lalipa/jv_id_asr_split
Updated • 3
How to use iqbalasrif/whisper-tiny-reset-parameter with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("automatic-speech-recognition", model="iqbalasrif/whisper-tiny-reset-parameter") # Load model directly
from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq
processor = AutoProcessor.from_pretrained("iqbalasrif/whisper-tiny-reset-parameter")
model = AutoModelForSpeechSeq2Seq.from_pretrained("iqbalasrif/whisper-tiny-reset-parameter")# Load model directly
from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq
processor = AutoProcessor.from_pretrained("iqbalasrif/whisper-tiny-reset-parameter")
model = AutoModelForSpeechSeq2Seq.from_pretrained("iqbalasrif/whisper-tiny-reset-parameter")This model is a fine-tuned version of openai/whisper-tiny.en on the lalipa/jv_id_asr_split jv_id_asr_source 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 | Cer |
|---|---|---|---|---|---|
| 10.8562 | 0.2041 | 30 | 10.8560 | 17.0625 | 24.9361 |
| 10.8557 | 0.4082 | 60 | 10.8553 | 20.3833 | 28.0200 |
| 10.8549 | 0.6122 | 90 | 10.8545 | 11.6823 | 17.2418 |
| 10.8542 | 0.8163 | 120 | 10.8539 | 10.5108 | 15.8910 |
| 10.8538 | 1.0204 | 150 | 10.8537 | 6.4322 | 9.6337 |
Base model
openai/whisper-tiny.en
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="iqbalasrif/whisper-tiny-reset-parameter")