Quantifying the Carbon Emissions of Machine Learning
Paper • 1910.09700 • Published • 47
How to use warmestman/whisper-larger-v3-mn-2000steps with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("automatic-speech-recognition", model="warmestman/whisper-larger-v3-mn-2000steps") # Load model directly
from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq
processor = AutoProcessor.from_pretrained("warmestman/whisper-larger-v3-mn-2000steps")
model = AutoModelForSpeechSeq2Seq.from_pretrained("warmestman/whisper-larger-v3-mn-2000steps")GPU - A100-80GB
This model is a fine-tuned version of openai/whisper-large-v3 on the None dataset.
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
The following hyperparameters were used during training:
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 0.4856 | 1.97 | 1000 | 0.496397 |
| 0.1312 | 3.94 | 2000 | 0.395565 |
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
@Ankhbayasgalan davaadorj