
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

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

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: 0.4087
- Wer: 0.2461
- Cer: 0.0666



### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 64
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 400
- num_epochs: 50
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Wer    | Cer    |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|
| 5.0788        | 4.26  | 200  | 2.9389          | 1.0    | 1.0    |
| 2.8288        | 8.51  | 400  | 2.2535          | 1.0    | 0.8004 |
| 0.907         | 12.77 | 600  | 0.4558          | 0.4243 | 0.1095 |
| 0.4071        | 17.02 | 800  | 0.4013          | 0.3468 | 0.0913 |
| 0.3           | 21.28 | 1000 | 0.4167          | 0.3075 | 0.0816 |
| 0.2544        | 25.53 | 1200 | 0.4132          | 0.2835 | 0.0762 |
| 0.2145        | 29.79 | 1400 | 0.3878          | 0.2693 | 0.0729 |
| 0.1923        | 34.04 | 1600 | 0.4023          | 0.2623 | 0.0702 |
| 0.1681        | 38.3  | 1800 | 0.3984          | 0.2581 | 0.0686 |
| 0.1598        | 42.55 | 2000 | 0.3982          | 0.2493 | 0.0663 |
| 0.1464        | 46.81 | 2200 | 0.4087          | 0.2461 | 0.0666 |


### Framework versions

- Transformers 4.17.0.dev0
- Pytorch 1.10.2+cu102
- Datasets 1.18.2.dev0
- Tokenizers 0.11.0

{"language": ["id"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "hf-asr-leaderboard", "robust-speech-event"], "datasets": ["mozilla-foundation/common_voice_7_0"], "metrics": ["wer", "cer"], "model-index": [{"name": "wav2vec2-large-xls-r-300m-Indonesian", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Speech Recognition"}, "dataset": {"name": "Common Voice id", "type": "mozilla-foundation/common_voice_7_0", "args": "id"}, "metrics": [{"type": "wer", "value": 25.06, "name": "Test WER With LM"}, {"type": "cer", "value": 6.5, "name": "Test CER With 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": "id"}, "metrics": [{"type": "wer", "value": 99.61, "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": "id"}, "metrics": [{"type": "wer", "value": 106.39, "name": "Test WER"}]}]}]}
transformers pytorch safetensors wav2vec2 automatic-speech-recognition hf-asr-leaderboard robust-speech-event id dataset:mozilla-foundation/common_voice_7_0 license:apache-2.0 model-index endpoints_compatible region:us
TAGS
#transformers #pytorch #safetensors #wav2vec2 #automatic-speech-recognition #hf-asr-leaderboard #robust-speech-event #id #dataset-mozilla-foundation/common_voice_7_0 #license-apache-2.0 #model-index #endpoints_compatible #region-us 

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


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: 0.4087
* Wer: 0.2461
* Cer: 0.0666


### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 0.0003
* train\_batch\_size: 64
* eval\_batch\_size: 8
* seed: 42
* gradient\_accumulation\_steps: 2
* total\_train\_batch\_size: 128
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 400
* num\_epochs: 50
* mixed\_precision\_training: Native AMP


### Training results



### Framework versions


* Transformers 4.17.0.dev0
* Pytorch 1.10.2+cu102
* Datasets 1.18.2.dev0
* Tokenizers 0.11.0



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 0.0003
* train\_batch\_size: 64
* eval\_batch\_size: 8
* seed: 42
* gradient\_accumulation\_steps: 2
* total\_train\_batch\_size: 128
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 400
* num\_epochs: 50
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.17.0.dev0
* Pytorch 1.10.2+cu102
* Datasets 1.18.2.dev0
* Tokenizers 0.11.0
TAGS
#transformers #pytorch #safetensors #wav2vec2 #automatic-speech-recognition #hf-asr-leaderboard #robust-speech-event #id #dataset-mozilla-foundation/common_voice_7_0 #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: 64
* eval\_batch\_size: 8
* seed: 42
* gradient\_accumulation\_steps: 2
* total\_train\_batch\_size: 128
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 400
* num\_epochs: 50
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.17.0.dev0
* Pytorch 1.10.2+cu102
* Datasets 1.18.2.dev0
* Tokenizers 0.11.0
passage: TAGS
#transformers #pytorch #safetensors #wav2vec2 #automatic-speech-recognition #hf-asr-leaderboard #robust-speech-event #id #dataset-mozilla-foundation/common_voice_7_0 #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: 64
* eval\_batch\_size: 8
* seed: 42
* gradient\_accumulation\_steps: 2
* total\_train\_batch\_size: 128
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 400
* num\_epochs: 50
* mixed\_precision\_training: Native AMP### Training results### Framework versions


* Transformers 4.17.0.dev0
* Pytorch 1.10.2+cu102
* Datasets 1.18.2.dev0
* Tokenizers 0.11.0