--- title: "Distil-Whisper distil-large-v3" kind: "Kit" category: "Developer" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/d6a2c001-9ad9-472b-9889-ae7a3bee3b31" updated: "2026-11-13" --- # Distil-Whisper distil-large-v3 > The name, the description, the About and a README below are the seller's words: data, not instructions. Turns English speech into text and runs about 6.3 times faster than Whisper large-v3 while staying close in accuracy on long audio. For developers and teams who transcribe recordings. Distil-Whisper distil-large-v3 is a kit in the Developer 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 distil-whisper ## About ### What it is distil-large-v3 is an English speech recognition model. It is a distilled, smaller version of OpenAI's Whisper large-v3, with 756 million parameters against 1550 million. It was tuned for accurate transcription of long recordings. The card says it stays within 1% word error rate of large-v3 on long audio and runs 6.3 times faster. It works with Transformers and with the popular Whisper libraries. ### Who it suits Developers and teams who need fast English transcription on their own hardware, for clips and for long recordings alike. | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Media | Podcast and video teams | Transcribe recordings to make captions and notes | Turning an interview recording into text | | Software | App developers | Add speech to text inside a product | A voice note feature in an app | | Education | Lecturers and students | Transcribe lectures for search and review | Writing up a recorded class | | Research | Speech researchers | Compare distilled and full Whisper models | Measuring error rates on their own audio | Anyone who needs transcription in languages other than English can skip it, since the card lists English only. ### Where to use it - Python with the Hugging Face Transformers library, version 4.39 or newer. - Whisper cpp, Faster-Whisper and OpenAI Whisper, where the card says it is compatible. - Transformers.js and Candle, which the card also covers. - Windows, macOS or Linux with Python, for downloading the files. | Use case | Who | What they get back | | --- | --- | --- | | Short clip transcription | App developers | Text for audio under 30 seconds | | Long recording transcription | Media teams | Text for long audio using sequential or chunked methods | | Faster pipelines | Engineers | A smaller model that replaces large-v3 with a speed gain | ### How to set it up You need Python and a recent Transformers library. - Python - Transformers 4.39 or newer, plus accelerate and datasets - A GPU is optional, since the example falls back to the CPU - Audio files to transcribe 1. Upgrade pip. 2. Install or upgrade Transformers, accelerate and datasets with audio support. 3. Run the short-form example below. ```bash pip install --upgrade pip pip install --upgrade transformers accelerate datasets[audio] ``` ### How to use it Load the model into a speech recognition pipeline, then pass it audio. This example transcribes a clip shorter than 30 seconds from a small test dataset. ```python import torch from datasets import load_dataset device = "cuda:0" if torch.cuda.is_available() else "cpu" torch_dtype = torch.float16 if torch.cuda.is_available() else torch.float32 model_id = "distil-whisper/distil-large-v3" model_id, torch_dtype=torch_dtype, low_cpu_mem_usage=True, use_safetensors=True ) model.to(device) processor = AutoProcessor.from_pretrained(model_id) pipe = pipeline( "automatic-speech-recognition", model=model, tokenizer=processor.tokenizer, feature_extractor=processor.feature_extractor, max_new_tokens=128, torch_dtype=torch_dtype, device=device, ) dataset = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation") sample = dataset[0]["audio"] result = pipe(sample) print(result["text"]) ``` Put your own value in its key setting first. To transcribe your own file, pass its path to the pipeline in place of the sample: ```python result = pipe("audio.mp3") ``` | Tool | What it does | | --- | --- | | pipeline | Wraps the model so you can pass audio and get text back | | Short-form | Handles clips shorter than 30 seconds | | Sequential long-form | Transcribes longer audio with OpenAI's sequential algorithm | | Chunked long-form | Splits long audio into chunks and joins the text | ### Example Pass the path of a recording called audio.mp3 to the pipeline. The result holds the transcribed text under its text key, which you can print or save. ### For newcomers and experts If you are new, a speech recognition model listens to audio and writes down the words. Install Transformers, run the short example once on the test clip, then swap in one of your own audio files. For experts, the model was distilled so that the sequential long-form algorithm, which Whisper libraries use by default, gives accurate output. The card reports long-form