---
license: apache-2.0
base_model: openai/whisper-medium
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: whisper-medium-myanmar
  results: []
datasets:
- chuuhtetnaing/myanmar-speech-dataset-openslr-80
language:
- my
pipeline_tag: automatic-speech-recognition
library_name: transformers
---

<!-- 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-myanmar

This model is a fine-tuned version of [openai/whisper-medium](https://huggingface.co/openai/whisper-medium) on the [chuuhtetnaing/myanmar-speech-dataset-openslr-80](https://huggingface.co/datasets/chuuhtetnaing/myanmar-speech-dataset-openslr-80) dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2282
- Wer: 49.4657

## Usage

```python
from datasets import Audio, load_dataset
from transformers import pipeline

# Load a sample audio
dataset = load_dataset("chuuhtetnaing/myanmar-speech-dataset-openslr-80")
dataset = dataset.cast_column("audio", Audio(sampling_rate=16000))
test_dataset = dataset['test']
input_speech = test_dataset[42]['audio']

pipe = pipeline(model='chuuhtetnaing/whisper-medium-myanmar')

output = pipe(input_speech, generate_kwargs={"language": "myanmar", "task": "transcribe"})
print(output['text']) # ကျမ ပြည်ပ မှာ ပညာသင် တော့ စာမေးပွဲ ကို တပတ်တခါ စစ်တယ်
```

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 40
- eval_batch_size: 40
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 200
- num_epochs: 50
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Wer     |
|:-------------:|:-----:|:----:|:---------------:|:-------:|
| 0.8546        | 1.0   | 57   | 0.5703          | 98.0855 |
| 0.2643        | 2.0   | 114  | 0.2404          | 84.9510 |
| 0.1982        | 3.0   | 171  | 0.1889          | 71.6385 |
| 0.1608        | 4.0   | 228  | 0.1781          | 68.4773 |
| 0.1212        | 5.0   | 285  | 0.1511          | 63.7133 |
| 0.1067        | 6.0   | 342  | 0.1427          | 60.2404 |
| 0.0682        | 7.0   | 399  | 0.1330          | 59.3500 |
| 0.0413        | 8.0   | 456  | 0.1322          | 56.9902 |
| 0.0249        | 9.0   | 513  | 0.1271          | 55.6545 |
| 0.0158        | 10.0  | 570  | 0.1430          | 54.8085 |
| 0.0124        | 11.0  | 627  | 0.1486          | 55.0312 |
| 0.0099        | 12.0  | 684  | 0.1550          | 53.7845 |
| 0.0082        | 13.0  | 741  | 0.1486          | 55.1647 |
| 0.0057        | 14.0  | 798  | 0.1747          | 53.6955 |
| 0.0041        | 15.0  | 855  | 0.1608          | 53.3393 |
| 0.0029        | 16.0  | 912  | 0.1596          | 50.6233 |
| 0.0013        | 17.0  | 969  | 0.1798          | 51.2912 |
| 0.0005        | 18.0  | 1026 | 0.1796          | 50.3562 |
| 0.0006        | 19.0  | 1083 | 0.1799          | 50.0890 |
| 0.0           | 20.0  | 1140 | 0.1849          | 50.2671 |
| 0.0001        | 21.0  | 1197 | 0.1878          | 50.0445 |
| 0.0           | 22.0  | 1254 | 0.1907          | 50.1781 |
| 0.0           | 23.0  | 1311 | 0.1929          | 50.0890 |
| 0.0           | 24.0  | 1368 | 0.1942          | 49.8664 |
| 0.0           | 25.0  | 1425 | 0.2019          | 50.0445 |
| 0.0           | 26.0  | 1482 | 0.2068          | 49.9555 |
| 0.0           | 27.0  | 1539 | 0.2103          | 50.0    |
| 0.0           | 28.0  | 1596 | 0.2129          | 49.9555 |
| 0.0           | 29.0  | 1653 | 0.2150          | 50.0    |
| 0.0           | 30.0  | 1710 | 0.2168          | 49.9555 |
| 0.0           | 31.0  | 1767 | 0.2183          | 49.9555 |
| 0.0           | 32.0  | 1824 | 0.2196          | 49.8664 |
| 0.0           | 33.0  | 1881 | 0.2208          | 49.6438 |
| 0.0           | 34.0  | 1938 | 0.2218          | 49.7329 |
| 0.0           | 35.0  | 1995 | 0.2227          | 49.5993 |
| 0.0           | 36.0  | 2052 | 0.2234          | 49.5548 |
| 0.0           | 37.0  | 2109 | 0.2242          | 49.5548 |
| 0.0           | 38.0  | 2166 | 0.2248          | 49.5102 |
| 0.0           | 39.0  | 2223 | 0.2253          | 49.5548 |
| 0.0           | 40.0  | 2280 | 0.2259          | 49.5548 |
| 0.0           | 41.0  | 2337 | 0.2263          | 49.5548 |
| 0.0           | 42.0  | 2394 | 0.2267          | 49.4657 |
| 0.0           | 43.0  | 2451 | 0.2271          | 49.5102 |
| 0.0           | 44.0  | 2508 | 0.2274          | 49.5102 |
| 0.0           | 45.0  | 2565 | 0.2276          | 49.4657 |
| 0.0           | 46.0  | 2622 | 0.2278          | 49.4657 |
| 0.0           | 47.0  | 2679 | 0.2280          | 49.5548 |
| 0.0           | 48.0  | 2736 | 0.2281          | 49.5102 |
| 0.0           | 49.0  | 2793 | 0.2282          | 49.5102 |
| 0.0           | 50.0  | 2850 | 0.2282          | 49.4657 |


### Framework versions

- Transformers 4.35.2
- Pytorch 2.1.1+cu121
- Datasets 2.14.5
- Tokenizers 0.15.1