Wav2vec2-xls-r-phoneme-300m-sv
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
One can simply download the file, rename it to vocab.json
and load a Wav2Vec2PhonemeCTCTokenizer.from_pretrained("./directory/with/vocab.json/")
.
This model is a fine-tuned version of wav2vec2-xls-r-300m on the COMMON_VOICE - SV-SE dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9707
- PER: 0.2215
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0005
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 2
- total_train_batch_size: 32
- total_eval_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 20.0
- mixed_precision_training: Native AMP
Training results
See Tensorboard traces
Framework versions
- Transformers 4.13.0.dev0
- Pytorch 1.8.1
- Datasets 1.16.2.dev0
- Tokenizers 0.10.3
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