Baybars/wav2vec2-xls-r-300m-cv8-turkish

This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the COMMON_VOICE - TR dataset. It achieves the following results on the evaluation set:

• Loss: 0.4164
• Wer: 0.3098
• Cer: 0.0764

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Language Model

N-gram language model is trained by mpoyraz on a Turkish Wikipedia articles using KenLM and ngram-lm-wiki repo was used to generate arpa LM and convert it into binary format.

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

• learning_rate: 0.0005
• train_batch_size: 64
• eval_batch_size: 8
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• lr_scheduler_warmup_steps: 500
• num_epochs: 100.0
• mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Wer	Cer
0.6356	9.09	500	0.5055	0.5536	0.1381
0.3847	18.18	1000	0.4002	0.4247	0.1065
0.3377	27.27	1500	0.4193	0.4167	0.1078
0.2175	36.36	2000	0.4351	0.3861	0.0974
0.2074	45.45	2500	0.3962	0.3622	0.0916
0.159	54.55	3000	0.4062	0.3526	0.0888
0.1882	63.64	3500	0.3991	0.3445	0.0850
0.1766	72.73	4000	0.4214	0.3396	0.0847
0.116	81.82	4500	0.4182	0.3265	0.0812
0.0718	90.91	5000	0.4259	0.3191	0.0781
0.019	100.0	5500	0.4164	0.3098	0.0764

Evaluation Commands

Please install unicode_tr package before running evaluation. It is used for Turkish text processing.

1. To evaluate on mozilla-foundation/common_voice_7_0 with split test

python eval.py --model_id Baybars/wav2vec2-xls-r-300m-cv8-turkish --dataset mozilla-foundation/common_voice_8_0 --config tr --split test

2. To evaluate on speech-recognition-community-v2/dev_data

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

Framework versions

• Transformers 4.17.0.dev0
• Pytorch 1.10.2+cu102
• Datasets 1.18.2.dev0
• Tokenizers 0.11.0