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--- |
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language: |
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- tr |
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license: apache-2.0 |
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tags: |
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- automatic-speech-recognition |
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- common_voice |
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- generated_from_trainer |
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- hf-asr-leaderboard |
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- robust-speech-event |
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- tr |
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datasets: |
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- common_voice |
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model-index: |
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- name: '' |
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results: [] |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# |
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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 COMMON_VOICE - TR dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.4164 |
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- Wer: 0.3098 |
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- Cer: 0.0764 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Language Model |
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N-gram language model is trained by [mpoyraz](https://huggingface.co/mpoyraz/wav2vec2-xls-r-300m-cv7-turkish) on a Turkish Wikipedia articles using KenLM and [ngram-lm-wiki](https://github.com/mpoyraz/ngram-lm-wiki) repo was used to generate arpa LM and convert it into binary format. |
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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: 0.0005 |
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- train_batch_size: 64 |
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- eval_batch_size: 8 |
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- seed: 42 |
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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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| 0.6356 | 9.09 | 500 | 0.5055 | 0.5536 | 0.1381 | |
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| 0.3847 | 18.18 | 1000 | 0.4002 | 0.4247 | 0.1065 | |
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| 0.3377 | 27.27 | 1500 | 0.4193 | 0.4167 | 0.1078 | |
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| 0.2175 | 36.36 | 2000 | 0.4351 | 0.3861 | 0.0974 | |
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| 0.2074 | 45.45 | 2500 | 0.3962 | 0.3622 | 0.0916 | |
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| 0.159 | 54.55 | 3000 | 0.4062 | 0.3526 | 0.0888 | |
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| 0.1882 | 63.64 | 3500 | 0.3991 | 0.3445 | 0.0850 | |
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| 0.1766 | 72.73 | 4000 | 0.4214 | 0.3396 | 0.0847 | |
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| 0.116 | 81.82 | 4500 | 0.4182 | 0.3265 | 0.0812 | |
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| 0.0718 | 90.91 | 5000 | 0.4259 | 0.3191 | 0.0781 | |
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| 0.019 | 100.0 | 5500 | 0.4164 | 0.3098 | 0.0764 | |
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## Evaluation Commands |
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Please install [unicode_tr](https://pypi.org/project/unicode_tr/) package before running evaluation. It is used for Turkish text processing. |
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1. To evaluate on `mozilla-foundation/common_voice_7_0` with split `test` |
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```bash |
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python eval.py --model_id Baybars/wav2vec2-xls-r-300m-cv8-turkish --dataset mozilla-foundation/common_voice_8_0 --config tr --split test |
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``` |
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2. To evaluate on `speech-recognition-community-v2/dev_data` |
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```bash |
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python eval.py --model_id Baybars/wav2vec2-xls-r-300m-cv8-turkish --dataset speech-recognition-community-v2/dev_data --config tr --split validation --chunk_length_s 5.0 --stride_length_s 1.0 |
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``` |
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### Framework versions |
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- Transformers 4.17.0.dev0 |
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- Pytorch 1.10.2+cu102 |
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- Datasets 1.18.2.dev0 |
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- Tokenizers 0.11.0 |
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