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# wav2vec2-xls-r-1b-cv8

This model is a fine-tuned version of [facebook/wav2vec2-xls-r-1b](https://huggingface.co/facebook/wav2vec2-xls-r-1b) on the MOZILLA-FOUNDATION/COMMON_VOICE_8_0 - PT dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2007
- Wer: 0.1838

## 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: 7.5e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 2000
- num_epochs: 30.0
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Wer    |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 2.1172        | 0.32  | 500   | 1.2852          | 0.9783 |
| 1.4152        | 0.64  | 1000  | 0.6434          | 0.6105 |
| 1.4342        | 0.96  | 1500  | 0.4844          | 0.3989 |
| 1.4657        | 1.29  | 2000  | 0.5080          | 0.4490 |
| 1.4961        | 1.61  | 2500  | 0.4764          | 0.4264 |
| 1.4515        | 1.93  | 3000  | 0.4519          | 0.4068 |
| 1.3924        | 2.25  | 3500  | 0.4472          | 0.4132 |
| 1.4524        | 2.57  | 4000  | 0.4455          | 0.3939 |
| 1.4328        | 2.89  | 4500  | 0.4369          | 0.4069 |
| 1.3456        | 3.22  | 5000  | 0.4234          | 0.3774 |
| 1.3725        | 3.54  | 5500  | 0.4387          | 0.3789 |
| 1.3812        | 3.86  | 6000  | 0.4298          | 0.3825 |
| 1.3282        | 4.18  | 6500  | 0.4025          | 0.3703 |
| 1.3326        | 4.5   | 7000  | 0.3917          | 0.3502 |
| 1.3028        | 4.82  | 7500  | 0.3889          | 0.3582 |
| 1.293         | 5.14  | 8000  | 0.3859          | 0.3496 |
| 1.321         | 5.47  | 8500  | 0.3875          | 0.3576 |
| 1.3165        | 5.79  | 9000  | 0.3927          | 0.3589 |
| 1.2701        | 6.11  | 9500  | 0.4058          | 0.3621 |
| 1.2718        | 6.43  | 10000 | 0.4211          | 0.3916 |
| 1.2683        | 6.75  | 10500 | 0.3968          | 0.3620 |
| 1.2643        | 7.07  | 11000 | 0.4128          | 0.3848 |
| 1.2485        | 7.4   | 11500 | 0.3849          | 0.3727 |
| 1.2608        | 7.72  | 12000 | 0.3770          | 0.3474 |
| 1.2388        | 8.04  | 12500 | 0.3774          | 0.3574 |
| 1.2524        | 8.36  | 13000 | 0.3789          | 0.3550 |
| 1.2458        | 8.68  | 13500 | 0.3770          | 0.3410 |
| 1.2505        | 9.0   | 14000 | 0.3638          | 0.3403 |
| 1.2254        | 9.32  | 14500 | 0.3770          | 0.3509 |
| 1.2459        | 9.65  | 15000 | 0.3592          | 0.3349 |
| 1.2049        | 9.97  | 15500 | 0.3600          | 0.3428 |
| 1.2097        | 10.29 | 16000 | 0.3626          | 0.3347 |
| 1.1988        | 10.61 | 16500 | 0.3740          | 0.3269 |
| 1.1671        | 10.93 | 17000 | 0.3548          | 0.3245 |
| 1.1532        | 11.25 | 17500 | 0.3394          | 0.3140 |
| 1.1459        | 11.58 | 18000 | 0.3349          | 0.3156 |
| 1.1511        | 11.9  | 18500 | 0.3272          | 0.3110 |
| 1.1465        | 12.22 | 19000 | 0.3348          | 0.3084 |
| 1.1426        | 12.54 | 19500 | 0.3193          | 0.3027 |
| 1.1278        | 12.86 | 20000 | 0.3318          | 0.3021 |
| 1.149         | 13.18 | 20500 | 0.3169          | 0.2947 |
| 1.114         | 13.5  | 21000 | 0.3224          | 0.2986 |
| 1.1249        | 13.83 | 21500 | 0.3227          | 0.2921 |
