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whisper-small-en-hi
Venkatesh4342/whisper-small-en-hi
whisper-small-en-hi is a automatic speech recognition model from Venkatesh4342. Use it when you need speech turned into text. It is set up for transformers. The card lists the license as apache-2.0.
should probably proofread and complete it, then remove this comment. --
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At a glance
- Task
- Automatic Speech Recognition
- Library
- transformers
- License
- apache-2.0
- Model type
- whisper
- Access
- Public
- Created
- Sep 10, 2023
- Updated
- Sep 28, 2023
- SHA
- 6e24d3fd
Base models
Model card
From the Hugging Face model README
<!-- 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. -->
whisper-small-en-hi
This model is a fine-tuned version of openai/whisper-small on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.3279
- Wer: 24.0479
Model description
Two datasets are used for two different languages, for hindi mozilla-foundation/common_voice_11_0 is used and for english google/fleurs is used. with combination of two dataset wer has decreased significantly.
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: 1e-05
- train_batch_size: 16
- 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: 500
- training_steps: 3000
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 0.059 | 2.52 | 1500 | 0.2881 | 24.7722 |
| 0.0084 | 5.03 | 3000 | 0.3279 | 24.0479 |
Framework versions
- Transformers 4.33.0
- Pytorch 1.11.0+cu113
- Datasets 2.1.0
- Tokenizers 0.13.3
- Task
- Automatic Speech Recognition
- Library
- transformers
- Type
- whisper
- License
- apache-2.0
- Created
- Sep 10, 2023
- Updated
- Sep 28, 2023