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Fangjun Kuang
Committed by GitHub
3bf986d0 1 parent ef16455c

Support non-streaming zipformer CTC ASR models (#2340) 

This PR adds support for non-streaming Zipformer CTC ASR models across 
multiple language bindings, WebAssembly, examples, and CI workflows.

- Introduces a new OfflineZipformerCtcModelConfig in C/C++, Python, Swift, Java, Kotlin, Go, Dart, Pascal, and C# APIs
- Updates initialization, freeing, and recognition logic to include Zipformer CTC in WASM and Node.js
- Adds example scripts and CI steps for downloading, building, and running Zipformer CTC models

Model doc is available at
https://k2-fsa.github.io/sherpa/onnx/pretrained_models/offline-ctc/icefall/zipformer.html
内嵌 并排对比
正在显示 71 个修改的文件 包含 2121 行增加 和 68 行删除
... ... @@ -6,6 +6,10 @@ cd dart-api-examples
pushd non-streaming-asr
echo '----------Zipformer CTC----------'
./run-zipformer-ctc.sh
rm -rf sherpa-onnx-*
echo '----------SenseVoice----------'
./run-sense-voice-with-hr.sh
./run-sense-voice.sh
... ... @@ -114,6 +118,10 @@ popd
pushd vad-with-non-streaming-asr
echo '----------Zipformer CTC----------'
./run-zipformer-ctc.sh
rm -rf sherpa-onnx-*
echo '----------Dolphin CTC----------'
./run-dolphin-ctc.sh
rm -rf sherpa-onnx-*
... ...
... ... @@ -6,43 +6,11 @@ cd ./version-test
./run.sh
ls -lh
cd ../speech-enhancement-gtcrn
./run.sh
ls -lh
cd ../kokoro-tts
./run-kokoro.sh
ls -lh
cd ../offline-tts
./run-matcha-zh.sh
ls -lh *.wav
./run-matcha-en.sh
ls -lh *.wav
./run-aishell3.sh
ls -lh *.wav
./run-piper.sh
ls -lh *.wav
./run-hf-fanchen.sh
ls -lh *.wav
ls -lh
pushd ../..
mkdir tts
cp -v dotnet-examples/kokoro-tts/*.wav ./tts
cp -v dotnet-examples/offline-tts/*.wav ./tts
popd
cd ../offline-speaker-diarization
./run.sh
rm -rfv *.onnx
rm -fv *.wav
rm -rfv sherpa-onnx-pyannote-*
cd ../offline-decode-files
./run-zipformer-ctc.sh
rm -rf sherpa-onnx-*
./run-dolphin-ctc.sh
rm -rf sherpa-onnx-dolphin-base-ctc-multi-lang-int8-2025-04-02
... ... @@ -82,6 +50,41 @@ rm -rf sherpa-onnx-*
./run-tdnn-yesno.sh
rm -rf sherpa-onnx-*
cd ../speech-enhancement-gtcrn
./run.sh
ls -lh
cd ../kokoro-tts
./run-kokoro.sh
ls -lh
cd ../offline-tts
./run-matcha-zh.sh
ls -lh *.wav
./run-matcha-en.sh
ls -lh *.wav
./run-aishell3.sh
ls -lh *.wav
./run-piper.sh
ls -lh *.wav
./run-hf-fanchen.sh
ls -lh *.wav
ls -lh
pushd ../..
mkdir tts
cp -v dotnet-examples/kokoro-tts/*.wav ./tts
cp -v dotnet-examples/offline-tts/*.wav ./tts
popd
cd ../offline-speaker-diarization
./run.sh
rm -rfv *.onnx
rm -fv *.wav
rm -rfv sherpa-onnx-pyannote-*
cd ../keyword-spotting-from-files
./run.sh
... ... @@ -115,5 +118,3 @@ rm -rf sherpa-onnx-*
cd ../spoken-language-identification
./run.sh
rm -rf sherpa-onnx-*
... ...
... ... @@ -10,6 +10,15 @@ arch=$(node -p "require('os').arch()")
platform=$(node -p "require('os').platform()")
node_version=$(node -p "process.versions.node.split('.')[0]")
echo "----------non-streaming ASR Zipformer CTC----------"
curl -SL -O https://github.com/k2-fsa/sherpa-onnx/releases/download/asr-models/sherpa-onnx-zipformer-ctc-zh-int8-2025-07-03.tar.bz2
tar xvf sherpa-onnx-zipformer-ctc-zh-int8-2025-07-03.tar.bz2
rm sherpa-onnx-zipformer-ctc-zh-int8-2025-07-03.tar.bz2
node ./test_asr_non_streaming_zipformer_ctc.js
rm -rf sherpa-onnx-zipformer-ctc-zh-int8-2025-07-03
echo "----------non-streaming ASR NeMo parakeet tdt----------"
curl -SL -O https://github.com/k2-fsa/sherpa-onnx/releases/download/asr-models/sherpa-onnx-nemo-parakeet-tdt-0.6b-v2-int8.tar.bz2
tar xvf sherpa-onnx-nemo-parakeet-tdt-0.6b-v2-int8.tar.bz2
... ...
... ... @@ -9,6 +9,15 @@ git status
ls -lh
ls -lh node_modules
# asr with offline zipformer ctc
curl -SL -O https://github.com/k2-fsa/sherpa-onnx/releases/download/asr-models/sherpa-onnx-zipformer-ctc-zh-int8-2025-07-03.tar.bz2
tar xvf sherpa-onnx-zipformer-ctc-zh-int8-2025-07-03.tar.bz2
rm sherpa-onnx-zipformer-ctc-zh-int8-2025-07-03.tar.bz2
node ./test-offline-zipformer-ctc.js
rm -rf sherpa-onnx-zipformer-ctc-zh-int8-2025-07-03
# asr with offline dolphin ctc
curl -SL -O https://github.com/k2-fsa/sherpa-onnx/releases/download/asr-models/sherpa-onnx-dolphin-base-ctc-multi-lang-int8-2025-04-02.tar.bz2
tar xvf sherpa-onnx-dolphin-base-ctc-multi-lang-int8-2025-04-02.tar.bz2
... ...
... ... @@ -9,6 +9,9 @@ ls -lh
./run-test-version.sh
./run-zipformer-ctc-asr.sh
rm -rf sherpa-onnx-zipformer-*
./run-decode-file-sense-voice-with-hr.sh
rm -rf sherpa-onnx-sense-voice-*
rm -rf dict lexicon.txt replace.fst test-hr.wav
... ...
... ... @@ -89,6 +89,7 @@ jobs:
make -j4 install
cp -v bin/sense-voice-simulate-streaming-alsa-cxx-api install/bin
cp -v bin/zipformer-ctc-simulate-streaming-alsa-cxx-api install/bin
rm -rf install/lib/pkgconfig
rm -fv install/lib/cargs.h
... ... @@ -135,6 +136,7 @@ jobs:
make -j4 install
cp -v bin/sense-voice-simulate-streaming-alsa-cxx-api install/bin
cp -v bin/zipformer-ctc-simulate-streaming-alsa-cxx-api install/bin
rm -rf install/lib/pkgconfig
rm -fv install/lib/cargs.h
... ...
... ... @@ -90,6 +90,7 @@ jobs:
make install
cp bin/sense-voice-simulate-streaming-alsa-cxx-api install/bin
cp bin/zipformer-ctc-simulate-streaming-alsa-cxx-api install/bin
ls -lh install/lib
... ...
... ... @@ -37,7 +37,7 @@ jobs:
strategy:
fail-fast: false
matrix:
os: [ubuntu-latest, macos-latest, macos-13, windows-latest]
os: [ubuntu-latest, macos-latest, macos-13, windows-latest, ubuntu-22.04-arm]
steps:
- uses: actions/checkout@v4
... ... @@ -56,7 +56,7 @@ jobs:
key: ${{ matrix.os }}
- name: Install Free pascal compiler (ubuntu)
if: matrix.os == 'ubuntu-latest'
if: matrix.os == 'ubuntu-latest' || matrix.os == 'ubuntu-22.04-arm'
shell: bash
run: |
sudo apt-get update
... ... @@ -156,6 +156,10 @@ jobs:
pushd non-streaming-asr
./run-zipformer-ctc.sh
rm -rf sherpa-onnx-*
echo "---"
./run-dolphin-ctc.sh
rm -rf sherpa-onnx-*
echo "---"
... ... @@ -264,9 +268,12 @@ jobs:
cd ./pascal-api-examples
pushd vad-with-non-streaming-asr
time ./run-vad-with-zipformer-ctc.sh
rm -rf sherpa-onnx-*
echo "---"
time ./run-vad-with-dolphin-ctc.sh
rm -rf sherpa-onnx-*
echo "---"
... ...
... ... @@ -165,6 +165,9 @@ jobs:
run: |
cd ./java-api-examples
./run-non-streaming-decode-file-zipformer-ctc.sh
rm -rf sherpa-onnx-zipformer-ctc-*
./run-non-streaming-decode-file-dolphin-ctc.sh
rm -rf sherpa-onnx-dolphin-*
... ...
... ... @@ -184,6 +184,10 @@ jobs:
go build
ls -lh
echo "Test Zipformer CTC"
./run-zipformer-ctc.sh
rm -rf sherpa-onnx-zipformer-*
echo "Test SenseVoice ctc"
./run-sense-voice-small-with-hr.sh
./run-sense-voice-small.sh
... ...
... ... @@ -19,12 +19,36 @@ jobs:
fail-fast: false
matrix:
os: [ubuntu-latest]
python-version: ["3.8"]
python-version: ["3.10"]
steps:
- uses: actions/checkout@v4
- name: Zipformer CTC (non-streaming)
shell: bash
run: |
git lfs install
names=(
sherpa-onnx-zipformer-ctc-zh-int8-2025-07-03
sherpa-onnx-zipformer-ctc-zh-2025-07-03
sherpa-onnx-zipformer-ctc-zh-fp16-2025-07-03
)
for name in ${names[@]}; do
git clone https://huggingface.co/csukuangfj/$name
pushd $name
git lfs pull
rm -rf .git
rm -rfv .gitattributes
ls -lh
popd
tar cjfv $name.tar.bz2 $name
rm -rf $name
ls -lh *.tar.bz2
done
- name: Vietnamese (zipformer)
if: false
shell: bash
run: |
rm -rf models
... ... @@ -76,6 +100,7 @@ jobs:
mv models/* .
- name: Publish to huggingface (Vietnamese zipformer)
if: false
env:
HF_TOKEN: ${{ secrets.HF_TOKEN }}
uses: nick-fields/retry@v3
... ...
... ... @@ -114,6 +114,7 @@ We also have spaces built using WebAssembly. They are listed below:
|Real-time speech recognition (Chinese + English) with Paraformer |[Click me][wasm-hf-streaming-asr-zh-en-paraformer]| [地址][wasm-ms-streaming-asr-zh-en-paraformer]|
|Real-time speech recognition (Chinese + English + Cantonese) with [Paraformer-large][Paraformer-large]|[Click me][wasm-hf-streaming-asr-zh-en-yue-paraformer]| [地址][wasm-ms-streaming-asr-zh-en-yue-paraformer]|
|Real-time speech recognition (English) |[Click me][wasm-hf-streaming-asr-en-zipformer] |[地址][wasm-ms-streaming-asr-en-zipformer]|
|VAD + speech recognition (Chinese) with [Zipformer CTC](https://k2-fsa.github.io/sherpa/onnx/pretrained_models/offline-ctc/icefall/zipformer.html#sherpa-onnx-zipformer-ctc-zh-int8-2025-07-03-chinese)|[Click me][wasm-hf-vad-asr-zh-zipformer-ctc-07-03]| [地址][wasm-ms-vad-asr-zh-zipformer-ctc-07-03]|
|VAD + speech recognition (Chinese + English + Korean + Japanese + Cantonese) with [SenseVoice][SenseVoice]|[Click me][wasm-hf-vad-asr-zh-en-ko-ja-yue-sense-voice]| [地址][wasm-ms-vad-asr-zh-en-ko-ja-yue-sense-voice]|
|VAD + speech recognition (English) with [Whisper][Whisper] tiny.en|[Click me][wasm-hf-vad-asr-en-whisper-tiny-en]| [地址][wasm-ms-vad-asr-en-whisper-tiny-en]|
|VAD + speech recognition (English) with [Moonshine tiny][Moonshine tiny]|[Click me][wasm-hf-vad-asr-en-moonshine-tiny-en]| [地址][wasm-ms-vad-asr-en-moonshine-tiny-en]|
... ... @@ -141,6 +142,7 @@ We also have spaces built using WebAssembly. They are listed below:
|----------------------------------------|------------------------------------|-----------------------------------|
| Speaker diarization | [Address][apk-speaker-diarization] | [点此][apk-speaker-diarization-cn]|
| Streaming speech recognition | [Address][apk-streaming-asr] | [点此][apk-streaming-asr-cn] |
| Simulated-streaming speech recognition | [Address][apk-simula-streaming-asr]| [点此][apk-simula-streaming-asr-cn]|
| Text-to-speech | [Address][apk-tts] | [点此][apk-tts-cn] |
| Voice activity detection (VAD) | [Address][apk-vad] | [点此][apk-vad-cn] |
| VAD + non-streaming speech recognition | [Address][apk-vad-asr] | [点此][apk-vad-asr-cn] |
... ... @@ -250,8 +252,10 @@ for more models. The following table lists only **SOME** of them.
|Name | Supported Languages| Description|
|-----|-----|----|
|[sherpa-onnx-nemo-parakeet-tdt-0.6b-v2-int8](https://k2-fsa.github.io/sherpa/onnx/pretrained_models/offline-transducer/nemo-transducer-models.html#sherpa-onnx-nemo-parakeet-tdt-0-6b-v2-int8-english)| English | It is converted from <https://huggingface.co/nvidia/parakeet-tdt-0.6b-v2>|
|[Whisper tiny.en](https://github.com/k2-fsa/sherpa-onnx/releases/download/asr-models/sherpa-onnx-whisper-tiny.en.tar.bz2)|English| See [also](https://k2-fsa.github.io/sherpa/onnx/pretrained_models/whisper/tiny.en.html)|
|[Moonshine tiny][Moonshine tiny]|English|See [also](https://github.com/usefulsensors/moonshine)|
|[sherpa-onnx-zipformer-ctc-zh-int8-2025-07-03](https://k2-fsa.github.io/sherpa/onnx/pretrained_models/offline-ctc/icefall/zipformer.html#sherpa-onnx-zipformer-ctc-zh-int8-2025-07-03-chinese)|Chinese| A Zipformer CTC model|
|[sherpa-onnx-sense-voice-zh-en-ja-ko-yue-2024-07-17][sherpa-onnx-sense-voice-zh-en-ja-ko-yue-2024-07-17]|Chinese, Cantonese, English, Korean, Japanese| 支持多种中文方言. See [also](https://k2-fsa.github.io/sherpa/onnx/sense-voice/index.html)|
|[sherpa-onnx-paraformer-zh-2024-03-09][sherpa-onnx-paraformer-zh-2024-03-09]|Chinese, English| 也支持多种中文方言. See [also](https://k2-fsa.github.io/sherpa/onnx/pretrained_models/offline-paraformer/paraformer-models.html#csukuangfj-sherpa-onnx-paraformer-zh-2024-03-09-chinese-english)|
|[sherpa-onnx-zipformer-ja-reazonspeech-2024-08-01][sherpa-onnx-zipformer-ja-reazonspeech-2024-08-01]|Japanese|See [also](https://k2-fsa.github.io/sherpa/onnx/pretrained_models/offline-transducer/zipformer-transducer-models.html#sherpa-onnx-zipformer-ja-reazonspeech-2024-08-01-japanese)|
... ... @@ -413,6 +417,8 @@ It uses sherpa-onnx for speech-to-text and text-to-speech.
