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--- |
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library_name: transformers |
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language: |
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- xh |
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license: cc-by-nc-4.0 |
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base_model: facebook/mms-1b-all |
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tags: |
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- generated_from_trainer |
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datasets: |
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- NCHLT_speech_corpus |
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metrics: |
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- wer |
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model-index: |
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- name: facebook mms-1b-all xhosa - Beijuka Bruno |
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results: |
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- task: |
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name: Automatic Speech Recognition |
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type: automatic-speech-recognition |
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dataset: |
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name: NCHLT_speech_corpus/Xhosa |
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type: NCHLT_speech_corpus |
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metrics: |
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- name: Wer |
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type: wer |
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value: 0.4469970462750246 |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# facebook mms-1b-all xhosa - Beijuka Bruno |
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This model is a fine-tuned version of [facebook/mms-1b-all](https://huggingface.co/facebook/mms-1b-all) on the NCHLT_speech_corpus/Xhosa dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.2822 |
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- Model Preparation Time: 0.0163 |
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- Wer: 0.4470 |
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- Cer: 0.0841 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 0.0003 |
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- train_batch_size: 4 |
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- eval_batch_size: 8 |
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- seed: 42 |
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- gradient_accumulation_steps: 8 |
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- total_train_batch_size: 32 |
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- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments |
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- lr_scheduler_type: linear |
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- lr_scheduler_warmup_steps: 100 |
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- num_epochs: 100 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Model Preparation Time | Wer | Cer | |
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|:-------------:|:-------:|:----:|:---------------:|:----------------------:|:------:|:------:| |
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| 53.6849 | 0.9955 | 167 | 0.2975 | 0.0163 | 0.3955 | 0.0660 | |
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| 2.6944 | 1.9955 | 334 | 0.2671 | 0.0163 | 0.3716 | 0.0612 | |
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| 2.4353 | 2.9955 | 501 | 0.2543 | 0.0163 | 0.3468 | 0.0580 | |
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| 2.2597 | 3.9955 | 668 | 0.2459 | 0.0163 | 0.3445 | 0.0565 | |
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| 2.1704 | 4.9955 | 835 | 0.2392 | 0.0163 | 0.3251 | 0.0547 | |
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| 2.0144 | 5.9955 | 1002 | 0.2353 | 0.0163 | 0.3160 | 0.0539 | |
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| 1.9278 | 6.9955 | 1169 | 0.2350 | 0.0163 | 0.3143 | 0.0539 | |
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| 1.849 | 7.9955 | 1336 | 0.2312 | 0.0163 | 0.3039 | 0.0520 | |
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| 1.811 | 8.9955 | 1503 | 0.2289 | 0.0163 | 0.3065 | 0.0530 | |
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| 1.7173 | 9.9955 | 1670 | 0.2311 | 0.0163 | 0.2967 | 0.0522 | |
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| 1.6504 | 10.9955 | 1837 | 0.2205 | 0.0163 | 0.2960 | 0.0511 | |
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| 1.5737 | 11.9955 | 2004 | 0.2227 | 0.0163 | 0.2904 | 0.0506 | |
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| 1.5547 | 12.9955 | 2171 | 0.2218 | 0.0163 | 0.2836 | 0.0502 | |
