metadata
language: it
license: mit
tags:
- whisper
- automatic-speech-recognition
- italian
- ctranslate2
- faster-whisper
- whisperx
- localai
- int8
datasets:
- mozilla-foundation/common_voice_25_0
- facebook/multilingual_librispeech
- facebook/voxpopuli
base_model: openai/whisper-medium
pipeline_tag: automatic-speech-recognition
whisper-medium-it-multi-ct2-int8
CTranslate2 INT8 quantized version of LocalAI-io/whisper-medium-it-multi for fast CPU inference.
Author: Ettore Di Giacinto
Brought to you by the LocalAI team. This model can be used directly with LocalAI.
Usage with LocalAI
This model is ready to use with LocalAI via the whisperx backend.
Save the following as whisperx-medium-it-multi.yaml in your LocalAI models directory:
name: whisperx-medium-it-multi
backend: whisperx
known_usecases:
- transcript
parameters:
model: LocalAI-io/whisper-medium-it-multi-ct2-int8
language: it
Then transcribe audio via the OpenAI-compatible endpoint:
curl http://localhost:8080/v1/audio/transcriptions \
-H "Content-Type: multipart/form-data" \
-F file="@audio.mp3" \
-F model="whisperx-medium-it-multi"
Usage
faster-whisper
from faster_whisper import WhisperModel
model = WhisperModel("LocalAI-io/whisper-medium-it-multi-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}")
Links
- HF Safetensors: LocalAI-io/whisper-medium-it-multi
- Project: github.com/mudler/italian-asr
- LocalAI: github.com/mudler/LocalAI