Automatic Speech Recognition
Transformers
TensorBoard
Safetensors
Irish
English
whisper
Generated from Trainer
Eval Results
Inference Endpoints
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Update README.md

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@@ -16,6 +16,7 @@ datasets:
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  metrics:
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  - bleu
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  - wer
 
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  model-index:
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  - name: Whisper Small GA-EN Speech Translation
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  results:
@@ -23,7 +24,9 @@ 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: IWSLT-2023, FLEURS, BiteSize, SpokenWords, Tatoeba, and Wikimedia + augmented
 
 
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  type: ymoslem/IWSLT2023-GA-EN
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  metrics:
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  - name: Bleu
@@ -69,8 +72,10 @@ The following hyperparameters were used during training:
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  - seed: 42
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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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  - training_steps: 3000
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  - mixed_precision_training: Native AMP
 
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  ### Training results
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@@ -96,7 +101,7 @@ The following hyperparameters were used during training:
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  | 0.1946 | 0.7881 | 1800 | 1.2820 | 26.17 | 42.46 | 64.9257 |
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  | 0.1588 | 0.8319 | 1900 | 1.3172 | 26.9 | 43.02 | 63.5299 |
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  | 0.1322 | 0.8757 | 2000 | 1.3248 | 27.78 | 43.53 | 63.8001 |
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- | 0.1134 | 0.9194 | 2100 | 1.3198 | 28.98 | 45.27 | 72.7600 |
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  | 0.1031 | 0.9632 | 2200 | 1.3502 | 29.18 | 44.77 | 68.3476 |
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  | 0.0518 | 1.0070 | 2300 | 1.3433 | 28.6 | 42.96 | 69.0230 |
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  | 0.0481 | 1.0508 | 2400 | 1.3715 | 29.01 | 44.46 | 69.6983 |
@@ -113,4 +118,4 @@ The following hyperparameters were used during training:
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  - Transformers 4.40.2
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  - Pytorch 2.2.0+cu121
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  - Datasets 2.19.1
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- - Tokenizers 0.19.1
 
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  metrics:
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  - bleu
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  - wer
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+ - chrf
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  model-index:
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  - name: Whisper Small GA-EN Speech Translation
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  results:
 
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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: >-
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+ IWSLT-2023, FLEURS, BiteSize, SpokenWords, Tatoeba, and Wikimedia +
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+ augmented
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  type: ymoslem/IWSLT2023-GA-EN
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  metrics:
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  - name: Bleu
 
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  - seed: 42
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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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+ - warmup_steps: 0
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  - training_steps: 3000
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  - mixed_precision_training: Native AMP
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+ - generation_max_length: 128
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  ### Training results
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  | 0.1946 | 0.7881 | 1800 | 1.2820 | 26.17 | 42.46 | 64.9257 |
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  | 0.1588 | 0.8319 | 1900 | 1.3172 | 26.9 | 43.02 | 63.5299 |
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  | 0.1322 | 0.8757 | 2000 | 1.3248 | 27.78 | 43.53 | 63.8001 |
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+ | 0.1134 | 0.9194 | **2100** | 1.3198 | 28.98 | 45.27 | 72.7600 |
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  | 0.1031 | 0.9632 | 2200 | 1.3502 | 29.18 | 44.77 | 68.3476 |
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  | 0.0518 | 1.0070 | 2300 | 1.3433 | 28.6 | 42.96 | 69.0230 |
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  | 0.0481 | 1.0508 | 2400 | 1.3715 | 29.01 | 44.46 | 69.6983 |
 
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  - Transformers 4.40.2
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  - Pytorch 2.2.0+cu121
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  - Datasets 2.19.1
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+ - Tokenizers 0.19.1