40 lines
1.3 KiB
C#
40 lines
1.3 KiB
C#
// Copyright (c) 2024 Xiaomi Corporation
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//
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// This file shows how to do spoken language identification with whisper.
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//
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// 1. Download a whisper multilingual model. We use a tiny model below.
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// Please refer to https://github.com/k2-fsa/sherpa-onnx/releases/tag/asr-models
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// to download more models.
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//
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// wget https://github.com/k2-fsa/sherpa-onnx/releases/download/asr-models/sherpa-onnx-whisper-tiny.tar.bz2
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// tar xvf sherpa-onnx-whisper-tiny.tar.bz2
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// rm sherpa-onnx-whisper-tiny.tar.bz2
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//
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// 2. Now run it
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//
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// dotnet run
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using SherpaOnnx;
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class SpokenLanguageIdentificationDemo
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{
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static void Main(string[] args)
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{
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var config = new SpokenLanguageIdentificationConfig();
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config.Whisper.Encoder = "./sherpa-onnx-whisper-tiny/tiny-encoder.int8.onnx";
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config.Whisper.Decoder = "./sherpa-onnx-whisper-tiny/tiny-decoder.int8.onnx";
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var slid = new SpokenLanguageIdentification(config);
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var filename = "./sherpa-onnx-whisper-tiny/test_wavs/0.wav";
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var waveReader = new WaveReader(filename);
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var s = slid.CreateStream();
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s.AcceptWaveform(waveReader.SampleRate, waveReader.Samples);
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var result = slid.Compute(s);
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Console.WriteLine($"Filename: {filename}");
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Console.WriteLine($"Detected language: {result.Lang}");
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}
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}
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