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| 1 |
+
---
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| 2 |
+
library_name: transformers
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| 3 |
+
license: apache-2.0
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| 4 |
+
base_model: rinna/japanese-hubert-base
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| 5 |
+
tags:
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| 6 |
+
- generated_from_trainer
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| 7 |
+
datasets:
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| 8 |
+
- common_voice_13_0
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| 9 |
+
metrics:
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| 10 |
+
- wer
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| 11 |
+
model-index:
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| 12 |
+
- name: Hubert-common_voice-ja-demo-roma-debug-40epochs-cosine
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| 13 |
+
results:
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| 14 |
+
- task:
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| 15 |
+
name: Automatic Speech Recognition
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| 16 |
+
type: automatic-speech-recognition
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| 17 |
+
dataset:
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| 18 |
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name: common_voice_13_0
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type: common_voice_13_0
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| 20 |
+
config: ja
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+
split: test
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args: ja
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| 23 |
+
metrics:
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| 24 |
+
- name: Wer
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+
type: wer
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| 26 |
+
value: 0.9991933857632587
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| 27 |
+
---
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| 28 |
+
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| 29 |
+
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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| 30 |
+
should probably proofread and complete it, then remove this comment. -->
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| 31 |
+
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| 32 |
+
# Hubert-common_voice-ja-demo-roma-debug-40epochs-cosine
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| 33 |
+
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| 34 |
+
This model is a fine-tuned version of [rinna/japanese-hubert-base](https://huggingface.co/rinna/japanese-hubert-base) on the common_voice_13_0 dataset.
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| 35 |
+
It achieves the following results on the evaluation set:
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| 36 |
+
- Loss: 0.5322
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| 37 |
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- Wer: 0.9992
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| 38 |
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- Cer: 0.1992
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| 39 |
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| 40 |
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## Model description
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| 41 |
+
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| 42 |
+
More information needed
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| 43 |
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## Intended uses & limitations
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| 45 |
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| 46 |
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More information needed
