|
--- |
|
language: |
|
- ky |
|
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 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 (with LM) |
|
type: wer |
|
value: 19.01 |
|
- name: Test CER (with LM) |
|
type: cer |
|
value: 5.38 |
|
- name: Test WER (no LM) |
|
type: wer |
|
value: 31.28 |
|
- name: Test CER (no LM) |
|
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. |
|
|
|
The kenlm language model is built using the text of the train and invalidated corpus splits. |
|
|
|
## 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 |
|
|
