	0.4303	0.2978
0.0079	325.57	14000	0.0643	0.4044	0.2980
0.0072	334.87	14400	0.0655	0.3828	0.2999
0.0081	344.18	14800	0.0668	0.4108	0.3046
0.0088	353.48	15200	0.0654	0.4019	0.2993
0.0088	362.78	15600	0.0681	0.4073	0.3091
0.0079	372.09	16000	0.0667	0.4204	0.3055
0.0072	381.39	16400	0.0656	0.4030	0.3028
0.0073	390.69	16800	0.0677	0.4032	0.3081
0.0069	399.99	17200	0.0669	0.4130	0.3021
0.0063	409.3	17600	0.0651	0.4072	0.2979
0.0059	418.6	18000	0.0640	0.4110	0.2969
0.0056	427.9	18400	0.0647	0.4229	0.2995
0.005	437.21	18800	0.0624	0.4118	0.2885
0.0046	446.51	19200	0.0615	0.4111	0.2841
0.0043	455.8	19600	0.0616	0.4071	0.2850
0.0038	465.11	20000	0.0624	0.4268	0.2867
0.0035	474.41	20400	0.0605	0.4117	0.2820
0.0035	483.71	20800	0.0602	0.4155	0.2819
0.0034	493.02	21200	0.0601	0.4165	0.2799

Framework versions

• Transformers 4.14.1
• Pytorch 1.10.0
• Datasets 1.16.1
• Tokenizers 0.10.3