[wasm-hf-streaming-asr-en-zipformer]: https://huggingface.co/spaces/k2-fsa/web-assembly-asr-sherpa-onnx-en
[wasm-ms-streaming-asr-en-zipformer]: https://modelscope.cn/studios/k2-fsa/web-assembly-asr-sherpa-onnx-en
[SenseVoice]: https://github.com/FunAudioLLM/SenseVoice
[wasm-hf-vad-asr-zh-zipformer-ctc-07-03]: https://huggingface.co/spaces/k2-fsa/web-assembly-vad-asr-sherpa-onnx-zh-zipformer-ctc
[wasm-ms-vad-asr-zh-zipformer-ctc-07-03]: https://modelscope.cn/studios/csukuangfj/web-assembly-vad-asr-sherpa-onnx-zh-zipformer-ctc/summary
[wasm-hf-vad-asr-zh-en-ko-ja-yue-sense-voice]: https://huggingface.co/spaces/k2-fsa/web-assembly-vad-asr-sherpa-onnx-zh-en-ja-ko-cantonese-sense-voice
[wasm-ms-vad-asr-zh-en-ko-ja-yue-sense-voice]: https://www.modelscope.cn/studios/csukuangfj/web-assembly-vad-asr-sherpa-onnx-zh-en-jp-ko-cantonese-sense-voice
[wasm-hf-vad-asr-en-whisper-tiny-en]: https://huggingface.co/spaces/k2-fsa/web-assembly-vad-asr-sherpa-onnx-en-whisper-tiny
... ... @@ -423,20 +429,20 @@ It uses sherpa-onnx for speech-to-text and text-to-speech.
[wasm-ms-vad-asr-en-zipformer-gigaspeech]: https://www.modelscope.cn/studios/k2-fsa/web-assembly-vad-asr-sherpa-onnx-en-zipformer-gigaspeech
[wasm-hf-vad-asr-zh-zipformer-wenetspeech]: https://huggingface.co/spaces/k2-fsa/web-assembly-vad-asr-sherpa-onnx-zh-zipformer-wenetspeech
[wasm-ms-vad-asr-zh-zipformer-wenetspeech]: https://www.modelscope.cn/studios/k2-fsa/web-assembly-vad-asr-sherpa-onnx-zh-zipformer-wenetspeech
[ReazonSpeech]: https://research.reazon.jp/_static/reazonspeech_nlp2023.pdf
[reazonspeech]: https://research.reazon.jp/_static/reazonspeech_nlp2023.pdf
[wasm-hf-vad-asr-ja-zipformer-reazonspeech]: https://huggingface.co/spaces/k2-fsa/web-assembly-vad-asr-sherpa-onnx-ja-zipformer
[wasm-ms-vad-asr-ja-zipformer-reazonspeech]: https://www.modelscope.cn/studios/csukuangfj/web-assembly-vad-asr-sherpa-onnx-ja-zipformer
[GigaSpeech2]: https://github.com/SpeechColab/GigaSpeech2
[gigaspeech2]: https://github.com/speechcolab/gigaspeech2
[wasm-hf-vad-asr-th-zipformer-gigaspeech2]: https://huggingface.co/spaces/k2-fsa/web-assembly-vad-asr-sherpa-onnx-th-zipformer
[wasm-ms-vad-asr-th-zipformer-gigaspeech2]: https://www.modelscope.cn/studios/csukuangfj/web-assembly-vad-asr-sherpa-onnx-th-zipformer
[TeleSpeech-ASR]: https://github.com/Tele-AI/TeleSpeech-ASR
[telespeech-asr]: https://github.com/tele-ai/telespeech-asr
[wasm-hf-vad-asr-zh-telespeech]: https://huggingface.co/spaces/k2-fsa/web-assembly-vad-asr-sherpa-onnx-zh-telespeech
[wasm-ms-vad-asr-zh-telespeech]: https://www.modelscope.cn/studios/k2-fsa/web-assembly-vad-asr-sherpa-onnx-zh-telespeech
[wasm-hf-vad-asr-zh-en-paraformer-large]: https://huggingface.co/spaces/k2-fsa/web-assembly-vad-asr-sherpa-onnx-zh-en-paraformer
[wasm-ms-vad-asr-zh-en-paraformer-large]: https://www.modelscope.cn/studios/k2-fsa/web-assembly-vad-asr-sherpa-onnx-zh-en-paraformer
[wasm-hf-vad-asr-zh-en-paraformer-small]: https://huggingface.co/spaces/k2-fsa/web-assembly-vad-asr-sherpa-onnx-zh-en-paraformer-small
[wasm-ms-vad-asr-zh-en-paraformer-small]: https://www.modelscope.cn/studios/k2-fsa/web-assembly-vad-asr-sherpa-onnx-zh-en-paraformer-small
[Dolphin]: https://github.com/DataoceanAI/Dolphin
[dolphin]: https://github.com/dataoceanai/dolphin
[wasm-ms-vad-asr-multi-lang-dolphin-base]: https://modelscope.cn/studios/csukuangfj/web-assembly-vad-asr-sherpa-onnx-multi-lang-dophin-ctc
[wasm-hf-vad-asr-multi-lang-dolphin-base]: https://huggingface.co/spaces/k2-fsa/web-assembly-vad-asr-sherpa-onnx-multi-lang-dophin-ctc
... ... @@ -450,6 +456,8 @@ It uses sherpa-onnx for speech-to-text and text-to-speech.
[apk-speaker-diarization-cn]: https://k2-fsa.github.io/sherpa/onnx/speaker-diarization/apk-cn.html
[apk-streaming-asr]: https://k2-fsa.github.io/sherpa/onnx/android/apk.html
[apk-streaming-asr-cn]: https://k2-fsa.github.io/sherpa/onnx/android/apk-cn.html
[apk-simula-streaming-asr]: https://k2-fsa.github.io/sherpa/onnx/android/apk-simulate-streaming-asr.html
[apk-simula-streaming-asr-cn]: https://k2-fsa.github.io/sherpa/onnx/android/apk-simulate-streaming-asr-cn.html
[apk-tts]: https://k2-fsa.github.io/sherpa/onnx/tts/apk-engine.html
[apk-tts-cn]: https://k2-fsa.github.io/sherpa/onnx/tts/apk-engine-cn.html
[apk-vad]: https://k2-fsa.github.io/sherpa/onnx/vad/apk.html
... ...
... ... @@ -45,6 +45,15 @@ if(SHERPA_ONNX_ENABLE_PORTAUDIO)
sherpa-onnx-cxx-api
portaudio_static
)
add_executable(zipformer-ctc-simulate-streaming-microphone-cxx-api
./zipformer-ctc-simulate-streaming-microphone-cxx-api.cc
${CMAKE_CURRENT_LIST_DIR}/../sherpa-onnx/csrc/microphone.cc
)
target_link_libraries(zipformer-ctc-simulate-streaming-microphone-cxx-api
sherpa-onnx-cxx-api
portaudio_static
)
endif()
if(SHERPA_ONNX_HAS_ALSA)
... ... @@ -57,10 +66,21 @@ if(SHERPA_ONNX_HAS_ALSA)
portaudio_static
)
add_executable(zipformer-ctc-simulate-streaming-alsa-cxx-api
./zipformer-ctc-simulate-streaming-alsa-cxx-api.cc
${CMAKE_CURRENT_LIST_DIR}/../sherpa-onnx/csrc/alsa.cc
)
target_link_libraries(zipformer-ctc-simulate-streaming-alsa-cxx-api
sherpa-onnx-cxx-api
portaudio_static
)
if(DEFINED ENV{SHERPA_ONNX_ALSA_LIB_DIR})
target_link_libraries(sense-voice-simulate-streaming-alsa-cxx-api -L$ENV{SHERPA_ONNX_ALSA_LIB_DIR} -lasound)
target_link_libraries(zipformer-ctc-simulate-streaming-alsa-cxx-api -L$ENV{SHERPA_ONNX_ALSA_LIB_DIR} -lasound)
else()
target_link_libraries(sense-voice-simulate-streaming-alsa-cxx-api asound)
target_link_libraries(zipformer-ctc-simulate-streaming-alsa-cxx-api asound)
endif()
endif()
... ...
// cxx-api-examples/zipformer-ctc-simulate-streaming-alsa-cxx-api.cc
// Copyright (c) 2025 Xiaomi Corporation
//
// This file demonstrates how to use zipformer CTC with sherpa-onnx's C++ API
// for streaming speech recognition from a microphone.
//
// clang-format off
//
// wget https://github.com/k2-fsa/sherpa-onnx/releases/download/asr-models/silero_vad.onnx
//
// wget https://github.com/k2-fsa/sherpa-onnx/releases/download/asr-models/sherpa-onnx-zipformer-ctc-zh-int8-2025-07-03.tar.bz2
// tar xvf sherpa-onnx-zipformer-ctc-zh-int8-2025-07-03.tar.bz2
// rm sherpa-onnx-zipformer-ctc-zh-int8-2025-07-03.tar.bz2
//
// clang-format on
#include <signal.h>
#include <stdio.h>
#include <stdlib.h>
#include <chrono> // NOLINT
#include <condition_variable> // NOLINT
#include <iostream>
#include <mutex> // NOLINT
#include <queue>
#include <thread> // NOLINT
#include <vector>
#include "sherpa-display.h" // NOLINT
#include "sherpa-onnx/c-api/cxx-api.h"
#include "sherpa-onnx/csrc/alsa.h"
std::queue<std::vector<float>> samples_queue;
std::condition_variable condition_variable;
std::mutex mutex;
bool stop = false;
static void Handler(int32_t /*sig*/) {
stop = true;
condition_variable.notify_one();
fprintf(stderr, "\nCaught Ctrl + C. Exiting...\n");
}
static void RecordCallback(sherpa_onnx::Alsa *alsa) {
int32_t chunk = 0.1 * alsa->GetActualSampleRate();
while (!stop) {
std::vector<float> samples = alsa->Read(chunk);
std::lock_guard<std::mutex> lock(mutex);
samples_queue.emplace(std::move(samples));
condition_variable.notify_one();
}
}
static sherpa_onnx::cxx::VoiceActivityDetector CreateVad() {
using namespace sherpa_onnx::cxx; // NOLINT
VadModelConfig config;
config.silero_vad.model = "./silero_vad.onnx";
config.silero_vad.threshold = 0.5;
config.silero_vad.min_silence_duration = 0.1;
config.silero_vad.min_speech_duration = 0.25;
config.silero_vad.max_speech_duration = 8;
config.sample_rate = 16000;
config.debug = false;
VoiceActivityDetector vad = VoiceActivityDetector::Create(config, 20);
if (!vad.Get()) {
std::cerr << "Failed to create VAD. Please check your config\n";
exit(-1);
}
return vad;
}
static sherpa_onnx::cxx::OfflineRecognizer CreateOfflineRecognizer() {
using namespace sherpa_onnx::cxx; // NOLINT
OfflineRecognizerConfig config;
config.model_config.zipformer_ctc.model =
"./sherpa-onnx-zipformer-ctc-zh-int8-2025-07-03/model.int8.onnx";
config.model_config.tokens =
"./sherpa-onnx-zipformer-ctc-zh-int8-2025-07-03/tokens.txt";
config.model_config.num_threads = 2;
config.model_config.debug = false;
std::cout << "Loading model\n";
OfflineRecognizer recognizer = OfflineRecognizer::Create(config);
if (!recognizer.Get()) {
std::cerr << "Please check your config\n";
exit(-1);
}
std::cout << "Loading model done\n";
return recognizer;
}
int32_t main(int32_t argc, const char *argv[]) {
const char *kUsageMessage = R"usage(
Usage:
wget https://github.com/k2-fsa/sherpa-onnx/releases/download/asr-models/silero_vad.onnx
wget https://github.com/k2-fsa/sherpa-onnx/releases/download/asr-models/sherpa-onnx-zipformer-ctc-zh-int8-2025-07-03.tar.bz2
tar xvf sherpa-onnx-zipformer-ctc-zh-int8-2025-07-03.tar.bz2
rm sherpa-onnx-zipformer-ctc-zh-int8-2025-07-03.tar.bz2
./zipformer-ctc-simulate-streaming-alsa-cxx-api device_name
The device name specifies which microphone to use in case there are several
on your system. You can use
arecord -l
to find all available microphones on your computer. For instance, if it outputs
**** List of CAPTURE Hardware Devices ****
card 3: UACDemoV10 [UACDemoV1.0], device 0: USB Audio [USB Audio]
Subdevices: 1/1
Subdevice #0: subdevice #0
and if you want to select card 3 and device 0 on that card, please use:
plughw:3,0
as the device_name.
)usage";
if (argc != 2) {
fprintf(stderr, "%s\n", kUsageMessage);
return -1;
}
signal(SIGINT, Handler);
using namespace sherpa_onnx::cxx; // NOLINT
auto vad = CreateVad();
auto recognizer = CreateOfflineRecognizer();
int32_t expected_sample_rate = 16000;
std::string device_name = argv[1];
sherpa_onnx::Alsa alsa(device_name.c_str());
fprintf(stderr, "Use recording device: %s\n", device_name.c_str());
if (alsa.GetExpectedSampleRate() != expected_sample_rate) {
fprintf(stderr, "sample rate: %d != %d\n", alsa.GetExpectedSampleRate(),
expected_sample_rate);
exit(-1);
}
int32_t window_size = 512; // samples, please don't change
int32_t offset = 0;
std::vector<float> buffer;
bool speech_started = false;
auto started_time = std::chrono::steady_clock::now();
SherpaDisplay display;
std::thread record_thread(RecordCallback, &alsa);
std::cout << "Started! Please speak\n";
while (!stop) {
{
std::unique_lock<std::mutex> lock(mutex);
while (samples_queue.empty() && !stop) {
condition_variable.wait(lock);
}
const auto &s = samples_queue.front();
buffer.insert(buffer.end(), s.begin(), s.end());
samples_queue.pop();
}
for (; offset + window_size < buffer.size(); offset += window_size) {
vad.AcceptWaveform(buffer.data() + offset, window_size);
if (!speech_started && vad.IsDetected()) {
speech_started = true;
started_time = std::chrono::steady_clock::now();
}
}
if (!speech_started) {
if (buffer.size() > 10 * window_size) {
offset -= buffer.size() - 10 * window_size;
buffer = {buffer.end() - 10 * window_size, buffer.end()};
}
}
auto current_time = std::chrono::steady_clock::now();
const float elapsed_seconds =
std::chrono::duration_cast<std::chrono::milliseconds>(current_time -
started_time)
.count() /
1000.;
if (speech_started && elapsed_seconds > 0.2) {
OfflineStream stream = recognizer.CreateStream();
stream.AcceptWaveform(expected_sample_rate, buffer.data(), buffer.size());
recognizer.Decode(&stream);
OfflineRecognizerResult result = recognizer.GetResult(&stream);
display.UpdateText(result.text);
display.Display();
started_time = std::chrono::steady_clock::now();
}
while (!vad.IsEmpty()) {
auto segment = vad.Front();
vad.Pop();
OfflineStream stream = recognizer.CreateStream();
stream.AcceptWaveform(expected_sample_rate, segment.samples.data(),
segment.samples.size());
recognizer.Decode(&stream);
OfflineRecognizerResult result = recognizer.GetResult(&stream);
display.UpdateText(result.text);
display.FinalizeCurrentSentence();
display.Display();
buffer.clear();
offset = 0;
speech_started = false;
}
}
record_thread.join();
return 0;
}
... ...
