metadata

language:
  - ca
license: apache-2.0
tags:
  - automatic-speech-recognition
  - mozilla-foundation/common_voice_8_0
  - collectivat/tv3_parla
  - projecte-aina/parlament_parla
  - generated_from_trainer
  - robust-speech-event
datasets:
  - mozilla-foundation/common_voice_8_0
  - collectivat/tv3_parla
  - projecte-aina/parlament_parla
model-index:
  - name: wav2vec2-xls-r-300m-ca
    results:
      - task:
          name: Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: mozilla-foundation/common_voice_8_0 ca
          type: mozilla-foundation/common_voice_8_0
          args: ca
        metrics:
          - name: Test WER
            type: wer
            value: 0.1522665117742443
          - name: Test CER
            type: cer
            value: 0.04078709154868726
      - task:
          name: Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: projecte-aina/parlament_parla ca
          type: projecte-aina/parlament_parla
          args: clean
        metrics:
          - name: Test WER
            type: wer
            value: 0.06541946111307212
          - name: Test CER
            type: cer
            value: 0.02205785796827398
      - task:
          name: Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: collectivat/tv3_parla ca
          type: collectivat/tv3_parla
          args: ca
        metrics:
          - name: Test WER
            type: wer
            value: 0.24485121453593564
          - name: Test CER
            type: cer
            value: 0.10753510718204506
      - task:
          name: Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: Robust Speech Event - Catalan Dev Data
          type: speech-recognition-community-v2/dev_data
          args: ca
        metrics:
          - name: Test WER
            type: wer
            value: 0.3325532856871798
          - name: Test CER
            type: cer
            value: 0.15916561314791403

wav2vec2-xls-r-300m-ca

This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the MOZILLA-FOUNDATION/COMMON_VOICE_8_0 - CA dataset. It achieves the following results on the evaluation set:

Loss: 0.2549
Wer: 0.1573

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: 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_steps: 2000
num_epochs: 12.0
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Wer
6.2099	0.09	500	3.4125	1.0
2.9961	0.18	1000	2.9224	1.0
2.2147	0.26	1500	0.6521	0.5568
1.3017	0.35	2000	0.3153	0.2761
1.1196	0.44	2500	0.2444	0.2367
1.0712	0.53	3000	0.2324	0.2132
1.052	0.62	3500	0.2173	0.2032
1.2813	2.13	4000	0.3326	0.2099
1.2365	2.4	4500	0.3224	0.2003
1.2193	2.66	5000	0.3198	0.1957
1.2072	2.93	5500	0.3063	0.1933
1.213	3.2	6000	0.3051	0.1980
1.2074	3.46	6500	0.3012	0.1879
1.1918	3.73	7000	0.2947	0.1829
1.1893	4.0	7500	0.2895	0.1807
1.1751	4.26	8000	0.2878	0.1776
1.1628	4.53	8500	0.2835	0.1731
1.1577	4.79	9000	0.2816	0.1761
1.1448	5.06	9500	0.2757	0.1740
1.1407	5.33	10000	0.2768	0.1798
1.1401	5.59	10500	0.2780	0.1816
1.1333	5.86	11000	0.2748	0.1750
1.1571	6.13	11500	0.2808	0.1708
1.1505	6.39	12000	0.2726	0.1692
1.1519	6.66	12500	0.2749	0.1654
1.136	6.93	13000	0.2765	0.1643
1.1326	7.19	13500	0.2706	0.1668
1.1342	7.46	14000	0.2665	0.1638
1.1286	7.72	14500	0.2669	0.1636
1.1243	7.99	15000	0.2619	0.1623
1.1173	8.26	15500	0.2652	0.1604
1.1129	8.52	16000	0.2610	0.1598
1.1091	8.79	16500	0.2608	0.1584
1.1053	9.06	17000	0.2633	0.1664
1.1004	9.32	17500	0.2594	0.1662
1.0995	9.59	18000	0.2623	0.1569
1.0964	9.86	18500	0.2624	0.1597
1.09	10.12	19000	0.2577	0.1578
1.089	10.39	19500	0.2574	0.1531
1.0864	10.66	20000	0.2556	0.1546
1.0806	10.92	20500	0.2548	0.1583
1.0842	11.19	21000	0.2550	0.1542
1.0805	11.45	21500	0.2561	0.1524
1.0722	11.72	22000	0.2540	0.1566
1.0763	11.99	22500	0.2549	0.1572

Framework versions

Transformers 4.16.0.dev0
Pytorch 1.10.1+cu102
Datasets 1.18.1
Tokenizers 0.11.0