End of training

README.md

@@ -1,199 +1,129 @@
 ---
 library_name: transformers
-tags: []
+license: cc-by-nc-4.0
+base_model: facebook/mms-1b-all
+tags:
+- generated_from_trainer
+metrics:
+- wer
+model-index:
+- name: ssc-cgg-mms-model-mix-adapt-max3-devtrain
+  results: []
 ---
 
-# Model Card for Model ID
-
-<!-- Provide a quick summary of what the model is/does. -->
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-
-
-## Model Details
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-### Model Description
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-<!-- Provide a longer summary of what this model is. -->
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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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+<!-- 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. -->
+
+# ssc-cgg-mms-model-mix-adapt-max3-devtrain
+
+This model is a fine-tuned version of [facebook/mms-1b-all](https://huggingface.co/facebook/mms-1b-all) on an unknown dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.3144
+- Cer: 0.1074
+- Wer: 0.5
+
+## 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: 0.0005
+- train_batch_size: 8
+- eval_batch_size: 6
+- seed: 42
+- gradient_accumulation_steps: 2
+- total_train_batch_size: 16
+- optimizer: Use OptimizerNames.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: 20
+- mixed_precision_training: Native AMP
+
+### Training results
+
+| Training Loss | Epoch   | Step  | Validation Loss | Cer    | Wer    |
+|:-------------:|:-------:|:-----:|:---------------:|:------:|:------:|
+| 2.8396        | 0.3118  | 200   | 0.7349          | 0.1882 | 0.7851 |
+| 0.808         | 0.6235  | 400   | 0.5227          | 0.1524 | 0.6640 |
+| 0.7968        | 0.9353  | 600   | 0.4932          | 0.1473 | 0.6426 |
+| 0.7337        | 1.2463  | 800   | 0.4622          | 0.1393 | 0.6229 |
+| 0.6984        | 1.5581  | 1000  | 0.4511          | 0.1387 | 0.6231 |
+| 0.7177        | 1.8698  | 1200  | 0.4409          | 0.1348 | 0.6050 |
+| 0.7086        | 2.1808  | 1400  | 0.4373          | 0.1351 | 0.6060 |
+| 0.6591        | 2.4926  | 1600  | 0.4194          | 0.1309 | 0.5879 |
+| 0.6929        | 2.8044  | 1800  | 0.4356          | 0.1325 | 0.5909 |
+| 0.686         | 3.1154  | 2000  | 0.4189          | 0.1296 | 0.5772 |
+| 0.6588        | 3.4271  | 2200  | 0.4104          | 0.1266 | 0.5709 |
+| 0.6267        | 3.7389  | 2400  | 0.4076          | 0.1280 | 0.5772 |
+| 0.6633        | 4.0499  | 2600  | 0.4059          | 0.1254 | 0.5677 |
+| 0.6479        | 4.3617  | 2800  | 0.3915          | 0.1246 | 0.5658 |
+| 0.6445        | 4.6734  | 3000  | 0.3884          | 0.1254 | 0.5673 |
+| 0.6554        | 4.9852  | 3200  | 0.3809          | 0.1223 | 0.5540 |
+| 0.6029        | 5.2962  | 3400  | 0.3913          | 0.1246 | 0.5662 |
+| 0.6445        | 5.6080  | 3600  | 0.3869          | 0.1224 | 0.5565 |
+| 0.5975        | 5.9197  | 3800  | 0.3775          | 0.1208 | 0.5505 |
+| 0.5668        | 6.2307  | 4000  | 0.3732          | 0.1201 | 0.5485 |
+| 0.6055        | 6.5425  | 4200  | 0.3715          | 0.1195 | 0.5464 |
+| 0.593         | 6.8542  | 4400  | 0.3723          | 0.1188 | 0.5450 |
+| 0.614         | 7.1652  | 4600  | 0.3653          | 0.1183 | 0.5446 |
+| 0.5791        | 7.4770  | 4800  | 0.3627          | 0.1198 | 0.5508 |
+| 0.5815        | 7.7888  | 5000  | 0.3634          | 0.1175 | 0.5386 |
+| 0.6083        | 8.0998  | 5200  | 0.3582          | 0.1167 | 0.5371 |
+| 0.5951        | 8.4115  | 5400  | 0.3568          | 0.1160 | 0.5325 |
+| 0.5479        | 8.7233  | 5600  | 0.3563          | 0.1160 | 0.5354 |
+| 0.5761        | 9.0343  | 5800  | 0.3494          | 0.1154 | 0.5329 |
+| 0.5584        | 9.3461  | 6000  | 0.3519          | 0.1150 | 0.5312 |
