pytorch tensorboard wav2vec2 automatic-speech-recognition gn dataset:mozilla-foundation/common_voice_7_0 transformers generated_from_trainer robust-speech-event hf-asr-leaderboard license:apache-2.0 model-index
---
language:
- gn
license: apache-2.0
tags:
- automatic-speech-recognition
- generated_from_trainer
- gn
- robust-speech-event
- hf-asr-leaderboard
datasets:
- mozilla-foundation/common_voice_7_0
model-index:
- name: wav2vec2-xls-r-gn-cv7
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: Common Voice 7
      type: mozilla-foundation/common_voice_7_0
      args: pt
    metrics:
    - name: Validation WER
      type: wer
      value: 73.02
    - name: Validation CER
      type: cer
      value: 17.79
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: Common Voice 7.0
      type: mozilla-foundation/common_voice_7_0
      args: gn
    metrics:
    - name: Test WER
      type: wer
      value: 62.65
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# wav2vec2-xls-r-gn-cv7

This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the common_voice dataset.
It achieves the following results on the evaluation set:
- Loss: 1.7197
- Wer: 0.7434

## 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: 0.0001
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 100
- training_steps: 13000
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch  | Step  | Validation Loss | Wer    |
|:-------------:|:------:|:-----:|:---------------:|:------:|
| 3.4669        | 6.24   | 100   | 3.3003          | 1.0    |
| 3.3214        | 12.48  | 200   | 3.2090          | 1.0    |
| 3.1619        | 18.73  | 300   | 2.6322          | 1.0    |
| 1.751         | 24.97  | 400   | 1.4089          | 0.9803 |
| 0.7997        | 31.24  | 500   | 0.9996          | 0.9211 |
| 0.4996        | 37.48  | 600   | 0.9879          | 0.8553 |
| 0.3677        | 43.73  | 700   | 0.9543          | 0.8289 |
| 0.2851        | 49.97  | 800   | 1.0627          | 0.8487 |
| 0.2556        | 56.24  | 900   | 1.0933          | 0.8355 |
| 0.2268        | 62.48  | 1000  | 0.9191          | 0.8026 |
| 0.1914        | 68.73  | 1100  | 0.9582          | 0.7961 |
| 0.1749        | 74.97  | 1200  | 1.0502          | 0.8092 |
| 0.157         | 81.24  | 1300  | 0.9998          | 0.7632 |
| 0.1505        | 87.48  | 1400  | 1.0076          | 0.7303 |
| 0.1278        | 93.73  | 1500  | 0.9321          | 0.75   |
| 0.1078        | 99.97  | 1600  | 1.0383          | 0.7697 |
| 0.1156        | 106.24 | 1700  | 1.0302          | 0.7763 |
| 0.1107        | 112.48 | 1800  | 1.0419          | 0.7763 |
| 0.091         | 118.73 | 1900  | 1.0694          | 0.75   |
| 0.0829        | 124.97 | 2000  | 1.0257          | 0.7829 |
| 0.0865        | 131.24 | 2100  | 1.2108          | 0.7368 |
| 0.0907        | 137.48 | 2200  | 1.0458          | 0.7697 |
| 0.0897        | 143.73 | 2300  | 1.1504          | 0.7895 |
| 0.0766        | 149.97 | 2400  | 1.1663          | 0.7237 |
| 0.0659        | 156.24 | 2500  | 1.1320          | 0.7632 |
| 0.0699        | 162.48 | 2600  | 1.2586          | 0.7434 |
| 0.0613        | 168.73 | 2700  | 1.1815          | 0.8158 |
| 0.0598        | 174.97 | 2800  | 1.3299          | 0.75   |
