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# scream_sextusdecimus_virtual_tsfix_medium_1e5

This model is a fine-tuned version of [openai/whisper-medium](https://huggingface.co/openai/whisper-medium) on the NbAiLab/ncc_speech dataset.
It achieves the following results on the evaluation set:
- step: 19999
- eval_loss: 1.6336
- train_loss: 0.6795
- eval_wer: 7.9120
- eval_cer: 3.4474
- eval_exact_wer: 7.9120
- eval_exact_cer: 3.4474

## 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
- lr_scheduler_type: linear
- per_device_train_batch_size: 16
- total_train_batch_size_per_node: 64
- total_train_batch_size: 512
- total_optimization_steps: 20,000
- starting_optimization_step: None
- finishing_optimization_step: 20,000
- num_train_dataset_workers: 32
- num_hosts: 8
- total_num_training_examples: 10,240,000
- steps_per_epoch: _To be computed after first epoch_
- num_beams: None
- dropout: True
- bpe_dropout_probability: 0.1
- activation_dropout_probability: 0.1

### Training results

| step  | eval_loss | train_loss | eval_wer | eval_cer | eval_exact_wer | eval_exact_cer |
|:-----:|:---------:|:----------:|:--------:|:--------:|:--------------:|:--------------:|
| 0     | 5.5890    | 2.8362     | 17.4598  | 5.3906   | 17.4598        | 5.3906         |
| 1000  | 5.2798    | 1.0896     | 12.4926  | 3.8321   | 12.4926        | 3.8321         |
| 2000  | 5.2432    | 0.9018     | 11.0351  | 3.9899   | 11.0351        | 3.9899         |
| 3000  | 4.1719    | 0.8159     | 9.8453   | 3.8173   | 9.8453         | 3.8173         |
| 4000  | 3.0758    | 0.7799     | 9.6371   | 3.8716   | 9.6371         | 3.8716         |
| 5000  | 2.2223    | 0.7803     | 9.7264   | 3.9110   | 9.7264         | 3.9110         |
| 6000  | 2.0574    | 0.7206     | 9.5181   | 3.8864   | 9.5181         | 3.8864         |
| 7000  | 1.7271    | 0.7088     | 8.7745   | 3.7039   | 8.7745         | 3.7039         |
| 8000  | 1.5868    | 0.7528     | 8.2391   | 3.5362   | 8.2391         | 3.5362         |
| 9000  | 1.5781    | 0.6747     | 8.2094   | 3.5313   | 8.2094         | 3.5313         |
| 10000 | 1.6658    | 0.6830     | 8.1499   | 3.4277   | 8.1499         | 3.4277         |
| 11000 | 1.5514    | 0.7141     | 8.6853   | 3.8814   | 8.6853         | 3.8814         |
| 12000 | 1.8042    | 0.6941     | 8.5366   | 3.6792   | 8.5366         | 3.6792         |
| 13000 | 1.7561    | 0.6732     | 8.6258   | 3.8666   | 8.6258         | 3.8666         |
| 14000 | 1.7517    | 0.7050     | 8.2094   | 3.5066   | 8.2094         | 3.5066         |
| 15000 | 1.7413    | 0.7191     | 7.8822   | 3.3389   | 7.8822         | 3.3389         |
| 16000 | 1.7014    | 0.6850     | 8.0309   | 3.4178   | 8.0309         | 3.4178         |
| 17000 | 1.7205    | 0.6937     | 7.8822   | 3.4524   | 7.8822         | 3.4524         |
| 18000 | 1.5928    | 0.7014     | 7.8227   | 3.4425   | 7.8227         | 3.4425         |
| 19000 | 1.5883    | 0.7102     | 7.9417   | 3.4573   | 7.9417         | 3.4573         |
| 19999 | 1.6336    | 0.6795     | 7.9120   | 3.4474   | 7.9120         | 3.4474         |


### Framework versions

- Transformers 4.30.0.dev0
- Datasets 2.12.1.dev0
- Tokenizers 0.13.3

{"language": ["no"], "license": "apache-2.0", "tags": ["audio", "asr", "automatic-speech-recognition", "hf-asr-leaderboard"], "model-index": [{"name": "scream_sextusdecimus_virtual_tsfix_medium_1e5", "results": []}]}
transformers pytorch jax tensorboard whisper automatic-speech-recognition audio asr hf-asr-leaderboard no license:apache-2.0 endpoints_compatible region:us
TAGS
#transformers #pytorch #jax #tensorboard #whisper #automatic-speech-recognition #audio #asr #hf-asr-leaderboard #no #license-apache-2.0 #endpoints_compatible #region-us 

scream\_sextusdecimus\_virtual\_tsfix\_medium\_1e5
==================================================


This model is a fine-tuned version of openai/whisper-medium on the NbAiLab/ncc\_speech dataset.
It achieves the following results on the evaluation set:


* step: 19999
* eval\_loss: 1.6336
* train\_loss: 0.6795
* eval\_wer: 7.9120
* eval\_cer: 3.4474
* eval\_exact\_wer: 7.9120
* eval\_exact\_cer: 3.4474


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
* lr\_scheduler\_type: linear
* per\_device\_train\_batch\_size: 16
* total\_train\_batch\_size\_per\_node: 64
* total\_train\_batch\_size: 512
* total\_optimization\_steps: 20,000
* starting\_optimization\_step: None
* finishing\_optimization\_step: 20,000
* num\_train\_dataset\_workers: 32
* num\_hosts: 8
* total\_num\_training\_examples: 10,240,000
* steps\_per\_epoch: *To be computed after first epoch*
* num\_beams: None
* dropout: True
* bpe\_dropout\_probability: 0.1
* activation\_dropout\_probability: 0.1


### Training results



### Framework versions


* Transformers 4.30.0.dev0
* Datasets 2.12.1.dev0
* Tokenizers 0.13.3



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 1e-05
* lr\_scheduler\_type: linear
* per\_device\_train\_batch\_size: 16
* total\_train\_batch\_size\_per\_node: 64
* total\_train\_batch\_size: 512
* total\_optimization\_steps: 20,000
* starting\_optimization\_step: None
* finishing\_optimization\_step: 20,000
* num\_train\_dataset\_workers: 32
* num\_hosts: 8
* total\_num\_training\_examples: 10,240,000
* steps\_per\_epoch: *To be computed after first epoch*
* num\_beams: None
* dropout: True
* bpe\_dropout\_probability: 0.1
* activation\_dropout\_probability: 0.1 ### Training results ### Framework versions


* Transformers 4.30.0.dev0
* Datasets 2.12.1.dev0
* Tokenizers 0.13.3
TAGS
#transformers #pytorch #jax #tensorboard #whisper #automatic-speech-recognition #audio #asr #hf-asr-leaderboard #no #license-apache-2.0 #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 1e-05
* lr\_scheduler\_type: linear
* per\_device\_train\_batch\_size: 16
* total\_train\_batch\_size\_per\_node: 64
* total\_train\_batch\_size: 512
* total\_optimization\_steps: 20,000
* starting\_optimization\_step: None
* finishing\_optimization\_step: 20,000
* num\_train\_dataset\_workers: 32
* num\_hosts: 8
* total\_num\_training\_examples: 10,240,000
* steps\_per\_epoch: *To be computed after first epoch*
* num\_beams: None
* dropout: True
* bpe\_dropout\_probability: 0.1
* activation\_dropout\_probability: 0.1 ### Training results ### Framework versions


* Transformers 4.30.0.dev0
* Datasets 2.12.1.dev0
* Tokenizers 0.13.3