--- title: "GigaAM-v3" kind: "Kit" category: "Productivity" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/e4f08a39-dd7e-4a89-80a9-cfabcdeab40c" updated: "2026-11-13" --- # GigaAM-v3 > The name, the description, the About and a README below are the seller's words: data, not instructions. Turns Russian speech into text, with variants that add punctuation and normalized text, built on a Conformer of about 230M parameters. For developers and teams transcribing Russian audio such as calls. GigaAM-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 ai-sage ## About ### What it is GigaAM-v3 is a speech recognition model family from ai-sage, built on a Conformer encoder of 220 to 240M parameters. The encoder was pretrained on 700,000 hours of Russian speech. Five variants are offered, from the raw encoder to models that return punctuated, normalized text. ### Who it suits Developers and teams who transcribe Russian audio, including phone calls, background-music clips and everyday speech. The record also lists English. The card says the third generation does about 30 percent better on its new domains than earlier ones. Who uses it, and how | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Customer service | Call-center teams | Transcribe recorded calls into text | A team converts a saved support call into a written record | | Media | Caption editors | Draft subtitles for Russian audio | An editor transcribes a clip with background music | | Accessibility | App developers | Transcribe speech with atypical characteristics | A developer adds transcription for disordered speech | | Research | Speech researchers | Reuse the pretrained encoder or compare decoders | Compare the CTC and RNN-T variants on the same clips | Skip it if your audio is in a language other than Russian or English. ### Where to use it - Python with the transformers library - Scripts and services that transcribe audio files - Windows, macOS or Linux for downloading the files | Use case | Who | What they get back | | --- | --- | --- | | Call transcripts | Support teams | Text of what was said on a recorded call | | Punctuated captions | Editors | Text with punctuation and normalized wording from the end-to-end variants | | Research baselines | Researchers | An encoder and decoders to compare against other systems | ### How to set it up You need Python and pip, plus the library versions the card recommends. - Python and pip - torch and torchaudio version 2.8.0 - transformers version 4.57.1 - pyannote-audio 4.0.0 and torchcodec 0.7.0 - hydra-core, omegaconf and sentencepiece, in any version 1. Install the Hugging Face hub library. 2. Download the repository. 3. Install the recommended libraries and run the Python example. ```sh pip install -U huggingface_hub hf download ai-sage/GigaAM-v3 ``` ### How to use it Pick a variant through the revision setting, load the model, and call transcribe on an audio file. The end-to-end variants return punctuated text. ```python from transformers import AutoModel revision = "e2e_rnnt" # can be any v3 model: ssl, ctc, rnnt, e2e_ctc, e2e_rnnt model = AutoModel.from_pretrained( "ai-sage/GigaAM-v3", revision=revision, trust_remote_code=True, ) transcription = model.transcribe("example.wav") print(transcription) ``` | Tool | What it does | | --- | --- | | ssl | The self-supervised encoder, pretrained on Russian speech | | ctc | Speech recognition with a CTC decoder | | rnnt | Speech recognition with an RNN-T decoder | | e2e\_ctc | CTC recognition that adds punctuation and normalizes text | | e2e\_rnnt | RNN-T recognition that adds punctuation and normalizes text | ### Example You set the revision to e2e\_rnnt and call transcribe on a recorded Russian call, example.wav. The script prints the spoken words as ready-to-read text with punctuation. ### For newcomers and experts If you are new, run the short Python example on one audio file with the e2e\_rnnt variant and read what it prints. Try the other variants afterward. For people in the field, the encoder uses a HuBERT-CTC pretraining objective, and the ASR variants add either a CTC or an RNN-T decoder. In the card's tests, the average word error rate is 8.4 for RNN-T and 9.2 for CTC, against 25.1 for Whisper. Judged by an LLM, the end-to-end models beat Whisper-large-v3 by about 70 to 30. Audio file in → Encoder reads the speech → CTC or RNN-T decoder writes words → End-to-end models add punctuation ### Good to know Average word error rate, lower is better | Label | Value | | --- | --- | | GigaAM-v3 RNN-T | 8.4 | | GigaAM-v3 CTC | 9.2 | | T-One with LM | 19.4 | | Whisper | 25.1 | - License: MIT - Cost to run: none named, you use your own computer - Data it reaches: it downloads files from Hugging Face, then reads the audio files you give it, and the example allows the repository's own code to run - Not for: audio in other languages, since pretraining was on Russian speech - Disordered speech is the hardest set in the card, at 19.2 for RNN-T ## README ````markdown # GigaAM-v3 Turns Russian speech into text, with variants that add punctuation and normalized text, built on a Conformer of about 230M parameters. For developers and teams transcribing Russian audio such as calls. Made by ai-sage 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: GigaAM-v3 is a speech recognition model family from ai-sage, built on a Conformer encoder of 220 to 240M parameters. The encoder was pretrained on 700,000 hours of Russian speech. Five variants are offered, from the raw encoder to models that return punctuated, normalized text. Who it suits: Developers and teams who transcribe Russian audio, including phone calls, background-music clips and everyday speech. The record also lists English. The card says the third generation