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--- |
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license: apache-2.0 |
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tags: |
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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-large-v2-kangri |
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results: |
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- task: |
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type: automatic-speech-recognition |
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name: Speech Recognition |
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dataset: |
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type: bridgeconn/snow-mountain |
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name: snow-moutain-Kangri |
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config: Kangri |
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split: train_500 |
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metrics: |
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- type: wer |
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value: 17.40 |
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name: WER |
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lower_is_better: true |
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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-large-v2-kangri |
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This model is a fine-tuned version of [vasista22/whisper-hindi-large-v2](https://huggingface.co/vasista22/whisper-hindi-large-v2) on the [bridgeconn/snow-mountain](https://huggingface.co/datasets/bridgeconn/snow-mountain) dataset for the low resource Indian language- Kangri. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.2967 |
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- Wer: 0.1740 |
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## Usage |
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In order to evaluate this model on an entire dataset, the evaluation codes available in the whisper-finetune repository can be used. |
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The same repository also provides the scripts for faster inference using whisper-jax. |
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## Training and evaluation data |
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Training Data: |
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- [Snow Mountain Dataset for Kangri Language](https://huggingface.co/datasets/bridgeconn/snow-mountain) |
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Evaluation Data: |
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- [Snow Mountain Dataset for Kangri Language](https://huggingface.co/datasets/bridgeconn/snow-mountain) |
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- [Kangri Translators Dataset ](https://drive.google.com/drive/folders/16BdOieekGRAo2bFOQDd4YhE2LpgiRnqQ?usp=share_link) |
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## Training procedure |
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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 |
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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. |
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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 |
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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 |
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more data being introduced. |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 1e-05 |
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- train_batch_size: 8 |
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- eval_batch_size: 8 |
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- seed: 42 |
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- distributed_type: multi-GPU |
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- num_devices: 2 |
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- total_train_batch_size: 16 |
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- total_eval_batch_size: 16 |
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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: 5000 |
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- mixed_precision_training: Native AMP |
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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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| 0.0001 | 40.0 | 1000 | 0.2442 | 0.1800 | |
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| 0.0 | 80.0 | 2000 | 0.2752 | 0.1764 | |
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| 0.0 | 120.0 | 3000 | 0.2870 | 0.1747 | |
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| 0.0 | 160.0 | 4000 | 0.2940 | 0.1745 | |
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| 0.0 | 200.0 | 5000 | 0.2967 | 0.1740 | |
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### Framework versions |
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- Transformers 4.28.0.dev0 |
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- Pytorch 2.0.0+cu117 |
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- Datasets 2.11.0 |
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- Tokenizers 0.13.3 |
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