test kaldi fbank melspectrom

paddlespeech/cls/exps/panns/predict.py

@@ -25,21 +25,89 @@ from paddlespeech.cls.models import SoundClassifier
 from paddlespeech.utils.dynamic_import import dynamic_import
 #from paddleaudio.features import LogMelSpectrogram
 
+import kaldi_native_fbank as kaldi_feature
+import numpy as np
+from paddle.audio.functional import power_to_db
+
 # yapf: disable
 parser = argparse.ArgumentParser(__doc__)
 parser.add_argument("--cfg_path", type=str, required=True)
 args = parser.parse_args()
 # yapf: enable
 
+fbank_opts = kaldi_feature.FbankOptions()
+fbank_opts.frame_opts.samp_freq = 32000
+fbank_opts.frame_opts.frame_shift_ms = 10
+fbank_opts.frame_opts.frame_length_ms = 32
+fbank_opts.mel_opts.num_bins = 64
+fbank_opts.mel_opts.low_freq = 50
+fbank_opts.mel_opts.high_freq = 14000
+fbank_opts.frame_opts.dither = 0
+fbank_opts.use_log_fbank = False
 
 def extract_features(file: str, **feat_conf) -> paddle.Tensor:
     file = os.path.abspath(os.path.expanduser(file))
-    waveform, _ = load_audio(file, sr=feat_conf['sr'])
+    waveform, _ = load_audio(file, sr=feat_conf['sr'], normal=False)
-    feature_extractor = LogMelSpectrogram(**feat_conf)
+    print(waveform.size)
-    feat = feature_extractor(paddle.to_tensor(waveform).unsqueeze(0))
+    print(waveform)
+
+    extractor = LogMelSpectrogram(**feat_conf)
+    feat = extractor(paddle.to_tensor(waveform).unsqueeze(0))
+    feat = paddle.transpose(feat, [0, 2, 1])
+    print(feat.shape)
+    print(feat)
+    return feat
+
+def extract_kaldi_features(file: str, **feat_conf) -> paddle.Tensor:
+    file = os.path.abspath(os.path.expanduser(file))
+    waveform, _ = load_audio(file, sr=feat_conf['sr'], normal=False)
+    print(waveform.size)
+    print(waveform)
+
+    extractor = kaldi_feature.OnlineFbank(fbank_opts)
+    extractor.accept_waveform(fbank_opts.frame_opts.samp_freq, waveform.data)
+    extractor.input_finished()
+    num_frames = extractor.num_frames_ready
+    kaldi_feat = []
+    feat = []
+    for i in range(num_frames):
+        feat.append(extractor.get_frame(i))
+    kaldi_feat.append(feat)
+    kaldi_feat = paddle.Tensor(np.array(kaldi_feat))
+    print(kaldi_feat)
+    kaldi_feat = paddle.transpose(kaldi_feat,[0, 2, 1])
+    kaldi_feat = power_to_db(kaldi_feat, 1.0, 1e-10, None)
+    kaldi_feat = paddle.transpose(kaldi_feat,[0, 2, 1])
+    print(kaldi_feat.shape)
+    print(kaldi_feat)
+    return kaldi_feat
+
+def feature_extractor(waveform: paddle.Tensor, **feat_conf) -> paddle.Tensor:
+    extractor = LogMelSpectrogram(**feat_conf)
+    feat = extractor(waveform)
     feat = paddle.transpose(feat, [0, 2, 1])
+    # print(feat.shape)
+    # print(feat)
     return feat
 
+def kaldi_feature_extractor(waveform: paddle.Tensor, **feat_conf) -> paddle.Tensor:
+    kaldi_feat = []
+    for b in range(waveform.shape[0]):
+        extractor = kaldi_feature.OnlineFbank(fbank_opts)
+        extractor.accept_waveform(fbank_opts.frame_opts.samp_freq, waveform[b].numpy().data)
+        extractor.input_finished()
+        num_frames = extractor.num_frames_ready
+        feat = []
+        for i in range(num_frames):
+            feat.append(extractor.get_frame(i))
+        kaldi_feat.append(feat)
+    kaldi_feat = paddle.Tensor(np.array(kaldi_feat))
+    kaldi_feat = paddle.transpose(kaldi_feat,[0, 2, 1])
+    kaldi_feat = power_to_db(kaldi_feat, 1.0, 1e-10, None)
+    kaldi_feat = paddle.transpose(kaldi_feat,[0, 2, 1])
+    # print(kaldi_feat.shape)
+    # print(kaldi_feat)
+    return kaldi_feat
 
 if __name__ == '__main__':
 
@@ -62,6 +130,7 @@ if __name__ == '__main__':
     model.eval()
 
     feat = extract_features(predicting_conf['audio_file'], **feat_conf)
+    #feat = extract_kaldi_features(predicting_conf['audio_file'], **feat_conf)
     logits = model(feat)
     probs = F.softmax(logits, axis=1).numpy()
 
@@ -71,3 +140,36 @@ if __name__ == '__main__':
     for idx in sorted_indices[:predicting_conf['top_k']]:
         msg += f'{ds_class.label_list[idx]}: {probs[0][idx]}\n'
     logger.info(msg)
+
+    #exit(1)
+
+    training_conf = config['training']
+    dev_ds = ds_class(**data_conf['dev'])
+    dev_sampler = paddle.io.BatchSampler(
+        dev_ds,
+        batch_size=training_conf['batch_size'],
+        shuffle=False,
+        drop_last=False)
+    dev_loader = paddle.io.DataLoader(
+        dev_ds,
+        batch_sampler=dev_sampler,
+        num_workers=training_conf['num_workers'],
+        return_list=True, )
+    num_corrects = 0
+    num_samples = 0
+    with logger.processing('Evaluation on validation dataset'):
+        for batch_idx, batch in enumerate(dev_loader):
+            waveforms, labels = batch
+            feats = feature_extractor(waveforms, **feat_conf)
+            #feats = kaldi_feature_extractor(waveforms, **feat_conf)
+
+            logits = model(feats)
+
+            preds = paddle.argmax(logits, axis=1)
+            num_corrects += (preds == labels).numpy().sum()
+            num_samples += feats.shape[0]
+
+    print_msg = '[Evaluation result]'
+    print_msg += ' dev_acc={:.4f}'.format(num_corrects / num_samples)
+
+    logger.eval(print_msg)

paddlespeech/cls/exps/panns/train_kaldi_feature_fbank.py

@@ -0,0 +1,197 @@
+# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+import argparse
+import os
+
+import paddle
+import yaml
+# from paddle.audio.features import LogMelSpectrogram
+from paddleaudio.utils import logger
+from paddleaudio.utils import Timer
+
+from paddlespeech.cls.models import SoundClassifier
+from paddlespeech.utils.dynamic_import import dynamic_import
+
+import kaldi_native_fbank as kaldi_feature
+import numpy as np
+from paddle.audio.functional import power_to_db
+
+# yapf: disable
+parser = argparse.ArgumentParser(__doc__)
+parser.add_argument("--cfg_path", type=str, required=True)
+args = parser.parse_args()
+# yapf: enable
+
+fbank_opts = kaldi_feature.FbankOptions()
+fbank_opts.frame_opts.samp_freq = 32000
+fbank_opts.frame_opts.frame_shift_ms = 10
+fbank_opts.frame_opts.frame_length_ms = 32
+fbank_opts.mel_opts.num_bins = 64
+fbank_opts.mel_opts.low_freq = 50
+fbank_opts.mel_opts.high_freq = 14000
+fbank_opts.frame_opts.dither = 0
+def kaldi_feature_extractor(waveform: paddle.Tensor, **feat_conf) -> paddle.Tensor:
+    kaldi_feat = []
+    for b in range(waveform.shape[0]):
+        extractor = kaldi_feature.OnlineFbank(fbank_opts)
+        extractor.accept_waveform(fbank_opts.frame_opts.samp_freq, waveform[b].numpy().data)
+        extractor.input_finished()
+        num_frames = extractor.num_frames_ready
+        feat = []
+        for i in range(num_frames):
+            feat.append(extractor.get_frame(i))
+        kaldi_feat.append(feat)
+    kaldi_feat = paddle.Tensor(np.array(kaldi_feat))
+    # print(kaldi_feat.shape)
+    # print(kaldi_feat)
+    return kaldi_feat
+
+if __name__ == "__main__":
+    nranks = paddle.distributed.get_world_size()
+    if paddle.distributed.get_world_size() > 1:
+        paddle.distributed.init_parallel_env()
+    local_rank = paddle.distributed.get_rank()
+
+    args.cfg_path = os.path.abspath(os.path.expanduser(args.cfg_path))
+    with open(args.cfg_path, 'r') as f:
+        config = yaml.safe_load(f)
+
+    model_conf = config['model']
+    data_conf = config['data']
+    feat_conf = config['feature']
+    training_conf = config['training']
+
+    # Dataset
+    ds_class = dynamic_import(data_conf['dataset'])
+    train_ds = ds_class(**data_conf['train'])
+    dev_ds = ds_class(**data_conf['dev'])
+    train_sampler = paddle.io.DistributedBatchSampler(
+        train_ds,
+        batch_size=training_conf['batch_size'],
+        shuffle=True,
+        drop_last=False)
+    train_loader = paddle.io.DataLoader(
+        train_ds,
+        batch_sampler=train_sampler,
+        num_workers=training_conf['num_workers'],
+        return_list=True,
+        use_buffer_reader=True, )
+
+    # Model
+    backbone_class = dynamic_import(model_conf['backbone'])
+    backbone = backbone_class(pretrained=True, extract_embedding=True)
+    model = SoundClassifier(backbone, num_class=data_conf['num_classes'])
+    model = paddle.DataParallel(model)
+    optimizer = paddle.optimizer.Adam(
+        learning_rate=training_conf['learning_rate'],
+        parameters=model.parameters())
+    criterion = paddle.nn.loss.CrossEntropyLoss()
+
+    steps_per_epoch = len(train_sampler)
+    timer = Timer(steps_per_epoch * training_conf['epochs'])
+    timer.start()
+
+    for epoch in range(1, training_conf['epochs'] + 1):
+        model.train()
+
+        avg_loss = 0
+        num_corrects = 0
+        num_samples = 0
+        for batch_idx, batch in enumerate(train_loader):
+            waveforms, labels = batch
+            feats = kaldi_feature_extractor(
+                waveforms, **feat_conf
+            )  # Need a padding when lengths of waveforms differ in a batch.
+
+            logits = model(feats)
+
+            loss = criterion(logits, labels)
+            loss.backward()
+            optimizer.step()
+            if isinstance(optimizer._learning_rate,
+                          paddle.optimizer.lr.LRScheduler):
+                optimizer._learning_rate.step()
+            optimizer.clear_grad()
+
+            # Calculate loss
+            avg_loss += float(loss)
+
+            # Calculate metrics
+            preds = paddle.argmax(logits, axis=1)
+            num_corrects += (preds == labels).numpy().sum()
+            num_samples += feats.shape[0]
+
+            timer.count()
+
+            if (batch_idx + 1
+                ) % training_conf['log_freq'] == 0 and local_rank == 0:
+                lr = optimizer.get_lr()
+                avg_loss /= training_conf['log_freq']
+                avg_acc = num_corrects / num_samples
+
+                print_msg = 'Epoch={}/{}, Step={}/{}'.format(
+                    epoch, training_conf['epochs'], batch_idx + 1,
+                    steps_per_epoch)
+                print_msg += ' loss={:.4f}'.format(avg_loss)
+                print_msg += ' acc={:.4f}'.format(avg_acc)
+                print_msg += ' lr={:.6f} step/sec={:.2f} | ETA {}'.format(
+                    lr, timer.timing, timer.eta)
+                logger.train(print_msg)
+
+                avg_loss = 0
+                num_corrects = 0
+                num_samples = 0
+
+        if epoch % training_conf[
+                'save_freq'] == 0 and batch_idx + 1 == steps_per_epoch and local_rank == 0:
+            dev_sampler = paddle.io.BatchSampler(
+                dev_ds,
+                batch_size=training_conf['batch_size'],
+                shuffle=False,
+                drop_last=False)
+            dev_loader = paddle.io.DataLoader(
+                dev_ds,
+                batch_sampler=dev_sampler,
+                num_workers=training_conf['num_workers'],
+                return_list=True, )
+
+            model.eval()
+            num_corrects = 0
+            num_samples = 0
+            with logger.processing('Evaluation on validation dataset'):
+                for batch_idx, batch in enumerate(dev_loader):
+                    waveforms, labels = batch
+                    feats = kaldi_feature_extractor(
+                        waveforms, **feat_conf
+                    )  # Need a padding when lengths of waveforms differ in a batch.
+
+                    logits = model(feats)
+
+                    preds = paddle.argmax(logits, axis=1)
+                    num_corrects += (preds == labels).numpy().sum()
+                    num_samples += feats.shape[0]
+
+            print_msg = '[Evaluation result]'
+            print_msg += ' dev_acc={:.4f}'.format(num_corrects / num_samples)
+
+            logger.eval(print_msg)
+
+            # Save model
+            save_dir = os.path.join(training_conf['checkpoint_dir'],
+                                    'epoch_{}'.format(epoch))
+            logger.info('Saving model checkpoint to {}'.format(save_dir))
+            paddle.save(model.state_dict(),
+                        os.path.join(save_dir, 'model.pdparams'))
+            paddle.save(optimizer.state_dict(),
+                        os.path.join(save_dir, 'model.pdopt'))

