---
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-lm
  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: 0.08108860330598514
       - name: Test CER
         type: cer
         value: 0.027241712812152218
  - 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: 0.1506717480848443
       - name: Test CER
         type: cer
         value: 0.09562445266717665
---

<!-- 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-300m-ca

This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the MOZILLA-FOUNDATION/COMMON_VOICE_8_0 - CA dataset.
It achieves the following results on the averaged across datasets test set:
- Loss: 0.2758
- Wer: 0.1792

## 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: 6.0
- mixed_precision_training: Native AMP

### Training results (without LM)

| 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 |


### Framework versions

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