EducativeCS2023/dummy_en_asr
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How to use RushitBhadaniya/whisper-en-tiny with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("automatic-speech-recognition", model="RushitBhadaniya/whisper-en-tiny") # Load model directly
from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq
processor = AutoProcessor.from_pretrained("RushitBhadaniya/whisper-en-tiny")
model = AutoModelForSpeechSeq2Seq.from_pretrained("RushitBhadaniya/whisper-en-tiny")This model is a fine-tuned version of openai/whisper-tiny on the EducativeCS2023/dummy_en_asr 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 |
|---|---|---|---|---|
| 1.7968 | 1.0 | 60 | 1.7344 | 99.5246 |
| 1.0839 | 2.0 | 120 | 1.5906 | 99.3661 |