--- title: "zipformer-asr-turkish-v1" kind: "Kit" category: "Developer" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/622e54b5-83d9-47eb-9eca-b7b1b7ab2293" updated: "2026-11-12" --- # zipformer-asr-turkish-v1 > The name, the description, the About and a README below are the seller's words: data, not instructions. Turns Turkish speech into text as it is spoken, with a small 20M-parameter streaming recognizer that runs in real time on one CPU core, for developers building voice features on edge devices. zipformer-asr-turkish-v1 is a kit in the Developer category, listed by Mindket. It is free. This is version 1, last updated on 2026-11-12. Download it on this page, a zip of its files. Nothing in it runs by itself. Made by oayk ## About ### What it is This is a Turkish speech recognizer of about 20M parameters, built to stream and to run on small devices. It turns audio into lowercase text as people speak. A single CPU core is enough for live use with the ONNX int8 files, at a 640 ms delay. Shorter 320 ms chunks and whole-recording decoding come from those same weights. ### Who it suits Developers who need Turkish speech to text on small machines will find it fits, as will researchers who compare Turkish recognizers. The output is plain spoken-form text with no punctuation. | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Edge devices | Embedded developers | Run Turkish recognition on a single CPU core | Live captions on a small device | | Customer service | Voice engineers | Transcribe Turkish speech as it arrives | A rough live transcript of a call | | Media | Subtitle teams | Draft Turkish text from audio before editing | A first pass for subtitles | | Research | Speech researchers | Compare error rates with other Turkish models | Checking scores on the FLEURS and MediaSpeech sets | If you need punctuated text or another language, you can skip it, since it outputs lowercase Turkish without punctuation. ### Where to use it - sherpa-onnx, which the card recommends for devices - A single desktop CPU thread, using the ONNX int8 files - PyTorch and icefall, for the research decoding setups in the card - Windows, macOS or Linux with Python, to download the files | Use case | Who | What they get back | | --- | --- | --- | | Live transcription | Device developers | Turkish text as the audio streams in | | Offline transcription | Analysts | Text from a whole recording, using full context | | Score comparison | Researchers | Word error rates on two public Turkish test sets | ### How to set it up You need Python and the packages the example imports. The audio must be 16 kHz mono. - Python on Windows, macOS or Linux - numpy, soundfile and sherpa\_onnx, which the example imports - 16 kHz mono audio, such as a WAV file 1. Install the Hugging Face hub package. 2. Download the model files. 3. Install the packages the example imports. 4. Point the example at the folder for the latency you want. ```sh pip install -U huggingface_hub hf download oayk/zipformer-asr-turkish-v1 ``` ### How to use it Run the streaming example with sherpa-onnx. It feeds 100 ms of audio at a time, the way a microphone would, and decodes as the audio arrives. Use the 320 ms folder for lower delay. ```python import numpy as np, soundfile as sf, sherpa_onnx d = "streaming-640ms" # or "streaming-320ms" rec = sherpa_onnx.OnlineRecognizer.from_transducer( tokens=f"{d}/tokens.txt", encoder=f"{d}/encoder.int8.onnx", decoder=f"{d}/decoder.int8.onnx", joiner=f"{d}/joiner.int8.onnx", num_threads=1, sample_rate=16000, feature_dim=80, decoding_method="greedy_search", enable_endpoint_detection=True) x, sr = sf.read("example.wav", dtype="float32") s = rec.create_stream() for i in range(0, len(x), 1600): # feed 100 ms at a time, like a microphone s.accept_waveform(sr, x[i:i + 1600]) while rec.is_ready(s): rec.decode_stream(s) ``` | Tool | What it does | | --- | --- | | streaming-640ms | ONNX int8 files for 640 ms chunks, the card's default | | streaming-320ms | ONNX int8 files for 320 ms chunks with lower delay | | offline-full-context | ONNX int8 files that read the whole recording at once | | sherpa\_onnx OnlineRecognizer | Python class that takes audio in pieces and decodes it | ### Example You feed a Turkish WAV file to the recognizer in 100 ms pieces. It decodes while the audio arrives and returns the words as lowercase Turkish, with numbers written out and no punctuation. ### For newcomers and experts Speech recognition turns spoken audio into written words. A good