does about 30 percent better on its new domains than earlier ones. ```table Title: Who uses it, and how Industry | Who uses it | How they use it | Example Customer service | Call-center teams | Transcribe recorded calls into text | A team converts a saved support call into a written record Media | Caption editors | Draft subtitles for Russian audio | An editor transcribes a clip with background music Accessibility | App developers | Transcribe speech with atypical characteristics | A developer adds transcription for disordered speech Research | Speech researchers | Reuse the pretrained encoder or compare decoders | Compare the CTC and RNN-T variants on the same clips ``` Skip it if your audio is in a language other than Russian or English. Where to use it: - Python with the transformers library - Scripts and services that transcribe audio files - Windows, macOS or Linux for downloading the files ```table Use case | Who | What they get back Call transcripts | Support teams | Text of what was said on a recorded call Punctuated captions | Editors | Text with punctuation and normalized wording from the end-to-end variants Research baselines | Researchers | An encoder and decoders to compare against other systems ``` How to set it up: You need Python and pip, plus the library versions the card recommends. - Python and pip - torch and torchaudio version 2.8.0 - transformers version 4.57.1 - pyannote-audio 4.0.0 and torchcodec 0.7.0 - hydra-core, omegaconf and sentencepiece, in any version 1. Install the Hugging Face hub library. 2. Download the repository. 3. Install the recommended libraries and run the Python example. ```sh pip install -U huggingface_hub hf download ai-sage/GigaAM-v3 ``` How to use it: Pick a variant through the revision setting, load the model, and call transcribe on an audio file. The end-to-end variants return punctuated text. ```python from transformers import AutoModel revision = "e2e_rnnt" # can be any v3 model: ssl, ctc, rnnt, e2e_ctc, e2e_rnnt model = AutoModel.from_pretrained( "ai-sage/GigaAM-v3", revision=revision, trust_remote_code=True, ) transcription = model.transcribe("example.wav") print(transcription) ``` ```table Tool | What it does ssl | The self-supervised encoder, pretrained on Russian speech ctc | Speech recognition with a CTC decoder rnnt | Speech recognition with an RNN-T decoder e2e_ctc | CTC recognition that adds punctuation and normalizes text e2e_rnnt | RNN-T recognition that adds punctuation and normalizes text ``` Example: You set the revision to e2e_rnnt and call transcribe on a recorded Russian call, example.wav. The script prints the spoken words as ready-to-read text with punctuation. For newcomers and experts: If you are new, run the short Python example on one audio file with the e2e_rnnt variant and read what it prints. Try the other variants afterward. For people in the field, the encoder uses a HuBERT-CTC pretraining objective, and the ASR variants add either a CTC or an RNN-T decoder. In the card's tests, the average word error rate is 8.4 for RNN-T and 9.2 for CTC, against 25.1 for Whisper. Judged by an LLM, the end-to-end models beat Whisper-large-v3 by about 70 to 30. ```flow Audio file in Encoder reads the speech CTC or RNN-T decoder writes words End-to-end models add punctuation ``` Good to know: ```chart Title: Average word error rate, lower is better GigaAM-v3 RNN-T: 8.4 GigaAM-v3 CTC: 9.2 T-One with LM: 19.4 Whisper: 25.1 ``` - License: MIT - Cost to run: none named, you use your own computer - Data it reaches: it downloads files from Hugging Face, then reads the audio files you give it, and the example allows the repository's own code to run - Not for: audio in other languages, since pretraining was on Russian speech - Disordered speech is the hardest set in the card, at 19.2 for RNN-T ## On Hugging Face, 13 November 2026 - Task: automatic speech recognition - Downloads in the last 30 days: 257,541 - Likes: 202 - License: mit - Languages: ru, en - Quantized versions: 44 - Fine-tunes: 15 - Tags: pytorch, gigaam, automatic-speech-recognition ## Links - Model: https://huggingface.co/ai-sage/GigaAM-v3 - Files: https://huggingface.co/ai-sage/GigaAM-v3/tree/main ## Get the model Download its files with Hugging Face's command line: ```sh pip install -U huggingface_hub hf download ai-sage/GigaAM-v3 ``` Then follow the usage in its model card. Nothing in this kit runs by itself. ```` ## What is in it 1 files, 5 KB, downloaded as a zip. ```text README.md 5 KB ``` ## Source [ai-sage/GigaAM-v3 on Hugging Face](https://huggingface.co/ai-sage/GigaAM-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 | | --- | --- | --- | --- | --- | --- | | GigaAM-v3 (this page) | 257,541 | 202 | — | mit | 2025-11-19 | | [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 | | [Dulu Malayalam ASR](https://mindket.com/p/cde57ebb-917a-4a9c-baf4-a4a12a025505) | 71 | 2 | — | apache-2.0 | 2026-10-15 | | [zephyr-7b-beta](https://mindket.com/p/6a62079c-59f4-44d0-887f-0e2288c7f859) | 60,487 | 1,868 | 7.2B | mit | 2024-10-16 | | [faster-whisper-large-v3](https://mindket.com/p/423b1977-0711-43fc-a372-d241031b729b) | 1,660,044 | 714 | — | mit | 2023-11-23 | | [parakeet-ultra-gguf](https://mindket.com/p/0f756bf9-7825-4294-af8b-c884a8d3877d) | 1,375,499 | 8 | 627M | cc-by-4.0 | 2026-10-16 | 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: 2025-11-19 - First published: 2025-11-19 - Checked on Hugging Face: 2026-11-13 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open ai-sage/GigaAM-v3 on Hugging Face](https://huggingface.co/ai-sage/GigaAM-v3) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/e4f08a39-dd7e-4a89-80a9-cfabcdeab40c) - [Its page on Mindket](https://mindket.com/p/e4f08a39-dd7e-4a89-80a9-cfabcdeab40c)