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enginex_bi_series-sherpa-onnx/dotnet-examples/online-decode-files/Program.cs

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C#

// Copyright (c) 2023 Xiaomi Corporation
// Copyright (c) 2023 by manyeyes
//
// This file shows how to use a streaming model to decode files
// Please refer to
// https://k2-fsa.github.io/sherpa/onnx/pretrained_models/online-transducer/zipformer-transducer-models.html
// to download streaming models
using CommandLine;
using CommandLine.Text;
using SherpaOnnx;
class OnlineDecodeFiles
{
class Options
{
[Option(Required = true, HelpText = "Path to tokens.txt")]
public string Tokens { get; set; } = string.Empty;
[Option(Required = false, Default = "cpu", HelpText = "Provider, e.g., cpu, coreml")]
public string Provider { get; set; } = string.Empty;
[Option(Required = false, HelpText = "Path to transducer encoder.onnx")]
public string Encoder { get; set; } = string.Empty;
[Option(Required = false, HelpText = "Path to transducer decoder.onnx")]
public string Decoder { get; set; } = string.Empty;
[Option(Required = false, HelpText = "Path to transducer joiner.onnx")]
public string Joiner { get; set; } = string.Empty;
[Option("paraformer-encoder", Required = false, HelpText = "Path to paraformer encoder.onnx")]
public string ParaformerEncoder { get; set; } = string.Empty;
[Option("paraformer-decoder", Required = false, HelpText = "Path to paraformer decoder.onnx")]
public string ParaformerDecoder { get; set; } = string.Empty;
[Option("zipformer2-ctc", Required = false, HelpText = "Path to zipformer2 CTC onnx model")]
public string Zipformer2Ctc { get; set; } = string.Empty;
[Option("num-threads", Required = false, Default = 1, HelpText = "Number of threads for computation")]
public int NumThreads { get; set; } = 1;
[Option("decoding-method", Required = false, Default = "greedy_search",
HelpText = "Valid decoding methods are: greedy_search, modified_beam_search")]
public string DecodingMethod { get; set; } = "greedy_search";
[Option(Required = false, Default = false, HelpText = "True to show model info during loading")]
public bool Debug { get; set; } = false;
[Option("sample-rate", Required = false, Default = 16000, HelpText = "Sample rate of the data used to train the model")]
public int SampleRate { get; set; } = 16000;
[Option("max-active-paths", Required = false, Default = 4,
HelpText = @"Used only when --decoding--method is modified_beam_search.
It specifies number of active paths to keep during the search")]
public int MaxActivePaths { get; set; } = 4;
[Option("enable-endpoint", Required = false, Default = false,
HelpText = "True to enable endpoint detection.")]
public bool EnableEndpoint { get; set; } = false;
[Option("rule1-min-trailing-silence", Required = false, Default = 2.4F,
HelpText = @"An endpoint is detected if trailing silence in seconds is
larger than this value even if nothing has been decoded. Used only when --enable-endpoint is true.")]
public float Rule1MinTrailingSilence { get; set; } = 2.4F;
[Option("rule2-min-trailing-silence", Required = false, Default = 1.2F,
HelpText = @"An endpoint is detected if trailing silence in seconds is
larger than this value after something that is not blank has been decoded. Used
only when --enable-endpoint is true.")]
public float Rule2MinTrailingSilence { get; set; } = 1.2F;
[Option("rule3-min-utterance-length", Required = false, Default = 20.0F,
HelpText = @"An endpoint is detected if the utterance in seconds is
larger than this value. Used only when --enable-endpoint is true.")]
public float Rule3MinUtteranceLength { get; set; } = 20.0F;
[Option("hotwords-file", Required = false, Default = "", HelpText = "Path to hotwords.txt")]
public string HotwordsFile { get; set; } = string.Empty;
[Option("hotwords-score", Required = false, Default = 1.5F, HelpText = "hotwords score")]
public float HotwordsScore { get; set; } = 1.5F;
[Option("rule-fsts", Required = false, Default = "",
HelpText = "If not empty, path to rule fst for inverse text normalization")]
public string RuleFsts { get; set; } = string.Empty;
[Option("files", Required = true, HelpText = "Audio files for decoding")]
public IEnumerable<string> Files { get; set; } = new string[] {};
}
static void Main(string[] args)
{
var parser = new CommandLine.Parser(with => with.HelpWriter = null);
var parserResult = parser.ParseArguments<Options>(args);
parserResult
.WithParsed<Options>(options => Run(options))
.WithNotParsed(errs => DisplayHelp(parserResult, errs));
}
private static void DisplayHelp<T>(ParserResult<T> result, IEnumerable<Error> errs)
{
string usage = @"
(1) Streaming transducer models
dotnet run \
--tokens=./sherpa-onnx-streaming-zipformer-bilingual-zh-en-2023-02-20/tokens.txt \
--encoder=./sherpa-onnx-streaming-zipformer-bilingual-zh-en-2023-02-20/encoder-epoch-99-avg-1.onnx \
