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--- |
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language: |
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- fa |
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license: apache-2.0 |
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base_model: makhataei/Whisper-Small-Common-Voice |
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tags: |
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- fa-asr |
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- generated_from_trainer |
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metrics: |
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- wer |
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model-index: |
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- name: Whisper Small Persian |
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results: [] |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# Whisper Small Persian |
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This model is a fine-tuned version of [makhataei/Whisper-Small-Common-Voice](https://huggingface.co/makhataei/Whisper-Small-Common-Voice) on the Ctejarat dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.4755 |
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- Wer: 26.8240 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 1e-07 |
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- train_batch_size: 11 |
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- eval_batch_size: 11 |
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- seed: 42 |
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- gradient_accumulation_steps: 8 |
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- total_train_batch_size: 88 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- lr_scheduler_warmup_steps: 500 |
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- training_steps: 10000 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Wer | |
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|:-------------:|:-------:|:-----:|:---------------:|:-------:| |
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| 0.9585 | 47.06 | 100 | 0.7643 | 39.2704 | |
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| 0.8272 | 94.12 | 200 | 0.7201 | 39.0558 | |
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| 0.6499 | 141.18 | 300 | 0.6593 | 39.6996 | |
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| 0.4717 | 188.24 | 400 | 0.5965 | 36.2661 | |
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| 0.2999 | 235.29 | 500 | 0.5380 | 34.3348 | |
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| 0.1746 | 282.35 | 600 | 0.4967 | 34.9785 | |
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| 0.0995 | 329.41 | 700 | 0.4795 | 35.8369 | |
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| 0.0509 | 376.47 | 800 | 0.4714 | 33.0472 | |
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| 0.0248 | 423.53 | 900 | 0.4669 | 30.9013 | |
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| 0.0144 | 470.59 | 1000 | 0.4643 | 30.6867 | |
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| 0.0093 | 517.65 | 1100 | 0.4625 | 29.8283 | |
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| 0.0066 | 564.71 | 1200 | 0.4618 | 29.3991 | |
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| 0.0051 | 611.76 | 1300 | 0.4616 | 29.6137 | |
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| 0.004 | 658.82 | 1400 | 0.4615 | 29.3991 | |
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| 0.0034 | 705.88 | 1500 | 0.4616 | 28.7554 | |
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| 0.0026 | 752.94 | 1600 | 0.4618 | 29.1845 | |
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| 0.0022 | 800.0 | 1700 | 0.4620 | 28.7554 | |
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| 0.0019 | 847.06 | 1800 | 0.4622 | 28.7554 | |
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| 0.0017 | 894.12 | 1900 | 0.4623 | 28.7554 | |
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| 0.0015 | 941.18 | 2000 | 0.4626 | 28.7554 | |
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| 0.0013 | 988.24 | 2100 | 0.4628 | 28.7554 | |
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| 0.0012 | 1035.29 | 2200 | 0.4630 | 28.3262 | |
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| 0.0011 | 1082.35 | 2300 | 0.4633 | 28.3262 | |
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| 0.001 | 1129.41 | 2400 | 0.4634 | 28.3262 | |
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| 0.0009 | 1176.47 | 2500 | 0.4636 | 28.3262 | |
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| 0.0008 | 1223.53 | 2600 | 0.4638 | 28.3262 | |
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| 0.0008 | 1270.59 | 2700 | 0.4640 | 27.8970 | |
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| 0.0007 | 1317.65 | 2800 | 0.4641 | 28.3262 | |
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| 0.0007 | 1364.71 | 2900 | 0.4644 | 28.3262 | |
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| 0.0006 | 1411.76 | 3000 | 0.4645 | 28.1116 | |
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| 0.0006 | 1458.82 | 3100 | 0.4647 | 27.8970 | |
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| 0.0005 | 1505.88 | 3200 | 0.4648 | 27.8970 | |
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| 0.0005 | 1552.94 | 3300 | 0.4650 | 28.1116 | |
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| 0.0005 | 1600.0 | 3400 | 0.4652 | 28.1116 | |
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| 0.0005 | 1647.06 | 3500 | 0.4654 | 27.8970 | |
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| 0.0004 | 1694.12 | 3600 | 0.4656 | 27.8970 | |
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| 0.0004 | 1741.18 | 3700 | 0.4657 | 27.8970 | |
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| 0.0004 | 1788.24 | 3800 | 0.4659 | 27.8970 | |
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| 0.0004 | 1835.29 | 3900 | 0.4661 | 27.4678 | |
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| 0.0004 | 1882.35 | 4000 | 0.4662 | 27.4678 | |
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| 0.0003 | 1929.41 | 4100 | 0.4664 | 27.4678 | |
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| 0.0003 | 1976.47 | 4200 | 0.4666 | 27.4678 | |
