---
library_name: transformers
license: apache-2.0
base_model: openai/whisper-medium
tags:
- generated_from_trainer
datasets:
- rbcurzon/ph_dialect_asr
metrics:
- wer
model-index:
- name: whisper-medium-ph
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: rbcurzon/ph_dialect_asr all
      type: rbcurzon/ph_dialect_asr
      args: all
    metrics:
    - name: Wer
      type: wer
      value: 0.1146545827633379
---

<!-- 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-medium-ph

This model is a fine-tuned version of [openai/whisper-medium](https://huggingface.co/openai/whisper-medium) on the rbcurzon/ph_dialect_asr all dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2901
- Wer: 0.1147

## Model description

More information needed

## Intended uses & limitations

This model is primarily designed for transcribing Tagalog, Bisaya, Ilocano, Waray, Kapampangan, Pangasinense, and Bikol voice notes and performing batch automatic speech recognition (ASR) for the same languages. It is also suitable for fine-tuning or domain adaptation for these specific speech tasks.

The model has several key limitations:
* It performs poorly in noisy or multi-speaker environments, leading to transcription errors.
* Accuracy is significantly reduced for noisy, accented, or dialectal speech.
* It is not optimized for real-time streaming.
* Like other Whisper-type models, it can produce plausible but incorrect words (hallucinations).

## 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
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- 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    |
|:-------------:|:------:|:----:|:---------------:|:------:|
| 0.1822        | 1.4818 | 1000 | 0.2656          | 0.1445 |
| 0.0706        | 2.9637 | 2000 | 0.2491          | 0.1270 |
| 0.0072        | 4.4448 | 3000 | 0.2729          | 0.1191 |
| 0.005         | 5.9266 | 4000 | 0.2810          | 0.1157 |
| 0.0009        | 7.4077 | 5000 | 0.2901          | 0.1147 |


### Framework versions

- Transformers 4.56.0.dev0
- Pytorch 2.8.0+cu128
- Datasets 4.0.0
- Tokenizers 0.21.4