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--- |
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language: |
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- uk |
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license: apache-2.0 |
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tags: |
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- automatic-speech-recognition |
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- mozilla-foundation/common_voice_7_0 |
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- generated_from_trainer |
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- uk |
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datasets: |
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- mozilla-foundation/common_voice_7_0 |
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model-index: |
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- name: wav2vec2-xls-r-300m-uk-with-lm |
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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 7 |
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type: mozilla-foundation/common_voice_7_0 |
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args: uk |
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metrics: |
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- name: Test WER |
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type: wer |
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value: 26.47 |
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- name: Test CER |
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type: cer |
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value: 2.90 |
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--- |
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# Ukrainian STT model (with Language Model) |
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🇺🇦 Join Ukrainian Speech Recognition Community - https://t.me/speech_recognition_uk |
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⭐ See other Ukrainian models - https://github.com/egorsmkv/speech-recognition-uk |
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- Have a look on an updated 300m model: https://huggingface.co/Yehor/wav2vec2-xls-r-300m-uk-with-small-lm |
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- Have a look on a better model with more parameters: https://huggingface.co/Yehor/wav2vec2-xls-r-1b-uk-with-lm |
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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_7_0 - UK dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.3015 |
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- Wer: 0.3377 |
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- Cer: 0.0708 |
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The above results present evaluation without the language model. |
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## Model description |
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On 100 test example the model shows the following results: |
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Without LM: |
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- WER: 0.2647 |
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- CER: 0.0469 |
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With LM: |
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- WER: 0.1568 |
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- CER: 0.0289 |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 0.0003 |
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- train_batch_size: 8 |
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- eval_batch_size: 8 |
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- seed: 42 |
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- gradient_accumulation_steps: 20 |
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- total_train_batch_size: 160 |
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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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| 3.0255 | 7.93 | 500 | 2.5514 | 0.9921 | 0.9047 | |
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| 1.3809 | 15.86 | 1000 | 0.4065 | 0.5361 | 0.1201 | |
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| 1.2355 | 23.8 | 1500 | 0.3474 | 0.4618 | 0.1033 | |
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| 1.1956 | 31.74 | 2000 | 0.3617 | 0.4580 | 0.1005 | |
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| 1.1416 | 39.67 | 2500 | 0.3182 | 0.4074 | 0.0891 | |
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| 1.0996 | 47.61 | 3000 | 0.3166 | 0.3985 | 0.0875 | |
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| 1.0427 | 55.55 | 3500 | 0.3116 | 0.3835 | 0.0828 | |
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| 0.9961 | 63.49 | 4000 | 0.3137 | 0.3757 | 0.0807 | |
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| 0.9575 | 71.42 | 4500 | 0.2992 | 0.3632 | 0.0771 | |
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| 0.9154 | 79.36 | 5000 | 0.3015 | 0.3502 | 0.0740 | |
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| 0.8994 | 87.3 | 5500 | 0.3004 | 0.3425 | 0.0723 | |
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| 0.871 | 95.24 | 6000 | 0.3016 | 0.3394 | 0.0713 | |
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### Framework versions |
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- Transformers 4.16.0.dev0 |
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- Pytorch 1.10.1+cu102 |
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- Datasets 1.18.1.dev0 |
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- Tokenizers 0.11.0 |
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