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---
license: apache-2.0
base_model: openai/whisper-small
tags:
- generated_from_trainer
model-index:
- name: whisper-small-diarization-0.2
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-small-diarization-0.2
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: 0.4057
- Speech Scored: 802.6678
- Speech Miss: 209.2589
- Speech Falarm: 9.0497
- Speaker Miss: 412.5650
- Speaker Falarm: 185.8256
- Speaker Error: 153.1979
- Speaker Correct: 1198.4045
- Diarization Error: 751.5885
- Frames: 1500.0
- Speaker Wide Frames: 1564.7402
- Speech Scored Ratio: 0.5351
- Speech Miss Ratio: 0.1395
- Speech Falarm Ratio: 0.0060
- Speaker Correct Ratio: 0.7989
- Speaker Miss Ratio: 0.2382
- Speaker Falarm Ratio: 0.1203
- Speaker Error Ratio: 0.0831
- Diarization Error Ratio: 0.4416
## 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: 16
- eval_batch_size: 24
- seed: 42
- 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 | 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 |
|:-------------:|:-----:|:----:|:---------------:|:-------------:|:-----------:|:-------------:|:------------:|:--------------:|:-------------:|:---------------:|:-----------------:|:------:|:-------------------:|:-------------------:|:-----------------:|:-------------------:|:---------------------:|:------------------:|:--------------------:|:-------------------:|:-----------------------:|
| 0.4753 | 1.0 | 225 | 0.4855 | 731.3740 | 280.5527 | 69.0541 | 614.8317 | 199.7986 | 151.0314 | 1127.7690 | 965.6617 | 1500.0 | 1564.7402 | 0.4876 | 0.1870 | 0.0460 | 0.7518 | 0.3772 | 0.1827 | 0.0796 | 0.6395 |
| 0.4572 | 2.0 | 450 | 0.4857 | 642.0541 | 369.8727 | 29.7105 | 773.0375 | 89.8413 | 131.1212 | 1124.9596 | 994.0 | 1500.0 | 1564.7402 | 0.4280 | 0.2466 | 0.0198 | 0.7500 | 0.4566 | 0.0839 | 0.0709 | 0.6114 |
| 0.4638 | 3.0 | 675 | 0.4618 | 748.0445 | 263.8823 | 43.8745 | 466.9677 | 356.8117 | 116.3025 | 1147.8718 | 940.0820 | 1500.0 | 1564.7402 | 0.4987 | 0.1759 | 0.0292 | 0.7652 | 0.2922 | 0.2364 | 0.0631 | 0.5917 |
| 0.4423 | 4.0 | 900 | 0.4477 | 740.9529 | 270.9738 | 30.4473 | 509.6905 | 235.4464 | 132.9747 | 1162.9712 | 878.1116 | 1500.0 | 1564.7402 | 0.4940 | 0.1806 | 0.0203 | 0.7753 | 0.3072 | 0.1581 | 0.0712 | 0.5365 |
| 0.4164 | 5.0 | 1125 | 0.4309 | 737.6809 | 274.2459 | 12.4037 | 512.4150 | 173.0157 | 138.8300 | 1178.9698 | 824.2607 | 1500.0 | 1564.7402 | 0.4918 | 0.1828 | 0.0083 | 0.7860 | 0.3010 | 0.1130 | 0.0754 | 0.4893 |
| 0.3924 | 6.0 | 1350 | 0.4112 | 812.0453 | 199.8814 | 13.5414 | 382.1125 | 253.1543 | 140.2999 | 1194.7111 | 775.5667 | 1500.0 | 1564.7402 | 0.5414 | 0.1333 | 0.0090 | 0.7965 | 0.2235 | 0.1713 | 0.0755 | 0.4702 |
| 0.3765 | 7.0 | 1575 | 0.4085 | 806.7515 | 205.1752 | 12.1369 | 405.6992 | 202.0323 | 149.4699 | 1197.7762 | 757.2014 | 1500.0 | 1564.7402 | 0.5378 | 0.1368 | 0.0081 | 0.7985 | 0.2361 | 0.1250 | 0.0829 | 0.4439 |
| 0.3814 | 8.0 | 1800 | 0.4051 | 802.6016 | 209.3252 | 9.5911 | 398.2677 | 213.9582 | 144.1378 | 1199.8329 | 756.3636 | 1500.0 | 1564.7402 | 0.5351 | 0.1396 | 0.0064 | 0.7999 | 0.2367 | 0.1275 | 0.0794 | 0.4436 |
| 0.3965 | 9.0 | 2025 | 0.4111 | 768.8736 | 243.0532 | 6.9250 | 474.9355 | 148.3069 | 146.9695 | 1194.2729 | 770.2119 | 1500.0 | 1564.7402 | 0.5126 | 0.1620 | 0.0046 | 0.7962 | 0.2742 | 0.0932 | 0.0806 | 0.4480 |
| 0.4048 | 10.0 | 2250 | 0.4057 | 802.6678 | 209.2589 | 9.0497 | 412.5650 | 185.8256 | 153.1979 | 1198.4045 | 751.5885 | 1500.0 | 1564.7402 | 0.5351 | 0.1395 | 0.0060 | 0.7989 | 0.2382 | 0.1203 | 0.0831 | 0.4416 |
### Framework versions
- Transformers 4.36.2
- Pytorch 2.0.0
- Datasets 2.16.1
- Tokenizers 0.15.0
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