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nhi_heldout-speaker-exp_MJM502_mms-1b-nhi-adapterft

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Safetensors

wav2vec2

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nhi_heldout-speaker-exp_MJM502_mms-1b-nhi-adapterft

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 ---
-library_name: transformers
-tags: []
 ---
-# Model Card for Model ID
-<!-- Provide a quick summary of what the model is/does. -->
-## Model Details
-### Model Description
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-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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 ---
+license: cc-by-nc-4.0
+base_model: facebook/mms-1b-all
+tags:
+- generated_from_trainer
+datasets:
+- audiofolder
+metrics:
+- wer
+model-index:
+- name: nhi_heldout-speaker-exp_MJM502_mms-1b-nhi-adapterft
+  results:
+  - task:
+      name: Automatic Speech Recognition
+      type: automatic-speech-recognition
+    dataset:
+      name: audiofolder
+      type: audiofolder
+      config: default
+      split: test
+      args: default
+    metrics:
+    - name: Wer
+      type: wer
+      value: 0.4966996699669967
 ---
+<!-- This model card has been generated automatically according to the information the Trainer had access to. You
+should probably proofread and complete it, then remove this comment. -->
+# nhi_heldout-speaker-exp_MJM502_mms-1b-nhi-adapterft
+This model is a fine-tuned version of [facebook/mms-1b-all](https://huggingface.co/facebook/mms-1b-all) on the audiofolder dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.7134
+- Wer: 0.4967
+- Cer: 0.1520
+## 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: 16
+- eval_batch_size: 32
+- 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: 100
+- mixed_precision_training: Native AMP
+### Training results
+| Training Loss | Epoch   | Step  | Validation Loss | Wer    | Cer    |
+|:-------------:|:-------:|:-----:|:---------------:|:------:|:------:|
+| 1.0124        | 1.6393  | 200   | 0.9195          | 0.7361 | 0.2359 |
+| 0.8011        | 3.2787  | 400   | 0.8468          | 0.7438 | 0.2192 |
+| 0.7141        | 4.9180  | 600   | 0.7913          | 0.6398 | 0.2030 |
+| 0.6767        | 6.5574  | 800   | 0.7461          | 0.6248 | 0.1987 |
+| 0.629         | 8.1967  | 1000  | 0.7092          | 0.6241 | 0.1901 |
+| 0.5964        | 9.8361  | 1200  | 0.7196          | 0.5959 | 0.1848 |
+| 0.5531        | 11.4754 | 1400  | 0.6797          | 0.6012 | 0.1894 |
+| 0.5541        | 13.1148 | 1600  | 0.6817          | 0.5659 | 0.1765 |
+| 0.5166        | 14.7541 | 1800  | 0.6663          | 0.5588 | 0.1686 |
+| 0.4807        | 16.3934 | 2000  | 0.6741          | 0.5567 | 0.1729 |
+| 0.5044        | 18.0328 | 2200  | 0.6592          | 0.5455 | 0.1685 |
+| 0.48          | 19.6721 | 2400  | 0.6720          | 0.5484 | 0.1698 |
+| 0.467         | 21.3115 | 2600  | 0.6492          | 0.5479 | 0.1661 |
+| 0.4634        | 22.9508 | 2800  | 0.6508          | 0.5430 | 0.1659 |
+| 0.4503        | 24.5902 | 3000  | 0.6551          | 0.5521 | 0.1705 |
+| 0.4373        | 26.2295 | 3200  | 0.6560          | 0.5328 | 0.1640 |
+| 0.4139        | 27.8689 | 3400  | 0.6635          | 0.5354 | 0.1642 |
+| 0.4093        | 29.5082 | 3600  | 0.6482          | 0.5310 | 0.1623 |
