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wav2vec2-large-xls-r-1b-frisian

Automatic Speech Recognition
Transformers
PyTorch
wav2vec2
Generated from Trainer
Eval Results
Inference Endpoints
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+ ---
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+ license: apache-2.0
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+ tags:
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+ - generated_from_trainer
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+ datasets:
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+ - common_voice_13_0
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+ metrics:
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+ - wer
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+ model-index:
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+ - name: wav2vec2-large-xls-r-1b-frisian
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+ results:
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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: common_voice_13_0
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+ type: common_voice_13_0
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+ config: fy-NL
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+ split: validation
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+ args: fy-NL
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+ metrics:
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+ - name: Wer
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+ type: wer
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+ value: 0.15077102723494865
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+ ---
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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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+
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+ # wav2vec2-large-xls-r-1b-frisian
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+
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+ This model is a fine-tuned version of [facebook/wav2vec2-xls-r-1b](https://huggingface.co/facebook/wav2vec2-xls-r-1b) on the common_voice_13_0 dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 0.2206
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+ - Wer: 0.1508
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+
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+ ## Model description
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+
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+ More information needed
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+
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+ ## Intended uses & limitations
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+
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+ More information needed
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+
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+ ## Training and evaluation data
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+
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+ More information needed
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+
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+ ## Training procedure
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+
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+ ### Training hyperparameters
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+
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+ The following hyperparameters were used during training:
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+ - learning_rate: 7e-05
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+ - train_batch_size: 16
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+ - eval_batch_size: 8
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+ - seed: 42
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+ - gradient_accumulation_steps: 2
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+ - total_train_batch_size: 32
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+ - optimizer: Adam with betas=(0.9,0.98) and epsilon=1e-08
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+ - lr_scheduler_type: linear
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+ - lr_scheduler_warmup_ratio: 0.1
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+ - num_epochs: 60
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+ - mixed_precision_training: Native AMP
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+
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+ ### Training results
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+
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+ | Training Loss | Epoch | Step | Validation Loss | Wer |
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+ |:-------------:|:-----:|:----:|:---------------:|:------:|
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+ | 4.9606 | 2.45 | 300 | 2.6184 | 1.0 |
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+ | 1.4992 | 4.9 | 600 | 0.4233 | 0.4143 |
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+ | 0.9757 | 7.35 | 900 | 0.2765 | 0.3021 |
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+ | 0.8773 | 9.8 | 1200 | 0.2529 | 0.2528 |
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+ | 0.7448 | 12.24 | 1500 | 0.2363 | 0.2258 |
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+ | 0.7039 | 14.69 | 1800 | 0.2258 | 0.2103 |
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+ | 0.6811 | 17.14 | 2100 | 0.2217 | 0.2074 |
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+ | 0.6279 | 19.59 | 2400 | 0.2050 | 0.1915 |
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+ | 0.5938 | 22.04 | 2700 | 0.2229 | 0.1922 |
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+ | 0.6227 | 24.49 | 3000 | 0.2088 | 0.2019 |
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+ | 0.5682 | 26.94 | 3300 | 0.2127 | 0.1874 |
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+ | 0.5939 | 29.39 | 3600 | 0.2044 | 0.1789 |
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+ | 0.5427 | 31.84 | 3900 | 0.2185 | 0.1791 |
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+ | 0.5551 | 34.41 | 4200 | 0.2097 | 0.1644 |
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+ | 0.5021 | 36.86 | 4500 | 0.2180 | 0.1678 |
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+ | 0.4589 | 39.31 | 4800 | 0.2076 | 0.1581 |
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+ | 0.5204 | 41.76 | 5100 | 0.2181 | 0.1587 |
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+ | 0.512 | 44.21 | 5400 | 0.2263 | 0.1607 |
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+ | 0.465 | 46.66 | 5700 | 0.2204 | 0.1493 |
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+ | 0.4482 | 49.11 | 6000 | 0.2143 | 0.1527 |
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+ | 0.3972 | 51.63 | 6300 | 0.2198 | 0.1617 |
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+ | 0.3168 | 54.09 | 6600 | 0.2170 | 0.1528 |
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+ | 0.2432 | 56.53 | 6900 | 0.2182 | 0.1529 |
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+ | 0.252 | 58.98 | 7200 | 0.2206 | 0.1508 |
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+
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+
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+ ### Framework versions
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+
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+ - Transformers 4.28.1
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+ - Pytorch 2.0.0+cu117
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+ - Datasets 2.11.0
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+ - Tokenizers 0.13.3