metadata
language:
- en
license: apache-2.0
tags:
- automatic-speech-recognition
- librispeech_asr
- robust-speech-event
- en
- generated_from_trainer
datasets:
- librispeech_asr
model-index:
- name: XLS-R-300M - English
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: LibriSpeech ASR
type: librispeech_asr
args: clean
metrics:
- name: Test WER
type: wer
value: 12.29
- name: Test CER
type: cer
value: 3.34
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Robust Speech Event - Dev Data
type: speech-recognition-community-v2/dev_data
args: en
metrics:
- name: Validation WER
type: wer
value: 36.75
- name: Validation CER
type: cer
value: 14.83
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the librispeech_asr dataset. It achieves the following results on the evaluation set:
- Loss: 0.1444
- Wer: 0.1167
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: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- 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: 1000
- num_epochs: 50
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 2.9365 | 4.17 | 500 | 2.9398 | 0.9999 |
| 1.5444 | 8.33 | 1000 | 0.5947 | 0.4289 |
| 1.1367 | 12.5 | 1500 | 0.2751 | 0.2366 |
| 0.9972 | 16.66 | 2000 | 0.2032 | 0.1797 |
| 0.9118 | 20.83 | 2500 | 0.1786 | 0.1479 |
| 0.8664 | 24.99 | 3000 | 0.1641 | 0.1408 |
| 0.8251 | 29.17 | 3500 | 0.1537 | 0.1267 |
| 0.793 | 33.33 | 4000 | 0.1525 | 0.1244 |
| 0.785 | 37.5 | 4500 | 0.1470 | 0.1184 |
| 0.7612 | 41.66 | 5000 | 0.1446 | 0.1177 |
| 0.7478 | 45.83 | 5500 | 0.1449 | 0.1176 |
| 0.7443 | 49.99 | 6000 | 0.1444 | 0.1167 |
Framework versions
- Transformers 4.17.0.dev0
- Pytorch 1.10.2+cu102
- Datasets 1.18.2.dev0
- Tokenizers 0.11.0