mirror of
https://github.com/snakers4/silero-vad.git
synced 2026-02-05 18:09:22 +08:00
修复CalculateProb方法计算句子EndOffset的bug
修改语法提示
This commit is contained in:
dongfp
@@ -1,5 +1,6 @@
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using Microsoft.ML.OnnxRuntime;
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using Microsoft.ML.OnnxRuntime.Tensors;
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using System;
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using System.Collections.Generic;
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using System.Linq;
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@@ -7,214 +8,208 @@ using System.Linq;
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namespace VADdotnet;
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public class SileroVadOnnxModel : IDisposable
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public class SileroVadOnnxModel : IDisposable
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{
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private readonly InferenceSession session;
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private float[][][] state;
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private float[][] context;
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private int lastSr = 0;
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private int lastBatchSize = 0;
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private static readonly List<int> SAMPLE_RATES = [8000, 16000];
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public SileroVadOnnxModel(string modelPath)
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{
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private readonly InferenceSession session;
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private float[][][] state;
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private float[][] context;
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private int lastSr = 0;
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private int lastBatchSize = 0;
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private static readonly List<int> SAMPLE_RATES = new List<int> { 8000, 16000 };
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public SileroVadOnnxModel(string modelPath)
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var sessionOptions = new SessionOptions
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{
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var sessionOptions = new SessionOptions();
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sessionOptions.InterOpNumThreads = 1;
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sessionOptions.IntraOpNumThreads = 1;
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sessionOptions.EnableCpuMemArena = true;
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InterOpNumThreads = 1,
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IntraOpNumThreads = 1,
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EnableCpuMemArena = true
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};
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session = new InferenceSession(modelPath, sessionOptions);
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session = new InferenceSession(modelPath, sessionOptions);
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ResetStates();
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}
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public void ResetStates()
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{
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state = new float[2][][];
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state[0] = new float[1][];
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state[1] = new float[1][];
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state[0][0] = new float[128];
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state[1][0] = new float[128];
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context = [];
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lastSr = 0;
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lastBatchSize = 0;
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}
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public void Dispose()
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{
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GC.SuppressFinalize(this);
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}
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public class ValidationResult(float[][] x, int sr)
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{
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public float[][] X { get; } = x;
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public int Sr { get; } = sr;
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}
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private static ValidationResult ValidateInput(float[][] x, int sr)
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{
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if (x.Length == 1)
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{
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x = [x[0]];
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}
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if (x.Length > 2)
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{
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throw new ArgumentException($"Incorrect audio data dimension: {x[0].Length}");
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}
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if (sr != 16000 && (sr % 16000 == 0))
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{
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int step = sr / 16000;
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float[][] reducedX = new float[x.Length][];
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for (int i = 0; i < x.Length; i++)
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{
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float[] current = x[i];
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float[] newArr = new float[(current.Length + step - 1) / step];
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for (int j = 0, index = 0; j < current.Length; j += step, index++)
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{
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newArr[index] = current[j];
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}
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reducedX[i] = newArr;
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}
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x = reducedX;
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sr = 16000;
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}
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if (!SAMPLE_RATES.Contains(sr))
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{
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throw new ArgumentException($"Only supports sample rates {string.Join(", ", SAMPLE_RATES)} (or multiples of 16000)");
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}
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if (((float)sr) / x[0].Length > 31.25)
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{
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throw new ArgumentException("Input audio is too short");
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}
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return new ValidationResult(x, sr);
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}
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private static float[][] Concatenate(float[][] a, float[][] b)
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{
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if (a.Length != b.Length)
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{
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throw new ArgumentException("The number of rows in both arrays must be the same.");
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}
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int rows = a.Length;
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int colsA = a[0].Length;
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int colsB = b[0].Length;
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float[][] result = new float[rows][];
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for (int i = 0; i < rows; i++)
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{
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result[i] = new float[colsA + colsB];
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Array.Copy(a[i], 0, result[i], 0, colsA);
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Array.Copy(b[i], 0, result[i], colsA, colsB);
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}
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return result;
