readme: ---
  tags:
  - generated_from_trainer
  model-index:
  - name: combined-onTorgoSentenceModel-TestedSpeakerM01
    results: []
  ---
  
  <!-- 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. -->
  
  # combined-onTorgoSentenceModel-TestedSpeakerM01
  
  This model is a fine-tuned version of [alexziweiwang/torgo-sentences-TestedSpeakerM01](https://huggingface.co/alexziweiwang/torgo-sentences-TestedSpeakerM01) on the None dataset.
  It achieves the following results on the evaluation set:
  - Loss: 1.3823
  - Wer: 0.8064
  
  ## 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: 4
  - eval_batch_size: 8
  - seed: 42
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - lr_scheduler_warmup_steps: 1000
  - num_epochs: 30
  
  ### Training results
  
  | Training Loss | Epoch | Step  | Validation Loss | Wer    |
  |:-------------:|:-----:|:-----:|:---------------:|:------:|
  | 28.7173       | 0.35  | 500   | 3.0048          | 0.9974 |
  | 2.5941        | 0.69  | 1000  | 2.1227          | 1.1821 |
  | 1.9528        | 1.04  | 1500  | 1.8921          | 1.2128 |
  | 1.5166        | 1.39  | 2000  | 1.3280          | 1.1795 |
  | 1.3394        | 1.74  | 2500  | 1.3925          | 1.0923 |
  | 1.2284        | 2.08  | 3000  | 1.4628          | 1.1064 |
  | 1.0785        | 2.43  | 3500  | 1.3823          | 1.0923 |
  | 1.0343        | 2.78  | 4000  | 1.2196          | 1.0923 |
  | 0.9979        | 3.12  | 4500  | 1.4429          | 1.0038 |
  | 0.8963        | 3.47  | 5000  | 1.3360          | 0.9731 |
  | 0.9169        | 3.82  | 5500  | 1.4309          | 0.9949 |
  | 0.7811        | 4.17  | 6000  | 1.4731          | 0.9974 |
  | 0.7811        | 4.51  | 6500  | 1.5859          | 0.9808 |
  | 0.7818        | 4.86  | 7000  | 1.3996          | 0.9923 |
  | 0.6952        | 5.21  | 7500  | 1.2495          | 1.0167 |
  | 0.6708        | 5.56  | 8000  | 1.7330          | 1.0103 |
  | 0.6724        | 5.9   | 8500  | 1.0807          | 0.9731 |
  | 0.6321        | 6.25  | 9000  | 1.4906          | 0.9833 |
  | 0.6564        | 6.6   | 9500  | 1.2854          | 0.9218 |
  | 0.6099        | 6.94  | 10000 | 1.4289          | 0.9321 |
  | 0.5643        | 7.29  | 10500 | 1.5796          | 0.9449 |
  | 0.5612        | 7.64  | 11000 | 1.3855          | 0.9013 |
  | 0.5746        | 7.99  | 11500 | 0.9218          | 0.8936 |
  | 0.5198        | 8.33  | 12000 | 1.0982          | 0.9436 |
  | 0.5754        | 8.68  | 12500 | 1.1686          | 0.9449 |
  | 0.5102        | 9.03  | 13000 | 1.1525          | 0.9321 |
  | 0.4721        | 9.38  | 13500 | 1.2355          | 0.8551 |
  | 0.4715        | 9.72  | 14000 | 1.2203          | 0.8897 |
  | 0.4961        | 10.07 | 14500 | 1.3226          | 0.8821 |
  | 0.4256        | 10.42 | 15000 | 1.4078          | 0.8795 |
  | 0.449         | 10.76 | 15500 | 1.1023          | 0.8731 |
  | 0.4141        | 11.11 | 16000 | 1.0989          | 0.9026 |
  | 0.4325        | 11.46 | 16500 | 1.3532          | 0.8936 |
  | 0.4034        | 11.81 | 17000 | 1.0583          | 0.8705 |
  | 0.4027        | 12.15 | 17500 | 1.0765          | 0.8692 |
  | 0.4031        | 12.5  | 18000 | 1.4424          | 0.8679 |
  | 0.4129        | 12.85 | 18500 | 1.1504          | 0.8654 |
  | 0.4291        | 13.19 | 19000 | 1.3790          | 0.8692 |
  | 0.3512        | 13.54 | 19500 | 1.2891          | 0.9077 |
  | 0.3499        | 13.89 | 20000 | 1.3068          | 0.8577 |
  | 0.3331        | 14.24 | 20500 | 1.5288          | 0.8833 |
  | 0.3626        | 14.58 | 21000 | 1.1218          | 0.9090 |
  | 0.495         | 14.93 | 21500 | 1.2152          | 0.8641 |
  | 0.3129        | 15.28 | 22000 | 1.4101          | 0.8974 |
  | 0.2945        | 15.62 | 22500 | 1.2965          | 0.8615 |
  | 0.3335        | 15.97 | 23000 | 1.3362          | 0.8282 |
  | 0.2902        | 16.32 | 23500 | 1.2603          | 0.8679 |
  | 0.294         | 16.67 | 24000 | 1.4634          | 0.8603 |
  | 0.2561        | 17.01 | 24500 | 1.0413          | 0.8333 |
  | 0.2654        | 17.36 | 25000 | 1.5734          | 0.8679 |
  | 0.2724        | 17.71 | 25500 | 1.6416          | 0.8756 |
  | 0.2581        | 18.06 | 26000 | 1.3018          | 0.8551 |
  | 0.2329        | 18.4  | 26500 | 1.5288          | 0.8654 |
  | 0.2484        | 18.75 | 27000 | 1.4365          | 0.8410 |
  | 0.2578        | 19.1  | 27500 | 1.4501          | 0.8564 |
  | 0.2237        | 19.44 | 28000 | 1.3333          | 0.8436 |
  | 0.2274        | 19.79 | 28500 | 1.3387          | 0.8256 |
  | 0.2253        | 20.14 | 29000 | 1.4898          | 0.8551 |
  | 0.2189        | 20.49 | 29500 | 1.6225          | 0.8692 |
  | 0.2006        | 20.83 | 30000 | 1.6085          | 0.8551 |
  | 0.2263        | 21.18 | 30500 | 1.5435          | 0.85   |
  | 0.2032        | 21