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README.md

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+---
+language: it
+license: mit
+tags:
+- whisper
+- automatic-speech-recognition
+- italian
+- localai
+datasets:
+- mozilla-foundation/common_voice_25_0
+- facebook/multilingual_librispeech
+- facebook/voxpopuli
+base_model: openai/whisper-base
+pipeline_tag: automatic-speech-recognition
+---
+
+# whisper-base-it-multi
+
+Fine-tuned [openai/whisper-base](https://huggingface.co/openai/whisper-base) (74M params) for Italian ASR on multiple datasets.
+
+**Author:** Ettore Di Giacinto
+
+Brought to you by the [LocalAI](https://github.com/mudler/LocalAI) team. This model can be used directly with [LocalAI](https://localai.io).
+
+## Results
+
+Evaluated on combined test set (Common Voice + MLS + VoxPopuli, 17,598 samples):
+
+| Step | WER |
+|------|-----|
+| 1000 | 28.48% |
+| 2000 | 26.73% |
+| 3000 | 24.45% |
+| 5000 | 23.26% |
+| 7000 | 21.79% |
+| 10000 | **21.4%** |
+
+## Training Details
+
+- **Base model:** openai/whisper-base (74M parameters)
+- **Datasets:** Common Voice 25.0 Italian (173k) + MLS Italian (60k) + VoxPopuli Italian (23k) = 255k train samples
+- **Steps:** 10,000
+- **Precision:** bf16 on NVIDIA GB10
+
+## Usage
+
+### Transformers
+
+```python
+from transformers import pipeline
+
+pipe = pipeline("automatic-speech-recognition", model="LocalAI-io/whisper-base-it-multi")
+result = pipe("audio.mp3", generate_kwargs={"language": "it", "task": "transcribe"})
+print(result["text"])
+```
+
+### CTranslate2 / faster-whisper
+
+For optimized CPU inference: [LocalAI-io/whisper-base-it-multi-ct2-int8](https://huggingface.co/LocalAI-io/whisper-base-it-multi-ct2-int8)
+
+## Links
+
+- **CTranslate2 INT8:** [LocalAI-io/whisper-base-it-multi-ct2-int8](https://huggingface.co/LocalAI-io/whisper-base-it-multi-ct2-int8)
+- **Code:** [github.com/localai-org/whisper-it](https://github.com/localai-org/whisper-it)
+- **LocalAI:** [github.com/mudler/LocalAI](https://github.com/mudler/LocalAI)