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1from datasets import Audio, load_dataset
2from transformers import pipeline
3
4# Load a sample audio
5dataset = load_dataset("chuuhtetnaing/myanmar-speech-dataset-openslr-80")
6dataset = dataset.cast_column("audio", Audio(sampling_rate=16000))
7test_dataset = dataset['test']
8input_speech = test_dataset[42]['audio']
9
10pipe = pipeline(model='chuuhtetnaing/whisper-medium-myanmar')
11
12output = pipe(input_speech, generate_kwargs={"language": "myanmar", "task": "transcribe"})
13print(output['text']) # ကျမ ပြည်ပ မှာ ပညာသင် တော့ စာမေးပွဲ ကို တပတ်တခါ စစ်တယ်| 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 |