paddlespeech/cls/exps/panns/train_kaldi_feature_fbank_logspectrom.py

@@ -0,0 +1,201 @@
+# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+import argparse
+import os
+
+import paddle
+import yaml
+# from paddle.audio.features import LogMelSpectrogram
+from paddleaudio.utils import logger
+from paddleaudio.utils import Timer
+
+from paddlespeech.cls.models import SoundClassifier
+from paddlespeech.utils.dynamic_import import dynamic_import
+
+import kaldi_native_fbank as kaldi_feature
+import numpy as np
+from paddle.audio.functional import power_to_db
+
+# yapf: disable
+parser = argparse.ArgumentParser(__doc__)
+parser.add_argument("--cfg_path", type=str, required=True)
+args = parser.parse_args()
+# yapf: enable
+
+fbank_opts = kaldi_feature.FbankOptions()
+fbank_opts.frame_opts.samp_freq = 32000
+fbank_opts.frame_opts.frame_shift_ms = 10
+fbank_opts.frame_opts.frame_length_ms = 32
+fbank_opts.mel_opts.num_bins = 64
+fbank_opts.mel_opts.low_freq = 50
+fbank_opts.mel_opts.high_freq = 14000
+fbank_opts.frame_opts.dither = 0
+fbank_opts.use_log_fbank = False
+def kaldi_feature_extractor(waveform: paddle.Tensor, **feat_conf) -> paddle.Tensor:
+    kaldi_feat = []
+    for b in range(waveform.shape[0]):
+        extractor = kaldi_feature.OnlineFbank(fbank_opts)
+        extractor.accept_waveform(fbank_opts.frame_opts.samp_freq, waveform[b].numpy().data)
+        extractor.input_finished()
+        num_frames = extractor.num_frames_ready
+        feat = []
+        for i in range(num_frames):
+            feat.append(extractor.get_frame(i))
+        kaldi_feat.append(feat)
+    kaldi_feat = paddle.Tensor(np.array(kaldi_feat))
+    kaldi_feat = paddle.transpose(kaldi_feat,[0, 2, 1])
+    kaldi_feat = power_to_db(kaldi_feat, 1.0, 1e-10, None)
+    kaldi_feat = paddle.transpose(kaldi_feat,[0, 2, 1])
+    # print(kaldi_feat.shape)
+    # print(kaldi_feat)
+    return kaldi_feat
+
+if __name__ == "__main__":
+    nranks = paddle.distributed.get_world_size()
+    if paddle.distributed.get_world_size() > 1:
+        paddle.distributed.init_parallel_env()
+    local_rank = paddle.distributed.get_rank()
+
+    args.cfg_path = os.path.abspath(os.path.expanduser(args.cfg_path))
+    with open(args.cfg_path, 'r') as f:
+        config = yaml.safe_load(f)
+
+    model_conf = config['model']
+    data_conf = config['data']
+    feat_conf = config['feature']
+    training_conf = config['training']
+
+    # Dataset
+    ds_class = dynamic_import(data_conf['dataset'])
+    train_ds = ds_class(**data_conf['train'])
+    dev_ds = ds_class(**data_conf['dev'])
+    train_sampler = paddle.io.DistributedBatchSampler(
+        train_ds,
+        batch_size=training_conf['batch_size'],
+        shuffle=True,
+        drop_last=False)
+    train_loader = paddle.io.DataLoader(
+        train_ds,
+        batch_sampler=train_sampler,
+        num_workers=training_conf['num_workers'],
+        return_list=True,
+        use_buffer_reader=True, )
+
+    # Model
+    backbone_class = dynamic_import(model_conf['backbone'])
+    backbone = backbone_class(pretrained=True, extract_embedding=True)
+    model = SoundClassifier(backbone, num_class=data_conf['num_classes'])
+    model = paddle.DataParallel(model)
+    optimizer = paddle.optimizer.Adam(
+        learning_rate=training_conf['learning_rate'],
+        parameters=model.parameters())
+    criterion = paddle.nn.loss.CrossEntropyLoss()
+
+    steps_per_epoch = len(train_sampler)
+    timer = Timer(steps_per_epoch * training_conf['epochs'])
+    timer.start()
+
+    for epoch in range(1, training_conf['epochs'] + 1):
+        model.train()
+
+        avg_loss = 0
+        num_corrects = 0
+        num_samples = 0
+        for batch_idx, batch in enumerate(train_loader):
+            waveforms, labels = batch
+            feats = kaldi_feature_extractor(
+                waveforms, **feat_conf
+            )  # Need a padding when lengths of waveforms differ in a batch.
+
+            logits = model(feats)
+
+            loss = criterion(logits, labels)
+            loss.backward()
+            optimizer.step()
+            if isinstance(optimizer._learning_rate,
+                          paddle.optimizer.lr.LRScheduler):
+                optimizer._learning_rate.step()
+            optimizer.clear_grad()
+
+            # Calculate loss
+            avg_loss += float(loss)
+
+            # Calculate metrics
+            preds = paddle.argmax(logits, axis=1)
+            num_corrects += (preds == labels).numpy().sum()
+            num_samples += feats.shape[0]
+
+            timer.count()
+
+            if (batch_idx + 1
+                ) % training_conf['log_freq'] == 0 and local_rank == 0:
+                lr = optimizer.get_lr()
+                avg_loss /= training_conf['log_freq']
+                avg_acc = num_corrects / num_samples
+
+                print_msg = 'Epoch={}/{}, Step={}/{}'.format(
+                    epoch, training_conf['epochs'], batch_idx + 1,
+                    steps_per_epoch)
+                print_msg += ' loss={:.4f}'.format(avg_loss)
+                print_msg += ' acc={:.4f}'.format(avg_acc)
+                print_msg += ' lr={:.6f} step/sec={:.2f} | ETA {}'.format(
+                    lr, timer.timing, timer.eta)
+                logger.train(print_msg)
+
+                avg_loss = 0
+                num_corrects = 0
+                num_samples = 0
+
+        if epoch % training_conf[
+                'save_freq'] == 0 and batch_idx + 1 == steps_per_epoch and local_rank == 0:
+            dev_sampler = paddle.io.BatchSampler(
+                dev_ds,
+                batch_size=training_conf['batch_size'],
+                shuffle=False,
+                drop_last=False)
+            dev_loader = paddle.io.DataLoader(
+                dev_ds,
+                batch_sampler=dev_sampler,
+                num_workers=training_conf['num_workers'],
+                return_list=True, )
+
+            model.eval()
+            num_corrects = 0
+            num_samples = 0
+            with logger.processing('Evaluation on validation dataset'):
+                for batch_idx, batch in enumerate(dev_loader):
+                    waveforms, labels = batch
+                    feats = kaldi_feature_extractor(
+                        waveforms, **feat_conf
+                    )  # Need a padding when lengths of waveforms differ in a batch.
+
+                    logits = model(feats)
+
+                    preds = paddle.argmax(logits, axis=1)
+                    num_corrects += (preds == labels).numpy().sum()
+                    num_samples += feats.shape[0]
+
+            print_msg = '[Evaluation result]'
+            print_msg += ' dev_acc={:.4f}'.format(num_corrects / num_samples)
+
+            logger.eval(print_msg)
+
+            # Save model
+            save_dir = os.path.join(training_conf['checkpoint_dir'],
+                                    'epoch_{}'.format(epoch))
+            logger.info('Saving model checkpoint to {}'.format(save_dir))
+            paddle.save(model.state_dict(),
+                        os.path.join(save_dir, 'model.pdparams'))
+            paddle.save(optimizer.state_dict(),
+                        os.path.join(save_dir, 'model.pdopt'))

