facebook/multilingual_librispeech
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How to use dashelruiz/whisper-small-es with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("automatic-speech-recognition", model="dashelruiz/whisper-small-es") # Load model directly
from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq
processor = AutoProcessor.from_pretrained("dashelruiz/whisper-small-es")
model = AutoModelForSpeechSeq2Seq.from_pretrained("dashelruiz/whisper-small-es")This model is a fine-tuned version of openai/whisper-small on the facebook/multilingual_librispeech 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 Ortho | Wer |
|---|---|---|---|---|---|
| 0.3349 | 0.02 | 500 | 0.1782 | 8.1526 | 8.1571 |
| 0.309 | 0.04 | 1000 | 0.1702 | 7.5899 | 7.5921 |
| 0.2814 | 0.05 | 1500 | 0.1680 | 8.0103 | 8.0124 |
| 0.3067 | 0.07 | 2000 | 0.1665 | 8.1007 | 8.1028 |
| 0.3223 | 0.09 | 2500 | 0.1751 | 9.2272 | 9.2294 |
| 0.2696 | 0.11 | 3000 | 0.1583 | 7.2374 | 7.2395 |
| 0.3203 | 0.13 | 3500 | 0.1542 | 6.9560 | 6.9559 |
| 0.2655 | 0.14 | 4000 | 0.1535 | 7.0848 | 7.0859 |
Base model
openai/whisper-small