mozilla-foundation/common_voice_13_0
Updated • 2.43k • 3
How to use mabrouk/whisper-small-yo with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("automatic-speech-recognition", model="mabrouk/whisper-small-yo") # Load model directly
from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq
processor = AutoProcessor.from_pretrained("mabrouk/whisper-small-yo")
model = AutoModelForSpeechSeq2Seq.from_pretrained("mabrouk/whisper-small-yo")# Load model directly
from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq
processor = AutoProcessor.from_pretrained("mabrouk/whisper-small-yo")
model = AutoModelForSpeechSeq2Seq.from_pretrained("mabrouk/whisper-small-yo")This model is a fine-tuned version of openai/whisper-small on the Common Voice 13 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 |
|---|---|---|---|---|---|
| 0.1023 | 5.88 | 500 | 0.8498 | 73.4063 | 54.6298 |
| 0.0129 | 11.76 | 1000 | 1.0323 | 76.4662 | 55.2126 |
| 0.0074 | 17.65 | 1500 | 1.1289 | 71.2914 | 53.1064 |
| 0.005 | 23.53 | 2000 | 1.1822 | 71.2764 | 52.1923 |
| 0.0048 | 29.41 | 2500 | 1.1737 | 72.5664 | 51.9407 |
| 0.0044 | 35.29 | 3000 | 1.1774 | 76.2412 | 55.2524 |
| 0.0041 | 41.18 | 3500 | 1.2009 | 72.5364 | 51.3843 |
| 0.004 | 47.06 | 4000 | 1.2013 | 71.5314 | 51.0134 |
Base model
openai/whisper-small
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="mabrouk/whisper-small-yo")