| 0.0577        | 181.24 | 2900  | 1.2035          | 0.7171 |
| 0.0576        | 187.48 | 3000  | 1.2134          | 0.7434 |
| 0.0518        | 193.73 | 3100  | 1.3406          | 0.7566 |
| 0.0524        | 199.97 | 3200  | 1.4251          | 0.75   |
| 0.0467        | 206.24 | 3300  | 1.3533          | 0.7697 |
| 0.0428        | 212.48 | 3400  | 1.2463          | 0.7368 |
| 0.0453        | 218.73 | 3500  | 1.4532          | 0.7566 |
| 0.0473        | 224.97 | 3600  | 1.3152          | 0.7434 |
| 0.0451        | 231.24 | 3700  | 1.2232          | 0.7368 |
| 0.0361        | 237.48 | 3800  | 1.2938          | 0.7171 |
| 0.045         | 243.73 | 3900  | 1.4148          | 0.7434 |
| 0.0422        | 249.97 | 4000  | 1.3786          | 0.7961 |
| 0.036         | 256.24 | 4100  | 1.4488          | 0.7697 |
| 0.0352        | 262.48 | 4200  | 1.2294          | 0.6776 |
| 0.0326        | 268.73 | 4300  | 1.2796          | 0.6974 |
| 0.034         | 274.97 | 4400  | 1.3805          | 0.7303 |
| 0.0305        | 281.24 | 4500  | 1.4994          | 0.7237 |
| 0.0325        | 287.48 | 4600  | 1.4330          | 0.6908 |
| 0.0338        | 293.73 | 4700  | 1.3091          | 0.7368 |
| 0.0306        | 299.97 | 4800  | 1.2174          | 0.7171 |
| 0.0299        | 306.24 | 4900  | 1.3527          | 0.7763 |
| 0.0287        | 312.48 | 5000  | 1.3651          | 0.7368 |
| 0.0274        | 318.73 | 5100  | 1.4337          | 0.7368 |
| 0.0258        | 324.97 | 5200  | 1.3831          | 0.6908 |
| 0.022         | 331.24 | 5300  | 1.3556          | 0.6974 |
| 0.021         | 337.48 | 5400  | 1.3836          | 0.7237 |
| 0.0241        | 343.73 | 5500  | 1.4352          | 0.7039 |
| 0.0229        | 349.97 | 5600  | 1.3904          | 0.7105 |
| 0.026         | 356.24 | 5700  | 1.4131          | 0.7171 |
| 0.021         | 362.48 | 5800  | 1.5426          | 0.6974 |
| 0.0191        | 368.73 | 5900  | 1.5960          | 0.7632 |
| 0.0227        | 374.97 | 6000  | 1.6240          | 0.7368 |
| 0.0204        | 381.24 | 6100  | 1.4301          | 0.7105 |
| 0.0175        | 387.48 | 6200  | 1.5554          | 0.75   |
| 0.0183        | 393.73 | 6300  | 1.6044          | 0.7697 |
| 0.0183        | 399.97 | 6400  | 1.5963          | 0.7368 |
| 0.016         | 406.24 | 6500  | 1.5679          | 0.7829 |
| 0.0178        | 412.48 | 6600  | 1.5928          | 0.7697 |
| 0.014         | 418.73 | 6700  | 1.7000          | 0.7632 |
| 0.0182        | 424.97 | 6800  | 1.5340          | 0.75   |
| 0.0148        | 431.24 | 6900  | 1.9274          | 0.7368 |
| 0.0148        | 437.48 | 7000  | 1.6437          | 0.7697 |
| 0.0173        | 443.73 | 7100  | 1.5468          | 0.75   |
| 0.0109        | 449.97 | 7200  | 1.6083          | 0.75   |
| 0.0167        | 456.24 | 7300  | 1.6732          | 0.75   |
| 0.0139        | 462.48 | 7400  | 1.5097          | 0.7237 |
| 0.013         | 468.73 | 7500  | 1.5947          | 0.7171 |
| 0.0128        | 474.97 | 7600  | 1.6260          | 0.7105 |
| 0.0166        | 481.24 | 7700  | 1.5756          | 0.7237 |
| 0.0127        | 487.48 | 7800  | 1.4506          | 0.6908 |
| 0.013         | 493.73 | 7900  | 1.4882          | 0.7368 |
| 0.0125        | 499.97 | 8000  | 1.5589          | 0.7829 |
