---
license: cc-by-4.0
language: tr
tags:
- automatic-speech-recognition
- mozilla-foundation/common_voice_7_0
- tr
- robust-speech-event
datasets:
- mozilla-foundation/common_voice_7_0
---

# wav2vec2-xls-r-300m-cv7-turkish

## Model description
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.

## Training and evaluation data
The following datasets were used for finetuning:
 - [Common Voice 7.0 TR](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0) All `validated` split except `test` split was used for training.
 - [MediaSpeech](https://www.openslr.org/108/)

## Training procedure
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.

### Training hyperparameters
The following hypermaters were used for finetuning:
- learning_rate 2e-4
- num_train_epochs 10
- warmup_steps 500
- freeze_feature_extractor
- mask_time_prob 0.1
- mask_feature_prob 0.05
- feat_proj_dropout 0.05
- attention_dropout 0.05
- final_dropout 0.05
- activation_dropout 0.05
- per_device_train_batch_size 8
- per_device_eval_batch_size 8
- gradient_accumulation_steps 8

### Framework versions
- Transformers 4.16.0.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](https://github.com/mpoyraz/ngram-lm-wiki) repo was used to generate arpa LM and convert it into binary format.