| 1.0968        | 14.15 | 22000 | 0.3033          | 0.2878 |
| 1.0851        | 14.47 | 22500 | 0.2996          | 0.2863 |
| 1.0985        | 14.79 | 23000 | 0.3011          | 0.2843 |
| 1.0808        | 15.11 | 23500 | 0.2932          | 0.2759 |
| 1.069         | 15.43 | 24000 | 0.2919          | 0.2750 |
| 1.0602        | 15.76 | 24500 | 0.2959          | 0.2713 |
| 1.0369        | 16.08 | 25000 | 0.2931          | 0.2754 |
| 1.0573        | 16.4  | 25500 | 0.2920          | 0.2722 |
| 1.051         | 16.72 | 26000 | 0.2855          | 0.2632 |
| 1.0279        | 17.04 | 26500 | 0.2850          | 0.2649 |
| 1.0496        | 17.36 | 27000 | 0.2817          | 0.2585 |
| 1.0516        | 17.68 | 27500 | 0.2961          | 0.2635 |
| 1.0244        | 18.01 | 28000 | 0.2781          | 0.2589 |
| 1.0099        | 18.33 | 28500 | 0.2783          | 0.2565 |
| 1.0016        | 18.65 | 29000 | 0.2719          | 0.2537 |
| 1.0157        | 18.97 | 29500 | 0.2621          | 0.2449 |
| 0.9572        | 19.29 | 30000 | 0.2582          | 0.2427 |
| 0.9802        | 19.61 | 30500 | 0.2707          | 0.2468 |
| 0.9577        | 19.94 | 31000 | 0.2563          | 0.2389 |
| 0.9562        | 20.26 | 31500 | 0.2592          | 0.2382 |
| 0.962         | 20.58 | 32000 | 0.2539          | 0.2341 |
| 0.9541        | 20.9  | 32500 | 0.2505          | 0.2288 |
| 0.9587        | 21.22 | 33000 | 0.2486          | 0.2302 |
| 0.9146        | 21.54 | 33500 | 0.2461          | 0.2269 |
| 0.9215        | 21.86 | 34000 | 0.2387          | 0.2228 |
| 0.9105        | 22.19 | 34500 | 0.2405          | 0.2222 |
| 0.8949        | 22.51 | 35000 | 0.2316          | 0.2191 |
| 0.9153        | 22.83 | 35500 | 0.2358          | 0.2180 |
| 0.8907        | 23.15 | 36000 | 0.2369          | 0.2168 |
| 0.8973        | 23.47 | 36500 | 0.2323          | 0.2120 |
| 0.8878        | 23.79 | 37000 | 0.2293          | 0.2104 |
| 0.8818        | 24.12 | 37500 | 0.2302          | 0.2132 |
| 0.8919        | 24.44 | 38000 | 0.2262          | 0.2083 |
| 0.8473        | 24.76 | 38500 | 0.2257          | 0.2040 |
| 0.8516        | 25.08 | 39000 | 0.2246          | 0.2031 |
| 0.8451        | 25.4  | 39500 | 0.2198          | 0.2000 |
| 0.8288        | 25.72 | 40000 | 0.2199          | 0.1990 |
| 0.8465        | 26.05 | 40500 | 0.2165          | 0.1972 |
| 0.8305        | 26.37 | 41000 | 0.2128          | 0.1957 |
| 0.8202        | 26.69 | 41500 | 0.2127          | 0.1937 |
| 0.8223        | 27.01 | 42000 | 0.2100          | 0.1934 |
| 0.8322        | 27.33 | 42500 | 0.2076          | 0.1905 |
| 0.8139        | 27.65 | 43000 | 0.2054          | 0.1880 |
| 0.8299        | 27.97 | 43500 | 0.2026          | 0.1868 |
| 0.7937        | 28.3  | 44000 | 0.2045          | 0.1872 |
| 0.7972        | 28.62 | 44500 | 0.2025          | 0.1861 |
| 0.809         | 28.94 | 45000 | 0.2026          | 0.1858 |
| 0.813         | 29.26 | 45500 | 0.2013          | 0.1838 |
| 0.7718        | 29.58 | 46000 | 0.2010          | 0.1837 |
| 0.7929        | 29.9  | 46500 | 0.2008          | 0.1840 |


### Framework versions

- Transformers 4.17.0.dev0
- Pytorch 1.10.2+cu102
- Datasets 1.18.3.dev0
- Tokenizers 0.11.0

