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# 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](https://huggingface.co/facebook/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

{"language": ["sv"], "tags": ["automatic-speech-recognition", "common_voice", "generated_from_trainer"], "datasets": ["common_voice"], "model-index": [{"name": "wav2vec2-xls-r-phoneme-300m-sv", "results": []}]}
transformers pytorch tensorboard wav2vec2 automatic-speech-recognition common_voice generated_from_trainer sv dataset:common_voice endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #common_voice #generated_from_trainer #sv #dataset-common_voice #endpoints_compatible #region-us 




# Wav2vec2-xls-r-phoneme-300m-sv

Note: The tokenizer was created from the official Swedish phoneme vocabulary as defined here: URL

One can simply download the file, rename it to 'URL' and load a 'Wav2Vec2PhonemeCTCTokenizer.from_pretrained("./directory/with/URL

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

# Wav2vec2-xls-r-phoneme-300m-sv

Note: The tokenizer was created from the official Swedish phoneme vocabulary as defined here: URL

One can simply download the file, rename it to 'URL' and load a 'Wav2Vec2PhonemeCTCTokenizer.from_pretrained("./directory/with/URL

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
TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #common_voice #generated_from_trainer #sv #dataset-common_voice #endpoints_compatible #region-us 
 # Wav2vec2-xls-r-phoneme-300m-sv

Note: The tokenizer was created from the official Swedish phoneme vocabulary as defined here: URL

One can simply download the file, rename it to 'URL' and load a 'Wav2Vec2PhonemeCTCTokenizer.from_pretrained("./directory/with/URL

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
passage: TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #common_voice #generated_from_trainer #sv #dataset-common_voice #endpoints_compatible #region-us 
# Wav2vec2-xls-r-phoneme-300m-sv

Note: The tokenizer was created from the official Swedish phoneme vocabulary as defined here: URL

One can simply download the file, rename it to 'URL' and load a 'Wav2Vec2PhonemeCTCTokenizer.from_pretrained("./directory/with/URL

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