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# scream_non_large_3e06_beams5_v2

This model is a fine-tuned version of [openai/whisper-large-v2](https://huggingface.co/openai/whisper-large-v2) on the NbAiLab/NCC_speech_all_v5 dataset.
It achieves the following results on the evaluation set:
- step: 29999
- eval_loss: 1.2308
- train_loss: 0.2858
- eval_wer: 10.3228
- eval_cer: 5.6236

## 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: 3e-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: 30,000
- starting_optimization_step: None
- finishing_optimization_step: 30,000
- num_train_dataset_workers: 32
- numb_hosts: 8
- total_num_training_examples: 4,800,000
- steps_per_epoch: 8543
- num_beams: 5

### Training results

| step  | eval_loss | train_loss | eval_wer | eval_cer |
|:-----:|:---------:|:----------:|:--------:|:--------:|
| 0     | 2.0006    | 0.8864     | 15.7125  | 8.0675   |
| 5000  | 1.4781    | 0.3285     | 10.3532  | 5.0340   |
| 10000 | 1.2340    | 0.3277     | 10.8709  | 5.7596   |
| 15000 | 1.1556    | 0.3272     | 10.3837  | 5.3515   |
| 20000 | 1.2220    | 0.2775     | 10.2314  | 5.5026   |
| 25000 | 1.2454    | 0.2367     | 10.1705  | 5.4976   |
| 29999 | 1.2308    | 0.2858     | 10.3228  | 5.6236   |


### Framework versions

- Transformers 4.29.0.dev0
- 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_3e06_beams5_v2", "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\_3e06\_beams5\_v2
====================================


This model is a fine-tuned version of openai/whisper-large-v2 on the NbAiLab/NCC\_speech\_all\_v5 dataset.
It achieves the following results on the evaluation set:


* step: 29999
* eval\_loss: 1.2308
* train\_loss: 0.2858
* eval\_wer: 10.3228
* eval\_cer: 5.6236


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: 3e-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: 30,000
* starting\_optimization\_step: None
* finishing\_optimization\_step: 30,000
* num\_train\_dataset\_workers: 32
* numb\_hosts: 8
* total\_num\_training\_examples: 4,800,000
* steps\_per\_epoch: 8543
* num\_beams: 5


### Training results



### Framework versions


* Transformers 4.29.0.dev0
* Datasets 2.11.0
* Tokenizers 0.13.3



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 3e-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: 30,000
* starting\_optimization\_step: None
* finishing\_optimization\_step: 30,000
* num\_train\_dataset\_workers: 32
* numb\_hosts: 8
* total\_num\_training\_examples: 4,800,000
* steps\_per\_epoch: 8543
* num\_beams: 5 ### Training results ### Framework versions


* Transformers 4.29.0.dev0
* 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: 3e-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: 30,000
* starting\_optimization\_step: None
* finishing\_optimization\_step: 30,000
* num\_train\_dataset\_workers: 32
* numb\_hosts: 8
* total\_num\_training\_examples: 4,800,000
* steps\_per\_epoch: 8543
* num\_beams: 5 ### Training results ### Framework versions


* Transformers 4.29.0.dev0
* 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: 3e-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: 30,000
* starting\_optimization\_step: None
* finishing\_optimization\_step: 30,000
* num\_train\_dataset\_workers: 32
* numb\_hosts: 8
* total\_num\_training\_examples: 4,800,000
* steps\_per\_epoch: 8543
* num\_beams: 5### Training results### Framework versions


* Transformers 4.29.0.dev0
* Datasets 2.11.0
* Tokenizers 0.13.3