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| 47 |
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| 48 |
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## Training and evaluation data
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| 49 |
+
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| 50 |
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More information needed
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| 51 |
+
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| 52 |
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## Training procedure
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| 53 |
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| 54 |
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### Training hyperparameters
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| 55 |
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The following hyperparameters were used during training:
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| 57 |
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- learning_rate: 3e-05
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| 58 |
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- train_batch_size: 16
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- eval_batch_size: 8
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- seed: 42
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- gradient_accumulation_steps: 2
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- total_train_batch_size: 32
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| 63 |
+
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
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| 64 |
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- lr_scheduler_type: cosine
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| 65 |
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- lr_scheduler_warmup_steps: 12500
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| 66 |
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- num_epochs: 40.0
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| 67 |
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- mixed_precision_training: Native AMP
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| 68 |
+
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| 69 |
+
### Training results
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| 70 |
+
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| 71 |
+
| Training Loss | Epoch | Step | Validation Loss | Wer | Cer |
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| 72 |
+
|:-------------:|:-------:|:-----:|:---------------:|:------:|:------:|
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| 73 |
+
| No log | 0.2660 | 100 | 16.8172 | 2.9494 | 3.4779 |
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| 74 |
+
| No log | 0.5319 | 200 | 16.5502 | 2.7606 | 2.9561 |
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| 75 |
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| No log | 0.7979 | 300 | 15.8340 | 1.9101 | 1.6839 |
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| 76 |
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| No log | 1.0638 | 400 | 13.2919 | 1.0 | 0.9276 |
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| 77 |
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| 12.6588 | 1.3298 | 500 | 7.8792 | 1.0 | 0.9276 |
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| 78 |
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| 12.6588 | 1.5957 | 600 | 6.1542 | 1.0 | 0.9276 |
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| 79 |
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| 12.6588 | 1.8617 | 700 | 5.7757 | 1.0 | 0.9276 |
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| 80 |
+
| 12.6588 | 2.1277 | 800 | 5.6188 | 1.0 | 0.9276 |
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| 81 |
+
| 12.6588 | 2.3936 | 900 | 5.4753 | 1.0 | 0.9276 |
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| 82 |
+
