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jed351
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whisper_medium_cantonese_cm_voice

Automatic Speech Recognition
Transformers
PyTorch
whisper
Generated from Trainer
Eval Results
Inference Endpoints
Model card Files Community
4
whisper_medium_cantonese_cm_voice
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---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- mozilla-foundation/common_voice_11_0
metrics:
- wer
model-index:
- name: whisper-large-v2-zh-hk-2gpu
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: mozilla-foundation/common_voice_11_0 zh-HK
      type: mozilla-foundation/common_voice_11_0
      config: None
      split: None
      args: zh-HK
    metrics:
    - name: Wer
      type: wer
      value: 0.45734639358860196
---

<!-- 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-large-v2-zh-hk-2gpu

This model is a fine-tuned version of [openai/whisper-medium](https://huggingface.co/openai/whisper-medium) on the mozilla-foundation/common_voice_11_0 zh-HK dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2237
- Wer: 0.4573

## 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: 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    |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.1544        | 1.14  | 1000 | 0.2260          | 0.5485 |
| 0.0745        | 2.28  | 2000 | 0.2132          | 0.4967 |
| 0.0213        | 3.42  | 3000 | 0.2114          | 0.4718 |
| 0.0117        | 4.57  | 4000 | 0.2196          | 0.4643 |
| 0.0014        | 5.71  | 5000 | 0.2237          | 0.4573 |


### Framework versions

- Transformers 4.26.0.dev0
- Pytorch 1.13.1
- Datasets 2.8.0
- Tokenizers 0.13.2