Add Pascal/Go/C#/Dart API for NeMo Canary ASR models (#2367)
Add support for the new NeMo Canary ASR model across multiple language bindings by introducing a Canary model configuration and setter method on the offline recognizer. - Define Canary model config in Pascal, Go, C#, Dart and update converter functions - Add SetConfig API for offline recognizer (Pascal, Go, C#, Dart) - Extend CI/workflows and example scripts to test non-streaming Canary decoding
This commit is contained in:
Fangjun Kuang
@@ -10,3 +10,4 @@ telespeech_ctc
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moonshine
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dolphin_ctc
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zipformer_ctc
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nemo_canary
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107
pascal-api-examples/non-streaming-asr/nemo_canary.pas
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107
pascal-api-examples/non-streaming-asr/nemo_canary.pas
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{ Copyright (c) 2025 Xiaomi Corporation }
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{
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This file shows how to use a non-streaming NeMo Canary model
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to decode files.
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You can download the model files from
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https://github.com/k2-fsa/sherpa-onnx/releases/tag/asr-models
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}
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program nemo_canary;
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{$mode objfpc}
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uses
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sherpa_onnx,
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DateUtils,
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SysUtils;
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var
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Wave: TSherpaOnnxWave;
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WaveFilename: AnsiString;
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Config: TSherpaOnnxOfflineRecognizerConfig;
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Recognizer: TSherpaOnnxOfflineRecognizer;
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Stream: TSherpaOnnxOfflineStream;
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RecognitionResult: TSherpaOnnxOfflineRecognizerResult;
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Start: TDateTime;
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Stop: TDateTime;
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Elapsed: Single;
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Duration: Single;
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RealTimeFactor: Single;
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begin
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Initialize(Config);
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Config.ModelConfig.Canary.Encoder := './sherpa-onnx-nemo-canary-180m-flash-en-es-de-fr-int8/encoder.int8.onnx';
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Config.ModelConfig.Canary.Decoder := './sherpa-onnx-nemo-canary-180m-flash-en-es-de-fr-int8/decoder.int8.onnx';
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Config.ModelConfig.Canary.SrcLang := 'en';
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Config.ModelConfig.Canary.TgtLang := 'en';
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Config.ModelConfig.Canary.UsePnc := True;
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Config.ModelConfig.Tokens := './sherpa-onnx-nemo-canary-180m-flash-en-es-de-fr-int8/tokens.txt';
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Config.ModelConfig.Provider := 'cpu';
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Config.ModelConfig.NumThreads := 1;
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Config.ModelConfig.Debug := False;
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WaveFilename := './sherpa-onnx-nemo-canary-180m-flash-en-es-de-fr-int8/test_wavs/en.wav';
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Wave := SherpaOnnxReadWave(WaveFilename);
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Recognizer := TSherpaOnnxOfflineRecognizer.Create(Config);
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Stream := Recognizer.CreateStream();
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Start := Now;
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Stream.AcceptWaveform(Wave.Samples, Wave.SampleRate);
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Recognizer.Decode(Stream);
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RecognitionResult := Recognizer.GetResult(Stream);
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Stop := Now;
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Elapsed := MilliSecondsBetween(Stop, Start) / 1000;
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Duration := Length(Wave.Samples) / Wave.SampleRate;
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RealTimeFactor := Elapsed / Duration;
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WriteLn(RecognitionResult.ToString);
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WriteLn(Format('NumThreads %d', [Config.ModelConfig.NumThreads]));
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WriteLn(Format('Elapsed %.3f s', [Elapsed]));
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WriteLn(Format('Wave duration %.3f s', [Duration]));
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WriteLn(Format('RTF = %.3f/%.3f = %.3f', [Elapsed, Duration, RealTimeFactor]));
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FreeAndNil(Stream);
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WriteLn('-----------Output German-----');
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Stream := Recognizer.CreateStream();
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Start := Now;
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Stream.AcceptWaveform(Wave.Samples, Wave.SampleRate);
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Config.ModelConfig.Canary.TgtLang := 'de';
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Recognizer.SetConfig(Config);
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Recognizer.Decode(Stream);
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RecognitionResult := Recognizer.GetResult(Stream);
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Stop := Now;
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Elapsed := MilliSecondsBetween(Stop, Start) / 1000;
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Duration := Length(Wave.Samples) / Wave.SampleRate;
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RealTimeFactor := Elapsed / Duration;
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WriteLn(RecognitionResult.ToString);
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WriteLn(Format('NumThreads %d', [Config.ModelConfig.NumThreads]));
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WriteLn(Format('Elapsed %.3f s', [Elapsed]));
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WriteLn(Format('Wave duration %.3f s', [Duration]));
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WriteLn(Format('RTF = %.3f/%.3f = %.3f', [Elapsed, Duration, RealTimeFactor]));
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{Free resources to avoid memory leak.
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Note: You don't need to invoke them for this simple script.
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However, you have to invoke them in your own large/complex project.
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}
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FreeAndNil(Stream);
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FreeAndNil(Recognizer);
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end.
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42
pascal-api-examples/non-streaming-asr/run-nemo-canary.sh
Executable file
42
pascal-api-examples/non-streaming-asr/run-nemo-canary.sh
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#!/usr/bin/env bash
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set -ex
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SCRIPT_DIR=$( cd -- "$( dirname -- "${BASH_SOURCE[0]}" )" &> /dev/null && pwd )
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SHERPA_ONNX_DIR=$(cd $SCRIPT_DIR/../.. && pwd)
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echo "SHERPA_ONNX_DIR: $SHERPA_ONNX_DIR"
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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
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mkdir -p ../../build
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pushd ../../build
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cmake \
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-DCMAKE_INSTALL_PREFIX=./install \
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-DSHERPA_ONNX_ENABLE_PYTHON=OFF \
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-DSHERPA_ONNX_ENABLE_TESTS=OFF \
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-DSHERPA_ONNX_ENABLE_CHECK=OFF \
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-DBUILD_SHARED_LIBS=ON \
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-DSHERPA_ONNX_ENABLE_PORTAUDIO=OFF \
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..
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cmake --build . --target install --config Release
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ls -lh lib
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popd
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fi
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if [ ! -f sherpa-onnx-nemo-canary-180m-flash-en-es-de-fr-int8/encoder.int8.onnx ]; then
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curl -SL -O https://github.com/k2-fsa/sherpa-onnx/releases/download/asr-models/sherpa-onnx-nemo-canary-180m-flash-en-es-de-fr-int8.tar.bz2
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tar xvf sherpa-onnx-nemo-canary-180m-flash-en-es-de-fr-int8.tar.bz2
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rm sherpa-onnx-nemo-canary-180m-flash-en-es-de-fr-int8.tar.bz2
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fi
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fpc \
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-dSHERPA_ONNX_USE_SHARED_LIBS \
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-Fu$SHERPA_ONNX_DIR/sherpa-onnx/pascal-api \
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-Fl$SHERPA_ONNX_DIR/build/install/lib \
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./nemo_canary.pas
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export LD_LIBRARY_PATH=$SHERPA_ONNX_DIR/build/install/lib:$LD_LIBRARY_PATH
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export DYLD_LIBRARY_PATH=$SHERPA_ONNX_DIR/build/install/lib:$DYLD_LIBRARY_PATH
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./nemo_canary
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