--- title: "Granite Speech 5.0 TurboCTC 470M GGUF" kind: "Kit" category: "Developer" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/f1576e4c-9b56-4251-9ecd-ddfd4132c910" updated: "2026-11-13" --- # Granite Speech 5.0 TurboCTC 470M GGUF > The name, the description, the About and a README below are the seller's words: data, not instructions. Converts speech to text in seven languages, as quantized GGUF versions of IBM's Granite Speech 5.0 TurboCTC 470M, built for transcribe.cpp. For developers adding local dictation or transcription. Granite Speech 5.0 TurboCTC 470M GGUF 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 vladers ## About ### What it is Granite Speech 5.0 TurboCTC 470M GGUF is a set of speech-to-text files made by vladers from IBM's Granite Speech model of the same name. It transcribes seven languages, English among them, and writes text with punctuation and capital letters. The files target the transcribe.cpp program and come in seven versions, from 4-bit to full 32-bit precision. ### Who it suits It suits developers who add dictation or transcription to an app that runs on the user's own computer, and people who want a compact recognizer whose build can be repeated and checked. | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Software | App developers | Add offline dictation to a desktop app | Use the Q8\_0 file the way Voiceour does | | Media | Editors and podcasters | Turn recorded interviews into text | Transcribe a 16 kHz WAV file | | Research | Speech researchers | Compare file sizes by word error rate | Read the per-file results in the card | Anyone who needs languages beyond those seven, or a finished app instead of model files, can skip it. ### Where to use it - transcribe.cpp, a C++ speech program that you build with CMake - A Windows, macOS or Linux computer with Python for the download - Apps that embed it, such as the macOS dictation app Voiceour | Use case | Who | What they get back | | --- | --- | --- | | Local dictation | App developers | Text from speech, with punctuation | | Interview transcripts | Editors | Written text from audio recordings | | Quantization checks | Researchers | Error rates for each file size | ### How to set it up - Python with pip, for the download - A C++ build setup with CMake, plus ffmpeg to convert audio - Disk space for the file you pick, from about 279 MB for Q4\_K\_M to 1.89 GB for F32 - The transcribe.cpp source code, taken from its GitHub page 1. Install the Hugging Face hub client. 2. Download one file, such as the Q8\_0 one. 3. Get the transcribe.cpp source code into a folder named transcribe.cpp, then build its command line tool with CMake. 4. Run the tool on a 16 kHz mono WAV file. ```sh pip install -U huggingface_hub hf download vladers/granite-speech-5.0-470m-turboctc-GGUF granite-speech-5.0-470m-turboctc-Q8_0.gguf --local-dir . ``` ```bash cmake -S transcribe.cpp -B build cmake --build build --target transcribe-cli -j ``` ### How to use it Convert your recording to a 16 kHz mono WAV with ffmpeg, then give the tool the model file and the audio. The card advises reading its Known behaviour notes before you try silence or tiny clips. ```bash ffmpeg -i in.mp3 -ar 16000 -ac 1 audio.wav ``` ```bash build/bin/transcribe-cli -m granite-speech-5.0-470m-turboctc-Q8_0.gguf audio.wav ``` | Tool | What it does | | --- | --- | | ffmpeg | Converts an audio file to 16 kHz mono WAV | | transcribe-cli | Runs the model file on the audio and returns text | | hf download | Fetches the model file from Hugging Face | ### Example You build transcribe-cli, download the Q8\_0 file, convert an MP3 interview with ffmpeg and run the tool on it. The card does not show a sample printout, so check the tool's own help for the output format. ### For newcomers and experts Speech recognition turns audio into text, and a smaller file trades a little accuracy for less disk and memory. Start with the Q8\_0 file, which the card calls the default choice. Of the 520 tensors in each file, 163 are compressed, namely the linear layers and pointwise convolutions, while the other 357 stay at full 32-bit precision. The BF16 file keeps IBM's original linear weights. The card measures every file on 19,330 