wav2vec2-xls-r-300m-cv8-turkish
Model description
This ASR model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on Turkish language.
Training and evaluation data
The following datasets were used for finetuning:
- Common Voice 8.0 TR All
validatedsplit excepttestsplit was used for training.
Training procedure
To support the datasets above, custom pre-processing and loading steps was performed and wav2vec2-turkish repo was used for that purpose.
Training hyperparameters
The following hypermaters were used for finetuning:
- learning_rate 2.5e-4
- num_train_epochs 20
- warmup_steps 500
- freeze_feature_extractor
- mask_time_prob 0.1
- mask_feature_prob 0.1
- feat_proj_dropout 0.05
- attention_dropout 0.05
- final_dropout 0.1
- activation_dropout 0.05
- per_device_train_batch_size 8
- per_device_eval_batch_size 8
- gradient_accumulation_steps 8
Framework versions
- Transformers 4.17.0.dev0
- Pytorch 1.10.1
- Datasets 1.17.0
- Tokenizers 0.10.3
Language Model
N-gram language model is trained on a Turkish Wikipedia articles using KenLM and ngram-lm-wiki repo was used to generate arpa LM and convert it into binary format.
Evaluation Commands
Please install unicode_tr package before running evaluation. It is used for Turkish text processing.
- To evaluate on
mozilla-foundation/common_voice_8_0with splittest
python eval.py --model_id mpoyraz/wav2vec2-xls-r-300m-cv8-turkish --dataset mozilla-foundation/common_voice_8_0 --config tr --split test
- To evaluate on
speech-recognition-community-v2/dev_data
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
Evaluation results:
| Dataset | WER | CER |
|---|---|---|
| Common Voice 8 TR test split | 10.61 | 2.67 |
| Speech Recognition Community dev data | 36.46 | 12.38 |