sz-mp commited on
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End of training

Browse files
Files changed (5) hide show
  1. README.md +8 -6
  2. all_results.json +12 -12
  3. eval_results.json +7 -7
  4. train_results.json +6 -6
  5. trainer_state.json +1255 -22
README.md CHANGED
@@ -2,6 +2,8 @@
2
  license: apache-2.0
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  base_model: facebook/wav2vec2-large-xlsr-53
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  tags:
 
 
5
  - generated_from_trainer
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  datasets:
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  - audiofolder
@@ -14,15 +16,15 @@ model-index:
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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: audiofolder
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  type: audiofolder
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  config: default
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  split: validation
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- args: default
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  metrics:
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  - name: Wer
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  type: wer
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- value: 0.9850068150840527
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  ---
27
 
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  <!-- This model card has been generated automatically according to the information the Trainer had access to. You
@@ -30,10 +32,10 @@ should probably proofread and complete it, then remove this comment. -->
30
 
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  # em_ctc
32
 
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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 audiofolder dataset.
34
  It achieves the following results on the evaluation set:
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- - Loss: 2.7311
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- - Wer: 0.9850
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38
  ## Model description
39
 
 
2
  license: apache-2.0
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  base_model: facebook/wav2vec2-large-xlsr-53
4
  tags:
5
+ - automatic-speech-recognition
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+ - wav_sub-P001
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  - generated_from_trainer
8
  datasets:
9
  - audiofolder
 
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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: WAV_SUB-P001 - TR
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  type: audiofolder
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  config: default
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  split: validation
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+ args: 'Config: tr, Training split: train+validation, Eval split: test'
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  metrics:
25
  - name: Wer
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  type: wer
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+ value: 0.9843253066787824
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  ---
29
 
30
  <!-- This model card has been generated automatically according to the information the Trainer had access to. You
 
32
 
33
  # em_ctc
34
 
35
+ This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the WAV_SUB-P001 - TR dataset.
36
  It achieves the following results on the evaluation set:
37
+ - Loss: 2.7286
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+ - Wer: 0.9843
39
 
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  ## Model description
41
 
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