using AliParaformerAsr; using CommandLine; using NAudio.Wave; internal static class Program { public class ProgramParams { [Option('i', "input", Required = true, HelpText = "Input wav file/folder path.")] public string WavFilePath { get; set; } [Option('m', "model", Default = "speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-onnx", HelpText = "Model path.")] public string Model { get; set; } } [STAThread] private static void Main(string[] args) { var argParams = Parser.Default.ParseArguments(args).Value; string modelPath = argParams.Model; if (!Directory.Exists(argParams.Model)) { modelPath = Path.Combine(AppDomain.CurrentDomain.BaseDirectory, modelPath); if (!Directory.Exists(modelPath)) { throw new DirectoryNotFoundException($"Model not found: {argParams.Model}"); } } string modelFilePath = Path.Combine(modelPath, "model_quant.onnx"); string configFilePath = Path.Combine(modelPath, "asr.yaml"); string mvnFilePath = Path.Combine(modelPath, "am.mvn"); string tokensFilePath = Path.Combine(modelPath, "tokens.json"); var offlineRecognizer = new OfflineRecognizer(modelFilePath, configFilePath, mvnFilePath, tokensFilePath, OnnxRumtimeTypes.CPU); List samples = new List(); TimeSpan total_duration = new TimeSpan(0L); if (File.Exists(argParams.WavFilePath)) { (var sample, var duration) = LoadWavFile(argParams.WavFilePath); samples.Add(sample); total_duration += duration; } else if (Directory.Exists(argParams.WavFilePath)) { var findWavCount = 0; foreach (var wavFilePath in Directory.EnumerateFiles(argParams.WavFilePath, "*.wav")) { (var sample, var duration) = LoadWavFile(wavFilePath); samples.Add(sample); total_duration += duration; findWavCount++; } Console.WriteLine($"Total WAV files found: {findWavCount} duration:{total_duration}"); } else { throw new Exception($"Invalid wav input path. {argParams.WavFilePath}"); } var start_time = DateTime.Now; 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 for (int i = 0; i < samples.Count; i += batchSize) { List temp_samples = samples.Skip(i).Take(batchSize).ToList(); List results = offlineRecognizer.GetResults(temp_samples); foreach (string result in results) { Console.WriteLine(result); Console.WriteLine(""); } } var end_time = DateTime.Now; double elapsed_milliseconds = (end_time - start_time).TotalMilliseconds; double rtf = elapsed_milliseconds / total_duration.TotalMilliseconds; Console.WriteLine("elapsed_milliseconds:{0}", elapsed_milliseconds.ToString()); Console.WriteLine("total_duration:{0}", total_duration.TotalMilliseconds.ToString()); // 实时因子是处理时间与音频时长的比值。 // 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。 // 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。 // 数值越小,表示处理速度越快。 // from chatgpt 解释 Console.WriteLine("Real-Time Factor :{0}", rtf.ToString()); Console.WriteLine("end!"); } private static (float[] sample, TimeSpan duration) LoadWavFile(string wavFilePath) { AudioFileReader _audioFileReader = new AudioFileReader(wavFilePath); byte[] datas = new byte[_audioFileReader.Length]; _audioFileReader.Read(datas, 0, datas.Length); var duration = _audioFileReader.TotalTime; float[] wavdata = new float[datas.Length / 4]; Buffer.BlockCopy(datas, 0, wavdata, 0, datas.Length); var sample = wavdata.Select((float x) => x * 32768f).ToArray(); return (sample, duration); } }