// cxx-api-examples/zipformer-ctc-simulate-streaming-microphone-cxx-api.cc
// Copyright (c) 2025 Xiaomi Corporation
//
// This file demonstrates how to use Zipformer CTC with sherpa-onnx's C++ API
// for streaming speech recognition from a microphone.
//
// clang-format off
//
// wget https://github.com/k2-fsa/sherpa-onnx/releases/download/asr-models/silero_vad.onnx
//
// wget https://github.com/k2-fsa/sherpa-onnx/releases/download/asr-models/sherpa-onnx-zipformer-ctc-zh-int8-2025-07-03.tar.bz2
// tar xvf sherpa-onnx-zipformer-ctc-zh-int8-2025-07-03.tar.bz2
// rm sherpa-onnx-zipformer-ctc-zh-int8-2025-07-03.tar.bz2
//
// clang-format on
#include <signal.h>
#include <stdio.h>
#include <stdlib.h>
#include <chrono> // NOLINT
#include <condition_variable> // NOLINT
#include <iostream>
#include <mutex> // NOLINT
#include <queue>
#include <vector>
#include "portaudio.h" // NOLINT
#include "sherpa-display.h" // NOLINT
#include "sherpa-onnx/c-api/cxx-api.h"
#include "sherpa-onnx/csrc/microphone.h"
std::queue<std::vector<float>> samples_queue;
std::condition_variable condition_variable;
std::mutex mutex;
bool stop = false;
static void Handler(int32_t /*sig*/) {
stop = true;
condition_variable.notify_one();
fprintf(stderr, "\nCaught Ctrl + C. Exiting...\n");
}
static int32_t RecordCallback(const void *input_buffer,
void * /*output_buffer*/,
unsigned long frames_per_buffer, // NOLINT
const PaStreamCallbackTimeInfo * /*time_info*/,
PaStreamCallbackFlags /*status_flags*/,
void * /*user_data*/) {
std::lock_guard<std::mutex> lock(mutex);
samples_queue.emplace(
reinterpret_cast<const float *>(input_buffer),
reinterpret_cast<const float *>(input_buffer) + frames_per_buffer);
condition_variable.notify_one();
return stop ? paComplete : paContinue;
}
static sherpa_onnx::cxx::VoiceActivityDetector CreateVad() {
using namespace sherpa_onnx::cxx; // NOLINT
VadModelConfig config;
config.silero_vad.model = "./silero_vad.onnx";
config.silero_vad.threshold = 0.5;
config.silero_vad.min_silence_duration = 0.1;
config.silero_vad.min_speech_duration = 0.25;
config.silero_vad.max_speech_duration = 8;
config.sample_rate = 16000;
config.debug = false;
VoiceActivityDetector vad = VoiceActivityDetector::Create(config, 20);
if (!vad.Get()) {
std::cerr << "Failed to create VAD. Please check your config\n";
exit(-1);
}
return vad;
}
static sherpa_onnx::cxx::OfflineRecognizer CreateOfflineRecognizer() {
using namespace sherpa_onnx::cxx; // NOLINT
OfflineRecognizerConfig config;
config.model_config.zipformer_ctc.model =
"./sherpa-onnx-zipformer-ctc-zh-int8-2025-07-03/model.int8.onnx";
config.model_config.tokens =
"./sherpa-onnx-zipformer-ctc-zh-int8-2025-07-03/tokens.txt";
config.model_config.num_threads = 2;
config.model_config.debug = false;
std::cout << "Loading model\n";
OfflineRecognizer recognizer = OfflineRecognizer::Create(config);
if (!recognizer.Get()) {
std::cerr << "Please check your config\n";
exit(-1);
}
std::cout << "Loading model done\n";
return recognizer;
}
int32_t main() {
signal(SIGINT, Handler);
using namespace sherpa_onnx::cxx; // NOLINT
auto vad = CreateVad();
auto recognizer = CreateOfflineRecognizer();
sherpa_onnx::Microphone mic;
PaDeviceIndex num_devices = Pa_GetDeviceCount();
if (num_devices == 0) {
std::cerr << " If you are using Linux, please try "
"./build/bin/zipformer-ctc-simulate-streaming-alsa-cxx-api\n";
return -1;
}
int32_t device_index = Pa_GetDefaultInputDevice();
const char *pDeviceIndex = std::getenv("SHERPA_ONNX_MIC_DEVICE");
if (pDeviceIndex) {
fprintf(stderr, "Use specified device: %s\n", pDeviceIndex);
device_index = atoi(pDeviceIndex);
}
mic.PrintDevices(device_index);
float mic_sample_rate = 16000;
const char *sample_rate_str = std::getenv("SHERPA_ONNX_MIC_SAMPLE_RATE");
if (sample_rate_str) {
fprintf(stderr, "Use sample rate %f for mic\n", mic_sample_rate);
mic_sample_rate = atof(sample_rate_str);
}
float sample_rate = 16000;
LinearResampler resampler;
if (mic_sample_rate != sample_rate) {
float min_freq = std::min(mic_sample_rate, sample_rate);
float lowpass_cutoff = 0.99 * 0.5 * min_freq;
int32_t lowpass_filter_width = 6;
resampler = LinearResampler::Create(mic_sample_rate, sample_rate,
lowpass_cutoff, lowpass_filter_width);
}
if (mic.OpenDevice(device_index, mic_sample_rate, 1, RecordCallback,
nullptr) == false) {
std::cerr << "Failed to open microphone device\n";
return -1;
}
int32_t window_size = 512; // samples, please don't change
int32_t offset = 0;
std::vector<float> buffer;
bool speech_started = false;
auto started_time = std::chrono::steady_clock::now();
SherpaDisplay display;
std::cout << "Started! Please speak\n";
while (!stop) {
{
std::unique_lock<std::mutex> lock(mutex);
while (samples_queue.empty() && !stop) {
condition_variable.wait(lock);
}
const auto &s = samples_queue.front();
if (!resampler.Get()) {
buffer.insert(buffer.end(), s.begin(), s.end());
} else {
auto resampled = resampler.Resample(s.data(), s.size(), false);
buffer.insert(buffer.end(), resampled.begin(), resampled.end());
}
samples_queue.pop();
}
for (; offset + window_size < buffer.size(); offset += window_size) {
vad.AcceptWaveform(buffer.data() + offset, window_size);
if (!speech_started && vad.IsDetected()) {
speech_started = true;
started_time = std::chrono::steady_clock::now();
}
}
if (!speech_started) {
if (buffer.size() > 10 * window_size) {
offset -= buffer.size() - 10 * window_size;
buffer = {buffer.end() - 10 * window_size, buffer.end()};
}
}
auto current_time = std::chrono::steady_clock::now();
const float elapsed_seconds =
std::chrono::duration_cast<std::chrono::milliseconds>(current_time -
started_time)
.count() /
1000.;
if (speech_started && elapsed_seconds > 0.2) {
OfflineStream stream = recognizer.CreateStream();
stream.AcceptWaveform(sample_rate, buffer.data(), buffer.size());
recognizer.Decode(&stream);
OfflineRecognizerResult result = recognizer.GetResult(&stream);
display.UpdateText(result.text);
display.Display();
started_time = std::chrono::steady_clock::now();
}
while (!vad.IsEmpty()) {
auto segment = vad.Front();
vad.Pop();
OfflineStream stream = recognizer.CreateStream();
stream.AcceptWaveform(sample_rate, segment.samples.data(),
segment.samples.size());
recognizer.Decode(&stream);
OfflineRecognizerResult result = recognizer.GetResult(&stream);
display.UpdateText(result.text);
display.FinalizeCurrentSentence();
display.Display();
buffer.clear();
offset = 0;
speech_started = false;
}
}
return 0;
}
... ...
// Copyright (c) 2025 Xiaomi Corporation
import 'dart:io';
import 'package:args/args.dart';
import 'package:sherpa_onnx/sherpa_onnx.dart' as sherpa_onnx;
import './init.dart';
void main(List<String> arguments) async {
await initSherpaOnnx();
final parser = ArgParser()
..addOption('model', help: 'Path to the Zipformer CTC model')
..addOption('tokens', help: 'Path to tokens.txt')
..addOption('input-wav', help: 'Path to input.wav to transcribe');
final res = parser.parse(arguments);
if (res['model'] == null ||
res['tokens'] == null ||
res['input-wav'] == null) {
print(parser.usage);
exit(1);
}
final model = res['model'] as String;
final tokens = res['tokens'] as String;
final inputWav = res['input-wav'] as String;
final zipformerCtc = sherpa_onnx.OfflineZipformerCtcModelConfig(model: model);
final modelConfig = sherpa_onnx.OfflineModelConfig(
zipformerCtc: zipformerCtc,
tokens: tokens,
debug: true,
numThreads: 1,
);
final config = sherpa_onnx.OfflineRecognizerConfig(model: modelConfig);
final recognizer = sherpa_onnx.OfflineRecognizer(config);
final waveData = sherpa_onnx.readWave(inputWav);
final stream = recognizer.createStream();
stream.acceptWaveform(
samples: waveData.samples, sampleRate: waveData.sampleRate);
recognizer.decode(stream);
final result = recognizer.getResult(stream);
print(result.text);
stream.free();
recognizer.free();
}
... ...
#!/usr/bin/env bash
set -ex
dart pub get
if [ ! -f ./sherpa-onnx-zipformer-ctc-zh-int8-2025-07-03/tokens.txt ]; then
curl -SL -O https://github.com/k2-fsa/sherpa-onnx/releases/download/asr-models/sherpa-onnx-zipformer-ctc-zh-int8-2025-07-03.tar.bz2
tar xvf sherpa-onnx-zipformer-ctc-zh-int8-2025-07-03.tar.bz2
rm sherpa-onnx-zipformer-ctc-zh-int8-2025-07-03.tar.bz2
fi
dart run \
./bin/zipformer-ctc.dart \
--model ./sherpa-onnx-zipformer-ctc-zh-int8-2025-07-03/model.int8.onnx \
--tokens ./sherpa-onnx-zipformer-ctc-zh-int8-2025-07-03/tokens.txt \
--input-wav ./sherpa-onnx-zipformer-ctc-zh-int8-2025-07-03/test_wavs/0.wav
... ...
// Copyright (c) 2025 Xiaomi Corporation
import 'dart:io';
import 'dart:typed_data';
import 'package:args/args.dart';
import 'package:sherpa_onnx/sherpa_onnx.dart' as sherpa_onnx;
import './init.dart';
void main(List<String> arguments) async {
await initSherpaOnnx();
final parser = ArgParser()
..addOption('silero-vad', help: 'Path to silero_vad.onnx')
..addOption('model', help: 'Path to the Zipformer CTC model')
..addOption('tokens', help: 'Path to tokens.txt')
..addOption('input-wav', help: 'Path to input.wav to transcribe');
final res = parser.parse(arguments);
if (res['silero-vad'] == null ||
res['model'] == null ||
res['tokens'] == null ||
res['input-wav'] == null) {
print(parser.usage);
exit(1);
}
// create VAD
final sileroVad = res['silero-vad'] as String;
final sileroVadConfig = sherpa_onnx.SileroVadModelConfig(
model: sileroVad,
minSilenceDuration: 0.25,
minSpeechDuration: 0.5,
maxSpeechDuration: 5.0,
);
final vadConfig = sherpa_onnx.VadModelConfig(
sileroVad: sileroVadConfig,
numThreads: 1,
debug: true,
);
final vad = sherpa_onnx.VoiceActivityDetector(
config: vadConfig, bufferSizeInSeconds: 10);
// create offline recognizer
final model = res['model'] as String;
final tokens = res['tokens'] as String;
final inputWav = res['input-wav'] as String;
final zipformerCtc = sherpa_onnx.OfflineZipformerCtcModelConfig(model: model);
final modelConfig = sherpa_onnx.OfflineModelConfig(
zipformerCtc: zipformerCtc,
tokens: tokens,
debug: true,
numThreads: 1,
);
final config = sherpa_onnx.OfflineRecognizerConfig(model: modelConfig);
final recognizer = sherpa_onnx.OfflineRecognizer(config);
final waveData = sherpa_onnx.readWave(inputWav);
if (waveData.sampleRate != 16000) {
print('Only 16000 Hz is supported. Given: ${waveData.sampleRate}');
exit(1);
}
int numSamples = waveData.samples.length;
int numIter = numSamples ~/ vadConfig.sileroVad.windowSize;
for (int i = 0; i != numIter; ++i) {
int start = i * vadConfig.sileroVad.windowSize;
vad.acceptWaveform(Float32List.sublistView(
waveData.samples, start, start + vadConfig.sileroVad.windowSize));
while (!vad.isEmpty()) {
final samples = vad.front().samples;
final startTime = vad.front().start.toDouble() / waveData.sampleRate;
final endTime =
startTime + samples.length.toDouble() / waveData.sampleRate;
final stream = recognizer.createStream();
stream.acceptWaveform(samples: samples, sampleRate: waveData.sampleRate);
recognizer.decode(stream);
final result = recognizer.getResult(stream);
stream.free();
print(
'${startTime.toStringAsPrecision(5)} -- ${endTime.toStringAsPrecision(5)} : ${result.text}');
vad.pop();
}
}
vad.flush();
while (!vad.isEmpty()) {
final samples = vad.front().samples;
final startTime = vad.front().start.toDouble() / waveData.sampleRate;
final endTime = startTime + samples.length.toDouble() / waveData.sampleRate;
final stream = recognizer.createStream();
stream.acceptWaveform(samples: samples, sampleRate: waveData.sampleRate);
recognizer.decode(stream);
final result = recognizer.getResult(stream);
stream.free();
print(
'${startTime.toStringAsPrecision(5)} -- ${endTime.toStringAsPrecision(5)} : ${result.text}');
vad.pop();
}
vad.free();
recognizer.free();
}
... ...
#!/usr/bin/env bash
set -ex
dart pub get
if [ ! -f ./sherpa-onnx-zipformer-ctc-zh-int8-2025-07-03/tokens.txt ]; then
curl -SL -O https://github.com/k2-fsa/sherpa-onnx/releases/download/asr-models/sherpa-onnx-zipformer-ctc-zh-int8-2025-07-03.tar.bz2
tar xvf sherpa-onnx-zipformer-ctc-zh-int8-2025-07-03.tar.bz2
rm sherpa-onnx-zipformer-ctc-zh-int8-2025-07-03.tar.bz2
fi
if [ ! -f ./lei-jun-test.wav ]; then
curl -SL -O https://github.com/k2-fsa/sherpa-onnx/releases/download/asr-models/lei-jun-test.wav
fi
if [[ ! -f ./silero_vad.onnx ]]; then
curl -SL -O https://github.com/k2-fsa/sherpa-onnx/releases/download/asr-models/silero_vad.onnx
fi
dart run \
./bin/zipformer-ctc.dart \
--silero-vad ./silero_vad.onnx \
--model ./sherpa-onnx-zipformer-ctc-zh-int8-2025-07-03/model.int8.onnx \
--tokens ./sherpa-onnx-zipformer-ctc-zh-int8-2025-07-03/tokens.txt \
--input-wav ./lei-jun-test.wav
... ...
... ... @@ -75,6 +75,9 @@ class OfflineDecodeFiles
[Option("nemo-ctc", Required = false, HelpText = "Path to model.onnx. Used only for NeMo CTC models")]
public string NeMoCtc { get; set; } = string.Empty;
[Option("zipformer-ctc", Required = false, HelpText = "Path to model.onnx. Used only for Zipformer CTC models")]
public string ZipformerCtc { get; set; } = string.Empty;
[Option("dolphin-model", Required = false, Default = "", HelpText = "Path to dolphin ctc model")]
public string DolphinModel { get; set; } = string.Empty;
... ... @@ -240,6 +243,10 @@ to download pre-trained Tdnn models.
