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---
language:
- gn
license: apache-2.0
tags:
- automatic-speech-recognition
- mozilla-foundation/common_voice_8_0
- generated_from_trainer
- gn
- robust-speech-event
- hf-asr-leaderboard
datasets:
- mozilla-foundation/common_voice_8_0
model-index:
- name: wav2vec2-large-xls-r-300m-gn-k1
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice 8
type: mozilla-foundation/common_voice_8_0
args: gn
metrics:
- name: Test WER
type: wer
value: 0.711890243902439
- name: Test CER
type: cer
value: 0.13311897106109324
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Robust Speech Event - Dev Data
type: speech-recognition-community-v2/dev_data
args: gn
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-gn-k1
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 - GN dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9220
- Wer: 0.6631
### 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-gn-k1 --dataset mozilla-foundation/common_voice_8_0 --config gn --split test --log_outputs
2. To evaluate on speech-recognition-community-v2/dev_data
NA
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.00018
- 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: 600
- num_epochs: 200
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:------:|:----:|:---------------:|:------:|
| 15.9402 | 8.32 | 100 | 6.9185 | 1.0 |
| 4.6367 | 16.64 | 200 | 3.7416 | 1.0 |
| 3.4337 | 24.96 | 300 | 3.2581 | 1.0 |
| 3.2307 | 33.32 | 400 | 2.8008 | 1.0 |
| 1.3182 | 41.64 | 500 | 0.8359 | 0.8171 |
| 0.409 | 49.96 | 600 | 0.8470 | 0.8323 |
| 0.2573 | 58.32 | 700 | 0.7823 | 0.7576 |
| 0.1969 | 66.64 | 800 | 0.8306 | 0.7424 |
| 0.1469 | 74.96 | 900 | 0.9225 | 0.7713 |
| 0.1172 | 83.32 | 1000 | 0.7903 | 0.6951 |
| 0.1017 | 91.64 | 1100 | 0.8519 | 0.6921 |
| 0.0851 | 99.96 | 1200 | 0.8129 | 0.6646 |
| 0.071 | 108.32 | 1300 | 0.8614 | 0.7043 |
| 0.061 | 116.64 | 1400 | 0.8414 | 0.6921 |
| 0.0552 | 124.96 | 1500 | 0.8649 | 0.6905 |
| 0.0465 | 133.32 | 1600 | 0.8575 | 0.6646 |
| 0.0381 | 141.64 | 1700 | 0.8802 | 0.6723 |
| 0.0338 | 149.96 | 1800 | 0.8731 | 0.6845 |
| 0.0306 | 158.32 | 1900 | 0.9003 | 0.6585 |
| 0.0236 | 166.64 | 2000 | 0.9408 | 0.6616 |
| 0.021 | 174.96 | 2100 | 0.9353 | 0.6723 |
| 0.0212 | 183.32 | 2200 | 0.9269 | 0.6570 |
| 0.0191 | 191.64 | 2300 | 0.9277 | 0.6662 |
| 0.0161 | 199.96 | 2400 | 0.9220 | 0.6631 |
### Framework versions
- Transformers 4.16.2
- Pytorch 1.10.0+cu111
- Datasets 1.18.3
- Tokenizers 0.11.0
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