using AliParaformerAsr;
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using CommandLine;
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using NAudio.Wave;
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internal static class Program
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{
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public class ProgramParams
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{
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[Option('i', "input", Required = true, HelpText = "Input wav file/folder path.")]
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public string WavFilePath { get; set; }
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[Option('m', "model", Default = "speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-onnx", HelpText = "Model path.")]
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public string Model { get; set; }
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}
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[STAThread]
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private static void Main(string[] args)
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{
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var argParams = Parser.Default.ParseArguments<ProgramParams>(args).Value;
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string modelPath = argParams.Model;
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if (!Directory.Exists(argParams.Model))
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{
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modelPath = Path.Combine(AppDomain.CurrentDomain.BaseDirectory, modelPath);
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if (!Directory.Exists(modelPath))
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{
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throw new DirectoryNotFoundException($"Model not found: {argParams.Model}");
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}
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}
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string modelFilePath = Path.Combine(modelPath, "model_quant.onnx");
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string configFilePath = Path.Combine(modelPath, "asr.yaml");
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string mvnFilePath = Path.Combine(modelPath, "am.mvn");
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string tokensFilePath = Path.Combine(modelPath, "tokens.json");
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var offlineRecognizer = new OfflineRecognizer(modelFilePath, configFilePath, mvnFilePath, tokensFilePath, OnnxRumtimeTypes.CPU);
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List<float[]> samples = new List<float[]>();
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TimeSpan total_duration = new TimeSpan(0L);
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if (File.Exists(argParams.WavFilePath))
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{
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(var sample, var duration) = LoadWavFile(argParams.WavFilePath);
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samples.Add(sample);
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total_duration += duration;
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}
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else if (Directory.Exists(argParams.WavFilePath))
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{
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var findWavCount = 0;
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foreach (var wavFilePath in Directory.EnumerateFiles(argParams.WavFilePath, "*.wav"))
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{
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(var sample, var duration) = LoadWavFile(wavFilePath);
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samples.Add(sample);
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total_duration += duration;
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findWavCount++;
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}
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Console.WriteLine($"Total WAV files found: {findWavCount} duration：{total_duration}");
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}
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else
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{
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throw new Exception($"Invalid wav input path. {argParams.WavFilePath}");
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}
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var start_time = DateTime.Now;
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int batchSize = 1; // 输入参数支持批处理，但是实际效果提升有限，感觉还是负优化，等GPU版本优化后再试
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for (int i = 0; i < samples.Count; i += batchSize)
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{
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List<float[]> temp_samples = samples.Skip(i).Take(batchSize).ToList();
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List<string> results = offlineRecognizer.GetResults(temp_samples);
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foreach (string result in results)
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{
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Console.WriteLine(result);
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Console.WriteLine("");
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}
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}
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var end_time = DateTime.Now;
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double elapsed_milliseconds = (end_time - start_time).TotalMilliseconds;
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double rtf = elapsed_milliseconds / total_duration.TotalMilliseconds;
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Console.WriteLine("elapsed_milliseconds:{0}", elapsed_milliseconds.ToString());
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Console.WriteLine("total_duration:{0}", total_duration.TotalMilliseconds.ToString());
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// 实时因子是处理时间与音频时长的比值。
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// 例如，如果一个 10 秒的音频片段需要 5 秒来处理，那么实时因子就是 0.5。
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// 如果处理时间和音频时长相等，那么实时因子就是 1，这意味着系统以实时速度进行处理。
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// 数值越小，表示处理速度越快。
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// from chatgpt 解释
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Console.WriteLine("Real-Time Factor :{0}", rtf.ToString());
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Console.WriteLine("end!");
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}
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private static (float[] sample, TimeSpan duration) LoadWavFile(string wavFilePath)
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{
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AudioFileReader _audioFileReader = new AudioFileReader(wavFilePath);
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byte[] datas = new byte[_audioFileReader.Length];
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_audioFileReader.Read(datas, 0, datas.Length);
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var duration = _audioFileReader.TotalTime;
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float[] wavdata = new float[datas.Length / 4];
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Buffer.BlockCopy(datas, 0, wavdata, 0, datas.Length);
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var sample = wavdata.Select((float x) => x * 32768f).ToArray();
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return (sample, duration);
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}
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}
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