mozilla-foundation/common_voice_17_0
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How to use makhataei/Whisper-Small-Common-Voice with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("automatic-speech-recognition", model="makhataei/Whisper-Small-Common-Voice") # Load model directly
from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq
processor = AutoProcessor.from_pretrained("makhataei/Whisper-Small-Common-Voice")
model = AutoModelForSpeechSeq2Seq.from_pretrained("makhataei/Whisper-Small-Common-Voice")This model is a fine-tuned version of makhataei/Whisper-Small-Common-Voice on the Common Voice 17.0 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 |
|---|---|---|---|---|
| 0.0013 | 0.15 | 100 | 0.7201 | 45.7700 |
| 0.0015 | 0.31 | 200 | 0.7220 | 45.7908 |
| 0.0019 | 0.46 | 300 | 0.7212 | 45.6382 |
| 0.0021 | 0.61 | 400 | 0.7229 | 45.7630 |
| 0.0025 | 0.77 | 500 | 0.7226 | 45.5943 |
| 0.0068 | 0.92 | 600 | 0.7229 | 46.1213 |
| 0.0293 | 1.07 | 700 | 0.7215 | 45.7075 |
| 0.0021 | 1.23 | 800 | 0.7215 | 45.6290 |
| 0.0025 | 1.38 | 900 | 0.7217 | 45.6151 |
| 0.0023 | 1.53 | 1000 | 0.7217 | 45.6220 |
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