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

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: 0.3641
- Wer: 0.2473
- Cer: 0.0758


## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 7.5e-05
- 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: 1000
- num_epochs: 50
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Wer    | Cer    |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|
| 6.1097        | 5.49  | 500  | 3.1422          | 1.0    | 1.0    |
| 2.985         | 10.98 | 1000 | 1.7357          | 0.9876 | 0.4125 |
| 1.0363        | 16.48 | 1500 | 0.4773          | 0.3510 | 0.1047 |
| 0.6111        | 21.97 | 2000 | 0.3937          | 0.2998 | 0.0910 |
| 0.4942        | 27.47 | 2500 | 0.3779          | 0.2776 | 0.0844 |
| 0.4421        | 32.96 | 3000 | 0.3745          | 0.2630 | 0.0807 |
| 0.4018        | 38.46 | 3500 | 0.3685          | 0.2553 | 0.0781 |
| 0.3759        | 43.95 | 4000 | 0.3618          | 0.2488 | 0.0761 |
| 0.3646        | 49.45 | 4500 | 0.3641          | 0.2473 | 0.0758 |


### Framework versions

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

{"language": ["sv"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "robust-speech-event", "hf-asr-leaderboard"], "datasets": ["mozilla-foundation/common_voice_8_0"], "metrics": ["wer", "cer"], "base_model": "facebook/wav2vec2-xls-r-300m", "model-index": [{"name": "wav2vec2-xls-r-300m-swedish", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Speech Recognition"}, "dataset": {"name": "Common Voice sv-SE", "type": "mozilla-foundation/common_voice_8_0", "args": "sv-SE"}, "metrics": [{"type": "wer", "value": 24.73, "name": "Test WER", "args": {"learning_rate": 7.5e-05, "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": 1000, "num_epochs": 50, "mixed_precision_training": "Native AMP"}}, {"type": "cer", "value": 7.58, "name": "Test CER", "args": {"learning_rate": 7.5e-05, "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": 1000, "num_epochs": 50, "mixed_precision_training": "Native AMP"}}]}]}]}
transformers pytorch wav2vec2 automatic-speech-recognition robust-speech-event hf-asr-leaderboard sv dataset:mozilla-foundation/common_voice_8_0 base_model:facebook/wav2vec2-xls-r-300m license:apache-2.0 model-index endpoints_compatible region:us
TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #robust-speech-event #hf-asr-leaderboard #sv #dataset-mozilla-foundation/common_voice_8_0 #base_model-facebook/wav2vec2-xls-r-300m #license-apache-2.0 #model-index #endpoints_compatible #region-us 

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


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: 0.3641
* Wer: 0.2473
* Cer: 0.0758


Training procedure
------------------


### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 7.5e-05
* 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: 1000
* 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: 7.5e-05
* 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: 1000
* 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 #wav2vec2 #automatic-speech-recognition #robust-speech-event #hf-asr-leaderboard #sv #dataset-mozilla-foundation/common_voice_8_0 #base_model-facebook/wav2vec2-xls-r-300m #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: 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: 1000
* 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 #wav2vec2 #automatic-speech-recognition #robust-speech-event #hf-asr-leaderboard #sv #dataset-mozilla-foundation/common_voice_8_0 #base_model-facebook/wav2vec2-xls-r-300m #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: 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: 1000
* 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