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# wav2vec2-large-asr-th-2

This model was trained from scratch on the None dataset.
It achieves the following results on the evaluation set:
- eval_loss: 0.1819
- eval_wer: 0.3346
- eval_cer: 0.0910
- eval_runtime: 735.0436
- eval_samples_per_second: 13.822
- eval_steps_per_second: 1.728
- epoch: 0.95
- step: 4000

## 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: 0.0001
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 3
- total_train_batch_size: 48
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 5000
- mixed_precision_training: Native AMP

### Framework versions

- Transformers 4.27.4
- Pytorch 1.13.1+cu116
- Datasets 2.11.0
- Tokenizers 0.13.2
{"language": ["th"], "library_name": "transformers", "tags": ["generated_from_trainer", "4-grams", "ASR with lm", "ASR with 4-gram", "speech-to-text with lm"], "datasets": ["mozilla-foundation/common_voice_10_0"], "metrics": ["cer", "wer"], "pipeline_tag": "automatic-speech-recognition", "model-index": [{"name": "wav2vec2-large-asr-th-2", "results": []}]}
transformers pytorch wav2vec2 automatic-speech-recognition generated_from_trainer 4-grams ASR with lm ASR with 4-gram speech-to-text with lm th dataset:mozilla-foundation/common_voice_10_0 endpoints_compatible region:us
TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #generated_from_trainer #4-grams #ASR with lm #ASR with 4-gram #speech-to-text with lm #th #dataset-mozilla-foundation/common_voice_10_0 #endpoints_compatible #region-us 




# wav2vec2-large-asr-th-2

This model was trained from scratch on the None dataset.
It achieves the following results on the evaluation set:
- eval_loss: 0.1819
- eval_wer: 0.3346
- eval_cer: 0.0910
- eval_runtime: 735.0436
- eval_samples_per_second: 13.822
- eval_steps_per_second: 1.728
- epoch: 0.95
- step: 4000

## 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: 0.0001
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 3
- total_train_batch_size: 48
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 5000
- mixed_precision_training: Native AMP

### Framework versions

- Transformers 4.27.4
- Pytorch 1.13.1+cu116
- Datasets 2.11.0
- Tokenizers 0.13.2
# wav2vec2-large-asr-th-2

This model was trained from scratch on the None dataset.
It achieves the following results on the evaluation set:
- eval_loss: 0.1819
- eval_wer: 0.3346
- eval_cer: 0.0910
- eval_runtime: 735.0436
- eval_samples_per_second: 13.822
- eval_steps_per_second: 1.728
- epoch: 0.95
- step: 4000 ## 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: 0.0001
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 3
- total_train_batch_size: 48
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 5000
- mixed_precision_training: Native AMP ### Framework versions

- Transformers 4.27.4
- Pytorch 1.13.1+cu116
- Datasets 2.11.0
- Tokenizers 0.13.2
TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #generated_from_trainer #4-grams #ASR with lm #ASR with 4-gram #speech-to-text with lm #th #dataset-mozilla-foundation/common_voice_10_0 #endpoints_compatible #region-us 
 # wav2vec2-large-asr-th-2

This model was trained from scratch on the None dataset.
It achieves the following results on the evaluation set:
- eval_loss: 0.1819
- eval_wer: 0.3346
- eval_cer: 0.0910
- eval_runtime: 735.0436
- eval_samples_per_second: 13.822
- eval_steps_per_second: 1.728
- epoch: 0.95
- step: 4000 ## 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: 0.0001
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 3
- total_train_batch_size: 48
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 5000
- mixed_precision_training: Native AMP ### Framework versions

- Transformers 4.27.4
- Pytorch 1.13.1+cu116
- Datasets 2.11.0
- Tokenizers 0.13.2
TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #generated_from_trainer #4-grams #ASR with lm #ASR with 4-gram #speech-to-text with lm #th #dataset-mozilla-foundation/common_voice_10_0 #endpoints_compatible #region-us 
# wav2vec2-large-asr-th-2

This model was trained from scratch on the None dataset.
It achieves the following results on the evaluation set:
- eval_loss: 0.1819
- eval_wer: 0.3346
- eval_cer: 0.0910
- eval_runtime: 735.0436
- eval_samples_per_second: 13.822
- eval_steps_per_second: 1.728
- epoch: 0.95
- step: 4000## 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: 0.0001
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 3
- total_train_batch_size: 48
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 5000
- mixed_precision_training: Native AMP### Framework versions

- Transformers 4.27.4
- Pytorch 1.13.1+cu116
- Datasets 2.11.0
- Tokenizers 0.13.2