metadata

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

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: 0.2036
WER: 0.2977

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 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