| 0.0141        | 506.24 | 8100  | 1.6328          | 0.7434 |
| 0.0115        | 512.48 | 8200  | 1.6586          | 0.7434 |
| 0.0117        | 518.73 | 8300  | 1.6043          | 0.7105 |
| 0.009         | 524.97 | 8400  | 1.6508          | 0.7237 |
| 0.0108        | 531.24 | 8500  | 1.4507          | 0.6974 |
| 0.011         | 537.48 | 8600  | 1.5942          | 0.7434 |
| 0.009         | 543.73 | 8700  | 1.8121          | 0.7697 |
| 0.0112        | 549.97 | 8800  | 1.6923          | 0.7697 |
| 0.0073        | 556.24 | 8900  | 1.7096          | 0.7368 |
| 0.0098        | 562.48 | 9000  | 1.7052          | 0.7829 |
| 0.0088        | 568.73 | 9100  | 1.6956          | 0.7566 |
| 0.0099        | 574.97 | 9200  | 1.4909          | 0.7171 |
| 0.0075        | 581.24 | 9300  | 1.6307          | 0.7697 |
| 0.0077        | 587.48 | 9400  | 1.6196          | 0.7961 |
| 0.0088        | 593.73 | 9500  | 1.6119          | 0.7566 |
| 0.0085        | 599.97 | 9600  | 1.4512          | 0.7368 |
| 0.0086        | 606.24 | 9700  | 1.5992          | 0.7237 |
| 0.0109        | 612.48 | 9800  | 1.4706          | 0.7368 |
| 0.0098        | 618.73 | 9900  | 1.3824          | 0.7171 |
| 0.0091        | 624.97 | 10000 | 1.4776          | 0.6974 |
| 0.0072        | 631.24 | 10100 | 1.4896          | 0.7039 |
| 0.0087        | 637.48 | 10200 | 1.5467          | 0.7368 |
| 0.007         | 643.73 | 10300 | 1.5493          | 0.75   |
| 0.0076        | 649.97 | 10400 | 1.5706          | 0.7303 |
| 0.0085        | 656.24 | 10500 | 1.5748          | 0.7237 |
| 0.0075        | 662.48 | 10600 | 1.5081          | 0.7105 |
| 0.0068        | 668.73 | 10700 | 1.4967          | 0.6842 |
| 0.0117        | 674.97 | 10800 | 1.4986          | 0.7105 |
| 0.0054        | 681.24 | 10900 | 1.5587          | 0.7303 |
| 0.0059        | 687.48 | 11000 | 1.5886          | 0.7171 |
| 0.0071        | 693.73 | 11100 | 1.5746          | 0.7171 |
| 0.0048        | 699.97 | 11200 | 1.6166          | 0.7237 |
| 0.0048        | 706.24 | 11300 | 1.6098          | 0.7237 |
| 0.0056        | 712.48 | 11400 | 1.5834          | 0.7237 |
| 0.0048        | 718.73 | 11500 | 1.5653          | 0.7171 |
| 0.0045        | 724.97 | 11600 | 1.6252          | 0.7237 |
| 0.0068        | 731.24 | 11700 | 1.6794          | 0.7171 |
| 0.0044        | 737.48 | 11800 | 1.6881          | 0.7039 |
| 0.008         | 743.73 | 11900 | 1.7393          | 0.75   |
| 0.0045        | 749.97 | 12000 | 1.6869          | 0.7237 |
| 0.0047        | 756.24 | 12100 | 1.7105          | 0.7303 |
| 0.0057        | 762.48 | 12200 | 1.7439          | 0.7303 |
| 0.004         | 768.73 | 12300 | 1.7871          | 0.7434 |
| 0.0061        | 774.97 | 12400 | 1.7812          | 0.7303 |
| 0.005         | 781.24 | 12500 | 1.7410          | 0.7434 |
| 0.0056        | 787.48 | 12600 | 1.7220          | 0.7303 |
| 0.0064        | 793.73 | 12700 | 1.7141          | 0.7434 |
| 0.0042        | 799.97 | 12800 | 1.7139          | 0.7368 |
| 0.0049        | 806.24 | 12900 | 1.7211          | 0.7434 |
| 0.0044        | 812.48 | 13000 | 1.7197          | 0.7434 |


### Framework versions

- Transformers 4.15.0
- Pytorch 1.10.0+cu111
- Datasets 1.18.0
- Tokenizers 0.10.3
