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# Whisper Small Chinese - Bingcheng Hu

This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) on the Common Voice 11.0 dataset.
It achieves the following results on the evaluation set:
- Loss: 1.7064
- Wer: 226.4151

## 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
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 5
- training_steps: 400
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Wer      |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.8417        | 1.08  | 20   | 1.4964          | 598.1132 |
| 1.2552        | 2.16  | 40   | 1.4901          | 367.9245 |
| 0.8195        | 3.24  | 60   | 1.4953          | 391.5094 |
| 0.6174        | 4.32  | 80   | 1.5091          | 475.4717 |
| 0.4594        | 5.41  | 100  | 1.5318          | 520.7547 |
| 0.489         | 6.49  | 120  | 1.5558          | 863.2075 |
| 0.4673        | 7.57  | 140  | 1.5719          | 663.2075 |
| 0.3976        | 8.65  | 160  | 1.5962          | 682.0755 |
| 0.3518        | 9.73  | 180  | 1.6160          | 623.5849 |
| 0.3043        | 10.81 | 200  | 1.6219          | 620.7547 |
| 0.2524        | 11.89 | 220  | 1.6505          | 598.1132 |
| 0.259         | 12.97 | 240  | 1.6543          | 329.2453 |
| 0.1696        | 14.05 | 260  | 1.6678          | 333.0189 |
| 0.1188        | 15.14 | 280  | 1.6746          | 329.2453 |
| 0.1366        | 16.22 | 300  | 1.6852          | 428.3019 |
| 0.1165        | 17.3  | 320  | 1.6979          | 262.2642 |
| 0.1062        | 18.38 | 340  | 1.7021          | 338.6792 |
| 0.0882        | 19.46 | 360  | 1.7047          | 313.2075 |
| 0.0891        | 20.54 | 380  | 1.7054          | 302.8302 |
| 0.0676        | 21.62 | 400  | 1.7064          | 226.4151 |


### Framework versions

- Transformers 4.28.0.dev0
- Pytorch 1.11.0+cu102
- Datasets 2.10.1
- Tokenizers 0.13.2

{"language": ["zh"], "license": "apache-2.0", "tags": ["whisper", "generated_from_trainer"], "datasets": ["mozilla-foundation/common_voice_11_0"], "metrics": ["wer"], "model-index": [{"name": "Whisper Small Chinese - Bingcheng Hu", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Automatic Speech Recognition"}, "dataset": {"name": "Common Voice 11.0", "type": "mozilla-foundation/common_voice_11_0", "config": "zh-CN", "split": "test[:1%]", "args": "config: chinese, split: test"}, "metrics": [{"type": "wer", "value": 226.41509433962264, "name": "Wer"}]}]}]}
transformers pytorch tensorboard whisper automatic-speech-recognition generated_from_trainer zh 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 #generated_from_trainer #zh #dataset-mozilla-foundation/common_voice_11_0 #license-apache-2.0 #model-index #endpoints_compatible #region-us 

Whisper Small Chinese - Bingcheng Hu
====================================


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


* Loss: 1.7064
* Wer: 226.4151


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
* gradient\_accumulation\_steps: 2
* total\_train\_batch\_size: 16
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 5
* training\_steps: 400
* mixed\_precision\_training: Native AMP


### Training results



### Framework versions


* Transformers 4.28.0.dev0
* Pytorch 1.11.0+cu102
* Datasets 2.10.1
* Tokenizers 0.13.2



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 1e-05
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 42
* gradient\_accumulation\_steps: 2
* total\_train\_batch\_size: 16
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 5
* training\_steps: 400
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.28.0.dev0
* Pytorch 1.11.0+cu102
* Datasets 2.10.1
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #tensorboard #whisper #automatic-speech-recognition #generated_from_trainer #zh #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
* gradient\_accumulation\_steps: 2
* total\_train\_batch\_size: 16
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 5
* training\_steps: 400
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.28.0.dev0
* Pytorch 1.11.0+cu102
* Datasets 2.10.1
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #tensorboard #whisper #automatic-speech-recognition #generated_from_trainer #zh #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
* gradient\_accumulation\_steps: 2
* total\_train\_batch\_size: 16
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 5
* training\_steps: 400
* mixed\_precision\_training: Native AMP### Training results### Framework versions


* Transformers 4.28.0.dev0
* Pytorch 1.11.0+cu102
* Datasets 2.10.1
* Tokenizers 0.13.2