liuh6
/

whisper-tiny_to_Chinese_accent

@@ -9,22 +9,9 @@ tags:
 - generated_from_trainer
 datasets:
 - Chinese_english
-metrics:
-- wer
 model-index:
 - name: Whisper tiny Chinese
-  results:
-  - task:
-      name: Automatic Speech Recognition
-      type: automatic-speech-recognition
-    dataset:
-      name: Chinese English
-      type: Chinese_english
-      args: 'config: default, split: test'
-    metrics:
-    - name: Wer
-      type: wer
-      value: 15.091342335186656
 ---
 <!-- This model card has been generated automatically according to the information the Trainer had access to. You
@@ -34,8 +21,13 @@ should probably proofread and complete it, then remove this comment. -->
 This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) on the Chinese English dataset.
 It achieves the following results on the evaluation set:
-- Loss: 0.3518
-- Wer: 15.0913
 ## Model description
@@ -61,15 +53,7 @@ The following hyperparameters were used during training:
 - optimizer: Use OptimizerNames.ADAMW_TORCH 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: 500
-- training_steps: 1000
-### Training results
-| Training Loss | Epoch  | Step | Validation Loss | Wer     |
-|:-------------:|:------:|:----:|:---------------:|:-------:|
-| 0.0283        | 1.1111 | 500  | 0.3560          | 16.2669 |
-| 0.003         | 2.2222 | 1000 | 0.3518          | 15.0913 |
 ### Framework versions

 - generated_from_trainer
 datasets:
 - Chinese_english
 model-index:
 - name: Whisper tiny Chinese
+  results: []
 ---
 <!-- This model card has been generated automatically according to the information the Trainer had access to. You
 This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) on the Chinese English dataset.
 It achieves the following results on the evaluation set:
+- eval_loss: 0.3515
+- eval_wer: 14.2176
+- eval_runtime: 205.6132
+- eval_samples_per_second: 3.059
+- eval_steps_per_second: 3.059
+- epoch: 2.2222
+- step: 1000
 ## Model description
 - optimizer: Use OptimizerNames.ADAMW_TORCH 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: 500
+- training_steps: 2000
 ### Framework versions

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