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# wav2vec2-base-finetuned-spgispeech-dev

This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the [kensho/spgispeech](https://huggingface.co/datasets/kensho/spgispeech) dev dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2897
- Wer: 0.1508

## 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: 16
- 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: 50
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Wer    |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 1.8285        | 2.22  | 1500  | 0.3361          | 0.2754 |
| 0.2582        | 4.44  | 3000  | 0.2643          | 0.2205 |
| 0.1697        | 6.66  | 4500  | 0.2467          | 0.2006 |
| 0.1314        | 8.88  | 6000  | 0.2711          | 0.1927 |
| 0.1084        | 11.09 | 7500  | 0.2521          | 0.1872 |
| 0.0922        | 13.31 | 9000  | 0.2588          | 0.1827 |
| 0.0818        | 15.53 | 10500 | 0.2572          | 0.1783 |
| 0.0712        | 17.75 | 12000 | 0.2720          | 0.1766 |
| 0.067         | 19.97 | 13500 | 0.2873          | 0.1751 |
| 0.0594        | 22.19 | 15000 | 0.2753          | 0.1704 |
| 0.0546        | 24.41 | 16500 | 0.2794          | 0.1694 |
| 0.0505        | 26.63 | 18000 | 0.2811          | 0.1665 |
| 0.0467        | 28.85 | 19500 | 0.2906          | 0.1657 |
| 0.0417        | 31.07 | 21000 | 0.3043          | 0.1661 |
| 0.0395        | 33.28 | 22500 | 0.3068          | 0.1627 |
| 0.0368        | 35.5  | 24000 | 0.3096          | 0.1617 |
| 0.0334        | 37.72 | 25500 | 0.3036          | 0.1581 |
| 0.0322        | 39.94 | 27000 | 0.2819          | 0.1564 |
| 0.0286        | 42.16 | 28500 | 0.2936          | 0.1544 |
| 0.0279        | 44.38 | 30000 | 0.2914          | 0.1534 |
| 0.0264        | 46.6  | 31500 | 0.2957          | 0.1519 |
| 0.0241        | 48.82 | 33000 | 0.2897          | 0.1508 |


### Framework versions

- Transformers 4.17.0
- Pytorch 1.12.1+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["kensho/spgispeech"], "widget": [{"example_title": "Finance Speech", "src": "https://drive.google.com/uc?id=151bzDnN_f0Dfjjrg36nI97tXM39t5Ka8"}], "model-index": [{"name": "wav2vec2-base-finetuned-spgispeech-dev", "results": []}]}
transformers pytorch tensorboard wav2vec2 automatic-speech-recognition generated_from_trainer dataset:kensho/spgispeech license:apache-2.0 endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-kensho/spgispeech #license-apache-2.0 #endpoints_compatible #region-us 

wav2vec2-base-finetuned-spgispeech-dev
======================================


This model is a fine-tuned version of facebook/wav2vec2-base on the kensho/spgispeech dev dataset.
It achieves the following results on the evaluation set:


* Loss: 0.2897
* Wer: 0.1508


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: 16
* 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: 50
* mixed\_precision\_training: Native AMP


### Training results



### Framework versions


* Transformers 4.17.0
* Pytorch 1.12.1+cu113
* Datasets 2.4.0
* Tokenizers 0.12.1



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 0.0001
* train\_batch\_size: 16
* 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: 50
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.17.0
* Pytorch 1.12.1+cu113
* Datasets 2.4.0
* Tokenizers 0.12.1
TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-kensho/spgispeech #license-apache-2.0 #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 0.0001
* train\_batch\_size: 16
* 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: 50
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.17.0
* Pytorch 1.12.1+cu113
* Datasets 2.4.0
* Tokenizers 0.12.1
passage: TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-kensho/spgispeech #license-apache-2.0 #endpoints_compatible #region-us 
### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 0.0001
* train\_batch\_size: 16
* 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: 50
* mixed\_precision\_training: Native AMP### Training results### Framework versions


* Transformers 4.17.0
* Pytorch 1.12.1+cu113
* Datasets 2.4.0
* Tokenizers 0.12.1