PolyAI/minds14
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How to use nomad-ai/whisper-tiny with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("automatic-speech-recognition", model="nomad-ai/whisper-tiny") # Load model directly
from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq
processor = AutoProcessor.from_pretrained("nomad-ai/whisper-tiny")
model = AutoModelForSpeechSeq2Seq.from_pretrained("nomad-ai/whisper-tiny")# Load model directly
from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq
processor = AutoProcessor.from_pretrained("nomad-ai/whisper-tiny")
model = AutoModelForSpeechSeq2Seq.from_pretrained("nomad-ai/whisper-tiny")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 |
|---|---|---|---|---|---|
| 1.7357 | 2.0 | 50 | 0.7179 | 0.2947 | 0.2412 |
| 0.2772 | 4.0 | 100 | 0.4758 | 0.2404 | 0.2113 |
| 0.081 | 6.0 | 150 | 0.5069 | 0.2628 | 0.2282 |
| 0.02 | 8.0 | 200 | 0.5289 | 0.2564 | 0.2297 |
| 0.0044 | 10.0 | 250 | 0.5366 | 0.2452 | 0.2251 |
| 0.0018 | 12.0 | 300 | 0.5565 | 0.2404 | 0.2251 |
| 0.0011 | 14.0 | 350 | 0.5668 | 0.2388 | 0.2259 |
| 0.0009 | 16.0 | 400 | 0.5762 | 0.2364 | 0.2251 |
| 0.0007 | 18.0 | 450 | 0.5847 | 0.2348 | 0.2243 |
| 0.0006 | 20.0 | 500 | 0.5913 | 0.2340 | 0.2243 |
Base model
openai/whisper-tiny
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="nomad-ai/whisper-tiny")