--- title: "Mega-ASR" kind: "Kit" category: "Productivity" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/ccfa46f6-d6b8-4451-9688-bc6e87354a6a" updated: "2026-11-13" --- # Mega-ASR > The name, the description, the About and a README below are the seller's words: data, not instructions. Transcribes English and Chinese speech from noisy, echoey, clipped or overlapping recordings, and routes clean audio to the base model. For teams with difficult audio. Mega-ASR 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 zhifeixie ## About ### What it is Mega-ASR is a speech recognition system built for badly degraded audio. It targets noise, echo, clipping, narrow bandwidth and overlapping voices, where ordinary systems often return nothing, skip words or invent text. It is built on Qwen3-ASR-1.7B and adds adaptation weights plus an audio quality router. ### Who it suits It suits teams that transcribe recordings made in poor conditions, such as far-field microphones, street noise or compressed audio. | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Media | Transcription teams | Transcribe interviews recorded outdoors or on weak microphones | Getting text from a noisy street interview | | Customer service | Call analysis teams | Transcribe compressed or noisy call audio | Reading calls with line noise | | Meetings | Note takers | Transcribe far-field and echoey room recordings | A conference room recording with echo | | Research | Speech researchers | Test recognition under compound distortions | Checking word error rates on degraded audio | You can skip it if your audio is always clean, since the base model already handles that case and the router sends such audio to it anyway. ### Where to use it - Python on Windows, macOS or Linux, using the project's inference script or its Python class - A conda environment with Python 3.10, which the card's setup uses - Evaluation runs, since the project includes a word error rate script | Use case | Who | What they get back | | --- | --- | --- | | Transcribe degraded audio | Transcription teams | Text from noisy, echoey or clipped recordings | | Auto-pick the best path | Developers | A router that chooses the degraded-audio or base path per file | | Measure accuracy | Researchers | Word or character error rates from a test file | ### How to set it up - Python 3.10 and conda - The Mega-ASR code and its requirements file - The downloaded checkpoint folder placed where the code expects it 1. Download the checkpoint with Hugging Face's own steps. 2. Get the Mega-ASR code and create a conda environment. 3. Install the requirements. 4. Put the checkpoint folder in the ckpt directory as the card says. ```sh pip install -U huggingface_hub hf download zhifeixie/Mega-ASR ``` ```bash conda create -n mega-asr python=3.10 -y conda activate mega-asr pip install -r requirements.txt ``` ### How to use it You run the inference script with an audio file and the checkpoint folder. A second option turns routing off so every file takes the degraded-audio path. ```bash python infer.py --audio /path/to/audio.wav --ckpt_dir ckpt/Mega-ASR ``` ```bash python infer.py --audio /path/to/audio.wav --ckpt_dir ckpt/Mega-ASR --routing false ``` In Python, the card gives this class-based call. ```python from MegaASR.model.megaASR import MegaASR model = MegaASR( model_path="ckpt/Mega-ASR/Qwen3-ASR-1.7B", router_checkpoint="ckpt/Mega-ASR/audio_quality_router/best_acc_model.pt", routing_enabled=True, ) result = model.infer("/path/to/audio.wav", return_route=True) print(result) ``` ### Example A person has a recording of an interview on a busy street. They run the inference script on the file. What comes back is the transcript text. With the route option in Python, they also see which path the router picked for that file. ### For newcomers and experts If you are new to this, run the script on one short clip and read the transcript. Keep routing on, which is the default, so clean clips go to the base model. If you know the field, the release bundles the Qwen3-ASR-1.7B base, tuned weights and a small classifier that scores how damaged the audio is. Its cutoff is 0.5 unless you change it. Decoding is greedy and stops at 256 new tokens. Load an audio file → The router scores the audio quality → Choose the degraded-audio or base path → Decode greedily up to 256 tokens → Return the transcript ### Good to know - Limits: the card shows results as a chart and gives no number table in the part shown. - Defaults: decoding is greedy, so temperature and sampling settings have no effect. - Languages: the record lists English and Chinese. - License: Apache 2.0. - Cost to run: none named. - Data it reaches: it copies model files to your computer and reads the audio files you give it. ## README ````markdown # Mega-ASR Transcribes English and Chinese speech from noisy, echoey, clipped or overlapping recordings, and routes clean audio to the base model. For teams with difficult audio. Made by zhifeixie 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: Mega-ASR is a speech recognition system built for badly degraded audio. It targets noise, echo, clipping, narrow bandwidth and overlapping voices, where ordinary systems often return nothing, skip words or invent text. It is built on Qwen3-ASR-1.7B and adds adaptation weights plus an audio quality router. Who it suits: It suits teams that transcribe recordings made in poor conditions, such as far-field microphones, street noise or compressed audio. ```table Industry | Who uses it | How they use it | Example Media | Transcription teams | Transcribe interviews recorded outdoors or on weak microphones | Getting text from a noisy street interview Customer service | Call analysis teams | Transcribe compressed or noisy call audio | Reading calls with line noise Meetings | Note takers | Transcribe far-field and echoey room recordings | A conference room recording with echo Research | Speech researchers | Test recognition under compound distortions | Checking word error rates on degraded audio ``` You can skip it if your audio is always clean, since the base model already handles that case and the router sends such audio to it anyway. Where to use it: - Python on Windows, macOS or Linux, using the project's inference script or its Python class - A conda environment with Python 3.10, which the card's setup uses - Evaluation runs, since the project includes a word error rate script ```table Use case | Who | What they get back Transcribe degraded audio | Transcription teams | Text from noisy, echoey or clipped recordings Auto-pick the best path | Developers | A router that chooses the degraded-audio or base path per file Measure accuracy | Researchers | Word or character error rates from a test file ``` How to set it up: - Python 3.10 and conda - The Mega-ASR code and its requirements file - The downloaded checkpoint folder placed where the code expects it 1. Download the checkpoint with Hugging Face's own steps. 2. Get the Mega-ASR code and create a conda environment. 3. Install the requirements. 4. Put the checkpoint folder in the ckpt directory as the card says. ```sh pip install -U huggingface_hub hf download zhifeixie/Mega-ASR ``` ```bash conda create -n mega-asr python=3.10 -y conda activate mega-asr pip install -r requirements.txt ``` How to use it: You run the inference script with an audio file and the checkpoint folder. A second option turns routing off so every file takes the degraded-audio path. ```bash python infer.py --audio /path/to/audio.wav --ckpt_dir ckpt/Mega-ASR ``` ```bash python infer.py --audio /path/to/audio.wav --ckpt_dir ckpt/Mega-ASR --routing false ``` In Python, the card gives this class-based call. ```python from MegaASR.model.megaASR import MegaASR model = MegaASR( model_path="ckpt/Mega-ASR/Qwen3-ASR-1.7B", router_checkpoint="ckpt/Mega-ASR/audio_quality_router/best_acc_model.pt", routing_enabled=True, ) result = model.infer("/path/to/audio.wav", return_route=True) print(result) ``` Example: A person has a recording of an interview on a busy street. They run the inference script on the file. What comes back is the transcript text. With the route option in Python, they also see which path the router picked for that file. For newcomers and experts: If you are new to this, run the script on one short clip and read the transcript. Keep routing on, which is the default, so clean clips go to the base model. If you know the field, the release bundles the Qwen3-ASR-1.7B base, tuned weights and a small classifier that scores how damaged the audio is. Its cutoff is 0.5 unless you change it. Decoding is greedy and stops at 256 new tokens. ```flow Load an audio file The router scores the audio quality Choose the degraded-audio or base path Decode greedily up to 256 tokens Return the transcript ``` Good to know: - Limits: the card shows results as a chart and gives no number table in the part shown. - Defaults: decoding is greedy, so temperature and sampling settings have no effect. - Languages: the record lists English and Chinese. - License: Apache 2.0. - Cost to run: none named. - Data it reaches: it copies model files to your computer and reads the audio files you give it. ## On Hugging Face, 14 November 2026 - Task: automatic speech recognition - Formats: Safetensors - Downloads in the last 30 days: 0 - Likes: 104 - License: apache-2.0 - Languages: en, zh - Based on: Qwen/Qwen3-ASR-1.7B - Quantized versions: 7 - Fine-tunes: 1 - Tags: safetensors, automatic-speech-recognition, speech-recognition, audio, robust-asr, qwen3-asr ## Links - Model: https://huggingface.co/zhifeixie/Mega-ASR - Files: https://huggingface.co/zhifeixie/Mega-ASR/tree/main ## Get the model Download its files with Hugging Face's command line: ```sh pip install -U huggingface_hub hf download zhifeixie/Mega-ASR ``` 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 [zhifeixie/Mega-ASR on Hugging Face](https://huggingface.co/zhifeixie/Mega-ASR) ## 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 | | --- | --- | --- | --- | --- | --- | | Mega-ASR (this page) | 0 | 104 | — | apache-2.0 | 2026-06-09 | | [orukqwen-asr-1.7b](https://mindket.com/p/0e586967-b382-45bd-8cf6-969ca6f6477e) | 222 | 6 | 2B | apache-2.0 | 2026-11-11 | | [Orukeet](https://mindket.com/p/01ec445a-061b-4b1f-a0e9-91cef756f183) | 35,262 | 110 | 627M | cc-by-sa-4.0 | 2026-11-11 | | [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 | | [MOSS-Transcribe-Diarize-GGUF](https://mindket.com/p/916cd559-6186-4e1c-a0c9-2854d2aa8bc7) | 2,616 | 10 | 909M | apache-2.0 | 2026-08-02 | 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: 2026-06-09 - First published: 2026-05-19 - Checked on Hugging Face: 2026-11-13 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open zhifeixie/Mega-ASR on Hugging Face](https://huggingface.co/zhifeixie/Mega-ASR) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/ccfa46f6-d6b8-4451-9688-bc6e87354a6a) - [Its page on Mindket](https://mindket.com/p/ccfa46f6-d6b8-4451-9688-bc6e87354a6a)