language:
- ug
license: apache-2.0
tags:
- automatic-speech-recognition
- mozilla-foundation/common_voice_8_0
- generated_from_trainer
- ug
- robust-speech-event
datasets:
- - mozilla-foundation/common_voice_8_0
model-index:
- name: XLS-R-300M Uyghur CV8
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice 8
type: mozilla-foundation/common_voice_8_0
args: ug
metrics:
- name: Test WER
type: wer
value: 30.5
- name: Test CER
type: cer
value: 5.8
XLS-R-300M Uyghur CV8
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the MOZILLA-FOUNDATION/COMMON_VOICE_8_0 - UG dataset. It achieves the following results on the evaluation set:
- Loss: 0.2026
- Wer: 0.3248
Model description
For a description of the model architecture, see facebook/wav2vec2-xls-r-300m
The model vocabulary consists of the alphabetic characters of the Perso-Arabic script for the Uyghur language, with punctuation removed.
Intended uses & limitations
This model is expected to be of some utility for low-fidelity use cases such as:
- Draft video captions
- Indexing of recorded broadcasts
The model is not reliable enough to use as a substitute for live captions for accessibility purposes, and it should not be used in a manner that would infringe the privacy of any of the contributors to the Common Voice dataset nor any other speakers.
Training and evaluation data
The combination of train and dev of common voice official splits were used as training data. The official test split was used as validation data as well as for final evaluation.
Training procedure
The featurization layers of the XLS-R model are frozen while tuning a final CTC/LM layer on the Uyghur CV8 example sentences. A ramped learning rate is used with an initial warmup phase of 2000 steps, a max of 0.0001, and cooling back towards 0 for the remainder of the 9400 steps (100 epochs).
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 2000
- num_epochs: 100.0
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 3.3036 | 5.32 | 500 | 3.2628 | 1.0 |
| 2.9734 | 10.63 | 1000 | 2.5677 | 0.9980 |
| 1.3466 | 15.95 | 1500 | 0.4455 | 0.6306 |
| 1.2424 | 21.28 | 2000 | 0.3603 | 0.5301 |
| 1.1655 | 26.59 | 2500 | 0.3165 | 0.4740 |
| 1.1026 | 31.91 | 3000 | 0.2930 | 0.4400 |
| 1.0655 | 37.23 | 3500 | 0.2675 | 0.4159 |
| 1.0239 | 42.55 | 4000 | 0.2580 | 0.3913 |
| 0.9938 | 47.87 | 4500 | 0.2373 | 0.3698 |
| 0.9655 | 53.19 | 5000 | 0.2379 | 0.3675 |
| 0.9374 | 58.51 | 5500 | 0.2486 | 0.3795 |
| 0.9065 | 63.83 | 6000 | 0.2243 | 0.3405 |
| 0.888 | 69.15 | 6500 | 0.2157 | 0.3277 |
| 0.8646 | 74.47 | 7000 | 0.2103 | 0.3288 |
| 0.8602 | 79.78 | 7500 | 0.2088 | 0.3238 |
| 0.8442 | 85.11 | 8000 | 0.2045 | 0.3266 |
| 0.8335 | 90.42 | 8500 | 0.2038 | 0.3241 |
| 0.8288 | 95.74 | 9000 | 0.2024 | 0.3280 |
Framework versions
- Transformers 4.17.0.dev0
- Pytorch 1.10.2+cu102
- Datasets 1.18.3
- Tokenizers 0.11.0