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# wav2vec2-common_voice-it-demo

This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the COMMON_VOICE - IT dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3484
- Wer: 0.2368

## 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: 0.0003
- 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: 500
- num_epochs: 10.0
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Wer    |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| No log        | 0.37  | 400   | 0.9124          | 0.7336 |
| 3.904         | 0.74  | 800   | 0.4753          | 0.5022 |
| 0.4384        | 1.1   | 1200  | 0.3941          | 0.3731 |
| 0.2985        | 1.47  | 1600  | 0.4007          | 0.3830 |
| 0.2719        | 1.84  | 2000  | 0.3576          | 0.3597 |
| 0.2719        | 2.21  | 2400  | 0.3571          | 0.3286 |
| 0.2158        | 2.57  | 2800  | 0.3465          | 0.3198 |
| 0.2054        | 2.94  | 3200  | 0.3162          | 0.2982 |
| 0.1783        | 3.31  | 3600  | 0.3295          | 0.3089 |
| 0.1495        | 3.68  | 4000  | 0.3248          | 0.3034 |
| 0.1495        | 4.04  | 4400  | 0.3101          | 0.3028 |
| 0.1397        | 4.41  | 4800  | 0.3588          | 0.3006 |
| 0.123         | 4.78  | 5200  | 0.3451          | 0.3041 |
| 0.115         | 5.15  | 5600  | 0.3333          | 0.2921 |
| 0.0947        | 5.51  | 6000  | 0.3331          | 0.2858 |
| 0.0947        | 5.88  | 6400  | 0.3536          | 0.2950 |
| 0.0952        | 6.25  | 6800  | 0.3344          | 0.2786 |
| 0.0778        | 6.62  | 7200  | 0.3363          | 0.2699 |
| 0.0744        | 6.99  | 7600  | 0.3246          | 0.2655 |
| 0.0648        | 7.35  | 8000  | 0.3390          | 0.2627 |
| 0.0648        | 7.72  | 8400  | 0.3405          | 0.2630 |
| 0.0591        | 8.09  | 8800  | 0.3367          | 0.2534 |
| 0.0527        | 8.46  | 9200  | 0.3448          | 0.2509 |
| 0.0461        | 8.82  | 9600  | 0.3379          | 0.2425 |
| 0.0408        | 9.19  | 10000 | 0.3491          | 0.2409 |
| 0.0408        | 9.56  | 10400 | 0.3456          | 0.2377 |
| 0.0393        | 9.93  | 10800 | 0.3488          | 0.2370 |


### Framework versions

- Transformers 4.29.2
- Pytorch 2.0.1+cu117
- Datasets 2.12.0
- Tokenizers 0.11.0

{"language": ["it"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "common_voice", "generated_from_trainer"], "datasets": ["common_voice"], "metrics": ["wer"], "model-index": [{"name": "wav2vec2-common_voice-it-demo", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Automatic Speech Recognition"}, "dataset": {"name": "COMMON_VOICE - IT", "type": "common_voice", "config": "it", "split": "test[80%:]", "args": "Config: it, Training split: train[85%:], Eval split: test[80%:]"}, "metrics": [{"type": "wer", "value": 0.23675718221172767, "name": "Wer"}]}]}]}
transformers pytorch tensorboard wav2vec2 automatic-speech-recognition common_voice generated_from_trainer it dataset:common_voice license:apache-2.0 model-index endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #common_voice #generated_from_trainer #it #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #region-us 

wav2vec2-common\_voice-it-demo
==============================


This model is a fine-tuned version of facebook/wav2vec2-large-xlsr-53 on the COMMON\_VOICE - IT dataset.
It achieves the following results on the evaluation set:


* Loss: 0.3484
* Wer: 0.2368


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: 0.0003
* 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: 500
* num\_epochs: 10.0
* mixed\_precision\_training: Native AMP


### Training results



### Framework versions


* Transformers 4.29.2
* Pytorch 2.0.1+cu117
* Datasets 2.12.0
* Tokenizers 0.11.0



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 0.0003
* 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: 500
* num\_epochs: 10.0
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.29.2
* Pytorch 2.0.1+cu117
* Datasets 2.12.0
* Tokenizers 0.11.0
TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #common_voice #generated_from_trainer #it #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 0.0003
* 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: 500
* num\_epochs: 10.0
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.29.2
* Pytorch 2.0.1+cu117
* Datasets 2.12.0
* Tokenizers 0.11.0