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--- |
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language: |
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- sv |
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tags: |
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- automatic-speech-recognition |
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- common_voice |
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- generated_from_trainer |
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datasets: |
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- common_voice |
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model-index: |
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- name: wav2vec2-xls-r-phoneme-300m-sv |
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results: [] |
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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-phoneme-300m-sv |
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**Note**: The tokenizer was created from the official Swedish phoneme vocabulary as defined here: https://github.com/microsoft/UniSpeech/blob/main/UniSpeech/examples/unispeech/data/sv/phonesMatches_reduced.json |
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One can simply download the file, rename it to `vocab.json` and load a `Wav2Vec2PhonemeCTCTokenizer.from_pretrained("./directory/with/vocab.json/")`. |
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This model is a fine-tuned version of [wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the COMMON_VOICE - SV-SE dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.9707 |
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- PER: 0.2215 |
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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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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: |
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- learning_rate: 0.0005 |
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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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- distributed_type: multi-GPU |
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- num_devices: 2 |
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- total_train_batch_size: 32 |
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- total_eval_batch_size: 16 |
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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: 500 |
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- num_epochs: 20.0 |
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- mixed_precision_training: Native AMP |
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### Training results |
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See Tensorboard traces |
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### Framework versions |
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- Transformers 4.13.0.dev0 |
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- Pytorch 1.8.1 |
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- Datasets 1.16.2.dev0 |
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- Tokenizers 0.10.3 |
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