first step is to run the streaming example on a short 16 kHz mono recording and read the text it produces. Under the hood sits a Zipformer2 encoder that only looks backward in time. A pruned RNN-T transducer writes the text, and an extra CTC head sits alongside it. An optional LSTM language model adds 10.2M parameters. With beam search, that language model and ILME, the card reports 5.28 percent on FLEURS with one normalizer and 6.50 with the other. Greedy ONNX int8 at 640 ms scores 8.05 and 6.80. The test sets are in-house and unreleased. You feed 16 kHz mono audio in small pieces → The encoder reads each chunk as it arrives → The transducer head decodes tokens from it → Lowercase Turkish text comes out FLEURS Turkish word error rate, balbal normalizer, lower is better | Label | Value | | --- | --- | | Google Chirp 3 cloud | 1.72 percent | | ElevenLabs Scribe cloud | 2.41 percent | | Whisper large-v3 | 3.77 percent | | Whisper large-v3-turbo | 4.42 percent | | This model, beam 8 with LM | 5.28 percent | | This model, greedy ONNX int8 | 6.80 percent | ### Good to know - License: apache-2.0, as listed on Hugging Face - Cost to run: none named. The cloud systems in the comparison are only reference scores - Data it reaches: the audio you feed it, and the source names no web or account access - Turkish is the only listed language - Not for: punctuated or capitalised output, since text comes out lowercase without punctuation - Scores use the authors' own runs and two different text normalizers ## README ````markdown # zipformer-asr-turkish-v1 Turns Turkish speech into text as it is spoken, with a small 20M-parameter streaming recognizer that runs in real time on one CPU core, for developers building voice features on edge devices. Made by oayk 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 is a Turkish speech recognizer of about 20M parameters, built to stream and to run on small devices. It turns audio into lowercase text as people speak. A single CPU core is enough for live use with the ONNX int8 files, at a 640 ms delay. Shorter 320 ms chunks and whole-recording decoding come from those same weights. Who it suits: Developers who need Turkish speech to text on small machines will find it fits, as will researchers who compare Turkish recognizers. The output is plain spoken-form text with no punctuation. ```table Industry | Who uses it | How they use it | Example Edge devices | Embedded developers | Run Turkish recognition on a single CPU core | Live captions on a small device Customer service | Voice engineers | Transcribe Turkish speech as it arrives | A rough live transcript of a call Media | Subtitle teams | Draft Turkish text from audio before editing | A first pass for subtitles Research | Speech researchers | Compare error rates with other Turkish models | Checking scores on the FLEURS and MediaSpeech sets ``` If you need punctuated text or another language, you can skip it, since it outputs lowercase Turkish without punctuation. Where to use it: - sherpa-onnx, which the card recommends for devices - A single desktop CPU thread, using the ONNX int8 files - PyTorch and icefall, for the research decoding setups in the card - Windows, macOS or Linux with Python, to download the files ```table Use case | Who | What they get back Live transcription | Device developers | Turkish text as the audio streams in Offline transcription | Analysts | Text from a whole recording, using full context Score comparison | Researchers | Word error rates on two public Turkish test sets ``` How to set it up: You need Python and the packages the example imports. The audio must be 16 kHz mono. - Python on Windows, macOS or Linux - numpy, soundfile and sherpa_onnx, which the example imports - 16 kHz mono audio, such as a WAV file 1. Install the Hugging Face hub package. 2. Download the model files. 3. Install the packages the example imports. 