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---
license: apache-2.0
base_model: openai/whisper-tiny
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: whisper-multi-diar-wer
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. -->
# whisper-multi-diar-wer
This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co./openai/whisper-tiny) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 10.0666
- Wer: 582.8864
- Cer: 168.6522
- Speech Scored: 693831
- Speech Miss: 52252
- Speech Falarm: 117030
- Speaker Miss: 52252
- Speaker Falarm: 117030
- Speaker Error: 187216
- Speaker Correct: 1437.5240
- Diarization Error: 356498
- Frames: 600
- Speaker Wide Frames: 746083
- Speech Scored Ratio: 1156.385
- Speech Miss Ratio: 87.0867
- Speech Falarm Ratio: 195.05
- Speaker Correct Ratio: 2.3959
- Speaker Miss Ratio: 0.0700
- Speaker Falarm Ratio: 0.1569
- Speaker Error Ratio: 0.2509
- Diarization Error Ratio: 0.4778
## 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: 24
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 48
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.05
- num_epochs: 10
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer | Cer | Speech Scored | Speech Miss | Speech Falarm | Speaker Miss | Speaker Falarm | Speaker Error | Speaker Correct | Diarization Error | Frames | Speaker Wide Frames | Speech Scored Ratio | Speech Miss Ratio | Speech Falarm Ratio | Speaker Correct Ratio | Speaker Miss Ratio | Speaker Falarm Ratio | Speaker Error Ratio | Diarization Error Ratio |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:|:-------------:|:-----------:|:-------------:|:------------:|:--------------:|:-------------:|:---------------:|:-----------------:|:------:|:-------------------:|:-------------------:|:-----------------:|:-------------------:|:---------------------:|:------------------:|:--------------------:|:-------------------:|:-----------------------:|
| 11.3437 | 1.0 | 42 | 10.7905 | 574.9471 | 166.1650 | 743633 | 2450 | 150302 | 2450 | 150302 | 202641 | 1427.9773 | 355393 | 600 | 746083 | 1239.3883 | 4.0833 | 250.5033 | 2.3800 | 0.0033 | 0.2015 | 0.2716 | 0.4763 |
| 10.3627 | 2.0 | 84 | 10.4901 | 578.0875 | 167.1479 | 735121 | 10962 | 136397 | 10962 | 136397 | 201434 | 1433.1820 | 348793 | 600 | 746083 | 1225.2017 | 18.27 | 227.3283 | 2.3886 | 0.0147 | 0.1828 | 0.2700 | 0.4675 |
| 9.9444 | 3.0 | 126 | 10.3015 | 569.6851 | 166.4943 | 715188 | 30895 | 127221 | 30895 | 127221 | 194291 | 1435.5347 | 352407 | 600 | 746083 | 1191.98 | 51.4917 | 212.035 | 2.3926 | 0.0414 | 0.1705 | 0.2604 | 0.4723 |
| 9.7658 | 4.0 | 168 | 10.2071 | 572.0536 | 166.8688 | 706081 | 40002 | 122962 | 40002 | 122962 | 191357 | 1436.2147 | 354321 | 600 | 746083 | 1176.8017 | 66.67 | 204.9367 | 2.3937 | 0.0536 | 0.1648 | 0.2565 | 0.4749 |
| 9.5093 | 5.0 | 210 | 10.1640 | 572.3712 | 166.9189 | 703250 | 42833 | 121335 | 42833 | 121335 | 190255 | 1436.8813 | 354423 | 600 | 746083 | 1172.0833 | 71.3883 | 202.225 | 2.3948 | 0.0574 | 0.1626 | 0.2550 | 0.4750 |
| 9.3069 | 6.0 | 252 | 10.1287 | 573.2534 | 167.0644 | 700202 | 45881 | 119938 | 45881 | 119938 | 189349 | 1436.9886 | 355168 | 600 | 746083 | 1167.0033 | 76.4683 | 199.8967 | 2.3950 | 0.0615 | 0.1608 | 0.2538 | 0.4760 |
| 9.2209 | 7.0 | 294 | 10.1009 | 582.8864 | 168.6522 | 698009 | 48074 | 118866 | 48074 | 118866 | 188639 | 1437.1880 | 355579 | 600 | 746083 | 1163.3483 | 80.1233 | 198.11 | 2.3953 | 0.0644 | 0.1593 | 0.2528 | 0.4766 |
| 9.0761 | 8.0 | 336 | 10.0912 | 582.8864 | 168.6522 | 695719 | 50364 | 117834 | 50364 | 117834 | 187684 | 1437.6227 | 355882 | 600 | 746083 | 1159.5317 | 83.94 | 196.39 | 2.3960 | 0.0675 | 0.1579 | 0.2516 | 0.4770 |
| 8.9928 | 9.0 | 378 | 10.0654 | 582.8864 | 168.6522 | 694031 | 52052 | 117145 | 52052 | 117145 | 187295 | 1437.4753 | 356492 | 600 | 746083 | 1156.7183 | 86.7533 | 195.2417 | 2.3958 | 0.0698 | 0.1570 | 0.2510 | 0.4778 |
| 8.9674 | 10.0 | 420 | 10.0666 | 582.8864 | 168.6522 | 693831 | 52252 | 117030 | 52252 | 117030 | 187216 | 1437.5240 | 356498 | 600 | 746083 | 1156.385 | 87.0867 | 195.05 | 2.3959 | 0.0700 | 0.1569 | 0.2509 | 0.4778 |
### Framework versions
- Transformers 4.36.2
- Pytorch 2.0.0
- Datasets 2.16.1
- Tokenizers 0.15.0
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