| 5.2353 | 2.6596 | 1000 | 5.3239 | 1.0 | 0.9276 |
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| 83 |
+
| 5.2353 | 2.9255 | 1100 | 5.1676 | 1.0 | 0.9276 |
|
| 84 |
+
| 5.2353 | 3.1915 | 1200 | 5.0084 | 1.0 | 0.9276 |
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| 85 |
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| 5.2353 | 3.4574 | 1300 | 4.8402 | 1.0 | 0.9276 |
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| 86 |
+
| 5.2353 | 3.7234 | 1400 | 4.6702 | 1.0 | 0.9276 |
|
| 87 |
+
| 4.4502 | 3.9894 | 1500 | 4.4957 | 1.0 | 0.9276 |
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| 88 |
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| 4.4502 | 4.2553 | 1600 | 4.3219 | 1.0 | 0.9276 |
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| 89 |
+
| 4.4502 | 4.5213 | 1700 | 4.1502 | 1.0 | 0.9276 |
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| 90 |
+
| 4.4502 | 4.7872 | 1800 | 3.9856 | 1.0 | 0.9276 |
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| 91 |
+
| 4.4502 | 5.0532 | 1900 | 3.8343 | 1.0 | 0.9276 |
|
| 92 |
+
| 3.7863 | 5.3191 | 2000 | 3.6907 | 1.0 | 0.9276 |
|
| 93 |
+
| 3.7863 | 5.5851 | 2100 | 3.5544 | 1.0 | 0.9276 |
|
| 94 |
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| 3.7863 | 5.8511 | 2200 | 3.4332 | 1.0 | 0.9276 |
|
| 95 |
+
| 3.7863 | 6.1170 | 2300 | 3.3063 | 1.0 | 0.9276 |
|
| 96 |
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| 3.7863 | 6.3830 | 2400 | 3.2075 | 1.0 | 0.9276 |
|
| 97 |
+
| 3.2473 | 6.6489 | 2500 | 3.1272 | 1.0 | 0.9276 |
|
| 98 |
+
| 3.2473 | 6.9149 | 2600 | 3.0657 | 1.0 | 0.9276 |
|
| 99 |
+
| 3.2473 | 7.1809 | 2700 | 3.0164 | 1.0 | 0.9276 |
|
| 100 |
+
| 3.2473 | 7.4468 | 2800 | 2.9748 | 1.0 | 0.9276 |
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| 101 |
+
| 3.2473 | 7.7128 | 2900 | 2.9447 | 1.0 | 0.9276 |
|
| 102 |
+
| 2.9649 | 7.9787 | 3000 | 2.9188 | 1.0 | 0.9276 |
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| 103 |
+
| 2.9649 | 8.2447 | 3100 | 2.9006 | 1.0 | 0.9276 |
|
| 104 |
+
| 2.9649 | 8.5106 | 3200 | 2.8877 | 1.0 | 0.9276 |
|
| 105 |
+
| 2.9649 | 8.7766 | 3300 | 2.8712 | 1.0 | 0.9276 |
|
| 106 |
+
| 2.9649 | 9.0426 | 3400 | 2.8529 | 1.0 | 0.9276 |
|
| 107 |
+
| 2.8673 | 9.3085 | 3500 | 2.8439 | 1.0 | 0.9276 |
|
| 108 |
+
| 2.8673 | 9.5745 | 3600 | 2.8313 | 1.0 | 0.9276 |
|
| 109 |
+
| 2.8673 | 9.8404 | 3700 | 2.8182 | 1.0 | 0.9276 |
|
| 110 |
+
| 2.8673 | 10.1064 | 3800 | 2.7311 | 1.0 | 0.9276 |
|
| 111 |
+
| 2.8673 | 10.3723 | 3900 | 2.4997 | 1.0 | 0.9276 |
|
| 112 |
+
| 2.6801 | 10.6383 | 4000 | 2.2398 | 1.0 | 0.8951 |
|
| 113 |
+
| 2.6801 | 10.9043 | 4100 | 1.9111 | 1.0 | 0.6154 |
|
| 114 |
+
| 2.6801 | 11.1702 | 4200 | 1.5447 | 1.0 | 0.4341 |
|
| 115 |
+
| 2.6801 | 11.4362 | 4300 | 1.3182 | 1.0 | 0.3959 |
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| 116 |
+
| 2.6801 | 11.7021 | 4400 | 1.1702 | 0.9996 | 0.3706 |
|
| 117 |
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| 1.5214 | 11.9681 | 4500 | 1.0558 | 0.9992 | 0.3214 |
|
| 118 |
+
| 1.5214 | 12.2340 | 4600 | 0.9717 | 0.9988 | 0.3024 |
|
| 119 |
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| 1.5214 | 12.5 | 4700 | 0.8959 | 0.9982 | 0.2874 |
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| 120 |
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| 1.5214 | 12.7660 | 4800 | 0.8399 | 0.9978 | 0.2747 |
|
| 121 |
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| 1.5214 | 13.0319 | 4900 | 0.7891 | 0.9974 | 0.2657 |
|
| 122 |
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| 0.8719 | 13.2979 | 5000 | 0.7484 | 0.9980 | 0.2580 |
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| 123 |
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| 0.8719 | 13.5638 | 5100 | 0.7145 | 0.9976 | 0.2523 |
|
| 124 |
+
| 0.8719 | 13.8298 | 5200 | 0.6852 | 0.9976 | 0.2481 |
|
| 125 |
+
| 0.8719 | 14.0957 | 5300 | 0.6618 | 0.9980 | 0.2487 |
|
| 126 |
+
| 0.8719 | 14.3617 | 5400 | 0.6400 | 0.9986 | 0.2477 |
|
| 127 |
+
| 0.6568 | 14.6277 | 5500 | 0.6200 | 0.9988 | 0.2449 |
|
| 128 |
+
| 0.6568 | 14.8936 | 5600 | 0.6032 | 0.9988 | 0.2421 |
