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# 

This model is a fine-tuned version of [facebook/wav2vec2-xls-r-1b](https://huggingface.co/facebook/wav2vec2-xls-r-1b) on the /WORKSPACE/DATA/HY/NOIZY_STUDENT_4/ - NA dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1693
- Wer: 0.2373
- Cer: 0.0429

## 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: 5e-05
- train_batch_size: 16
- eval_batch_size: 64
- seed: 842
- gradient_accumulation_steps: 8
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.98) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- training_steps: 5000
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Wer    | Cer    |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|
| 1.255         | 7.24  | 500  | 0.2978          | 0.4294 | 0.0758 |
| 1.0058        | 14.49 | 1000 | 0.1883          | 0.2838 | 0.0483 |
| 0.9371        | 21.73 | 1500 | 0.1813          | 0.2627 | 0.0457 |
| 0.8999        | 28.98 | 2000 | 0.1693          | 0.2373 | 0.0429 |
| 0.8814        | 36.23 | 2500 | 0.1760          | 0.2420 | 0.0435 |
| 0.8364        | 43.47 | 3000 | 0.1765          | 0.2416 | 0.0419 |
| 0.8019        | 50.72 | 3500 | 0.1758          | 0.2311 | 0.0398 |
| 0.7665        | 57.96 | 4000 | 0.1745          | 0.2240 | 0.0399 |
| 0.7376        | 65.22 | 4500 | 0.1717          | 0.2190 | 0.0385 |
| 0.716         | 72.46 | 5000 | 0.1700          | 0.2147 | 0.0382 |


### Framework versions

- Transformers 4.17.0.dev0
- Pytorch 1.10.2
- Datasets 1.18.4.dev0
- Tokenizers 0.11.0

{"language": ["hy"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "generated_from_trainer", "hf-asr-leaderboard", "hy", "mozilla-foundation/common_voice_8_0", "robust-speech-event"], "datasets": ["common_voice"], "model-index": [{"name": "wav2vec2-xls-r-1b-hy-cv", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Speech Recognition"}, "dataset": {"name": "Common Voice hy-AM", "type": "mozilla-foundation/common_voice_8_0", "args": "hy-AM"}, "metrics": [{"type": "wer", "value": 10.811865729898516, "name": "WER LM"}, {"type": "cer", "value": 2.2205361659079412, "name": "CER LM"}]}, {"task": {"type": "automatic-speech-recognition", "name": "Speech Recognition"}, "dataset": {"name": "Robust Speech Event - Dev Data", "type": "speech-recognition-community-v2/dev_data", "args": "hy"}, "metrics": [{"type": "wer", "value": 18.219363037089988, "name": "Test WER"}, {"type": "cer", "value": 7.075988867335752, "name": "Test CER"}]}]}]}
transformers pytorch tensorboard wav2vec2 automatic-speech-recognition generated_from_trainer hf-asr-leaderboard hy mozilla-foundation/common_voice_8_0 robust-speech-event dataset:common_voice license:apache-2.0 model-index endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #hf-asr-leaderboard #hy #mozilla-foundation/common_voice_8_0 #robust-speech-event #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #region-us 


This model is a fine-tuned version of facebook/wav2vec2-xls-r-1b on the /WORKSPACE/DATA/HY/NOIZY\_STUDENT\_4/ - NA dataset.
It achieves the following results on the evaluation set:


* Loss: 0.1693
* Wer: 0.2373
* Cer: 0.0429


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: 5e-05
* train\_batch\_size: 16
* eval\_batch\_size: 64
* seed: 842
* gradient\_accumulation\_steps: 8
* total\_train\_batch\_size: 128
* optimizer: Adam with betas=(0.9,0.98) and epsilon=1e-08
* lr\_scheduler\_type: cosine
* lr\_scheduler\_warmup\_ratio: 0.1
* training\_steps: 5000
* mixed\_precision\_training: Native AMP


### Training results



### Framework versions


* Transformers 4.17.0.dev0
* Pytorch 1.10.2
* Datasets 1.18.4.dev0
* Tokenizers 0.11.0



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 5e-05
* train\_batch\_size: 16
* eval\_batch\_size: 64
* seed: 842
* gradient\_accumulation\_steps: 8
* total\_train\_batch\_size: 128
* optimizer: Adam with betas=(0.9,0.98) and epsilon=1e-08
* lr\_scheduler\_type: cosine
* lr\_scheduler\_warmup\_ratio: 0.1
* training\_steps: 5000
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.17.0.dev0
* Pytorch 1.10.2
* Datasets 1.18.4.dev0
* Tokenizers 0.11.0
TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #hf-asr-leaderboard #hy #mozilla-foundation/common_voice_8_0 #robust-speech-event #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 5e-05
* train\_batch\_size: 16
* eval\_batch\_size: 64
* seed: 842
* gradient\_accumulation\_steps: 8
* total\_train\_batch\_size: 128
* optimizer: Adam with betas=(0.9,0.98) and epsilon=1e-08
* lr\_scheduler\_type: cosine
* lr\_scheduler\_warmup\_ratio: 0.1
* training\_steps: 5000
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.17.0.dev0
* Pytorch 1.10.2
* Datasets 1.18.4.dev0
* Tokenizers 0.11.0
passage: TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #hf-asr-leaderboard #hy #mozilla-foundation/common_voice_8_0 #robust-speech-event #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #region-us 
### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 5e-05
* train\_batch\_size: 16
* eval\_batch\_size: 64
* seed: 842
* gradient\_accumulation\_steps: 8
* total\_train\_batch\_size: 128
* optimizer: Adam with betas=(0.9,0.98) and epsilon=1e-08
* lr\_scheduler\_type: cosine
* lr\_scheduler\_warmup\_ratio: 0.1
* training\_steps: 5000
* mixed\_precision\_training: Native AMP### Training results### Framework versions


* Transformers 4.17.0.dev0
* Pytorch 1.10.2
* Datasets 1.18.4.dev0
* Tokenizers 0.11.0