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openai/whisper-small
on a merged Romanian speech corpus (128-mel input features).| Metric | Normalized | Raw |
|---|---|---|
| WER | 0.0927 | 0.096 |
| CER | 0.0282 | 0.029 |
eval_results.json.1from transformers import WhisperProcessor, WhisperForConditionalGeneration
2import torch
3
4processor = WhisperProcessor.from_pretrained("IonGrozea/whisper-small_ro-80mel")
5model = WhisperForConditionalGeneration.from_pretrained("IonGrozea/whisper-small_ro-80mel")
6
7inputs = processor(audio_array, sampling_rate=16000, return_tensors="pt")
8with torch.no_grad():
9 ids = model.generate(inputs.input_features, language="romanian",
10 task="transcribe", num_beams=5)
11print(processor.batch_decode(ids, skip_special_tokens=True)[0])final_best/ directory instead.