metadata
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-base
pipeline_tag: automatic-speech-recognition
whisper-base-it-multi-yodas-asr_only-nocv-ct2-int8
CTranslate2 INT8 quantized version of LocalAI-io/whisper-base-it-multi-yodas-asr_only-nocv for fast CPU inference.
Author: Ettore Di Giacinto
Brought to you by the LocalAI team. This model can be used directly with LocalAI.
Training
- Base model: openai/whisper-base, fine-tuned on MLS + VoxPopuli + YODAS-Granary (asr_only) Italian
- Quantization: INT8 via CTranslate2
Usage
faster-whisper
from faster_whisper import WhisperModel
model = WhisperModel("LocalAI-io/whisper-base-it-multi-yodas-asr_only-nocv-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-base-it-multi-yodas-asr_only-nocv-ct2-int8", device="cpu", compute_type="int8")
result = model.transcribe("audio.mp3", language="it")
Links
- HF Safetensors: LocalAI-io/whisper-base-it-multi-yodas-asr_only-nocv
- Code: github.com/mudler/italian-asr
- LocalAI: github.com/mudler/LocalAI