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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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- 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: mpoyraz/wav2vec2-xls-r-300m-cv6-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 6.1 |
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type: common_voice |
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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: 8.83 |
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- name: Test CER |
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type: cer |
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value: 2.37 |
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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: 32.81 |
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- name: Test CER |
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type: cer |
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value: 11.22 |
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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 - Test Data |
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type: speech-recognition-community-v2/eval_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: 34.86 |
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--- |
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# wav2vec2-xls-r-300m-cv6-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 6.1 TR](https://huggingface.co/datasets/common_voice) All `validated` split except `test` split was used for training.
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- [MediaSpeech](https://www.openslr.org/108/)
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## Training procedure
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To support both of 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 2e-4
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- num_train_epochs 10
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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 1.18.3
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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 `common_voice` with split `test`
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```bash
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python eval.py --model_id mpoyraz/wav2vec2-xls-r-300m-cv6-turkish --dataset common_voice --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-cv6-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 6.1 TR test split| 8.83 | 2.37 |
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|Speech Recognition Community dev data| 32.81 | 11.22 |
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