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ASR-test

Automatic Speech Recognition
Transformers
Safetensors
Vietnamese
wav2vec2
Generated from Trainer
Eval Results
Inference Endpoints
Model card Files Community
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End of training

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Files changed (3) hide show
  1. README.md +50 -8
  2. adapter.vie.safetensors +1 -1
  3. model.safetensors +1 -1
README.md CHANGED
@@ -10,7 +10,7 @@ datasets:
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  metrics:
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  - wer
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  model-index:
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- - name: ASR-test
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  results:
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  - task:
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  name: Automatic Speech Recognition
@@ -21,18 +21,18 @@ model-index:
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  metrics:
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  - name: Wer
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  type: wer
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- value: 0.4502295081967213
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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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  should probably proofread and complete it, then remove this comment. -->
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- # ASR-test
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  This model is a fine-tuned version of [facebook/mms-1b-all](https://huggingface.co/facebook/mms-1b-all) on the HTV news dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.8796
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- - Wer: 0.4502
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  ## Model description
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@@ -58,19 +58,61 @@ The following hyperparameters were used during training:
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  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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  - lr_scheduler_type: linear
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  - lr_scheduler_warmup_steps: 100
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- - num_epochs: 1
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  - mixed_precision_training: Native AMP
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  ### Training results
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  | Training Loss | Epoch | Step | Validation Loss | Wer |
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  |:-------------:|:-----:|:----:|:---------------:|:------:|
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- | 4.8401 | 0.92 | 100 | 0.8796 | 0.4502 |
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ### Framework versions
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  - Transformers 4.37.0.dev0
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- - Pytorch 2.1.0+cu118
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  - Datasets 2.15.0
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  - Tokenizers 0.15.0
 
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  metrics:
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  - wer
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  model-index:
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+ - name: ASR-test-1
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  results:
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  - task:
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  name: Automatic Speech Recognition
 
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  metrics:
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  - name: Wer
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  type: wer
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+ value: 0.2882930019620667
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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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  should probably proofread and complete it, then remove this comment. -->
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+ # ASR-test-1
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  This model is a fine-tuned version of [facebook/mms-1b-all](https://huggingface.co/facebook/mms-1b-all) on the HTV news dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.5663
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+ - Wer: 0.2883
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  ## Model description
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  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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  - lr_scheduler_type: linear
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  - lr_scheduler_warmup_steps: 100
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+ - num_epochs: 40
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  - mixed_precision_training: Native AMP
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  ### Training results
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  | Training Loss | Epoch | Step | Validation Loss | Wer |
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  |:-------------:|:-----:|:----:|:---------------:|:------:|
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+ | 4.772 | 0.92 | 100 | 0.8456 | 0.4411 |
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+ | 1.1042 | 1.83 | 200 | 0.7041 | 0.4076 |
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+ | 0.9814 | 2.75 | 300 | 0.7243 | 0.3782 |
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+ | 0.9096 | 3.67 | 400 | 0.6771 | 0.3655 |
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+ | 0.8823 | 4.59 | 500 | 0.6265 | 0.3627 |
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+ | 0.8435 | 5.5 | 600 | 0.6200 | 0.3543 |
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+ | 0.8157 | 6.42 | 700 | 0.6414 | 0.3417 |
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+ | 0.822 | 7.34 | 800 | 0.5872 | 0.3431 |
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+ | 0.7852 | 8.26 | 900 | 0.6012 | 0.3387 |
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+ | 0.7533 | 9.17 | 1000 | 0.6023 | 0.3256 |
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+ | 0.7609 | 10.09 | 1100 | 0.5837 | 0.3444 |
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+ | 0.7568 | 11.01 | 1200 | 0.5791 | 0.3311 |
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+ | 0.7091 | 11.93 | 1300 | 0.6227 | 0.3206 |
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+ | 0.7098 | 12.84 | 1400 | 0.5766 | 0.3266 |
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+ | 0.7006 | 13.76 | 1500 | 0.6084 | 0.3117 |
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+ | 0.6673 | 14.68 | 1600 | 0.5857 | 0.3120 |
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+ | 0.6832 | 15.6 | 1700 | 0.5754 | 0.3338 |
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+ | 0.6646 | 16.51 | 1800 | 0.5963 | 0.3117 |
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+ | 0.6524 | 17.43 | 1900 | 0.5816 | 0.3137 |
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+ | 0.6385 | 18.35 | 2000 | 0.5691 | 0.3257 |
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+ | 0.6433 | 19.27 | 2100 | 0.5929 | 0.3105 |
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+ | 0.6129 | 20.18 | 2200 | 0.5709 | 0.3067 |
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+ | 0.624 | 21.1 | 2300 | 0.5686 | 0.3168 |
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+ | 0.6128 | 22.02 | 2400 | 0.5867 | 0.3080 |
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+ | 0.584 | 22.94 | 2500 | 0.5680 | 0.3101 |
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+ | 0.5956 | 23.85 | 2600 | 0.5611 | 0.3023 |
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+ | 0.5825 | 24.77 | 2700 | 0.5821 | 0.2999 |
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+ | 0.56 | 25.69 | 2800 | 0.5622 | 0.3012 |
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+ | 0.56 | 26.61 | 2900 | 0.5590 | 0.3053 |
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+ | 0.5523 | 27.52 | 3000 | 0.5758 | 0.2967 |
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+ | 0.5335 | 28.44 | 3100 | 0.5649 | 0.3090 |
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+ | 0.5686 | 29.36 | 3200 | 0.5703 | 0.2931 |
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+ | 0.5488 | 30.28 | 3300 | 0.5709 | 0.2921 |
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+ | 0.5249 | 31.19 | 3400 | 0.5646 | 0.2973 |
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+ | 0.5278 | 32.11 | 3500 | 0.5628 | 0.2933 |
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+ | 0.5252 | 33.03 | 3600 | 0.5663 | 0.2927 |
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+ | 0.5092 | 33.94 | 3700 | 0.5618 | 0.2922 |
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+ | 0.5099 | 34.86 | 3800 | 0.5616 | 0.2954 |
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+ | 0.5031 | 35.78 | 3900 | 0.5670 | 0.2913 |
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+ | 0.4959 | 36.7 | 4000 | 0.5679 | 0.2923 |
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+ | 0.4936 | 37.61 | 4100 | 0.5675 | 0.2912 |
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+ | 0.5012 | 38.53 | 4200 | 0.5661 | 0.2897 |
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+ | 0.4819 | 39.45 | 4300 | 0.5663 | 0.2883 |
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  ### Framework versions
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  - Transformers 4.37.0.dev0
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+ - Pytorch 2.1.0+cu121
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  - Datasets 2.15.0
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  - Tokenizers 0.15.0
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