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# openai/whisper-medium.en

This model is a fine-tuned version of [openai/whisper-medium.en](https://huggingface.co/openai/whisper-medium.en) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2960
- Wer: 10.3463

## 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     |
|:-------------:|:-----:|:----:|:---------------:|:-------:|
| 0.3449        | 0.12  | 500  | 0.2603          | 10.9738 |
| 0.3078        | 1.07  | 1000 | 0.2300          | 10.4144 |
| 0.0824        | 2.01  | 1500 | 0.2341          | 9.5919  |
| 0.1783        | 2.13  | 2000 | 0.2283          | 10.2529 |
| 0.0161        | 3.08  | 2500 | 0.2648          | 10.2387 |
| 0.0088        | 4.02  | 3000 | 0.2778          | 10.1778 |
| 0.0053        | 4.14  | 3500 | 0.2852          | 10.5260 |
| 0.0083        | 5.09  | 4000 | 0.2960          | 10.3463 |


### Framework versions

- Transformers 4.27.0.dev0
- Pytorch 1.13.1+cu117
- Datasets 2.9.1.dev0
- Tokenizers 0.13.2

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["wer"], "model-index": [{"name": "openai/whisper-medium.en", "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": 12.35, "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": 3.42, "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": 2.06, "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": 5.28, "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": 9.04, "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": 35.92, "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": 5.84, "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": 17.55, "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/URL
==========


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


* Loss: 0.2960
* Wer: 10.3463


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 4.27.0.dev0
* Pytorch 1.13.1+cu117
* Datasets 2.9.1.dev0
* Tokenizers 0.13.2



### 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 4.27.0.dev0
* Pytorch 1.13.1+cu117
* Datasets 2.9.1.dev0
* Tokenizers 0.13.2
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 4.27.0.dev0
* Pytorch 1.13.1+cu117
* Datasets 2.9.1.dev0
* Tokenizers 0.13.2