speechx/examples/u2pp_ol/wenetspeech/1.sh

@@ -0,0 +1,11 @@
+#!/bin/bash
+ 
+now=`date +'%Y-%m-%d %H:%M:%S'`
+start_time=$(date --date="$now" +%s);
+
+compute_my_fbank_main --wav_rspecifier=scp:aishell_test.scp
+#compute_my_fbank_main --wav_rspecifier=scp:aishell_test.scp --feature_wspecifier=ark,t:out.log
+ 
+now=`date +'%Y-%m-%d %H:%M:%S'`
+end_time=$(date --date="$now" +%s);
+echo "openblas fbank used time:"$((end_time-start_time))"s"

speechx/examples/u2pp_ol/wenetspeech/2.sh

@@ -0,0 +1,10 @@
+#!/bin/bash
+ 
+now=`date +'%Y-%m-%d %H:%M:%S'`
+start_time=$(date --date="$now" +%s);
+
+compute_my_fbank_main_kaldi_native_fbank scp:aishell_test.scp 
+ 
+now=`date +'%Y-%m-%d %H:%M:%S'`
+end_time=$(date --date="$now" +%s);
+echo "openblas fbank used time:"$((end_time-start_time))"s"

speechx/examples/u2pp_ol/wenetspeech/local/feat.sh

@@ -3,7 +3,7 @@ set -e
 
 . path.sh
 
-nj=20
+nj=1
 stage=-1
 stop_stage=100
 
@@ -18,25 +18,20 @@ model_dir=$ckpt_dir/asr1_chunk_conformer_u2pp_wenetspeech_static_1.3.0.model/
 aishell_wav_scp=aishell_test.scp
 
 
-if [ ${stage} -le 1 ] && [ ${stop_stage} -ge 1 ]; then
+#if [ ${stage} -le 1 ] && [ ${stop_stage} -ge 1 ]; then
-    cmvn_json2kaldi_main \
+#    cmvn_json2kaldi_main \
-        --json_file  $model_dir/mean_std.json \
+#        --json_file  $model_dir/mean_std.json \
-        --cmvn_write_path $exp/cmvn.ark \
+#        --cmvn_write_path $exp/cmvn.ark \
-        --binary=false
+#        --binary=false
-    
+#    
-    echo "convert json cmvn to kaldi ark."
+#    echo "convert json cmvn to kaldi ark."
-fi
+#fi
 
 if [ ${stage} -le 2 ] && [ ${stop_stage} -ge 2 ]; then
-    ./local/split_data.sh $data $data/$aishell_wav_scp $aishell_wav_scp $nj
+    utils/run.pl JOB=1:$nj feat.log \
-    
+    compute_my_fbank_main \
-    utils/run.pl JOB=1:$nj $data/split${nj}/JOB/feat.log \
+        --wav_rspecifier=scp:aishell_test.scp \
-    compute_fbank_main \
+        --feature_wspecifier=ark,scp:fbank.ark,fbank.scp
-        --num_bins 80 \
-        --cmvn_file=$exp/cmvn.ark \
-        --streaming_chunk=36 \
-        --wav_rspecifier=scp:$data/split${nj}/JOB/${aishell_wav_scp} \
-        --feature_wspecifier=ark,scp:$data/split${nj}/JOB/fbank.ark,$data/split${nj}/JOB/fbank.scp
     
     echo "compute fbank feature."
 fi

speechx/examples/u2pp_ol/wenetspeech/out.log1

@@ -0,0 +1,691 @@
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+-8.82558, -11.6979, -11.699, -13.981, -13.8982, -13.903, -13.776, -13.7374, -14.3227, -14.9152, -13.886, -12.4982, -12.7228, -12.573, -12.8912, -14.4912, -14.0427, -13.4277, -12.779, -12.0823, -12.0622, -11.4417, -12.3722, -13.2667, -12.5714, -11.6921, -13.0874, -12.3964, -11.6474, -11.5671, -10.502, -10.7924, -10.6835, -9.91974, -10.9239, -10.615, -10.01, -10.1378, -10.6469, -11.5103, -12.4925, -11.2807, -11.3185, -11.8098, -10.9415, -10.342, -10.7609, -10.8784, -10.271, -10.2186, -10.4686, -11.5373, -11.2079, -10.6656, -10.947, -10.6037, -10.0065, -10.6095, -10.6326, -10.3874, -10.2368, -10.4214, -10.0003, -11.0533, 
+-9.13967, -10.2735, -12.0678, -14.8134, -14.4851, -13.7333, -13.0208, -13.2405, -14.7998, -15.9424, -14.5893, -13.4138, -12.9445, -12.6325, -12.0286, -12.941, -15.3692, -13.6518, -12.7468, -12.3832, -12.2747, -12.0068, -11.6499, -12.7247, -11.8028, -11.5855, -11.9803, -11.8751, -12.6062, -11.1553, -10.9452, -11.54, -10.9822, -10.8617, -10.6505, -10.6587, -10.7149, -10.7465, -11.0425, -12.1492, -11.2965, -10.7564, -10.2035, -11.3869, -11.3484, -10.7822, -10.9317, -10.6223, -10.5761, -10.8089, -10.9973, -11.1797, -9.65973, -10.0946, -10.5298, -10.9181, -10.7129, -10.8537, -10.584, -10.5094, -10.2683, -10.0038, -9.62004, -11.2506, 
+-10.4231, -12.6501, -12.4927, -14.1918, -14.0978, -12.2777, -11.7058, -12.5313, -14.6283, -13.8049, -13.9207, -15.1431, -14.6916, -13.1957, -11.9127, -12.5612, -13.959, -13.4631, -13.3393, -14.8278, -12.9571, -12.5968, -12.1996, -12.122, -11.4348, -12.6729, -12.0168, -11.2412, -12.217, -10.9437, -10.2903, -11.3424, -11.6397, -11.5869, -11.4112, -10.9438, -11.7455, -11.8506, -11.3784, -10.9938, -10.4471, -10.2286, -10.529, -11.3989, -11.1688, -10.5883, -10.3761, -10.6879, -10.7765, -11.4602, -11.31, -11.2337, -9.99914, -10.4147, -10.093, -10.2397, -11.363, -10.7187, -9.84955, -9.78283, -10.5614, -10.131, -9.84335, -11.2062, 
+-10.6744, -12.7995, -12.5721, -13.9973, -14.0736, -12.8899, -11.7835, -12.3784, -12.5953, -12.4269, -12.7629, -12.4633, -12.4992, -12.2858, -11.3761, -10.7091, -10.8321, -10.657, -9.96763, -9.6023, -9.22422, -8.95221, -8.55736, -8.93704, -8.17114, -8.026, -8.21458, -8.75029, -9.54363, -9.68585, -9.86807, -9.12036, -10.1185, -11.0674, -11.7358, -10.8677, -11.2346, -10.6482, -10.8894, -10.8251, -10.631, -9.69207, -10.8556, -11.6042, -10.6275, -10.245, -10.3371, -10.2504, -10.4957, -11.0589, -11.0985, -10.961, -10.4596, -10.2014, -9.91269, -10.0596, -10.7745, -9.86364, -9.23433, -9.44065, -9.50153, -9.1127, -9.6258, -10.597, 
+-11.1081, -11.559, -11.8556, -12.6256, -12.6111, -11.9687, -10.8498, -11.0579, -11.4343, -12.7429, -10.9841, -10.5583, -11.8437, -10.2871, -9.9781, -10.5219, -9.91745, -9.75031, -9.38156, -8.5858, -8.11469, -8.35669, -8.10883, -7.96548, -7.28325, -6.83626, -7.16229, -7.52316, -8.11884, -8.81341, -8.84978, -7.72615, -8.84633, -10.4875, -10.0658, -9.11796, -10.1976, -9.27394, -9.36652, -9.98985, -9.30055, -9.13327, -10.0096, -8.79563, -9.1681, -8.47975, -9.23549, -9.41146, -9.18698, -9.94378, -10.1294, -9.97156, -9.93035, -8.45161, -8.54684, -8.76805, -9.05598, -8.46721, -7.6965, -7.81994, -8.10225, -8.42176, -8.59724, -9.2462, 
+-10.0454, -9.95068, -9.68494, -10.1606, -11.0191, -10.3852, -10.2596, -11.2478, -12.6063, -12.7736, -10.7083, -10.8366, -10.2445, -10.722, -11.1283, -11.2255, -11.1637, -10.4312, -9.27792, -9.43784, -9.91833, -10.3974, -10.2871, -9.15634, -8.70388, -8.65139, -8.92885, -9.03251, -9.06566, -10.0864, -9.82848, -9.21997, -9.88439, -10.0704, -9.73048, -9.04436, -9.75701, -9.15485, -9.25959, -9.61354, -8.87261, -9.22437, -9.52519, -8.39018, -9.08388, -8.87203, -9.5971, -9.61628, -9.18465, -9.99037, -8.70591, -9.37348, -9.22974, -7.65808, -7.98348, -8.41762, -8.88954, -8.21053, -7.28461, -7.45137, -7.68607, -8.65724, -8.6268, -8.80145, 
+
+openblas fbank used time:1s

speechx/speechx/CMakeLists.txt

@@ -8,6 +8,12 @@ ${CMAKE_CURRENT_SOURCE_DIR}/kaldi
 )
 add_subdirectory(kaldi)
 
