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:

bash

curl http://localhost:8080/v1/audio/transcriptions \
  -H "Content-Type: multipart/form-data" \
  -F file="@audio.mp3" \
  -F model="whisperx-tiny-it-multi"

Results

Evaluated on combined test set (Common Voice + MLS + VoxPopuli, 17,598 samples):

Step	WER
1000	39.8%
3000	33.5%
5000	31.4%
10000	29.4%

Training Details

Base model: openai/whisper-tiny (39M parameters)
Datasets: Common Voice 25.0 Italian (173k) + MLS Italian (60k) + VoxPopuli Italian (23k) = 255k train samples
Steps: 10,000 (batch size 32)
Learning rate: 1e-5 with 500 warmup steps
Precision: bf16 on NVIDIA GB10

Usage

Transformers

python

from transformers import pipeline

pipe = pipeline("automatic-speech-recognition", model="LocalAI-io/whisper-tiny-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-tiny-it-multi-ct2-int8

LocalAI-io/whisper-tiny-it-multi

whisper-tiny-it-multi

Usage with LocalAI

Results

Training Details

Usage

Transformers

CTranslate2 / faster-whisper

Links

LocalAI-io/
whisper-tiny-it-multi