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---
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
---
<!-- 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: 0.3641
- 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
|