{
config.ModelConfig.Dolphin.Model = options.DolphinModel;
}
else if (!string.IsNullOrEmpty(options.ZipformerCtc))
{
config.ModelConfig.ZipformerCtc.Model = options.ZipformerCtc;
}
else if (!string.IsNullOrEmpty(options.TeleSpeechCtc))
{
config.ModelConfig.TeleSpeechCtc = options.TeleSpeechCtc;
... ...
#!/usr/bin/env bash
set -ex
if [ ! -f ./sherpa-onnx-zipformer-ctc-zh-int8-2025-07-03/tokens.txt ]; then
curl -SL -O https://github.com/k2-fsa/sherpa-onnx/releases/download/asr-models/sherpa-onnx-zipformer-ctc-zh-int8-2025-07-03.tar.bz2
tar xvf sherpa-onnx-zipformer-ctc-zh-int8-2025-07-03.tar.bz2
rm sherpa-onnx-zipformer-ctc-zh-int8-2025-07-03.tar.bz2
fi
dotnet run \
--tokens=./sherpa-onnx-zipformer-ctc-zh-int8-2025-07-03/tokens.txt \
--zipformer-ctc=./sherpa-onnx-zipformer-ctc-zh-int8-2025-07-03/model.int8.onnx \
--num-threads=1 \
--files ./sherpa-onnx-zipformer-ctc-zh-int8-2025-07-03/test_wavs/0.wav \
./sherpa-onnx-zipformer-ctc-zh-int8-2025-07-03/test_wavs/1.wav \
./sherpa-onnx-zipformer-ctc-zh-int8-2025-07-03/test_wavs/8k.wav
... ...
... ... @@ -104,6 +104,27 @@ class OfflineDolphinModelConfig {
final String model;
}
class OfflineZipformerCtcModelConfig {
const OfflineZipformerCtcModelConfig({this.model = ''});
factory OfflineZipformerCtcModelConfig.fromJson(Map<String, dynamic> json) {
return OfflineZipformerCtcModelConfig(
model: json['model'] as String? ?? '',
);
}
@override
String toString() {
return 'OfflineZipformerCtcModelConfig(model: $model)';
}
Map<String, dynamic> toJson() => {
'model': model,
};
final String model;
}
class OfflineWhisperModelConfig {
const OfflineWhisperModelConfig(
{this.encoder = '',
... ... @@ -288,6 +309,7 @@ class OfflineModelConfig {
this.moonshine = const OfflineMoonshineModelConfig(),
this.fireRedAsr = const OfflineFireRedAsrModelConfig(),
this.dolphin = const OfflineDolphinModelConfig(),
this.zipformerCtc = const OfflineZipformerCtcModelConfig(),
required this.tokens,
this.numThreads = 1,
this.debug = true,
... ... @@ -336,6 +358,10 @@ class OfflineModelConfig {
? OfflineDolphinModelConfig.fromJson(
json['dolphin'] as Map<String, dynamic>)
: const OfflineDolphinModelConfig(),
zipformerCtc: json['zipformerCtc'] != null
? OfflineZipformerCtcModelConfig.fromJson(
json['zipformerCtc'] as Map<String, dynamic>)
: const OfflineZipformerCtcModelConfig(),
tokens: json['tokens'] as String,
numThreads: json['numThreads'] as int? ?? 1,
debug: json['debug'] as bool? ?? true,
... ... @@ -349,7 +375,7 @@ class OfflineModelConfig {
@override
String toString() {
return 'OfflineModelConfig(transducer: $transducer, paraformer: $paraformer, nemoCtc: $nemoCtc, whisper: $whisper, tdnn: $tdnn, senseVoice: $senseVoice, moonshine: $moonshine, fireRedAsr: $fireRedAsr, dolphin: $dolphin, tokens: $tokens, numThreads: $numThreads, debug: $debug, provider: $provider, modelType: $modelType, modelingUnit: $modelingUnit, bpeVocab: $bpeVocab, telespeechCtc: $telespeechCtc)';
return 'OfflineModelConfig(transducer: $transducer, paraformer: $paraformer, nemoCtc: $nemoCtc, whisper: $whisper, tdnn: $tdnn, senseVoice: $senseVoice, moonshine: $moonshine, fireRedAsr: $fireRedAsr, dolphin: $dolphin, zipformerCtc: $zipformerCtc, tokens: $tokens, numThreads: $numThreads, debug: $debug, provider: $provider, modelType: $modelType, modelingUnit: $modelingUnit, bpeVocab: $bpeVocab, telespeechCtc: $telespeechCtc)';
}
Map<String, dynamic> toJson() => {
... ... @@ -362,6 +388,7 @@ class OfflineModelConfig {
'moonshine': moonshine.toJson(),
'fireRedAsr': fireRedAsr.toJson(),
'dolphin': dolphin.toJson(),
'zipformerCtc': zipformerCtc.toJson(),
'tokens': tokens,
'numThreads': numThreads,
'debug': debug,
... ... @@ -381,6 +408,7 @@ class OfflineModelConfig {
final OfflineMoonshineModelConfig moonshine;
final OfflineFireRedAsrModelConfig fireRedAsr;
final OfflineDolphinModelConfig dolphin;
final OfflineZipformerCtcModelConfig zipformerCtc;
final String tokens;
final int numThreads;
... ... @@ -578,6 +606,8 @@ class OfflineRecognizer {
config.model.fireRedAsr.decoder.toNativeUtf8();
c.ref.model.dolphin.model = config.model.dolphin.model.toNativeUtf8();
c.ref.model.zipformerCtc.model =
config.model.zipformerCtc.model.toNativeUtf8();
c.ref.model.tokens = config.model.tokens.toNativeUtf8();
... ... @@ -623,6 +653,7 @@ class OfflineRecognizer {
calloc.free(c.ref.model.modelType);
calloc.free(c.ref.model.provider);
calloc.free(c.ref.model.tokens);
calloc.free(c.ref.model.zipformerCtc.model);
calloc.free(c.ref.model.dolphin.model);
calloc.free(c.ref.model.fireRedAsr.decoder);
calloc.free(c.ref.model.fireRedAsr.encoder);
... ...
... ... @@ -266,6 +266,10 @@ final class SherpaOnnxOfflineDolphinModelConfig extends Struct {
external Pointer<Utf8> model;
}
final class SherpaOnnxOfflineZipformerCtcModelConfig extends Struct {
external Pointer<Utf8> model;
}
final class SherpaOnnxOfflineWhisperModelConfig extends Struct {
external Pointer<Utf8> encoder;
external Pointer<Utf8> decoder;
... ... @@ -333,6 +337,7 @@ final class SherpaOnnxOfflineModelConfig extends Struct {
external SherpaOnnxOfflineMoonshineModelConfig moonshine;
external SherpaOnnxOfflineFireRedAsrModelConfig fireRedAsr;
external SherpaOnnxOfflineDolphinModelConfig dolphin;
external SherpaOnnxOfflineZipformerCtcModelConfig zipformerCtc;
}
final class SherpaOnnxOfflineRecognizerConfig extends Struct {
... ...
... ... @@ -28,6 +28,8 @@ func main() {
flag.StringVar(&config.ModelConfig.NemoCTC.Model, "nemo-ctc", "", "Path to the NeMo CTC model")
flag.StringVar(&config.ModelConfig.ZipformerCtc.Model, "zipformer-ctc", "", "Path to the Zipformer CTC model")
flag.StringVar(&config.ModelConfig.Dolphin.Model, "dolphin-model", "", "Path to the Dolphin CTC model")
flag.StringVar(&config.ModelConfig.FireRedAsr.Encoder, "fire-red-asr-encoder", "", "Path to the FireRedAsr encoder model")
... ...
#!/usr/bin/env bash
set -ex
if [ ! -f ./sherpa-onnx-zipformer-ctc-zh-int8-2025-07-03/tokens.txt ]; then
curl -SL -O https://github.com/k2-fsa/sherpa-onnx/releases/download/asr-models/sherpa-onnx-zipformer-ctc-zh-int8-2025-07-03.tar.bz2
tar xvf sherpa-onnx-zipformer-ctc-zh-int8-2025-07-03.tar.bz2
rm sherpa-onnx-zipformer-ctc-zh-int8-2025-07-03.tar.bz2
fi
go mod tidy
go build
./non-streaming-decode-files \
--zipformer-ctc ./sherpa-onnx-zipformer-ctc-zh-int8-2025-07-03/model.int8.onnx \
--tokens ./sherpa-onnx-zipformer-ctc-zh-int8-2025-07-03/tokens.txt \
--debug 0 \
./sherpa-onnx-zipformer-ctc-zh-int8-2025-07-03/test_wavs/0.wav
... ...
... ... @@ -15,6 +15,7 @@ export { Samples,
OfflineTdnnModelConfig,
OfflineSenseVoiceModelConfig,
OfflineMoonshineModelConfig,
OfflineZipformerCtcModelConfig,
OfflineModelConfig,
OfflineLMConfig,
OfflineRecognizerConfig,
... ...
... ... @@ -45,7 +45,23 @@ static SherpaOnnxOfflineParaformerModelConfig GetOfflineParaformerModelConfig(
return c;
}
static SherpaOnnxOfflineDolphinModelConfig GetOfflineDolphinfig(
static SherpaOnnxOfflineZipformerCtcModelConfig
GetOfflineZipformerCtcModelConfig(Napi::Object obj) {
SherpaOnnxOfflineZipformerCtcModelConfig c;
memset(&c, 0, sizeof(c));
if (!obj.Has("zipformerCtc") || !obj.Get("zipformerCtc").IsObject()) {
return c;
}
Napi::Object o = obj.Get("zipformerCtc").As<Napi::Object>();
SHERPA_ONNX_ASSIGN_ATTR_STR(model, model);
return c;
}
static SherpaOnnxOfflineDolphinModelConfig GetOfflineDolphinModelConfig(
Napi::Object obj) {
SherpaOnnxOfflineDolphinModelConfig c;
memset(&c, 0, sizeof(c));
... ... @@ -185,7 +201,8 @@ static SherpaOnnxOfflineModelConfig GetOfflineModelConfig(Napi::Object obj) {
c.sense_voice = GetOfflineSenseVoiceModelConfig(o);
c.moonshine = GetOfflineMoonshineModelConfig(o);
c.fire_red_asr = GetOfflineFireRedAsrModelConfig(o);
c.dolphin = GetOfflineDolphinfig(o);
c.dolphin = GetOfflineDolphinModelConfig(o);
c.zipformer_ctc = GetOfflineZipformerCtcModelConfig(o);
SHERPA_ONNX_ASSIGN_ATTR_STR(tokens, tokens);
SHERPA_ONNX_ASSIGN_ATTR_INT32(num_threads, numThreads);
... ... @@ -312,6 +329,7 @@ CreateOfflineRecognizerWrapper(const Napi::CallbackInfo &info) {
SHERPA_ONNX_DELETE_C_STR(c.model_config.fire_red_asr.decoder);
SHERPA_ONNX_DELETE_C_STR(c.model_config.dolphin.model);
SHERPA_ONNX_DELETE_C_STR(c.model_config.zipformer_ctc.model);
SHERPA_ONNX_DELETE_C_STR(c.model_config.tokens);
SHERPA_ONNX_DELETE_C_STR(c.model_config.provider);
... ...
... ... @@ -55,6 +55,10 @@ export class OfflineDolphinModelConfig {
public model: string = '';
}
export class OfflineZipformerCtcModelConfig {
public model: string = '';
}
export class OfflineWhisperModelConfig {
public encoder: string = '';
public decoder: string = '';
... ... @@ -97,6 +101,7 @@ export class OfflineModelConfig {
public senseVoice: OfflineSenseVoiceModelConfig = new OfflineSenseVoiceModelConfig();
public moonshine: OfflineMoonshineModelConfig = new OfflineMoonshineModelConfig();
public dolphin: OfflineDolphinModelConfig = new OfflineDolphinModelConfig();
public zipformerCtc: OfflineZipformerCtcModelConfig = new OfflineZipformerCtcModelConfig();
}
export class OfflineLMConfig {
... ...
// Copyright 2025 Xiaomi Corporation
// This file shows how to use an offline Zipformer CTC model,
// i.e., non-streaming Zipformer CTC model,
// to decode files.
import com.k2fsa.sherpa.onnx.*;
public class NonStreamingDecodeFileZipformerCtc {
public static void main(String[] args) {
// please refer to
// https://github.com/k2-fsa/sherpa-onnx/releases/download/asr-models/sherpa-onnx-zipformer-ctc-zh-int8-2025-07-03.tar.bz2
// to download model files
String model = "./sherpa-onnx-zipformer-ctc-zh-int8-2025-07-03/model.int8.onnx";
String tokens = "./sherpa-onnx-zipformer-ctc-zh-int8-2025-07-03/tokens.txt";
String waveFilename = "./sherpa-onnx-zipformer-ctc-zh-int8-2025-07-03/test_wavs/0.wav";
WaveReader reader = new WaveReader(waveFilename);
OfflineZipformerCtcModelConfig zipformerCtc =
OfflineZipformerCtcModelConfig.builder().setModel(model).build();
OfflineModelConfig modelConfig =
OfflineModelConfig.builder()
.setZipformerCtc(zipformerCtc)
.setTokens(tokens)
.setNumThreads(1)
.setDebug(true)
.build();
OfflineRecognizerConfig config =
OfflineRecognizerConfig.builder()
.setOfflineModelConfig(modelConfig)
.setDecodingMethod("greedy_search")
.build();
OfflineRecognizer recognizer = new OfflineRecognizer(config);
OfflineStream stream = recognizer.createStream();
stream.acceptWaveform(reader.getSamples(), reader.getSampleRate());
recognizer.decode(stream);
String text = recognizer.getResult(stream).getText();
System.out.printf("filename:%s\nresult:%s\n", waveFilename, text);
stream.release();
recognizer.release();
}
}
... ...
#!/usr/bin/env bash
set -ex
if [[ ! -f ../build/lib/libsherpa-onnx-jni.dylib && ! -f ../build/lib/libsherpa-onnx-jni.so ]]; then
mkdir -p ../build
pushd ../build
cmake \
-DSHERPA_ONNX_ENABLE_PYTHON=OFF \
-DSHERPA_ONNX_ENABLE_TESTS=OFF \
-DSHERPA_ONNX_ENABLE_CHECK=OFF \
-DBUILD_SHARED_LIBS=ON \
-DSHERPA_ONNX_ENABLE_PORTAUDIO=OFF \
-DSHERPA_ONNX_ENABLE_JNI=ON \
..
make -j4
ls -lh lib
popd
fi
if [ ! -f ../sherpa-onnx/java-api/build/sherpa-onnx.jar ]; then
pushd ../sherpa-onnx/java-api
make
popd
fi
if [ ! -f ./sherpa-onnx-zipformer-ctc-zh-int8-2025-07-03/tokens.txt ]; then
curl -SL -O https://github.com/k2-fsa/sherpa-onnx/releases/download/asr-models/sherpa-onnx-zipformer-ctc-zh-int8-2025-07-03.tar.bz2
tar xvf sherpa-onnx-zipformer-ctc-zh-int8-2025-07-03.tar.bz2
rm sherpa-onnx-zipformer-ctc-zh-int8-2025-07-03.tar.bz2
fi
java \
-Djava.library.path=$PWD/../build/lib \
-cp ../sherpa-onnx/java-api/build/sherpa-onnx.jar \
NonStreamingDecodeFileZipformerCtc.java
... ...