+include_directories(
+${CMAKE_CURRENT_SOURCE_DIR}
+${CMAKE_CURRENT_SOURCE_DIR}/kaldi-native-fbank
+)
+add_subdirectory(kaldi-native-fbank)
+
 include_directories(
 ${CMAKE_CURRENT_SOURCE_DIR}
 ${CMAKE_CURRENT_SOURCE_DIR}/utils

speechx/speechx/frontend/audio/CMakeLists.txt

@@ -20,10 +20,12 @@ target_link_libraries(${bin_name} utils kaldi-util kaldi-matrix gflags glog)
 set(BINS 
   compute_linear_spectrogram_main
   compute_fbank_main
+  compute_my_fbank_main
+  compute_my_fbank_main_kaldi_native_fbank
 )
 
 foreach(bin_name IN LISTS BINS)
   add_executable(${bin_name} ${CMAKE_CURRENT_SOURCE_DIR}/${bin_name}.cc)
-  target_include_directories(${bin_name} PRIVATE ${SPEECHX_ROOT} ${SPEECHX_ROOT}/kaldi)
+  target_include_directories(${bin_name} PRIVATE ${SPEECHX_ROOT} ${SPEECHX_ROOT}/kaldi ${SPEECHX_ROOT}/kaldi-native-fbank/csrc/)
-  target_link_libraries(${bin_name} PUBLIC frontend utils kaldi-util gflags glog)
+  target_link_libraries(${bin_name} PUBLIC frontend utils kaldi-util gflags glog kaldi-native-fbank-core)
 endforeach()

speechx/speechx/frontend/audio/compute_my_fbank_main.cc

@@ -0,0 +1,189 @@
+// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+// todo refactor, repalce with gtest
+
+#include "base/flags.h"
+#include "base/log.h"
+#include "frontend/audio/audio_cache.h"
+#include "frontend/audio/data_cache.h"
+#include "frontend/audio/fbank.h"
+#include "frontend/audio/feature_cache.h"
+#include "frontend/audio/frontend_itf.h"
+#include "frontend/audio/normalizer.h"
+#include "kaldi/feat/wave-reader.h"
+#include "kaldi/util/kaldi-io.h"
+#include "kaldi/util/table-types.h"
+
+DEFINE_string(wav_rspecifier, "", "test wav scp path");
+DEFINE_string(feature_wspecifier, "", "output feats wspecifier");
+// DEFINE_string(cmvn_file, "", "read cmvn");
+DEFINE_double(streaming_chunk, 0.36, "streaming feature chunk size");
+DEFINE_int32(num_bins, 64, "fbank num bins");
+DEFINE_int32(sample_rate, 16000, "sampe rate: 16k, 8k.");
+
+int main(int argc, char* argv[]) {
+    gflags::SetUsageMessage("Usage:");
+    gflags::ParseCommandLineFlags(&argc, &argv, false);
+    google::InitGoogleLogging(argv[0]);
+    google::InstallFailureSignalHandler();
+    FLAGS_logtostderr = 1;
+
+    CHECK_GT(FLAGS_wav_rspecifier.size(), 0);
+    // CHECK_GT(FLAGS_feature_wspecifier.size(), 0);
+    kaldi::SequentialTableReader<kaldi::WaveHolder> wav_reader(
+        FLAGS_wav_rspecifier);
+    kaldi::SequentialTableReader<kaldi::WaveInfoHolder> wav_info_reader(
+        FLAGS_wav_rspecifier);
+    kaldi::BaseFloatMatrixWriter feat_writer(FLAGS_feature_wspecifier);
+
+    int32 num_done = 0, num_err = 0;
+
+    // feature pipeline: wave cache --> povey window
+    // -->fbank --> global cmvn -> feat cache
+
+    std::unique_ptr<ppspeech::FrontendInterface> data_source(
+        new ppspeech::AudioCache(3600 * 1600, false));
+
+    kaldi::FbankOptions opt;
+    // opt.frame_opts.samp_freq = 32000;
+    opt.frame_opts.samp_freq = 16000;
+    opt.frame_opts.frame_length_ms = 32;
+    opt.frame_opts.frame_shift_ms = 10;
+    opt.mel_opts.num_bins = 64;
+    // opt.mel_opts.low_freq = 50;
+    // opt.mel_opts.high_freq = 14000;
+    opt.frame_opts.dither = 0.0;
+
+    LOG(INFO) << "frame_length_ms: " << opt.frame_opts.frame_length_ms;
+    LOG(INFO) << "frame_shift_ms: " << opt.frame_opts.frame_shift_ms;
+    LOG(INFO) << "num_bins: " << opt.mel_opts.num_bins;
+    LOG(INFO) << "dither: " << opt.frame_opts.dither;
+
+    std::unique_ptr<ppspeech::FrontendInterface> fbank(
+        new ppspeech::Fbank(opt, std::move(data_source)));
+
+    // std::unique_ptr<ppspeech::FrontendInterface> cmvn(
+    //     new ppspeech::CMVN(FLAGS_cmvn_file, std::move(fbank)));
+
+    // the feature cache output feature chunk by chunk.
+    ppspeech::FeatureCacheOptions feat_cache_opts;
+    ppspeech::FeatureCache feature_cache(feat_cache_opts, std::move(fbank));
+    LOG(INFO) << "fbank: " << true;
+    LOG(INFO) << "feat dim: " << feature_cache.Dim();
+
+
+    float streaming_chunk = FLAGS_streaming_chunk;
+    int chunk_sample_size = streaming_chunk * FLAGS_sample_rate;
+    LOG(INFO) << "sr: " << FLAGS_sample_rate;
+    LOG(INFO) << "chunk size (sec): " << streaming_chunk;
+    LOG(INFO) << "chunk size (sample): " << chunk_sample_size;
+
+    
+    for (; !wav_reader.Done() && !wav_info_reader.Done();
+         wav_reader.Next(), wav_info_reader.Next()) {
+        const std::string& utt = wav_reader.Key();
+        const kaldi::WaveData& wave_data = wav_reader.Value();
+
+        const std::string& utt2 = wav_info_reader.Key();
+        const kaldi::WaveInfo& wave_info = wav_info_reader.Value();
+
+        CHECK(utt == utt2)
+            << "wav reader and wav info reader using diff rspecifier!!!";
+        LOG(INFO) << "utt: " << utt;
+        LOG(INFO) << "samples: " << wave_info.SampleCount();
+        LOG(INFO) << "dur: " << wave_info.Duration() << " sec";
+        CHECK(wave_info.SampFreq() == FLAGS_sample_rate)
+            << "need " << FLAGS_sample_rate << " get " << wave_info.SampFreq();
+
+        // load first channel wav
+        int32 this_channel = 0;
+        kaldi::SubVector<kaldi::BaseFloat> waveform(wave_data.Data(),
+                                                    this_channel);
+        // compute feat chunk by chunk
+        int tot_samples = waveform.Dim();
+        int sample_offset = 0;
+        std::vector<kaldi::Vector<BaseFloat>> feats;
+        int feature_rows = 0;
+
+        for (int i = 0; i < tot_samples; i++){
+            waveform(i) = waveform(i) / 32768;
+        }
+        // printf("%f %f %f %f %f %f\n", waveform(0), waveform(1), waveform(3),
+        //                             waveform(tot_samples - 3), waveform(tot_samples - 2), waveform(tot_samples - 1));
+        while (sample_offset < tot_samples) {
+            // cur chunk size
+            int cur_chunk_size =
+                std::min(chunk_sample_size, tot_samples - sample_offset);
+
+            // get chunk wav
+            kaldi::Vector<kaldi::BaseFloat> wav_chunk(cur_chunk_size);
+            for (int i = 0; i < cur_chunk_size; ++i) {
+                wav_chunk(i) = waveform(sample_offset + i);
+            }
+
+            // compute feat
+            feature_cache.Accept(wav_chunk);
+
+            // send finish signal
+            if (cur_chunk_size < chunk_sample_size) {
+                feature_cache.SetFinished();
+            }
+
+            // read feat
+            kaldi::Vector<BaseFloat> features;
+            bool flag = true;
+            do {
+                flag = feature_cache.Read(&features);
+                if (flag && features.Dim() != 0) {
+                    feats.push_back(features);
+                    feature_rows += features.Dim() / feature_cache.Dim();
+                }
+            } while (flag == true && features.Dim() != 0);
+
+            // forward offset
+            sample_offset += cur_chunk_size;
+        }
+
+        int cur_idx = 0;
+        kaldi::Matrix<kaldi::BaseFloat> features(feature_rows,
+                                                 feature_cache.Dim());
+        for (auto feat : feats) {
+            int num_rows = feat.Dim() / feature_cache.Dim();
+            for (int row_idx = 0; row_idx < num_rows; ++row_idx) {
+                for (size_t col_idx = 0; col_idx < feature_cache.Dim();
+                     ++col_idx) {
+                    features(cur_idx, col_idx) =
+                        feat(row_idx * feature_cache.Dim() + col_idx);
+                }
+                ++cur_idx;
+            }
+        }
+        LOG(INFO) << "feat shape: " << features.NumRows() << " , "
+                  << features.NumCols();
+        // feat_writer.Write(utt, features);
+
+        // reset frontend pipeline state
+        feature_cache.Reset();
+
+        if (num_done % 50 == 0 && num_done != 0)
+            VLOG(2) << "Processed " << num_done << " utterances";
+
+        num_done++;
+    }
+
+    LOG(INFO) << "Done " << num_done << " utterances, " << num_err
+              << " with errors.";
+    return (num_done != 0 ? 0 : 1);
+}

