itaihay
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wav2vec_asr_swbd

Automatic Speech Recognition

Transformers

PyTorch

TensorBoard

wav2vec2

Generated from Trainer

Model card Files Files and versions Metrics Training metrics Community

itaihay commited on May 21, 2022

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1 Parent(s): 3de613a

update model card README.md

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@@ -12,10 +12,10 @@ should probably proofread and complete it, then remove this comment. -->
 # wav2vec_asr_swbd
-This model is a fine-tuned version of [facebook/wav2vec2-large-robust-ft-swbd-300h](https://huggingface.co/facebook/wav2vec2-large-robust-ft-swbd-300h) on an unknown dataset.
 It achieves the following results on the evaluation set:
-- Loss: 0.5214
-- Wer: 0.7269
 ## Model description
@@ -42,27 +42,83 @@ The following hyperparameters were used during training:
 - total_train_batch_size: 80
 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
 - lr_scheduler_type: linear
-- lr_scheduler_warmup_steps: 150
 - num_epochs: 20
 - mixed_precision_training: Native AMP
 ### Training results
-| Training Loss | Epoch | Step | Validation Loss | Wer    |
-|:-------------:|:-----:|:----:|:---------------:|:------:|
-| 3.0565        | 0.09  | 250  | 3.0007          | 1.0    |
-| 1.4935        | 0.18  | 500  | 1.0356          | 0.8593 |
-| 1.1937        | 0.26  | 750  | 0.7995          | 0.5973 |
-| 1.0645        | 0.35  | 1000 | 0.6634          | 0.7798 |
-| 0.9923        | 0.44  | 1250 | 0.6355          | 0.7755 |
-| 0.9494        | 0.53  | 1500 | 0.6208          | 0.7647 |
-| 0.9503        | 0.61  | 1750 | 0.5996          | 0.5880 |
-| 0.9111        | 0.7   | 2000 | 0.5775          | 0.5689 |
-| 0.9027        | 0.79  | 2250 | 0.5761          | 0.6729 |
-| 0.8943        | 0.88  | 2500 | 0.5407          | 0.6768 |
-| 0.8671        | 0.96  | 2750 | 0.5257          | 0.6955 |
-| 0.823         | 1.05  | 3000 | 0.5520          | 0.8153 |
-| 0.8279        | 1.14  | 3250 | 0.5214          | 0.7269 |
 ### Framework versions

