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---
language:
- sv-SE
license: apache-2.0
tags:
- automatic-speech-recognition
- robust-speech-event
datasets:
- mozilla-foundation/common_voice_8_0
metrics:
- wer
- cer
model-index:
- name: wav2vec2-xls-r-300m-swedish
results:
- task:
type: automatic-speech-recognition # Required. Example: automatic-speech-recognition
name: Speech Recognition # Optional. Example: Speech Recognition
dataset:
type: mozilla-foundation/common_voice_8_0 # Required. Example: common_voice. Use dataset id from https://hf.co/datasets
name: Common Voice sv-SE # Required. Example: Common Voice zh-CN
args: sv-SE # Optional. Example: zh-CN
metrics:
- type: wer # Required. Example: wer
value: 38.57 # Required. Example: 20.90
name: Test WER # Optional. Example: Test WER
args:
- 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: 1000
- num_epochs: 30
- mixed_precision_training: Native AMP # Optional. Example for BLEU: max_order
- type: cer # Required. Example: wer
value: 10.98 # Required. Example: 20.90
name: Test CER # Optional. Example: Test WER
args:
- 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: 1000
- num_epochs: 30
- mixed_precision_training: Native AMP # Optional. Example for BLEU: max_order
---
<!-- 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-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: 0.4286
- Wer: 0.2729
- Cer: 0.0858
### 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: 1000
- num_epochs: 30
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer | Cer |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|
| 4.6203 | 5.49 | 500 | 2.8904 | 1.0 | 1.0 |
| 1.147 | 10.98 | 1000 | 0.5255 | 0.4107 | 0.1304 |
| 0.5246 | 16.48 | 1500 | 0.4598 | 0.3342 | 0.1058 |
| 0.378 | 21.97 | 2000 | 0.4316 | 0.2991 | 0.0949 |
| 0.298 | 27.47 | 2500 | 0.4286 | 0.2729 | 0.0858 |
### Framework versions
- Transformers 4.17.0.dev0
- Pytorch 1.10.2+cu102
- Datasets 1.18.2.dev0
- Tokenizers 0.11.0
|