thennal/IMaSC
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How to use kavyamanohar/XLSR-WithLM-Malayalam with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("automatic-speech-recognition", model="kavyamanohar/XLSR-WithLM-Malayalam") # Load model directly
from transformers import AutoProcessor, AutoModelForCTC
processor = AutoProcessor.from_pretrained("kavyamanohar/XLSR-WithLM-Malayalam")
model = AutoModelForCTC.from_pretrained("kavyamanohar/XLSR-WithLM-Malayalam")This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the IMASC, Indic TTS Malayalam, OpenSLR Malayalam Train split datasets. It achieves the following results on the evaluation set:
Trigram Language Model Trained using KENLM Library on kavyamanohar/ml-sentences dataset
The following hyperparameters were used during training:
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 1.4912 | 0.1165 | 1000 | 0.5497 | 0.7011 |
| 0.5377 | 0.2330 | 2000 | 0.3292 | 0.5364 |
| 0.4343 | 0.3494 | 3000 | 0.2475 | 0.4424 |
| 0.3678 | 0.4659 | 4000 | 0.2145 | 0.4014 |
| 0.3345 | 0.5824 | 5000 | 0.1898 | 0.3774 |
| 0.3029 | 0.6989 | 6000 | 0.1718 | 0.3441 |
| 0.2685 | 0.8153 | 7000 | 0.1517 | 0.3135 |
| 0.2385 | 0.9318 | 8000 | 0.1395 | 0.2952 |
Base model
facebook/wav2vec2-xls-r-300m