
<!-- 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_sft_de

This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the Common Voice 11.0 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1840
- Wer: 28.6081

## 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: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 10000
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Wer     |
|:-------------:|:-----:|:-----:|:---------------:|:-------:|
| 0.2232        | 0.08  | 1000  | 0.2541          | 70.0448 |
| 0.2266        | 0.16  | 2000  | 0.2393          | 40.9830 |
| 0.22          | 0.24  | 3000  | 0.2278          | 42.1428 |
| 0.2105        | 0.32  | 4000  | 0.2184          | 41.9078 |
| 0.2108        | 0.4   | 5000  | 0.2090          | 38.9711 |
| 0.1869        | 0.48  | 6000  | 0.2034          | 28.8377 |
| 0.161         | 0.56  | 7000  | 0.1974          | 25.5598 |
| 0.1667        | 0.64  | 8000  | 0.1911          | 27.8122 |
| 0.1891        | 0.72  | 9000  | 0.1860          | 28.4944 |
| 0.1917        | 0.8   | 10000 | 0.1840          | 28.6081 |


### Framework versions

- Transformers 4.28.0.dev0
- Pytorch 2.0.0
- Datasets 2.11.0
- Tokenizers 0.13.3

{"language": ["de"], "license": "apache-2.0", "tags": ["hf-asr-leaderboard", "generated_from_trainer"], "datasets": ["mozilla-foundation/common_voice_11_0"], "metrics": ["wer"], "model-index": [{"name": "whisper_sft_de", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Automatic Speech Recognition"}, "dataset": {"name": "Common Voice 11.0", "type": "mozilla-foundation/common_voice_11_0", "config": "de", "split": "validation[:10000]", "args": "config: german, split: test"}, "metrics": [{"type": "wer", "value": 28.608091133538355, "name": "Wer"}]}]}]}
transformers pytorch tensorboard whisper automatic-speech-recognition hf-asr-leaderboard generated_from_trainer de dataset:mozilla-foundation/common_voice_11_0 license:apache-2.0 model-index endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #whisper #automatic-speech-recognition #hf-asr-leaderboard #generated_from_trainer #de #dataset-mozilla-foundation/common_voice_11_0 #license-apache-2.0 #model-index #endpoints_compatible #region-us 

whisper\_sft\_de
================


This model is a fine-tuned version of openai/whisper-small on the Common Voice 11.0 dataset.
It achieves the following results on the evaluation set:


* Loss: 0.1840
* Wer: 28.6081


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: 8
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 500
* training\_steps: 10000
* mixed\_precision\_training: Native AMP


### Training results



### Framework versions


* Transformers 4.28.0.dev0
* Pytorch 2.0.0
* Datasets 2.11.0
* Tokenizers 0.13.3



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 1e-05
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 500
* training\_steps: 10000
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.28.0.dev0
* Pytorch 2.0.0
* Datasets 2.11.0
* Tokenizers 0.13.3
TAGS
#transformers #pytorch #tensorboard #whisper #automatic-speech-recognition #hf-asr-leaderboard #generated_from_trainer #de #dataset-mozilla-foundation/common_voice_11_0 #license-apache-2.0 #model-index #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 1e-05
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 500
* training\_steps: 10000
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.28.0.dev0
* Pytorch 2.0.0
* Datasets 2.11.0
* Tokenizers 0.13.3
TAGS
#transformers #pytorch #tensorboard #whisper #automatic-speech-recognition #hf-asr-leaderboard #generated_from_trainer #de #dataset-mozilla-foundation/common_voice_11_0 #license-apache-2.0 #model-index #endpoints_compatible #region-us 
### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 1e-05
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 500
* training\_steps: 10000
* mixed\_precision\_training: Native AMP### Training results### Framework versions


* Transformers 4.28.0.dev0
* Pytorch 2.0.0
* Datasets 2.11.0
* Tokenizers 0.13.3