Model save
Browse files
README.md
ADDED
|
@@ -0,0 +1,106 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
---
|
| 2 |
+
library_name: transformers
|
| 3 |
+
license: apache-2.0
|
| 4 |
+
base_model: facebook/wav2vec2-base
|
| 5 |
+
tags:
|
| 6 |
+
- generated_from_trainer
|
| 7 |
+
datasets:
|
| 8 |
+
- timit_asr
|
| 9 |
+
metrics:
|
| 10 |
+
- wer
|
| 11 |
+
model-index:
|
| 12 |
+
- name: repo_name
|
| 13 |
+
results:
|
| 14 |
+
- task:
|
| 15 |
+
name: Automatic Speech Recognition
|
| 16 |
+
type: automatic-speech-recognition
|
| 17 |
+
dataset:
|
| 18 |
+
name: timit_asr
|
| 19 |
+
type: timit_asr
|
| 20 |
+
config: clean
|
| 21 |
+
split: None
|
| 22 |
+
args: clean
|
| 23 |
+
metrics:
|
| 24 |
+
- name: Wer
|
| 25 |
+
type: wer
|
| 26 |
+
value: 0.22107366825167116
|
| 27 |
+
---
|
| 28 |
+
|
| 29 |
+
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
|
| 30 |
+
should probably proofread and complete it, then remove this comment. -->
|
| 31 |
+
|
| 32 |
+
# repo_name
|
| 33 |
+
|
| 34 |
+
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the timit_asr dataset.
|
| 35 |
+
It achieves the following results on the evaluation set:
|
| 36 |
+
- Loss: 0.5351
|
| 37 |
+
- Wer: 0.2211
|
| 38 |
+
|
| 39 |
+
## Model description
|
| 40 |
+
|
| 41 |
+
More information needed
|
| 42 |
+
|
| 43 |
+
## Intended uses & limitations
|
| 44 |
+
|
| 45 |
+
More information needed
|
| 46 |
+
|
| 47 |
+
## Training and evaluation data
|
| 48 |
+
|
| 49 |
+
More information needed
|
| 50 |
+
|
| 51 |
+
## Training procedure
|
| 52 |
+
|
| 53 |
+
### Training hyperparameters
|
| 54 |
+
|
| 55 |
+
The following hyperparameters were used during training:
|
| 56 |
+
- learning_rate: 0.0001
|
| 57 |
+
- train_batch_size: 8
|
| 58 |
+
- eval_batch_size: 8
|
| 59 |
+
- seed: 42
|
| 60 |
+
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
|
| 61 |
+
- lr_scheduler_type: linear
|
| 62 |
+
- lr_scheduler_warmup_steps: 1000
|
| 63 |
+
- num_epochs: 30
|
| 64 |
+
- mixed_precision_training: Native AMP
|
| 65 |
+
|
| 66 |
+
### Training results
|
| 67 |
+
|
| 68 |
+
| Training Loss | Epoch | Step | Validation Loss | Wer |
|
| 69 |
+
|:-------------:|:-------:|:-----:|:---------------:|:------:|
|
| 70 |
+
| 3.5252 | 1.0040 | 500 | 1.6991 | 0.9701 |
|
| 71 |
+
| 0.854 | 2.0080 | 1000 | 0.5187 | 0.4025 |
|
| 72 |
+
| 0.4211 | 3.0120 | 1500 | 0.4289 | 0.3326 |
|
| 73 |
+
| 0.2871 | 4.0161 | 2000 | 0.3947 | 0.2896 |
|
| 74 |
+
| 0.2266 | 5.0201 | 2500 | 0.4034 | 0.2881 |
|
| 75 |
+
| 0.1789 | 6.0241 | 3000 | 0.4833 | 0.2926 |
|
| 76 |
+
| 0.1638 | 7.0281 | 3500 | 0.4342 | 0.2776 |
|
| 77 |
+
| 0.15 | 8.0321 | 4000 | 0.4643 | 0.2750 |
|
| 78 |
+
| 0.1251 | 9.0361 | 4500 | 0.4449 | 0.2642 |
|
| 79 |
+
| 0.1064 | 10.0402 | 5000 | 0.4785 | 0.2578 |
|
| 80 |
+
| 0.0986 | 11.0442 | 5500 | 0.4480 | 0.2627 |
|
| 81 |
+
| 0.0883 | 12.0482 | 6000 | 0.4876 | 0.2603 |
|
| 82 |
+
| 0.0784 | 13.0522 | 6500 | 0.5100 | 0.2519 |
|
| 83 |
+
| 0.0721 | 14.0562 | 7000 | 0.4795 | 0.2536 |
|
| 84 |
+
| 0.0696 | 15.0602 | 7500 | 0.4797 | 0.2456 |
|
| 85 |
+
| 0.0598 | 16.0643 | 8000 | 0.5064 | 0.2410 |
|
| 86 |
+
| 0.0575 | 17.0683 | 8500 | 0.5075 | 0.2362 |
|
| 87 |
+
| 0.0508 | 18.0723 | 9000 | 0.5062 | 0.2420 |
|
| 88 |
+
| 0.048 | 19.0763 | 9500 | 0.5078 | 0.2397 |
|
| 89 |
+
| 0.0402 | 20.0803 | 10000 | 0.5511 | 0.2341 |
|
| 90 |
+
| 0.0429 | 21.0843 | 10500 | 0.4835 | 0.2330 |
|
| 91 |
+
| 0.0362 | 22.0884 | 11000 | 0.5800 | 0.2308 |
|
| 92 |
+
| 0.0333 | 23.0924 | 11500 | 0.5250 | 0.2306 |
|
| 93 |
+
| 0.0285 | 24.0964 | 12000 | 0.5242 | 0.2288 |
|
| 94 |
+
| 0.0296 | 25.1004 | 12500 | 0.4995 | 0.2238 |
|
| 95 |
+
| 0.0264 | 26.1044 | 13000 | 0.5296 | 0.2236 |
|
| 96 |
+
| 0.0245 | 27.1084 | 13500 | 0.5530 | 0.2233 |
|
| 97 |
+
| 0.0214 | 28.1124 | 14000 | 0.5376 | 0.2209 |
|
| 98 |
+
| 0.0214 | 29.1165 | 14500 | 0.5351 | 0.2211 |
|
| 99 |
+
|
| 100 |
+
|
| 101 |
+
### Framework versions
|
| 102 |
+
|
| 103 |
+
- Transformers 4.56.2
|
| 104 |
+
- Pytorch 2.8.0+cu126
|
| 105 |
+
- Datasets 2.21.0
|
| 106 |
+
- Tokenizers 0.22.1
|