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# wav2vec2_xlsr50k_korean_phoneme_aihub-40m

This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on 40 minutes of [the Korean Spontaneous Conversational speech dataset](https://aihub.or.kr/aihubdata/data/view.do?currMenu=115&topMenu=100&aihubDataSe=realm&dataSetSn=109).

## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data

- Training data: AIHUB 자유대화 음성(일반남녀) dataset
- Num of speakers for training: 2
- Num of audio files: 425
- Total audio length: about 41 minutes
- No evaluation (will be updated soon)

## Training procedure

- Token preprocessing: convert hangeul-jamo to MFA-v1-based phoneset (no longer available; v2 is based on ipa-format)
- Vocab size: 46

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 200

### Training results



### Framework versions

- Transformers 4.21.3
- Pytorch 1.8.1+cu102
- Datasets 2.4.0
- Tokenizers 0.12.1

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "wav2vec2_xlsr50k_korean_phoneme_aihub-40m", "results": []}]}
transformers pytorch tensorboard wav2vec2 automatic-speech-recognition generated_from_trainer license:apache-2.0 endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us 




# wav2vec2_xlsr50k_korean_phoneme_aihub-40m

This model is a fine-tuned version of facebook/wav2vec2-large-xlsr-53 on 40 minutes of the Korean Spontaneous Conversational speech dataset.

## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data

- Training data: AIHUB 자유대화 음성(일반남녀) dataset
- Num of speakers for training: 2
- Num of audio files: 425
- Total audio length: about 41 minutes
- No evaluation (will be updated soon)

## Training procedure

- Token preprocessing: convert hangeul-jamo to MFA-v1-based phoneset (no longer available; v2 is based on ipa-format)
- Vocab size: 46

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 200

### Training results



### Framework versions

- Transformers 4.21.3
- Pytorch 1.8.1+cu102
- Datasets 2.4.0
- Tokenizers 0.12.1

# wav2vec2_xlsr50k_korean_phoneme_aihub-40m

This model is a fine-tuned version of facebook/wav2vec2-large-xlsr-53 on 40 minutes of the Korean Spontaneous Conversational speech dataset. ## Model description

More information needed ## Intended uses & limitations

More information needed ## Training and evaluation data

- Training data: AIHUB 자유대화 음성(일반남녀) dataset
- Num of speakers for training: 2
- Num of audio files: 425
- Total audio length: about 41 minutes
- No evaluation (will be updated soon) ## Training procedure

- Token preprocessing: convert hangeul-jamo to MFA-v1-based phoneset (no longer available; v2 is based on ipa-format)
- Vocab size: 46 ### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 200 ### Training results ### Framework versions

- Transformers 4.21.3
- Pytorch 1.8.1+cu102
- Datasets 2.4.0
- Tokenizers 0.12.1
TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us 
 # wav2vec2_xlsr50k_korean_phoneme_aihub-40m

This model is a fine-tuned version of facebook/wav2vec2-large-xlsr-53 on 40 minutes of the Korean Spontaneous Conversational speech dataset. ## Model description

More information needed ## Intended uses & limitations

More information needed ## Training and evaluation data

- Training data: AIHUB 자유대화 음성(일반남녀) dataset
- Num of speakers for training: 2
- Num of audio files: 425
- Total audio length: about 41 minutes
- No evaluation (will be updated soon) ## Training procedure

- Token preprocessing: convert hangeul-jamo to MFA-v1-based phoneset (no longer available; v2 is based on ipa-format)
- Vocab size: 46 ### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 200 ### Training results ### Framework versions

- Transformers 4.21.3
- Pytorch 1.8.1+cu102
- Datasets 2.4.0
- Tokenizers 0.12.1
passage: TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us 
# wav2vec2_xlsr50k_korean_phoneme_aihub-40m

This model is a fine-tuned version of facebook/wav2vec2-large-xlsr-53 on 40 minutes of the Korean Spontaneous Conversational speech dataset.## Model description

More information needed## Intended uses & limitations

More information needed## Training and evaluation data

- Training data: AIHUB 자유대화 음성(일반남녀) dataset
- Num of speakers for training: 2
- Num of audio files: 425
- Total audio length: about 41 minutes
- No evaluation (will be updated soon)## Training procedure

- Token preprocessing: convert hangeul-jamo to MFA-v1-based phoneset (no longer available; v2 is based on ipa-format)
- Vocab size: 46### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 200### Training results### Framework versions

- Transformers 4.21.3
- Pytorch 1.8.1+cu102
- Datasets 2.4.0
- Tokenizers 0.12.1