File size: 5,317 Bytes
0bbdad1 d46221f f8a8e11 0bbdad1 d46221f 0bbdad1 d46221f 0bbdad1 d46221f 0bbdad1 89dcecc d46221f 0bbdad1 d46221f f8a8e11 0bbdad1 d46221f 0bbdad1 d46221f 0bbdad1 d46221f 0bbdad1 89dcecc 0bbdad1 c8b6183 0bbdad1 c8b6183 89dcecc 0bbdad1 |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 |
---
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: 28.74
- name: Test CER
type: cer
value: 5.38
---
<!-- 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. -->
# XLS-R-300M Uyghur 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 - UG dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2036
- WER: 0.2977
## 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 alphabetic characters of the [Perso-Arabic script for the Uyghur language](https://omniglot.com/writing/uyghur.htm), 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 18500 steps (100 epochs).
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- 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: 2000
- num_epochs: 100.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 3.2892 | 2.66 | 500 | 3.2415 | 1.0 |
| 2.9206 | 5.32 | 1000 | 2.4381 | 1.0056 |
| 1.4909 | 7.97 | 1500 | 0.5428 | 0.6705 |
| 1.3395 | 10.64 | 2000 | 0.4207 | 0.5995 |
| 1.2718 | 13.3 | 2500 | 0.3743 | 0.5648 |
| 1.1798 | 15.95 | 3000 | 0.3225 | 0.4927 |
| 1.1392 | 18.61 | 3500 | 0.3097 | 0.4627 |
| 1.1143 | 21.28 | 4000 | 0.2996 | 0.4505 |
| 1.0923 | 23.93 | 4500 | 0.2841 | 0.4229 |
| 1.0516 | 26.59 | 5000 | 0.2705 | 0.4113 |
| 1.051 | 29.25 | 5500 | 0.2622 | 0.4078 |
| 1.021 | 31.91 | 6000 | 0.2611 | 0.4009 |
| 0.9886 | 34.57 | 6500 | 0.2498 | 0.3921 |
| 0.984 | 37.23 | 7000 | 0.2521 | 0.3845 |
| 0.9631 | 39.89 | 7500 | 0.2413 | 0.3791 |
| 0.9353 | 42.55 | 8000 | 0.2391 | 0.3612 |
| 0.922 | 45.21 | 8500 | 0.2363 | 0.3571 |
| 0.9116 | 47.87 | 9000 | 0.2285 | 0.3668 |
| 0.8951 | 50.53 | 9500 | 0.2256 | 0.3729 |
| 0.8865 | 53.19 | 10000 | 0.2228 | 0.3663 |
| 0.8792 | 55.85 | 10500 | 0.2221 | 0.3656 |
| 0.8682 | 58.51 | 11000 | 0.2228 | 0.3323 |
| 0.8492 | 61.17 | 11500 | 0.2167 | 0.3446 |
| 0.8365 | 63.83 | 12000 | 0.2156 | 0.3321 |
| 0.8298 | 66.49 | 12500 | 0.2142 | 0.3400 |
| 0.808 | 69.15 | 13000 | 0.2079 | 0.3148 |
| 0.7999 | 71.81 | 13500 | 0.2117 | 0.3225 |
| 0.7871 | 74.47 | 14000 | 0.2088 | 0.3174 |
| 0.7858 | 77.13 | 14500 | 0.2060 | 0.3008 |
| 0.7764 | 79.78 | 15000 | 0.2128 | 0.3146 |
| 0.7684 | 82.45 | 15500 | 0.2086 | 0.3101 |
| 0.7717 | 85.11 | 16000 | 0.2048 | 0.3069 |
| 0.7435 | 87.76 | 16500 | 0.2027 | 0.3055 |
| 0.7378 | 90.42 | 17000 | 0.2059 | 0.2993 |
| 0.7406 | 93.08 | 17500 | 0.2040 | 0.2966 |
| 0.7361 | 95.74 | 18000 | 0.2056 | 0.3000 |
| 0.7379 | 98.4 | 18500 | 0.2031 | 0.2976 |
### Framework versions
- Transformers 4.16.0.dev0
- Pytorch 1.10.1+cu102
- Datasets 1.18.2.dev0
- Tokenizers 0.11.0
|