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# openai/whisper-medium

This model is a fine-tuned version of [openai/whisper-medium](https://huggingface.co/openai/whisper-medium) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3622
- Wer: 10.6091

## 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: 32
- eval_batch_size: 32
- 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: 4000
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Wer     |
|:-------------:|:-----:|:----:|:---------------:|:-------:|
| 0.5314        | 0.12  | 500  | 0.2762          | 13.2758 |
| 0.1619        | 1.09  | 1000 | 0.2541          | 11.6909 |
| 0.069         | 2.05  | 1500 | 0.2768          | 10.2892 |
| 0.0756        | 3.02  | 2000 | 0.2756          | 11.6142 |
| 0.0324        | 3.14  | 2500 | 0.2961          | 11.1800 |
| 0.0171        | 4.11  | 3000 | 0.3322          | 11.1689 |
| 0.0046        | 5.07  | 3500 | 0.3653          | 10.5858 |
| 0.0091        | 6.03  | 4000 | 0.3622          | 10.6091 |


### Framework versions

- Transformers 4.29.0
- Pytorch 1.14.0a0+44dac51
- Datasets 2.12.0
- Tokenizers 0.13.3

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["wer"], "model-index": [{"name": "openai/whisper-medium", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Automatic Speech Recognition"}, "dataset": {"name": "rishabhjain16/infer_myst", "type": "rishabhjain16/infer_myst", "config": "en", "split": "test"}, "metrics": [{"type": "wer", "value": 11.96, "name": "WER"}]}, {"task": {"type": "automatic-speech-recognition", "name": "Automatic Speech Recognition"}, "dataset": {"name": "rishabhjain16/infer_pfs", "type": "rishabhjain16/infer_pfs", "config": "en", "split": "test"}, "metrics": [{"type": "wer", "value": 3.12, "name": "WER"}]}, {"task": {"type": "automatic-speech-recognition", "name": "Automatic Speech Recognition"}, "dataset": {"name": "rishabhjain16/infer_cmu", "type": "rishabhjain16/infer_cmu", "config": "en", "split": "test"}, "metrics": [{"type": "wer", "value": 8.92, "name": "WER"}]}, {"task": {"type": "automatic-speech-recognition", "name": "Automatic Speech Recognition"}, "dataset": {"name": "rishabhjain16/libritts_dev_clean", "type": "rishabhjain16/libritts_dev_clean", "config": "en", "split": "test"}, "metrics": [{"type": "wer", "value": 5.39, "name": "WER"}]}, {"task": {"type": "automatic-speech-recognition", "name": "Automatic Speech Recognition"}, "dataset": {"name": "rishabhjain16/infer_pf_swedish", "type": "rishabhjain16/infer_pf_swedish", "config": "en", "split": "test"}, "metrics": [{"type": "wer", "value": 7.74, "name": "WER"}]}, {"task": {"type": "automatic-speech-recognition", "name": "Automatic Speech Recognition"}, "dataset": {"name": "rishabhjain16/infer_pf_german", "type": "rishabhjain16/infer_pf_german", "config": "en", "split": "test"}, "metrics": [{"type": "wer", "value": 36.21, "name": "WER"}]}, {"task": {"type": "automatic-speech-recognition", "name": "Automatic Speech Recognition"}, "dataset": {"name": "rishabhjain16/infer_pf_italian", "type": "rishabhjain16/infer_pf_italian", "config": "en", "split": "test"}, "metrics": [{"type": "wer", "value": 4.16, "name": "WER"}]}, {"task": {"type": "automatic-speech-recognition", "name": "Automatic Speech Recognition"}, "dataset": {"name": "rishabhjain16/infer_so_chinese", "type": "rishabhjain16/infer_so_chinese", "config": "en", "split": "test"}, "metrics": [{"type": "wer", "value": 14.4, "name": "WER"}]}]}]}
transformers pytorch tensorboard whisper automatic-speech-recognition generated_from_trainer license:apache-2.0 model-index endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #whisper #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #model-index #endpoints_compatible #region-us 

openai/whisper-medium
=====================


This model is a fine-tuned version of openai/whisper-medium on the None dataset.
It achieves the following results on the evaluation set:


* Loss: 0.3622
* Wer: 10.6091


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: 32
* eval\_batch\_size: 32
* 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: 4000
* mixed\_precision\_training: Native AMP


### Training results



### Framework versions


* Transformers 4.29.0
* Pytorch 1.14.0a0+44dac51
* Datasets 2.12.0
* Tokenizers 0.13.3



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 1e-05
* train\_batch\_size: 32
* eval\_batch\_size: 32
* 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: 4000
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.29.0
* Pytorch 1.14.0a0+44dac51
* Datasets 2.12.0
* Tokenizers 0.13.3
TAGS
#transformers #pytorch #tensorboard #whisper #automatic-speech-recognition #generated_from_trainer #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: 32
* eval\_batch\_size: 32
* 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: 4000
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.29.0
* Pytorch 1.14.0a0+44dac51
* Datasets 2.12.0
* Tokenizers 0.13.3