... ... @@ -253,6 +253,13 @@ function testOfflineAsr() {
rm sherpa-onnx-zipformer-multi-zh-hans-2023-9-2.tar.bz2
fi
if [ ! -f ./sherpa-onnx-zipformer-ctc-zh-int8-2025-07-03/model.int8.onnx ]; then
curl -SL -O https://github.com/k2-fsa/sherpa-onnx/releases/download/asr-models/sherpa-onnx-zipformer-ctc-zh-int8-2025-07-03.tar.bz2
tar xvf sherpa-onnx-zipformer-ctc-zh-int8-2025-07-03.tar.bz2
rm sherpa-onnx-zipformer-ctc-zh-int8-2025-07-03.tar.bz2
fi
out_filename=test_offline_asr.jar
kotlinc-jvm -include-runtime -d $out_filename \
test_offline_asr.kt \
... ...
package com.k2fsa.sherpa.onnx
fun main() {
val types = arrayOf(0, 2, 5, 6, 15, 21, 24, 25)
val types = arrayOf(0, 2, 5, 6, 15, 21, 24, 25, 31)
for (type in types) {
test(type)
}
... ... @@ -19,6 +19,7 @@ fun test(type: Int) {
21 -> "./sherpa-onnx-moonshine-tiny-en-int8/test_wavs/0.wav"
24 -> "./sherpa-onnx-fire-red-asr-large-zh_en-2025-02-16/test_wavs/0.wav"
25 -> "./sherpa-onnx-dolphin-base-ctc-multi-lang-int8-2025-04-02/test_wavs/0.wav"
31 -> "./sherpa-onnx-zipformer-ctc-zh-int8-2025-07-03/test_wavs/0.wav"
else -> null
}
... ...
... ... @@ -123,6 +123,7 @@ The following tables list the examples in this folder.
|[./test_asr_non_streaming_moonshine.js](./test_asr_non_streaming_moonshine.js)|Non-streaming speech recognition from a file using [Moonshine](https://github.com/usefulsensors/moonshine)|
|[./test_vad_with_non_streaming_asr_moonshine.js](./test_vad_with_non_streaming_asr_moonshine.js)| Non-streaming speech recognition from a file using [Moonshine](https://github.com/usefulsensors/moonshine) + [Silero VAD](https://github.com/snakers4/silero-vad)|
|[./test_asr_non_streaming_nemo_ctc.js](./test_asr_non_streaming_nemo_ctc.js)|Non-streaming speech recognition from a file using a [NeMo](https://github.com/NVIDIA/NeMo) CTC model with greedy search|
|[./test_asr_non_streaming_zipformer_ctc.js](./test_asr_non_streaming_zipformer_ctc.js)|Non-streaming speech recognition from a file using a Zipformer CTC model with greedy search|
|[./test_asr_non_streaming_nemo_parakeet_tdt_v2.js](./test_asr_non_streaming_nemo_parakeet_tdt_v2.js)|Non-streaming speech recognition from a file using a [NeMo](https://github.com/NVIDIA/NeMo) [parakeet-tdt-0.6b-v2](https://k2-fsa.github.io/sherpa/onnx/pretrained_models/offline-transducer/nemo-transducer-models.html#sherpa-onnx-nemo-parakeet-tdt-0-6b-v2-int8-english) model with greedy search|
|[./test_asr_non_streaming_dolphin_ctc.js](./test_asr_non_streaming_dolphin_ctc.js)|Non-streaming speech recognition from a file using a [Dolphinhttps://github.com/DataoceanAI/Dolphin]) CTC model with greedy search|
|[./test_asr_non_streaming_paraformer.js](./test_asr_non_streaming_paraformer.js)|Non-streaming speech recognition from a file using [Paraformer](https://github.com/alibaba-damo-academy/FunASR)|
... ... @@ -137,6 +138,7 @@ The following tables list the examples in this folder.
|[./test_vad_asr_non_streaming_whisper_microphone.js](./test_vad_asr_non_streaming_whisper_microphone.js)|VAD + Non-streaming speech recognition from a microphone using [Whisper](https://github.com/openai/whisper)|
|[./test_vad_asr_non_streaming_moonshine_microphone.js](./test_vad_asr_non_streaming_moonshine_microphone.js)|VAD + Non-streaming speech recognition from a microphone using [Moonshine](https://github.com/usefulsensors/moonshine)|
|[./test_vad_asr_non_streaming_nemo_ctc_microphone.js](./test_vad_asr_non_streaming_nemo_ctc_microphone.js)|VAD + Non-streaming speech recognition from a microphone using a [NeMo](https://github.com/NVIDIA/NeMo) CTC model with greedy search|
|[./test_vad_asr_non_streaming_zipformer_ctc_microphone.js](./test_vad_asr_non_streaming_zipformer_ctc_microphone.js)|VAD + Non-streaming speech recognition from a microphone using a Zipformer CTC model with greedy search|
|[./test_vad_asr_non_streaming_paraformer_microphone.js](./test_vad_asr_non_streaming_paraformer_microphone.js)|VAD + Non-streaming speech recognition from a microphone using [Paraformer](https://github.com/alibaba-damo-academy/FunASR)|
|[./test_vad_asr_non_streaming_sense_voice_microphone.js](./test_vad_asr_non_streaming_sense_voice_microphone.js)|VAD + Non-streaming speech recognition from a microphone using [SenseVoice](https://github.com/FunAudioLLM/SenseVoice)|
... ... @@ -372,6 +374,21 @@ rm sherpa-onnx-nemo-parakeet-tdt-0.6b-v2-int8.tar.bz2
node ./test_asr_non_streaming_nemo_parakeet_tdt_v2.js
```
### Non-streaming speech recognition with Zipformer CTC models
```bash
wget https://github.com/k2-fsa/sherpa-onnx/releases/download/asr-models/sherpa-onnx-zipformer-ctc-zh-int8-2025-07-03.tar.bz2
tar xvf sherpa-onnx-zipformer-ctc-zh-int8-2025-07-03.tar.bz2
rm sherpa-onnx-zipformer-ctc-zh-int8-2025-07-03.tar.bz2
node ./test_asr_non_streaming_zipformer_ctc.js
# To run VAD + non-streaming ASR with Paraformer using a microphone
npm install naudiodon2
node ./test_vad_asr_non_streaming_zipformer_ctc_microphone.js
```
### Non-streaming speech recognition with NeMo CTC models
```bash
... ...
// Copyright (c) 2025 Xiaomi Corporation
const sherpa_onnx = require('sherpa-onnx-node');
// Please download test files from
// https://github.com/k2-fsa/sherpa-onnx/releases/tag/asr-models
const config = {
'featConfig': {
'sampleRate': 16000,
'featureDim': 80,
},
'modelConfig': {
'zipformerCtc': {
'model': './sherpa-onnx-zipformer-ctc-zh-int8-2025-07-03/model.int8.onnx',
},
'tokens': './sherpa-onnx-zipformer-ctc-zh-int8-2025-07-03/tokens.txt',
'numThreads': 2,
'provider': 'cpu',
'debug': 1,
}
};
const waveFilename =
'./sherpa-onnx-zipformer-ctc-zh-int8-2025-07-03/test_wavs/0.wav';
const recognizer = new sherpa_onnx.OfflineRecognizer(config);
console.log('Started')
let start = Date.now();
const stream = recognizer.createStream();
const wave = sherpa_onnx.readWave(waveFilename);
stream.acceptWaveform({sampleRate: wave.sampleRate, samples: wave.samples});
recognizer.decode(stream);
result = recognizer.getResult(stream)
let stop = Date.now();
console.log('Done')
const elapsed_seconds = (stop - start) / 1000;
const duration = wave.samples.length / wave.sampleRate;
const real_time_factor = elapsed_seconds / duration;
console.log('Wave duration', duration.toFixed(3), 'seconds')
console.log('Elapsed', elapsed_seconds.toFixed(3), 'seconds')
console.log(
`RTF = ${elapsed_seconds.toFixed(3)}/${duration.toFixed(3)} =`,
real_time_factor.toFixed(3))
console.log(waveFilename)
console.log('result\n', result)
... ...
// Copyright (c) 2025 Xiaomi Corporation (authors: Fangjun Kuang)
//
const portAudio = require('naudiodon2');
// console.log(portAudio.getDevices());
const sherpa_onnx = require('sherpa-onnx-node');
function createRecognizer() {
// Please download test files from
// https://github.com/k2-fsa/sherpa-onnx/releases/tag/asr-models
const config = {
'featConfig': {
'sampleRate': 16000,
'featureDim': 80,
},
'modelConfig': {
'zipformerCtc': {
'model':
'./sherpa-onnx-zipformer-ctc-zh-int8-2025-07-03/model.int8.onnx',
},
'tokens': './sherpa-onnx-zipformer-ctc-zh-int8-2025-07-03/tokens.txt',
'numThreads': 2,
'provider': 'cpu',
'debug': 1,
}
};
return new sherpa_onnx.OfflineRecognizer(config);
}
function createVad() {
// please download silero_vad.onnx from
// https://github.com/k2-fsa/sherpa-onnx/releases/download/asr-models/silero_vad.onnx
const config = {
sileroVad: {
model: './silero_vad.onnx',
threshold: 0.5,
minSpeechDuration: 0.25,
minSilenceDuration: 0.5,
windowSize: 512,
},
sampleRate: 16000,
debug: true,
numThreads: 1,
};
const bufferSizeInSeconds = 60;
return new sherpa_onnx.Vad(config, bufferSizeInSeconds);
}
const recognizer = createRecognizer();
const vad = createVad();
const bufferSizeInSeconds = 30;
const buffer =
new sherpa_onnx.CircularBuffer(bufferSizeInSeconds * vad.config.sampleRate);
const ai = new portAudio.AudioIO({
inOptions: {
channelCount: 1,
closeOnError: true, // Close the stream if an audio error is detected, if
// set false then just log the error
deviceId: -1, // Use -1 or omit the deviceId to select the default device
sampleFormat: portAudio.SampleFormatFloat32,
sampleRate: vad.config.sampleRate
}
});
let printed = false;
let index = 0;
ai.on('data', data => {
const windowSize = vad.config.sileroVad.windowSize;
buffer.push(new Float32Array(data.buffer));
while (buffer.size() > windowSize) {
const samples = buffer.get(buffer.head(), windowSize);
buffer.pop(windowSize);
vad.acceptWaveform(samples);
}
while (!vad.isEmpty()) {
const segment = vad.front();
vad.pop();
const stream = recognizer.createStream();
stream.acceptWaveform({
samples: segment.samples,
sampleRate: recognizer.config.featConfig.sampleRate
});
recognizer.decode(stream);
const r = recognizer.getResult(stream);
if (r.text.length > 0) {
const text = r.text.toLowerCase().trim();
console.log(`${index}: ${text}`);
const filename = `${index}-${text}-${
new Date()
.toLocaleTimeString('en-US', {hour12: false})
.split(' ')[0]}.wav`;
sherpa_onnx.writeWave(
filename,
{samples: segment.samples, sampleRate: vad.config.sampleRate});
index += 1;
}
}
});
ai.start();
console.log('Started! Please speak')
... ...
... ... @@ -154,6 +154,23 @@ rm sherpa-onnx-dolphin-base-ctc-multi-lang-int8-2025-04-02.tar.bz2
node ./test-offline-dolphin-ctc.js
```
## ./test-offline-zipformer-ctc.js
[./test-offline-zipformer-ctc.js](./test-offline-zipformer-ctc.js) demonstrates
how to decode a file with a Zipformer CTC model. In the code we use
[sherpa-onnx-zipformer-ctc-zh-int8-2025-07-03](https://k2-fsa.github.io/sherpa/onnx/pretrained_models/offline-ctc/icefall/zipformer.html#sherpa-onnx-zipformer-ctc-zh-int8-2025-07-03-chinese).
You can use the following command to run it:
```bash
wget https://github.com/k2-fsa/sherpa-onnx/releases/download/asr-models/sherpa-onnx-zipformer-ctc-zh-int8-2025-07-03.tar.bz2
tar xvf sherpa-onnx-zipformer-ctc-zh-int8-2025-07-03.tar.bz2
rm sherpa-onnx-zipformer-ctc-zh-int8-2025-07-03.tar.bz2
node ./test-offline-zipformer-ctc.js
```
## ./test-offline-nemo-ctc.js
[./test-offline-nemo-ctc.js](./test-offline-nemo-ctc.js) demonstrates
... ...
// Copyright (c) 2025 Xiaomi Corporation (authors: Fangjun Kuang)
//
const fs = require('fs');
const {Readable} = require('stream');
const wav = require('wav');
const sherpa_onnx = require('sherpa-onnx');
function createOfflineRecognizer() {
let config = {
modelConfig: {
zipformerCtc: {
model: './sherpa-onnx-zipformer-ctc-zh-int8-2025-07-03/model.int8.onnx',
},
tokens: './sherpa-onnx-zipformer-ctc-zh-int8-2025-07-03/tokens.txt',
}
};
return sherpa_onnx.createOfflineRecognizer(config);
}
const recognizer = createOfflineRecognizer();
const stream = recognizer.createStream();
const waveFilename =
'./sherpa-onnx-zipformer-ctc-zh-int8-2025-07-03/test_wavs/0.wav';
const wave = sherpa_onnx.readWave(waveFilename);
stream.acceptWaveform(wave.sampleRate, wave.samples);
recognizer.decode(stream);
const text = recognizer.getResult(stream).text;
console.log(text);
stream.free();
recognizer.free();
... ...
... ... @@ -9,3 +9,4 @@ sense_voice
telespeech_ctc
moonshine
dolphin_ctc
zipformer_ctc
... ...
#!/usr/bin/env bash
set -ex
SCRIPT_DIR=$( cd -- "$( dirname -- "${BASH_SOURCE[0]}" )" &> /dev/null && pwd )
SHERPA_ONNX_DIR=$(cd $SCRIPT_DIR/../.. && pwd)
echo "SHERPA_ONNX_DIR: $SHERPA_ONNX_DIR"
if [[ ! -f ../../build/install/lib/libsherpa-onnx-c-api.dylib && ! -f ../../build/install/lib/libsherpa-onnx-c-api.so && ! -f ../../build/install/lib/sherpa-onnx-c-api.dll ]]; then
mkdir -p ../../build
pushd ../../build
cmake \
-DCMAKE_INSTALL_PREFIX=./install \
-DSHERPA_ONNX_ENABLE_PYTHON=OFF \
-DSHERPA_ONNX_ENABLE_TESTS=OFF \
-DSHERPA_ONNX_ENABLE_CHECK=OFF \
-DBUILD_SHARED_LIBS=ON \
-DSHERPA_ONNX_ENABLE_PORTAUDIO=OFF \
..
cmake --build . --target install --config Release
ls -lh lib
popd
fi
if [ ! -f ./sherpa-onnx-zipformer-ctc-zh-int8-2025-07-03/tokens.txt ]; then
curl -SL -O https://github.com/k2-fsa/sherpa-onnx/releases/download/asr-models/sherpa-onnx-zipformer-ctc-zh-int8-2025-07-03.tar.bz2
tar xvf sherpa-onnx-zipformer-ctc-zh-int8-2025-07-03.tar.bz2
rm sherpa-onnx-zipformer-ctc-zh-int8-2025-07-03.tar.bz2
fi
fpc \
-dSHERPA_ONNX_USE_SHARED_LIBS \
-Fu$SHERPA_ONNX_DIR/sherpa-onnx/pascal-api \
-Fl$SHERPA_ONNX_DIR/build/install/lib \
./zipformer_ctc.pas
export LD_LIBRARY_PATH=$SHERPA_ONNX_DIR/build/install/lib:$LD_LIBRARY_PATH
export DYLD_LIBRARY_PATH=$SHERPA_ONNX_DIR/build/install/lib:$DYLD_LIBRARY_PATH
./zipformer_ctc
... ...
