Kamshat
/

wav2vec2-base-issai-colab

@@ -1,199 +1,149 @@
 ---
-library_name: transformers
-tags: []
 ---
-# Model Card for Model ID
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 ---
+language:
+- kz
+license: apache-2.0
+base_model: facebook/wav2vec2-large-xlsr-53
+tags:
+- hf-asr-leaderboard
+- generated_from_trainer
+datasets:
+- ISSAI_KSC2
+metrics:
+- wer
+model-index:
+- name: Kammi
+  results:
+  - task:
+      name: Automatic Speech Recognition
+      type: automatic-speech-recognition
+    dataset:
+      name: BilalS96/ISSAI_KSC2
+      type: ISSAI_KSC2
+      args: 'config: kzk, split: test'
+    metrics:
+    - name: Wer
+      type: wer
+      value: 0.38223702730599607
 ---
+<!-- 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. -->
+# Kammi
+This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the BilalS96/ISSAI_KSC2 dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.6797
+- Wer: 0.3822
+## 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: 2
+- total_train_batch_size: 8
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- lr_scheduler_warmup_steps: 500
+- num_epochs: 30
+- mixed_precision_training: Native AMP
+### Training results
+| Training Loss | Epoch   | Step  | Validation Loss | Wer    |
+|:-------------:|:-------:|:-----:|:---------------:|:------:|
+| 5.2923        | 0.4278  | 400   | 4.4181          | 1.0    |
+| 3.3189        | 0.8556  | 800   | 3.6105          | 1.0    |
+| 3.2115        | 1.2834  | 1200  | 3.3715          | 1.0    |
+| 3.148         | 1.7112  | 1600  | 3.1163          | 1.0    |
+| 3.0788        | 2.1390  | 2000  | 3.2185          | 1.0    |
+| 2.9677        | 2.5668  | 2400  | 2.7724          | 1.0000 |
+| 2.3283        | 2.9947  | 2800  | 1.7294          | 0.9985 |
+| 1.6653        | 3.4225  | 3200  | 1.3565          | 0.9627 |
+| 1.4308        | 3.8503  | 3600  | 1.1434          | 0.9235 |
+| 1.2196        | 4.2781  | 4000  | 0.9823          | 0.8583 |
+| 1.0644        | 4.7059  | 4400  | 0.8573          | 0.8191 |
+| 0.9649        | 5.1337  | 4800  | 0.8064          | 0.7725 |
+| 0.849         | 5.5615  | 5200  | 0.7391          | 0.7389 |
+| 0.8208        | 5.9893  | 5600  | 0.7014          | 0.6868 |
+| 0.6995        | 6.4171  | 6000  | 0.6765          | 0.6687 |
+| 0.703         | 6.8449  | 6400  | 0.6347          | 0.6476 |
+| 0.6136        | 7.2727  | 6800  | 0.6371          | 0.6226 |
+| 0.5957        | 7.7005  | 7200  | 0.6068          | 0.6000 |
+| 0.5616        | 8.1283  | 7600  | 0.5877          | 0.5774 |
+| 0.5128        | 8.5561  | 8000  | 0.5878          | 0.5605 |
+| 0.5093        | 8.9840  | 8400  | 0.5502          | 0.5469 |
+| 0.4544        | 9.4118  | 8800  | 0.5823          | 0.5424 |
+| 0.4622        | 9.8396  | 9200  | 0.5546          | 0.5219 |
+| 0.424         | 10.2674 | 9600  | 0.5910          | 0.5247 |
+| 0.4041        | 10.6952 | 10000 | 0.5735          | 0.5130 |
+| 0.3956        | 11.1230 | 10400 | 0.5673          | 0.5005 |
