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---
license: apache-2.0
base_model: openai/whisper-small
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: Teamim-small_WeightDecay-0.05_Augmented_Combined-Data_date-11-07-2024_12-42
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Teamim-small_WeightDecay-0.05_Augmented_Combined-Data_date-11-07-2024_12-42
This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1474
- Wer: 9.8084
- Avg Precision Exact: 0.9128
- Avg Recall Exact: 0.9142
- Avg F1 Exact: 0.9131
- Avg Precision Letter Shift: 0.9270
- Avg Recall Letter Shift: 0.9286
- Avg F1 Letter Shift: 0.9274
- Avg Precision Word Level: 0.9292
- Avg Recall Word Level: 0.9309
- Avg F1 Word Level: 0.9297
- Avg Precision Word Shift: 0.9711
- Avg Recall Word Shift: 0.9735
- Avg F1 Word Shift: 0.9719
- Precision Median Exact: 1.0
- Recall Median Exact: 1.0
- F1 Median Exact: 1.0
- Precision Max Exact: 1.0
- Recall Max Exact: 1.0
- F1 Max Exact: 1.0
- Precision Min Exact: 0.0
- Recall Min Exact: 0.0
- F1 Min Exact: 0.0
- Precision Min Letter Shift: 0.0
- Recall Min Letter Shift: 0.0
- F1 Min Letter Shift: 0.0
- Precision Min Word Level: 0.0
- Recall Min Word Level: 0.0
- F1 Min Word Level: 0.0
- Precision Min Word Shift: 0.1429
- Recall Min Word Shift: 0.125
- F1 Min Word Shift: 0.1333
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 8
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 200000
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer | Avg Precision Exact | Avg Recall Exact | Avg F1 Exact | Avg Precision Letter Shift | Avg Recall Letter Shift | Avg F1 Letter Shift | Avg Precision Word Level | Avg Recall Word Level | Avg F1 Word Level | Avg Precision Word Shift | Avg Recall Word Shift | Avg F1 Word Shift | Precision Median Exact | Recall Median Exact | F1 Median Exact | Precision Max Exact | Recall Max Exact | F1 Max Exact | Precision Min Exact | Recall Min Exact | F1 Min Exact | Precision Min Letter Shift | Recall Min Letter Shift | F1 Min Letter Shift | Precision Min Word Level | Recall Min Word Level | F1 Min Word Level | Precision Min Word Shift | Recall Min Word Shift | F1 Min Word Shift |
|:-------------:|:-------:|:------:|:---------------:|:--------:|:-------------------:|:----------------:|:------------:|:--------------------------:|:-----------------------:|:-------------------:|:------------------------:|:---------------------:|:-----------------:|:------------------------:|:---------------------:|:-----------------:|:----------------------:|:-------------------:|:---------------:|:-------------------:|:----------------:|:------------:|:-------------------:|:----------------:|:------------:|:--------------------------:|:-----------------------:|:-------------------:|:------------------------:|:---------------------:|:-----------------:|:------------------------:|:---------------------:|:-----------------:|
| No log | 0.0001 | 1 | 9.9314 | 148.2331 | 0.0007 | 0.0023 | 0.0010 | 0.0225 | 0.0232 | 0.0218 | 0.0100 | 0.0586 | 0.0155 | 0.1303 | 0.1348 | 0.1270 | 0.0 | 0.0 | 0.0 | 0.1 | 0.5 | 0.1538 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 0.048 | 0.5167 | 10000 | 0.1206 | 17.0868 | 0.8509 | 0.8591 | 0.8544 | 0.8713 | 0.8797 | 0.8749 | 0.8748 | 0.8830 | 0.8783 | 0.9382 | 0.9482 | 0.9423 | 0.9231 | 0.9231 | 0.9286 | 1.0 | 1.0 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 0.0182 | 1.0334 | 20000 | 0.1214 | 14.3208 | 0.8671 | 0.8696 | 0.8679 | 0.8853 | 0.8879 | 0.8861 | 0.8885 | 0.8911 | 0.8893 | 0.9504 | 0.9534 | 0.9513 | 0.9286 | 0.9333 | 0.9474 | 1.0 | 1.0 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 0.0136 | 1.5501 | 30000 | 0.1261 | 13.5939 | 0.8761 | 0.8797 | 0.8775 | 0.8943 | 0.8981 | 0.8957 | 0.8971 | 0.9008 | 0.8984 | 0.9531 | 0.9572 | 0.9546 | 0.9412 | 1.0 | 0.9600 | 1.0 | 1.0 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 0.0039 | 2.0668 | 40000 | 0.1274 | 12.8292 | 0.8843 | 0.8873 | 0.8853 | 0.9000 | 0.9033 | 0.9012 | 0.9024 | 0.9057 | 0.9036 | 0.9551 | 0.9597 | 0.9569 | 1.0 | 1.0 | 0.9630 | 1.0 | 1.0 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 0.0034 | 2.5834 | 50000 | 0.1294 | 12.4579 | 0.8877 | 0.8890 | 0.8879 | 0.9032 | 0.9046 | 0.9035 | 0.9059 | 0.9078 | 0.9064 | 0.9593 | 0.9618 | 0.9600 | 1.0 | 1.0 | 0.9655 | 1.0 | 1.0 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.1429 | 0.125 | 0.1333 |
