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--- |
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language: |
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- ca |
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license: apache-2.0 |
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tags: |
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- automatic-speech-recognition |
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- collectivat/tv3_parla |
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- generated_from_trainer |
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- hf-asr-leaderboard |
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- mozilla-foundation/common_voice_8_0 |
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- projecte-aina/parlament_parla |
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- robust-speech-event |
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datasets: |
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- mozilla-foundation/common_voice_8_0 |
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- collectivat/tv3_parla |
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- projecte-aina/parlament_parla |
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model-index: |
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- name: wav2vec2-xls-r-1b-ca |
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results: |
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- task: |
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name: Speech Recognition |
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type: automatic-speech-recognition |
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dataset: |
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name: mozilla-foundation/common_voice_8_0 ca |
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type: mozilla-foundation/common_voice_8_0 |
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args: ca |
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metrics: |
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- name: Test WER |
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type: wer |
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value: 11.030639657300516 |
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- name: Test CER |
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type: cer |
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value: 2.8405630530040634 |
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- task: |
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name: Speech Recognition |
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type: automatic-speech-recognition |
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dataset: |
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name: projecte-aina/parlament_parla ca |
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type: projecte-aina/parlament_parla |
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args: clean |
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metrics: |
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- name: Test WER |
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type: wer |
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value: 6.483115660665961 |
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- name: Test CER |
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type: cer |
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value: 2.0212863746191828 |
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- task: |
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name: Speech Recognition |
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type: automatic-speech-recognition |
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dataset: |
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name: collectivat/tv3_parla ca |
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type: collectivat/tv3_parla |
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args: ca |
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metrics: |
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- name: Test WER |
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type: wer |
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value: 17.917773414943988 |
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- name: Test CER |
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type: cer |
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value: 8.872589572206396 |
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- task: |
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name: Speech Recognition |
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type: automatic-speech-recognition |
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dataset: |
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name: Robust Speech Event - Catalan Dev Data |
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type: speech-recognition-community-v2/dev_data |
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args: ca |
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metrics: |
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- name: Test WER |
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type: wer |
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value: 27.126683954209097 |
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- name: Test CER |
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type: cer |
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value: 14.213308815078726 |
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- task: |
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name: Automatic Speech Recognition |
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type: automatic-speech-recognition |
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dataset: |
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name: Robust Speech Event - Test Data |
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type: speech-recognition-community-v2/eval_data |
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args: ca |
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metrics: |
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- name: Test WER |
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type: wer |
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value: 18.7 |
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--- |
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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-1b-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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## 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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### Training results |
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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 hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 2e-05 |
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- train_batch_size: 8 |
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- eval_batch_size: 8 |
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- seed: 42 |
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- gradient_accumulation_steps: 8 |
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- total_train_batch_size: 64 |
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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: 10.0 |
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- mixed_precision_training: Native AMP |
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### Framework versions |
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- Transformers 4.17.0.dev0 |
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- Pytorch 1.10.2+cu102 |
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- Datasets 1.18.3 |
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- Tokenizers 0.11.0 |
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# Thanks |
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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. |
