google/fleurs
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How to use simpragma/breeze-listen-dsw-base-te with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("automatic-speech-recognition", model="simpragma/breeze-listen-dsw-base-te") # Load model directly
from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq
processor = AutoProcessor.from_pretrained("simpragma/breeze-listen-dsw-base-te")
model = AutoModelForSpeechSeq2Seq.from_pretrained("simpragma/breeze-listen-dsw-base-te")This model is a fine-tuned version of openai/whisper-base on the google/fleurs te_in 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 |
|---|---|---|---|---|
| 0.2937 | 2.03 | 200 | 0.3237 | 42.5614 |
| 0.1611 | 5.02 | 400 | 0.2756 | 38.9148 |
| 0.0889 | 8.01 | 600 | 0.2930 | 38.1106 |
| 0.0456 | 11.0 | 800 | 0.3372 | 37.4544 |
| 0.0229 | 13.03 | 1000 | 0.3982 | 37.9258 |
| 0.0103 | 16.02 | 1200 | 0.4473 | 38.2678 |
| 0.0042 | 19.02 | 1400 | 0.4836 | 37.8980 |
| 0.0025 | 22.01 | 1600 | 0.5083 | 37.7317 |
| 0.002 | 24.04 | 1800 | 0.5220 | 37.8010 |
| 0.0018 | 27.03 | 2000 | 0.5269 | 37.9027 |
Base model
openai/whisper-base