language:
- uz
license: apache-2.0
tags:
- automatic-speech-recognition
- mozilla-foundation/common_voice_8_0
- generated_from_trainer
- robust-speech-event
datasets:
- mozilla-foundation/common_voice_8_0
model-index:
- name: XLS-R-300M Uzbek CV8
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice 8
type: mozilla-foundation/common_voice_8_0
args: uz
metrics:
- name: Test WER (no LM)
type: wer
value: 32.88
- name: Test CER (no LM)
type: cer
value: 6.53
XLS-R-300M Uzbek CV8
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the MOZILLA-FOUNDATION/COMMON_VOICE_8_0 - UZ dataset. It achieves the following results on the evaluation set:
- Loss: 0.3063
- Wer: 0.3852
- Cer: 0.0777
Model description
For a description of the model architecture, see facebook/wav2vec2-xls-r-300m
The model vocabulary consists of the Modern Latin alphabet for Uzbek, with punctuation removed. Note that the characters <‘> and <’> do not count as punctuation, as <‘> modifies <o> and <g>, and <’> indicates the glottal stop or a long vowel.
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 50% of the train common voice official split was used as training data. The 50% of the official dev split was used as validation data, and the full test set was used for final evaluation.
The kenlm language model was compiled from the target sentences of the train + other datasets.
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 32
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 100.0
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer | Cer |
|---|---|---|---|---|---|
| 3.1401 | 3.25 | 500 | 3.1146 | 1.0 | 1.0 |
| 2.7484 | 6.49 | 1000 | 2.2842 | 1.0065 | 0.7069 |
| 1.0899 | 9.74 | 1500 | 0.5414 | 0.6125 | 0.1351 |
| 0.9465 | 12.99 | 2000 | 0.4566 | 0.5635 | 0.1223 |
| 0.8771 | 16.23 | 2500 | 0.4212 | 0.5366 | 0.1161 |
| 0.8346 | 19.48 | 3000 | 0.3994 | 0.5144 | 0.1102 |
| 0.8127 | 22.73 | 3500 | 0.3819 | 0.4944 | 0.1051 |
| 0.7833 | 25.97 | 4000 | 0.3705 | 0.4798 | 0.1011 |
| 0.7603 | 29.22 | 4500 | 0.3661 | 0.4704 | 0.0992 |
| 0.7424 | 32.47 | 5000 | 0.3529 | 0.4577 | 0.0957 |
| 0.7251 | 35.71 | 5500 | 0.3410 | 0.4473 | 0.0928 |
| 0.7106 | 38.96 | 6000 | 0.3401 | 0.4428 | 0.0919 |
| 0.7027 | 42.21 | 6500 | 0.3355 | 0.4353 | 0.0905 |
| 0.6927 | 45.45 | 7000 | 0.3308 | 0.4296 | 0.0885 |
| 0.6828 | 48.7 | 7500 | 0.3246 | 0.4204 | 0.0863 |
| 0.6706 | 51.95 | 8000 | 0.3250 | 0.4233 | 0.0868 |
| 0.6629 | 55.19 | 8500 | 0.3264 | 0.4159 | 0.0849 |
| 0.6556 | 58.44 | 9000 | 0.3213 | 0.4100 | 0.0835 |
| 0.6484 | 61.69 | 9500 | 0.3182 | 0.4124 | 0.0837 |
| 0.6407 | 64.93 | 10000 | 0.3171 | 0.4050 | 0.0825 |
| 0.6375 | 68.18 | 10500 | 0.3150 | 0.4039 | 0.0822 |
| 0.6363 | 71.43 | 11000 | 0.3129 | 0.3991 | 0.0810 |
| 0.6307 | 74.67 | 11500 | 0.3114 | 0.3986 | 0.0807 |
| 0.6232 | 77.92 | 12000 | 0.3103 | 0.3895 | 0.0790 |
| 0.6216 | 81.17 | 12500 | 0.3086 | 0.3891 | 0.0790 |
| 0.6174 | 84.41 | 13000 | 0.3082 | 0.3881 | 0.0785 |
| 0.6196 | 87.66 | 13500 | 0.3059 | 0.3875 | 0.0782 |
| 0.6174 | 90.91 | 14000 | 0.3084 | 0.3862 | 0.0780 |
| 0.6169 | 94.16 | 14500 | 0.3070 | 0.3860 | 0.0779 |
| 0.6166 | 97.4 | 15000 | 0.3066 | 0.3855 | 0.0778 |
Framework versions
- Transformers 4.16.2
- Pytorch 1.10.2+cu102
- Datasets 1.18.3
- Tokenizers 0.11.0