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---
language:
- as
license: apache-2.0
tags:
- automatic-speech-recognition
- mozilla-foundation/common_voice_8_0
- generated_from_trainer
- as
- robust-speech-event
- model_for_talk
- hf-asr-leaderboard
datasets:
- common_voice
model-index:
- name: wav2vec2-large-xls-r-300m-as-with-LM-v2
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice 8
type: mozilla-foundation/common_voice_8_0
args: hsb
metrics:
- name: Test WER
type: wer
value: []
- name: Test CER
type: cer
value: []
---
<!-- 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. -->
### Note: Files are missing. Probably, didn't get (git)pushed properly. :(
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: 1.1679
- Wer: 0.5761
## 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: 0.000111
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 300
- num_epochs: 200
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:------:|:----:|:---------------:|:------:|
| 8.3852 | 10.51 | 200 | 3.6402 | 1.0 |
| 3.5374 | 21.05 | 400 | 3.3894 | 1.0 |
| 2.8645 | 31.56 | 600 | 1.3143 | 0.8303 |
| 1.1784 | 42.1 | 800 | 0.9417 | 0.6661 |
| 0.7805 | 52.62 | 1000 | 0.9292 | 0.6237 |
| 0.5973 | 63.15 | 1200 | 0.9489 | 0.6014 |
| 0.4784 | 73.67 | 1400 | 0.9916 | 0.5962 |
| 0.4138 | 84.21 | 1600 | 1.0272 | 0.6121 |
| 0.3491 | 94.72 | 1800 | 1.0412 | 0.5984 |
| 0.3062 | 105.26 | 2000 | 1.0769 | 0.6005 |
| 0.2707 | 115.77 | 2200 | 1.0708 | 0.5752 |
| 0.2459 | 126.31 | 2400 | 1.1285 | 0.6009 |
| 0.2234 | 136.82 | 2600 | 1.1209 | 0.5949 |
| 0.2035 | 147.36 | 2800 | 1.1348 | 0.5842 |
| 0.1876 | 157.87 | 3000 | 1.1480 | 0.5872 |
| 0.1669 | 168.41 | 3200 | 1.1496 | 0.5838 |
| 0.1595 | 178.92 | 3400 | 1.1721 | 0.5778 |
| 0.1505 | 189.46 | 3600 | 1.1654 | 0.5744 |
| 0.1486 | 199.97 | 3800 | 1.1679 | 0.5761 |
### Framework versions
- Transformers 4.16.1
- Pytorch 1.10.0+cu111
- Datasets 1.18.2
- Tokenizers 0.11.0
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