|
| 129 |
+
| 0.6568 | 15.1596 | 5700 | 0.5875 | 0.9984 | 0.2395 |
|
| 130 |
+
| 0.6568 | 15.4255 | 5800 | 0.5776 | 0.9990 | 0.2409 |
|
| 131 |
+
| 0.6568 | 15.6915 | 5900 | 0.5617 | 0.9994 | 0.2360 |
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| 132 |
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| 0.548 | 15.9574 | 6000 | 0.5485 | 0.9982 | 0.2347 |
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| 133 |
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| 0.548 | 16.2234 | 6100 | 0.5394 | 0.9988 | 0.2334 |
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| 134 |
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| 0.548 | 16.4894 | 6200 | 0.5322 | 0.9984 | 0.2317 |
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| 135 |
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| 0.548 | 16.7553 | 6300 | 0.5243 | 0.9970 | 0.2320 |
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| 136 |
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| 0.548 | 17.0213 | 6400 | 0.5121 | 0.9986 | 0.2272 |
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| 137 |
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| 0.4681 | 17.2872 | 6500 | 0.5070 | 0.9990 | 0.2266 |
|
| 138 |
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| 0.4681 | 17.5532 | 6600 | 0.5014 | 0.9992 | 0.2263 |
|
| 139 |
+
| 0.4681 | 17.8191 | 6700 | 0.4943 | 0.9986 | 0.2242 |
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| 140 |
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| 0.4681 | 18.0851 | 6800 | 0.4930 | 0.9988 | 0.2228 |
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| 141 |
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| 0.4681 | 18.3511 | 6900 | 0.4969 | 0.9986 | 0.2245 |
|
| 142 |
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| 0.4198 | 18.6170 | 7000 | 0.4883 | 0.9986 | 0.2225 |
|
| 143 |
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| 0.4198 | 18.8830 | 7100 | 0.4805 | 0.9986 | 0.2215 |
|
| 144 |
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| 0.4198 | 19.1489 | 7200 | 0.4777 | 0.9984 | 0.2208 |
|
| 145 |
+
| 0.4198 | 19.4149 | 7300 | 0.4718 | 0.9988 | 0.2209 |
|
| 146 |
+
| 0.4198 | 19.6809 | 7400 | 0.4721 | 0.9984 | 0.2199 |
|
| 147 |
+
| 0.3795 | 19.9468 | 7500 | 0.4675 | 0.9984 | 0.2205 |
|
| 148 |
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| 0.3795 | 20.2128 | 7600 | 0.4692 | 0.9988 | 0.2162 |
|
| 149 |
+
| 0.3795 | 20.4787 | 7700 | 0.4732 | 0.9986 | 0.2173 |
|
| 150 |
+
| 0.3795 | 20.7447 | 7800 | 0.4654 | 0.9982 | 0.2173 |
|
| 151 |
+
| 0.3795 | 21.0106 | 7900 | 0.4557 | 0.9986 | 0.2158 |
|
| 152 |
+
| 0.3504 | 21.2766 | 8000 | 0.4562 | 0.9982 | 0.2144 |
|
| 153 |
+
| 0.3504 | 21.5426 | 8100 | 0.4679 | 0.9982 | 0.2144 |
|
| 154 |
+
| 0.3504 | 21.8085 | 8200 | 0.4584 | 0.9990 | 0.2169 |
|
| 155 |
+
| 0.3504 | 22.0745 | 8300 | 0.4561 | 0.9982 | 0.2134 |
|
| 156 |
+
| 0.3504 | 22.3404 | 8400 | 0.4595 | 0.9988 | 0.2143 |
|
| 157 |
+
| 0.3134 | 22.6064 | 8500 | 0.4544 | 0.9986 | 0.2155 |
|
| 158 |
+
| 0.3134 | 22.8723 | 8600 | 0.4544 | 0.9984 | 0.2134 |
|
| 159 |
+
| 0.3134 | 23.1383 | 8700 | 0.4552 | 0.9984 | 0.2129 |
|
| 160 |
+
| 0.3134 | 23.4043 | 8800 | 0.4524 | 0.9984 | 0.2121 |
|
| 161 |
+
| 0.3134 | 23.6702 | 8900 | 0.4554 | 0.9986 | 0.2113 |
|
| 162 |
+
| 0.3014 | 23.9362 | 9000 | 0.4617 | 0.9982 | 0.2103 |
|
| 163 |
+
| 0.3014 | 24.2021 | 9100 | 0.4606 | 0.9978 | 0.2130 |
|
| 164 |
+
| 0.3014 | 24.4681 | 9200 | 0.4561 | 0.9974 | 0.2105 |
|
| 165 |
+
| 0.3014 | 24.7340 | 9300 | 0.4566 | 0.9984 | 0.2089 |
|
| 166 |
+
| 0.3014 | 25.0 | 9400 | 0.4486 | 0.9990 | 0.2119 |
|
| 167 |
+
| 0.2791 | 25.2660 | 9500 | 0.4542 | 0.9990 | 0.2117 |
|
| 168 |
+
| 0.2791 | 25.5319 | 9600 | 0.4540 | 0.9986 | 0.2095 |
|
| 169 |
+
| 0.2791 | 25.7979 | 9700 | 0.4419 | 0.9984 | 0.2091 |
|
| 170 |
+
| 0.2791 | 26.0638 | 9800 | 0.4569 | 0.9982 | 0.2074 |
|
| 171 |
+
| 0.2791 | 26.3298 | 9900 | 0.4543 | 0.9984 | 0.2090 |
|
| 172 |
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| 0.2564 | 26.5957 | 10000 | 0.4689 | 0.9982 | 0.2088 |
