Instructions to use istomin9192/whisper-small-sr with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use istomin9192/whisper-small-sr with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="istomin9192/whisper-small-sr")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("istomin9192/whisper-small-sr") model = AutoModelForSpeechSeq2Seq.from_pretrained("istomin9192/whisper-small-sr", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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README.md
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@@ -45,21 +45,21 @@ Fine-tuned **OpenAI Whisper Small**.
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This model was fine-tuned on a **mixture of publicly available Serbian speech corpora**, including:
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- Additional Serbian corpora used in the training pipeline
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## Training procedure
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### Training results
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- Text normalization used for WER:
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- punctuation removed
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- lowercased
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- numbers converted to words
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This model was fine-tuned on a **mixture of publicly available Serbian speech corpora**, including:
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- Mozilla Common Voice 24.0, evaluated on **CV test (sr)**
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- FLEURS Serbian
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- ParlaSpeech-RS (subset of the full dataset)
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- Additional Serbian corpora used in the training pipeline
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## Training procedure
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- Epochs: 8
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- Batch size: 32
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- Optimizer: AdamW
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- LR: 6e-5 with warmup (50 steps) + cosine decay to min_lr = 1e-7
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- Mixed precision: bfloat16
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- SpecAugment: frequency + time masking
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- Sampling: weighted sampling across datasets
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### Training results
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- Text normalization used for WER:
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- punctuation removed
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- lowercased
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- Cyrillic → Latin conversion
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- numbers converted to words
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