flozi00/asr-german-mixed
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How to use basha3la2/whisper-base-de-v2 with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("automatic-speech-recognition", model="basha3la2/whisper-base-de-v2") # Load model directly
from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq
processor = AutoProcessor.from_pretrained("basha3la2/whisper-base-de-v2")
model = AutoModelForSpeechSeq2Seq.from_pretrained("basha3la2/whisper-base-de-v2", device_map="auto")This model is a fine-tuned version of basha3la2/whisper-base-de on the ASR German Mixed 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 |
|---|---|---|---|---|
| 0.3295 | 0.0667 | 1000 | 0.2994 | 17.9237 |
| 0.2862 | 0.1333 | 2000 | 0.2807 | 17.3261 |
| 0.2699 | 0.2 | 3000 | 0.2681 | 16.6313 |
| 0.2515 | 0.2667 | 4000 | 0.2593 | 16.3450 |
| 0.2861 | 0.3333 | 5000 | 0.2519 | 15.7891 |
| 0.2420 | 0.4 | 6000 | 0.2460 | 15.6604 |
| 0.2410 | 0.4667 | 7000 | 0.2411 | 15.3088 |
| 0.2486 | 0.5333 | 8000 | 0.2364 | 15.0777 |
| 0.2304 | 0.6 | 9000 | 0.2326 | 14.8723 |
| 0.2325 | 0.6667 | 10000 | 0.2301 | 14.6350 |
| 0.2225 | 0.7333 | 11000 | 0.2274 | 14.4608 |
| 0.2466 | 0.8 | 12000 | 0.2256 | 14.3146 |
| 0.2200 | 0.8667 | 13000 | 0.2239 | 14.2793 |
| 0.2319 | 0.9333 | 14000 | 0.2229 | 14.2002 |
| 0.2508 | 1.0 | 15000 | 0.2225 | 14.1615 |