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---
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license: apache-2.0
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language: tr
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tags:
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- automatic-speech-recognition
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- common_voice
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- mozilla-foundation/common_voice_8_0
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- tr
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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: mpoyraz/wav2vec2-xls-r-300m-cv8-turkish
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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: tr
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metrics:
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- name: Test WER
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type: wer
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value: 10.61
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- name: Test CER
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type: cer
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value: 2.67
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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: Robust Speech Event - Dev Data
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type: speech-recognition-community-v2/dev_data
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args: tr
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metrics:
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- name: Test WER
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type: wer
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value: 36.46
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- name: Test CER
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type: cer
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value: 12.38
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---
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# wav2vec2-xls-r-300m-cv8-turkish
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## Model description
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This ASR model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on Turkish language.
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## Training and evaluation data
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The following datasets were used for finetuning:
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- [Common Voice 8.0 TR](https://huggingface.co/datasets/mozilla-foundation/common_voice_8_0) All `validated` split except `test` split was used for training.
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## Training procedure
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To support the datasets above, custom pre-processing and loading steps was performed and [wav2vec2-turkish](https://github.com/mpoyraz/wav2vec2-turkish) repo was used for that purpose.
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### Training hyperparameters
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The following hypermaters were used for finetuning:
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- learning_rate 2.5e-4
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- num_train_epochs 20
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- warmup_steps 500
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- freeze_feature_extractor
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- mask_time_prob 0.1
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- mask_feature_prob 0.1
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- feat_proj_dropout 0.05
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- attention_dropout 0.05
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- final_dropout 0.1
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- activation_dropout 0.05
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- per_device_train_batch_size 8
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- per_device_eval_batch_size 8
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- gradient_accumulation_steps 8
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### Framework versions
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- Transformers 4.17.0.dev0
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- Pytorch 1.10.1
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- Datasets 1.17.0
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- Tokenizers 0.10.3
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## Language Model
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N-gram language model is trained 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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## 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_8_0` with split `test`
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```bash
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python eval.py --model_id mpoyraz/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 mpoyraz/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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## Evaluation results:
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| Dataset | WER | CER |
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|---|---|---|
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|Common Voice 8 TR test split| 10.61 | 2.67 |
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|Speech Recognition Community dev data| 36.46 | 12.38 |
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