Jzuluaga/atcosim_corpus
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How to use bhattasp/w_f1_tiny with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("automatic-speech-recognition", model="bhattasp/w_f1_tiny") # Load model directly
from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq
processor = AutoProcessor.from_pretrained("bhattasp/w_f1_tiny")
model = AutoModelForSpeechSeq2Seq.from_pretrained("bhattasp/w_f1_tiny")This model is a fine-tuned version of openai/whisper-tiny on the atcosim 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 |
|---|---|---|---|---|
| 0.0239 | 2.0921 | 1000 | 0.0797 | 3.1048 |
| 0.0064 | 4.1841 | 2000 | 0.0682 | 2.7118 |
Base model
openai/whisper-tiny