using System.Linq;
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using System.Text;
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using System.Threading.Tasks;
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using Microsoft.ML;
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using Microsoft.ML.OnnxRuntime;
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using Microsoft.ML.OnnxRuntime.Tensors;
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using Microsoft.Extensions.Logging;
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using AliFsmnVadSharp.Model;
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using AliFsmnVadSharp.Utils;
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namespace AliFsmnVadSharp
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{
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public class AliFsmnVad
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{
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private InferenceSession _onnxSession;
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private readonly ILogger<AliFsmnVad> _logger;
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private string _frontend;
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private WavFrontend _wavFrontend;
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private int _batchSize = 1;
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private int _max_end_sil = int.MinValue;
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private EncoderConfEntity _encoderConfEntity;
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private VadPostConfEntity _vad_post_conf;
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public AliFsmnVad(string modelFilePath, string configFilePath, string mvnFilePath, int batchSize = 1)
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{
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Microsoft.ML.OnnxRuntime.SessionOptions options = new Microsoft.ML.OnnxRuntime.SessionOptions();
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options.AppendExecutionProvider_CPU(0);
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options.InterOpNumThreads = 1;
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_onnxSession = new InferenceSession(modelFilePath, options);
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VadYamlEntity vadYamlEntity = YamlHelper.ReadYaml<VadYamlEntity>(configFilePath);
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_wavFrontend = new WavFrontend(mvnFilePath, vadYamlEntity.frontend_conf);
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_frontend = vadYamlEntity.frontend;
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_vad_post_conf = vadYamlEntity.vad_post_conf;
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_batchSize = batchSize;
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_max_end_sil = _max_end_sil != int.MinValue ? _max_end_sil : vadYamlEntity.vad_post_conf.max_end_silence_time;
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_encoderConfEntity = vadYamlEntity.encoder_conf;
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ILoggerFactory loggerFactory = new LoggerFactory();
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_logger = new Logger<AliFsmnVad>(loggerFactory);
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}
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public SegmentEntity[] GetSegments(List<float[]> samples)
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{
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int waveform_nums = samples.Count;
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_batchSize = Math.Min(waveform_nums, _batchSize);
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SegmentEntity[] segments = new SegmentEntity[waveform_nums];
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for (int beg_idx = 0; beg_idx < waveform_nums; beg_idx += _batchSize)
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{
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int end_idx = Math.Min(waveform_nums, beg_idx + _batchSize);
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List<float[]> waveform_list = new List<float[]>();
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for (int i = beg_idx; i < end_idx; i++)
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{
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waveform_list.Add(samples[i]);
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}
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List<VadInputEntity> vadInputEntitys = ExtractFeats(waveform_list);
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try
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{
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int t_offset = 0;
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int step = Math.Min(waveform_list.Max(x => x.Length), 6000);
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bool is_final = true;
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List<VadOutputEntity> vadOutputEntitys = Infer(vadInputEntitys);
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for (int batch_num = beg_idx; batch_num < end_idx; batch_num++)
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{
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var scores = vadOutputEntitys[batch_num - beg_idx].Scores;
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SegmentEntity[] segments_part = vadInputEntitys[batch_num].VadScorer.DefaultCall(scores, waveform_list[batch_num - beg_idx], is_final: is_final, max_end_sil: _max_end_sil, online: false);
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if (segments_part.Length > 0)
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{
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#pragma warning disable CS8602 // 解引用可能出现空引用。
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if (segments[batch_num] == null)
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{
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segments[batch_num] = new SegmentEntity();
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}
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segments[batch_num].Segment.AddRange(segments_part[0].Segment); //
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#pragma warning restore CS8602 // 解引用可能出现空引用。
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}
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}
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}
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catch (OnnxRuntimeException ex)
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{
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_logger.LogWarning("input wav is silence or noise");
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segments = null;
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}
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// for (int batch_num = 0; batch_num < _batchSize; batch_num++)
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// {
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// List<float[]> segment_waveforms = new List<float[]>();
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// foreach (int[] segment in segments[beg_idx + batch_num].Segment)
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// {
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// // (int)(16000 * (segment[0] / 1000.0) * 2);
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// int frame_length = (((6000 * 400) / 400 - 1) * 160 + 400) / 60 / 1000;
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// int frame_start = segment[0] * frame_length;
