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: 0.4958060228262364
            name: Wer
          - type: bleu
            value: 0.2629057639184852
            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: 0.5099
Wer: 0.4958
Cer: 0.0885
Bleu: 0.2629

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
11.4061	50.0	50	4.6415	1.0007	0.9640	0.0
1.789	100.0	100	0.3026	0.4457	0.0734	0.3064
0.08	150.0	150	0.3223	0.4304	0.0711	0.3275
0.0473	200.0	200	0.3547	0.4426	0.0742	0.3156
0.0364	250.0	250	0.3786	0.4556	0.0761	0.2972
0.0298	300.0	300	0.4070	0.4629	0.0800	0.2875
0.0279	350.0	350	0.4190	0.4688	0.0799	0.2864
0.0253	400.0	400	0.4353	0.4755	0.0818	0.2757
0.0198	450.0	450	0.4808	0.5066	0.0887	0.2432
0.0216	500.0	500	0.4699	0.4780	0.0815	0.2777
0.0194	550.0	550	0.4745	0.4895	0.0877	0.2643
0.0201	600.0	600	0.5035	0.4971	0.0881	0.2647
0.0153	650.0	650	0.5099	0.4958	0.0885	0.2629

Framework versions

Transformers 4.44.2
Pytorch 2.4.1+cu121
Datasets 3.0.0
Tokenizers 0.19.1