PolyAI/minds14
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How to use wozniakos10/whisper-tiny-en with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("automatic-speech-recognition", model="wozniakos10/whisper-tiny-en") # pip install -U transformers accelerate
# Load model directly
from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq
processor = AutoProcessor.from_pretrained("wozniakos10/whisper-tiny-en")
model = AutoModelForSpeechSeq2Seq.from_pretrained("wozniakos10/whisper-tiny-en", device_map="auto")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 | Model Preparation Time | Wer Ortho | Wer |
|---|---|---|---|---|---|---|
| 0.6958 | 1.0 | 28 | 0.5557 | 0.0034 | 0.3930 | 0.3878 |
| 0.4037 | 2.0 | 56 | 0.4946 | 0.0034 | 0.3171 | 0.3176 |
| 0.1433 | 3.0 | 84 | 0.5048 | 0.0034 | 0.3041 | 0.3028 |
| 0.0384 | 4.0 | 112 | 0.5354 | 0.0034 | 0.3035 | 0.3046 |
| 0.0227 | 5.0 | 140 | 0.5596 | 0.0034 | 0.3171 | 0.3200 |
| 0.0077 | 6.0 | 168 | 0.5725 | 0.0034 | 0.3183 | 0.3188 |
| 0.0028 | 7.0 | 196 | 0.5812 | 0.0034 | 0.3004 | 0.3022 |
| 0.0016 | 8.0 | 224 | 0.5847 | 0.0034 | 0.3171 | 0.3200 |
| 0.0016 | 9.0 | 252 | 0.5862 | 0.0034 | 0.3196 | 0.3223 |
| 0.0015 | 10.0 | 280 | 0.5860 | 0.0034 | 0.3196 | 0.3223 |
Base model
openai/whisper-tiny