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# fine-tuned-vctkdataset

This model is a fine-tuned version of [jonatasgrosman/wav2vec2-large-xlsr-53-english](https://huggingface.co/jonatasgrosman/wav2vec2-large-xlsr-53-english) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1289
- Wer: 0.1950

## 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: 0.0001
- train_batch_size: 32
- 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: 1000
- num_epochs: 30
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Wer    |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| No log        | 1.0   | 125  | 2.8536          | 1.0    |
| No log        | 2.0   | 250  | 1.6881          | 1.0168 |
| No log        | 3.0   | 375  | 0.3762          | 0.4656 |
| 1.8553        | 4.0   | 500  | 0.1728          | 0.2567 |
| 1.8553        | 5.0   | 625  | 0.1234          | 0.2073 |
| 1.8553        | 6.0   | 750  | 0.1231          | 0.2007 |
| 1.8553        | 7.0   | 875  | 0.1225          | 0.2013 |
| 0.1847        | 8.0   | 1000 | 0.1211          | 0.1959 |
| 0.1847        | 9.0   | 1125 | 0.1217          | 0.2007 |
| 0.1847        | 10.0  | 1250 | 0.1221          | 0.1980 |
| 0.1847        | 11.0  | 1375 | 0.1258          | 0.1983 |
| 0.1016        | 12.0  | 1500 | 0.1307          | 0.1959 |
| 0.1016        | 13.0  | 1625 | 0.1179          | 0.1942 |
| 0.1016        | 14.0  | 1750 | 0.1248          | 0.1983 |
| 0.1016        | 15.0  | 1875 | 0.1281          | 0.1954 |
| 0.0756        | 16.0  | 2000 | 0.1199          | 0.1967 |
| 0.0756        | 17.0  | 2125 | 0.1284          | 0.1967 |
| 0.0756        | 18.0  | 2250 | 0.1338          | 0.1982 |
| 0.0756        | 19.0  | 2375 | 0.1270          | 0.1987 |
| 0.0699        | 20.0  | 2500 | 0.1298          | 0.1964 |
| 0.0699        | 21.0  | 2625 | 0.1319          | 0.1950 |
| 0.0699        | 22.0  | 2750 | 0.1254          | 0.1925 |
| 0.0699        | 23.0  | 2875 | 0.1285          | 0.1949 |
| 0.0491        | 24.0  | 3000 | 0.1303          | 0.1948 |
| 0.0491        | 25.0  | 3125 | 0.1293          | 0.1964 |
| 0.0491        | 26.0  | 3250 | 0.1294          | 0.1969 |
| 0.0491        | 27.0  | 3375 | 0.1279          | 0.1952 |
| 0.043         | 28.0  | 3500 | 0.1318          | 0.1942 |
| 0.043         | 29.0  | 3625 | 0.1287          | 0.1950 |
| 0.043         | 30.0  | 3750 | 0.1289          | 0.1950 |


### Framework versions

- Transformers 4.29.0.dev0
- Pytorch 2.0.0+cu118
- Datasets 1.14.0
- Tokenizers 0.13.3

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["wer"], "model-index": [{"name": "fine-tuned-vctkdataset", "results": []}]}
transformers pytorch tensorboard wav2vec2 automatic-speech-recognition generated_from_trainer license:apache-2.0 endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us 

fine-tuned-vctkdataset
======================


This model is a fine-tuned version of jonatasgrosman/wav2vec2-large-xlsr-53-english on the None dataset.
It achieves the following results on the evaluation set:


* Loss: 0.1289
* Wer: 0.1950


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: 0.0001
* train\_batch\_size: 32
* 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: 1000
* num\_epochs: 30
* mixed\_precision\_training: Native AMP


### Training results



### Framework versions


* Transformers 4.29.0.dev0
* Pytorch 2.0.0+cu118
* Datasets 1.14.0
* Tokenizers 0.13.3



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 0.0001
* train\_batch\_size: 32
* 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: 1000
* num\_epochs: 30
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.29.0.dev0
* Pytorch 2.0.0+cu118
* Datasets 1.14.0
* Tokenizers 0.13.3
TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 0.0001
* train\_batch\_size: 32
* 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: 1000
* num\_epochs: 30
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.29.0.dev0
* Pytorch 2.0.0+cu118
* Datasets 1.14.0
* Tokenizers 0.13.3
TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us 
### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 0.0001
* train\_batch\_size: 32
* 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: 1000
* num\_epochs: 30
* mixed\_precision\_training: Native AMP### Training results### Framework versions


* Transformers 4.29.0.dev0
* Pytorch 2.0.0+cu118
* Datasets 1.14.0
* Tokenizers 0.13.3