{"language": ["pt"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "generated_from_trainer", "hf-asr-leaderboard", "mozilla-foundation/common_voice_8_0", "pt", "robust-speech-event"], "datasets": ["mozilla-foundation/common_voice_8_0"], "model-index": [{"name": "wav2vec2-xls-r-1b-cv8", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Automatic Speech Recognition"}, "dataset": {"name": "Common Voice 8", "type": "mozilla-foundation/common_voice_8_0", "args": "pt"}, "metrics": [{"type": "wer", "value": 17.7, "name": "Test WER"}, {"type": "cer", "value": 5.21, "name": "Test CER"}]}, {"task": {"type": "automatic-speech-recognition", "name": "Automatic Speech Recognition"}, "dataset": {"name": "Robust Speech Event - Dev Data", "type": "speech-recognition-community-v2/dev_data", "args": "sv"}, "metrics": [{"type": "wer", "value": 45.68, "name": "Test WER"}, {"type": "cer", "value": 18.67, "name": "Test CER"}, {"type": "wer", "value": 45.29, "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": "pt"}, "metrics": [{"type": "wer", "value": 48.03, "name": "Test WER"}]}]}]}
transformers pytorch wav2vec2 automatic-speech-recognition generated_from_trainer hf-asr-leaderboard mozilla-foundation/common_voice_8_0 pt robust-speech-event dataset:mozilla-foundation/common_voice_8_0 license:apache-2.0 model-index endpoints_compatible region:us
TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #generated_from_trainer #hf-asr-leaderboard #mozilla-foundation/common_voice_8_0 #pt #robust-speech-event #dataset-mozilla-foundation/common_voice_8_0 #license-apache-2.0 #model-index #endpoints_compatible #region-us 

wav2vec2-xls-r-1b-cv8
=====================


This model is a fine-tuned version of facebook/wav2vec2-xls-r-1b on the MOZILLA-FOUNDATION/COMMON\_VOICE\_8\_0 - PT dataset.
It achieves the following results on the evaluation set:


* Loss: 0.2007
* Wer: 0.1838


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: 7.5e-05
* train\_batch\_size: 4
* eval\_batch\_size: 4
* seed: 42
* gradient\_accumulation\_steps: 4
* total\_train\_batch\_size: 16
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 2000
* num\_epochs: 30.0
* mixed\_precision\_training: Native AMP


### Training results



### Framework versions


* Transformers 4.17.0.dev0
* Pytorch 1.10.2+cu102
* Datasets 1.18.3.dev0
* Tokenizers 0.11.0



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 7.5e-05
* train\_batch\_size: 4
* eval\_batch\_size: 4
* seed: 42
* gradient\_accumulation\_steps: 4
* total\_train\_batch\_size: 16
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 2000
* num\_epochs: 30.0
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.17.0.dev0
* Pytorch 1.10.2+cu102
* Datasets 1.18.3.dev0
* Tokenizers 0.11.0
TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #generated_from_trainer #hf-asr-leaderboard #mozilla-foundation/common_voice_8_0 #pt #robust-speech-event #dataset-mozilla-foundation/common_voice_8_0 #license-apache-2.0 #model-index #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 7.5e-05
* train\_batch\_size: 4
* eval\_batch\_size: 4
* seed: 42
* gradient\_accumulation\_steps: 4
* total\_train\_batch\_size: 16
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 2000
* num\_epochs: 30.0
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.17.0.dev0
* Pytorch 1.10.2+cu102
* Datasets 1.18.3.dev0
* Tokenizers 0.11.0
passage: TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #generated_from_trainer #hf-asr-leaderboard #mozilla-foundation/common_voice_8_0 #pt #robust-speech-event #dataset-mozilla-foundation/common_voice_8_0 #license-apache-2.0 #model-index #endpoints_compatible #region-us 
### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 7.5e-05
* train\_batch\_size: 4
* eval\_batch\_size: 4
* seed: 42
* gradient\_accumulation\_steps: 4
* total\_train\_batch\_size: 16
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 2000
* num\_epochs: 30.0
* mixed\_precision\_training: Native AMP### Training results### Framework versions


* Transformers 4.17.0.dev0
* Pytorch 1.10.2+cu102
* Datasets 1.18.3.dev0
* Tokenizers 0.11.0