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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/UK/COMPOSED_DATASET/ - NA dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1092
- Wer: 0.1752
- Cer: 0.0323

## 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: 42
- 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: 12000
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Wer    | Cer    |
|:-------------:|:-----:|:-----:|:---------------:|:------:|:------:|
| 1.7005        | 1.61  | 500   | 0.4082          | 0.5584 | 0.1164 |
| 1.1555        | 3.22  | 1000  | 0.2020          | 0.2953 | 0.0557 |
| 1.0927        | 4.82  | 1500  | 0.1708          | 0.2584 | 0.0480 |
| 1.0707        | 6.43  | 2000  | 0.1563          | 0.2405 | 0.0450 |
| 1.0728        | 8.04  | 2500  | 0.1620          | 0.2442 | 0.0463 |
| 1.0268        | 9.65  | 3000  | 0.1588          | 0.2378 | 0.0458 |
| 1.0328        | 11.25 | 3500  | 0.1466          | 0.2352 | 0.0442 |
| 1.0249        | 12.86 | 4000  | 0.1552          | 0.2341 | 0.0449 |
| 1.016         | 14.47 | 4500  | 0.1602          | 0.2435 | 0.0473 |
| 1.0164        | 16.08 | 5000  | 0.1491          | 0.2337 | 0.0444 |
| 0.9935        | 17.68 | 5500  | 0.1539          | 0.2373 | 0.0458 |
| 0.9626        | 19.29 | 6000  | 0.1458          | 0.2305 | 0.0434 |
| 0.9505        | 20.9  | 6500  | 0.1368          | 0.2157 | 0.0407 |
| 0.9389        | 22.51 | 7000  | 0.1437          | 0.2231 | 0.0426 |
| 0.9129        | 24.12 | 7500  | 0.1313          | 0.2076 | 0.0394 |
| 0.9118        | 25.72 | 8000  | 0.1292          | 0.2040 | 0.0384 |
| 0.8848        | 27.33 | 8500  | 0.1299          | 0.2028 | 0.0384 |
| 0.8667        | 28.94 | 9000  | 0.1228          | 0.1945 | 0.0367 |
| 0.8641        | 30.55 | 9500  | 0.1223          | 0.1939 | 0.0364 |
| 0.8516        | 32.15 | 10000 | 0.1184          | 0.1876 | 0.0349 |
| 0.8379        | 33.76 | 10500 | 0.1137          | 0.1821 | 0.0338 |
| 0.8235        | 35.37 | 11000 | 0.1127          | 0.1779 | 0.0331 |
| 0.8112        | 36.98 | 11500 | 0.1103          | 0.1766 | 0.0327 |
| 0.8069        | 38.59 | 12000 | 0.1092          | 0.1752 | 0.0323 |


### Framework versions

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

{"language": ["uk"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "generated_from_trainer", "hf-asr-leaderboard", "mozilla-foundation/common_voice_8_0", "robust-speech-event"], "datasets": ["common_voice"], "model-index": [{"name": "wav2vec2-xls-r-1b-hy", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Speech Recognition"}, "dataset": {"name": "Common Voice uk", "type": "mozilla-foundation/common_voice_8_0", "args": "uk"}, "metrics": [{"type": "wer", "value": 10.406342913776015, "name": "WER LM"}, {"type": "cer", "value": 2.0387492208601703, "name": "CER LM"}]}, {"task": {"type": "automatic-speech-recognition", "name": "Automatic Speech Recognition"}, "dataset": {"name": "Robust Speech Event - Dev Data", "type": "speech-recognition-community-v2/dev_data", "args": "uk"}, "metrics": [{"type": "wer", "value": 40.57, "name": "Test WER"}]}, {"task": {"type": "automatic-speech-recognition", "name": "Automatic Speech Recognition"}, "dataset": {"name": "Robust Speech Event - Test Data", "type": "speech-recognition-community-v2/eval_data", "args": "uk"}, "metrics": [{"type": "wer", "value": 28.95, "name": "Test WER"}]}]}]}
transformers pytorch tensorboard wav2vec2 automatic-speech-recognition generated_from_trainer hf-asr-leaderboard mozilla-foundation/common_voice_8_0 robust-speech-event uk dataset:common_voice license:apache-2.0 model-index endpoints_compatible has_space region:us
TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #hf-asr-leaderboard #mozilla-foundation/common_voice_8_0 #robust-speech-event #uk #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #has_space #region-us 


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


* Loss: 0.1092
* Wer: 0.1752
* Cer: 0.0323


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: 42
* 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: 12000
* 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: 42
* 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: 12000
* 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 #mozilla-foundation/common_voice_8_0 #robust-speech-event #uk #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #has_space #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 5e-05
* train\_batch\_size: 16
* eval\_batch\_size: 64
* seed: 42
* 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: 12000
* 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 #mozilla-foundation/common_voice_8_0 #robust-speech-event #uk #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #has_space #region-us 
### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 5e-05
* train\_batch\_size: 16
* eval\_batch\_size: 64
* seed: 42
* 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: 12000
* 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