File size: 2,437 Bytes
b6bc6ea 6c655b3 b6bc6ea f8fdeed b6bc6ea | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 | ---
language: it
license: mit
tags:
- whisper
- automatic-speech-recognition
- italian
- ctranslate2
- faster-whisper
- whisperx
- localai
- int8
datasets:
- mozilla-foundation/common_voice_25_0
base_model: openai/whisper-tiny
pipeline_tag: automatic-speech-recognition
---
# whisper-tiny-it-ct2-int8
[CTranslate2](https://github.com/OpenNMT/CTranslate2) INT8 quantized version of [LocalAI-io/whisper-tiny-it](https://huggingface.co/LocalAI-io/whisper-tiny-it) for fast CPU inference.
**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).
## Usage with LocalAI
This model is ready to use with [LocalAI](https://localai.io) via the `whisperx` backend.
Save the following as `whisperx-tiny-it.yaml` in your LocalAI models directory:
```yaml
name: whisperx-tiny-it
backend: whisperx
known_usecases:
- transcript
parameters:
model: LocalAI-io/whisper-tiny-it-ct2-int8
language: it
```
Then transcribe audio via the OpenAI-compatible endpoint:
```bash
curl http://localhost:8080/v1/audio/transcriptions \
-H "Content-Type: multipart/form-data" \
-F file="@audio.mp3" \
-F model="whisperx-tiny-it"
```
## Model Details
- **Base model:** openai/whisper-tiny, fine-tuned on Common Voice 25.0 Italian
- **Quantization:** INT8 via CTranslate2
- **Size:** 39MB (vs 578MB for the original fp32 model)
- **WER:** 27.1% on Common Voice 25.0 Italian test set
## Usage
### faster-whisper
```python
from faster_whisper import WhisperModel
model = WhisperModel("LocalAI-io/whisper-tiny-it-ct2-int8", device="cpu", compute_type="int8")
segments, info = model.transcribe("audio.mp3", language="it")
for segment in segments:
print(f"[{segment.start:.1f}s - {segment.end:.1f}s] {segment.text}")
```
### WhisperX
```python
import whisperx
model = whisperx.load_model("LocalAI-io/whisper-tiny-it-ct2-int8", device="cpu", compute_type="int8")
result = model.transcribe("audio.mp3", language="it")
```
### LocalAI
This model is compatible with [LocalAI](https://github.com/mudler/LocalAI) for local, self-hosted AI inference.
## Links
- **HF Safetensors version:** [LocalAI-io/whisper-tiny-it](https://huggingface.co/LocalAI-io/whisper-tiny-it)
- **Code:** [github.com/mudler/italian-asr](https://github.com/mudler/italian-asr)
- **LocalAI:** [github.com/mudler/LocalAI](https://github.com/mudler/LocalAI)
|