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# model_en

This model is a fine-tuned version of [facebook/wav2vec2-large](https://huggingface.co/facebook/wav2vec2-large) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.8610
- Wer: 0.2641

## 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: 4
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 200

### Training results

| Training Loss | Epoch  | Step  | Validation Loss | Wer    |
|:-------------:|:------:|:-----:|:---------------:|:------:|
| 6.3443        | 3.05   | 250   | 3.0966          | 1.0    |
| 2.9847        | 6.1    | 500   | 3.0603          | 1.0    |
| 2.9263        | 9.15   | 750   | 2.9131          | 1.0    |
| 2.2584        | 12.19  | 1000  | 1.4318          | 0.6575 |
| 1.2603        | 15.24  | 1250  | 1.1964          | 0.4994 |
| 0.9182        | 18.29  | 1500  | 1.1494          | 0.4485 |
| 0.7462        | 21.34  | 1750  | 1.2171          | 0.4357 |
| 0.6129        | 24.39  | 2000  | 1.0557          | 0.3468 |
| 0.5364        | 27.44  | 2250  | 1.1069          | 0.4222 |
| 0.4607        | 30.48  | 2500  | 1.3270          | 0.3370 |
| 0.4139        | 33.53  | 2750  | 1.1814          | 0.3658 |
| 0.3587        | 36.58  | 3000  | 1.2423          | 0.3419 |
| 0.321         | 39.63  | 3250  | 1.2931          | 0.3211 |
| 0.2961        | 42.68  | 3500  | 1.1409          | 0.3315 |
| 0.2635        | 45.73  | 3750  | 1.4537          | 0.3241 |
| 0.2498        | 48.78  | 4000  | 1.2643          | 0.3192 |
| 0.2352        | 51.82  | 4250  | 1.2789          | 0.3278 |
| 0.2193        | 54.87  | 4500  | 1.4220          | 0.3021 |
| 0.2068        | 57.92  | 4750  | 1.3567          | 0.3713 |
| 0.2055        | 60.97  | 5000  | 1.5375          | 0.3051 |
| 0.198         | 64.02  | 5250  | 1.2676          | 0.2782 |
| 0.1835        | 67.07  | 5500  | 1.3905          | 0.2825 |
| 0.1655        | 70.12  | 5750  | 1.7000          | 0.2978 |
| 0.1677        | 73.17  | 6000  | 1.4250          | 0.2812 |
| 0.1522        | 76.22  | 6250  | 1.4220          | 0.2941 |
| 0.1522        | 79.27  | 6500  | 1.5195          | 0.3021 |
| 0.1344        | 82.32  | 6750  | 1.3749          | 0.2996 |
| 0.1298        | 85.36  | 7000  | 1.6663          | 0.2849 |
| 0.1293        | 88.41  | 7250  | 1.4564          | 0.2892 |
| 0.1264        | 91.46  | 7500  | 1.4373          | 0.2935 |
| 0.1243        | 94.51  | 7750  | 1.6572          | 0.2972 |
| 0.1141        | 97.56  | 8000  | 1.4936          | 0.2892 |
| 0.1086        | 100.61 | 8250  | 1.5231          | 0.2868 |
| 0.1056        | 103.65 | 8500  | 1.3733          | 0.2763 |
| 0.098         | 106.7  | 8750  | 1.4887          | 0.2923 |
| 0.0984        | 109.75 | 9000  | 1.3779          | 0.2923 |
| 0.0916        | 112.8  | 9250  | 1.4868          | 0.2604 |
| 0.0881        | 115.85 | 9500  | 1.7991          | 0.2996 |
| 0.0846        | 118.9  | 9750  | 1.5845          | 0.2849 |
| 0.0861        | 121.95 | 10000 | 1.6684          | 0.2794 |
| 0.0806        | 124.99 | 10250 | 1.5774          | 0.3039 |
| 0.0822        | 128.05 | 10500 | 1.5928          | 0.2886 |
| 0.0788        | 131.1  | 10750 | 1.6158          | 0.2880 |
| 0.0704        | 134.15 | 11000 | 1.7679          | 0.2941 |
| 0.0721        | 137.19 | 11250 | 1.7055          | 0.2629 |
| 0.0723        | 140.24 | 11500 | 1.5473          | 0.2653 |
| 0.0676        | 143.29 | 11750 | 1.8963          | 0.2745 |
| 0.0665        | 146.34 | 12000 | 1.6367          | 0.2739 |
| 0.0618        | 149.39 | 12250 | 1.6757          | 0.2745 |
| 0.0595        | 152.44 | 12500 | 1.5900          | 0.2745 |
| 0.056         | 155.48 | 12750 | 1.5362          | 0.2794 |
| 0.0587        | 158.53 | 13000 | 1.4616          | 0.2684 |
| 0.0519        | 161.58 | 13250 | 1.6867          | 0.2549 |
| 0.0569        | 164.63 | 13500 | 1.8294          | 0.2574 |
| 0.0497        | 167.68 | 13750 | 1.7844          | 0.2868 |
| 0.0531        | 170.73 | 14000 | 1.7564          | 0.2770 |
| 0.0489        | 173.78 | 14250 | 1.5811          | 0.2629 |
| 0.0524        | 176.82 | 14500 | 1.6925          | 0.2684 |
| 0.0431        | 179.87 | 14750 | 1.7236          | 0.2653 |
| 0.0457        | 182.92 | 15000 | 1.7460          | 0.2512 |
| 0.045         | 185.97 | 15250 | 1.8096          | 0.2610 |
| 0.0402        | 189.02 | 15500 | 1.8795          | 0.2635 |
| 0.0529        | 192.07 | 15750 | 1.8310          | 0.2616 |
| 0.0396        | 195.12 | 16000 | 1.8380          | 0.2635 |
| 0.0432        | 198.17 | 16250 | 1.8610          | 0.2641 |


### Framework versions

- Transformers 4.11.3
- Pytorch 1.9.0
- Datasets 1.13.3
- Tokenizers 0.10.3

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

model\_en
=========


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


* Loss: 1.8610
* Wer: 0.2641


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: 4
* eval\_batch\_size: 8
* seed: 42
* gradient\_accumulation\_steps: 2
* total\_train\_batch\_size: 8
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 1000
* num\_epochs: 200


### Training results



### Framework versions


* Transformers 4.11.3
* Pytorch 1.9.0
* Datasets 1.13.3
* Tokenizers 0.10.3



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 0.0001
* train\_batch\_size: 4
* eval\_batch\_size: 8
* seed: 42
* gradient\_accumulation\_steps: 2
* total\_train\_batch\_size: 8
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 1000
* num\_epochs: 200 ### Training results ### Framework versions


* Transformers 4.11.3
* Pytorch 1.9.0
* Datasets 1.13.3
* Tokenizers 0.10.3
TAGS
#transformers #pytorch #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: 4
* eval\_batch\_size: 8
* seed: 42
* gradient\_accumulation\_steps: 2
* total\_train\_batch\_size: 8
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 1000
* num\_epochs: 200 ### Training results ### Framework versions


* Transformers 4.11.3
* Pytorch 1.9.0
* Datasets 1.13.3
* Tokenizers 0.10.3
passage: TAGS
#transformers #pytorch #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: 4
* eval\_batch\_size: 8
* seed: 42
* gradient\_accumulation\_steps: 2
* total\_train\_batch\_size: 8
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 1000
* num\_epochs: 200### Training results### Framework versions


* Transformers 4.11.3
* Pytorch 1.9.0
* Datasets 1.13.3
* Tokenizers 0.10.3