speechx/speechx/frontend/audio/compute_my_fbank_main_kaldi_native_fbank.cc

@@ -0,0 +1,96 @@
+/**
+ * Copyright (c)  2022  Xiaomi Corporation (authors: Fangjun Kuang)
+ *
+ * See LICENSE for clarification regarding multiple authors
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <iostream>
+
+#include "base/flags.h"
+#include "base/log.h"
+#include "frontend/audio/audio_cache.h"
+#include "frontend/audio/data_cache.h"
+#include "frontend/audio/fbank.h"
+#include "frontend/audio/feature_cache.h"
+#include "frontend/audio/frontend_itf.h"
+#include "frontend/audio/normalizer.h"
+
+#include "kaldi-native-fbank/csrc/online-feature.h"
+
+#include "kaldi/feat/wave-reader.h"
+#include "kaldi/util/kaldi-io.h"
+#include "kaldi/util/table-types.h"
+#include <fstream>
+
+int main(int argc, char* argv[]) {
+  kaldi::SequentialTableReader<kaldi::WaveHolder> wav_reader(
+        argv[1]);
+  kaldi::SequentialTableReader<kaldi::WaveInfoHolder> wav_info_reader(
+        argv[1]);
+
+  knf::FbankOptions opts;
+  opts.frame_opts.samp_freq = 16000;
+  opts.frame_opts.frame_length_ms = 32;
+  opts.frame_opts.frame_shift_ms = 10;
+  opts.mel_opts.num_bins = 64;
+  opts.frame_opts.dither = 0.0;
+
+  for (; !wav_reader.Done() && !wav_info_reader.Done();
+         wav_reader.Next(), wav_info_reader.Next()) {
+        const std::string& utt = wav_reader.Key();
+        const kaldi::WaveData& wave_data = wav_reader.Value();
+
+        const std::string& utt2 = wav_info_reader.Key();
+        const kaldi::WaveInfo& wave_info = wav_info_reader.Value();
+
+        CHECK(utt == utt2)
+            << "wav reader and wav info reader using diff rspecifier!!!";
+        LOG(INFO) << "utt: " << utt;
+        LOG(INFO) << "samples: " << wave_info.SampleCount();
+        LOG(INFO) << "dur: " << wave_info.Duration() << " sec";
+        CHECK(wave_info.SampFreq() == 16000)
+            << "need " << 16000 << " get " << wave_info.SampFreq();
+
+        // load first channel wav
+        int32 this_channel = 0;
+        kaldi::SubVector<kaldi::BaseFloat> waveform(wave_data.Data(),
+                                                    this_channel);
+        // compute feat chunk by chunk
+        int tot_samples = waveform.Dim();
+        int sample_offset = 0;
+        std::vector<kaldi::Vector<BaseFloat>> feats;
+        int feature_rows = 0;
+
+        for (int i = 0; i < tot_samples; i++){
+            waveform(i) = waveform(i) / 32768;
+        }
+
+        knf::OnlineFbank fbank(opts);
+        fbank.AcceptWaveform(16000, waveform.Data(), tot_samples);
+
+        std::ostringstream os;
+
+        int32_t n = fbank.NumFramesReady();
+        for (int32_t i = 0; i != n; ++i) {
+          const float *frame = fbank.GetFrame(i);
+          for (int32_t k = 0; k != opts.mel_opts.num_bins; ++k) {
+            // os << frame[k] << ", ";
+          }
+          // os << "\n";
+        }
+        // std::cout << os.str() << "\n";
+  }
+  return 0;
+}

speechx/speechx/kaldi-native-fbank/CMakeLists.txt

@@ -0,0 +1 @@
+add_subdirectory(csrc)

speechx/speechx/kaldi-native-fbank/csrc/CMakeLists.txt

@@ -0,0 +1,12 @@
+
+set(sources
+  feature-fbank.cc
+  feature-functions.cc
+  feature-window.cc
+  fftsg.c
+  mel-computations.cc
+  online-feature.cc
+  rfft.cc
+)
+
+add_library(kaldi-native-fbank-core ${sources})

speechx/speechx/kaldi-native-fbank/csrc/feature-fbank.cc

@@ -0,0 +1,117 @@
+/**
+ * Copyright (c)  2022  Xiaomi Corporation (authors: Fangjun Kuang)
+ *
+ * See LICENSE for clarification regarding multiple authors
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+// This file is copied/modified from kaldi/src/feat/feature-fbank.cc
+//
+#include "kaldi-native-fbank/csrc/feature-fbank.h"
+
+#include <cmath>
+
+#include "kaldi-native-fbank/csrc/feature-functions.h"
+
+namespace knf {
+
+static void Sqrt(float *in_out, int32_t n) {
+  for (int32_t i = 0; i != n; ++i) {
+    in_out[i] = std::sqrt(in_out[i]);
+  }
+}
+
+std::ostream &operator<<(std::ostream &os, const FbankOptions &opts) {
+  os << opts.ToString();
+  return os;
+}
+
+FbankComputer::FbankComputer(const FbankOptions &opts)
+    : opts_(opts), rfft_(opts.frame_opts.PaddedWindowSize()) {
+  if (opts.energy_floor > 0.0f) {
+    log_energy_floor_ = logf(opts.energy_floor);
+  }
+
+  // We'll definitely need the filterbanks info for VTLN warping factor 1.0.
+  // [note: this call caches it.]
+  GetMelBanks(1.0f);
+}
+
+FbankComputer::~FbankComputer() {
+  for (auto iter = mel_banks_.begin(); iter != mel_banks_.end(); ++iter)
+    delete iter->second;
+}
+
+const MelBanks *FbankComputer::GetMelBanks(float vtln_warp) {
+  MelBanks *this_mel_banks = nullptr;
+
+  // std::map<float, MelBanks *>::iterator iter = mel_banks_.find(vtln_warp);
+  auto iter = mel_banks_.find(vtln_warp);
+  if (iter == mel_banks_.end()) {
+    this_mel_banks = new MelBanks(opts_.mel_opts, opts_.frame_opts, vtln_warp);
+    mel_banks_[vtln_warp] = this_mel_banks;
+  } else {
+    this_mel_banks = iter->second;
+  }
+  return this_mel_banks;
+}
+
+void FbankComputer::Compute(float signal_raw_log_energy, float vtln_warp,
+                            std::vector<float> *signal_frame, float *feature) {
+  const MelBanks &mel_banks = *(GetMelBanks(vtln_warp));
+
+  KNF_CHECK_EQ(signal_frame->size(), opts_.frame_opts.PaddedWindowSize());
+
+  // Compute energy after window function (not the raw one).
+  if (opts_.use_energy && !opts_.raw_energy) {
+    signal_raw_log_energy = std::log(
+        std::max<float>(InnerProduct(signal_frame->data(), signal_frame->data(),
+                                     signal_frame->size()),
+                        std::numeric_limits<float>::epsilon()));
+  }
+  rfft_.Compute(signal_frame->data());  // signal_frame is modified in-place
+  ComputePowerSpectrum(signal_frame);
+
+  // Use magnitude instead of power if requested.
+  if (!opts_.use_power) {
+    Sqrt(signal_frame->data(), signal_frame->size() / 2 + 1);
+  }
+
+  int32_t mel_offset = ((opts_.use_energy && !opts_.htk_compat) ? 1 : 0);
+
+  // Its length is opts_.mel_opts.num_bins
+  float *mel_energies = feature + mel_offset;
+
+  // Sum with mel filter banks over the power spectrum
+  mel_banks.Compute(signal_frame->data(), mel_energies);
+
+  if (opts_.use_log_fbank) {
+    // Avoid log of zero (which should be prevented anyway by dithering).
+    for (int32_t i = 0; i != opts_.mel_opts.num_bins; ++i) {
+      auto t = std::max(mel_energies[i], std::numeric_limits<float>::epsilon());
+      mel_energies[i] = std::log(t);
+    }
+  }
+
+  // Copy energy as first value (or the last, if htk_compat == true).
+  if (opts_.use_energy) {
+    if (opts_.energy_floor > 0.0 && signal_raw_log_energy < log_energy_floor_) {
+      signal_raw_log_energy = log_energy_floor_;
+    }
+    int32_t energy_index = opts_.htk_compat ? opts_.mel_opts.num_bins : 0;
+    feature[energy_index] = signal_raw_log_energy;
+  }
+}
+
+}  // namespace knf

speechx/speechx/kaldi-native-fbank/csrc/feature-fbank.h

@@ -0,0 +1,132 @@
+/**
+ * Copyright (c)  2022  Xiaomi Corporation (authors: Fangjun Kuang)
+ *
+ * See LICENSE for clarification regarding multiple authors
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+// This file is copied/modified from kaldi/src/feat/feature-fbank.h
+
+#ifndef KALDI_NATIVE_FBANK_CSRC_FEATURE_FBANK_H_
+#define KALDI_NATIVE_FBANK_CSRC_FEATURE_FBANK_H_
+
+#include <map>
+
+#include "kaldi-native-fbank/csrc/feature-window.h"
+#include "kaldi-native-fbank/csrc/mel-computations.h"
+#include "kaldi-native-fbank/csrc/rfft.h"
+
+namespace knf {
+
+struct FbankOptions {
+  FrameExtractionOptions frame_opts;
+  MelBanksOptions mel_opts;
+  // append an extra dimension with energy to the filter banks
+  bool use_energy = false;
+  float energy_floor = 0.0f;  // active iff use_energy==true
+
+  // If true, compute log_energy before preemphasis and windowing
+  // If false, compute log_energy after preemphasis ans windowing
+  bool raw_energy = true;  // active iff use_energy==true
+
+  // If true, put energy last (if using energy)
+  // If false, put energy first
+  bool htk_compat = false;  // active iff use_energy==true
+
+  // if true (default), produce log-filterbank, else linear
+  bool use_log_fbank = true;
+
+  // if true (default), use power in filterbank
+  // analysis, else magnitude.
+  bool use_power = true;
+
+  FbankOptions() { mel_opts.num_bins = 23; }
+
+  std::string ToString() const {
+    std::ostringstream os;
+    os << "frame_opts: \n";
+    os << frame_opts << "\n";
+    os << "\n";
+
+    os << "mel_opts: \n";
+    os << mel_opts << "\n";
+
+    os << "use_energy: " << use_energy << "\n";
+    os << "energy_floor: " << energy_floor << "\n";
+    os << "raw_energy: " << raw_energy << "\n";
+    os << "htk_compat: " << htk_compat << "\n";
+    os << "use_log_fbank: " << use_log_fbank << "\n";
+    os << "use_power: " << use_power << "\n";
+    return os.str();
+  }
+};
+
+std::ostream &operator<<(std::ostream &os, const FbankOptions &opts);
+
+class FbankComputer {
+ public:
+  using Options = FbankOptions;
+
+  explicit FbankComputer(const FbankOptions &opts);
+  ~FbankComputer();
+
+  int32_t Dim() const {
+    return opts_.mel_opts.num_bins + (opts_.use_energy ? 1 : 0);
+  }
+
+  // if true, compute log_energy_pre_window but after dithering and dc removal
+  bool NeedRawLogEnergy() const { return opts_.use_energy && opts_.raw_energy; }
+
+  const FrameExtractionOptions &GetFrameOptions() const {
+    return opts_.frame_opts;
+  }
+
+  const FbankOptions &GetOptions() const { return opts_; }
+
+  /**
+     Function that computes one frame of features from
+     one frame of signal.
+
+     @param [in] signal_raw_log_energy The log-energy of the frame of the signal
+         prior to windowing and pre-emphasis, or
+         log(numeric_limits<float>::min()), whichever is greater.  Must be
+         ignored by this function if this class returns false from
+         this->NeedsRawLogEnergy().
+     @param [in] vtln_warp  The VTLN warping factor that the user wants
+         to be applied when computing features for this utterance.  Will
+         normally be 1.0, meaning no warping is to be done.  The value will
+         be ignored for feature types that don't support VLTN, such as
+         spectrogram features.
+     @param [in] signal_frame  One frame of the signal,
+       as extracted using the function ExtractWindow() using the options
+       returned by this->GetFrameOptions().  The function will use the
+       vector as a workspace, which is why it's a non-const pointer.
+     @param [out] feature  Pointer to a vector of size this->Dim(), to which
+         the computed feature will be written. It should be pre-allocated.
+  */
+  void Compute(float signal_raw_log_energy, float vtln_warp,
+               std::vector<float> *signal_frame, float *feature);
+
+ private:
+  const MelBanks *GetMelBanks(float vtln_warp);
+
+  FbankOptions opts_;
+  float log_energy_floor_;
+  std::map<float, MelBanks *> mel_banks_;  // float is VTLN coefficient.
+  Rfft rfft_;
+};
+
+}  // namespace knf
+
+#endif  // KALDI_NATIVE_FBANK_CSRC_FEATURE_FBANK_H_