+| 0.5523        | 9.6578  | 6200  | 0.3508          | 0.1157 | 0.5361 |
+| 0.5625        | 9.9696  | 6400  | 0.3428          | 0.1139 | 0.5272 |
+| 0.5237        | 10.2806 | 6600  | 0.3448          | 0.1140 | 0.5268 |
+| 0.5102        | 10.5924 | 6800  | 0.3417          | 0.1137 | 0.5249 |
+| 0.5779        | 10.9041 | 7000  | 0.3414          | 0.1135 | 0.5232 |
+| 0.525         | 11.2151 | 7200  | 0.3385          | 0.1128 | 0.5232 |
+| 0.5485        | 11.5269 | 7400  | 0.3372          | 0.1120 | 0.5185 |
+| 0.5494        | 11.8387 | 7600  | 0.3333          | 0.1114 | 0.5177 |
+| 0.5044        | 12.1496 | 7800  | 0.3308          | 0.1113 | 0.5140 |
+| 0.5421        | 12.4614 | 8000  | 0.3342          | 0.1106 | 0.5121 |
+| 0.5278        | 12.7732 | 8200  | 0.3269          | 0.1113 | 0.5135 |
+| 0.5121        | 13.0842 | 8400  | 0.3393          | 0.1120 | 0.5178 |
+| 0.5305        | 13.3959 | 8600  | 0.3332          | 0.1109 | 0.5155 |
+| 0.5061        | 13.7077 | 8800  | 0.3306          | 0.1102 | 0.5122 |
+| 0.5042        | 14.0187 | 9000  | 0.3264          | 0.1098 | 0.5127 |
+| 0.4954        | 14.3305 | 9200  | 0.3256          | 0.1099 | 0.5128 |
+| 0.504         | 14.6422 | 9400  | 0.3261          | 0.1097 | 0.5104 |
+| 0.5442        | 14.9540 | 9600  | 0.3199          | 0.1087 | 0.5070 |
+| 0.4786        | 15.2650 | 9800  | 0.3255          | 0.1091 | 0.5072 |
+| 0.5219        | 15.5768 | 10000 | 0.3225          | 0.1084 | 0.5053 |
+| 0.4802        | 15.8885 | 10200 | 0.3227          | 0.1087 | 0.5067 |
+| 0.5           | 16.1995 | 10400 | 0.3230          | 0.1083 | 0.5062 |
+| 0.4789        | 16.5113 | 10600 | 0.3210          | 0.1085 | 0.5061 |
+| 0.4613        | 16.8231 | 10800 | 0.3224          | 0.1081 | 0.5047 |
+| 0.4906        | 17.1341 | 11000 | 0.3196          | 0.1085 | 0.5061 |
+| 0.495         | 17.4458 | 11200 | 0.3190          | 0.1082 | 0.5048 |
+| 0.4796        | 17.7576 | 11400 | 0.3174          | 0.1084 | 0.5041 |
+| 0.4687        | 18.0686 | 11600 | 0.3178          | 0.1082 | 0.5043 |
+| 0.4707        | 18.3804 | 11800 | 0.3167          | 0.1081 | 0.5028 |
+| 0.5062        | 18.6921 | 12000 | 0.3145          | 0.1077 | 0.5034 |
+| 0.4891        | 19.0031 | 12200 | 0.3149          | 0.1079 | 0.5044 |
+| 0.4901        | 19.3149 | 12400 | 0.3150          | 0.1072 | 0.5018 |
+| 0.4705        | 19.6267 | 12600 | 0.3147          | 0.1075 | 0.5004 |
+| 0.4639        | 19.9384 | 12800 | 0.3144          | 0.1074 | 0.5    |
+
+
+### Framework versions
+
+- Transformers 4.52.1
+- Pytorch 2.9.1+cu128
+- Datasets 3.6.0
+- Tokenizers 0.21.4

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config.json

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+  "do_stable_layer_norm": true,
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+  "num_attention_heads": 16,
+  "num_codevector_groups": 2,
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+  "num_conv_pos_embedding_groups": 16,
+  "num_conv_pos_embeddings": 128,
+  "num_feat_extract_layers": 7,
+  "num_hidden_layers": 48,
+  "num_negatives": 100,
+  "output_hidden_size": 1280,
+  "pad_token_id": 57,
+  "proj_codevector_dim": 1024,
+  "tdnn_dilation": [
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+  "torch_dtype": "float32",
+  "transformers_version": "4.52.1",
+  "use_weighted_layer_sum": false,
+  "vocab_size": 60,
+  "xvector_output_dim": 512
+}

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preprocessor_config.json

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+  "padding_side": "right",
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+  "sampling_rate": 16000
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vocab.json

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-{
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-  }
-}
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