metadata

language:
  - hsb
license: apache-2.0
tags:
  - automatic-speech-recognition
  - mozilla-foundation/common_voice_8_0
  - generated_from_trainer
  - hsb
  - robust-speech-event
  - model_for_talk
  - hf-asr-leaderboard
datasets:
  - mozilla-foundation/common_voice_8_0
model-index:
  - name: wav2vec2-large-xls-r-300m-hsb-v1
    results:
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: Common Voice 8
          type: mozilla-foundation/common_voice_8_0
          args: hsb
        metrics:
          - name: Test WER
            type: wer
            value: 0.4393
          - name: Test CER
            type: cer
            value: 0.1036
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: Robust Speech Event - Dev Data
          type: speech-recognition-community-v2/dev_data
          args: hsb
        metrics:
          - name: Test WER
            type: wer
            value: NA
          - name: Test CER
            type: cer
            value: NA

wav2vec2-large-xls-r-300m-hsb-v1

This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the MOZILLA-FOUNDATION/COMMON_VOICE_8_0 - HSB dataset. It achieves the following results on the evaluation set:

Loss: 0.5684
Wer: 0.4402

Evaluation Commands

To evaluate on mozilla-foundation/common_voice_8_0 with test split

python eval.py --model_id DrishtiSharma/wav2vec2-large-xls-r-300m-hsb-v1 --dataset mozilla-foundation/common_voice_8_0 --config hsb --split test --log_outputs

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

Upper Sorbian language isn't available in speech-recognition-community-v2/dev_data

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 0.00045
train_batch_size: 16
eval_batch_size: 8
seed: 42
gradient_accumulation_steps: 2
total_train_batch_size: 32
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_steps: 500
num_epochs: 50
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Wer
8.972	3.23	100	3.7498	1.0
3.3401	6.45	200	3.2320	1.0
3.2046	9.68	300	3.1741	0.9806
2.4031	12.9	400	1.0579	0.8996
1.0427	16.13	500	0.7989	0.7557
0.741	19.35	600	0.6405	0.6299
0.5699	22.58	700	0.6129	0.5928
0.4607	25.81	800	0.6548	0.5695
0.3827	29.03	900	0.6268	0.5190
0.3282	32.26	1000	0.5919	0.5016
0.2764	35.48	1100	0.5953	0.4805
0.2335	38.71	1200	0.5717	0.4728
0.2106	41.94	1300	0.5674	0.4569
0.1859	45.16	1400	0.5685	0.4502
0.1592	48.39	1500	0.5684	0.4402

Framework versions

Transformers 4.16.1
Pytorch 1.10.0+cu111
Datasets 1.18.2
Tokenizers 0.11.0