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---
language:
- hsb
license: apache-2.0
tags:
- automatic-speech-recognition
- mozilla-foundation/common_voice_8_0
- generated_from_trainer
- hsb
- robust-speech-event
- model_for_talk
- hf-asr-leaderboard
datasets:
- mozilla-foundation/common_voice_8_0
model-index:
- name: wav2vec2-large-xls-r-300m-hsb-v1
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: 0.4393
- name: Test CER
type: cer
value: 0.1036
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Robust Speech Event - Dev Data
type: speech-recognition-community-v2/dev_data
args: hsb
metrics:
- name: Test WER
type: wer
value: NA
- name: Test CER
type: cer
value: NA
---
<!-- 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-hsb-v1
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the MOZILLA-FOUNDATION/COMMON_VOICE_8_0 - HSB dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5684
- Wer: 0.4402
### Evaluation Commands
1. To evaluate on mozilla-foundation/common_voice_8_0 with test split
python eval.py --model_id DrishtiSharma/wav2vec2-large-xls-r-300m-hsb-v1 --dataset mozilla-foundation/common_voice_8_0 --config hsb --split test --log_outputs
2. To evaluate on speech-recognition-community-v2/dev_data
Upper Sorbian language isn't available in speech-recognition-community-v2/dev_data
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.00045
- 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: 500
- num_epochs: 50
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 8.972 | 3.23 | 100 | 3.7498 | 1.0 |
| 3.3401 | 6.45 | 200 | 3.2320 | 1.0 |
| 3.2046 | 9.68 | 300 | 3.1741 | 0.9806 |
| 2.4031 | 12.9 | 400 | 1.0579 | 0.8996 |
| 1.0427 | 16.13 | 500 | 0.7989 | 0.7557 |
| 0.741 | 19.35 | 600 | 0.6405 | 0.6299 |
| 0.5699 | 22.58 | 700 | 0.6129 | 0.5928 |
| 0.4607 | 25.81 | 800 | 0.6548 | 0.5695 |
| 0.3827 | 29.03 | 900 | 0.6268 | 0.5190 |
| 0.3282 | 32.26 | 1000 | 0.5919 | 0.5016 |
| 0.2764 | 35.48 | 1100 | 0.5953 | 0.4805 |
| 0.2335 | 38.71 | 1200 | 0.5717 | 0.4728 |
| 0.2106 | 41.94 | 1300 | 0.5674 | 0.4569 |
| 0.1859 | 45.16 | 1400 | 0.5685 | 0.4502 |
| 0.1592 | 48.39 | 1500 | 0.5684 | 0.4402 |
### Framework versions
- Transformers 4.16.1
- Pytorch 1.10.0+cu111
- Datasets 1.18.2
- Tokenizers 0.11.0
|