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ptah23
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whisper-tiny-en-US

Automatic Speech Recognition
Transformers
PyTorch
whisper
Generated from Trainer
Eval Results
Inference Endpoints
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update model card README.md

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@@ -22,7 +22,7 @@ model-index:
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  metrics:
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  - name: Wer
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  type: wer
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- value: 0.20146619603584034
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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
@@ -32,9 +32,9 @@ should probably proofread and complete it, then remove this comment. -->
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  This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) on the PolyAI/minds14 dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.6756
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- - Wer Ortho: 0.2044
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- - Wer: 0.2015
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  ## Model description
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@@ -59,21 +59,53 @@ 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: constant_with_warmup
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- - lr_scheduler_warmup_steps: 50
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- - training_steps: 4000
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  ### Training results
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- | Training Loss | Epoch | Step | Validation Loss | Wer Ortho | Wer |
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- |:-------------:|:------:|:----:|:---------------:|:---------:|:------:|
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- | 0.0007 | 17.86 | 500 | 0.5138 | 0.1941 | 0.1920 |
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- | 0.0002 | 35.71 | 1000 | 0.5565 | 0.1958 | 0.1936 |
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- | 0.0001 | 53.57 | 1500 | 0.5851 | 0.1981 | 0.1958 |
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- | 0.0001 | 71.43 | 2000 | 0.6081 | 0.2030 | 0.1998 |
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- | 0.0 | 89.29 | 2500 | 0.6273 | 0.2038 | 0.2009 |
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- | 0.0 | 107.14 | 3000 | 0.6441 | 0.2021 | 0.1996 |
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- | 0.0 | 125.0 | 3500 | 0.6602 | 0.2035 | 0.2007 |
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- | 0.0 | 142.86 | 4000 | 0.6756 | 0.2044 | 0.2015 |
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ### Framework versions
 
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  metrics:
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  - name: Wer
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  type: wer
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+ value: 0.19386369807222373
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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 [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) on the PolyAI/minds14 dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.5874
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+ - Wer Ortho: 0.1978
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+ - Wer: 0.1939
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  ## Model description
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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: constant_with_warmup
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+ - lr_scheduler_warmup_steps: 5
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+ - training_steps: 400
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  ### Training results
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+ | Training Loss | Epoch | Step | Validation Loss | Wer Ortho | Wer |
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+ |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|
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+ | No log | 0.36 | 10 | 0.5445 | 0.1970 | 0.1930 |
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+ | No log | 0.71 | 20 | 0.5571 | 0.1973 | 0.1939 |
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+ | 0.0009 | 1.07 | 30 | 0.5571 | 0.1981 | 0.1936 |
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+ | 0.0009 | 1.43 | 40 | 0.5535 | 0.1958 | 0.1920 |
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+ | 0.0024 | 1.79 | 50 | 0.5568 | 0.1967 | 0.1930 |
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+ | 0.0024 | 2.14 | 60 | 0.5679 | 0.2024 | 0.1996 |
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+ | 0.0024 | 2.5 | 70 | 0.5574 | 0.1970 | 0.1944 |
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+ | 0.0019 | 2.86 | 80 | 0.5454 | 0.1956 | 0.1936 |
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+ | 0.0019 | 3.21 | 90 | 0.5541 | 0.2044 | 0.1982 |
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+ | 0.0043 | 3.57 | 100 | 0.5330 | 0.1998 | 0.1936 |
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+ | 0.0043 | 3.93 | 110 | 0.5524 | 0.1981 | 0.1958 |
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+ | 0.0043 | 4.29 | 120 | 0.5482 | 0.1958 | 0.1933 |
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+ | 0.0043 | 4.64 | 130 | 0.5554 | 0.1984 | 0.1952 |
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+ | 0.0043 | 5.0 | 140 | 0.5634 | 0.1998 | 0.1949 |
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+ | 0.001 | 5.36 | 150 | 0.5526 | 0.1990 | 0.1930 |
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+ | 0.001 | 5.71 | 160 | 0.5511 | 0.1973 | 0.1920 |
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+ | 0.001 | 6.07 | 170 | 0.5548 | 0.1953 | 0.1917 |
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+ | 0.0006 | 6.43 | 180 | 0.5589 | 0.2047 | 0.1998 |
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+ | 0.0006 | 6.79 | 190 | 0.5657 | 0.2027 | 0.1982 |
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+ | 0.0004 | 7.14 | 200 | 0.5686 | 0.1961 | 0.1933 |
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+ | 0.0004 | 7.5 | 210 | 0.5714 | 0.1944 | 0.1911 |
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+ | 0.0004 | 7.86 | 220 | 0.5710 | 0.1976 | 0.1941 |
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+ | 0.0002 | 8.21 | 230 | 0.5656 | 0.1973 | 0.1925 |
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+ | 0.0002 | 8.57 | 240 | 0.5661 | 0.1981 | 0.1928 |
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+ | 0.0001 | 8.93 | 250 | 0.5688 | 0.1984 | 0.1930 |
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+ | 0.0001 | 9.29 | 260 | 0.5720 | 0.1967 | 0.1920 |
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+ | 0.0001 | 9.64 | 270 | 0.5745 | 0.1970 | 0.1930 |
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+ | 0.0001 | 10.0 | 280 | 0.5758 | 0.1964 | 0.1925 |
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+ | 0.0001 | 10.36 | 290 | 0.5767 | 0.1970 | 0.1930 |
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+ | 0.0001 | 10.71 | 300 | 0.5780 | 0.1978 | 0.1941 |
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+ | 0.0001 | 11.07 | 310 | 0.5793 | 0.1978 | 0.1941 |
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+ | 0.0001 | 11.43 | 320 | 0.5803 | 0.1976 | 0.1939 |
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+ | 0.0001 | 11.79 | 330 | 0.5815 | 0.1973 | 0.1933 |
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+ | 0.0001 | 12.14 | 340 | 0.5824 | 0.1973 | 0.1933 |
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+ | 0.0001 | 12.5 | 350 | 0.5833 | 0.1973 | 0.1933 |
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+ | 0.0001 | 12.86 | 360 | 0.5843 | 0.1970 | 0.1933 |
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+ | 0.0001 | 13.21 | 370 | 0.5850 | 0.1976 | 0.1936 |
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+ | 0.0001 | 13.57 | 380 | 0.5856 | 0.1978 | 0.1939 |
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+ | 0.0001 | 13.93 | 390 | 0.5865 | 0.1978 | 0.1939 |
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+ | 0.0001 | 14.29 | 400 | 0.5874 | 0.1978 | 0.1939 |
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  ### Framework versions