Create README.md

README.md

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+---
+language:
+- as
+license: apache-2.0
+tags:
+- automatic-speech-recognition
+- mozilla-foundation/common_voice_8_0
+- generated_from_trainer
+- as
+- robust-speech-event
+- model_for_talk
+datasets:
+- common_voice
+model-index:
+- name: wav2vec2-large-xls-r-300m-as-with-LM-v2
+  results:
+  - task: 
+      name: Automatic Speech Recognition 
+      type: automatic-speech-recognition
+    dataset:
+      name: Common Voice 8
+      type: mozilla-foundation/common_voice_8_0
+      args: hsb
+    metrics:
+       - name: Test WER
+         type: wer
+         value: []
+       - name: Test CER
+         type: cer
+         value: []
+---
+
+<!-- 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-large-xls-r-300m-as-v9
+
+This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the common_voice dataset.
+It achieves the following results on the evaluation set:
+- Loss: 1.1679
+- Wer: 0.5761
+
+## 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: 0.000111
+- 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: 300
+- num_epochs: 200
+- mixed_precision_training: Native AMP
+
+### Training results
+
+| Training Loss | Epoch  | Step | Validation Loss | Wer    |
+|:-------------:|:------:|:----:|:---------------:|:------:|
+| 8.3852        | 10.51  | 200  | 3.6402          | 1.0    |
+| 3.5374        | 21.05  | 400  | 3.3894          | 1.0    |
+| 2.8645        | 31.56  | 600  | 1.3143          | 0.8303 |
+| 1.1784        | 42.1   | 800  | 0.9417          | 0.6661 |
+| 0.7805        | 52.62  | 1000 | 0.9292          | 0.6237 |
+| 0.5973        | 63.15  | 1200 | 0.9489          | 0.6014 |
+| 0.4784        | 73.67  | 1400 | 0.9916          | 0.5962 |
+| 0.4138        | 84.21  | 1600 | 1.0272          | 0.6121 |
+| 0.3491        | 94.72  | 1800 | 1.0412          | 0.5984 |
+| 0.3062        | 105.26 | 2000 | 1.0769          | 0.6005 |
+| 0.2707        | 115.77 | 2200 | 1.0708          | 0.5752 |
+| 0.2459        | 126.31 | 2400 | 1.1285          | 0.6009 |
+| 0.2234        | 136.82 | 2600 | 1.1209          | 0.5949 |
+| 0.2035        | 147.36 | 2800 | 1.1348          | 0.5842 |
+| 0.1876        | 157.87 | 3000 | 1.1480          | 0.5872 |
+| 0.1669        | 168.41 | 3200 | 1.1496          | 0.5838 |
+| 0.1595        | 178.92 | 3400 | 1.1721          | 0.5778 |
+| 0.1505        | 189.46 | 3600 | 1.1654          | 0.5744 |
+| 0.1486        | 199.97 | 3800 | 1.1679          | 0.5761 |
+
+
+### Framework versions
+
+- Transformers 4.16.1
+- Pytorch 1.10.0+cu111
+- Datasets 1.18.2
+- Tokenizers 0.11.0