End of training
Browse files- README.md +4 -31
- config.json +2 -2
- pytorch_model.bin +2 -2
- training_args.bin +1 -1
README.md
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- generated_from_trainer
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datasets:
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- common_voice_13_0
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metrics:
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- wer
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model-index:
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- name: wav2vec2-large-xlsr-53-demo-colab
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results:
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- task:
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name: Automatic Speech Recognition
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type: automatic-speech-recognition
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dataset:
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name: common_voice_13_0
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type: common_voice_13_0
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config: sah
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split: test
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args: sah
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metrics:
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- name: Wer
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type: wer
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value: 0.5698038864511508
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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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# wav2vec2-large-xlsr-53-demo-colab
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This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the common_voice_13_0 dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.8836
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- Wer: 0.5698
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## Model description
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- gradient_accumulation_steps: 2
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- total_train_batch_size: 32
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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- lr_scheduler_type:
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- lr_scheduler_warmup_steps: 500
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- num_epochs:
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### Training results
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| Training Loss | Epoch | Step | Validation Loss | Wer |
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| 0.1354 | 14.04 | 400 | 0.8703 | 0.6377 |
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| 0.1297 | 28.07 | 800 | 0.8601 | 0.6317 |
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| 0.0937 | 42.11 | 1200 | 0.9103 | 0.6320 |
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| 0.0751 | 56.14 | 1600 | 0.8848 | 0.6044 |
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| 0.0582 | 70.18 | 2000 | 0.8630 | 0.5770 |
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| 0.0492 | 84.21 | 2400 | 0.8889 | 0.5786 |
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| 0.0402 | 98.25 | 2800 | 0.8836 | 0.5698 |
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### Framework versions
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- Transformers 4.34.0
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- Pytorch 2.1.0+
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- Datasets 2.14.5
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- Tokenizers 0.14.1
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- generated_from_trainer
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datasets:
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- common_voice_13_0
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model-index:
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- name: wav2vec2-large-xlsr-53-demo-colab
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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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# wav2vec2-large-xlsr-53-demo-colab
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This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the common_voice_13_0 dataset.
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## Model description
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- gradient_accumulation_steps: 2
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- total_train_batch_size: 32
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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- lr_scheduler_type: constant
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- lr_scheduler_warmup_steps: 500
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- num_epochs: 20
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### Training results
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### Framework versions
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- Transformers 4.34.0
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- Pytorch 2.1.0+cu121
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- Datasets 2.14.5
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- Tokenizers 0.14.1
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config.json
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"num_hidden_layers": 24,
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"num_negatives": 100,
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"output_hidden_size": 1024,
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"pad_token_id":
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"proj_codevector_dim": 768,
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"tdnn_dilation": [
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1,
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"torch_dtype": "float32",
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"transformers_version": "4.34.0",
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"use_weighted_layer_sum": false,
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"vocab_size":
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"xvector_output_dim": 512
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}
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"num_hidden_layers": 24,
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"num_negatives": 100,
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"output_hidden_size": 1024,
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"pad_token_id": 45,
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"proj_codevector_dim": 768,
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"tdnn_dilation": [
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1,
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"torch_dtype": "float32",
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"transformers_version": "4.34.0",
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"use_weighted_layer_sum": false,
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"vocab_size": 48,
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"xvector_output_dim": 512
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}
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pytorch_model.bin
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training_args.bin
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