metadata

license: apache-2.0
tags:
  - generated_from_trainer
base_model: facebook/wav2vec2-xls-r-300m
datasets:
  - common_voice_17_0
metrics:
  - wer
model-index:
  - name: xls-r-300m-hbs-phoneme-unfrozen-batch16
    results:
      - task:
          type: automatic-speech-recognition
          name: Automatic Speech Recognition
        dataset:
          name: common_voice_17_0
          type: common_voice_17_0
          config: hsb
          split: test
          args: hsb
        metrics:
          - type: wer
            value: 0.4111996251171509
            name: Wer

xls-r-300m-hbs-phoneme-unfrozen-batch16

This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the common_voice_17_0 dataset. It achieves the following results on the evaluation set:

Loss: 0.7105
Wer: 0.4112
Cer: 0.0948

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: 0.0003
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: 100
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Wer	Cer
3.6184	3.2258	100	3.4215	1.0	1.0
3.2927	6.4516	200	3.2247	1.0	1.0
3.2291	9.6774	300	3.2021	1.0	1.0000
1.4844	12.9032	400	1.3507	0.9857	0.2837
0.4136	16.1290	500	0.6982	0.6567	0.1608
0.2346	19.3548	600	0.6496	0.5956	0.1466
0.1401	22.5806	700	0.6680	0.5565	0.1314
0.1535	25.8065	800	0.6597	0.5026	0.1190
0.1165	29.0323	900	0.7085	0.5112	0.1224
0.076	32.2581	1000	0.7359	0.5026	0.1195
0.083	35.4839	1100	0.7144	0.4991	0.1205
0.0985	38.7097	1200	0.6907	0.4756	0.1120
0.052	41.9355	1300	0.6806	0.4700	0.1105
0.0347	45.1613	1400	0.7097	0.4588	0.1091
0.0432	48.3871	1500	0.7086	0.4649	0.1093
0.0626	51.6129	1600	0.6947	0.4393	0.1029
0.0474	54.8387	1700	0.6915	0.4468	0.1058
0.057	58.0645	1800	0.7068	0.4358	0.1020
0.0373	61.2903	1900	0.7140	0.4419	0.1037
0.0994	64.5161	2000	0.6966	0.4208	0.0987
0.0503	67.7419	2100	0.6997	0.4306	0.0988
0.0418	70.9677	2200	0.7105	0.4353	0.1006
0.036	74.1935	2300	0.7320	0.4356	0.1024
0.0171	77.4194	2400	0.7132	0.4257	0.0994
0.0234	80.6452	2500	0.7059	0.4171	0.0967
0.0335	83.8710	2600	0.7449	0.4140	0.0973
0.0288	87.0968	2700	0.7028	0.4157	0.0964
0.0344	90.3226	2800	0.7181	0.4112	0.0960
0.0298	93.5484	2900	0.7150	0.4105	0.0951
0.0532	96.7742	3000	0.7164	0.4119	0.0950
0.0058	100.0	3100	0.7105	0.4112	0.0948

Framework versions

Transformers 4.42.0.dev0
Pytorch 2.3.1+cu121
Datasets 2.19.2
Tokenizers 0.19.1