speechx/speechx/kaldi-native-fbank/csrc/feature-functions.cc

@@ -0,0 +1,49 @@
+/**
+ * Copyright (c)  2022  Xiaomi Corporation (authors: Fangjun Kuang)
+ *
+ * See LICENSE for clarification regarding multiple authors
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+// This file is copied/modified from kaldi/src/feat/feature-functions.cc
+
+#include "kaldi-native-fbank/csrc/feature-functions.h"
+
+#include <cstdint>
+#include <vector>
+
+namespace knf {
+
+void ComputePowerSpectrum(std::vector<float> *complex_fft) {
+  int32_t dim = complex_fft->size();
+
+  // now we have in complex_fft, first half of complex spectrum
+  // it's stored as [real0, realN/2, real1, im1, real2, im2, ...]
+
+  float *p = complex_fft->data();
+  int32_t half_dim = dim / 2;
+  float first_energy = p[0] * p[0];
+  float last_energy = p[1] * p[1];  // handle this special case
+
+  for (int32_t i = 1; i < half_dim; ++i) {
+    float real = p[i * 2];
+    float im = p[i * 2 + 1];
+    p[i] = real * real + im * im;
+  }
+  p[0] = first_energy;
+  p[half_dim] = last_energy;  // Will actually never be used, and anyway
+  // if the signal has been bandlimited sensibly this should be zero.
+}
+
+}  // namespace knf

speechx/speechx/kaldi-native-fbank/csrc/feature-functions.h

@@ -0,0 +1,38 @@
+/**
+ * Copyright (c)  2022  Xiaomi Corporation (authors: Fangjun Kuang)
+ *
+ * See LICENSE for clarification regarding multiple authors
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+// This file is copied/modified from kaldi/src/feat/feature-functions.h
+#ifndef KALDI_NATIVE_FBANK_CSRC_FEATURE_FUNCTIONS_H
+#define KALDI_NATIVE_FBANK_CSRC_FEATURE_FUNCTIONS_H
+
+#include <vector>
+namespace knf {
+
+// ComputePowerSpectrum converts a complex FFT (as produced by the FFT
+// functions in csrc/rfft.h), and converts it into
+// a power spectrum.  If the complex FFT is a vector of size n (representing
+// half of the complex FFT of a real signal of size n, as described there),
+// this function computes in the first (n/2) + 1 elements of it, the
+// energies of the fft bins from zero to the Nyquist frequency.  Contents of the
+// remaining (n/2) - 1 elements are undefined at output.
+
+void ComputePowerSpectrum(std::vector<float> *complex_fft);
+
+}  // namespace knf
+
+#endif  // KALDI_NATIVE_FBANK_CSRC_FEATURE_FUNCTIONS_H

speechx/speechx/kaldi-native-fbank/csrc/feature-window.cc

@@ -0,0 +1,236 @@
+// kaldi-native-fbank/csrc/feature-window.cc
+//
+// Copyright (c)  2022  Xiaomi Corporation (authors: Fangjun Kuang)
+
+// This file is copied/modified from kaldi/src/feat/feature-window.cc
+
+#include "kaldi-native-fbank/csrc/feature-window.h"
+
+#include <cmath>
+#include <vector>
+
+#ifndef M_2PI
+#define M_2PI 6.283185307179586476925286766559005
+#endif
+
+namespace knf {
+
+std::ostream &operator<<(std::ostream &os, const FrameExtractionOptions &opts) {
+  os << opts.ToString();
+  return os;
+}
+
+FeatureWindowFunction::FeatureWindowFunction(const FrameExtractionOptions &opts)
+    : window_(opts.WindowSize()) {
+  int32_t frame_length = opts.WindowSize();
+  KNF_CHECK_GT(frame_length, 0);
+
+  float *window_data = window_.data();
+
+  double a = M_2PI / (frame_length - 1);
+  for (int32_t i = 0; i < frame_length; i++) {
+    double i_fl = static_cast<double>(i);
+    if (opts.window_type == "hanning") {
+      window_data[i] = 0.5 - 0.5 * cos(a * i_fl);
+    } else if (opts.window_type == "sine") {
+      // when you are checking ws wikipedia, please
+      // note that 0.5 * a = M_PI/(frame_length-1)
+      window_data[i] = sin(0.5 * a * i_fl);
+    } else if (opts.window_type == "hamming") {
+      window_data[i] = 0.54 - 0.46 * cos(a * i_fl);
+    } else if (opts.window_type ==
+               "povey") {  // like hamming but goes to zero at edges.
+      window_data[i] = pow(0.5 - 0.5 * cos(a * i_fl), 0.85);
+    } else if (opts.window_type == "rectangular") {
+      window_data[i] = 1.0;
+    } else if (opts.window_type == "blackman") {
+      window_data[i] = opts.blackman_coeff - 0.5 * cos(a * i_fl) +
+                       (0.5 - opts.blackman_coeff) * cos(2 * a * i_fl);
+    } else {
+      KNF_LOG(FATAL) << "Invalid window type " << opts.window_type;
+    }
+  }
+}
+
+void FeatureWindowFunction::Apply(float *wave) const {
+  int32_t window_size = window_.size();
+  const float *p = window_.data();
+  for (int32_t k = 0; k != window_size; ++k) {
+    wave[k] *= p[k];
+  }
+}
+
+int64_t FirstSampleOfFrame(int32_t frame, const FrameExtractionOptions &opts) {
+  int64_t frame_shift = opts.WindowShift();
+  if (opts.snip_edges) {
+    return frame * frame_shift;
+  } else {
+    int64_t midpoint_of_frame = frame_shift * frame + frame_shift / 2,
+            beginning_of_frame = midpoint_of_frame - opts.WindowSize() / 2;
+    return beginning_of_frame;
+  }
+}
+
+int32_t NumFrames(int64_t num_samples, const FrameExtractionOptions &opts,
+                  bool flush /*= true*/) {
+  int64_t frame_shift = opts.WindowShift();
+  int64_t frame_length = opts.WindowSize();
+  if (opts.snip_edges) {
+    // with --snip-edges=true (the default), we use a HTK-like approach to
+    // determining the number of frames-- all frames have to fit completely into
+    // the waveform, and the first frame begins at sample zero.
+    if (num_samples < frame_length)
+      return 0;
+    else
+      return (1 + ((num_samples - frame_length) / frame_shift));
+    // You can understand the expression above as follows: 'num_samples -
+    // frame_length' is how much room we have to shift the frame within the
+    // waveform; 'frame_shift' is how much we shift it each time; and the ratio
+    // is how many times we can shift it (integer arithmetic rounds down).
+  } else {
+    // if --snip-edges=false, the number of frames is determined by rounding the
+    // (file-length / frame-shift) to the nearest integer.  The point of this
+    // formula is to make the number of frames an obvious and predictable
+    // function of the frame shift and signal length, which makes many
+    // segmentation-related questions simpler.
+    //
+    // Because integer division in C++ rounds toward zero, we add (half the
+    // frame-shift minus epsilon) before dividing, to have the effect of
+    // rounding towards the closest integer.
+    int32_t num_frames = (num_samples + (frame_shift / 2)) / frame_shift;
+
+    if (flush) return num_frames;
+
+    // note: 'end' always means the last plus one, i.e. one past the last.
+    int64_t end_sample_of_last_frame =
+        FirstSampleOfFrame(num_frames - 1, opts) + frame_length;
+
+    // the following code is optimized more for clarity than efficiency.
+    // If flush == false, we can't output frames that extend past the end
+    // of the signal.
+    while (num_frames > 0 && end_sample_of_last_frame > num_samples) {
+      num_frames--;
+      end_sample_of_last_frame -= frame_shift;
+    }
+    return num_frames;
+  }
+}
+
+void ExtractWindow(int64_t sample_offset, const std::vector<float> &wave,
+                   int32_t f, const FrameExtractionOptions &opts,
+                   const FeatureWindowFunction &window_function,
+                   std::vector<float> *window,
+                   float *log_energy_pre_window /*= nullptr*/) {
+  KNF_CHECK(sample_offset >= 0 && wave.size() != 0);
+
+  int32_t frame_length = opts.WindowSize();
+  int32_t frame_length_padded = opts.PaddedWindowSize();
+
+  int64_t num_samples = sample_offset + wave.size();
+  int64_t start_sample = FirstSampleOfFrame(f, opts);
+  int64_t end_sample = start_sample + frame_length;
+
+  if (opts.snip_edges) {
+    KNF_CHECK(start_sample >= sample_offset && end_sample <= num_samples);
+  } else {
+    KNF_CHECK(sample_offset == 0 || start_sample >= sample_offset);
+  }
+
+  if (window->size() != frame_length_padded) {
+    window->resize(frame_length_padded);
+  }
+
+  // wave_start and wave_end are start and end indexes into 'wave', for the
+  // piece of wave that we're trying to extract.
+  int32_t wave_start = int32_t(start_sample - sample_offset);
+  int32_t wave_end = wave_start + frame_length;
+
+  if (wave_start >= 0 && wave_end <= wave.size()) {
+    // the normal case-- no edge effects to consider.
+    std::copy(wave.begin() + wave_start,
+              wave.begin() + wave_start + frame_length, window->data());
+  } else {
+    // Deal with any end effects by reflection, if needed.  This code will only
+    // be reached for about two frames per utterance, so we don't concern
+    // ourselves excessively with efficiency.
+    int32_t wave_dim = wave.size();
+    for (int32_t s = 0; s < frame_length; ++s) {
+      int32_t s_in_wave = s + wave_start;
+      while (s_in_wave < 0 || s_in_wave >= wave_dim) {
+        // reflect around the beginning or end of the wave.
+        // e.g. -1 -> 0, -2 -> 1.
+        // dim -> dim - 1, dim + 1 -> dim - 2.
+        // the code supports repeated reflections, although this
+        // would only be needed in pathological cases.
+        if (s_in_wave < 0)
+          s_in_wave = -s_in_wave - 1;
+        else
+          s_in_wave = 2 * wave_dim - 1 - s_in_wave;
+      }
+      (*window)[s] = wave[s_in_wave];
+    }
+  }
+
+  ProcessWindow(opts, window_function, window->data(), log_energy_pre_window);
+}
+
+static void RemoveDcOffset(float *d, int32_t n) {
+  float sum = 0;
+  for (int32_t i = 0; i != n; ++i) {
+    sum += d[i];
+  }
+
+  float mean = sum / n;
+
+  for (int32_t i = 0; i != n; ++i) {
+    d[i] -= mean;
+  }
+}
+
+float InnerProduct(const float *a, const float *b, int32_t n) {
+  float sum = 0;
+  for (int32_t i = 0; i != n; ++i) {
+    sum += a[i] * b[i];
+  }
+  return sum;
+}
+
+static void Preemphasize(float *d, int32_t n, float preemph_coeff) {
+  if (preemph_coeff == 0.0) {
+    return;
+  }
+
+  KNF_CHECK(preemph_coeff >= 0.0 && preemph_coeff <= 1.0);
+
+  for (int32_t i = n - 1; i > 0; --i) {
+    d[i] -= preemph_coeff * d[i - 1];
+  }
+  d[0] -= preemph_coeff * d[0];
+}
+
+void ProcessWindow(const FrameExtractionOptions &opts,
+                   const FeatureWindowFunction &window_function, float *window,
+                   float *log_energy_pre_window /*= nullptr*/) {
+  int32_t frame_length = opts.WindowSize();
+
+  // TODO(fangjun): Remove dither
+  KNF_CHECK_EQ(opts.dither, 0);
+
+  if (opts.remove_dc_offset) {
+    RemoveDcOffset(window, frame_length);
+  }
+
+  if (log_energy_pre_window != NULL) {
+    float energy = std::max<float>(InnerProduct(window, window, frame_length),
+                                   std::numeric_limits<float>::epsilon());
+    *log_energy_pre_window = std::log(energy);
+  }
+
+  if (opts.preemph_coeff != 0.0) {
+    Preemphasize(window, frame_length, opts.preemph_coeff);
+  }
+
+  window_function.Apply(window);
+}
+
+}  // namespace knf

