Anujgr8
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wav2vec2-base-Malyalam-large

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 ---
+license: apache-2.0
+base_model: facebook/wav2vec2-base-960h
+tags:
+- generated_from_trainer
+metrics:
+- wer
+model-index:
+- name: wav2vec2-base-Malyalam-large
+  results: []
 ---
+<!-- 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-base-Malyalam-large
+This model is a fine-tuned version of [facebook/wav2vec2-base-960h](https://huggingface.co/facebook/wav2vec2-base-960h) on the None dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.4124
+- Wer: 0.4294
+- Cer: 0.0876
+## 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: 6
+- eval_batch_size: 8
+- seed: 42
+- gradient_accumulation_steps: 4
+- total_train_batch_size: 24
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- lr_scheduler_warmup_steps: 500
+- training_steps: 3000
+- mixed_precision_training: Native AMP
+### Training results
+| Training Loss | Epoch   | Step | Validation Loss | Wer    | Cer    |
+|:-------------:|:-------:|:----:|:---------------:|:------:|:------:|
+| 5.755         | 2.1622  | 300  | 3.4394          | 1.0    | 1.0    |
+| 1.5526        | 4.3243  | 600  | 0.7064          | 0.7488 | 0.1865 |
+| 0.6682        | 6.4865  | 900  | 0.5412          | 0.6252 | 0.1383 |
+| 0.4698        | 8.6486  | 1200 | 0.4891          | 0.5767 | 0.1271 |
+| 0.3632        | 10.8108 | 1500 | 0.3958          | 0.5024 | 0.1004 |
+| 0.2918        | 12.9730 | 1800 | 0.4565          | 0.5020 | 0.1055 |
+| 0.2335        | 15.1351 | 2100 | 0.4298          | 0.4723 | 0.0981 |
+| 0.1956        | 17.2973 | 2400 | 0.3805          | 0.4466 | 0.0888 |
+| 0.1665        | 19.4595 | 2700 | 0.4039          | 0.4311 | 0.0889 |
+| 0.1447        | 21.6216 | 3000 | 0.4124          | 0.4294 | 0.0876 |
+### Framework versions
+- Transformers 4.41.2
+- Pytorch 2.3.0+cu121
+- Datasets 1.18.3
+- Tokenizers 0.19.1