README.md

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
datasets:
  - common_voice
model-index:
  - name: wav2vec2-large-xls-r-300m-hsb-v2
    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: []
          - name: Test CER
            type: cer
            value: []

wav2vec2-large-xls-r-300m-hsb-v2

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

• Loss: 0.5328
• Wer: 0.4596

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

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
8.5979	3.23	100	3.5602	1.0
3.303	6.45	200	3.2238	1.0
3.2034	9.68	300	3.2002	0.9888
2.7986	12.9	400	1.2408	0.9210
1.3869	16.13	500	0.7973	0.7462
1.0228	19.35	600	0.6722	0.6788
0.8311	22.58	700	0.6100	0.6150
0.717	25.81	800	0.6236	0.6013
0.6264	29.03	900	0.6031	0.5575
0.5494	32.26	1000	0.5656	0.5309
0.4781	35.48	1100	0.5289	0.4996
0.4311	38.71	1200	0.5375	0.4768
0.3902	41.94	1300	0.5246	0.4703
0.3508	45.16	1400	0.5382	0.4696
0.3199	48.39	1500	0.5328	0.4596

Framework versions

• Transformers 4.16.1
• Pytorch 1.10.0+cu111
• Datasets 1.18.2
• Tokenizers 0.11.0