File size: 5,230 Bytes
6a65d17 b3113ac 6a65d17 b3113ac 6a65d17 0741dd7 c7bc7d9 6a65d17 c7bc7d9 6a65d17 c7bc7d9 0741dd7 c7bc7d9 0741dd7 6a65d17 c7bc7d9 6a65d17 b3113ac c8acc40 b3113ac 6a65d17 c7bc7d9 ac84ada 6a65d17 c8acc40 6a65d17 c7bc7d9 6a65d17 e7b488d b88e18b c8acc40 b88e18b 6a65d17 b88e18b 6a65d17 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 |
---
language:
- uz
license: apache-2.0
tags:
- automatic-speech-recognition
- generated_from_trainer
- hf-asr-leaderboard
- mozilla-foundation/common_voice_8_0
- 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 (with LM)
type: wer
value: 15.065
- name: Test CER (with LM)
type: cer
value: 3.077
- 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](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the MOZILLA-FOUNDATION/COMMON_VOICE_8_0 - UZ dataset.
It achieves the following results on the validation 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](https://huggingface.co/facebook/wav2vec2-xls-r-300m)
The model vocabulary consists of the [Modern Latin alphabet for Uzbek](https://en.wikipedia.org/wiki/Uzbek_alphabet), 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.
The decoder uses a kenlm language model built on common_voice text.
## 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 of the model without LM, while the model with LM was evaluated only on 500 examples from the `test` set.
The kenlm language model was compiled from the target sentences of the train + other dataset splits.
### 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
|