metadata

language:
  - ca
license: apache-2.0
tags:
  - automatic-speech-recognition
  - collectivat/tv3_parla
  - generated_from_trainer
  - hf-asr-leaderboard
  - mozilla-foundation/common_voice_8_0
  - projecte-aina/parlament_parla
  - 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: 13.170091241317552
          - name: Test CER
            type: cer
            value: 3.356726205534543
      - 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: 8.048005647723262
          - name: Test CER
            type: cer
            value: 2.240912911020065
      - 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: 23.320629787889285
          - name: Test CER
            type: cer
            value: 10.43921620208999
      - task:
          name: Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: speech-recognition-community-v2/dev_data ca
          type: speech-recognition-community-v2/dev_data
          args: ca
        metrics:
          - name: Test WER
            type: wer
            value: 31.99671115046487
          - name: Test CER
            type: cer
            value: 15.820020687277324
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: Robust Speech Event - Test Data
          type: speech-recognition-community-v2/eval_data
          args: ca
        metrics:
          - name: Test WER
            type: wer
            value: 22.04

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, the tv3_parla and parlament_parla datasets. It achieves the following results on the evaluation set (for the three datasets):

Loss: 0.2472
Wer: 0.1499

Model description

Please check the original facebook/wav2vec2-xls-r-1b Model card. This is just a finetuned version of that model.

Intended uses & limitations

As any model trained on crowdsourced data, this model can show the biases and particularities of the data and model used to train this model. Moreover, since this is a speech recognition model, it may underperform for some lower-resourced dialects for the catalan language.

Training and evaluation data

More information needed

Training procedure

The data is preprocessed to remove characters not on the catalan alphabet. Moreover, numbers are verbalized using code provided by @ccoreilly, which can be found on the text/ folder or here.

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: 18.0
mixed_precision_training: Native AMP

Training results

Check the Tensorboard tab to check the training profile and evaluation results along training. The model was evaluated on the test splits for each of the datasets used during training.

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
1.0835	12.25	23000	0.2586	0.1521
1.0883	12.52	23500	0.2583	0.1519
1.0888	12.79	24000	0.2551	0.1582
1.0933	13.05	24500	0.2628	0.1537
1.0799	13.32	25000	0.2600	0.1508
1.0804	13.59	25500	0.2620	0.1475
1.0814	13.85	26000	0.2537	0.1517
1.0693	14.12	26500	0.2560	0.1542
1.0724	14.38	27000	0.2540	0.1574
1.0704	14.65	27500	0.2548	0.1626
1.0729	14.92	28000	0.2548	0.1601
1.0724	15.18	28500	0.2511	0.1512
1.0655	15.45	29000	0.2498	0.1490
1.0608	15.98	30000	0.2487	0.1481
1.0541	16.52	31000	0.2468	0.1504
1.0584	17.05	32000	0.2467	0.1493
1.0507	17.58	33000	0.2481	0.1517

Framework versions

Transformers 4.16.0.dev0
Pytorch 1.10.1+cu102
Datasets 1.18.3
Tokenizers 0.11.0

Thanks

Want to thank both @ccoreilly and @gullabi who have contributed with their own resources and knowledge into making this model possible.