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}
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private static float[][] GetLastColumns(float[][] array, int contextSize)
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{
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int rows = array.Length;
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int cols = array[0].Length;
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if (contextSize > cols)
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{
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throw new ArgumentException("contextSize cannot be greater than the number of columns in the array.");
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}
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float[][] result = new float[rows][];
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for (int i = 0; i < rows; i++)
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{
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result[i] = new float[contextSize];
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Array.Copy(array[i], cols - contextSize, result[i], 0, contextSize);
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}
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return result;
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}
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public float[] Call(float[][] x, int sr)
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{
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var result = ValidateInput(x, sr);
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x = result.X;
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sr = result.Sr;
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int numberSamples = sr == 16000 ? 512 : 256;
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if (x[0].Length != numberSamples)
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{
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throw new ArgumentException($"Provided number of samples is {x[0].Length} (Supported values: 256 for 8000 sample rate, 512 for 16000)");
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}
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int batchSize = x.Length;
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int contextSize = sr == 16000 ? 64 : 32;
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if (lastBatchSize == 0)
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{
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ResetStates();
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}
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if (lastSr != 0 && lastSr != sr)
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{
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ResetStates();
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}
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if (lastBatchSize != 0 && lastBatchSize != batchSize)
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{
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ResetStates();
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}
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public void ResetStates()
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if (context.Length == 0)
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{
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state = new float[2][][];
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state[0] = new float[1][];
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state[1] = new float[1][];
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state[0][0] = new float[128];
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state[1][0] = new float[128];
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context = Array.Empty<float[]>();
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lastSr = 0;
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lastBatchSize = 0;
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}
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public void Dispose()
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{
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session?.Dispose();
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}
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public class ValidationResult
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{
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public float[][] X { get; }
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public int Sr { get; }
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public ValidationResult(float[][] x, int sr)
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context = new float[batchSize][];
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for (int i = 0; i < batchSize; i++)
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{
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X = x;
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Sr = sr;
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context[i] = new float[contextSize];
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}
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}
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private ValidationResult ValidateInput(float[][] x, int sr)
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{
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if (x.Length == 1)
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x = Concatenate(context, x);
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var inputs = new List<NamedOnnxValue>
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{
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x = new float[][] { x[0] };
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}
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if (x.Length > 2)
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{
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throw new ArgumentException($"Incorrect audio data dimension: {x[0].Length}");
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}
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if (sr != 16000 && (sr % 16000 == 0))
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{
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int step = sr / 16000;
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float[][] reducedX = new float[x.Length][];
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for (int i = 0; i < x.Length; i++)
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{
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float[] current = x[i];
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float[] newArr = new float[(current.Length + step - 1) / step];
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for (int j = 0, index = 0; j < current.Length; j += step, index++)
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{
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newArr[index] = current[j];
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}
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reducedX[i] = newArr;
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}
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x = reducedX;
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sr = 16000;
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}
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if (!SAMPLE_RATES.Contains(sr))
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{
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throw new ArgumentException($"Only supports sample rates {string.Join(", ", SAMPLE_RATES)} (or multiples of 16000)");
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}
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if (((float)sr) / x[0].Length > 31.25)
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{
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throw new ArgumentException("Input audio is too short");
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}
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return new ValidationResult(x, sr);
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}
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private static float[][] Concatenate(float[][] a, float[][] b)
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{
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if (a.Length != b.Length)
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{
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throw new ArgumentException("The number of rows in both arrays must be the same.");
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}
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int rows = a.Length;
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int colsA = a[0].Length;
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int colsB = b[0].Length;
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float[][] result = new float[rows][];
