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# wav2vec2-large-xls-r-300m-as

This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the common_voice dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8318
- Wer: 0.5174

## 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: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.12
- num_epochs: 120

### Training results

| Training Loss | Epoch | Step | Validation Loss | Wer    |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 4.882         | 25.0  | 400  | 1.2290          | 0.8182 |
| 0.8275        | 50.0  | 800  | 0.6835          | 0.5398 |
| 0.337         | 75.0  | 1200 | 0.7789          | 0.5107 |
| 0.2113        | 100.0 | 1600 | 0.8318          | 0.5174 |


### Framework versions

- Transformers 4.16.2
- Pytorch 1.10.1+cu102
- Datasets 1.17.1.dev0
- Tokenizers 0.10.3

### Test Evaluation

Common Voice Assamese Test Set (v7.0)

- WER: 0.7224
- CER: 0.2882
{"language": ["as"], "license": "apache-2.0", "tags": ["generated_from_trainer", "robust-speech-event", "hf-asr-leaderboard"], "datasets": ["common_voice"], "model-index": [{"name": "wav2vec2-large-xls-r-300m-as", "results": []}]}
transformers pytorch tensorboard wav2vec2 automatic-speech-recognition generated_from_trainer robust-speech-event hf-asr-leaderboard as dataset:common_voice license:apache-2.0 endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #robust-speech-event #hf-asr-leaderboard #as #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us 

wav2vec2-large-xls-r-300m-as
============================


This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the common\_voice dataset.
It achieves the following results on the evaluation set:


* Loss: 0.8318
* Wer: 0.5174


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: 16
* eval\_batch\_size: 8
* seed: 42
* gradient\_accumulation\_steps: 2
* total\_train\_batch\_size: 32
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_ratio: 0.12
* num\_epochs: 120


### Training results



### Framework versions


* Transformers 4.16.2
* Pytorch 1.10.1+cu102
* Datasets 1.17.1.dev0
* Tokenizers 0.10.3


### Test Evaluation


Common Voice Assamese Test Set (v7.0)


* WER: 0.7224
* CER: 0.2882



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 0.0003
* train\_batch\_size: 16
* eval\_batch\_size: 8
* seed: 42
* gradient\_accumulation\_steps: 2
* total\_train\_batch\_size: 32
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_ratio: 0.12
* num\_epochs: 120 ### Training results ### Framework versions


* Transformers 4.16.2
* Pytorch 1.10.1+cu102
* Datasets 1.17.1.dev0
* Tokenizers 0.10.3 ### Test Evaluation


Common Voice Assamese Test Set (v7.0)


* WER: 0.7224
* CER: 0.2882
TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #robust-speech-event #hf-asr-leaderboard #as #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 0.0003
* train\_batch\_size: 16
* eval\_batch\_size: 8
* seed: 42
* gradient\_accumulation\_steps: 2
* total\_train\_batch\_size: 32
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_ratio: 0.12
* num\_epochs: 120 ### Training results ### Framework versions


* Transformers 4.16.2
* Pytorch 1.10.1+cu102
* Datasets 1.17.1.dev0
* Tokenizers 0.10.3 ### Test Evaluation


Common Voice Assamese Test Set (v7.0)


* WER: 0.7224
* CER: 0.2882
passage: TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #robust-speech-event #hf-asr-leaderboard #as #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us 
### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 0.0003
* train\_batch\_size: 16
* eval\_batch\_size: 8
* seed: 42
* gradient\_accumulation\_steps: 2
* total\_train\_batch\_size: 32
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_ratio: 0.12
* num\_epochs: 120### Training results### Framework versions


* Transformers 4.16.2
* Pytorch 1.10.1+cu102
* Datasets 1.17.1.dev0
* Tokenizers 0.10.3### Test Evaluation


Common Voice Assamese Test Set (v7.0)


* WER: 0.7224
* CER: 0.2882