English clips and lists speed on an Apple M4 Pro. IBM's Granite Speech checkpoint → Converted to seven GGUF files → Pick a file by size and accuracy → transcribe.cpp loads it → Audio goes in and text comes out Word error rate on the confirmation set in percent | Label | Value | | --- | --- | | F32 | 5.30% | | Q8\_0 | 5.30% | | Q6\_K | 5.29% | | Q5\_K\_M | 5.32% | | Q4\_K\_M | 5.39% | | Parakeet TDT 0.6B v3 | 7.21% | File size by version in MB | Label | Value | | --- | --- | | Q4\_K\_M | 279 MB | | Q5\_K\_M | 336 MB | | Q6\_K | 392 MB | | Q8\_0 | 506 MB | | F16 | 948 MB | ### Good to know - Versions: seven files, from Q4\_K\_M at 279 MB to F32 at 1.89 GB - Accuracy: on the 17,858-clip English confirmation set, Q4\_K\_M scores 5.39 percent word error against 5.30 for the full-precision file - Size: the record's rough 4-bit estimate is about 237 MB, while the card lists 279 MB for Q4\_K\_M - Languages: seven, and the accuracy tests cover English only - License: Apache 2.0 - Cost to run: none named - Data it reaches: the audio you process on your own computer, while the pip and git steps fetch packages and code from the web - Not for: languages outside the seven, or a finished app with a window ## README ````markdown # Granite Speech 5.0 TurboCTC 470M GGUF Converts speech to text in seven languages, as quantized GGUF versions of IBM's Granite Speech 5.0 TurboCTC 470M, built for transcribe.cpp. For developers adding local dictation or transcription. Made by vladers 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: Granite Speech 5.0 TurboCTC 470M GGUF is a set of speech-to-text files made by vladers from IBM's Granite Speech model of the same name. It transcribes seven languages, English among them, and writes text with punctuation and capital letters. The files target the transcribe.cpp program and come in seven versions, from 4-bit to full 32-bit precision. Who it suits: It suits developers who add dictation or transcription to an app that runs on the user's own computer, and people who want a compact recognizer whose build can be repeated and checked. ```table Industry | Who uses it | How they use it | Example Software | App developers | Add offline dictation to a desktop app | Use the Q8_0 file the way Voiceour does Media | Editors and podcasters | Turn recorded interviews into text | Transcribe a 16 kHz WAV file Research | Speech researchers | Compare file sizes by word error rate | Read the per-file results in the card ``` Anyone who needs languages beyond those seven, or a finished app instead of model files, can skip it. Where to use it: - transcribe.cpp, a C++ speech program that you build with CMake - A Windows, macOS or Linux computer with Python for the download - Apps that embed it, such as the macOS dictation app Voiceour ```table Use case | Who | What they get back Local dictation | App developers | Text from speech, with punctuation Interview transcripts | Editors | Written text from audio recordings Quantization checks | Researchers | Error rates for each file size ``` How to set it up: - Python with pip, for the download - A C++ build setup with CMake, plus ffmpeg to convert audio - Disk space for the file you pick, from about 279 MB for Q4_K_M to 1.89 GB for F32 - The transcribe.cpp source code, taken from its GitHub page 1. Install the Hugging Face hub client. 2. Download one file, such as the Q8_0 one. 3. Get the transcribe.cpp source code into a folder named transcribe.cpp, then build its command line tool with CMake. 4. Run the tool on a 16 kHz mono WAV file. ```sh pip install -U huggingface_hub hf download vladers/granite-speech-5.0-470m-turboctc-GGUF granite-speech-5.0-470m-turboctc-Q8_0.gguf --local-dir . ``` ```bash cmake -S transcribe.cpp -B build cmake --build build --target transcribe-cli -j ``` How to use it: Convert your recording to a 16 kHz mono WAV with ffmpeg, then give the tool the model file and the audio. The card advises reading its Known behaviour notes before you try silence or tiny clips. ```bash ffmpeg -i in.mp3 -ar 16000 -ac 1 audio.wav ``` ```bash build/bin/transcribe-cli -m granite-speech-5.0-470m-turboctc-Q8_0.gguf audio.wav ``` ```table Tool | What it does ffmpeg | Converts an audio file to 