PolyAI/minds14
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How to use shamsaH/whisper-tiny-minds14-en-us with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("automatic-speech-recognition", model="shamsaH/whisper-tiny-minds14-en-us") # Load model directly
from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq
processor = AutoProcessor.from_pretrained("shamsaH/whisper-tiny-minds14-en-us")
model = AutoModelForSpeechSeq2Seq.from_pretrained("shamsaH/whisper-tiny-minds14-en-us", 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 | Wer | Wer Ortho |
|---|---|---|---|---|---|
| 1.7385 | 1.0 | 29 | 0.7435 | 0.3751 | 0.4170 |
| 0.5314 | 2.0 | 58 | 0.5790 | 0.3442 | 0.3757 |
| 0.3243 | 3.0 | 87 | 0.5545 | 0.3393 | 0.3732 |
| 0.2605 | 4.0 | 116 | 0.5519 | 0.3374 | 0.3701 |
| 0.2126 | 5.0 | 145 | 0.5595 | 0.3251 | 0.3578 |
| 0.1212 | 6.0 | 174 | 0.5680 | 0.3251 | 0.3547 |
| 0.1061 | 7.0 | 203 | 0.5810 | 0.3239 | 0.3523 |
| 0.0719 | 8.0 | 232 | 0.5878 | 0.3196 | 0.3486 |
| 0.0834 | 9.0 | 261 | 0.5930 | 0.3165 | 0.3448 |
| 0.0464 | 10.0 | 290 | 0.5955 | 0.3165 | 0.3442 |
Base model
openai/whisper-tiny