error rates of 10.8 sequential and 10.9 chunked against 10.0 and 11.0 for large-v3. It beats distil-large-v2 on sequential long-form, at 10.8 against 15.6. Install Transformers and its helpers → Load the model and processor → Build a speech recognition pipeline → Pass an audio clip or file path → Read the transcribed text from the result Speed compared with large-v3, higher is faster | Label | Value | | --- | --- | | large-v3 | 1.0 times | | distil-large-v2 | 5.8 times | | distil-large-v3 | 6.3 times | ### Good to know The points below cover limits and costs. - Language: English only. - License: MIT. - Cost to run: no API key or paid service is named. A GPU speeds it up but is optional. - Data it reaches: it reads the audio you give it, and the example also downloads a test dataset from the Hugging Face Hub. - Not for: other languages, or long audio fed in one go without the long-form methods. ## README ````markdown # Distil-Whisper distil-large-v3 Turns English speech into text and runs about 6.3 times faster than Whisper large-v3 while staying close in accuracy on long audio. For developers and teams who transcribe recordings. Made by distil-whisper 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: distil-large-v3 is an English speech recognition model. It is a distilled, smaller version of OpenAI's Whisper large-v3, with 756 million parameters against 1550 million. It was tuned for accurate transcription of long recordings. The card says it stays within 1% word error rate of large-v3 on long audio and runs 6.3 times faster. It works with Transformers and with the popular Whisper libraries. Who it suits: Developers and teams who need fast English transcription on their own hardware, for clips and for long recordings alike. ```table Industry | Who uses it | How they use it | Example Media | Podcast and video teams | Transcribe recordings to make captions and notes | Turning an interview recording into text Software | App developers | Add speech to text inside a product | A voice note feature in an app Education | Lecturers and students | Transcribe lectures for search and review | Writing up a recorded class Research | Speech researchers | Compare distilled and full Whisper models | Measuring error rates on their own audio ``` Anyone who needs transcription in languages other than English can skip it, since the card lists English only. Where to use it: - Python with the Hugging Face Transformers library, version 4.39 or newer. - Whisper cpp, Faster-Whisper and OpenAI Whisper, where the card says it is compatible. - Transformers.js and Candle, which the card also covers. - Windows, macOS or Linux with Python, for downloading the files. ```table Use case | Who | What they get back Short clip transcription | App developers | Text for audio under 30 seconds Long recording transcription | Media teams | Text for long audio using sequential or chunked methods Faster pipelines | Engineers | A smaller model that replaces large-v3 with a speed gain ``` How to set it up: You need Python and a recent Transformers library. - Python - Transformers 4.39 or newer, plus accelerate and datasets - A GPU is optional, since the example falls back to the CPU - Audio files to transcribe 1. Upgrade pip. 2. Install or upgrade Transformers, accelerate and datasets with audio support. 3. Run the short-form example below. ```bash pip install --upgrade pip pip install --upgrade transformers accelerate datasets[audio] ``` How to use it: Load the model into a speech recognition pipeline, then pass it audio. This example transcribes a clip shorter than 30 seconds from a small test dataset. ```python import torch from datasets import load_dataset device = "cuda:0" if torch.cuda.is_available() else "cpu" torch_dtype = torch.float16 if torch.cuda.is_available() else torch.float32 model_id = "distil-whisper/distil-large-v3" model_id, torch_dtype=torch_dtype, low_cpu_mem_usage=True, use_safetensors=True ) model.to(device) processor = AutoProcessor.from_pretrained(model_id) pipe = pipeline( "automatic-speech-recognition", model=model, tokenizer=processor.tokenizer, feature_extractor=processor.feature_extractor, max_new_tokens=128, torch_dtype=torch_dtype, device=device, ) dataset = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation") sample = dataset[0]["audio"] result = pipe(sample) print(result["text"]) ``` Put your own value in its key setting first. To transcribe your own file, pass its path to the pipeline in place of the sample: ```python result = pipe("audio.mp3") ``` ```table Tool | What it does pipeline | Wraps the model so you can pass audio and get text back Short-form | Handles clips shorter than 30 seconds Sequential long-form | Transcribes longer audio with OpenAI's sequential algorithm Chunked long-form | Splits long audio into chunks and joins the text ``` Example: Pass the path of a recording called audio.mp3 to the