| 0.0067 | 3.1001 | 60000 | 0.1309 | 11.7593 | 0.8955 | 0.8991 | 0.8969 | 0.9115 | 0.9152 | 0.9129 | 0.9139 | 0.9177 | 0.9154 | 0.9626 | 0.9675 | 0.9646 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.1429 | 0.1 | 0.1176 |
| 0.0048 | 3.6168 | 70000 | 0.1306 | 11.5926 | 0.8960 | 0.8986 | 0.8969 | 0.9125 | 0.9152 | 0.9134 | 0.9152 | 0.9178 | 0.9161 | 0.9637 | 0.9670 | 0.9648 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.1429 | 0.1111 | 0.125 |
| 0.0022 | 4.1335 | 80000 | 0.1356 | 11.8884 | 0.8918 | 0.8928 | 0.8919 | 0.9073 | 0.9084 | 0.9074 | 0.9095 | 0.9106 | 0.9096 | 0.9605 | 0.9632 | 0.9613 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.1429 | 0.125 | 0.1333 |
| 0.002 | 4.6502 | 90000 | 0.1348 | 11.0356 | 0.9012 | 0.9002 | 0.9004 | 0.9155 | 0.9145 | 0.9146 | 0.9181 | 0.9170 | 0.9172 | 0.9666 | 0.9664 | 0.9661 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.1429 | 0.1111 | 0.125 |
| 0.001 | 5.1669 | 100000 | 0.1398 | 11.1929 | 0.8983 | 0.9002 | 0.8989 | 0.9134 | 0.9154 | 0.9140 | 0.9156 | 0.9176 | 0.9162 | 0.9648 | 0.9678 | 0.9658 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.1429 | 0.125 | 0.1333 |
| 0.0016 | 5.6836 | 110000 | 0.1426 | 10.9821 | 0.9019 | 0.9032 | 0.9022 | 0.9168 | 0.9182 | 0.9171 | 0.9191 | 0.9207 | 0.9196 | 0.9644 | 0.9670 | 0.9652 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.1429 | 0.125 | 0.1333 |
| 0.0002 | 6.2003 | 120000 | 0.1417 | 10.5384 | 0.9032 | 0.9045 | 0.9035 | 0.9179 | 0.9193 | 0.9182 | 0.9203 | 0.9214 | 0.9205 | 0.9680 | 0.9701 | 0.9686 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.1429 | 0.125 | 0.1333 |
| 0.0004 | 6.7170 | 130000 | 0.1439 | 10.5604 | 0.9022 | 0.9048 | 0.9031 | 0.9167 | 0.9195 | 0.9177 | 0.9190 | 0.9218 | 0.9200 | 0.9656 | 0.9699 | 0.9673 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.1429 | 0.125 | 0.1333 |
| 0.0005 | 7.2336 | 140000 | 0.1460 | 10.4188 | 0.9048 | 0.9056 | 0.9048 | 0.9193 | 0.9202 | 0.9194 | 0.9215 | 0.9224 | 0.9216 | 0.9673 | 0.9696 | 0.9681 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.1429 | 0.125 | 0.1333 |
| 0.0002 | 7.7503 | 150000 | 0.1396 | 10.1860 | 0.9068 | 0.9072 | 0.9066 | 0.9203 | 0.9209 | 0.9203 | 0.9228 | 0.9233 | 0.9227 | 0.9687 | 0.9705 | 0.9691 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.1429 | 0.125 | 0.1333 |
| 0.0001 | 8.2670 | 160000 | 0.1442 | 10.1136 | 0.9074 | 0.9068 | 0.9068 | 0.9214 | 0.9209 | 0.9208 | 0.9237 | 0.9234 | 0.9232 | 0.9697 | 0.9701 | 0.9695 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.1429 | 0.125 | 0.1333 |
| 0.0005 | 8.7837 | 170000 | 0.1432 | 9.9688 | 0.9098 | 0.9107 | 0.9099 | 0.9243 | 0.9253 | 0.9244 | 0.9266 | 0.9277 | 0.9268 | 0.9702 | 0.9717 | 0.9705 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.1429 | 0.125 | 0.1333 |
| 0.0 | 9.3004 | 180000 | 0.1467 | 10.0538 | 0.9093 | 0.9098 | 0.9092 | 0.9239 | 0.9246 | 0.9239 | 0.9261 | 0.9268 | 0.9261 | 0.9703 | 0.9716 | 0.9705 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.1429 | 0.125 | 0.1333 |
| 0.0 | 9.8171 | 190000 | 0.1449 | 9.9342 | 0.9119 | 0.9125 | 0.9118 | 0.9262 | 0.9269 | 0.9261 | 0.9283 | 0.9288 | 0.9282 | 0.9702 | 0.9715 | 0.9704 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.1429 | 0.125 | 0.1333 |
| 0.0001 | 10.3338 | 200000 | 0.1474 | 9.8084 | 0.9128 | 0.9142 | 0.9131 | 0.9270 | 0.9286 | 0.9274 | 0.9292 | 0.9309 | 0.9297 | 0.9711 | 0.9735 | 0.9719 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.1429 | 0.125 | 0.1333 |
### Framework versions
- Transformers 4.41.2
- Pytorch 2.2.1
- Datasets 2.20.0
- Tokenizers 0.19.1
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