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| 0.0003 | 2023.53 | 4300 | 0.4668 | 27.4678 | |
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| 0.0003 | 2070.59 | 4400 | 0.4670 | 27.4678 | |
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| 0.0003 | 2117.65 | 4500 | 0.4672 | 27.4678 | |
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| 0.0003 | 2164.71 | 4600 | 0.4674 | 27.4678 | |
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| 0.0002 | 2211.76 | 4700 | 0.4676 | 27.4678 | |
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| 0.0002 | 2258.82 | 4800 | 0.4678 | 27.2532 | |
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| 0.0002 | 2305.88 | 4900 | 0.4680 | 27.2532 | |
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| 0.0002 | 2352.94 | 5000 | 0.4682 | 27.0386 | |
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| 0.0002 | 2400.0 | 5100 | 0.4684 | 27.0386 | |
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| 0.0002 | 2447.06 | 5200 | 0.4685 | 27.0386 | |
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| 0.0002 | 2494.12 | 5300 | 0.4688 | 27.0386 | |
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| 0.0002 | 2541.18 | 5400 | 0.4689 | 27.0386 | |
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| 0.0002 | 2588.24 | 5500 | 0.4691 | 27.0386 | |
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| 0.0002 | 2635.29 | 5600 | 0.4693 | 27.0386 | |
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| 0.0002 | 2682.35 | 5700 | 0.4695 | 27.0386 | |
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| 0.0002 | 2729.41 | 5800 | 0.4697 | 27.0386 | |
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| 0.0002 | 2776.47 | 5900 | 0.4699 | 27.0386 | |
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| 0.0002 | 2823.53 | 6000 | 0.4700 | 27.0386 | |
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| 0.0001 | 2870.59 | 6100 | 0.4702 | 27.0386 | |
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| 0.0001 | 2917.65 | 6200 | 0.4704 | 27.0386 | |
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| 0.0001 | 2964.71 | 6300 | 0.4706 | 27.0386 | |
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| 0.0001 | 3011.76 | 6400 | 0.4708 | 27.0386 | |
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| 0.0001 | 3058.82 | 6500 | 0.4710 | 27.0386 | |
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| 0.0001 | 3105.88 | 6600 | 0.4712 | 27.2532 | |
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| 0.0001 | 3152.94 | 6700 | 0.4714 | 27.2532 | |
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| 0.0001 | 3200.0 | 6800 | 0.4716 | 27.0386 | |
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| 0.0001 | 3247.06 | 6900 | 0.4718 | 27.0386 | |
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| 0.0001 | 3294.12 | 7000 | 0.4720 | 27.0386 | |
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| 0.0001 | 3341.18 | 7100 | 0.4721 | 27.0386 | |
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| 0.0001 | 3388.24 | 7200 | 0.4723 | 26.8240 | |
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| 0.0001 | 3435.29 | 7300 | 0.4725 | 26.8240 | |
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| 0.0001 | 3482.35 | 7400 | 0.4727 | 26.6094 | |
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| 0.0001 | 3529.41 | 7500 | 0.4728 | 26.6094 | |
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| 0.0001 | 3576.47 | 7600 | 0.4730 | 26.6094 | |
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| 0.0001 | 3623.53 | 7700 | 0.4732 | 26.6094 | |
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| 0.0001 | 3670.59 | 7800 | 0.4733 | 26.6094 | |
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| 0.0001 | 3717.65 | 7900 | 0.4735 | 26.6094 | |
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| 0.0001 | 3764.71 | 8000 | 0.4736 | 26.6094 | |
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| 0.0001 | 3811.76 | 8100 | 0.4738 | 26.6094 | |
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| 0.0001 | 3858.82 | 8200 | 0.4739 | 26.6094 | |
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| 0.0001 | 3905.88 | 8300 | 0.4741 | 26.6094 | |
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| 0.0001 | 3952.94 | 8400 | 0.4742 | 26.6094 | |
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| 0.0001 | 4000.0 | 8500 | 0.4744 | 26.6094 | |
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| 0.0001 | 4047.06 | 8600 | 0.4745 | 26.6094 | |
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| 0.0001 | 4094.12 | 8700 | 0.4746 | 26.6094 | |
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| 0.0001 | 4141.18 | 8800 | 0.4748 | 26.6094 | |
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| 0.0001 | 4188.24 | 8900 | 0.4749 | 26.6094 | |
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| 0.0001 | 4235.29 | 9000 | 0.4750 | 26.6094 | |
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| 0.0001 | 4282.35 | 9100 | 0.4751 | 26.6094 | |
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| 0.0001 | 4329.41 | 9200 | 0.4751 | 27.0386 | |
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| 0.0001 | 4376.47 | 9300 | 0.4752 | 27.0386 | |
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| 0.0001 | 4423.53 | 9400 | 0.4753 | 27.0386 | |
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| 0.0001 | 4470.59 | 9500 | 0.4754 | 27.0386 | |
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| 0.0001 | 4517.65 | 9600 | 0.4754 | 27.0386 | |
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| 0.0001 | 4564.71 | 9700 | 0.4755 | 26.8240 | |
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| 0.0001 | 4611.76 | 9800 | 0.4755 | 26.8240 | |
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| 0.0001 | 4658.82 | 9900 | 0.4755 | 26.8240 | |
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| 0.0001 | 4705.88 | 10000 | 0.4755 | 26.8240 | |
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### Framework versions |
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- Transformers 4.35.2 |
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- Pytorch 2.0.1+cu117 |
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- Datasets 2.15.0 |
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- Tokenizers 0.15.0 |
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