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// int frame_end = segment[1] * frame_length;
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// float[] segment_waveform = new float[frame_end - frame_start];
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// Array.Copy(waveform_list[batch_num], frame_start, segment_waveform, 0, segment_waveform.Length);
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// segment_waveforms.Add(segment_waveform);
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// }
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// segments[beg_idx + batch_num].Waveform.AddRange(segment_waveforms);
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// }
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}
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return segments;
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}
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public SegmentEntity[] GetSegmentsByStep(List<float[]> samples)
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{
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int waveform_nums = samples.Count;
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_batchSize=Math.Min(waveform_nums, _batchSize);
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SegmentEntity[] segments = new SegmentEntity[waveform_nums];
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for (int beg_idx = 0; beg_idx < waveform_nums; beg_idx += _batchSize)
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{
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int end_idx = Math.Min(waveform_nums, beg_idx + _batchSize);
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List<float[]> waveform_list = new List<float[]>();
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for (int i = beg_idx; i < end_idx; i++)
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{
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waveform_list.Add(samples[i]);
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}
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List<VadInputEntity> vadInputEntitys = ExtractFeats(waveform_list);
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int feats_len = vadInputEntitys.Max(x => x.SpeechLength);
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List<float[]> in_cache = new List<float[]>();
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in_cache = PrepareCache(in_cache);
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try
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{
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int step = Math.Min(vadInputEntitys.Max(x => x.SpeechLength), 6000 * 400);
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bool is_final = true;
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for (int t_offset = 0; t_offset < (int)(feats_len); t_offset += Math.Min(step, feats_len - t_offset))
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{
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if (t_offset + step >= feats_len - 1)
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{
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step = feats_len - t_offset;
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is_final = true;
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}
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else
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{
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is_final = false;
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}
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List<VadInputEntity> vadInputEntitys_step = new List<VadInputEntity>();
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foreach (VadInputEntity vadInputEntity in vadInputEntitys)
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{
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VadInputEntity vadInputEntity_step = new VadInputEntity();
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float[]? feats = vadInputEntity.Speech;
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int curr_step = Math.Min(feats.Length - t_offset, step);
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if (curr_step <= 0)
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{
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vadInputEntity_step.Speech = new float[32000];
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vadInputEntity_step.SpeechLength = 0;
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vadInputEntity_step.InCaches = in_cache;
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vadInputEntity_step.Waveform = new float[(((int)(32000) / 400 - 1) * 160 + 400)];
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vadInputEntitys_step.Add(vadInputEntity_step);
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continue;
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}
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float[]? feats_step = new float[curr_step];
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Array.Copy(feats, t_offset, feats_step, 0, feats_step.Length);
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float[]? waveform = vadInputEntity.Waveform;
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float[]? waveform_step = new float[Math.Min(waveform.Length, ((int)(t_offset + step) / 400 - 1) * 160 + 400) - t_offset / 400 * 160];
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Array.Copy(waveform, t_offset / 400 * 160, waveform_step, 0, waveform_step.Length);
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vadInputEntity_step.Speech = feats_step;
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vadInputEntity_step.SpeechLength = feats_step.Length;
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vadInputEntity_step.InCaches = vadInputEntity.InCaches;
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vadInputEntity_step.Waveform = waveform_step;
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vadInputEntitys_step.Add(vadInputEntity_step);
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}
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List<VadOutputEntity> vadOutputEntitys = Infer(vadInputEntitys_step);
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for (int batch_num = 0; batch_num < _batchSize; batch_num++)
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{
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vadInputEntitys[batch_num].InCaches = vadOutputEntitys[batch_num].OutCaches;
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var scores = vadOutputEntitys[batch_num].Scores;
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SegmentEntity[] segments_part = vadInputEntitys[batch_num].VadScorer.DefaultCall(scores, vadInputEntitys_step[batch_num].Waveform, is_final: is_final, max_end_sil: _max_end_sil, online: false);
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if (segments_part.Length > 0)
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{
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#pragma warning disable CS8602 // 解引用可能出现空引用。
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if (segments[beg_idx + batch_num] == null)
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{
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segments[beg_idx + batch_num] = new SegmentEntity();
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}
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if (segments_part[0] != null)
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{
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segments[beg_idx + batch_num].Segment.AddRange(segments_part[0].Segment);
