Create README.md

README.md

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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 \
+```
+
+