ivrit-faster-whisper-turbo-d4
This model is a conversion of the ivrit-ai/whisper-large-v3-turbo-d4-p1-take2 model to the Faster-Whisper format, offering significantly faster inference times.
Model Overview
- Base Model: ivrit-ai/whisper-large-v3-turbo-d4-p1-take2
- Converted to: Faster-Whisper (for faster ASR with minimal performance loss)
- Language: Hebrew (
he
) - Quantization: Float32
All credits go to ivrit-ai for developing the original Whisper model.
How to Use the Model
To use the model in your projects, follow the steps below to load and transcribe audio:
# Import the Faster Whisper module
import faster_whisper
# Load the model from Hugging Face
model = faster_whisper.WhisperModel("israelisraeli/ivrit-faster-whisper-turbo-d4", device="cuda")
# Transcribe the audio file to JSON
segs, _ = model.transcribe("AUDIOFILE_efiTheTigger.mp3", language="he")
# Format the output into a list of dictionaries with timestamps and text
transcribed_segments_with_timestamps = [
{"start": s.start, "end": s.end, "text": s.text} for s in segs
]
import json
# Save the result to a JSON file
with open("transcribed_segments_with_timestamps.json", "w", encoding="utf-8") as json_file:
json.dump(
transcribed_segments_with_timestamps, json_file, ensure_ascii=False, indent=4
)
print("Transcription saved to transcribed_segments_with_timestamps.json")
Conversion process
Tokenizer Conversion
from transformers import AutoTokenizer
# Load the tokenizer from the original Whisper model files
tokenizer_directory = "path_to_whisper_model_files"
tokenizer = AutoTokenizer.from_pretrained(tokenizer_directory)
# Save the tokenizer into a single JSON file
tokenizer.save_pretrained("path_to_save_directory", legacy_format=False)
Model Conversion to Faster-Whisper
To convert the original ivrit-ai/whisper-large-v3-turbo-d4-p1-take2 model to the Faster-Whisper format, i used the CTranslate2 library. The following command was used for the conversion:
ct2-transformers-converter \
--model ./whisper-large-v3-turbo-d4-p1-take2 \
--output_dir ./ivrit-faster-whisper-turbo-d4 \
--copy_files tokenizer.json preprocessor_config.json \
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Base model
ivrit-ai/whisper-large-v3-turbo-d4-p1-take2