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}
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#pragma warning restore CS8602 // 解引用可能出现空引用。
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}
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}
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}
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}
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catch (OnnxRuntimeException ex)
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{
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_logger.LogWarning("input wav is silence or noise");
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segments = null;
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}
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// for (int batch_num = 0; batch_num < _batchSize; batch_num++)
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// {
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// List<float[]> segment_waveforms=new List<float[]>();
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// foreach (int[] segment in segments[beg_idx + batch_num].Segment)
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// {
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// // (int)(16000 * (segment[0] / 1000.0) * 2);
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// int frame_length = (((6000 * 400) / 400 - 1) * 160 + 400) / 60 / 1000;
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// int frame_start = segment[0] * frame_length;
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// int frame_end = segment[1] * frame_length;
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// if(frame_end > waveform_list[batch_num].Length)
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// {
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// break;
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// }
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// float[] segment_waveform = new float[frame_end - frame_start];
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// Array.Copy(waveform_list[batch_num], frame_start, segment_waveform, 0, segment_waveform.Length);
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// segment_waveforms.Add(segment_waveform);
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// }
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// segments[beg_idx + batch_num].Waveform.AddRange(segment_waveforms);
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// }
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}
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return segments;
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}
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private List<float[]> PrepareCache(List<float[]> in_cache)
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{
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if (in_cache.Count > 0)
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{
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return in_cache;
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}
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int fsmn_layers = _encoderConfEntity.fsmn_layers;
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int proj_dim = _encoderConfEntity.proj_dim;
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int lorder = _encoderConfEntity.lorder;
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for (int i = 0; i < fsmn_layers; i++)
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{
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float[] cache = new float[1 * proj_dim * (lorder - 1) * 1];
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in_cache.Add(cache);
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}
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return in_cache;
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}
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private List<VadInputEntity> ExtractFeats(List<float[]> waveform_list)
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{
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List<float[]> in_cache = new List<float[]>();
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in_cache = PrepareCache(in_cache);
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List<VadInputEntity> vadInputEntitys = new List<VadInputEntity>();
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foreach (var waveform in waveform_list)
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{
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float[] fbanks = _wavFrontend.GetFbank(waveform);
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float[] features = _wavFrontend.LfrCmvn(fbanks);
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VadInputEntity vadInputEntity = new VadInputEntity();
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vadInputEntity.Waveform = waveform;
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vadInputEntity.Speech = features;
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vadInputEntity.SpeechLength = features.Length;
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vadInputEntity.InCaches = in_cache;
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vadInputEntity.VadScorer = new E2EVadModel(_vad_post_conf);
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vadInputEntitys.Add(vadInputEntity);
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}
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return vadInputEntitys;
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}
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/// <summary>
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/// 一维数组转3维数组
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/// </summary>
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/// <param name="obj"></param>
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/// <param name="len">一维长</param>
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/// <param name="wid">二维长</param>
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/// <returns></returns>
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public static T[,,] DimOneToThree<T>(T[] oneDimObj, int len, int wid)
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{
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if (oneDimObj.Length % (len * wid) != 0)
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return null;
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int height = oneDimObj.Length / (len * wid);
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T[,,] threeDimObj = new T[len, wid, height];
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for (int i = 0; i < oneDimObj.Length; i++)
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{
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threeDimObj[i / (wid * height), (i / height) % wid, i % height] = oneDimObj[i];
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}
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return threeDimObj;
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}
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private List<VadOutputEntity> Infer(List<VadInputEntity> vadInputEntitys)
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{
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List<VadOutputEntity> vadOutputEntities = new List<VadOutputEntity>();
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foreach (VadInputEntity vadInputEntity in vadInputEntitys)
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{
