README.md

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-tiny
pipeline_tag: automatic-speech-recognition

whisper-tiny-it-multi-ct2-int8

CTranslate2 INT8 quantized version of LocalAI-io/whisper-tiny-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-tiny-it-multi.yaml in your LocalAI models directory:

name: whisperx-tiny-it-multi
backend: whisperx
known_usecases:
  - transcript
parameters:
  model: LocalAI-io/whisper-tiny-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-tiny-it-multi"

Usage

faster-whisper

from faster_whisper import WhisperModel

model = WhisperModel("LocalAI-io/whisper-tiny-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}")

WhisperX

import whisperx

model = whisperx.load_model("LocalAI-io/whisper-tiny-it-multi-ct2-int8", device="cpu", compute_type="int8")
result = model.transcribe("audio.mp3", language="it")

Links

• HF Safetensors: LocalAI-io/whisper-tiny-it-multi
• Code: github.com/localai-org/italian-whisper
• LocalAI: github.com/mudler/LocalAI