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israelisraeli
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ivrit-faster-whisper-turbo-d4

Automatic Speech Recognition
Hebrew
faster-whisper
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ivrit-faster-whisper-turbo-d4
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---
language:
- he
base_model:
- ivrit-ai/whisper-large-v3-turbo-d4-p1-take2
pipeline_tag: automatic-speech-recognition
tags:
- faster-whisper
---

# 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**](https://github.com/guillaumekln/faster-whisper) format, offering significantly faster inference times.

### Model Overview

- **Base Model**: [ivrit-ai/whisper-large-v3-turbo-d4-p1-take2](https://huggingface.co/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:

```python
# 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

```python
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](https://huggingface.co/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:

```bash
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 \
```