{ Copyright (c) 2025 Xiaomi Corporation }
{
This file shows how to use a non-streaming Zipformer CTC model
to decode files.
You can download the model files from
https://github.com/k2-fsa/sherpa-onnx/releases/tag/asr-models
}
program zipformer_ctc;
{$mode objfpc}
uses
sherpa_onnx,
DateUtils,
SysUtils;
var
Wave: TSherpaOnnxWave;
WaveFilename: AnsiString;
Config: TSherpaOnnxOfflineRecognizerConfig;
Recognizer: TSherpaOnnxOfflineRecognizer;
Stream: TSherpaOnnxOfflineStream;
RecognitionResult: TSherpaOnnxOfflineRecognizerResult;
Start: TDateTime;
Stop: TDateTime;
Elapsed: Single;
Duration: Single;
RealTimeFactor: Single;
begin
Initialize(Config);
Config.ModelConfig.ZipformerCtc.Model := './sherpa-onnx-zipformer-ctc-zh-int8-2025-07-03/model.int8.onnx';
Config.ModelConfig.Tokens := './sherpa-onnx-zipformer-ctc-zh-int8-2025-07-03/tokens.txt';
Config.ModelConfig.Provider := 'cpu';
Config.ModelConfig.NumThreads := 1;
Config.ModelConfig.Debug := False;
WaveFilename := './sherpa-onnx-zipformer-ctc-zh-int8-2025-07-03/test_wavs/0.wav';
Wave := SherpaOnnxReadWave(WaveFilename);
Recognizer := TSherpaOnnxOfflineRecognizer.Create(Config);
Stream := Recognizer.CreateStream();
Start := Now;
Stream.AcceptWaveform(Wave.Samples, Wave.SampleRate);
Recognizer.Decode(Stream);
RecognitionResult := Recognizer.GetResult(Stream);
Stop := Now;
Elapsed := MilliSecondsBetween(Stop, Start) / 1000;
Duration := Length(Wave.Samples) / Wave.SampleRate;
RealTimeFactor := Elapsed / Duration;
WriteLn(RecognitionResult.ToString);
WriteLn(Format('NumThreads %d', [Config.ModelConfig.NumThreads]));
WriteLn(Format('Elapsed %.3f s', [Elapsed]));
WriteLn(Format('Wave duration %.3f s', [Duration]));
WriteLn(Format('RTF = %.3f/%.3f = %.3f', [Elapsed, Duration, RealTimeFactor]));
{Free resources to avoid memory leak.
Note: You don't need to invoke them for this simple script.
However, you have to invoke them in your own large/complex project.
}
FreeAndNil(Stream);
FreeAndNil(Recognizer);
end.
... ...
... ... @@ -2,3 +2,5 @@
vad_with_whisper
vad_with_sense_voice
vad_with_moonshine
vad_with_zipformer_ctc
vad_with_dolphin
... ...
#!/usr/bin/env bash
set -ex
SCRIPT_DIR=$( cd -- "$( dirname -- "${BASH_SOURCE[0]}" )" &> /dev/null && pwd )
SHERPA_ONNX_DIR=$(cd $SCRIPT_DIR/../.. && pwd)
echo "SHERPA_ONNX_DIR: $SHERPA_ONNX_DIR"
if [[ ! -f ../../build/install/lib/libsherpa-onnx-c-api.dylib && ! -f ../../build/install/lib/libsherpa-onnx-c-api.so && ! -f ../../build/install/lib/sherpa-onnx-c-api.dll ]]; then
mkdir -p ../../build
pushd ../../build
cmake \
-DCMAKE_INSTALL_PREFIX=./install \
-DSHERPA_ONNX_ENABLE_PYTHON=OFF \
-DSHERPA_ONNX_ENABLE_TESTS=OFF \
-DSHERPA_ONNX_ENABLE_CHECK=OFF \
-DBUILD_SHARED_LIBS=ON \
-DSHERPA_ONNX_ENABLE_PORTAUDIO=OFF \
..
cmake --build . --target install --config Release
popd
fi
if [[ ! -f ./silero_vad.onnx ]]; then
curl -SL -O https://github.com/k2-fsa/sherpa-onnx/releases/download/asr-models/silero_vad.onnx
fi
if [ ! -f ./lei-jun-test.wav ]; then
curl -SL -O https://github.com/k2-fsa/sherpa-onnx/releases/download/asr-models/lei-jun-test.wav
fi
if [ ! -f ./sherpa-onnx-zipformer-ctc-zh-int8-2025-07-03/tokens.txt ]; then
curl -SL -O https://github.com/k2-fsa/sherpa-onnx/releases/download/asr-models/sherpa-onnx-zipformer-ctc-zh-int8-2025-07-03.tar.bz2
tar xvf sherpa-onnx-zipformer-ctc-zh-int8-2025-07-03.tar.bz2
rm sherpa-onnx-zipformer-ctc-zh-int8-2025-07-03.tar.bz2
fi
fpc \
-dSHERPA_ONNX_USE_SHARED_LIBS \
-Fu$SHERPA_ONNX_DIR/sherpa-onnx/pascal-api \
-Fl$SHERPA_ONNX_DIR/build/install/lib \
./vad_with_zipformer_ctc.pas
export LD_LIBRARY_PATH=$SHERPA_ONNX_DIR/build/install/lib:$LD_LIBRARY_PATH
export DYLD_LIBRARY_PATH=$SHERPA_ONNX_DIR/build/install/lib:$DYLD_LIBRARY_PATH
./vad_with_zipformer_ctc
... ...
{ Copyright (c) 2025 Xiaomi Corporation }
{
This file shows how to use a non-streaming Zipformer CTC model
with silero VAD to decode files.
You can download the model files from
https://github.com/k2-fsa/sherpa-onnx/releases/tag/asr-models
}
program vad_with_zipformer_ctc;
{$mode objfpc}
uses
sherpa_onnx,
SysUtils;
function CreateVad(): TSherpaOnnxVoiceActivityDetector;
var
Config: TSherpaOnnxVadModelConfig;
SampleRate: Integer;
WindowSize: Integer;
begin
Initialize(Config);
SampleRate := 16000; {Please don't change it unless you know the details}
WindowSize := 512; {Please don't change it unless you know the details}
Config.SileroVad.Model := './silero_vad.onnx';
Config.SileroVad.MinSpeechDuration := 0.5;
Config.SileroVad.MinSilenceDuration := 0.5;
Config.SileroVad.Threshold := 0.5;
Config.SileroVad.WindowSize := WindowSize;
Config.NumThreads:= 1;
Config.Debug:= True;
Config.Provider:= 'cpu';
Config.SampleRate := SampleRate;
Result := TSherpaOnnxVoiceActivityDetector.Create(Config, 30);
end;
function CreateOfflineRecognizer(): TSherpaOnnxOfflineRecognizer;
var
Config: TSherpaOnnxOfflineRecognizerConfig;
begin
Initialize(Config);
Config.ModelConfig.ZipformerCtc.Model := './sherpa-onnx-zipformer-ctc-zh-int8-2025-07-03/model.int8.onnx';
Config.ModelConfig.Tokens := './sherpa-onnx-zipformer-ctc-zh-int8-2025-07-03/tokens.txt';
Config.ModelConfig.Provider := 'cpu';
Config.ModelConfig.NumThreads := 1;
Config.ModelConfig.Debug := False;
Result := TSherpaOnnxOfflineRecognizer.Create(Config);
end;
var
Wave: TSherpaOnnxWave;
Recognizer: TSherpaOnnxOfflineRecognizer;
Vad: TSherpaOnnxVoiceActivityDetector;
Offset: Integer;
WindowSize: Integer;
SpeechSegment: TSherpaOnnxSpeechSegment;
Start: Single;
Duration: Single;
Stream: TSherpaOnnxOfflineStream;
RecognitionResult: TSherpaOnnxOfflineRecognizerResult;
begin
Vad := CreateVad();
Recognizer := CreateOfflineRecognizer();
Wave := SherpaOnnxReadWave('./lei-jun-test.wav');
if Wave.SampleRate <> Vad.Config.SampleRate then
begin
WriteLn(Format('Expected sample rate: %d. Given: %d',
[Vad.Config.SampleRate, Wave.SampleRate]));
Exit;
end;
WindowSize := Vad.Config.SileroVad.WindowSize;
Offset := 0;
while Offset + WindowSize <= Length(Wave.Samples) do
begin
Vad.AcceptWaveform(Wave.Samples, Offset, WindowSize);
Offset += WindowSize;
while not Vad.IsEmpty do
begin
SpeechSegment := Vad.Front();
Vad.Pop();
Stream := Recognizer.CreateStream();
Stream.AcceptWaveform(SpeechSegment.Samples, Wave.SampleRate);
Recognizer.Decode(Stream);
RecognitionResult := Recognizer.GetResult(Stream);
Start := SpeechSegment.Start / Wave.SampleRate;
Duration := Length(SpeechSegment.Samples) / Wave.SampleRate;
WriteLn(Format('%.3f -- %.3f %s',
[Start, Start + Duration, RecognitionResult.Text]));
FreeAndNil(Stream);
end;
end;
Vad.Flush;
while not Vad.IsEmpty do
begin
SpeechSegment := Vad.Front();
Vad.Pop();
Stream := Recognizer.CreateStream();
Stream.AcceptWaveform(SpeechSegment.Samples, Wave.SampleRate);
Recognizer.Decode(Stream);
RecognitionResult := Recognizer.GetResult(Stream);
Start := SpeechSegment.Start / Wave.SampleRate;
Duration := Length(SpeechSegment.Samples) / Wave.SampleRate;
WriteLn(Format('%.3f -- %.3f %s',
[Start, Start + Duration, RecognitionResult.Text]));
FreeAndNil(Stream);
end;
FreeAndNil(Recognizer);
FreeAndNil(Vad);
end.
... ...
#!/usr/bin/env python3
"""
This file shows how to use a non-streaming zipformer CTC model from icefall
to decode files.
Please download model files from
https://github.com/k2-fsa/sherpa-onnx/releases/tag/asr-models
"""
from pathlib import Path
import sherpa_onnx
import soundfile as sf
def create_recognizer():
model = "./sherpa-onnx-zipformer-ctc-zh-int8-2025-07-03/model.int8.onnx"
tokens = "./sherpa-onnx-zipformer-ctc-zh-int8-2025-07-03/tokens.txt"
test_wav = "./sherpa-onnx-zipformer-ctc-zh-int8-2025-07-03/test_wavs/0.wav"
if not Path(model).is_file() or not Path(test_wav).is_file():
raise ValueError(
"""Please download model files from
https://github.com/k2-fsa/sherpa-onnx/releases/tag/asr-models
"""
)
return (
sherpa_onnx.OfflineRecognizer.from_zipformer_ctc(
model=model,
tokens=tokens,
debug=True,
),
test_wav,
)
def main():
recognizer, wave_filename = create_recognizer()
audio, sample_rate = sf.read(wave_filename, dtype="float32", always_2d=True)
audio = audio[:, 0] # only use the first channel
# audio is a 1-D float32 numpy array normalized to the range [-1, 1]
# sample_rate does not need to be 16000 Hz
stream = recognizer.create_stream()
stream.accept_waveform(sample_rate, audio)
recognizer.decode_stream(stream)
print(wave_filename)
print(stream.result)
if __name__ == "__main__":
main()
... ...
... ... @@ -344,7 +344,7 @@ def get_models():
""",
),
Model(
model_name="sherpa-onnx-streaming-zipformer-ctc-fp16-zh-2025-06-30",
model_name="sherpa-onnx-streaming-zipformer-ctc-zh-fp16-2025-06-30",
idx=19,
lang="zh",
short_name="large_zipformer_fp16",
... ... @@ -363,6 +363,26 @@ def get_models():
popd
""",
),
Model(
model_name="sherpa-onnx-streaming-zipformer-ctc-zh-int8-2025-06-30",
idx=20,
lang="zh",
short_name="large_zipformer_int8",
rule_fsts="itn_zh_number.fst",
cmd="""
if [ ! -f itn_zh_number.fst ]; then
curl -SL -O https://github.com/k2-fsa/sherpa-onnx/releases/download/asr-models/itn_zh_number.fst
fi
pushd $model_name
rm -fv bpe.model
rm -rf test_wavs
ls -lh
popd
""",
),
]
return models
... ...
... ... @@ -551,6 +551,23 @@ def get_models():
popd
""",
),
Model(
model_name="sherpa-onnx-zipformer-ctc-zh-int8-2025-07-03",
idx=31,
lang="zh",
lang2="Chinese",
short_name="zipformer_2025_07_03",
cmd="""
pushd $model_name
rm -rfv test_wavs
rm -rfv bbpe.model
ls -lh
popd
""",
),
]
return models
... ...
... ... @@ -27,6 +27,7 @@ namespace SherpaOnnx
Moonshine = new OfflineMoonshineModelConfig();
FireRedAsr = new OfflineFireRedAsrModelConfig();
Dolphin = new OfflineDolphinModelConfig();
ZipformerCtc = new OfflineZipformerCtcModelConfig();
}
public OfflineTransducerModelConfig Transducer;
public OfflineParaformerModelConfig Paraformer;
... ... @@ -60,5 +61,6 @@ namespace SherpaOnnx
public OfflineMoonshineModelConfig Moonshine;
public OfflineFireRedAsrModelConfig FireRedAsr;
public OfflineDolphinModelConfig Dolphin;
public OfflineZipformerCtcModelConfig ZipformerCtc;
}
}
... ...
/// Copyright (c) 2025 Xiaomi Corporation (authors: Fangjun Kuang)
using System.Runtime.InteropServices;
namespace SherpaOnnx
{
[StructLayout(LayoutKind.Sequential)]
public struct OfflineZipformerCtcModelConfig
{
public OfflineZipformerCtcModelConfig()
{
Model = "";
}
[MarshalAs(UnmanagedType.LPStr)]
public string Model;
}
}
... ...
../../../../go-api-examples/non-streaming-decode-files/run-zipformer-ctc.sh
\ No newline at end of file
... ...
... ... @@ -398,6 +398,10 @@ type OfflineNemoEncDecCtcModelConfig struct {
Model string // Path to the model, e.g., model.onnx or model.int8.onnx
}
type OfflineZipformerCtcModelConfig struct {
Model string // Path to the model, e.g., model.onnx or model.int8.onnx
}
type OfflineDolphinModelConfig struct {
Model string // Path to the model, e.g., model.onnx or model.int8.onnx
}
... ... @@ -439,16 +443,17 @@ type OfflineLMConfig struct {
}
type OfflineModelConfig struct {
Transducer OfflineTransducerModelConfig
Paraformer OfflineParaformerModelConfig
NemoCTC OfflineNemoEncDecCtcModelConfig
Whisper OfflineWhisperModelConfig
Tdnn OfflineTdnnModelConfig
SenseVoice OfflineSenseVoiceModelConfig
Moonshine OfflineMoonshineModelConfig
FireRedAsr OfflineFireRedAsrModelConfig
Dolphin OfflineDolphinModelConfig
Tokens string // Path to tokens.txt
Transducer OfflineTransducerModelConfig
Paraformer OfflineParaformerModelConfig
NemoCTC OfflineNemoEncDecCtcModelConfig
Whisper OfflineWhisperModelConfig
Tdnn OfflineTdnnModelConfig
SenseVoice OfflineSenseVoiceModelConfig
Moonshine OfflineMoonshineModelConfig
FireRedAsr OfflineFireRedAsrModelConfig
Dolphin OfflineDolphinModelConfig
ZipformerCtc OfflineZipformerCtcModelConfig
Tokens string // Path to tokens.txt
// Number of threads to use for neural network computation
NumThreads int
... ... @@ -540,6 +545,7 @@ func newCOfflineRecognizerConfig(config *OfflineRecognizerConfig) *C.struct_Sher
c.model_config.fire_red_asr.decoder = C.CString(config.ModelConfig.FireRedAsr.Decoder)
c.model_config.dolphin.model = C.CString(config.ModelConfig.Dolphin.Model)
c.model_config.zipformer_ctc.model = C.CString(config.ModelConfig.ZipformerCtc.Model)
c.model_config.tokens = C.CString(config.ModelConfig.Tokens)
... ... @@ -653,11 +659,22 @@ func freeCOfflineRecognizerConfig(c *C.struct_SherpaOnnxOfflineRecognizerConfig)
C.free(unsafe.Pointer(c.model_config.fire_red_asr.encoder))
c.model_config.fire_red_asr.encoder = nil
}
if c.model_config.fire_red_asr.decoder != nil {
C.free(unsafe.Pointer(c.model_config.fire_red_asr.decoder))
c.model_config.fire_red_asr.decoder = nil
}
if c.model_config.dolphin.model != nil {
C.free(unsafe.Pointer(c.model_config.dolphin.model))
c.model_config.dolphin.model = nil
}
if c.model_config.zipformer_ctc.model != nil {
C.free(unsafe.Pointer(c.model_config.zipformer_ctc.model))
c.model_config.zipformer_ctc.model = nil
}
if c.model_config.tokens != nil {
C.free(unsafe.Pointer(c.model_config.tokens))
c.model_config.tokens = nil
... ...
