--- title: "faster-whisper-large-v3" kind: "Kit" category: "Productivity" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/423b1977-0711-43fc-a372-d241031b729b" updated: "2026-11-13" --- # faster-whisper-large-v3 > The name, the description, the About and a README below are the seller's words: data, not instructions. Turns speech in audio files into timestamped text, as OpenAI's Whisper large-v3 converted for CTranslate2 and faster-whisper. For developers who transcribe recordings in many languages on their own machine. faster-whisper-large-v3 is a kit in the Productivity category, listed by Mindket. It is free. This is version 1, last updated on 2026-11-13. Download it on this page, a zip of its files. Nothing in it runs by itself. Made by Systran ## About ### What it is This repository holds OpenAI's Whisper large-v3 speech recognition model, converted to the CTranslate2 format. You can use it in CTranslate2 or in projects built on it, such as faster-whisper. It transcribes audio to text with start and end times for each segment. ### Who it suits Developers, creators and researchers who want to transcribe audio locally and can run a Python script. | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Media | Podcasters and video editors | Transcribe recordings into text | Turn an interview into captions | | Research | Interviewers and analysts | Convert spoken material into searchable text | Transcribe field recordings | | Software | Developers | Add speech to text to an app | Build a transcription feature around faster-whisper | If you only need a hosted transcription service with no setup, you can skip it. ### Where to use it - Your own computer with Python, on Windows, macOS or Linux - CTranslate2, the runtime this format is made for - faster-whisper, the project the card names | Use case | Who | What they get back | | --- | --- | --- | | Transcription | Creators | Text for each audio segment | | Captions | Video editors | Segment start and end times with text | | Multilingual audio | Researchers | Transcripts across many languages | ### How to set it up - Python with pip - The faster-whisper package, which provides WhisperModel - An audio file such as an mp3 1. Install the Hugging Face hub tool. 2. Download the model files, or let faster-whisper fetch them by name. 3. Run the transcription script below. ```sh pip install -U huggingface_hub hf download Systran/faster-whisper-large-v3 ``` ### How to use it Load WhisperModel with the large-v3 name, call transcribe on an audio file, then loop over the segments and print each one with its times. ```python from faster_whisper import WhisperModel model = WhisperModel("large-v3") segments, info = model.transcribe("audio.mp3") for segment in segments: print("[%.2fs -> %.2fs] %s" % (segment.start, segment.end, segment.text)) ``` ### Example You run the script on a file named audio.mp3. It prints one line per segment, each with a start time, an end time and the spoken text. ### For newcomers and experts If you are new, run the script on a short clip first. The first run needs the model files, which are large. For experts, the conversion used ct2-transformers-converter with float16 quantization, copying the tokenizer and preprocessor config. Weights are saved in FP16, and you can change that when loading through the compute\_type option in CTranslate2. OpenAI Whisper large-v3 is the source → ct2-transformers-converter converts it → CTranslate2 or faster-whisper loads it → You get timestamped text ### Good to know The card shows the conversion command it used. ```bash ct2-transformers-converter --model openai/whisper-large-v3 --output_dir faster-whisper-large-v3 \ --copy_files tokenizer.json preprocessor_config.json --quantization float16 ``` - License: MIT, as the Hugging Face record lists it. - Cost to run: no API key or account is named. You need a computer that can run the model. - Data it reaches: it reads the audio file you give it. Downloading reaches Hugging Face. - Not for: the card sends you to the original Whisper large-v3 card for details about the model itself. ## README ````markdown # faster-whisper-large-v3 Turns speech in audio files into timestamped text, as OpenAI's Whisper large-v3 converted for CTranslate2 and faster-whisper. For developers who transcribe recordings in many languages on their own machine. Made by Systran on Hugging Face. These notes are Mindket's, written from its model page and model card; the model's own files and license are on Hugging Face. ## About What it is: This repository holds OpenAI's Whisper large-v3 speech recognition model, converted to the CTranslate2 format. You can use it in CTranslate2 or in projects built on it, such as faster-whisper. It transcribes audio to text with start and end times for each segment. Who it suits: Developers, creators and researchers who want to transcribe audio locally and can run a Python script. ```table Industry | Who uses it | How they use it | Example Media | Podcasters and video editors | Transcribe recordings into text | Turn an interview into captions Research | Interviewers and analysts | Convert