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Model: tristayqc/my_zh_CN_asr_cv13_model
Source: Original Platform

README.md

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+---
+license: apache-2.0
+base_model: jonatasgrosman/wav2vec2-large-xlsr-53-chinese-zh-cn
+tags:
+- generated_from_trainer
+datasets:
+- common_voice_13_0
+metrics:
+- wer
+- cer
+model-index:
+- name: my_zh_CN_asr_cv13_model
+  results:
+  - task:
+      name: Automatic Speech Recognition
+      type: automatic-speech-recognition
+    dataset:
+      name: common_voice_13_0
+      type: common_voice_13_0
+      config: zh-CN
+      split: train
+      args: zh-CN
+    metrics:
+    - name: Wer
+      type: wer
+      value: 0.375
+    - name: Cer
+      type: cer
+      value: 0.0674
+---
+
+<!-- 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. -->
+
+# my_zh_CN_asr_cv13_model
+
+This model is a fine-tuned version of [jonatasgrosman/wav2vec2-large-xlsr-53-chinese-zh-cn](https://huggingface.co/jonatasgrosman/wav2vec2-large-xlsr-53-chinese-zh-cn) on the common_voice_13_0 dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.1614
+- Cer: 0.0674
+- Wer: 0.375
+
+## 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: 1e-05
+- train_batch_size: 8
+- eval_batch_size: 8
+- seed: 42
+- gradient_accumulation_steps: 2
+- total_train_batch_size: 16
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- lr_scheduler_warmup_steps: 500
+- training_steps: 2000
+- mixed_precision_training: Native AMP
+
+### Training results
+
+| Training Loss | Epoch   | Step | Validation Loss | Cer    | Wer   |
+|:-------------:|:-------:|:----:|:---------------:|:------:|:-----:|
+| 0.0489        | 249.002 | 1000 | 0.1566          | 0.0638 | 0.375 |
+| 0.0224        | 499.002 | 2000 | 0.1614          | 0.0674 | 0.375 |
+
+
+### Framework versions
+
+- Transformers 4.40.1
+- Pytorch 2.2.1+cu121
+- Datasets 2.19.0
+- Tokenizers 0.19.1