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# scream_non_large_1e06_verbosity6

This model is a fine-tuned version of [NbAiLab/scream_non_large_1e06_beams5_constantlr_long](https://huggingface.co/NbAiLab/scream_non_large_1e06_beams5_constantlr_long) on the NbAiLab/NCC_speech_all_v5 dataset.
It achieves the following results on the evaluation set:
- step: 24999
- eval_loss: 1.2068
- train_loss: 0.1588
- eval_wer: 10.8404
- eval_cer: 5.9410

## 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-06
- lr_scheduler_type: linear
- per_device_train_batch_size: 5
- total_train_batch_size_per_node: 20
- total_train_batch_size: 160
- total_optimization_steps: 25,000
- starting_optimization_step: None
- finishing_optimization_step: 25,000
- num_train_dataset_workers: 32
- num_hosts: 8
- total_num_training_examples: 4,000,000
- steps_per_epoch: 3948
- num_beams: 5

### Training results

| step  | eval_loss | train_loss | eval_wer | eval_cer |
|:-----:|:---------:|:----------:|:--------:|:--------:|
| 0     | 1.1990    | 0.1742     | 10.4750  | 5.6387   |
| 2500  | 1.2217    | 0.1679     | 10.5359  | 5.7294   |
| 5000  | 1.2039    | 0.1717     | 10.5968  | 5.7949   |
| 7500  | 1.2121    | 0.1653     | 10.6577  | 5.8050   |
| 10000 | 1.2018    | 0.1494     | 10.6577  | 5.8453   |
| 12500 | 1.2066    | 0.1707     | 10.3228  | 5.7496   |
| 15000 | 1.2399    | 0.1671     | 10.8100  | 5.8201   |
| 17500 | 1.2301    | 0.1737     | 10.9013  | 5.9209   |
| 20000 | 1.2481    | 0.1520     | 11.1145  | 6.0015   |
| 22500 | 1.2333    | 0.1635     | 11.1754  | 6.1577   |
| 24999 | 1.2068    | 0.1588     | 10.8404  | 5.9410   |


### Framework versions

- Transformers 4.28.1
- Datasets 2.11.0
- Tokenizers 0.13.3

{"language": ["no"], "license": "apache-2.0", "tags": ["audio", "asr", "automatic-speech-recognition", "hf-asr-leaderboard"], "model-index": [{"name": "scream_non_large_1e06_verbosity6", "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\_non\_large\_1e06\_verbosity6
====================================


This model is a fine-tuned version of NbAiLab/scream\_non\_large\_1e06\_beams5\_constantlr\_long on the NbAiLab/NCC\_speech\_all\_v5 dataset.
It achieves the following results on the evaluation set:


* step: 24999
* eval\_loss: 1.2068
* train\_loss: 0.1588
* eval\_wer: 10.8404
* eval\_cer: 5.9410


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-06
* lr\_scheduler\_type: linear
* per\_device\_train\_batch\_size: 5
* total\_train\_batch\_size\_per\_node: 20
* total\_train\_batch\_size: 160
* total\_optimization\_steps: 25,000
* starting\_optimization\_step: None
* finishing\_optimization\_step: 25,000
* num\_train\_dataset\_workers: 32
* num\_hosts: 8
* total\_num\_training\_examples: 4,000,000
* steps\_per\_epoch: 3948
* num\_beams: 5


### Training results



### Framework versions


* Transformers 4.28.1
* Datasets 2.11.0
* Tokenizers 0.13.3



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 1e-06
* lr\_scheduler\_type: linear
* per\_device\_train\_batch\_size: 5
* total\_train\_batch\_size\_per\_node: 20
* total\_train\_batch\_size: 160
* total\_optimization\_steps: 25,000
* starting\_optimization\_step: None
* finishing\_optimization\_step: 25,000
* num\_train\_dataset\_workers: 32
* num\_hosts: 8
* total\_num\_training\_examples: 4,000,000
* steps\_per\_epoch: 3948
* num\_beams: 5 ### Training results ### Framework versions


* Transformers 4.28.1
* Datasets 2.11.0
* 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-06
* lr\_scheduler\_type: linear
* per\_device\_train\_batch\_size: 5
* total\_train\_batch\_size\_per\_node: 20
* total\_train\_batch\_size: 160
* total\_optimization\_steps: 25,000
* starting\_optimization\_step: None
* finishing\_optimization\_step: 25,000
* num\_train\_dataset\_workers: 32
* num\_hosts: 8
* total\_num\_training\_examples: 4,000,000
* steps\_per\_epoch: 3948
* num\_beams: 5 ### Training results ### Framework versions


* Transformers 4.28.1
* Datasets 2.11.0
* 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-06
* lr\_scheduler\_type: linear
* per\_device\_train\_batch\_size: 5
* total\_train\_batch\_size\_per\_node: 20
* total\_train\_batch\_size: 160
* total\_optimization\_steps: 25,000
* starting\_optimization\_step: None
* finishing\_optimization\_step: 25,000
* num\_train\_dataset\_workers: 32
* num\_hosts: 8
* total\_num\_training\_examples: 4,000,000
* steps\_per\_epoch: 3948
* num\_beams: 5### Training results### Framework versions


* Transformers 4.28.1
* Datasets 2.11.0
* Tokenizers 0.13.3