
# 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, the [tv3_parla](https://huggingface.co/datasets/collectivat/tv3_parla) and [parlament_parla](https://huggingface.co/datasets/projecte-aina/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](https://huggingface.co/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](https://github.com/ccoreilly), which can be found on the text/ folder or [here](https://github.com/CollectivaT-dev/catotron-cpu/blob/master/text/numbers_ca.py).

### 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](https://github.com/ccoreilly) and [@gullabi](https://github.com/gullabi) who have contributed with their own resources and knowledge into making this model possible.

{"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": {"type": "automatic-speech-recognition", "name": "Speech Recognition"}, "dataset": {"name": "mozilla-foundation/common_voice_8_0 ca", "type": "mozilla-foundation/common_voice_8_0", "args": "ca"}, "metrics": [{"type": "wer", "value": 13.170091241317552, "name": "Test WER"}, {"type": "cer", "value": 3.356726205534543, "name": "Test CER"}]}, {"task": {"type": "automatic-speech-recognition", "name": "Speech Recognition"}, "dataset": {"name": "projecte-aina/parlament_parla ca", "type": "projecte-aina/parlament_parla", "args": "clean"}, "metrics": [{"type": "wer", "value": 8.048005647723262, "name": "Test WER"}, {"type": "cer", "value": 2.240912911020065, "name": "Test CER"}]}, {"task": {"type": "automatic-speech-recognition", "name": "Speech Recognition"}, "dataset": {"name": "collectivat/tv3_parla ca", "type": "collectivat/tv3_parla", "args": "ca"}, "metrics": [{"type": "wer", "value": 23.320629787889285, "name": "Test WER"}, {"type": "cer", "value": 10.43921620208999, "name": "Test CER"}]}, {"task": {"type": "automatic-speech-recognition", "name": "Speech Recognition"}, "dataset": {"name": "speech-recognition-community-v2/dev_data ca", "type": "speech-recognition-community-v2/dev_data", "args": "ca"}, "metrics": [{"type": "wer", "value": 31.99671115046487, "name": "Test WER"}, {"type": "cer", "value": 15.820020687277324, "name": "Test CER"}]}, {"task": {"type": "automatic-speech-recognition", "name": "Automatic Speech Recognition"}, "dataset": {"name": "Robust Speech Event - Test Data", "type": "speech-recognition-community-v2/eval_data", "args": "ca"}, "metrics": [{"type": "wer", "value": 22.04, "name": "Test WER"}]}]}]}
transformers pytorch tensorboard wav2vec2 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 ca dataset:mozilla-foundation/common_voice_8_0 dataset:collectivat/tv3_parla dataset:projecte-aina/parlament_parla license:apache-2.0 model-index endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #wav2vec2 #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 #ca #dataset-mozilla-foundation/common_voice_8_0 #dataset-collectivat/tv3_parla #dataset-projecte-aina/parlament_parla #license-apache-2.0 #model-index #endpoints_compatible #region-us 

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.



### 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.



### 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. ### 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.
TAGS
#transformers #pytorch #tensorboard #wav2vec2 #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 #ca #dataset-mozilla-foundation/common_voice_8_0 #dataset-collectivat/tv3_parla #dataset-projecte-aina/parlament_parla #license-apache-2.0 #model-index #endpoints_compatible #region-us 
 ### 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. ### 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.
passage: TAGS
#transformers #pytorch #tensorboard #wav2vec2 #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 #ca #dataset-mozilla-foundation/common_voice_8_0 #dataset-collectivat/tv3_parla #dataset-projecte-aina/parlament_parla #license-apache-2.0 #model-index #endpoints_compatible #region-us 
### 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.### 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.