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wav2vec2-large-mms-1b-nhi-adapterft-ilv

Safetensors

wav2vec2

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wav2vec2-large-mms-1b-nhi-adapterft-ilv

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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
-<!-- Provide a longer summary of what this model is. -->
-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: wav2vec2-large-mms-1b-nhi-adapterft-ilv
+  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: 0.3611111111111111
 ---
+<!-- 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. -->
+# wav2vec2-large-mms-1b-nhi-adapterft-ilv
+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: 0.5244
+- Wer: 0.3611
+- Cer: 0.1060
+## 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: 20
+- 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: 200
+- mixed_precision_training: Native AMP
+### Training results
+| Training Loss | Epoch    | Step  | Validation Loss | Wer    | Cer    |
+|:-------------:|:--------:|:-----:|:---------------:|:------:|:------:|
+| 1.0326        | 1.6529   | 200   | 0.6958          | 0.6481 | 0.1936 |
+| 0.8213        | 3.3058   | 400   | 0.6097          | 0.5563 | 0.1615 |
+| 0.7669        | 4.9587   | 600   | 0.5498          | 0.5405 | 0.1580 |
+| 0.7058        | 6.6116   | 800   | 0.5066          | 0.5015 | 0.1430 |
+| 0.6592        | 8.2645   | 1000  | 0.5060          | 0.4907 | 0.1388 |
+| 0.6518        | 9.9174   | 1200  | 0.4823          | 0.4772 | 0.1357 |
+| 0.6181        | 11.5702  | 1400  | 0.4753          | 0.4691 | 0.1331 |
+| 0.5987        | 13.2231  | 1600  | 0.4944          | 0.4761 | 0.1369 |
+| 0.5762        | 14.8760  | 1800  | 0.4588          | 0.4510 | 0.1259 |
+| 0.5702        | 16.5289  | 2000  | 0.4705          | 0.4533 | 0.1272 |
+| 0.5308        | 18.1818  | 2200  | 0.4797          | 0.4549 | 0.1287 |
+| 0.5317        | 19.8347  | 2400  | 0.4807          | 0.4680 | 0.1310 |
+| 0.5179        | 21.4876  | 2600  | 0.4509          | 0.4286 | 0.1235 |
+| 0.5116        | 23.1405  | 2800  | 0.4451          | 0.4363 | 0.1242 |
+| 0.4869        | 24.7934  | 3000  | 0.4246          | 0.4267 | 0.1192 |
+| 0.47          | 26.4463  | 3200  | 0.4474          | 0.4340 | 0.1249 |
+| 0.4663        | 28.0992  | 3400  | 0.4379          | 0.4120 | 0.1191 |
+| 0.4557        | 29.7521  | 3600  | 0.4476          | 0.4236 | 0.1212 |
+| 0.4564        | 31.4050  | 3800  | 0.4248          | 0.4062 | 0.1171 |
+| 0.4454        | 33.0579  | 4000  | 0.4370          | 0.4120 | 0.1189 |
+| 0.4435        | 34.7107  | 4200  | 0.4282          | 0.4090 | 0.1182 |
+| 0.439         | 36.3636  | 4400  | 0.4233          | 0.4008 | 0.1158 |
+| 0.4299        | 38.0165  | 4600  | 0.4301          | 0.4101 | 0.1177 |
+| 0.4125        | 39.6694  | 4800  | 0.4426          | 0.4074 | 0.1190 |
+| 0.401         | 41.3223  | 5000  | 0.4378          | 0.4140 | 0.1186 |
+| 0.4034        | 42.9752  | 5200  | 0.4308          | 0.3993 | 0.1161 |
+| 0.4052        | 44.6281  | 5400  | 0.4399          | 0.4001 | 0.1160 |
+| 0.3889        | 46.2810  | 5600  | 0.4443          | 0.4047 | 0.1169 |
+| 0.3976        | 47.9339  | 5800  | 0.4345          | 0.3835 | 0.1171 |
+| 0.3737        | 49.5868  | 6000  | 0.4505          | 0.3916 | 0.1133 |
+| 0.3845        | 51.2397  | 6200  | 0.4342          | 0.3966 | 0.1139 |
