Downloads · 30 days
14
10% of all-time downloads
LocalAI-io/whisper-tiny-it-multi-ct2-int8
whisper-tiny-it-multi-ct2-int8 is a automatic speech recognition model from LocalAI-io. Use it when you need speech turned into text. The card lists the license as mit.
CTranslate2 INT8 quantized version of LocalAI-io/whisper-tiny-it-multi for fast CPU inference.
Downloads · 30 days
14
10% of all-time downloads
All-time downloads
144
Public
Repo size
40.5 MB
Likes
0
Public
Click a slice to open those files.
.bin40.5 MB · 86%
From the Hugging Face model README
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.
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"
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}")
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")