mozilla-foundation/common_voice_13_0
Updated • 1.72k • 5
How to use maag/whisper_tiny_cs with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("automatic-speech-recognition", model="maag/whisper_tiny_cs") # Load model directly
from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq
processor = AutoProcessor.from_pretrained("maag/whisper_tiny_cs")
model = AutoModelForSpeechSeq2Seq.from_pretrained("maag/whisper_tiny_cs")This model is a fine-tuned version of openai/whisper-tiny on the mozilla-foundation/common_voice_13_0 cs dataset. It achieves the following results on the evaluation set:
More information needed
More information needed
More information needed
The following hyperparameters were used during training:
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 0.1007 | 21.86 | 1000 | 0.5430 | 44.0802 |
| 0.013 | 43.72 | 2000 | 0.6489 | 44.9182 |
| 0.0079 | 65.57 | 3000 | 0.6782 | 45.5169 |
Base model
openai/whisper-tiny