PereLluis13 commited on
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update model

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README.md CHANGED
@@ -77,20 +77,20 @@ model-index:
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  <!-- This model card has been generated automatically according to the information the Trainer had access to. You
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  should probably proofread and complete it, then remove this comment. -->
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- # wav2vec2-xls-r-300m-ca-lm
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- 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.
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- It achieves the following results on the averaged across datasets test set (without the LM):
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- - Loss: 0.2758
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- - Wer: 0.1792
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  ## Model description
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- More information needed
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  ## Intended uses & limitations
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- More information needed
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  ## Training and evaluation data
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@@ -98,6 +98,8 @@ More information needed
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  ## Training procedure
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  ### Training hyperparameters
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  The following hyperparameters were used during training:
@@ -110,10 +112,12 @@ The following hyperparameters were used during training:
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  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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  - lr_scheduler_type: linear
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  - lr_scheduler_warmup_steps: 2000
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- - num_epochs: 6.0
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  - mixed_precision_training: Native AMP
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- ### Training results (without LM)
 
 
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  | Training Loss | Epoch | Step | Validation Loss | Wer |
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  |:-------------:|:-----:|:-----:|:---------------:|:------:|
@@ -162,10 +166,32 @@ The following hyperparameters were used during training:
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  | 1.0805 | 11.45 | 21500 | 0.2561 | 0.1524 |
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  | 1.0722 | 11.72 | 22000 | 0.2540 | 0.1566 |
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  | 1.0763 | 11.99 | 22500 | 0.2549 | 0.1572 |
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ### Framework versions
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  - Transformers 4.16.0.dev0
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  - Pytorch 1.10.1+cu102
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- - Datasets 1.18.1
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  - Tokenizers 0.11.0
 
 
 
 
 
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  <!-- This model card has been generated automatically according to the information the Trainer had access to. You
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  should probably proofread and complete it, then remove this comment. -->
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+ # wav2vec2-xls-r-300m-ca
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+ 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.
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+ It achieves the following results on the evaluation set (for the three datasets and without the LM):
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+ - Loss: 0.2472
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+ - Wer: 0.1499
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  ## Model description
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+ 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.
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  ## Intended uses & limitations
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+ 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.
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  ## Training and evaluation data
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  ## Training procedure
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+ 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).
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+
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  ### Training hyperparameters
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  The following hyperparameters were used during training:
 
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  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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  - lr_scheduler_type: linear
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  - lr_scheduler_warmup_steps: 2000
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+ - num_epochs: 18.0
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  - mixed_precision_training: Native AMP
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+ ### Training results
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+
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+ 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.
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  | Training Loss | Epoch | Step | Validation Loss | Wer |
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  |:-------------:|:-----:|:-----:|:---------------:|:------:|
 
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  | 1.0805 | 11.45 | 21500 | 0.2561 | 0.1524 |
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  | 1.0722 | 11.72 | 22000 | 0.2540 | 0.1566 |
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  | 1.0763 | 11.99 | 22500 | 0.2549 | 0.1572 |
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+ | 1.0835 | 12.25 | 23000 | 0.2586 | 0.1521 |
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+ | 1.0883 | 12.52 | 23500 | 0.2583 | 0.1519 |
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+ | 1.0888 | 12.79 | 24000 | 0.2551 | 0.1582 |
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+ | 1.0933 | 13.05 | 24500 | 0.2628 | 0.1537 |
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+ | 1.0799 | 13.32 | 25000 | 0.2600 | 0.1508 |
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+ | 1.0804 | 13.59 | 25500 | 0.2620 | 0.1475 |
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+ | 1.0814 | 13.85 | 26000 | 0.2537 | 0.1517 |
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+ | 1.0693 | 14.12 | 26500 | 0.2560 | 0.1542 |
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+ | 1.0724 | 14.38 | 27000 | 0.2540 | 0.1574 |
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+ | 1.0704 | 14.65 | 27500 | 0.2548 | 0.1626 |
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+ | 1.0729 | 14.92 | 28000 | 0.2548 | 0.1601 |
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+ | 1.0724 | 15.18 | 28500 | 0.2511 | 0.1512 |
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+ | 1.0655 | 15.45 | 29000 | 0.2498 | 0.1490 |
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+ | 1.0608 | 15.98 | 30000 | 0.2487 | 0.1481 |
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+ | 1.0541 | 16.52 | 31000 | 0.2468 | 0.1504 |
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+ | 1.0584 | 17.05 | 32000 | 0.2467 | 0.1493 |
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+ | 1.0507 | 17.58 | 33000 | 0.2481 | 0.1517 |
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+
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  ### Framework versions
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  - Transformers 4.16.0.dev0
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  - Pytorch 1.10.1+cu102
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+ - Datasets 1.18.3
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  - Tokenizers 0.11.0
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+
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+ # Thanks
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+
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+ 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.
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