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distil-large-v3-hi-ft

Automatic Speech Recognition
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whisper
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distil-large-v3-hi-ft
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---
license: mit
base_model: distil-whisper/distil-large-v3
tags:
- generated_from_trainer
datasets:
- common_voice_16_1
metrics:
- wer
model-index:
- name: distil-whisper/distil-large-v3
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: common_voice_16_1
      type: common_voice_16_1
      config: hi
      split: test
      args: hi
    metrics:
    - name: Wer
      type: wer
      value: 0.3297535347291973
---

<!-- 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. -->

# distil-whisper/distil-large-v3

This model is a fine-tuned version of [distil-whisper/distil-large-v3](https://huggingface.co/distil-whisper/distil-large-v3) on the common_voice_16_1 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6148
- Wer: 0.3298

## Model description

More information needed

## 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: 0.0001
- train_batch_size: 32
- eval_batch_size: 32
- 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: 5000
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Wer    |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.125         | 4.5   | 1000 | 0.4658          | 0.4300 |
| 0.0412        | 9.01  | 2000 | 0.5247          | 0.3960 |
| 0.0077        | 13.51 | 3000 | 0.5476          | 0.3535 |
| 0.0007        | 18.02 | 4000 | 0.5731          | 0.3398 |
| 0.0001        | 22.52 | 5000 | 0.6148          | 0.3298 |


### Framework versions

- Transformers 4.40.0.dev0
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.1