End of training

README.md

@@ -1,199 +1,117 @@
 ---
 library_name: transformers
-tags: []
+license: apache-2.0
+base_model: facebook/wav2vec2-large-xlsr-53
+tags:
+- generated_from_trainer
+datasets:
+- audiofolder
+metrics:
+- wer
+model-index:
+- name: w2v2-lmk_augmented
+  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: 0.4878048780487805
 ---
 
-# Model Card for Model ID
-
-<!-- Provide a quick summary of what the model is/does. -->
-
-
-
-## Model Details
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-### Model Description
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-<!-- 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.
-
-- **Developed by:** [More Information Needed]
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-## Uses
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-## How to Get Started with the Model
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-## Training Details
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-### Training Data
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-#### Metrics
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-## Environmental Impact
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-Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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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. -->
+
+# w2v2-lmk_augmented
+
+This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the audiofolder dataset.
+It achieves the following results on the evaluation set:
+- Loss: 1.2406
+- Wer: 0.4878
+- Cer: 0.1858
+
+## 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: 0.0001
+- train_batch_size: 8
+- eval_batch_size: 8
+- seed: 42
+- gradient_accumulation_steps: 2
+- total_train_batch_size: 16
+- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED 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: 300
+- num_epochs: 100
+- mixed_precision_training: Native AMP
+
+### Training results
+
+| Training Loss | Epoch   | Step | Validation Loss | Wer    | Cer    |
+|:-------------:|:-------:|:----:|:---------------:|:------:|:------:|
+| 8.5498        | 2.7123  | 100  | 4.0528          | 1.0    | 1.0    |
+| 3.1716        | 5.4110  | 200  | 2.9634          | 1.0    | 1.0    |
+| 2.9756        | 8.1096  | 300  | 2.8924          | 1.0    | 1.0    |
+| 2.8279        | 10.8219 | 400  | 2.5968          | 1.0    | 1.0    |
+| 2.2866        | 13.5205 | 500  | 1.7827          | 0.9895 | 0.6283 |
+| 1.619         | 16.2192 | 600  | 1.3242          | 0.9443 | 0.4021 |
+| 1.2926        | 18.9315 | 700  | 1.1299          | 0.7875 | 0.2833 |
+| 1.0181        | 21.6301 | 800  | 1.1390          | 0.6585 | 0.2513 |
+| 0.8774        | 24.3288 | 900  | 1.0760          | 0.6132 | 0.2338 |
+| 0.7471        | 27.0274 | 1000 | 0.9959          | 0.5889 | 0.2155 |
+| 0.6542        | 29.7397 | 1100 | 1.0575          | 0.5575 | 0.2117 |
+| 0.5632        | 32.4384 | 1200 | 1.0240          | 0.5784 | 0.2171 |
+| 0.4834        | 35.1370 | 1300 | 1.0971          | 0.5505 | 0.1912 |
+| 0.4716        | 37.8493 | 1400 | 1.1336          | 0.5749 | 0.2056 |
+| 0.45          | 40.5479 | 1500 | 1.0703          | 0.5679 | 0.2079 |
+| 0.394         | 43.2466 | 1600 | 1.1579          | 0.5645 | 0.2178 |
+| 0.3588        | 45.9589 | 1700 | 1.0555          | 0.5296 | 0.1896 |
+| 0.3217        | 48.6575 | 1800 | 1.2323          | 0.5575 | 0.2102 |
+| 0.3245        | 51.3562 | 1900 | 1.1639          | 0.5401 | 0.2018 |
+| 0.289         | 54.0548 | 2000 | 1.1304          | 0.5122 | 0.1927 |
+| 0.28          | 56.7671 | 2100 | 1.2295          | 0.5296 | 0.2003 |
+| 0.2521        | 59.4658 | 2200 | 1.1612          | 0.5226 | 0.1950 |
+| 0.2624        | 62.1644 | 2300 | 1.1982          | 0.5157 | 0.2003 |
+| 0.2402        | 64.8767 | 2400 | 1.2075          | 0.5296 | 0.1988 |
+| 0.2258        | 67.5753 | 2500 | 1.2091          | 0.5366 | 0.2003 |
+| 0.2232        | 70.2740 | 2600 | 1.1830          | 0.5296 | 0.1957 |
+| 0.2181        | 72.9863 | 2700 | 1.2001          | 0.5157 | 0.1942 |
+| 0.2214        | 75.6849 | 2800 | 1.1942          | 0.5052 | 0.1889 |
+| 0.1752        | 78.3836 | 2900 | 1.1873          | 0.5087 | 0.1896 |
+| 0.1891        | 81.0822 | 3000 | 1.2159          | 0.5192 | 0.1927 |
+| 0.1733        | 83.7945 | 3100 | 1.2105          | 0.5017 | 0.1881 |
+| 0.1982        | 86.4932 | 3200 | 1.2331          | 0.5087 | 0.1874 |
+| 0.1681        | 89.1918 | 3300 | 1.1848          | 0.4808 | 0.1790 |
+| 0.1631        | 91.9041 | 3400 | 1.2273          | 0.4878 | 0.1858 |
+| 0.1579        | 94.6027 | 3500 | 1.2334          | 0.4948 | 0.1843 |
+| 0.1795        | 97.3014 | 3600 | 1.2399          | 0.4878 | 0.1851 |
+| 0.1592        | 100.0   | 3700 | 1.2406          | 0.4878 | 0.1858 |
+
+
+### Framework versions
+
+- Transformers 4.57.1
+- Pytorch 2.8.0+cu128
+- Datasets 3.0.0
+- Tokenizers 0.22.1

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