metadata

language:
  - kmr
  - ku
license: apache-2.0
tags:
  - automatic-speech-recognition
  - mozilla-foundation/common_voice_8_0
  - generated_from_trainer
  - kmr
  - robust-speech-event
  - model_for_talk
  - hf-asr-leaderboard
datasets:
  - mozilla-foundation/common_voice_8_0
model-index:
  - name: Akashpb13/xlsr_kurmanji_kurdish
    results:
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: Common Voice 8
          type: mozilla-foundation/common_voice_8_0
          args: kmr
        metrics:
          - name: Test WER
            type: wer
            value: 0.33073206986250464
          - name: Test CER
            type: cer
            value: 0.08035244447163924
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: Robust Speech Event - Dev Data
          type: speech-recognition-community-v2/dev_data
          args: kmr
        metrics:
          - name: Test WER
            type: wer
            value: 0.33073206986250464
          - name: Test CER
            type: cer
            value: 0.08035244447163924

Akashpb13/xlsr_kurmanji_kurdish

This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the MOZILLA-FOUNDATION/COMMON_VOICE_7_0 - hu dataset. It achieves the following results on the evaluation set (which is 10 percent of train data set merged with invalidated data, reported, other, and dev datasets):

Loss: 0.292389
Wer: 0.388585

Model description

"facebook/wav2vec2-xls-r-300m" was finetuned.

Intended uses & limitations

More information needed

Training and evaluation data

Training data - Common voice Kurmanji Kurdish train.tsv, dev.tsv, invalidated.tsv, reported.tsv, and other.tsv Only those points were considered where upvotes were greater than downvotes and duplicates were removed after concatenation of all the datasets given in common voice 7.0

Training procedure

For creating the training dataset, all possible datasets were appended and 90-10 split was used.

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 0.000096
train_batch_size: 16
eval_batch_size: 16
seed: 13
gradient_accumulation_steps: 16
lr_scheduler_type: cosine_with_restarts
lr_scheduler_warmup_steps: 200
num_epochs: 100
mixed_precision_training: Native AMP

Training results

Step	Training Loss	Validation Loss	Wer
200	4.382500	3.183725	1.000000
400	2.870200	0.996664	0.781117
600	0.609900	0.333755	0.445052
800	0.326800	0.305729	0.403157
1000	0.255000	0.290734	0.391621
1200	0.226300	0.292389	0.388585

Framework versions

Transformers 4.16.0.dev0
Pytorch 1.10.0+cu102
Datasets 1.18.1
Tokenizers 0.10.3

Evaluation Commands

To evaluate on mozilla-foundation/common_voice_8_0 with split test

python eval.py --model_id Akashpb13/xlsr_kurmanji_kurdish --dataset mozilla-foundation/common_voice_8_0 --config kmr --split test