README.md · marinone94/xls-r-300m-sv-robust at 6d7b2237b76d07fe2942c92eaf6b54edf5350b5c

metadata

language:
  - sv-SE
license: cc0-1.0
tags:
  - automatic-speech-recognition
  - mozilla-foundation/common_voice_8_0
  - generated_from_trainer
  - sv
  - robust-speech-event
  - model_for_talk
datasets:
  - common_voice
model-index:
  - name: XLS-R-300M - Swedish
    results:
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: mozilla-foundation/common_voice_8_0
          type: mozilla-foundation/common_voice_8_0
          args: sv-SE
        metrics:
          - name: Test WER
            type: wer
            value: 8.72
          - name: Test CER
            type: cer
            value: 3.05
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: speech-recognition-community-v2/dev_data
          type: speech-recognition-community-v2/dev_data
          args: sv
        metrics:
          - name: Test WER
            type: wer
            value: 19.67
          - name: Test CER
            type: cer
            value: 8.94

This model is a fine-tuned version of KBLab/wav2vec2-large-voxrex on the MOZILLA-FOUNDATION/COMMON_VOICE_8_0 - SV-SE dataset. It achieves the following results on the evaluation set:

Loss: 0.1595
Wer: 0.1200

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: 0.00025
train_batch_size: 32
eval_batch_size: 32
seed: 42
gradient_accumulation_steps: 4
total_train_batch_size: 128
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_ratio: 0.25
num_epochs: 100.0
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Wer
3.0418	5.49	500	3.0176	1.0
1.1819	10.98	1000	0.2562	0.2168
1.0032	16.48	1500	0.1746	0.1546
0.9077	21.97	2000	0.1600	0.1339
0.8687	27.47	2500	0.1647	0.1378
0.8081	32.96	3000	0.1608	0.1353
0.7923	38.46	3500	0.1534	0.1277
0.7349	43.95	4000	0.1546	0.1303
0.7199	49.45	4500	0.1617	0.1277
0.7028	54.94	5000	0.1572	0.1287
0.6912	60.44	5500	0.1560	0.1249
0.6492	65.93	6000	0.1542	0.1260
0.6407	71.43	6500	0.1605	0.1240
0.6222	76.92	7000	0.1577	0.1219
0.6039	82.42	7500	0.1645	0.1249
0.5928	87.91	8000	0.1590	0.1214
0.6022	93.4	8500	0.1597	0.1213
0.5814	98.9	9000	0.1599	0.1199

Framework versions

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