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| 1.5517 | 13.9955 | 2338 | 0.2236 | 0.0163 | 0.2917 | 0.0507 | |
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| 1.5031 | 14.9955 | 2505 | 0.2162 | 0.0163 | 0.2826 | 0.0493 | |
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| 1.4097 | 15.9955 | 2672 | 0.2187 | 0.0163 | 0.2773 | 0.0492 | |
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| 1.4143 | 16.9955 | 2839 | 0.2176 | 0.0163 | 0.2809 | 0.0485 | |
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| 1.36 | 17.9955 | 3006 | 0.2200 | 0.0163 | 0.2675 | 0.0479 | |
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| 1.3312 | 18.9955 | 3173 | 0.2163 | 0.0163 | 0.2672 | 0.0482 | |
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| 1.3369 | 19.9955 | 3340 | 0.2199 | 0.0163 | 0.2678 | 0.0481 | |
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| 1.3036 | 20.9955 | 3507 | 0.2224 | 0.0163 | 0.2714 | 0.0482 | |
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| 1.2558 | 21.9955 | 3674 | 0.2244 | 0.0163 | 0.2656 | 0.0478 | |
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| 1.2058 | 22.9955 | 3841 | 0.2192 | 0.0163 | 0.2642 | 0.0481 | |
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| 1.167 | 23.9955 | 4008 | 0.2225 | 0.0163 | 0.2561 | 0.0471 | |
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| 1.1849 | 24.9955 | 4175 | 0.2275 | 0.0163 | 0.2610 | 0.0473 | |
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| 1.1378 | 25.9955 | 4342 | 0.2246 | 0.0163 | 0.2610 | 0.0474 | |
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| 1.1095 | 26.9955 | 4509 | 0.2295 | 0.0163 | 0.2538 | 0.0464 | |
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| 1.1042 | 27.9955 | 4676 | 0.2243 | 0.0163 | 0.2518 | 0.0462 | |
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| 1.0537 | 28.9955 | 4843 | 0.2293 | 0.0163 | 0.2531 | 0.0467 | |
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| 1.0335 | 29.9955 | 5010 | 0.2264 | 0.0163 | 0.2531 | 0.0456 | |
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| 1.0453 | 30.9955 | 5177 | 0.2232 | 0.0163 | 0.2525 | 0.0452 | |
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| 1.0099 | 31.9955 | 5344 | 0.2285 | 0.0163 | 0.2551 | 0.0467 | |
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| 0.9826 | 32.9955 | 5511 | 0.2345 | 0.0163 | 0.2570 | 0.0470 | |
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| 0.9615 | 33.9955 | 5678 | 0.2361 | 0.0163 | 0.2587 | 0.0471 | |
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| 0.9583 | 34.9955 | 5845 | 0.2340 | 0.0163 | 0.2528 | 0.0452 | |
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| 0.9421 | 35.9955 | 6012 | 0.2339 | 0.0163 | 0.2443 | 0.0452 | |
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| 0.9268 | 36.9955 | 6179 | 0.2350 | 0.0163 | 0.2518 | 0.0455 | |
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| 0.9003 | 37.9955 | 6346 | 0.2380 | 0.0163 | 0.2472 | 0.0451 | |
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| 0.9068 | 38.9955 | 6513 | 0.2424 | 0.0163 | 0.2515 | 0.0459 | |
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| 0.8845 | 39.9955 | 6680 | 0.2466 | 0.0163 | 0.2525 | 0.0462 | |
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| 0.88 | 40.9955 | 6847 | 0.2423 | 0.0163 | 0.2387 | 0.0441 | |
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| 0.8683 | 41.9955 | 7014 | 0.2448 | 0.0163 | 0.2528 | 0.0463 | |
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| 0.8535 | 42.9955 | 7181 | 0.2498 | 0.0163 | 0.2492 | 0.0455 | |
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| 0.8413 | 43.9955 | 7348 | 0.2431 | 0.0163 | 0.2511 | 0.0463 | |
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| 0.8147 | 44.9955 | 7515 | 0.2416 | 0.0163 | 0.2426 | 0.0449 | |
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| 0.8062 | 45.9955 | 7682 | 0.2483 | 0.0163 | 0.2479 | 0.0455 | |
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| 0.7876 | 46.9955 | 7849 | 0.2477 | 0.0163 | 0.2531 | 0.0463 | |
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| 0.8108 | 47.9955 | 8016 | 0.2469 | 0.0163 | 0.2462 | 0.0454 | |
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| 0.7689 | 48.9955 | 8183 | 0.2539 | 0.0163 | 0.2489 | 0.0460 | |
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| 0.7609 | 49.9955 | 8350 | 0.2535 | 0.0163 | 0.2453 | 0.0446 | |
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| 0.7442 | 50.9955 | 8517 | 0.2603 | 0.0163 | 0.2485 | 0.0459 | |
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### Framework versions |
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- Transformers 4.47.1 |
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- Pytorch 2.1.0+cu118 |
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- Datasets 3.2.0 |
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- Tokenizers 0.21.0 |
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