Chillarmo/common_voice_20_armenian
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How to use Chillarmo/wav2vec2-common_voice_20-hy-mms-finetune with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("automatic-speech-recognition", model="Chillarmo/wav2vec2-common_voice_20-hy-mms-finetune") # Load model directly
from transformers import AutoProcessor, AutoModelForCTC
processor = AutoProcessor.from_pretrained("Chillarmo/wav2vec2-common_voice_20-hy-mms-finetune")
model = AutoModelForCTC.from_pretrained("Chillarmo/wav2vec2-common_voice_20-hy-mms-finetune")This model is a fine-tuned version of facebook/mms-1b-all on the COMMON_VOICE_20_ARMENIAN dataset. It achieves the following results on the evaluation set:
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The following hyperparameters were used during training:
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| No log | 0.2137 | 100 | 0.2269 | 0.3076 |
| No log | 0.4274 | 200 | 0.1915 | 0.2824 |
| No log | 0.6410 | 300 | 0.1993 | 0.2964 |
| No log | 0.8547 | 400 | 0.1832 | 0.2735 |
| 0.9965 | 1.0684 | 500 | 0.1764 | 0.2649 |
| 0.9965 | 1.2821 | 600 | 0.1733 | 0.2625 |
| 0.9965 | 1.4957 | 700 | 0.1725 | 0.2592 |
| 0.9965 | 1.7094 | 800 | 0.1706 | 0.2581 |
| 0.9965 | 1.9231 | 900 | 0.1681 | 0.2585 |
| 0.2922 | 2.1368 | 1000 | 0.1694 | 0.2591 |
| 0.2922 | 2.3504 | 1100 | 0.1701 | 0.2575 |
| 0.2922 | 2.5641 | 1200 | 0.1701 | 0.2614 |
| 0.2922 | 2.7778 | 1300 | 0.1654 | 0.2535 |
| 0.2922 | 2.9915 | 1400 | 0.1644 | 0.2517 |
| 0.2788 | 3.2051 | 1500 | 0.1636 | 0.2540 |
| 0.2788 | 3.4188 | 1600 | 0.1616 | 0.2512 |
| 0.2788 | 3.6325 | 1700 | 0.1600 | 0.2470 |
| 0.2788 | 3.8462 | 1800 | 0.1587 | 0.2464 |
Base model
facebook/mms-1b-all