speechx/speechx/kaldi-native-fbank/csrc/feature-window.h

@@ -0,0 +1,178 @@
+// kaldi-native-fbank/csrc/feature-window.h
+//
+// Copyright (c)  2022  Xiaomi Corporation (authors: Fangjun Kuang)
+
+// This file is copied/modified from kaldi/src/feat/feature-window.h
+
+#ifndef KALDI_NATIVE_FEAT_CSRC_FEATURE_WINDOW_H_
+#define KALDI_NATIVE_FEAT_CSRC_FEATURE_WINDOW_H_
+
+#include <sstream>
+#include <string>
+#include <vector>
+
+#include "kaldi-native-fbank/csrc/log.h"
+
+namespace knf {
+
+inline int32_t RoundUpToNearestPowerOfTwo(int32_t n) {
+  // copied from kaldi/src/base/kaldi-math.cc
+  KNF_CHECK_GT(n, 0);
+  n--;
+  n |= n >> 1;
+  n |= n >> 2;
+  n |= n >> 4;
+  n |= n >> 8;
+  n |= n >> 16;
+  return n + 1;
+}
+
+struct FrameExtractionOptions {
+  float samp_freq = 16000;
+  float frame_shift_ms = 10.0f;   // in milliseconds.
+  float frame_length_ms = 25.0f;  // in milliseconds.
+  float dither = 1.0f;            // Amount of dithering, 0.0 means no dither.
+  float preemph_coeff = 0.97f;    // Preemphasis coefficient.
+  bool remove_dc_offset = true;   // Subtract mean of wave before FFT.
+  std::string window_type = "povey";  // e.g. Hamming window
+  // May be "hamming", "rectangular", "povey", "hanning", "sine", "blackman"
+  // "povey" is a window I made to be similar to Hamming but to go to zero at
+  // the edges, it's pow((0.5 - 0.5*cos(n/N*2*pi)), 0.85) I just don't think the
+  // Hamming window makes sense as a windowing function.
+  bool round_to_power_of_two = true;
+  float blackman_coeff = 0.42f;
+  bool snip_edges = true;
+  // bool allow_downsample = false;
+  // bool allow_upsample = false;
+
+  // Used for streaming feature extraction. It indicates the number
+  // of feature frames to keep in the recycling vector. -1 means to
+  // keep all feature frames.
+  int32_t max_feature_vectors = -1;
+
+  int32_t WindowShift() const {
+    return static_cast<int32_t>(samp_freq * 0.001f * frame_shift_ms);
+  }
+  int32_t WindowSize() const {
+    return static_cast<int32_t>(samp_freq * 0.001f * frame_length_ms);
+  }
+  int32_t PaddedWindowSize() const {
+    return (round_to_power_of_two ? RoundUpToNearestPowerOfTwo(WindowSize())
+                                  : WindowSize());
+  }
+  std::string ToString() const {
+    std::ostringstream os;
+#define KNF_PRINT(x) os << #x << ": " << x << "\n"
+    KNF_PRINT(samp_freq);
+    KNF_PRINT(frame_shift_ms);
+    KNF_PRINT(frame_length_ms);
+    KNF_PRINT(dither);
+    KNF_PRINT(preemph_coeff);
+    KNF_PRINT(remove_dc_offset);
+    KNF_PRINT(window_type);
+    KNF_PRINT(round_to_power_of_two);
+    KNF_PRINT(blackman_coeff);
+    KNF_PRINT(snip_edges);
+    // KNF_PRINT(allow_downsample);
+    // KNF_PRINT(allow_upsample);
+    KNF_PRINT(max_feature_vectors);
+#undef KNF_PRINT
+    return os.str();
+  }
+};
+
+std::ostream &operator<<(std::ostream &os, const FrameExtractionOptions &opts);
+
+class FeatureWindowFunction {
+ public:
+  FeatureWindowFunction() = default;
+  explicit FeatureWindowFunction(const FrameExtractionOptions &opts);
+  /**
+   * @param wave Pointer to a 1-D array of shape [window_size].
+   *             It is modified in-place: wave[i] = wave[i] * window_[i].
+   * @param
+   */
+  void Apply(float *wave) const;
+
+ private:
+  std::vector<float> window_;  // of size opts.WindowSize()
+};
+
+int64_t FirstSampleOfFrame(int32_t frame, const FrameExtractionOptions &opts);
+
+/**
+   This function returns the number of frames that we can extract from a wave
+   file with the given number of samples in it (assumed to have the same
+   sampling rate as specified in 'opts').
+
+      @param [in] num_samples  The number of samples in the wave file.
+      @param [in] opts     The frame-extraction options class
+
+      @param [in] flush   True if we are asserting that this number of samples
+   is 'all there is', false if we expecting more data to possibly come in.  This
+   only makes a difference to the answer
+   if opts.snips_edges== false.  For offline feature extraction you always want
+   flush == true.  In an online-decoding context, once you know (or decide) that
+   no more data is coming in, you'd call it with flush == true at the end to
+   flush out any remaining data.
+*/
+int32_t NumFrames(int64_t num_samples, const FrameExtractionOptions &opts,
+                  bool flush = true);
+
+/*
+  ExtractWindow() extracts a windowed frame of waveform (possibly with a
+  power-of-two, padded size, depending on the config), including all the
+  processing done by ProcessWindow().
+
+  @param [in] sample_offset  If 'wave' is not the entire waveform, but
+                   part of it to the left has been discarded, then the
+                   number of samples prior to 'wave' that we have
+                   already discarded.  Set this to zero if you are
+                   processing the entire waveform in one piece, or
+                   if you get 'no matching function' compilation
+                   errors when updating the code.
+  @param [in] wave  The waveform
+  @param [in] f     The frame index to be extracted, with
+                    0 <= f < NumFrames(sample_offset + wave.Dim(), opts, true)
+  @param [in] opts  The options class to be used
+  @param [in] window_function  The windowing function, as derived from the
+                    options class.
+  @param [out] window  The windowed, possibly-padded waveform to be
+                     extracted.  Will be resized as needed.
+  @param [out] log_energy_pre_window  If non-NULL, the log-energy of
+                   the signal prior to pre-emphasis and multiplying by
+                   the windowing function will be written to here.
+*/
+void ExtractWindow(int64_t sample_offset, const std::vector<float> &wave,
+                   int32_t f, const FrameExtractionOptions &opts,
+                   const FeatureWindowFunction &window_function,
+                   std::vector<float> *window,
+                   float *log_energy_pre_window = nullptr);
+
+/**
+  This function does all the windowing steps after actually
+  extracting the windowed signal: depending on the
+  configuration, it does dithering, dc offset removal,
+  preemphasis, and multiplication by the windowing function.
+   @param [in] opts  The options class to be used
+   @param [in] window_function  The windowing function-- should have
+                    been initialized using 'opts'.
+   @param [in,out] window  A vector of size opts.WindowSize().  Note:
+      it will typically be a sub-vector of a larger vector of size
+      opts.PaddedWindowSize(), with the remaining samples zero,
+      as the FFT code is more efficient if it operates on data with
+      power-of-two size.
+   @param [out]   log_energy_pre_window If non-NULL, then after dithering and
+      DC offset removal, this function will write to this pointer the log of
+      the total energy (i.e. sum-squared) of the frame.
+ */
+void ProcessWindow(const FrameExtractionOptions &opts,
+                   const FeatureWindowFunction &window_function, float *window,
+                   float *log_energy_pre_window = nullptr);
+
+// Compute the inner product of two vectors
+float InnerProduct(const float *a, const float *b, int32_t n);
+
+}  // namespace knf
+
+#endif  // KALDI_NATIVE_FEAT_CSRC_FEATURE_WINDOW_H_

