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ndeclarke
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wav2vec2-mms-1b-CV17.0-training_set_variations

Automatic Speech Recognition
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wav2vec2
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ndeclarke commited on
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End of training

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  1. README.md +25 -30
  2. adapter.tam-256.safetensors +1 -1
  3. model.safetensors +1 -1
README.md CHANGED
@@ -1,20 +1,20 @@
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  ---
 
 
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  base_model: facebook/mms-1b-all
 
 
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  datasets:
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  - common_voice_17_0
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- library_name: transformers
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- license: cc-by-nc-4.0
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  metrics:
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  - wer
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  - bleu
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- tags:
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- - generated_from_trainer
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  model-index:
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  - name: wav2vec2-mms-1b-CV17.0-training_set_variations
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  results:
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  - task:
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- type: automatic-speech-recognition
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  name: Automatic Speech Recognition
 
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  dataset:
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  name: common_voice_17_0
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  type: common_voice_17_0
@@ -22,12 +22,12 @@ model-index:
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  split: validation
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  args: ta
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  metrics:
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- - type: wer
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- value: 0.41655589677774213
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- name: Wer
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- - type: bleu
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- value: 0.3602695476100989
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- name: Bleu
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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
@@ -37,10 +37,10 @@ should probably proofread and complete it, then remove this comment. -->
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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 common_voice_17_0 dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.3166
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- - Wer: 0.4166
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- - Cer: 0.0689
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- - Bleu: 0.3603
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  ## Model description
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@@ -73,21 +73,16 @@ The following hyperparameters were used during training:
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  ### Training results
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- | Training Loss | Epoch | Step | Validation Loss | Wer | Cer | Bleu |
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- |:-------------:|:------:|:----:|:---------------:|:------:|:------:|:------:|
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- | 11.8671 | 3.125 | 50 | 3.8104 | 1.0011 | 0.9249 | 0.0 |
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- | 1.5965 | 6.25 | 100 | 0.2796 | 0.4122 | 0.0680 | 0.3517 |
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- | 0.2054 | 9.375 | 150 | 0.2320 | 0.3751 | 0.0620 | 0.4040 |
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- | 0.1702 | 12.5 | 200 | 0.2367 | 0.3794 | 0.0633 | 0.3939 |
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- | 0.1495 | 15.625 | 250 | 0.2527 | 0.4168 | 0.0680 | 0.3457 |
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- | 0.1457 | 18.75 | 300 | 0.2536 | 0.3973 | 0.0662 | 0.3723 |
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- | 0.1264 | 21.875 | 350 | 0.2765 | 0.4175 | 0.0693 | 0.3546 |
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- | 0.1092 | 25.0 | 400 | 0.2711 | 0.4032 | 0.0673 | 0.3701 |
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- | 0.0952 | 28.125 | 450 | 0.2828 | 0.4139 | 0.0691 | 0.3605 |
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- | 0.0919 | 31.25 | 500 | 0.2972 | 0.4283 | 0.0721 | 0.3355 |
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- | 0.0909 | 34.375 | 550 | 0.2971 | 0.4155 | 0.0686 | 0.3567 |
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- | 0.0744 | 37.5 | 600 | 0.3086 | 0.4241 | 0.0705 | 0.3461 |
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- | 0.069 | 40.625 | 650 | 0.3166 | 0.4166 | 0.0689 | 0.3603 |
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  ### Framework versions
 
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  ---
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+ library_name: transformers
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+ license: cc-by-nc-4.0
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  base_model: facebook/mms-1b-all
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+ tags:
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+ - generated_from_trainer
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  datasets:
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  - common_voice_17_0
 
 
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  metrics:
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  - wer
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  - bleu
 
 
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  model-index:
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  - name: wav2vec2-mms-1b-CV17.0-training_set_variations
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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_17_0
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  type: common_voice_17_0
 
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  split: validation
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  args: ta
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  metrics:
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+ - name: Wer
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+ type: wer
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+ value: 0.4250355227574827
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+ - name: Bleu
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+ type: bleu
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+ value: 0.3461897882903843
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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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  This model is a fine-tuned version of [facebook/mms-1b-all](https://huggingface.co/facebook/mms-1b-all) on the common_voice_17_0 dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.3320
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+ - Wer: 0.4250
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+ - Cer: 0.0720
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+ - Bleu: 0.3462
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  ## Model description
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  ### Training results
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+ | Training Loss | Epoch | Step | Validation Loss | Wer | Cer | Bleu |
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+ |:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|
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+ | 11.5156 | 6.25 | 50 | 5.5076 | 1.0 | 0.9701 | 0.0 |
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+ | 1.8133 | 12.5 | 100 | 0.2759 | 0.4067 | 0.0674 | 0.3610 |
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+ | 0.1751 | 18.75 | 150 | 0.2414 | 0.3828 | 0.0639 | 0.3924 |
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+ | 0.1315 | 25.0 | 200 | 0.2556 | 0.3887 | 0.0649 | 0.3901 |
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+ | 0.1 | 31.25 | 250 | 0.2842 | 0.4168 | 0.0700 | 0.3520 |
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+ | 0.0842 | 37.5 | 300 | 0.2997 | 0.4133 | 0.0699 | 0.3571 |
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+ | 0.0717 | 43.75 | 350 | 0.3210 | 0.4260 | 0.0732 | 0.3431 |
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+ | 0.0608 | 50.0 | 400 | 0.3320 | 0.4250 | 0.0720 | 0.3462 |
 
 
 
 
 
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  ### Framework versions
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