Helsinki-NLP/opus-100
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How to use smangrul/xls-r-mr-model with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("automatic-speech-recognition", model="smangrul/xls-r-mr-model") # Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("smangrul/xls-r-mr-model", dtype="auto")This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the MOZILLA-FOUNDATION/COMMON_VOICE_8_0 - MR and OPENSLR - SLR64 - MR datasets. It achieves the following results on the evaluation set:
| Without LM | With LM |
|---|---|
| 40.513437625350984 | 31.04693140794224 |
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The following hyperparameters were used during training:
| Step | Training Loss | Validation Loss | Wer |
|---|---|---|---|
| 400 | 3.794000 | 3.532227 | 1.000000 |
| 800 | 3.362400 | 3.359044 | 1.000000 |
| 1200 | 2.293900 | 1.011279 | 0.829924 |
| 1600 | 1.233000 | 0.502743 | 0.593662 |
| 2000 | 0.962600 | 0.412519 | 0.496992 |
| 2400 | 0.831800 | 0.402903 | 0.493783 |
| 2800 | 0.737000 | 0.389773 | 0.469314 |
| 3200 | 0.677100 | 0.373987 | 0.436021 |
| 3600 | 0.634400 | 0.383823 | 0.432010 |
| 4000 | 0.586000 | 0.375610 | 0.419575 |
| 4400 | 0.561000 | 0.387891 | 0.418371 |
| 4800 | 0.518500 | 0.386357 | 0.417569 |
| 5200 | 0.515300 | 0.415069 | 0.430004 |
| 5600 | 0.478100 | 0.399211 | 0.408744 |
| 6000 | 0.468100 | 0.424542 | 0.402327 |
| 6400 | 0.439400 | 0.430979 | 0.410750 |
| 6800 | 0.429600 | 0.427700 | 0.409146 |
| 7200 | 0.400300 | 0.451111 | 0.419976 |
| 7600 | 0.395100 | 0.463446 | 0.405134 |
| 8000 | 0.381800 | 0.454752 | 0.407942 |
| 8400 | 0.371500 | 0.461547 | 0.404733 |
| 8800 | 0.362500 | 0.461543 | 0.411151 |
| 9200 | 0.338200 | 0.468299 | 0.417168 |
| 9600 | 0.338800 | 0.480989 | 0.412355 |
| 10000 | 0.317600 | 0.475700 | 0.410750 |
| 10400 | 0.315100 | 0.478920 | 0.403530 |
| 10800 | 0.296200 | 0.480600 | 0.398315 |
| 11200 | 0.299000 | 0.477083 | 0.393502 |
| 11600 | 0.290000 | 0.465646 | 0.393903 |
| 12000 | 0.290900 | 0.490041 | 0.405937 |
| 12400 | 0.275600 | 0.489354 | 0.399519 |
| 12800 | 0.272600 | 0.494580 | 0.395909 |
| 13200 | 0.265900 | 0.497918 | 0.397112 |
| 13600 | 0.266300 | 0.498627 | 0.397513 |
| 14000 | 0.259600 | 0.504610 | 0.401524 |
To evaluate on mozilla-foundation/common_voice_8_0 with split test
python eval.py --model_id smangrul/xls-r-mr-model --dataset mozilla-foundation/common_voice_8_0 --config mr --split test