4. Point the example at the folder for the latency you want. ```sh pip install -U huggingface_hub hf download oayk/zipformer-asr-turkish-v1 ``` How to use it: Run the streaming example with sherpa-onnx. It feeds 100 ms of audio at a time, the way a microphone would, and decodes as the audio arrives. Use the 320 ms folder for lower delay. ```python import numpy as np, soundfile as sf, sherpa_onnx d = "streaming-640ms" # or "streaming-320ms" rec = sherpa_onnx.OnlineRecognizer.from_transducer( tokens=f"{d}/tokens.txt", encoder=f"{d}/encoder.int8.onnx", decoder=f"{d}/decoder.int8.onnx", joiner=f"{d}/joiner.int8.onnx", num_threads=1, sample_rate=16000, feature_dim=80, decoding_method="greedy_search", enable_endpoint_detection=True) x, sr = sf.read("example.wav", dtype="float32") s = rec.create_stream() for i in range(0, len(x), 1600): # feed 100 ms at a time, like a microphone s.accept_waveform(sr, x[i:i + 1600]) while rec.is_ready(s): rec.decode_stream(s) ``` ```table Tool | What it does streaming-640ms | ONNX int8 files for 640 ms chunks, the card's default streaming-320ms | ONNX int8 files for 320 ms chunks with lower delay offline-full-context | ONNX int8 files that read the whole recording at once sherpa_onnx OnlineRecognizer | Python class that takes audio in pieces and decodes it ``` Example: You feed a Turkish WAV file to the recognizer in 100 ms pieces. It decodes while the audio arrives and returns the words as lowercase Turkish, with numbers written out and no punctuation. For newcomers and experts: Speech recognition turns spoken audio into written words. A good first step is to run the streaming example on a short 16 kHz mono recording and read the text it produces. Under the hood sits a Zipformer2 encoder that only looks backward in time. A pruned RNN-T transducer writes the text, and an extra CTC head sits alongside it. An optional LSTM language model adds 10.2M parameters. With beam search, that language model and ILME, the card reports 5.28 percent on FLEURS with one normalizer and 6.50 with the other. Greedy ONNX int8 at 640 ms scores 8.05 and 6.80. The test sets are in-house and unreleased. ```flow You feed 16 kHz mono audio in small pieces The encoder reads each chunk as it arrives The transducer head decodes tokens from it Lowercase Turkish text comes out ``` ```chart Title: FLEURS Turkish word error rate, balbal normalizer, lower is better Google Chirp 3 cloud: 1.72 percent ElevenLabs Scribe cloud: 2.41 percent Whisper large-v3: 3.77 percent Whisper large-v3-turbo: 4.42 percent This model, beam 8 with LM: 5.28 percent This model, greedy ONNX int8: 6.80 percent ``` Good to know: - License: apache-2.0, as listed on Hugging Face - Cost to run: none named. The cloud systems in the comparison are only reference scores - Data it reaches: the audio you feed it, and the source names no web or account access - Turkish is the only listed language - Not for: punctuated or capitalised output, since text comes out lowercase without punctuation - Scores use the authors' own runs and two different text normalizers ## On Hugging Face, 13 November 2026 - Task: automatic speech recognition - Library: icefall - Downloads in the last 30 days: 0 - Likes: 6 - License: apache-2.0 - Languages: tr - Tags: icefall, onnx, automatic-speech-recognition, speech, turkish, streaming, zipformer, transducer, rnn-t, sherpa-onnx, edge ## Links - Model: https://huggingface.co/oayk/zipformer-asr-turkish-v1 - Files: https://huggingface.co/oayk/zipformer-asr-turkish-v1/tree/main ## Get the model Download its files with Hugging Face's command line: ```sh pip install -U huggingface_hub hf download oayk/zipformer-asr-turkish-v1 ``` 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 [oayk/zipformer-asr-turkish-v1 on Hugging Face](https://huggingface.co/oayk/zipformer-asr-turkish-v1) ## 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 | | --- | --- | --- | --- | --- | --- | | zipformer-asr-turkish-v1 (this page) | 0 | 6 | — | apache-2.0 | 2026-11-10 | | [parakeet-tdt-0.6b-v3](https://mindket.com/p/f8461a86-a5fe-403c-99b0-03415facc120) | 529,594 | 1,272 | 627M | cc-by-4.0 | 2026-08-05 | | [Parakeet RNNT 1.1B](https://mindket.com/p/e1c1ffcc-4cad-4d3b-b5a8-570b9678cc53) | 8,709 | 189 | 1.1B | cc-by-4.0 | 2026-07-06 | | [Realtime-Venus](https://mindket.com/p/45d3544c-f19f-439a-9ca0-cf4fef04dee6) | 2,101 | 135 | — | apache-2.0 | 2026-10-26 | | [Ming-3.0-audio-flash](https://mindket.com/p/fd44df1d-31b5-4ff0-8479-ed2a012a793a) | 2,122 | 87 | 128B | mit | 2026-10-29 | | [RubatoCodec](https://mindket.com/p/d379055b-79cb-481c-b464-606aa8844898) | 670 | 18 | 246M | apache-2.0 | 2026-10-28 | 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: 2026-11-10 - First published: 2026-11-10 - Checked on Hugging Face: 2026-11-12 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open oayk/zipformer-asr-turkish-v1 on Hugging Face](https://huggingface.co/oayk/zipformer-asr-turkish-v1) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/622e54b5-83d9-47eb-9eca-b7b1b7ab2293) - [Its page on Mindket](https://mindket.com/p/622e54b5-83d9-47eb-9eca-b7b1b7ab2293)