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---
language:
- ky
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 Kyrgiz CV8
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice 8
type: mozilla-foundation/common_voice_8_0
args: ky
metrics:
- name: Test WER
type: wer
value: 31.28
- name: Test CER
type: cer
value: 7.66
---
<!-- 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 Kyrgiz 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 - KY dataset.
It achieves the following results on the validation set:
- Loss: 0.5497
- Wer: 0.2945
- Cer: 0.0791
## 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 cyrillic alphabet 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`, `dev` and `other` of common voice official splits were used as training data. The half of the official `test` split was used as validation data, as and the full `test` set was used for final evaluation.
## Training procedure
The featurization layers of the XLS-R model are frozen while tuning a final CTC/LM layer on the Kyrgiz CV8 example sentences. A ramped learning rate is used with an initial warmup phase of 500 steps, a max of 0.0001, and cooling back towards 0 for the remainder of the 8100 steps (300 epochs).
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- 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: 300.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer | Cer |
|:-------------:|:------:|:----:|:---------------:|:------:|:------:|
| 3.1079 | 18.51 | 500 | 2.6795 | 0.9996 | 0.9825 |
| 0.8506 | 37.04 | 1000 | 0.4323 | 0.3718 | 0.0961 |
| 0.6821 | 55.55 | 1500 | 0.4105 | 0.3311 | 0.0878 |
| 0.6091 | 74.07 | 2000 | 0.4281 | 0.3168 | 0.0851 |
| 0.5429 | 92.58 | 2500 | 0.4525 | 0.3147 | 0.0842 |
| 0.5063 | 111.11 | 3000 | 0.4619 | 0.3144 | 0.0839 |
| 0.4661 | 129.62 | 3500 | 0.4660 | 0.3039 | 0.0818 |
| 0.4353 | 148.15 | 4000 | 0.4695 | 0.3083 | 0.0820 |
| 0.4048 | 166.65 | 4500 | 0.4909 | 0.3085 | 0.0824 |
| 0.3852 | 185.18 | 5000 | 0.5074 | 0.3048 | 0.0812 |
| 0.3567 | 203.69 | 5500 | 0.5111 | 0.3012 | 0.0810 |
| 0.3451 | 222.22 | 6000 | 0.5225 | 0.2982 | 0.0804 |
| 0.325 | 240.73 | 6500 | 0.5270 | 0.2955 | 0.0796 |
| 0.3089 | 259.25 | 7000 | 0.5381 | 0.2929 | 0.0793 |
| 0.2941 | 277.76 | 7500 | 0.5565 | 0.2923 | 0.0794 |
| 0.2945 | 296.29 | 8000 | 0.5495 | 0.2951 | 0.0789 |
### Framework versions
- Transformers 4.17.0.dev0
- Pytorch 1.10.2+cu102
- Datasets 1.18.3
- Tokenizers 0.11.0
|