metadata

language:
  - hu
license: apache-2.0
tags:
  - automatic-speech-recognition
  - generated_from_trainer
  - hf-asr-leaderboard
  - hu
  - model_for_talk
  - mozilla-foundation/common_voice_8_0
  - robust-speech-event
datasets:
  - mozilla-foundation/common_voice_8_0
base_model: facebook/wav2vec2-xls-r-300m
model-index:
  - name: Akashpb13/xlsr_hungarian_new
    results:
      - task:
          type: automatic-speech-recognition
          name: Automatic Speech Recognition
        dataset:
          name: Common Voice 8
          type: mozilla-foundation/common_voice_8_0
          args: hu
        metrics:
          - type: wer
            value: 0.2851621517163838
            name: Test WER
          - type: cer
            value: 0.06112982522287432
            name: Test CER
      - task:
          type: automatic-speech-recognition
          name: Automatic Speech Recognition
        dataset:
          name: Robust Speech Event - Dev Data
          type: speech-recognition-community-v2/dev_data
          args: hu
        metrics:
          - type: wer
            value: 0.2851621517163838
            name: Test WER
          - type: cer
            value: 0.06112982522287432
            name: Test CER
      - task:
          type: automatic-speech-recognition
          name: Automatic Speech Recognition
        dataset:
          name: Robust Speech Event - Test Data
          type: speech-recognition-community-v2/eval_data
          args: hu
        metrics:
          - type: wer
            value: 47.15
            name: Test WER

Akashpb13/xlsr_hungarian_new

This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the MOZILLA-FOUNDATION/COMMON_VOICE_8_0 - hu dataset. It achieves the following results on evaluation set (which is 10 percent of train data set merged with invalidated data, reported, other and dev datasets):

Loss: 0.197464
Wer: 0.330094

Model description

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

Intended uses & limitations

More information needed

Training and evaluation data

Training data - Common voice hungarian 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 train dataset, all possible datasets were appended and 90-10 split was used.

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 0.000095637994662983496
train_batch_size: 16
eval_batch_size: 16
seed: 13
gradient_accumulation_steps: 16
lr_scheduler_type: cosine_with_restarts
lr_scheduler_warmup_steps: 500
num_epochs: 100
mixed_precision_training: Native AMP

Training results

Step	Training Loss	Validation Loss	Wer
500	4.785300	0.952295	0.796236
1000	0.535800	0.217474	0.381613
1500	0.258400	0.205524	0.345056
2000	0.202800	0.198680	0.336264
2500	0.182700	0.197464	0.330094

Framework versions

Transformers 4.16.0.dev0
Pytorch 1.10.0+cu102
Datasets 1.18.3
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_hungarian_new --dataset mozilla-foundation/common_voice_8_0 --config hu --split test