+| 0.3643        | 52.8926  | 6400  | 0.4454          | 0.3854 | 0.1146 |
+| 0.3563        | 54.5455  | 6600  | 0.4385          | 0.3881 | 0.1143 |
+| 0.3438        | 56.1983  | 6800  | 0.4524          | 0.3958 | 0.1174 |
+| 0.3374        | 57.8512  | 7000  | 0.4328          | 0.3893 | 0.1161 |
+| 0.3404        | 59.5041  | 7200  | 0.4532          | 0.3927 | 0.1149 |
+| 0.3319        | 61.1570  | 7400  | 0.4425          | 0.3947 | 0.1173 |
+| 0.3277        | 62.8099  | 7600  | 0.4270          | 0.3958 | 0.1148 |
+| 0.3284        | 64.4628  | 7800  | 0.4608          | 0.3843 | 0.1131 |
+| 0.3202        | 66.1157  | 8000  | 0.4453          | 0.3873 | 0.1126 |
+| 0.3229        | 67.7686  | 8200  | 0.4657          | 0.3908 | 0.1162 |
+| 0.3131        | 69.4215  | 8400  | 0.4316          | 0.3746 | 0.1127 |
+| 0.3115        | 71.0744  | 8600  | 0.4376          | 0.3796 | 0.1123 |
+| 0.3045        | 72.7273  | 8800  | 0.4581          | 0.3943 | 0.1170 |
+| 0.2979        | 74.3802  | 9000  | 0.4444          | 0.3769 | 0.1126 |
+| 0.2977        | 76.0331  | 9200  | 0.4447          | 0.3939 | 0.1162 |
+| 0.2972        | 77.6860  | 9400  | 0.4415          | 0.3796 | 0.1134 |
+| 0.2856        | 79.3388  | 9600  | 0.4607          | 0.3827 | 0.1134 |
+| 0.2868        | 80.9917  | 9800  | 0.4467          | 0.3816 | 0.1116 |
+| 0.2872        | 82.6446  | 10000 | 0.4480          | 0.3692 | 0.1102 |
+| 0.2849        | 84.2975  | 10200 | 0.4510          | 0.3850 | 0.1131 |
+| 0.2792        | 85.9504  | 10400 | 0.4585          | 0.3792 | 0.1109 |
+| 0.2634        | 87.6033  | 10600 | 0.4712          | 0.3827 | 0.1129 |
+| 0.2662        | 89.2562  | 10800 | 0.4711          | 0.375  | 0.1099 |
+| 0.2642        | 90.9091  | 11000 | 0.4591          | 0.3900 | 0.1137 |
+| 0.2553        | 92.5620  | 11200 | 0.4583          | 0.3657 | 0.1094 |
+| 0.2431        | 94.2149  | 11400 | 0.4818          | 0.3808 | 0.1131 |
+| 0.2593        | 95.8678  | 11600 | 0.4577          | 0.3719 | 0.1089 |
+| 0.2577        | 97.5207  | 11800 | 0.4555          | 0.3827 | 0.1108 |
+| 0.2515        | 99.1736  | 12000 | 0.4579          | 0.3735 | 0.1099 |
+| 0.2566        | 100.8264 | 12200 | 0.4683          | 0.3704 | 0.1093 |
+| 0.2492        | 102.4793 | 12400 | 0.4587          | 0.3711 | 0.1082 |
+| 0.2388        | 104.1322 | 12600 | 0.4686          | 0.3634 | 0.1085 |
+| 0.2449        | 105.7851 | 12800 | 0.4637          | 0.3673 | 0.1083 |
+| 0.2444        | 107.4380 | 13000 | 0.4676          | 0.3654 | 0.1088 |
+| 0.2423        | 109.0909 | 13200 | 0.4711          | 0.3711 | 0.1089 |
+| 0.2236        | 110.7438 | 13400 | 0.4650          | 0.3619 | 0.1086 |
+| 0.2367        | 112.3967 | 13600 | 0.4638          | 0.3677 | 0.1083 |
+| 0.2237        | 114.0496 | 13800 | 0.4805          | 0.3681 | 0.1074 |
+| 0.2307        | 115.7025 | 14000 | 0.4724          | 0.3669 | 0.1085 |
+| 0.221         | 117.3554 | 14200 | 0.4734          | 0.3669 | 0.1085 |
+| 0.2166        | 119.0083 | 14400 | 0.4852          | 0.3758 | 0.1117 |
+| 0.2137        | 120.6612 | 14600 | 0.4801          | 0.3681 | 0.1102 |
+| 0.2076        | 122.3140 | 14800 | 0.4822          | 0.3596 | 0.1091 |
+| 0.2087        | 123.9669 | 15000 | 0.4835          | 0.3677 | 0.1093 |
+| 0.2076        | 125.6198 | 15200 | 0.4771          | 0.3684 | 0.1098 |
+| 0.1987        | 127.2727 | 15400 | 0.4868          | 0.3681 | 0.1079 |
+| 0.2051        | 128.9256 | 15600 | 0.4771          | 0.3688 | 0.1094 |