--decoder=./sherpa-onnx-streaming-zipformer-bilingual-zh-en-2023-02-20/decoder-epoch-99-avg-1.onnx \
--joiner=./sherpa-onnx-streaming-zipformer-bilingual-zh-en-2023-02-20/joiner-epoch-99-avg-1.onnx \
--num-threads=2 \
--decoding-method=modified_beam_search \
--debug=false \
--files ./sherpa-onnx-streaming-zipformer-bilingual-zh-en-2023-02-20/test_wavs/0.wav \
./sherpa-onnx-streaming-zipformer-bilingual-zh-en-2023-02-20/test_wavs/1.wav
(2) Streaming Zipformer2 Ctc models
dotnet run -c Release \
--tokens ./sherpa-onnx-streaming-zipformer-ctc-multi-zh-hans-2023-12-13/tokens.txt \
--zipformer2-ctc ./sherpa-onnx-streaming-zipformer-ctc-multi-zh-hans-2023-12-13/ctc-epoch-20-avg-1-chunk-16-left-128.onnx \
--files ./sherpa-onnx-streaming-zipformer-ctc-multi-zh-hans-2023-12-13/test_wavs/DEV_T0000000000.wav \
./sherpa-onnx-streaming-zipformer-ctc-multi-zh-hans-2023-12-13/test_wavs/DEV_T0000000001.wav \
./sherpa-onnx-streaming-zipformer-ctc-multi-zh-hans-2023-12-13/test_wavs/DEV_T0000000002.wav \
./sherpa-onnx-streaming-zipformer-ctc-multi-zh-hans-2023-12-13/test_wavs/TEST_MEETING_T0000000113.wav \
./sherpa-onnx-streaming-zipformer-ctc-multi-zh-hans-2023-12-13/test_wavs/TEST_MEETING_T0000000219.wav \
./sherpa-onnx-streaming-zipformer-ctc-multi-zh-hans-2023-12-13/test_wavs/TEST_MEETING_T0000000351.wav
(3) Streaming Paraformer models
dotnet run \
--tokens=./sherpa-onnx-streaming-paraformer-bilingual-zh-en/tokens.txt \
--paraformer-encoder=./sherpa-onnx-streaming-paraformer-bilingual-zh-en/encoder.int8.onnx \
--paraformer-decoder=./sherpa-onnx-streaming-paraformer-bilingual-zh-en/decoder.int8.onnx \
--num-threads=2 \
--decoding-method=greedy_search \
--debug=false \
--files ./sherpa-onnx-streaming-paraformer-bilingual-zh-en/test_wavs/0.wav \
./sherpa-onnx-streaming-paraformer-bilingual-zh-en/test_wavs/1.wav
Please refer to
https://k2-fsa.github.io/sherpa/onnx/pretrained_models/online-transducer/index.html
https://k2-fsa.github.io/sherpa/onnx/pretrained_models/online-paraformer/index.html
https://k2-fsa.github.io/sherpa/onnx/pretrained_models/online-ctc/index.html
to download pre-trained streaming models.
";
var helpText = HelpText.AutoBuild(result, h =>
{
h.AdditionalNewLineAfterOption = false;
h.Heading = usage;
h.Copyright = "Copyright (c) 2023 Xiaomi Corporation";
return HelpText.DefaultParsingErrorsHandler(result, h);
}, e => e);
Console.WriteLine(helpText);
}
private static void Run(Options options)
{
var config = new OnlineRecognizerConfig();
config.FeatConfig.SampleRate = options.SampleRate;
// All models from icefall using feature dim 80.
// You can change it if your model has a different feature dim.
config.FeatConfig.FeatureDim = 80;
config.ModelConfig.Transducer.Encoder = options.Encoder;
config.ModelConfig.Transducer.Decoder = options.Decoder;
config.ModelConfig.Transducer.Joiner = options.Joiner;
config.ModelConfig.Paraformer.Encoder = options.ParaformerEncoder;
config.ModelConfig.Paraformer.Decoder = options.ParaformerDecoder;
config.ModelConfig.Zipformer2Ctc.Model = options.Zipformer2Ctc;
config.ModelConfig.Tokens = options.Tokens;
config.ModelConfig.Provider = options.Provider;
config.ModelConfig.NumThreads = options.NumThreads;
config.ModelConfig.Debug = options.Debug ? 1 : 0;
config.DecodingMethod = options.DecodingMethod;
config.MaxActivePaths = options.MaxActivePaths;
config.EnableEndpoint = options.EnableEndpoint ? 1 : 0;
config.Rule1MinTrailingSilence = options.Rule1MinTrailingSilence;
config.Rule2MinTrailingSilence = options.Rule2MinTrailingSilence;
config.Rule3MinUtteranceLength = options.Rule3MinUtteranceLength;
config.HotwordsFile = options.HotwordsFile;
config.HotwordsScore = options.HotwordsScore;
config.RuleFsts = options.RuleFsts;
var recognizer = new OnlineRecognizer(config);
var files = options.Files.ToArray();
// We create a separate stream for each file
var streams = new List<OnlineStream>();
streams.EnsureCapacity(files.Length);
for (int i = 0; i != files.Length; ++i)
{
var s = recognizer.CreateStream();
var waveReader = new WaveReader(files[i]);
s.AcceptWaveform(waveReader.SampleRate, waveReader.Samples);
var tailPadding = new float[(int)(waveReader.SampleRate * 0.3)];
s.AcceptWaveform(waveReader.SampleRate, tailPadding);
s.InputFinished();
streams.Add(s);
}
while (true)
{
var readyStreams = streams.Where(s => recognizer.IsReady(s));
if (!readyStreams.Any())
{
break;
}
recognizer.Decode(readyStreams);
}
// display results
for (int i = 0; i != files.Length; ++i)
{
var r = recognizer.GetResult(streams[i]);
var text = r.Text;
var tokens = r.Tokens;
Console.WriteLine("--------------------");
Console.WriteLine(files[i]);
Console.WriteLine("text: {0}", text);
Console.WriteLine("tokens: [{0}]", string.Join(", ", tokens));
Console.Write("timestamps: [");
r.Timestamps.ToList().ForEach(i => Console.Write(string.Format("{0:0.00}", i) + ", "));
Console.WriteLine("]");
}
Console.WriteLine("--------------------");
}
}