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| 173 |
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| 0.2564 | 26.8617 | 10100 | 0.4590 | 0.9984 | 0.2089 |
|
| 174 |
+
| 0.2564 | 27.1277 | 10200 | 0.4986 | 0.9986 | 0.2093 |
|
| 175 |
+
| 0.2564 | 27.3936 | 10300 | 0.4693 | 0.9990 | 0.2100 |
|
| 176 |
+
| 0.2564 | 27.6596 | 10400 | 0.5128 | 0.9982 | 0.2085 |
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| 177 |
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| 0.2449 | 27.9255 | 10500 | 0.4512 | 0.9984 | 0.2099 |
|
| 178 |
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| 0.2449 | 28.1915 | 10600 | 0.4651 | 0.9994 | 0.2091 |
|
| 179 |
+
| 0.2449 | 28.4574 | 10700 | 0.4604 | 0.9984 | 0.2068 |
|
| 180 |
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| 0.2449 | 28.7234 | 10800 | 0.4687 | 0.9990 | 0.2080 |
|
| 181 |
+
| 0.2449 | 28.9894 | 10900 | 0.4688 | 0.9994 | 0.2064 |
|
| 182 |
+
| 0.2258 | 29.2553 | 11000 | 0.4759 | 0.9994 | 0.2092 |
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| 183 |
+
| 0.2258 | 29.5213 | 11100 | 0.4816 | 0.9988 | 0.2068 |
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| 184 |
+
| 0.2258 | 29.7872 | 11200 | 0.4750 | 0.9988 | 0.2053 |
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| 185 |
+
| 0.2258 | 30.0532 | 11300 | 0.4753 | 0.9986 | 0.2048 |
|
| 186 |
+
| 0.2258 | 30.3191 | 11400 | 0.4829 | 0.9992 | 0.2060 |
|
| 187 |
+
| 0.2124 | 30.5851 | 11500 | 0.4800 | 0.9986 | 0.2081 |
|
| 188 |
+
| 0.2124 | 30.8511 | 11600 | 0.5290 | 0.9990 | 0.2061 |
|
| 189 |
+
| 0.2124 | 31.1170 | 11700 | 0.5369 | 0.9988 | 0.2055 |
|
| 190 |
+
| 0.2124 | 31.3830 | 11800 | 0.5170 | 0.9978 | 0.2041 |
|
| 191 |
+
| 0.2124 | 31.6489 | 11900 | 0.5229 | 0.9990 | 0.2070 |
|
| 192 |
+
| 0.2007 | 31.9149 | 12000 | 0.5035 | 0.9986 | 0.2060 |
|
| 193 |
+
| 0.2007 | 32.1809 | 12100 | 0.5103 | 0.9974 | 0.2049 |
|
| 194 |
+
| 0.2007 | 32.4468 | 12200 | 0.4868 | 0.9972 | 0.2032 |
|
| 195 |
+
| 0.2007 | 32.7128 | 12300 | 0.4867 | 0.9996 | 0.2043 |
|
| 196 |
+
| 0.2007 | 32.9787 | 12400 | 0.5049 | 0.9982 | 0.2040 |
|
| 197 |
+
| 0.1867 | 33.2447 | 12500 | 0.5126 | 0.9984 | 0.2040 |
|
| 198 |
+
| 0.1867 | 33.5106 | 12600 | 0.5321 | 0.9992 | 0.2037 |
|
| 199 |
+
| 0.1867 | 33.7766 | 12700 | 0.5187 | 0.9978 | 0.2040 |
|
| 200 |
+
| 0.1867 | 34.0426 | 12800 | 0.5319 | 0.9990 | 0.2064 |
|
| 201 |
+
| 0.1867 | 34.3085 | 12900 | 0.5275 | 0.9980 | 0.2041 |
|
| 202 |
+
| 0.1749 | 34.5745 | 13000 | 0.5433 | 0.9982 | 0.2043 |
|
| 203 |
+
| 0.1749 | 34.8404 | 13100 | 0.5094 | 0.9984 | 0.2023 |
|
| 204 |
+
| 0.1749 | 35.1064 | 13200 | 0.5363 | 0.9990 | 0.2004 |
|
| 205 |
+
| 0.1749 | 35.3723 | 13300 | 0.5331 | 0.9994 | 0.2022 |
|
| 206 |
+
| 0.1749 | 35.6383 | 13400 | 0.5053 | 0.9990 | 0.2009 |
|
| 207 |
+
| 0.1604 | 35.9043 | 13500 | 0.5157 | 0.9990 | 0.2026 |
|
| 208 |
+
| 0.1604 | 36.1702 | 13600 | 0.5299 | 0.9990 | 0.2018 |
|
| 209 |
+
| 0.1604 | 36.4362 | 13700 | 0.5117 | 0.9996 | 0.2050 |
|
| 210 |
+
| 0.1604 | 36.7021 | 13800 | 0.5067 | 0.9994 | 0.2038 |
|
| 211 |
+
| 0.1604 | 36.9681 | 13900 | 0.4994 | 0.9996 | 0.2028 |
|
| 212 |
+
| 0.1412 | 37.2340 | 14000 | 0.5346 | 0.9984 | 0.2024 |
|
| 213 |
+
| 0.1412 | 37.5 | 14100 | 0.5350 | 0.9994 | 0.2015 |
|
| 214 |
+
| 0.1412 | 37.7660 | 14200 | 0.5237 | 0.9990 | 0.2010 |
|
| 215 |
+
| 0.1412 | 38.0319 | 14300 | 0.5305 | 0.9992 | 0.1993 |
|
| 216 |
+
| 0.1412 | 38.2979 | 14400 | 0.5309 | 0.9986 | 0.1973 |
|
| 217 |
+
| 0.1286 | 38.5638 | 14500 | 0.5270 | 0.9992 | 0.1992 |
|
| 218 |
+
| 0.1286 | 38.8298 | 14600 | 0.5363 | 0.9990 | 0.1999 |
|
| 219 |
+
| 0.1286 | 39.0957 | 14700 | 0.5347 | 0.9990 | 0.1999 |
|
| 220 |
+
| 0.1286 | 39.3617 | 14800 | 0.5319 | 0.9990 | 0.1999 |
|
| 221 |
+
| 0.1286 | 39.6277 | 14900 | 0.5322 | 0.9994 | 0.1995 |
|
| 222 |
+
| 0.1217 | 39.8936 | 15000 | 0.5322 | 0.9992 | 0.1992 |
|
| 223 |
+
|
| 224 |
+
|
| 225 |
+
### Framework versions
|
| 226 |
+
|
| 227 |
+
- Transformers 4.47.0.dev0
|
| 228 |
+
- Pytorch 2.5.1+cu124
|
| 229 |
+
- Datasets 3.1.0
|
| 230 |
+
- Tokenizers 0.20.3
|