README.md

metadata

library_name: transformers
language:
  - zul
license: cc-by-nc-4.0
base_model: facebook/mms-1b-all
tags:
  - generated_from_trainer
datasets:
  - NCHLT_speech_corpus
metrics:
  - wer
model-index:
  - name: facebook mms-1b-all zulu - Beijuka Bruno
    results:
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: NCHLT_speech_corpus/Zulu
          type: NCHLT_speech_corpus
        metrics:
          - name: Wer
            type: wer
            value: 0.6532026601330067

facebook mms-1b-all zulu - Beijuka Bruno

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

• Loss: 0.2443
• Model Preparation Time: 0.0119
• Wer: 0.6532
• Cer: 0.1372

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: 0.0003
• train_batch_size: 4
• eval_batch_size: 8
• seed: 42
• gradient_accumulation_steps: 4
• total_train_batch_size: 16
• optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
• lr_scheduler_type: linear
• lr_scheduler_warmup_steps: 100
• num_epochs: 100

Training results

Training Loss	Epoch	Step	Validation Loss	Model Preparation Time	Wer	Cer
2.9828	1.0	2523	0.2050	0.0119	0.3254	0.0439
0.2496	2.0	5046	0.1529	0.0119	0.2548	0.0359
0.2165	3.0	7569	0.1458	0.0119	0.2314	0.0314
0.2017	4.0	10092	0.1427	0.0119	0.2376	0.0324
0.1926	5.0	12615	0.1349	0.0119	0.2220	0.0301
0.1791	6.0	15138	0.1320	0.0119	0.2104	0.0296
0.1704	7.0	17661	0.1294	0.0119	0.2200	0.0303
0.1639	8.0	20184	0.1298	0.0119	0.2159	0.0303
0.1581	9.0	22707	0.1304	0.0119	0.2124	0.0295
0.154	10.0	25230	0.1306	0.0119	0.2046	0.0284
0.1489	11.0	27753	0.1291	0.0119	0.2032	0.0282
0.1454	12.0	30276	0.1301	0.0119	0.2072	0.0298
0.1424	13.0	32799	0.1287	0.0119	0.2088	0.0293
0.138	14.0	35322	0.1303	0.0119	0.1994	0.0279
0.1368	15.0	37845	0.1292	0.0119	0.2267	0.0339
0.1339	16.0	40368	0.1259	0.0119	0.2470	0.0342
0.1319	17.0	42891	0.1294	0.0119	0.2092	0.0291
0.1285	18.0	45414	0.1324	0.0119	0.2023	0.0284
0.125	19.0	47937	0.1307	0.0119	0.2059	0.0287
0.1234	20.0	50460	0.1283	0.0119	0.2048	0.0290
0.1197	21.0	52983	0.1273	0.0119	0.2068	0.0288
0.1196	22.0	55506	0.1306	0.0119	0.2126	0.0293
0.1157	23.0	58029	0.1302	0.0119	0.2050	0.0289
0.1134	24.0	60552	0.1289	0.0119	0.2061	0.0287

Framework versions

• Transformers 4.47.0
• Pytorch 2.1.0+cu118
• Datasets 3.2.0
• Tokenizers 0.21.0