+| 0.3694        | 11.5508 | 10800 | 0.5336          | 0.4940 |
+| 0.3675        | 11.9786 | 11200 | 0.5304          | 0.4886 |
+| 0.338         | 12.4064 | 11600 | 0.6132          | 0.4859 |
+| 0.3355        | 12.8342 | 12000 | 0.6146          | 0.4872 |
+| 0.3251        | 13.2620 | 12400 | 0.5979          | 0.4753 |
+| 0.309         | 13.6898 | 12800 | 0.5721          | 0.4657 |
+| 0.3065        | 14.1176 | 13200 | 0.5849          | 0.4598 |
+| 0.2824        | 14.5455 | 13600 | 0.5872          | 0.4644 |
+| 0.2875        | 14.9733 | 14000 | 0.5864          | 0.4540 |
+| 0.2663        | 15.4011 | 14400 | 0.5885          | 0.4513 |
+| 0.2711        | 15.8289 | 14800 | 0.6090          | 0.4553 |
+| 0.2566        | 16.2567 | 15200 | 0.6312          | 0.4532 |
+| 0.2524        | 16.6845 | 15600 | 0.6248          | 0.4450 |
+| 0.2528        | 17.1123 | 16000 | 0.6329          | 0.4390 |
+| 0.2381        | 17.5401 | 16400 | 0.6040          | 0.4370 |
+| 0.2336        | 17.9679 | 16800 | 0.5855          | 0.4327 |
+| 0.2184        | 18.3957 | 17200 | 0.6107          | 0.4327 |
+| 0.2253        | 18.8235 | 17600 | 0.6087          | 0.4316 |
+| 0.2169        | 19.2513 | 18000 | 0.6169          | 0.4261 |
+| 0.2142        | 19.6791 | 18400 | 0.6025          | 0.4321 |
+| 0.2125        | 20.1070 | 18800 | 0.6478          | 0.4261 |
+| 0.1994        | 20.5348 | 19200 | 0.6504          | 0.4238 |
+| 0.2025        | 20.9626 | 19600 | 0.6580          | 0.4229 |
+| 0.1954        | 21.3904 | 20000 | 0.6401          | 0.4170 |
+| 0.1939        | 21.8182 | 20400 | 0.6443          | 0.4119 |
+| 0.1865        | 22.2460 | 20800 | 0.6588          | 0.4140 |
+| 0.1847        | 22.6738 | 21200 | 0.6463          | 0.4087 |
+| 0.185         | 23.1016 | 21600 | 0.6490          | 0.4058 |
+| 0.1796        | 23.5294 | 22000 | 0.6653          | 0.4070 |
+| 0.1745        | 23.9572 | 22400 | 0.6452          | 0.4042 |
+| 0.173         | 24.3850 | 22800 | 0.6895          | 0.4018 |
+| 0.1653        | 24.8128 | 23200 | 0.6482          | 0.4017 |
+| 0.165         | 25.2406 | 23600 | 0.6620          | 0.3962 |
+| 0.1622        | 25.6684 | 24000 | 0.6702          | 0.3971 |
+| 0.1565        | 26.0963 | 24400 | 0.6899          | 0.3985 |
+| 0.1563        | 26.5241 | 24800 | 0.7042          | 0.3932 |
+| 0.1555        | 26.9519 | 25200 | 0.7017          | 0.3931 |
+| 0.1548        | 27.3797 | 25600 | 0.6751          | 0.3895 |
+| 0.1543        | 27.8075 | 26000 | 0.6831          | 0.3895 |
+| 0.1464        | 28.2353 | 26400 | 0.6765          | 0.3842 |
+| 0.1475        | 28.6631 | 26800 | 0.6842          | 0.3858 |
+| 0.144         | 29.0909 | 27200 | 0.6904          | 0.3851 |
+| 0.1461        | 29.5187 | 27600 | 0.6821          | 0.3834 |
+| 0.1417        | 29.9465 | 28000 | 0.6797          | 0.3822 |
+### Framework versions
+- Transformers 4.41.1
+- Pytorch 2.3.0+cu121
+- Datasets 2.19.1
+- Tokenizers 0.19.1

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