README.md

metadata

language:
  - sv-SE
license: cc0-1.0
tags:
  - automatic-speech-recognition
  - mozilla-foundation/common_voice_7_0
  - sv
  - generated_from_trainer
  - robust-speech-event
  - model_for_talk
datasets:
  - mozilla-foundation/common_voice_7_0
model-index:
  - name: XLS-R-300M-voxrex - Swedish
    results:
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: Common Voice 7
          type: mozilla-foundation/common_voice_7_0
          args: sv-SE
        metrics:
          - name: Test WER
            type: wer
            value: 18.89
          - name: Test CER
            type: cer
            value: 6.63
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: Robust Speech Event - Dev Data
          type: Robust Speech Event - Dev Data
          args: sv
        metrics:
          - name: Test WER
            type: wer
            value: 30.65
          - name: Test CER
            type: cer
            value: 13.56

This model is a fine-tuned version of KBLab/wav2vec2-large-voxrex on the MOZILLA-FOUNDATION/COMMON_VOICE_7_0 - SV-SE dataset. It achieves the following results on the evaluation set:

• Loss: 0.2201
• Wer: 0.1778

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: 7.5e-05
• train_batch_size: 8
• eval_batch_size: 8
• seed: 42
• gradient_accumulation_steps: 4
• 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: 2000
• num_epochs: 50.0
• mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Wer
3.1522	1.45	500	3.1290	1.0
2.9576	2.91	1000	2.9633	1.0
1.9853	4.36	1500	0.8902	0.6104
1.5867	5.81	2000	0.4793	0.3664
1.4608	7.27	2500	0.3816	0.3095
1.3496	8.72	3000	0.3415	0.2783
1.3058	10.17	3500	0.3072	0.2519
1.2533	11.63	4000	0.2877	0.2381
1.2535	13.08	4500	0.2791	0.2320
1.2273	14.53	5000	0.2726	0.2282
1.2083	15.99	5500	0.2638	0.2212
1.1606	17.44	6000	0.2531	0.2174
1.1545	18.89	6500	0.2468	0.2109
1.1344	20.35	7000	0.2494	0.2050
1.1173	21.8	7500	0.2447	0.1980
1.1081	23.26	8000	0.2428	0.1998
1.1023	24.71	8500	0.2329	0.1951
1.0923	26.16	9000	0.2388	0.1962
1.0798	27.61	9500	0.2363	0.1944
1.0769	29.07	10000	0.2342	0.1913
1.0672	30.52	10500	0.2250	0.1875
1.0735	31.97	11000	0.2305	0.1874
1.0628	33.43	11500	0.2291	0.1851
1.0451	34.88	12000	0.2263	0.1856
1.0299	36.34	12500	0.2257	0.1834
1.0368	37.79	13000	0.2230	0.1808
1.0322	39.24	13500	0.2231	0.1833
1.0451	40.7	14000	0.2197	0.1817
1.0304	42.15	14500	0.2241	0.1813
1.0102	43.6	15000	0.2233	0.1795
1.0135	45.06	15500	0.2200	0.1794
1.014	46.51	16000	0.2207	0.1779
1.0071	47.96	16500	0.2205	0.1784
0.9729	49.42	17000	0.2204	0.1777

Framework versions

• Transformers 4.16.0.dev0
• Pytorch 1.10.1+cu102
• Datasets 1.17.1.dev0
• Tokenizers 0.11.0