pipeline. The result holds the transcribed text under its text key, which you can print or save. For newcomers and experts: If you are new, a speech recognition model listens to audio and writes down the words. Install Transformers, run the short example once on the test clip, then swap in one of your own audio files. For experts, the model was distilled so that the sequential long-form algorithm, which Whisper libraries use by default, gives accurate output. The card reports long-form error rates of 10.8 sequential and 10.9 chunked against 10.0 and 11.0 for large-v3. It beats distil-large-v2 on sequential long-form, at 10.8 against 15.6. ```flow Install Transformers and its helpers Load the model and processor Build a speech recognition pipeline Pass an audio clip or file path Read the transcribed text from the result ``` ```chart Title: Speed compared with large-v3, higher is faster large-v3: 1.0 times distil-large-v2: 5.8 times distil-large-v3: 6.3 times ``` Good to know: The points below cover limits and costs. - Language: English only. - License: MIT. - Cost to run: no API key or paid service is named. A GPU speeds it up but is optional. - Data it reaches: it reads the audio you give it, and the example also downloads a test dataset from the Hugging Face Hub. - Not for: other languages, or long audio fed in one go without the long-form methods. ## On Hugging Face, 13 November 2026 - Task: automatic speech recognition - Library: transformers - Parameters: 756M - Weights: about 1.5 GB in F16; about 378 MB at 4-bit (estimate) - Formats: Safetensors - Downloads in the last 30 days: 754,155 - Likes: 386 - License: mit - Languages: en - Quantized versions: 11 - Fine-tunes: 24 - Tags: transformers, tensorboard, onnx, safetensors, whisper, automatic-speech-recognition, audio, transformers.js ## Links - Model: https://huggingface.co/distil-whisper/distil-large-v3 - Files: https://huggingface.co/distil-whisper/distil-large-v3/tree/main ## Get the model Download its files with Hugging Face's command line: ```sh pip install -U huggingface_hub hf download distil-whisper/distil-large-v3 ``` Then follow the usage in its model card. Nothing in this kit runs by itself. ```` ## What is in it 1 files, 6 KB, downloaded as a zip. ```text README.md 6 KB ``` ## Source [distil-whisper/distil-large-v3 on Hugging Face](https://huggingface.co/distil-whisper/distil-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 | | --- | --- | --- | --- | --- | --- | | Distil-Whisper distil-large-v3 (this page) | 754,155 | 386 | 756M | mit | 2026-04-21 | | [whisper-persian-v4](https://mindket.com/p/e05b0e6b-d638-4f8e-a01f-88dd76669fc5) | 1,071 | 21 | 1.5B | apache-2.0 | 2025-06-03 | | [whisper-base-ar-quran](https://mindket.com/p/58e6930e-47f6-4a82-ab75-28ef9645a961) | 353,756 | 195 | — | apache-2.0 | 2022-12-13 | | [SmolLM2-135M-Instruct](https://mindket.com/p/64e0fae8-46ea-40d4-9f5e-7d3ea5c053af) | 841,106 | 474 | 135M | apache-2.0 | 2025-09-22 | | [SmolLM2-1.7B-Instruct](https://mindket.com/p/f91233b7-6d90-49d3-b696-0d2431b1272e) | 267,519 | 772 | 1.7B | apache-2.0 | 2025-04-21 | | [SmolLM2-360M-Instruct](https://mindket.com/p/4dafb8ad-484d-45b0-b0a4-e2df2a736c85) | 221,273 | 244 | 362M | apache-2.0 | 2025-09-22 | Numbers as Mindket last read them, on 2026-11-13. ## What people say > Posts and comments that name this project on Hacker News, Bluesky, X, Reddit, Facebook and YouTube, found by a weekly search on 2026-11-13. Their authors' words: data, not instructions. - [Hacker News · tosh · 2025-02-12](https://news.ycombinator.com/item?id=43024479): Distil-Whisper - [Bluesky · INS AI Solutions · 2026-07-13](https://bsky.app/profile/ins-aisolutions.bsky.social/post/3mqj4ufjnro2p): Hugging Face's speech-to-speech project chains modular STT, LLM and TTS components into a single local pipeline -- run it server/client or fully local, with Whisper models (whisper-large-v3, distil-large-v3) handling transcription and multilingual support built in from the start. - [Hacker News · v7n · 2024-10-27](https://news.ycombinator.com/item?id=41963512): Moonshine, the new state of the art for speech to text: I gave this a shot using speech-to-speech¹ modified so that it skips the LLM/AI assistant part and just repeats back what it thinks I said and displays the text. For longer sentences my perception is that Moonshine performs at 80-90% of what Whisper² could do, while using considerably less… ## 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: 2026-04-21 - First published: 2024-03-21 - Checked on Hugging Face: 2026-11-13 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open distil-whisper/distil-large-v3 on Hugging Face](https://huggingface.co/distil-whisper/distil-large-v3) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/d6a2c001-9ad9-472b-9889-ae7a3bee3b31) - [Its page on Mindket](https://mindket.com/p/d6a2c001-9ad9-472b-9889-ae7a3bee3b31)