metadata

base_model: facebook/mms-1b-all
datasets:
  - common_voice_17_0
library_name: transformers
license: cc-by-nc-4.0
metrics:
  - wer
  - bleu
tags:
  - generated_from_trainer
model-index:
  - name: wav2vec2-mms-1b-CV17.0-training_set_variations
    results:
      - task:
          type: automatic-speech-recognition
          name: Automatic Speech Recognition
        dataset:
          name: common_voice_17_0
          type: common_voice_17_0
          config: ta
          split: validation
          args: ta
        metrics:
          - type: wer
            value: 0.3597180870859695
            name: Wer
          - type: bleu
            value: 0.4226157099926465
            name: Bleu

wav2vec2-mms-1b-CV17.0-training_set_variations

This model is a fine-tuned version of facebook/mms-1b-all on the common_voice_17_0 dataset. It achieves the following results on the evaluation set:

Loss: 0.2047
Wer: 0.3597
Cer: 0.0579
Bleu: 0.4226

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: 0.001
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_ratio: 0.15
training_steps: 2000
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Wer	Cer	Bleu
6.3615	0.3906	100	0.2954	0.4162	0.0682	0.3508
0.2115	0.7812	200	0.2266	0.3822	0.0619	0.3888
0.1868	1.1719	300	0.2227	0.3755	0.0608	0.3981
0.1913	1.5625	400	0.2274	0.3912	0.0637	0.3779
0.1896	1.9531	500	0.2263	0.3858	0.0631	0.3867
0.1769	2.3438	600	0.2176	0.3785	0.0618	0.3942
0.1752	2.7344	700	0.2162	0.3816	0.0614	0.3887
0.1777	3.125	800	0.2098	0.3606	0.0582	0.4260
0.1747	3.5156	900	0.2078	0.3657	0.0585	0.4111
0.1672	3.9062	1000	0.2075	0.3770	0.0595	0.3920
0.1583	4.2969	1100	0.2060	0.3631	0.0580	0.4137
0.1713	4.6875	1200	0.2064	0.3664	0.0587	0.4118
0.1563	5.0781	1300	0.2047	0.3597	0.0579	0.4226

Framework versions

Transformers 4.44.2
Pytorch 2.4.1+cu121
Datasets 3.0.0
Tokenizers 0.19.1