+| 0.3878        | 31.1475 | 3800  | 0.6557          | 0.5158 | 0.1598 |
+| 0.3967        | 32.7869 | 4000  | 0.6564          | 0.5253 | 0.1615 |
+| 0.3724        | 34.4262 | 4200  | 0.6564          | 0.5121 | 0.1587 |
+| 0.3791        | 36.0656 | 4400  | 0.6628          | 0.5172 | 0.1589 |
+| 0.3608        | 37.7049 | 4600  | 0.6710          | 0.5231 | 0.1626 |
+| 0.364         | 39.3443 | 4800  | 0.6523          | 0.5088 | 0.1575 |
+| 0.3472        | 40.9836 | 5000  | 0.6687          | 0.5251 | 0.1623 |
+| 0.3432        | 42.6230 | 5200  | 0.6622          | 0.5200 | 0.1598 |
+| 0.3459        | 44.2623 | 5400  | 0.6517          | 0.5128 | 0.1569 |
+| 0.3135        | 45.9016 | 5600  | 0.6543          | 0.5184 | 0.1606 |
+| 0.3158        | 47.5410 | 5800  | 0.6571          | 0.5176 | 0.1590 |
+| 0.3266        | 49.1803 | 6000  | 0.6657          | 0.5146 | 0.1567 |
+| 0.2958        | 50.8197 | 6200  | 0.6676          | 0.5099 | 0.1563 |
+| 0.2872        | 52.4590 | 6400  | 0.6734          | 0.5119 | 0.1572 |
+| 0.2789        | 54.0984 | 6600  | 0.6743          | 0.5166 | 0.1575 |
+| 0.287         | 55.7377 | 6800  | 0.6944          | 0.5111 | 0.1557 |
+| 0.2911        | 57.3770 | 7000  | 0.6754          | 0.5052 | 0.1548 |
+| 0.2782        | 59.0164 | 7200  | 0.6671          | 0.5095 | 0.1551 |
+| 0.2694        | 60.6557 | 7400  | 0.6752          | 0.5040 | 0.1532 |
+| 0.2598        | 62.2951 | 7600  | 0.6878          | 0.5113 | 0.1562 |
+| 0.2688        | 63.9344 | 7800  | 0.6622          | 0.5064 | 0.1548 |
+| 0.2633        | 65.5738 | 8000  | 0.6940          | 0.5075 | 0.1557 |
+| 0.2454        | 67.2131 | 8200  | 0.6961          | 0.5025 | 0.1522 |
+| 0.245         | 68.8525 | 8400  | 0.7007          | 0.5048 | 0.1540 |
+| 0.24          | 70.4918 | 8600  | 0.6965          | 0.5088 | 0.1552 |
+| 0.2456        | 72.1311 | 8800  | 0.6918          | 0.5092 | 0.1553 |
+| 0.2429        | 73.7705 | 9000  | 0.7023          | 0.5075 | 0.1550 |
+| 0.2283        | 75.4098 | 9200  | 0.7149          | 0.5084 | 0.1555 |
+| 0.2236        | 77.0492 | 9400  | 0.7001          | 0.5083 | 0.1547 |
+| 0.2299        | 78.6885 | 9600  | 0.6943          | 0.5035 | 0.1539 |
+| 0.2184        | 80.3279 | 9800  | 0.7046          | 0.5068 | 0.1535 |
+| 0.2142        | 81.9672 | 10000 | 0.6988          | 0.4985 | 0.1524 |
+| 0.2123        | 83.6066 | 10200 | 0.7084          | 0.4987 | 0.1520 |
+| 0.2197        | 85.2459 | 10400 | 0.7023          | 0.5002 | 0.1514 |
+| 0.2012        | 86.8852 | 10600 | 0.7108          | 0.5024 | 0.1526 |
+| 0.2079        | 88.5246 | 10800 | 0.7081          | 0.4986 | 0.1525 |
+| 0.209         | 90.1639 | 11000 | 0.7056          | 0.5047 | 0.1533 |
+| 0.1943        | 91.8033 | 11200 | 0.7143          | 0.5005 | 0.1518 |
+| 0.1965        | 93.4426 | 11400 | 0.7077          | 0.4988 | 0.1522 |
+| 0.1886        | 95.0820 | 11600 | 0.7179          | 0.4960 | 0.1516 |
+| 0.2085        | 96.7213 | 11800 | 0.7170          | 0.4979 | 0.1520 |
+| 0.1812        | 98.3607 | 12000 | 0.7129          | 0.4978 | 0.1523 |
+| 0.1904        | 100.0   | 12200 | 0.7134          | 0.4967 | 0.1520 |
+### Framework versions
+- Transformers 4.41.2
+- Pytorch 2.4.0
+- Datasets 3.2.0
+- Tokenizers 0.19.1

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+size 8834400