PolyAI/minds14
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How to use ezrab/whisper-tiny-minds14 with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("automatic-speech-recognition", model="ezrab/whisper-tiny-minds14") # Load model directly
from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq
processor = AutoProcessor.from_pretrained("ezrab/whisper-tiny-minds14")
model = AutoModelForSpeechSeq2Seq.from_pretrained("ezrab/whisper-tiny-minds14")# Load model directly
from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq
processor = AutoProcessor.from_pretrained("ezrab/whisper-tiny-minds14")
model = AutoModelForSpeechSeq2Seq.from_pretrained("ezrab/whisper-tiny-minds14")This model is a fine-tuned version of openai/whisper-tiny on the PolyAI/minds14 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 |
|---|---|---|---|---|---|
| 0.9105 | 1.7857 | 50 | 0.6418 | 0.4115 | 0.3937 |
| 0.2535 | 3.5714 | 100 | 0.5773 | 0.3337 | 0.3164 |
| 0.0887 | 5.3571 | 150 | 0.6295 | 0.3368 | 0.3182 |
| 0.0288 | 7.1429 | 200 | 0.6449 | 0.3381 | 0.3211 |
| 0.0198 | 8.9286 | 250 | 0.6932 | 0.4170 | 0.4203 |
| 0.0092 | 10.7143 | 300 | 0.6835 | 0.3152 | 0.3058 |
| 0.0134 | 12.5 | 350 | 0.7404 | 0.3288 | 0.3264 |
| 0.0096 | 14.2857 | 400 | 0.7067 | 0.3374 | 0.3312 |
| 0.0073 | 16.0714 | 450 | 0.7303 | 0.3122 | 0.3081 |
| 0.0056 | 17.8571 | 500 | 0.7381 | 0.2850 | 0.2851 |
Base model
openai/whisper-tiny
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="ezrab/whisper-tiny-minds14")