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for (int i = 0; i < rows; i++)
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{
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result[i] = new float[colsA + colsB];
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Array.Copy(a[i], 0, result[i], 0, colsA);
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Array.Copy(b[i], 0, result[i], colsA, colsB);
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}
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return result;
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}
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private static float[][] GetLastColumns(float[][] array, int contextSize)
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{
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int rows = array.Length;
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int cols = array[0].Length;
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if (contextSize > cols)
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{
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throw new ArgumentException("contextSize cannot be greater than the number of columns in the array.");
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}
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float[][] result = new float[rows][];
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for (int i = 0; i < rows; i++)
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{
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result[i] = new float[contextSize];
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Array.Copy(array[i], cols - contextSize, result[i], 0, contextSize);
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}
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return result;
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}
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public float[] Call(float[][] x, int sr)
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{
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var result = ValidateInput(x, sr);
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x = result.X;
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sr = result.Sr;
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int numberSamples = sr == 16000 ? 512 : 256;
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if (x[0].Length != numberSamples)
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{
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throw new ArgumentException($"Provided number of samples is {x[0].Length} (Supported values: 256 for 8000 sample rate, 512 for 16000)");
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}
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int batchSize = x.Length;
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int contextSize = sr == 16000 ? 64 : 32;
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if (lastBatchSize == 0)
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{
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ResetStates();
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}
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if (lastSr != 0 && lastSr != sr)
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{
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ResetStates();
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}
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if (lastBatchSize != 0 && lastBatchSize != batchSize)
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{
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ResetStates();
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}
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if (context.Length == 0)
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{
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context = new float[batchSize][];
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for (int i = 0; i < batchSize; i++)
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{
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context[i] = new float[contextSize];
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}
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}
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x = Concatenate(context, x);
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var inputs = new List<NamedOnnxValue>
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{
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NamedOnnxValue.CreateFromTensor("input", new DenseTensor<float>(x.SelectMany(a => a).ToArray(), new[] { x.Length, x[0].Length })),
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NamedOnnxValue.CreateFromTensor("sr", new DenseTensor<long>(new[] { (long)sr }, new[] { 1 })),
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NamedOnnxValue.CreateFromTensor("state", new DenseTensor<float>(state.SelectMany(a => a.SelectMany(b => b)).ToArray(), new[] { state.Length, state[0].Length, state[0][0].Length }))
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NamedOnnxValue.CreateFromTensor("input", new DenseTensor<float>(x.SelectMany(a => a).ToArray(), [x.Length, x[0].Length])),
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NamedOnnxValue.CreateFromTensor("sr", new DenseTensor<long>(new[] { (long)sr }, [1])),
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NamedOnnxValue.CreateFromTensor("state", new DenseTensor<float>(state.SelectMany(a => a.SelectMany(b => b)).ToArray(), [state.Length, state[0].Length, state[0][0].Length]))
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};
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using (var outputs = session.Run(inputs))
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using var outputs = session.Run(inputs);
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var output = outputs.First(o => o.Name == "output").AsTensor<float>();
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var newState = outputs.First(o => o.Name == "stateN").AsTensor<float>();
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context = GetLastColumns(x, contextSize);
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lastSr = sr;
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lastBatchSize = batchSize;
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state = new float[newState.Dimensions[0]][][];
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for (int i = 0; i < newState.Dimensions[0]; i++)
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{
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state[i] = new float[newState.Dimensions[1]][];
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for (int j = 0; j < newState.Dimensions[1]; j++)
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{
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var output = outputs.First(o => o.Name == "output").AsTensor<float>();
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var newState = outputs.First(o => o.Name == "stateN").AsTensor<float>();
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context = GetLastColumns(x, contextSize);
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lastSr = sr;
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lastBatchSize = batchSize;
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state = new float[newState.Dimensions[0]][][];
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for (int i = 0; i < newState.Dimensions[0]; i++)
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state[i][j] = new float[newState.Dimensions[2]];
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for (int k = 0; k < newState.Dimensions[2]; k++)
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{
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state[i] = new float[newState.Dimensions[1]][];
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for (int j = 0; j < newState.Dimensions[1]; j++)
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{
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state[i][j] = new float[newState.Dimensions[2]];
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for (int k = 0; k < newState.Dimensions[2]; k++)
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{
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state[i][j][k] = newState[i, j, k];
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}
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}
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state[i][j][k] = newState[i, j, k];
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}
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return output.ToArray();
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}
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}
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return [.. output];
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}
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}
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