whisper-large-v2-kangri
This model is a fine-tuned version of vasista22/whisper-hindi-large-v2 on the bridgeconn/snow-mountain dataset for the low resource Indian language- Kangri. It achieves the following results on the evaluation set:
- Loss: 0.2967
- Wer: 0.1740
Usage
In order to evaluate this model on an entire dataset, the evaluation codes available in the whisper-finetune repository can be used.
The same repository also provides the scripts for faster inference using whisper-jax.
Training and evaluation data
Training Data:
Evaluation Data:
Training procedure
We implemented Cross-Lingual Phoneme Recognition - a process that leverages patterns in resource-rich languages such as Hindi to recognize utterances in resource-poor languages such as Kangri. By fine-tuning a pre-trained model of the Whisper-Hindi-Large-V2 on a customised dataset - we have achieved SoTa accuracy. A customised dataset - consisting of the brigdeconn/snow-mountain and sentences collected from Kangri translators was created. This was then split using the 80/20 split rule. The results were evaluated with 5000 steps. The model decreases the word error rate by 0.6% after the initial 1000 steps. The Validation Loss increases due to more data being introduced.
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 2
- total_train_batch_size: 16
- total_eval_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 5000
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 0.0001 | 40.0 | 1000 | 0.2442 | 0.1800 |
| 0.0 | 80.0 | 2000 | 0.2752 | 0.1764 |
| 0.0 | 120.0 | 3000 | 0.2870 | 0.1747 |
| 0.0 | 160.0 | 4000 | 0.2940 | 0.1745 |
| 0.0 | 200.0 | 5000 | 0.2967 | 0.1740 |
Framework versions
- Transformers 4.28.0.dev0
- Pytorch 2.0.0+cu117
- Datasets 2.11.0
- Tokenizers 0.13.3
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