lucio/xls-r-uyghur-cv8

XLS-R-300M Uyghur CV8

This model is a fine-tuned version of 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: 0.2026
• Wer: 0.3248

Model description

For a description of the model architecture, see facebook/wav2vec2-xls-r-300m

The model vocabulary consists of the alphabetic characters of the Perso-Arabic script for the Uyghur language, 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 9400 steps (100 epochs).

Training hyperparameters

The following hyperparameters were used during training:

• learning_rate: 0.0001
• train_batch_size: 16
• eval_batch_size: 8
• seed: 42
• gradient_accumulation_steps: 4
• total_train_batch_size: 64
• 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
3.3036	5.32	500	3.2628	1.0
2.9734	10.63	1000	2.5677	0.9980
1.3466	15.95	1500	0.4455	0.6306
1.2424	21.28	2000	0.3603	0.5301
1.1655	26.59	2500	0.3165	0.4740
1.1026	31.91	3000	0.2930	0.4400
1.0655	37.23	3500	0.2675	0.4159
1.0239	42.55	4000	0.2580	0.3913
0.9938	47.87	4500	0.2373	0.3698
0.9655	53.19	5000	0.2379	0.3675
0.9374	58.51	5500	0.2486	0.3795
0.9065	63.83	6000	0.2243	0.3405
0.888	69.15	6500	0.2157	0.3277
0.8646	74.47	7000	0.2103	0.3288
0.8602	79.78	7500	0.2088	0.3238
0.8442	85.11	8000	0.2045	0.3266
0.8335	90.42	8500	0.2038	0.3241
0.8288	95.74	9000	0.2024	0.3280

Framework versions

• Transformers 4.17.0.dev0
• Pytorch 1.10.2+cu102
• Datasets 1.18.3
• Tokenizers 0.11.0