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 acoustic model is a fine-tuned version of facebook/wav2vec2-xls-r-1b for Finnish ASR. The model has been fine-tuned with 275.6 hours of Finnish transcribed speech data. It achieves the following results on the Common Voice 7 test set together with language model (Finnish KenLM):

• Wer: 4.19
• Cer: 0.90

Model description

TODO

Intended uses & limitations

TODO

Training and evaluation data

This model was fine-tuned with 275.6 hours of Finnish transcribed speech data from following datasets:

Dataset	Hours	% of total hours
Common Voice 7.0 Finnish train+evaluation+other splits	9.70 h	3.52 %
Finnish parliament session 2	0.24 h	0.09 %
VoxPopuli Finnish	21.97 h	7.97 %
CSS10 Finnish	10.32 h	3.74 %
Aalto Finnish Parliament ASR Corpus	228.00 h	82.73 %
Finnish Broadcast Corpus	5.37 h	1.95 %

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: 8-bit 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