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int batchSize = 1;//_batchSize
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var inputMeta = _onnxSession.InputMetadata;
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var container = new List<NamedOnnxValue>();
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int[] dim = new int[] { batchSize, vadInputEntity.Speech.Length / 400 / batchSize, 400 };
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var tensor = new DenseTensor<float>(vadInputEntity.Speech, dim, false);
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container.Add(NamedOnnxValue.CreateFromTensor<float>("speech", tensor));
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int i = 0;
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foreach (var cache in vadInputEntity.InCaches)
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{
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int[] cache_dim = new int[] { 1, 128, cache.Length / 128 / 1, 1 };
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var cache_tensor = new DenseTensor<float>(cache, cache_dim, false);
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container.Add(NamedOnnxValue.CreateFromTensor<float>("in_cache" + i.ToString(), cache_tensor));
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i++;
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}
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IDisposableReadOnlyCollection<DisposableNamedOnnxValue> results = _onnxSession.Run(container);
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var resultsArray = results.ToArray();
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VadOutputEntity vadOutputEntity = new VadOutputEntity();
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for (int j = 0; j < resultsArray.Length; j++)
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{
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if (resultsArray[j].Name.Equals("logits"))
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{
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Tensor<float> tensors = resultsArray[0].AsTensor<float>();
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var _scores = DimOneToThree<float>(tensors.ToArray(), 1, tensors.Dimensions[1]);
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vadOutputEntity.Scores = _scores;
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}
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if (resultsArray[j].Name.StartsWith("out_cache"))
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{
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vadOutputEntity.OutCaches.Add(resultsArray[j].AsEnumerable<float>().ToArray());
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}
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}
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vadOutputEntities.Add(vadOutputEntity);
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}
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return vadOutputEntities;
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}
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private float[] PadSequence(List<VadInputEntity> modelInputs)
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{
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int max_speech_length = modelInputs.Max(x => x.SpeechLength);
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int speech_length = max_speech_length * modelInputs.Count;
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float[] speech = new float[speech_length];
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float[,] xxx = new float[modelInputs.Count, max_speech_length];
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for (int i = 0; i < modelInputs.Count; i++)
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{
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if (max_speech_length == modelInputs[i].SpeechLength)
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{
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for (int j = 0; j < xxx.GetLength(1); j++)
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{
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#pragma warning disable CS8602 // 解引用可能出现空引用。
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xxx[i, j] = modelInputs[i].Speech[j];
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#pragma warning restore CS8602 // 解引用可能出现空引用。
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}
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continue;
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}
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float[] nullspeech = new float[max_speech_length - modelInputs[i].SpeechLength];
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float[]? curr_speech = modelInputs[i].Speech;
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float[] padspeech = new float[max_speech_length];
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// ///////////////////////////////////////////////////
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var arr_neg_mean = _onnxSession.ModelMetadata.CustomMetadataMap["neg_mean"].ToString().Split(',').ToArray();
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double[] neg_mean = arr_neg_mean.Select(x => (double)Convert.ToDouble(x)).ToArray();
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var arr_inv_stddev = _onnxSession.ModelMetadata.CustomMetadataMap["inv_stddev"].ToString().Split(',').ToArray();
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double[] inv_stddev = arr_inv_stddev.Select(x => (double)Convert.ToDouble(x)).ToArray();
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int dim = neg_mean.Length;
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for (int j = 0; j < max_speech_length; j++)
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{
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int k = new Random().Next(0, dim);
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padspeech[j] = (float)((float)(0 + neg_mean[k]) * inv_stddev[k]);
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}
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Array.Copy(curr_speech, 0, padspeech, 0, curr_speech.Length);
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for (int j = 0; j < padspeech.Length; j++)
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{
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#pragma warning disable CS8602 // 解引用可能出现空引用。
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xxx[i, j] = padspeech[j];
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#pragma warning restore CS8602 // 解引用可能出现空引用。
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}
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}
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int s = 0;
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for (int i = 0; i < xxx.GetLength(0); i++)
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{
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for (int j = 0; j < xxx.GetLength(1); j++)
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{
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speech[s] = xxx[i, j];
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s++;
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}
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}
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return speech;
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}
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}
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}
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