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# whisper-small-aishell

This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the aishell zh-cn dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1770
- Wer: 0.4068

## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data

train data：aishell train
test data：aishell test

## 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
- distributed_type: multi-GPU
- num_devices: 4
- gradient_accumulation_steps: 4
- total_train_batch_size: 512
- total_eval_batch_size: 128
- 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    |Cer     |
|:-------------:|:-----:|:-----:|:---------------:|:------:|:------:|
| 0.042         | 4.26  | 1000  | 0.1227          | 0.3990 |
| 0.0134        | 8.52  | 2000  | 0.1312          | 0.4004 |
| 0.0042        | 12.78 | 3000  | 0.1402          | 0.4027 |0.051   |
| 0.0022        | 17.04 | 4000  | 0.1479          | 0.4045 |
| 0.001         | 21.3  | 5000  | 0.1568          | 0.4069 |
| 0.0007        | 25.56 | 6000  | 0.1568          | 0.3990 |
| 0.0004        | 29.82 | 7000  | 0.1644          | 0.4037 |
| 0.0003        | 34.08 | 8000  | 0.1697          | 0.4045 |
| 0.0002        | 38.34 | 9000  | 0.1751          | 0.4072 |
| 0.0002        | 42.6  | 10000 | 0.1770          | 0.4068 |


### Framework versions

- Transformers 4.28.0.dev0
- Pytorch 1.13.0
- Datasets 2.10.1
- Tokenizers 0.13.2

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["aishell"], "metrics": ["wer"], "model-index": [{"name": "whisper-small-aishell", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Automatic Speech Recognition"}, "dataset": {"name": "aishell", "type": "aishell", "config": "zh-cn", "split": "test", "args": "zh-cn"}, "metrics": [{"type": "wer", "value": 0.4067725752508361, "name": "Wer"}]}]}]}
transformers pytorch tensorboard whisper automatic-speech-recognition generated_from_trainer dataset:aishell license:apache-2.0 model-index endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #whisper #automatic-speech-recognition #generated_from_trainer #dataset-aishell #license-apache-2.0 #model-index #endpoints_compatible #region-us 

whisper-small-aishell
=====================


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


* Loss: 0.1770
* Wer: 0.4068


Model description
-----------------


More information needed


Intended uses & limitations
---------------------------


More information needed


Training and evaluation data
----------------------------


train data：aishell train
test data：aishell test


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
* distributed\_type: multi-GPU
* num\_devices: 4
* gradient\_accumulation\_steps: 4
* total\_train\_batch\_size: 512
* total\_eval\_batch\_size: 128
* 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 1.13.0
* Datasets 2.10.1
* 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
* distributed\_type: multi-GPU
* num\_devices: 4
* gradient\_accumulation\_steps: 4
* total\_train\_batch\_size: 512
* total\_eval\_batch\_size: 128
* 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 1.13.0
* Datasets 2.10.1
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #tensorboard #whisper #automatic-speech-recognition #generated_from_trainer #dataset-aishell #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
* distributed\_type: multi-GPU
* num\_devices: 4
* gradient\_accumulation\_steps: 4
* total\_train\_batch\_size: 512
* total\_eval\_batch\_size: 128
* 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 1.13.0
* Datasets 2.10.1
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #tensorboard #whisper #automatic-speech-recognition #generated_from_trainer #dataset-aishell #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
* distributed\_type: multi-GPU
* num\_devices: 4
* gradient\_accumulation\_steps: 4
* total\_train\_batch\_size: 512
* total\_eval\_batch\_size: 128
* 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 1.13.0
* Datasets 2.10.1
* Tokenizers 0.13.2