16 kHz mono WAV transcribe-cli | Runs the model file on the audio and returns text hf download | Fetches the model file from Hugging Face ``` Example: You build transcribe-cli, download the Q8_0 file, convert an MP3 interview with ffmpeg and run the tool on it. The card does not show a sample printout, so check the tool's own help for the output format. For newcomers and experts: Speech recognition turns audio into text, and a smaller file trades a little accuracy for less disk and memory. Start with the Q8_0 file, which the card calls the default choice. Of the 520 tensors in each file, 163 are compressed, namely the linear layers and pointwise convolutions, while the other 357 stay at full 32-bit precision. The BF16 file keeps IBM's original linear weights. The card measures every file on 19,330 English clips and lists speed on an Apple M4 Pro. ```flow IBM's Granite Speech checkpoint Converted to seven GGUF files Pick a file by size and accuracy transcribe.cpp loads it Audio goes in and text comes out ``` ```chart Title: Word error rate on the confirmation set in percent F32: 5.30% Q8_0: 5.30% Q6_K: 5.29% Q5_K_M: 5.32% Q4_K_M: 5.39% Parakeet TDT 0.6B v3: 7.21% ``` ```chart Title: File size by version in MB Q4_K_M: 279 MB Q5_K_M: 336 MB Q6_K: 392 MB Q8_0: 506 MB F16: 948 MB ``` Good to know: - Versions: seven files, from Q4_K_M at 279 MB to F32 at 1.89 GB - Accuracy: on the 17,858-clip English confirmation set, Q4_K_M scores 5.39 percent word error against 5.30 for the full-precision file - Size: the record's rough 4-bit estimate is about 237 MB, while the card lists 279 MB for Q4_K_M - Languages: seven, and the accuracy tests cover English only - License: Apache 2.0 - Cost to run: none named - Data it reaches: the audio you process on your own computer, while the pip and git steps fetch packages and code from the web - Not for: languages outside the seven, or a finished app with a window ## On Hugging Face, 13 November 2026 - Task: automatic speech recognition - Parameters: 473M - Weights: about 237 MB at 4-bit (estimate) - Formats: GGUF - Downloads in the last 30 days: 431 - Likes: 4 - License: apache-2.0 - Languages: en, fr, de, es, pt, it, ja - Based on: ibm-granite/granite-speech-5.0-470m-turboctc - Tags: gguf, transcribe.cpp, speech-recognition, granite, automatic-speech-recognition, multilingual ## Links - Model: https://huggingface.co/vladers/granite-speech-5.0-470m-turboctc-GGUF - Files: https://huggingface.co/vladers/granite-speech-5.0-470m-turboctc-GGUF/tree/main ## Get the model Download its files with Hugging Face's command line: ```sh pip install -U huggingface_hub hf download vladers/granite-speech-5.0-470m-turboctc-GGUF ``` 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 [vladers/granite-speech-5.0-470m-turboctc-GGUF on Hugging Face](https://huggingface.co/vladers/granite-speech-5.0-470m-turboctc-GGUF) ## 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 | | --- | --- | --- | --- | --- | --- | | Granite Speech 5.0 TurboCTC 470M GGUF (this page) | 431 | 4 | 473M | apache-2.0 | 2026-11-05 | | [Qwen3-ASR-1.7B GGUF](https://mindket.com/p/10bca442-bcbf-45b9-9c6c-930624df43f9) | 11,523 | 17 | 2.3B | apache-2.0 | 2026-08-02 | | [Whistle](https://mindket.com/p/2a83a355-5760-461d-9833-a5d15efc8cc8) | 94,489 | 570 | — | apache-2.0 | 2026-11-05 | | [parakeet-ultra](https://mindket.com/p/5647b30a-a58e-4b54-b4fc-18517ea9dde6) | 51,675 | 142 | 627M | cc-by-4.0 | 2026-09-23 | | [Moondream Parakeet Redux](https://mindket.com/p/0b472c3b-79ae-4a8c-a542-ddffc53eee4c) | 9,577 | 320 | 149M | cc-by-4.0 | 2026-09-23 | | [Earlybird by sahilmahendrakar](https://mindket.com/p/63374aaf-908f-49b3-9cfc-811cda44e66e) | 89 | 24 | — | mit | 2026-11-11 | 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: 2026-11-05 - First published: 2026-11-04 - Checked on Hugging Face: 2026-11-13 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open vladers/granite-speech-5.0-470m-turboctc-GGUF on Hugging Face](https://huggingface.co/vladers/granite-speech-5.0-470m-turboctc-GGUF) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/f1576e4c-9b56-4251-9ecd-ddfd4132c910) - [Its page on Mindket](https://mindket.com/p/f1576e4c-9b56-4251-9ecd-ddfd4132c910)