wav2vec2-classic-300m-norwegian-colab

README.md

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+---
+license: apache-2.0
+base_model: facebook/wav2vec2-xls-r-300m
+tags:
+- generated_from_trainer
+datasets:
+- nb_samtale
+metrics:
+- wer
+model-index:
+- name: wav2vec2-classic-300m-norwegian-colab
+  results:
+  - task:
+      name: Automatic Speech Recognition
+      type: automatic-speech-recognition
+    dataset:
+      name: nb_samtale
+      type: nb_samtale
+      config: annotations
+      split: test
+      args: annotations
+    metrics:
+    - name: Wer
+      type: wer
+      value: 0.7528477035956058
+---
+
+<!-- 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-classic-300m-norwegian-colab
+
+This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the nb_samtale dataset.
+It achieves the following results on the evaluation set:
+- Loss: 2.2190
+- Wer: 0.7528
+
+## 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: 16
+- eval_batch_size: 8
+- seed: 42
+- gradient_accumulation_steps: 2
+- total_train_batch_size: 32
+- 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    |
+|:-------------:|:-----:|:----:|:---------------:|:------:|
+| 4.8141        | 2.57  | 400  | 3.0571          | 1.0    |
+| 3.0777        | 5.14  | 800  | 2.9987          | 1.0    |
+| 2.7311        | 7.72  | 1200 | 2.5705          | 0.9829 |
+| 2.1302        | 10.29 | 1600 | 1.8399          | 0.9225 |
+| 1.6827        | 12.86 | 2000 | 1.6372          | 0.8559 |
+| 1.312         | 15.43 | 2400 | 1.8908          | 0.9172 |
+| 0.9979        | 18.01 | 2800 | 1.7908          | 0.7890 |
+| 0.7456        | 20.58 | 3200 | 1.8110          | 0.7720 |
+| 0.592         | 23.15 | 3600 | 2.0024          | 0.7686 |
+| 0.4946        | 25.72 | 4000 | 2.1173          | 0.7702 |
+| 0.4093        | 28.3  | 4400 | 2.2190          | 0.7528 |
+
+
+### Framework versions
+
+- Transformers 4.35.2
+- Pytorch 2.1.0+cu118
+- Datasets 2.15.0
+- Tokenizers 0.15.0

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