+| 0.2024        | 130.5785 | 15800 | 0.4885          | 0.3634 | 0.1090 |
+| 0.1957        | 132.2314 | 16000 | 0.4980          | 0.3642 | 0.1088 |
+| 0.2073        | 133.8843 | 16200 | 0.5003          | 0.3654 | 0.1112 |
+| 0.1859        | 135.5372 | 16400 | 0.4980          | 0.3596 | 0.1083 |
+| 0.1835        | 137.1901 | 16600 | 0.4923          | 0.3661 | 0.1088 |
+| 0.1928        | 138.8430 | 16800 | 0.4814          | 0.3592 | 0.1067 |
+| 0.1814        | 140.4959 | 17000 | 0.4935          | 0.3696 | 0.1098 |
+| 0.1901        | 142.1488 | 17200 | 0.5006          | 0.3657 | 0.1076 |
+| 0.1857        | 143.8017 | 17400 | 0.4996          | 0.3627 | 0.1097 |
+| 0.1805        | 145.4545 | 17600 | 0.4981          | 0.3688 | 0.1080 |
+| 0.1752        | 147.1074 | 17800 | 0.4923          | 0.3553 | 0.1063 |
+| 0.1794        | 148.7603 | 18000 | 0.4871          | 0.3549 | 0.1046 |
+| 0.1777        | 150.4132 | 18200 | 0.4999          | 0.3557 | 0.1077 |
+| 0.1786        | 152.0661 | 18400 | 0.4887          | 0.3615 | 0.1078 |
+| 0.1718        | 153.7190 | 18600 | 0.4924          | 0.3611 | 0.1065 |
+| 0.1694        | 155.3719 | 18800 | 0.4956          | 0.3665 | 0.1074 |
+| 0.1766        | 157.0248 | 19000 | 0.5133          | 0.3623 | 0.1069 |
+| 0.1694        | 158.6777 | 19200 | 0.5171          | 0.3661 | 0.1077 |
+| 0.1658        | 160.3306 | 19400 | 0.5001          | 0.3573 | 0.1075 |
+| 0.1616        | 161.9835 | 19600 | 0.5067          | 0.3665 | 0.1084 |
+| 0.1646        | 163.6364 | 19800 | 0.5044          | 0.3650 | 0.1080 |
+| 0.1587        | 165.2893 | 20000 | 0.5077          | 0.3580 | 0.1060 |
+| 0.1666        | 166.9421 | 20200 | 0.5005          | 0.3580 | 0.1053 |
+| 0.1669        | 168.5950 | 20400 | 0.5033          | 0.3596 | 0.1067 |
+| 0.153         | 170.2479 | 20600 | 0.5211          | 0.3634 | 0.1070 |
+| 0.1495        | 171.9008 | 20800 | 0.5117          | 0.3580 | 0.1056 |
+| 0.1537        | 173.5537 | 21000 | 0.5139          | 0.3615 | 0.1048 |
+| 0.152         | 175.2066 | 21200 | 0.5254          | 0.3692 | 0.1086 |
+| 0.1533        | 176.8595 | 21400 | 0.5257          | 0.3665 | 0.1083 |
+| 0.1473        | 178.5124 | 21600 | 0.5215          | 0.3627 | 0.1063 |
+| 0.15          | 180.1653 | 21800 | 0.5261          | 0.3738 | 0.1089 |
+| 0.1521        | 181.8182 | 22000 | 0.5267          | 0.3646 | 0.1069 |
+| 0.1491        | 183.4711 | 22200 | 0.5251          | 0.3654 | 0.1068 |
+| 0.1462        | 185.1240 | 22400 | 0.5202          | 0.3623 | 0.1066 |
+| 0.1527        | 186.7769 | 22600 | 0.5166          | 0.3580 | 0.1048 |
+| 0.151         | 188.4298 | 22800 | 0.5199          | 0.3619 | 0.1058 |
+| 0.1447        | 190.0826 | 23000 | 0.5265          | 0.3588 | 0.1053 |
+| 0.1567        | 191.7355 | 23200 | 0.5228          | 0.3630 | 0.1062 |
+| 0.1446        | 193.3884 | 23400 | 0.5256          | 0.3615 | 0.1059 |
+| 0.1494        | 195.0413 | 23600 | 0.5229          | 0.3611 | 0.1059 |
+| 0.1496        | 196.6942 | 23800 | 0.5221          | 0.3619 | 0.1066 |
+| 0.1431        | 198.3471 | 24000 | 0.5249          | 0.3619 | 0.1062 |
+| 0.1441        | 200.0    | 24200 | 0.5244          | 0.3611 | 0.1060 |
+### Framework versions
+- Transformers 4.41.2
+- Pytorch 2.4.0
+- Datasets 2.19.1
+- Tokenizers 0.19.1

adapter.nhi.safetensors ADDED Viewed

	@@ -0,0 +1,3 @@

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