metadata

license: cc-by-nc-4.0
base_model: facebook/mms-1b-all
tags:
  - generated_from_trainer
datasets:
  - common_voice_16_0
metrics:
  - wer
model-index:
  - name: breeze-listen-w2v2-id
    results:
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: common_voice_16_0
          type: common_voice_16_0
          config: id
          split: test
          args: id
        metrics:
          - name: Wer
            type: wer
            value: 0.1455760839290688

breeze-listen-w2v2-id

This model is a fine-tuned version of facebook/mms-1b-all on the common_voice_16_0 dataset. It achieves the following results on the evaluation set:

Loss: 0.1253
Wer: 0.1456

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: 4
eval_batch_size: 8
seed: 42
distributed_type: multi-GPU
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	0.1	200	3.2671	1.0
No log	0.19	400	2.8741	1.0007
3.8381	0.29	600	2.7612	0.9955
3.8381	0.38	800	2.6333	0.9981
2.6996	0.48	1000	2.3074	0.9771
2.6996	0.58	1200	2.0155	0.9286
2.6996	0.67	1400	1.9155	0.8947
2.2919	0.77	1600	1.6412	0.8814
2.2919	0.87	1800	1.4531	0.8285
1.5872	0.96	2000	0.1813	0.2060
1.5872	1.06	2200	0.1636	0.1806
1.5872	1.15	2400	0.1558	0.1744
0.2659	1.25	2600	0.1522	0.1647
0.2659	1.35	2800	0.1553	0.1664
0.2436	1.44	3000	0.1841	0.1961
0.2436	1.54	3200	0.1419	0.1640
0.2436	1.64	3400	0.1456	0.1714
0.2464	1.73	3600	0.1402	0.1607
0.2464	1.83	3800	0.1345	0.1528
0.2292	1.92	4000	0.1342	0.1556
0.2292	2.02	4200	0.1334	0.1552
0.2292	2.12	4400	0.1352	0.1543
0.2209	2.21	4600	0.1350	0.1538
0.2209	2.31	4800	0.1342	0.1530
0.2136	2.41	5000	0.1320	0.1540
0.2136	2.5	5200	0.1369	0.1569
0.2136	2.6	5400	0.1314	0.1517
0.2154	2.69	5600	0.1304	0.1506
0.2154	2.79	5800	0.1320	0.1507
0.2123	2.89	6000	0.1319	0.1524
0.2123	2.98	6200	0.1292	0.1524
0.2123	3.08	6400	0.1283	0.1488
0.2109	3.18	6600	0.1258	0.1492
0.2109	3.27	6800	0.1291	0.1488
0.2103	3.37	7000	0.1278	0.1484
0.2103	3.46	7200	0.1250	0.1478
0.2103	3.56	7400	0.1277	0.1482
0.1986	3.66	7600	0.1256	0.1476
0.1986	3.75	7800	0.1258	0.1468
0.1954	3.85	8000	0.1256	0.1465
0.1954	3.95	8200	0.1253	0.1456

Framework versions

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