... ... @@ -212,6 +212,21 @@ def get_models():
git diff
""",
),
Model(
model_name="sherpa-onnx-zipformer-ctc-zh-int8-2025-07-03",
hf="k2-fsa/web-assembly-vad-asr-sherpa-onnx-zh-zipformer-ctc",
ms="csukuangfj/web-assembly-vad-asr-sherpa-onnx-zh-zipformer-ctc",
short_name="vad-asr-zh-zipformer-ctc",
cmd="""
pushd $model_name
mv model.int8.onnx ../zipformer-ctc.onnx
mv tokens.txt ../
popd
rm -rf $model_name
sed -i.bak 's/Zipformer/Zipformer CTC supporting Chinese 中文/g' ../index.html
git diff
""",
),
]
return models
... ...
... ... @@ -484,6 +484,9 @@ static sherpa_onnx::OfflineRecognizerConfig GetOfflineRecognizerConfig(
recognizer_config.model_config.dolphin.model =
SHERPA_ONNX_OR(config->model_config.dolphin.model, "");
recognizer_config.model_config.zipformer_ctc.model =
SHERPA_ONNX_OR(config->model_config.zipformer_ctc.model, "");
recognizer_config.lm_config.model =
SHERPA_ONNX_OR(config->lm_config.model, "");
recognizer_config.lm_config.scale =
... ...
... ... @@ -451,6 +451,10 @@ SHERPA_ONNX_API typedef struct SherpaOnnxOfflineDolphinModelConfig {
const char *model;
} SherpaOnnxOfflineDolphinModelConfig;
SHERPA_ONNX_API typedef struct SherpaOnnxOfflineZipformerCtcModelConfig {
const char *model;
} SherpaOnnxOfflineZipformerCtcModelConfig;
SHERPA_ONNX_API typedef struct SherpaOnnxOfflineModelConfig {
SherpaOnnxOfflineTransducerModelConfig transducer;
SherpaOnnxOfflineParaformerModelConfig paraformer;
... ... @@ -474,6 +478,7 @@ SHERPA_ONNX_API typedef struct SherpaOnnxOfflineModelConfig {
SherpaOnnxOfflineMoonshineModelConfig moonshine;
SherpaOnnxOfflineFireRedAsrModelConfig fire_red_asr;
SherpaOnnxOfflineDolphinModelConfig dolphin;
SherpaOnnxOfflineZipformerCtcModelConfig zipformer_ctc;
} SherpaOnnxOfflineModelConfig;
SHERPA_ONNX_API typedef struct SherpaOnnxOfflineRecognizerConfig {
... ...
... ... @@ -252,6 +252,9 @@ OfflineRecognizer OfflineRecognizer::Create(
c.model_config.dolphin.model = config.model_config.dolphin.model.c_str();
c.model_config.zipformer_ctc.model =
config.model_config.zipformer_ctc.model.c_str();
c.lm_config.model = config.lm_config.model.c_str();
c.lm_config.scale = config.lm_config.scale;
... ...
... ... @@ -241,6 +241,10 @@ struct SHERPA_ONNX_API OfflineDolphinModelConfig {
std::string model;
};
struct SHERPA_ONNX_API OfflineZipformerCtcModelConfig {
std::string model;
};
struct SHERPA_ONNX_API OfflineMoonshineModelConfig {
std::string preprocessor;
std::string encoder;
... ... @@ -267,6 +271,7 @@ struct SHERPA_ONNX_API OfflineModelConfig {
OfflineMoonshineModelConfig moonshine;
OfflineFireRedAsrModelConfig fire_red_asr;
OfflineDolphinModelConfig dolphin;
OfflineZipformerCtcModelConfig zipformer_ctc;
};
struct SHERPA_ONNX_API OfflineLMConfig {
... ...
... ... @@ -113,6 +113,16 @@ std::unique_ptr<OfflineCtcModel> OfflineCtcModel::Create(
const OfflineModelConfig &config) {
if (!config.dolphin.model.empty()) {
return std::make_unique<OfflineDolphinModel>(config);
} else if (!config.nemo_ctc.model.empty()) {
return std::make_unique<OfflineNemoEncDecCtcModel>(config);
} else if (!config.tdnn.model.empty()) {
return std::make_unique<OfflineTdnnCtcModel>(config);
} else if (!config.zipformer_ctc.model.empty()) {
return std::make_unique<OfflineZipformerCtcModel>(config);
} else if (!config.wenet_ctc.model.empty()) {
return std::make_unique<OfflineWenetCtcModel>(config);
} else if (!config.telespeech_ctc.empty()) {
return std::make_unique<OfflineTeleSpeechCtcModel>(config);
}
// TODO(fangjun): Refactor it. We don't need to use model_type here
... ... @@ -167,6 +177,16 @@ std::unique_ptr<OfflineCtcModel> OfflineCtcModel::Create(
Manager *mgr, const OfflineModelConfig &config) {
if (!config.dolphin.model.empty()) {
return std::make_unique<OfflineDolphinModel>(mgr, config);
} else if (!config.nemo_ctc.model.empty()) {
return std::make_unique<OfflineNemoEncDecCtcModel>(mgr, config);
} else if (!config.tdnn.model.empty()) {
return std::make_unique<OfflineTdnnCtcModel>(mgr, config);
} else if (!config.zipformer_ctc.model.empty()) {
return std::make_unique<OfflineZipformerCtcModel>(mgr, config);
} else if (!config.wenet_ctc.model.empty()) {
return std::make_unique<OfflineWenetCtcModel>(mgr, config);
} else if (!config.telespeech_ctc.empty()) {
return std::make_unique<OfflineTeleSpeechCtcModel>(mgr, config);
}
// TODO(fangjun): Refactor it. We don't need to use model_type here
... ...
... ... @@ -33,6 +33,7 @@ java_files += OfflineWhisperModelConfig.java
java_files += OfflineFireRedAsrModelConfig.java
java_files += OfflineMoonshineModelConfig.java
java_files += OfflineNemoEncDecCtcModelConfig.java
java_files += OfflineZipformerCtcModelConfig.java
java_files += OfflineSenseVoiceModelConfig.java
java_files += OfflineDolphinModelConfig.java
java_files += OfflineModelConfig.java
... ...
... ... @@ -11,6 +11,7 @@ public class OfflineModelConfig {
private final OfflineNemoEncDecCtcModelConfig nemo;
private final OfflineSenseVoiceModelConfig senseVoice;
private final OfflineDolphinModelConfig dolphin;
private final OfflineZipformerCtcModelConfig zipformerCtc;
private final String teleSpeech;
private final String tokens;
private final int numThreads;
... ... @@ -28,6 +29,7 @@ public class OfflineModelConfig {
this.fireRedAsr = builder.fireRedAsr;
this.moonshine = builder.moonshine;
this.nemo = builder.nemo;
this.zipformerCtc = builder.zipformerCtc;
this.senseVoice = builder.senseVoice;
this.dolphin = builder.dolphin;
this.teleSpeech = builder.teleSpeech;
... ... @@ -52,7 +54,7 @@ public class OfflineModelConfig {
return transducer;
}
public OfflineWhisperModelConfig getZipformer2Ctc() {
public OfflineWhisperModelConfig getWhisper() {
return whisper;
}
... ... @@ -68,6 +70,14 @@ public class OfflineModelConfig {
return dolphin;
}
public OfflineNemoEncDecCtcModelConfig getNemo() {
return nemo;
}
public OfflineZipformerCtcModelConfig getZipformerCtc() {
return zipformerCtc;
}
public String getTokens() {
return tokens;
}
... ... @@ -109,6 +119,7 @@ public class OfflineModelConfig {
private OfflineNemoEncDecCtcModelConfig nemo = OfflineNemoEncDecCtcModelConfig.builder().build();
private OfflineSenseVoiceModelConfig senseVoice = OfflineSenseVoiceModelConfig.builder().build();
private OfflineDolphinModelConfig dolphin = OfflineDolphinModelConfig.builder().build();
private OfflineZipformerCtcModelConfig zipformerCtc = OfflineZipformerCtcModelConfig.builder().build();
private String teleSpeech = "";
private String tokens = "";
private int numThreads = 1;
... ... @@ -142,6 +153,11 @@ public class OfflineModelConfig {
return this;
}
public Builder setZipformerCtc(OfflineZipformerCtcModelConfig zipformerCtc) {
this.zipformerCtc = zipformerCtc;
return this;
}
public Builder setTeleSpeech(String teleSpeech) {
this.teleSpeech = teleSpeech;
return this;
... ...
// Copyright 2025 Xiaomi Corporation
package com.k2fsa.sherpa.onnx;
public class OfflineZipformerCtcModelConfig {
private final String model;
private OfflineZipformerCtcModelConfig(Builder builder) {
this.model = builder.model;
}
public static Builder builder() {
return new Builder();
}
public String getModel() {
return model;
}
public static class Builder {
private String model = "";
public OfflineZipformerCtcModelConfig build() {
return new OfflineZipformerCtcModelConfig(this);
}
public Builder setModel(String model) {
this.model = model;
return this;
}
}
}
... ...
... ... @@ -269,6 +269,21 @@ static OfflineRecognizerConfig GetOfflineConfig(JNIEnv *env, jobject config) {
ans.model_config.nemo_ctc.model = p;
env->ReleaseStringUTFChars(s, p);
// zipformer ctc
fid =
env->GetFieldID(model_config_cls, "zipformerCtc",
"Lcom/k2fsa/sherpa/onnx/OfflineZipformerCtcModelConfig;");
jobject zipformer_ctc_config = env->GetObjectField(model_config, fid);
jclass zipformer_ctc_config_cls = env->GetObjectClass(zipformer_ctc_config);
fid =
env->GetFieldID(zipformer_ctc_config_cls, "model", "Ljava/lang/String;");
s = (jstring)env->GetObjectField(zipformer_ctc_config, fid);
p = env->GetStringUTFChars(s, nullptr);
ans.model_config.zipformer_ctc.model = p;
env->ReleaseStringUTFChars(s, p);
// dolphin
fid = env->GetFieldID(model_config_cls, "dolphin",
"Lcom/k2fsa/sherpa/onnx/OfflineDolphinModelConfig;");
... ...
... ... @@ -29,6 +29,10 @@ data class OfflineDolphinModelConfig(
var model: String = "",
)
data class OfflineZipformerCtcModelConfig(
var model: String = "",
)
data class OfflineWhisperModelConfig(
var encoder: String = "",
var decoder: String = "",
... ... @@ -64,6 +68,7 @@ data class OfflineModelConfig(
var nemo: OfflineNemoEncDecCtcModelConfig = OfflineNemoEncDecCtcModelConfig(),
var senseVoice: OfflineSenseVoiceModelConfig = OfflineSenseVoiceModelConfig(),
var dolphin: OfflineDolphinModelConfig = OfflineDolphinModelConfig(),
var zipformerCtc: OfflineZipformerCtcModelConfig = OfflineZipformerCtcModelConfig(),
var teleSpeech: String = "",
var numThreads: Int = 1,
var debug: Boolean = false,
... ... @@ -559,6 +564,16 @@ fun getOfflineModelConfig(type: Int): OfflineModelConfig? {
modelType = "nemo_transducer",
)
}
31 -> {
val modelDir = "sherpa-onnx-zipformer-ctc-zh-int8-2025-07-03"
return OfflineModelConfig(
zipformerCtc = OfflineZipformerCtcModelConfig(
model = "$modelDir/model.int8.onnx",
),
tokens = "$modelDir/tokens.txt",
)
}
}
return null
}
... ...
... ... @@ -412,6 +412,7 @@ fun getModelConfig(type: Int): OnlineModelConfig? {
model = "$modelDir/model.onnx",
),
tokens = "$modelDir/tokens.txt",
modelType = "zipformer2",
)
}
... ... @@ -422,6 +423,7 @@ fun getModelConfig(type: Int): OnlineModelConfig? {
model = "$modelDir/model.fp16.onnx",
),
tokens = "$modelDir/tokens.txt",
modelType = "zipformer2",
)
}
... ...
... ... @@ -284,6 +284,11 @@ type
function ToString: AnsiString;
end;
TSherpaOnnxOfflineZipformerCtcModelConfig = record
Model: AnsiString;
function ToString: AnsiString;
end;
TSherpaOnnxOfflineWhisperModelConfig = record
Encoder: AnsiString;
Decoder: AnsiString;
... ... @@ -346,6 +351,7 @@ type
Moonshine: TSherpaOnnxOfflineMoonshineModelConfig;
FireRedAsr: TSherpaOnnxOfflineFireRedAsrModelConfig;
Dolphin: TSherpaOnnxOfflineDolphinModelConfig;
ZipformerCtc: TSherpaOnnxOfflineZipformerCtcModelConfig;
class operator Initialize({$IFDEF FPC}var{$ELSE}out{$ENDIF} Dest: TSherpaOnnxOfflineModelConfig);
function ToString: AnsiString;
end;
... ... @@ -726,6 +732,9 @@ type
SherpaOnnxOfflineDolphinModelConfig = record
Model: PAnsiChar;
end;
SherpaOnnxOfflineZipformerCtcModelConfig = record
Model: PAnsiChar;
end;
SherpaOnnxOfflineWhisperModelConfig = record
Encoder: PAnsiChar;
Decoder: PAnsiChar;
... ... @@ -773,6 +782,7 @@ type
Moonshine: SherpaOnnxOfflineMoonshineModelConfig;
FireRedAsr: SherpaOnnxOfflineFireRedAsrModelConfig;
Dolphin: SherpaOnnxOfflineDolphinModelConfig;
ZipformerCtc: SherpaOnnxOfflineZipformerCtcModelConfig;
end;
SherpaOnnxOfflineRecognizerConfig = record
... ... @@ -1536,6 +1546,12 @@ begin
[Self.Model]);
end;
function TSherpaOnnxOfflineZipformerCtcModelConfig.ToString: AnsiString;
begin
Result := Format('TSherpaOnnxOfflineZipformerCtcModelConfig(Model := %s)',
[Self.Model]);
end;
function TSherpaOnnxOfflineWhisperModelConfig.ToString: AnsiString;
begin
Result := Format('TSherpaOnnxOfflineWhisperModelConfig(' +
... ... @@ -1610,14 +1626,15 @@ begin
'SenseVoice := %s, ' +
'Moonshine := %s, ' +
'FireRedAsr := %s, ' +
'Dolphin := %s' +
'Dolphin := %s, ' +
'ZipformerCtc := %s' +
')',
[Self.Transducer.ToString, Self.Paraformer.ToString,
Self.NeMoCtc.ToString, Self.Whisper.ToString, Self.Tdnn.ToString,
Self.Tokens, Self.NumThreads, Self.Debug.ToString, Self.Provider,
Self.ModelType, Self.ModelingUnit, Self.BpeVocab,
Self.TeleSpeechCtc, Self.SenseVoice.ToString, Self.Moonshine.ToString,
Self.FireRedAsr.ToString, Self.Dolphin.ToString
Self.FireRedAsr.ToString, Self.Dolphin.ToString, Self.ZipformerCtc.ToString
]);
end;
... ... @@ -1688,6 +1705,7 @@ begin
C.ModelConfig.FireRedAsr.Decoder := PAnsiChar(Config.ModelConfig.FireRedAsr.Decoder);
C.ModelConfig.Dolphin.Model := PAnsiChar(Config.ModelConfig.Dolphin.Model);
C.ModelConfig.ZipformerCtc.Model := PAnsiChar(Config.ModelConfig.ZipformerCtc.Model);
C.LMConfig.Model := PAnsiChar(Config.LMConfig.Model);
C.LMConfig.Scale := Config.LMConfig.Scale;
... ...
