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--- |
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language: |
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- uz |
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license: apache-2.0 |
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tags: |
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- automatic-speech-recognition |
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- generated_from_trainer |
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- hf-asr-leaderboard |
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- mozilla-foundation/common_voice_8_0 |
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- robust-speech-event |
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datasets: |
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- mozilla-foundation/common_voice_8_0 |
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model-index: |
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- name: XLS-R-300M Uzbek CV8 |
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results: |
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- task: |
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name: Automatic Speech Recognition |
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type: automatic-speech-recognition |
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dataset: |
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name: Common Voice 8 |
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type: mozilla-foundation/common_voice_8_0 |
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args: uz |
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metrics: |
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- name: Test WER (with LM) |
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type: wer |
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value: 15.065 |
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- name: Test CER (with LM) |
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type: cer |
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value: 3.077 |
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- name: Test WER (no LM) |
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type: wer |
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value: 32.88 |
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- name: Test CER (no LM) |
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type: cer |
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value: 6.53 |
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--- |
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# XLS-R-300M Uzbek CV8 |
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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 - UZ dataset. |
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It achieves the following results on the validation set: |
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- Loss: 0.3063 |
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- Wer: 0.3852 |
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- Cer: 0.0777 |
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## Model description |
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For a description of the model architecture, see [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) |
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The model vocabulary consists of the [Modern Latin alphabet for Uzbek](https://en.wikipedia.org/wiki/Uzbek_alphabet), with punctuation removed. |
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Note that the characters <‘> and <’> do not count as punctuation, as <‘> modifies \<o\> and \<g\>, and <’> indicates the glottal stop or a long vowel. |
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The decoder uses a kenlm language model built on common_voice text. |
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## Intended uses & limitations |
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This model is expected to be of some utility for low-fidelity use cases such as: |
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- Draft video captions |
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- Indexing of recorded broadcasts |
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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. |
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## Training and evaluation data |
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The 50% of the `train` common voice official split was used as training data. The 50% of the official `dev` split was used as validation data, and the full `test` set was used for final evaluation of the model without LM, while the model with LM was evaluated only on 500 examples from the `test` set. |
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The kenlm language model was compiled from the target sentences of the train + other dataset splits. |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 3e-05 |
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- train_batch_size: 32 |
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- eval_batch_size: 8 |
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- seed: 42 |
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- gradient_accumulation_steps: 4 |
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- total_train_batch_size: 128 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- lr_scheduler_warmup_steps: 500 |
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- num_epochs: 100.0 |
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- mixed_precision_training: Native AMP |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Wer | Cer | |
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|:-------------:|:-----:|:-----:|:---------------:|:------:|:------:| |
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| 3.1401 | 3.25 | 500 | 3.1146 | 1.0 | 1.0 | |
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| 2.7484 | 6.49 | 1000 | 2.2842 | 1.0065 | 0.7069 | |
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| 1.0899 | 9.74 | 1500 | 0.5414 | 0.6125 | 0.1351 | |
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| 0.9465 | 12.99 | 2000 | 0.4566 | 0.5635 | 0.1223 | |
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| 0.8771 | 16.23 | 2500 | 0.4212 | 0.5366 | 0.1161 | |
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| 0.8346 | 19.48 | 3000 | 0.3994 | 0.5144 | 0.1102 | |
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| 0.8127 | 22.73 | 3500 | 0.3819 | 0.4944 | 0.1051 | |
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| 0.7833 | 25.97 | 4000 | 0.3705 | 0.4798 | 0.1011 | |
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| 0.7603 | 29.22 | 4500 | 0.3661 | 0.4704 | 0.0992 | |
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| 0.7424 | 32.47 | 5000 | 0.3529 | 0.4577 | 0.0957 | |
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| 0.7251 | 35.71 | 5500 | 0.3410 | 0.4473 | 0.0928 | |
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| 0.7106 | 38.96 | 6000 | 0.3401 | 0.4428 | 0.0919 | |
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| 0.7027 | 42.21 | 6500 | 0.3355 | 0.4353 | 0.0905 | |
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| 0.6927 | 45.45 | 7000 | 0.3308 | 0.4296 | 0.0885 | |
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| 0.6828 | 48.7 | 7500 | 0.3246 | 0.4204 | 0.0863 | |
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| 0.6706 | 51.95 | 8000 | 0.3250 | 0.4233 | 0.0868 | |
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| 0.6629 | 55.19 | 8500 | 0.3264 | 0.4159 | 0.0849 | |
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| 0.6556 | 58.44 | 9000 | 0.3213 | 0.4100 | 0.0835 | |
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| 0.6484 | 61.69 | 9500 | 0.3182 | 0.4124 | 0.0837 | |
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| 0.6407 | 64.93 | 10000 | 0.3171 | 0.4050 | 0.0825 | |
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| 0.6375 | 68.18 | 10500 | 0.3150 | 0.4039 | 0.0822 | |
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| 0.6363 | 71.43 | 11000 | 0.3129 | 0.3991 | 0.0810 | |
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| 0.6307 | 74.67 | 11500 | 0.3114 | 0.3986 | 0.0807 | |
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| 0.6232 | 77.92 | 12000 | 0.3103 | 0.3895 | 0.0790 | |
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| 0.6216 | 81.17 | 12500 | 0.3086 | 0.3891 | 0.0790 | |
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| 0.6174 | 84.41 | 13000 | 0.3082 | 0.3881 | 0.0785 | |
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| 0.6196 | 87.66 | 13500 | 0.3059 | 0.3875 | 0.0782 | |
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| 0.6174 | 90.91 | 14000 | 0.3084 | 0.3862 | 0.0780 | |
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| 0.6169 | 94.16 | 14500 | 0.3070 | 0.3860 | 0.0779 | |
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| 0.6166 | 97.4 | 15000 | 0.3066 | 0.3855 | 0.0778 | |
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### Framework versions |
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- Transformers 4.16.2 |
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- Pytorch 1.10.2+cu102 |
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- Datasets 1.18.3 |
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- Tokenizers 0.11.0 |
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