 # wav2vec_asr_swbd
+This model is a fine-tuned version of [facebook/wav2vec2-large-robust-ft-swbd-300h](https://huggingface.co/facebook/wav2vec2-large-robust-ft-swbd-300h) on the None dataset.
 It achieves the following results on the evaluation set:
+- Loss: 0.3052
+- Wer: 0.5302
 ## Model description
 - total_train_batch_size: 80
 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
 - lr_scheduler_type: linear
+- lr_scheduler_warmup_steps: 500
 - num_epochs: 20
 - mixed_precision_training: Native AMP
 ### Training results
+| Training Loss | Epoch | Step  | Validation Loss | Wer    |
+|:-------------:|:-----:|:-----:|:---------------:|:------:|
+| 1.5445        | 0.29  | 500   | 0.9114          | 0.6197 |
+| 0.9397        | 0.58  | 1000  | 0.5057          | 0.5902 |
+| 0.8557        | 0.86  | 1500  | 0.4465          | 0.6264 |
+| 0.7716        | 1.15  | 2000  | 0.4182          | 0.5594 |
+| 0.7659        | 1.44  | 2500  | 0.4111          | 0.7048 |
+| 0.7406        | 1.73  | 3000  | 0.3927          | 0.5944 |
+| 0.6857        | 2.02  | 3500  | 0.3852          | 0.7118 |
+| 0.7113        | 2.31  | 4000  | 0.3775          | 0.5608 |
+| 0.6804        | 2.59  | 4500  | 0.3885          | 0.5759 |
+| 0.6654        | 2.88  | 5000  | 0.3703          | 0.7226 |
+| 0.6569        | 3.17  | 5500  | 0.3688          | 0.5972 |
+| 0.6335        | 3.46  | 6000  | 0.3661          | 0.7278 |
+| 0.6309        | 3.75  | 6500  | 0.3579          | 0.6324 |
+| 0.6231        | 4.03  | 7000  | 0.3620          | 0.5770 |
+| 0.6171        | 4.32  | 7500  | 0.3640          | 0.5772 |
+| 0.6191        | 4.61  | 8000  | 0.3553          | 0.6075 |
+| 0.6142        | 4.9   | 8500  | 0.3543          | 0.6126 |
+| 0.5905        | 5.19  | 9000  | 0.3601          | 0.6319 |
+| 0.5846        | 5.48  | 9500  | 0.3429          | 0.7343 |
+| 0.5874        | 5.76  | 10000 | 0.3429          | 0.5962 |
+| 0.5768        | 6.05  | 10500 | 0.3381          | 0.7410 |
+| 0.5783        | 6.34  | 11000 | 0.3391          | 0.5823 |
+| 0.5835        | 6.63  | 11500 | 0.3447          | 0.5821 |
+| 0.5817        | 6.92  | 12000 | 0.3314          | 0.6890 |
+| 0.5459        | 7.2   | 12500 | 0.3363          | 0.5727 |
+| 0.5575        | 7.49  | 13000 | 0.3363          | 0.7387 |
+| 0.5505        | 7.78  | 13500 | 0.3368          | 0.5685 |
+| 0.55          | 8.07  | 14000 | 0.3330          | 0.5587 |
+| 0.5523        | 8.36  | 14500 | 0.3338          | 0.5484 |
+| 0.5116        | 8.65  | 15000 | 0.3350          | 0.4351 |
+| 0.5263        | 8.93  | 15500 | 0.3254          | 0.6235 |
+| 0.5265        | 9.22  | 16000 | 0.3297          | 0.6207 |
+| 0.5265        | 9.51  | 16500 | 0.3279          | 0.6143 |
+| 0.5172        | 9.8   | 17000 | 0.3260          | 0.5800 |
+| 0.5028        | 10.09 | 17500 | 0.3259          | 0.5774 |
+| 0.5062        | 10.37 | 18000 | 0.3259          | 0.5552 |
+| 0.5112        | 10.66 | 18500 | 0.3201          | 0.6625 |
+| 0.5149        | 10.95 | 19000 | 0.3184          | 0.6865 |
+| 0.4939        | 11.24 | 19500 | 0.3152          | 0.6116 |
+| 0.5065        | 11.53 | 20000 | 0.3172          | 0.5246 |
+| 0.5129        | 11.82 | 20500 | 0.3129          | 0.5908 |
+| 0.4909        | 12.1  | 21000 | 0.3152          | 0.6075 |
+| 0.4865        | 12.39 | 21500 | 0.3160          | 0.5037 |
+| 0.4805        | 12.68 | 22000 | 0.3139          | 0.5458 |
+| 0.4691        | 12.97 | 22500 | 0.3225          | 0.5815 |
+| 0.4534        | 13.26 | 23000 | 0.3168          | 0.5614 |
+| 0.4661        | 13.54 | 23500 | 0.3135          | 0.6053 |
+| 0.4636        | 13.83 | 24000 | 0.3120          | 0.5142 |
+| 0.4554        | 14.12 | 24500 | 0.3127          | 0.5552 |
+| 0.4602        | 14.41 | 25000 | 0.3117          | 0.5562 |
+| 0.4521        | 14.7  | 25500 | 0.3106          | 0.4995 |
+| 0.4369        | 14.99 | 26000 | 0.3100          | 0.5663 |
+| 0.4249        | 15.27 | 26500 | 0.3110          | 0.5262 |
+| 0.4321        | 15.56 | 27000 | 0.3106          | 0.5183 |
+| 0.4293        | 15.85 | 27500 | 0.3091          | 0.5311 |
+| 0.4537        | 16.14 | 28000 | 0.3134          | 0.4986 |
+| 0.4258        | 16.43 | 28500 | 0.3138          | 0.4487 |
+| 0.4347        | 16.71 | 29000 | 0.3091          | 0.5011 |
+| 0.4615        | 17.0  | 29500 | 0.3068          | 0.5616 |
+| 0.4163        | 17.29 | 30000 | 0.3115          | 0.5426 |
+| 0.4074        | 17.58 | 30500 | 0.3079          | 0.5341 |
+| 0.4121        | 17.87 | 31000 | 0.3047          | 0.5619 |
+| 0.4219        | 18.16 | 31500 | 0.3085          | 0.5051 |
+| 0.4049        | 18.44 | 32000 | 0.3084          | 0.5116 |
+| 0.4119        | 18.73 | 32500 | 0.3071          | 0.5028 |
+| 0.4129        | 19.02 | 33000 | 0.3064          | 0.5030 |
+| 0.4143        | 19.31 | 33500 | 0.3040          | 0.5086 |
+| 0.4013        | 19.6  | 34000 | 0.3057          | 0.5271 |
+| 0.4162        | 19.88 | 34500 | 0.3052          | 0.5302 |
 ### Framework versions