README.md

metadata

license: apache-2.0
base_model: openai/whisper-medium
tags:
  - generated_from_trainer
datasets:
  - >-
    fsicoli/cv17-fleurs-coraa-mls-ted-alcaim-cf-cdc-lapsbm-lapsmail-sydney-lingualibre-voxforge-tatoeba
metrics:
  - wer
model-index:
  - name: whisper-medium-pt-3000h
    results:
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: >-
            fsicoli/cv17-fleurs-coraa-mls-ted-alcaim-cf-cdc-lapsbm-lapsmail-sydney-lingualibre-voxforge-tatoeba
            default
          type: >-
            fsicoli/cv17-fleurs-coraa-mls-ted-alcaim-cf-cdc-lapsbm-lapsmail-sydney-lingualibre-voxforge-tatoeba
          args: default
        metrics:
          - name: Wer
            type: wer
            value: 0.11007210455159983

whisper-medium-pt-3000h

This model is a fine-tuned version of openai/whisper-medium on the fsicoli/cv17-fleurs-coraa-mls-ted-alcaim-cf-cdc-lapsbm-lapsmail-sydney-lingualibre-voxforge-tatoeba default dataset. It achieves the following results on the evaluation set:

• Loss: 0.9306
• Wer: 0.1101

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: 5e-06
• 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: 10000
• num_epochs: 10.0
• mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Wer
0.4423	0.2	20000	0.4723	0.1633
0.4963	0.39	40000	0.4921	0.1547
0.3853	0.59	60000	0.5099	0.1470
0.37	0.79	80000	0.4753	0.1439
0.3615	0.98	100000	0.5074	0.1386
0.2394	1.18	120000	0.4858	0.1341
0.227	1.38	140000	0.5758	0.1323
0.2461	1.57	160000	0.5067	0.1322
0.2078	1.77	180000	0.5087	0.1291
0.2138	1.97	200000	0.5201	0.1273
0.1188	2.16	220000	0.6359	0.1265
0.1009	2.36	240000	0.6229	0.1253
0.1394	2.56	260000	0.5734	0.1231
0.1383	2.75	280000	0.5914	0.1213
0.1332	2.95	300000	0.6174	0.1212
0.0634	3.15	320000	0.6461	0.1190
0.0667	3.34	340000	0.6330	0.1211
0.0546	3.54	360000	0.6927	0.1190
0.1029	3.74	380000	0.6777	0.1184
0.0664	3.93	400000	0.6367	0.1161
0.0665	4.13	420000	0.7467	0.1171
0.0695	4.33	440000	0.7332	0.1164
0.0708	4.52	460000	0.7141	0.1171
0.0695	4.72	480000	0.6869	0.1169
0.0758	4.92	500000	0.7360	0.1153
0.061	5.11	520000	0.7594	0.1161
0.0804	5.31	540000	0.7640	0.1158
0.0963	5.51	560000	0.7848	0.1157
0.0815	5.7	580000	0.7635	0.1145
0.0794	5.9	600000	0.7566	0.1134
0.0907	6.1	620000	0.8152	0.1147
0.0664	6.29	640000	0.8405	0.1123
0.0654	6.49	660000	0.8278	0.1119
0.0652	6.69	680000	0.8267	0.1134
0.1043	6.88	700000	0.8254	0.1122
0.0383	7.08	720000	0.8719	0.1122
0.0461	7.28	740000	0.8640	0.1130
0.0791	7.47	760000	0.8990	0.1122
0.0587	7.67	780000	0.9107	0.1122
0.0578	7.87	800000	0.9060	0.1124
0.0218	8.06	820000	0.8845	0.1111
0.0125	8.26	840000	0.9072	0.1112
0.0172	8.46	860000	0.8899	0.1107
0.0204	8.65	880000	0.9149	0.1108
0.0145	8.85	900000	0.9097	0.1103
0.0146	9.05	920000	0.9084	0.1107
0.0166	9.24	940000	0.9053	0.1103
0.0177	9.44	960000	0.9193	0.1100
0.0157	9.64	980000	0.9212	0.1101
0.0096	9.83	1000000	0.9313	0.1103

Framework versions

• Transformers 4.39.0.dev0
• Pytorch 2.2.1+cu121
• Datasets 2.18.1.dev0
• Tokenizers 0.15.0