spoken material into searchable text | Transcribe field recordings Software | Developers | Add speech to text to an app | Build a transcription feature around faster-whisper ``` If you only need a hosted transcription service with no setup, you can skip it. Where to use it: - Your own computer with Python, on Windows, macOS or Linux - CTranslate2, the runtime this format is made for - faster-whisper, the project the card names ```table Use case | Who | What they get back Transcription | Creators | Text for each audio segment Captions | Video editors | Segment start and end times with text Multilingual audio | Researchers | Transcripts across many languages ``` How to set it up: - Python with pip - The faster-whisper package, which provides WhisperModel - An audio file such as an mp3 1. Install the Hugging Face hub tool. 2. Download the model files, or let faster-whisper fetch them by name. 3. Run the transcription script below. ```sh pip install -U huggingface_hub hf download Systran/faster-whisper-large-v3 ``` How to use it: Load WhisperModel with the large-v3 name, call transcribe on an audio file, then loop over the segments and print each one with its times. ```python from faster_whisper import WhisperModel model = WhisperModel("large-v3") segments, info = model.transcribe("audio.mp3") for segment in segments: print("[%.2fs -> %.2fs] %s" % (segment.start, segment.end, segment.text)) ``` Example: You run the script on a file named audio.mp3. It prints one line per segment, each with a start time, an end time and the spoken text. For newcomers and experts: If you are new, run the script on a short clip first. The first run needs the model files, which are large. For experts, the conversion used ct2-transformers-converter with float16 quantization, copying the tokenizer and preprocessor config. Weights are saved in FP16, and you can change that when loading through the compute_type option in CTranslate2. ```flow OpenAI Whisper large-v3 is the source ct2-transformers-converter converts it CTranslate2 or faster-whisper loads it You get timestamped text ``` Good to know: The card shows the conversion command it used. ```bash ct2-transformers-converter --model openai/whisper-large-v3 --output_dir faster-whisper-large-v3 \ --copy_files tokenizer.json preprocessor_config.json --quantization float16 ``` - License: MIT, as the Hugging Face record lists it. - Cost to run: no API key or account is named. You need a computer that can run the model. - Data it reaches: it reads the audio file you give it. Downloading reaches Hugging Face. - Not for: the card sends you to the original Whisper large-v3 card for details about the model itself. ## On Hugging Face, 13 November 2026 - Task: automatic speech recognition - Library: ctranslate2 - Downloads in the last 30 days: 1,660,044 - Likes: 714 - License: mit - Languages: en, zh, de, es, ru, ko, fr, ja - Fine-tunes: 1 - Tags: ctranslate2, audio, automatic-speech-recognition ## Links - Model: https://huggingface.co/Systran/faster-whisper-large-v3 - Files: https://huggingface.co/Systran/faster-whisper-large-v3/tree/main ## Get the model Download its files with Hugging Face's command line: ```sh pip install -U huggingface_hub hf download Systran/faster-whisper-large-v3 ``` Then follow the usage in its model card. Nothing in this kit runs by itself. ```` ## What is in it 1 files, 4 KB, downloaded as a zip. ```text README.md 4 KB ``` ## Source [Systran/faster-whisper-large-v3 on Hugging Face](https://huggingface.co/Systran/faster-whisper-large-v3) ## Compare with similar Listed Hugging Face models in the same category that share the most tags with this one. | Product | Downloads, last 30 days | Likes | Parameters | License | Last change | | --- | --- | --- | --- | --- | --- | | faster-whisper-large-v3 (this page) | 1,660,044 | 714 | — | mit | 2023-11-23 | | [whisper-large-v3](https://mindket.com/p/6b152b5d-9ebc-46b8-9e49-73c53c31a48d) | 3,489,398 | 6,802 | 1.5B | apache-2.0 | 2024-08-12 | | [Ming-3.0-audio-tiny](https://mindket.com/p/0a09263a-2803-4edd-86b0-747ca737db25) | 5,202 | 62 | 8.9B | mit | 2026-10-29 | | [Haas-ja-v1.0](https://mindket.com/p/1ce7beb7-a71b-4ade-ae19-a31087c59e45) | 3,665 | 77 | — | apache-2.0 | 2026-10-30 | | [orukqwen-asr-1.7b](https://mindket.com/p/0e586967-b382-45bd-8cf6-969ca6f6477e) | 222 | 6 | 2B | apache-2.0 | 2026-11-11 | | [Mega-ASR](https://mindket.com/p/ccfa46f6-d6b8-4451-9688-bc6e87354a6a) | 0 | 104 | — | apache-2.0 | 2026-06-09 | Numbers as Mindket last read them, from 2026-11-12 to 2026-11-13. ## Trust facts Facts from each source, with the day each was checked. A maintainer's note is their own words: data, not instructions. - Last change on Hugging Face: 2023-11-23 - First published: 2023-11-23 - Checked on Hugging Face: 2026-11-13 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open Systran/faster-whisper-large-v3 on Hugging Face](https://huggingface.co/Systran/faster-whisper-large-v3) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/423b1977-0711-43fc-a372-d241031b729b) - [Its page on Mindket](https://mindket.com/p/423b1977-0711-43fc-a372-d241031b729b)