README.md

metadata

license: apache-2.0
language: fi
metrics:
  - wer
  - cer
tags:
  - generated_from_trainer
  - automatic-speech-recognition
  - fi
  - finnish
  - robust-speech-event
datasets:
  - mozilla-foundation/common_voice_7_0
model-index:
  - name: wav2vec2-xlsr-1b-finnish-lm-v2
    results:
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: Common Voice 7
          type: mozilla-foundation/common_voice_7_0
          args: fi
        metrics:
          - name: Test WER
            type: wer
            value: 4.19
          - name: Test CER
            type: cer
            value: 0.9

wav2vec2-xlsr-1b-finnish-lm-v2

This model is a fine-tuned version of facebook/wav2vec2-xls-r-1b. It achieves the following results on the evaluation set with LM:

• Wer: 4.19
• Cer: 0.90

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: 32
• eval_batch_size: 8
• seed: 42
• 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
0.7778	0.17	500	0.2851	0.3572
0.5506	0.34	1000	0.1595	0.2130
0.6569	0.5	1500	0.1458	0.2046
0.5997	0.67	2000	0.1374	0.1975
0.542	0.84	2500	0.1390	0.1956
0.4815	1.01	3000	0.1266	0.1813
0.6982	1.17	3500	0.1441	0.1965
0.4522	1.34	4000	0.1232	0.1822
0.4655	1.51	4500	0.1209	0.1702
0.4069	1.68	5000	0.1149	0.1688
0.4226	1.84	5500	0.1121	0.1560
0.3993	2.01	6000	0.1091	0.1557
0.406	2.18	6500	0.1115	0.1553
0.4098	2.35	7000	0.1144	0.1560
0.3995	2.51	7500	0.1028	0.1476
0.4101	2.68	8000	0.1129	0.1511
0.3636	2.85	8500	0.1025	0.1517
0.3534	3.02	9000	0.1068	0.1480
0.3836	3.18	9500	0.1072	0.1459
0.3531	3.35	10000	0.0928	0.1367
0.3649	3.52	10500	0.1042	0.1426
0.3645	3.69	11000	0.0979	0.1433
0.3685	3.85	11500	0.0947	0.1346
0.3325	4.02	12000	0.0991	0.1352
0.3497	4.19	12500	0.0919	0.1358
0.3303	4.36	13000	0.0888	0.1272
0.3323	4.52	13500	0.0888	0.1277
0.3452	4.69	14000	0.0894	0.1279
0.337	4.86	14500	0.0917	0.1289
0.3114	5.03	15000	0.0942	0.1313
0.3099	5.19	15500	0.0902	0.1239
0.3079	5.36	16000	0.0871	0.1256
0.3293	5.53	16500	0.0861	0.1263
0.3123	5.7	17000	0.0876	0.1203
0.3093	5.86	17500	0.0848	0.1226
0.2903	6.03	18000	0.0914	0.1221
0.297	6.2	18500	0.0841	0.1185
0.2797	6.37	19000	0.0858	0.1165
0.2878	6.53	19500	0.0874	0.1161
0.2974	6.7	20000	0.0835	0.1173
0.3051	6.87	20500	0.0835	0.1178
0.2941	7.04	21000	0.0852	0.1155
0.258	7.21	21500	0.0832	0.1132
0.2778	7.37	22000	0.0829	0.1110
0.2751	7.54	22500	0.0822	0.1069
0.2887	7.71	23000	0.0819	0.1103
0.2509	7.88	23500	0.0787	0.1055
0.2501	8.04	24000	0.0807	0.1076
0.2399	8.21	24500	0.0784	0.1052
0.2539	8.38	25000	0.0772	0.1075
0.248	8.55	25500	0.0772	0.1055
0.2689	8.71	26000	0.0763	0.1027
0.2855	8.88	26500	0.0756	0.1035
0.2421	9.05	27000	0.0771	0.0998
0.2497	9.22	27500	0.0756	0.0971
0.2367	9.38	28000	0.0741	0.0974
0.2473	9.55	28500	0.0739	0.0982
0.2396	9.72	29000	0.0756	0.0991
0.2602	9.89	29500	0.0737	0.0975

Framework versions

• Transformers 4.17.0.dev0
• Pytorch 1.10.2+cu102
• Datasets 1.18.3
• Tokenizers 0.11.0