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mms-1b-all-bem-genbed-m-model

Automatic Speech Recognition
Transformers
Safetensors
wav2vec2
genbed
mms
Generated from Trainer
Inference Endpoints
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metadata
library_name: transformers
license: cc-by-nc-4.0
base_model: facebook/mms-1b-all
tags:
  - automatic-speech-recognition
  - genbed
  - mms
  - generated_from_trainer
metrics:
  - wer
model-index:
  - name: mms-1b-all-bem-genbed-m-model
    results: []

mms-1b-all-bem-genbed-m-model

This model is a fine-tuned version of facebook/mms-1b-all on the GENBED - BEM dataset. It achieves the following results on the evaluation set:

  • Loss: 0.2887
  • Wer: 0.4119

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: 4
  • eval_batch_size: 8
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • lr_scheduler_warmup_steps: 100
  • num_epochs: 30.0
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Wer
6.6677 0.1379 100 1.1908 0.9662
0.7337 0.2759 200 0.4132 0.5658
0.5661 0.4138 300 0.3767 0.5508
0.523 0.5517 400 0.3591 0.5009
0.5391 0.6897 500 0.3530 0.5054
0.4978 0.8276 600 0.3496 0.5096
0.4737 0.9655 700 0.3478 0.5079
0.4878 1.1034 800 0.3390 0.4737
0.4737 1.2414 900 0.3304 0.4905
0.4603 1.3793 1000 0.3328 0.4842
0.4793 1.5172 1100 0.3252 0.4640
0.4359 1.6552 1200 0.3252 0.4659
0.457 1.7931 1300 0.3204 0.4717
0.469 1.9310 1400 0.3185 0.4697
0.4894 2.0690 1500 0.3167 0.4622
0.4386 2.2069 1600 0.3185 0.4646
0.4441 2.3448 1700 0.3099 0.4607
0.444 2.4828 1800 0.3154 0.4551
0.4065 2.6207 1900 0.3138 0.4623
0.4163 2.7586 2000 0.3087 0.4377
0.4518 2.8966 2100 0.3054 0.4495
0.4208 3.0345 2200 0.3037 0.4512
0.381 3.1724 2300 0.3074 0.4386
0.4203 3.3103 2400 0.2989 0.4244
0.4556 3.4483 2500 0.3080 0.4835
0.4143 3.5862 2600 0.2956 0.4222
0.4055 3.7241 2700 0.3023 0.4581
0.4102 3.8621 2800 0.2955 0.4412
0.4451 4.0 2900 0.2944 0.4181
0.3857 4.1379 3000 0.2985 0.4426
0.4071 4.2759 3100 0.2918 0.4290
0.4 4.4138 3200 0.2951 0.4321
0.4257 4.5517 3300 0.3035 0.4608
0.3929 4.6897 3400 0.2965 0.4131
0.3957 4.8276 3500 0.2938 0.4397
0.3974 4.9655 3600 0.2887 0.4119
0.3733 5.1034 3700 0.2890 0.4050
0.382 5.2414 3800 0.2917 0.4233
0.3669 5.3793 3900 0.2900 0.4313

Framework versions

  • Transformers 4.46.0.dev0
  • Pytorch 2.4.1+cu121
  • Datasets 3.0.1
  • Tokenizers 0.20.0