metadata

language:
  - hi
license: cc-by-nc-4.0
base_model: facebook/mms-1b-all
tags:
  - automatic-speech-recognition
  - mozilla-foundation/common_voice_16_0
  - mms
  - generated_from_trainer
datasets:
  - common_voice_16_0
metrics:
  - wer
model-index:
  - name: wav2vec2-common_voice-hi-mms-demo
    results:
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: MOZILLA-FOUNDATION/COMMON_VOICE_16_0 - HI
          type: common_voice_16_0
          config: hi
          split: test
          args: 'Config: hi, Training split: train+validation, Eval split: test'
        metrics:
          - name: Wer
            type: wer
            value: 0.2516432655283731

wav2vec2-common_voice-hi-mms-demo

This model is a fine-tuned version of facebook/mms-1b-all on the MOZILLA-FOUNDATION/COMMON_VOICE_16_0 - HI dataset. It achieves the following results on the evaluation set:

Loss: 0.2672
Wer: 0.2516

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: 8
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: 4.0
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Wer
No log	0.11	100	0.4487	0.3565
No log	0.23	200	0.3544	0.3317
No log	0.34	300	0.3693	0.3088
No log	0.45	400	0.3404	0.3040
1.5084	0.56	500	0.3346	0.2995
1.5084	0.68	600	0.3411	0.2936
1.5084	0.79	700	0.3175	0.2887
1.5084	0.9	800	0.3159	0.2898
1.5084	1.02	900	0.3139	0.3045
0.3485	1.13	1000	0.3067	0.2958
0.3485	1.24	1100	0.2969	0.2767
0.3485	1.35	1200	0.2916	0.2714
0.3485	1.47	1300	0.2893	0.2663
0.3485	1.58	1400	0.3183	0.2985
0.3152	1.69	1500	0.2961	0.2688
0.3152	1.81	1600	0.2848	0.2665
0.3152	1.92	1700	0.2844	0.2656
0.3152	2.03	1800	0.2855	0.2707
0.3152	2.14	1900	0.2887	0.2686
0.3058	2.26	2000	0.2858	0.2657
0.3058	2.37	2100	0.2814	0.2629
0.3058	2.48	2200	0.2809	0.2633
0.3058	2.6	2300	0.2779	0.2613
0.3058	2.71	2400	0.2745	0.2581
0.2861	2.82	2500	0.2769	0.2618
0.2861	2.93	2600	0.2742	0.2576
0.2861	3.05	2700	0.2730	0.2575
0.2861	3.16	2800	0.2727	0.2564
0.2861	3.27	2900	0.2726	0.2563
0.2839	3.39	3000	0.2713	0.2576
0.2839	3.5	3100	0.2690	0.2537
0.2839	3.61	3200	0.2706	0.2540
0.2839	3.72	3300	0.2687	0.2542
0.2839	3.84	3400	0.2671	0.2521
0.2706	3.95	3500	0.2673	0.2522

Framework versions

Transformers 4.38.0.dev0
Pytorch 2.1.2+cu121
Datasets 2.16.1
Tokenizers 0.15.1