... ... @@ -528,6 +528,87 @@ class OfflineRecognizer(object):
return self
@classmethod
def from_zipformer_ctc(
cls,
model: str,
tokens: str,
num_threads: int = 1,
sample_rate: int = 16000,
feature_dim: int = 80,
decoding_method: str = "greedy_search",
debug: bool = False,
provider: str = "cpu",
rule_fsts: str = "",
rule_fars: str = "",
hr_dict_dir: str = "",
hr_rule_fsts: str = "",
hr_lexicon: str = "",
):
"""
Please refer to
`<https://k2-fsa.github.io/sherpa/onnx/pretrained_models/offline-ctc/icefall/index.html>`_
to download pre-trained models for different languages, e.g., Chinese,
English, etc.
Args:
model:
Path to ``model.onnx``.
tokens:
Path to ``tokens.txt``. Each line in ``tokens.txt`` contains two
columns::
symbol integer_id
num_threads:
Number of threads for neural network computation.
sample_rate:
Sample rate of the training data used to train the model.
feature_dim:
Dimension of the feature used to train the model.
decoding_method:
Valid values are greedy_search.
debug:
True to show debug messages.
provider:
onnxruntime execution providers. Valid values are: cpu, cuda, coreml.
rule_fsts:
If not empty, it specifies fsts for inverse text normalization.
If there are multiple fsts, they are separated by a comma.
rule_fars:
If not empty, it specifies fst archives for inverse text normalization.
If there are multiple archives, they are separated by a comma.
"""
self = cls.__new__(cls)
model_config = OfflineModelConfig(
zipformer_ctc=OfflineZipformerCtcModelConfig(model=model),
tokens=tokens,
num_threads=num_threads,
debug=debug,
provider=provider,
)
feat_config = FeatureExtractorConfig(
sampling_rate=sample_rate,
feature_dim=feature_dim,
)
recognizer_config = OfflineRecognizerConfig(
feat_config=feat_config,
model_config=model_config,
decoding_method=decoding_method,
rule_fsts=rule_fsts,
rule_fars=rule_fars,
hr=HomophoneReplacerConfig(
dict_dir=hr_dict_dir,
lexicon=hr_lexicon,
rule_fsts=hr_rule_fsts,
),
)
self.recognizer = _Recognizer(recognizer_config)
self.config = recognizer_config
return self
@classmethod
def from_nemo_ctc(
cls,
model: str,
... ...
... ... @@ -16,3 +16,6 @@ tts-kokoro-en
tts-kokoro-zh-en
speech-enhancement-gtcrn
decode-file-sense-voice-with-hr
test-version
zipformer-ctc-asr
dolphin-ctc-asr
... ...
... ... @@ -346,6 +346,14 @@ func sherpaOnnxOfflineParaformerModelConfig(
)
}
func sherpaOnnxOfflineZipformerCtcModelConfig(
model: String = ""
) -> SherpaOnnxOfflineZipformerCtcModelConfig {
return SherpaOnnxOfflineZipformerCtcModelConfig(
model: toCPointer(model)
)
}
func sherpaOnnxOfflineNemoEncDecCtcModelConfig(
model: String = ""
) -> SherpaOnnxOfflineNemoEncDecCtcModelConfig {
... ... @@ -449,7 +457,9 @@ func sherpaOnnxOfflineModelConfig(
senseVoice: SherpaOnnxOfflineSenseVoiceModelConfig = sherpaOnnxOfflineSenseVoiceModelConfig(),
moonshine: SherpaOnnxOfflineMoonshineModelConfig = sherpaOnnxOfflineMoonshineModelConfig(),
fireRedAsr: SherpaOnnxOfflineFireRedAsrModelConfig = sherpaOnnxOfflineFireRedAsrModelConfig(),
dolphin: SherpaOnnxOfflineDolphinModelConfig = sherpaOnnxOfflineDolphinModelConfig()
dolphin: SherpaOnnxOfflineDolphinModelConfig = sherpaOnnxOfflineDolphinModelConfig(),
zipformerCtc: SherpaOnnxOfflineZipformerCtcModelConfig =
sherpaOnnxOfflineZipformerCtcModelConfig()
) -> SherpaOnnxOfflineModelConfig {
return SherpaOnnxOfflineModelConfig(
transducer: transducer,
... ... @@ -468,7 +478,8 @@ func sherpaOnnxOfflineModelConfig(
sense_voice: senseVoice,
moonshine: moonshine,
fire_red_asr: fireRedAsr,
dolphin: dolphin
dolphin: dolphin,
zipformer_ctc: zipformerCtc
)
}
... ...
#!/usr/bin/env bash
set -ex
if [ ! -d ../build-swift-macos ]; then
echo "Please run ../build-swift-macos.sh first!"
exit 1
fi
if [ ! -f ./sherpa-onnx-zipformer-ctc-zh-int8-2025-07-03/model.int8.onnx ]; then
echo "Please download the pre-trained model for testing."
echo "You can refer to"
echo ""
echo "https://k2-fsa.github.io/sherpa/onnx/pretrained_models/offline-ctc/icefall/zipformer.html#sherpa-onnx-zipformer-ctc-zh-int8-2025-07-03-chinese"
echo ""
echo "for help"
curl -SL -O https://github.com/k2-fsa/sherpa-onnx/releases/download/asr-models/sherpa-onnx-zipformer-ctc-zh-int8-2025-07-03.tar.bz2
tar xvf sherpa-onnx-zipformer-ctc-zh-int8-2025-07-03.tar.bz2
rm sherpa-onnx-zipformer-ctc-zh-int8-2025-07-03.tar.bz2
ls -lh sherpa-onnx-zipformer-ctc-zh-int8-2025-07-03
fi
if [ ! -e ./zipformer-ctc-asr ]; then
# Note: We use -lc++ to link against libc++ instead of libstdc++
swiftc \
-lc++ \
-I ../build-swift-macos/install/include \
-import-objc-header ./SherpaOnnx-Bridging-Header.h \
./zipformer-ctc-asr.swift ./SherpaOnnx.swift \
-L ../build-swift-macos/install/lib/ \
-l sherpa-onnx \
-l onnxruntime \
-o zipformer-ctc-asr
strip zipformer-ctc-asr
else
echo "./zipformer-ctc-asr exists - skip building"
fi
export DYLD_LIBRARY_PATH=$PWD/../build-swift-macos/install/lib:$DYLD_LIBRARY_PATH
./zipformer-ctc-asr
... ...
import AVFoundation
extension AudioBuffer {
func array() -> [Float] {
return Array(UnsafeBufferPointer(self))
}
}
extension AVAudioPCMBuffer {
func array() -> [Float] {
return self.audioBufferList.pointee.mBuffers.array()
}
}
func run() {
let model = "./sherpa-onnx-zipformer-ctc-zh-int8-2025-07-03/model.int8.onnx"
let tokens = "./sherpa-onnx-zipformer-ctc-zh-int8-2025-07-03/tokens.txt"
let zipformerCtc = sherpaOnnxOfflineZipformerCtcModelConfig(
model: model
)
let modelConfig = sherpaOnnxOfflineModelConfig(
tokens: tokens,
debug: 0,
zipformerCtc: zipformerCtc
)
let featConfig = sherpaOnnxFeatureConfig(
sampleRate: 16000,
featureDim: 80
)
var config = sherpaOnnxOfflineRecognizerConfig(
featConfig: featConfig,
modelConfig: modelConfig
)
let recognizer = SherpaOnnxOfflineRecognizer(config: &config)
let filePath = "./sherpa-onnx-zipformer-ctc-zh-int8-2025-07-03/test_wavs/0.wav"
let fileURL: NSURL = NSURL(fileURLWithPath: filePath)
let audioFile = try! AVAudioFile(forReading: fileURL as URL)
let audioFormat = audioFile.processingFormat
assert(audioFormat.channelCount == 1)
assert(audioFormat.commonFormat == AVAudioCommonFormat.pcmFormatFloat32)
let audioFrameCount = UInt32(audioFile.length)
let audioFileBuffer = AVAudioPCMBuffer(pcmFormat: audioFormat, frameCapacity: audioFrameCount)
try! audioFile.read(into: audioFileBuffer!)
let array: [Float]! = audioFileBuffer?.array()
let result = recognizer.decode(samples: array, sampleRate: Int(audioFormat.sampleRate))
print("\nresult is:\n\(result.text)")
if result.timestamps.count != 0 {
print("\ntimestamps is:\n\(result.timestamps)")
}
}
@main
struct App {
static func main() {
run()
}
}
... ...
... ... @@ -43,6 +43,10 @@ function freeConfig(config, Module) {
freeConfig(config.dolphin, Module)
}
if ('zipformerCtc' in config) {
freeConfig(config.zipformerCtc, Module)
}
if ('moonshine' in config) {
freeConfig(config.moonshine, Module)
}
... ... @@ -627,6 +631,23 @@ function initSherpaOnnxOfflineDolphinModelConfig(config, Module) {
}
}
function initSherpaOnnxOfflineZipformerCtcModelConfig(config, Module) {
const n = Module.lengthBytesUTF8(config.model || '') + 1;
const buffer = Module._malloc(n);
const len = 1 * 4; // 1 pointer
const ptr = Module._malloc(len);
Module.stringToUTF8(config.model || '', buffer, n);
Module.setValue(ptr, buffer, 'i8*');
return {
buffer: buffer, ptr: ptr, len: len,
}
}
function initSherpaOnnxOfflineWhisperModelConfig(config, Module) {
const encoderLen = Module.lengthBytesUTF8(config.encoder || '') + 1;
const decoderLen = Module.lengthBytesUTF8(config.decoder || '') + 1;
... ... @@ -840,6 +861,12 @@ function initSherpaOnnxOfflineModelConfig(config, Module) {
};
}
if (!('zipformerCtc' in config)) {
config.zipformerCtc = {
model: '',
};
}
if (!('whisper' in config)) {
config.whisper = {
encoder: '',
... ... @@ -906,9 +933,12 @@ function initSherpaOnnxOfflineModelConfig(config, Module) {
const dolphin =
initSherpaOnnxOfflineDolphinModelConfig(config.dolphin, Module);
const zipformerCtc =
initSherpaOnnxOfflineZipformerCtcModelConfig(config.zipformerCtc, Module);
const len = transducer.len + paraformer.len + nemoCtc.len + whisper.len +
tdnn.len + 8 * 4 + senseVoice.len + moonshine.len + fireRedAsr.len +
dolphin.len;
dolphin.len + zipformerCtc.len;
const ptr = Module._malloc(len);
... ... @@ -1010,11 +1040,14 @@ function initSherpaOnnxOfflineModelConfig(config, Module) {
Module._CopyHeap(dolphin.ptr, dolphin.len, ptr + offset);
offset += dolphin.len;
Module._CopyHeap(zipformerCtc.ptr, zipformerCtc.len, ptr + offset);
offset += zipformerCtc.len;
return {
buffer: buffer, ptr: ptr, len: len, transducer: transducer,
paraformer: paraformer, nemoCtc: nemoCtc, whisper: whisper, tdnn: tdnn,
senseVoice: senseVoice, moonshine: moonshine, fireRedAsr: fireRedAsr,
dolphin: dolphin
dolphin: dolphin, zipformerCtc: zipformerCtc
}
}
... ...
... ... @@ -13,6 +13,7 @@ extern "C" {
static_assert(sizeof(SherpaOnnxOfflineTransducerModelConfig) == 3 * 4, "");
static_assert(sizeof(SherpaOnnxOfflineParaformerModelConfig) == 4, "");
static_assert(sizeof(SherpaOnnxOfflineZipformerCtcModelConfig) == 4, "");
static_assert(sizeof(SherpaOnnxOfflineDolphinModelConfig) == 4, "");
static_assert(sizeof(SherpaOnnxOfflineNemoEncDecCtcModelConfig) == 4, "");
static_assert(sizeof(SherpaOnnxOfflineWhisperModelConfig) == 5 * 4, "");
... ... @@ -31,7 +32,8 @@ static_assert(sizeof(SherpaOnnxOfflineModelConfig) ==
sizeof(SherpaOnnxOfflineSenseVoiceModelConfig) +
sizeof(SherpaOnnxOfflineMoonshineModelConfig) +
sizeof(SherpaOnnxOfflineFireRedAsrModelConfig) +
sizeof(SherpaOnnxOfflineDolphinModelConfig),
sizeof(SherpaOnnxOfflineDolphinModelConfig) +
sizeof(SherpaOnnxOfflineZipformerCtcModelConfig),
"");
static_assert(sizeof(SherpaOnnxFeatureConfig) == 2 * 4, "");
... ... @@ -77,6 +79,7 @@ void PrintOfflineRecognizerConfig(SherpaOnnxOfflineRecognizerConfig *config) {
auto moonshine = &model_config->moonshine;
auto fire_red_asr = &model_config->fire_red_asr;
auto dolphin = &model_config->dolphin;
auto zipformer_ctc = &model_config->zipformer_ctc;
fprintf(stdout, "----------offline transducer model config----------\n");
fprintf(stdout, "encoder: %s\n", transducer->encoder);
... ... @@ -117,6 +120,9 @@ void PrintOfflineRecognizerConfig(SherpaOnnxOfflineRecognizerConfig *config) {
fprintf(stdout, "----------offline Dolphin model config----------\n");
fprintf(stdout, "model: %s\n", dolphin->model);
fprintf(stdout, "----------offline zipformer ctc model config----------\n");
fprintf(stdout, "model: %s\n", zipformer_ctc->model);
fprintf(stdout, "tokens: %s\n", model_config->tokens);
fprintf(stdout, "num_threads: %d\n", model_config->num_threads);
fprintf(stdout, "provider: %s\n", model_config->provider);
... ...
... ... @@ -117,6 +117,10 @@ function initOfflineRecognizer() {
};
} else if (fileExists('dolphin.onnx')) {
config.modelConfig.dolphin = {model: './dolphin.onnx'};
} else if (fileExists('zipformer-ctc.onnx')) {
// you need to rename model.int8.onnx from zipformer CTC to
// zipformer-ctc.onnx
config.modelConfig.zipformerCtc = {model: './zipformer-ctc.onnx'};
} else {
console.log('Please specify a model.');
alert('Please specify a model.');
... ...