speechx/speechx/kaldi-native-fbank/csrc/log.cc

@@ -0,0 +1,143 @@
+/**
+ * Copyright (c)  2022  Xiaomi Corporation (authors: Fangjun Kuang)
+ *
+ * See LICENSE for clarification regarding multiple authors
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/*
+ * Stack trace related stuff is from kaldi.
+ * Refer to
+ * https://github.com/kaldi-asr/kaldi/blob/master/src/base/kaldi-error.cc
+ */
+
+#include "kaldi-native-fbank/csrc/log.h"
+
+#ifdef KNF_HAVE_EXECINFO_H
+#include <execinfo.h> // To get stack trace in error messages.
+#ifdef KNF_HAVE_CXXABI_H
+#include <cxxabi.h> // For name demangling.
+// Useful to decode the stack trace, but only used if we have execinfo.h
+#endif // KNF_HAVE_CXXABI_H
+#endif // KNF_HAVE_EXECINFO_H
+
+#include <stdlib.h>
+
+#include <ctime>
+#include <iomanip>
+#include <string>
+
+namespace knf {
+
+std::string GetDateTimeStr() {
+  std::ostringstream os;
+  std::time_t t = std::time(nullptr);
+  std::tm tm = *std::localtime(&t);
+  os << std::put_time(&tm, "%F %T"); // yyyy-mm-dd hh:mm:ss
+  return os.str();
+}
+
+static bool LocateSymbolRange(const std::string &trace_name, std::size_t *begin,
+                              std::size_t *end) {
+  // Find the first '_' with leading ' ' or '('.
+  *begin = std::string::npos;
+  for (std::size_t i = 1; i < trace_name.size(); ++i) {
+    if (trace_name[i] != '_') {
+      continue;
+    }
+    if (trace_name[i - 1] == ' ' || trace_name[i - 1] == '(') {
+      *begin = i;
+      break;
+    }
+  }
+  if (*begin == std::string::npos) {
+    return false;
+  }
+  *end = trace_name.find_first_of(" +", *begin);
+  return *end != std::string::npos;
+}
+
+#ifdef KNF_HAVE_EXECINFO_H
+static std::string Demangle(const std::string &trace_name) {
+#ifndef KNF_HAVE_CXXABI_H
+  return trace_name;
+#else  // KNF_HAVE_CXXABI_H
+  // Try demangle the symbol. We are trying to support the following formats
+  // produced by different platforms:
+  //
+  // Linux:
+  //   ./kaldi-error-test(_ZN5kaldi13UnitTestErrorEv+0xb) [0x804965d]
+  //
+  // Mac:
+  //   0 server 0x000000010f67614d _ZNK5kaldi13MessageLogger10LogMessageEv + 813
+  //
+  // We want to extract the name e.g., '_ZN5kaldi13UnitTestErrorEv' and
+  // demangle it info a readable name like kaldi::UnitTextError.
+  std::size_t begin, end;
+  if (!LocateSymbolRange(trace_name, &begin, &end)) {
+    return trace_name;
+  }
+  std::string symbol = trace_name.substr(begin, end - begin);
+  int status;
+  char *demangled_name = abi::__cxa_demangle(symbol.c_str(), 0, 0, &status);
+  if (status == 0 && demangled_name != nullptr) {
+    symbol = demangled_name;
+    free(demangled_name);
+  }
+  return trace_name.substr(0, begin) + symbol +
+         trace_name.substr(end, std::string::npos);
+#endif // KNF_HAVE_CXXABI_H
+}
+#endif // KNF_HAVE_EXECINFO_H
+
+std::string GetStackTrace() {
+  std::string ans;
+#ifdef KNF_HAVE_EXECINFO_H
+  constexpr const std::size_t kMaxTraceSize = 50;
+  constexpr const std::size_t kMaxTracePrint = 50; // Must be even.
+                                                   // Buffer for the trace.
+  void *trace[kMaxTraceSize];
+  // Get the trace.
+  std::size_t size = backtrace(trace, kMaxTraceSize);
+  // Get the trace symbols.
+  char **trace_symbol = backtrace_symbols(trace, size);
+  if (trace_symbol == nullptr)
+    return ans;
+
+  // Compose a human-readable backtrace string.
+  ans += "[ Stack-Trace: ]\n";
+  if (size <= kMaxTracePrint) {
+    for (std::size_t i = 0; i < size; ++i) {
+      ans += Demangle(trace_symbol[i]) + "\n";
+    }
+  } else { // Print out first+last (e.g.) 5.
+    for (std::size_t i = 0; i < kMaxTracePrint / 2; ++i) {
+      ans += Demangle(trace_symbol[i]) + "\n";
+    }
+    ans += ".\n.\n.\n";
+    for (std::size_t i = size - kMaxTracePrint / 2; i < size; ++i) {
+      ans += Demangle(trace_symbol[i]) + "\n";
+    }
+    if (size == kMaxTraceSize)
+      ans += ".\n.\n.\n"; // Stack was too long, probably a bug.
+  }
+
+  // We must free the array of pointers allocated by backtrace_symbols(),
+  // but not the strings themselves.
+  free(trace_symbol);
+#endif // KNF_HAVE_EXECINFO_H
+  return ans;
+}
+
+} // namespace knf

speechx/speechx/kaldi-native-fbank/csrc/log.h

@@ -0,0 +1,383 @@
+/**
+ * Copyright (c)  2022  Xiaomi Corporation (authors: Fangjun Kuang)
+ *
+ * See LICENSE for clarification regarding multiple authors
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+// The content in this file is copied/modified from
+// https://github.com/k2-fsa/k2/blob/master/k2/csrc/log.h
+#ifndef KALDI_NATIVE_FBANK_CSRC_LOG_H_
+#define KALDI_NATIVE_FBANK_CSRC_LOG_H_
+
+#include <stdio.h>
+
+#include <mutex>  // NOLINT
+#include <sstream>
+#include <string>
+
+namespace knf {
+
+#if KNF_ENABLE_CHECK
+
+#if defined(NDEBUG)
+constexpr bool kDisableDebug = true;
+#else
+constexpr bool kDisableDebug = false;
+#endif
+
+enum class LogLevel {
+  kTrace = 0,
+  kDebug = 1,
+  kInfo = 2,
+  kWarning = 3,
+  kError = 4,
+  kFatal = 5,  // print message and abort the program
+};
+
+// They are used in KNF_LOG(xxx), so their names
+// do not follow the google c++ code style
+//
+// You can use them in the following way:
+//
+//  KNF_LOG(TRACE) << "some message";
+//  KNF_LOG(DEBUG) << "some message";
+#ifndef _MSC_VER
+constexpr LogLevel TRACE = LogLevel::kTrace;
+constexpr LogLevel DEBUG = LogLevel::kDebug;
+constexpr LogLevel INFO = LogLevel::kInfo;
+constexpr LogLevel WARNING = LogLevel::kWarning;
+constexpr LogLevel ERROR = LogLevel::kError;
+constexpr LogLevel FATAL = LogLevel::kFatal;
+#else
+#define TRACE LogLevel::kTrace
+#define DEBUG LogLevel::kDebug
+#define INFO LogLevel::kInfo
+#define WARNING LogLevel::kWarning
+#define ERROR LogLevel::kError
+#define FATAL LogLevel::kFatal
+#endif
+
+std::string GetStackTrace();
+
+/* Return the current log level.
+
+
+   If the current log level is TRACE, then all logged messages are printed out.
+
+   If the current log level is DEBUG, log messages with "TRACE" level are not
+   shown and all other levels are printed out.
+
+   Similarly, if the current log level is INFO, log message with "TRACE" and
+   "DEBUG" are not shown and all other levels are printed out.
+
+   If it is FATAL, then only FATAL messages are shown.
+ */
+inline LogLevel GetCurrentLogLevel() {
+  static LogLevel log_level = INFO;
+  static std::once_flag init_flag;
+  std::call_once(init_flag, []() {
+    const char *env_log_level = std::getenv("KNF_LOG_LEVEL");
+    if (env_log_level == nullptr) return;
+
+    std::string s = env_log_level;
+    if (s == "TRACE")
+      log_level = TRACE;
+    else if (s == "DEBUG")
+      log_level = DEBUG;
+    else if (s == "INFO")
+      log_level = INFO;
+    else if (s == "WARNING")
+      log_level = WARNING;
+    else if (s == "ERROR")
+      log_level = ERROR;
+    else if (s == "FATAL")
+      log_level = FATAL;
+    else
+      fprintf(stderr,
+              "Unknown KNF_LOG_LEVEL: %s"
+              "\nSupported values are: "
+              "TRACE, DEBUG, INFO, WARNING, ERROR, FATAL",
+              s.c_str());
+  });
+  return log_level;
+}
+
+inline bool EnableAbort() {
+  static std::once_flag init_flag;
+  static bool enable_abort = false;
+  std::call_once(init_flag, []() {
+    enable_abort = (std::getenv("KNF_ABORT") != nullptr);
+  });
+  return enable_abort;
+}
+
+class Logger {
+ public:
+  Logger(const char *filename, const char *func_name, uint32_t line_num,
+         LogLevel level)
+      : filename_(filename),
+        func_name_(func_name),
+        line_num_(line_num),
+        level_(level) {
+    cur_level_ = GetCurrentLogLevel();
+    fprintf(stderr, "here\n");
+    switch (level) {
+      case TRACE:
+        if (cur_level_ <= TRACE) fprintf(stderr, "[T] ");
+        break;
+      case DEBUG:
+        if (cur_level_ <= DEBUG) fprintf(stderr, "[D] ");
+        break;
+      case INFO:
+        if (cur_level_ <= INFO) fprintf(stderr, "[I] ");
+        break;
+      case WARNING:
+        if (cur_level_ <= WARNING) fprintf(stderr, "[W] ");
+        break;
+      case ERROR:
+        if (cur_level_ <= ERROR) fprintf(stderr, "[E] ");
+        break;
+      case FATAL:
+        if (cur_level_ <= FATAL) fprintf(stderr, "[F] ");
+        break;
+    }
+
+    if (cur_level_ <= level_) {
+      fprintf(stderr, "%s:%u:%s ", filename, line_num, func_name);
+    }
+  }
+
+  ~Logger() noexcept(false) {
+    static constexpr const char *kErrMsg = R"(
+    Some bad things happened. Please read the above error messages and stack
+    trace. If you are using Python, the following command may be helpful:
+
+      gdb --args python /path/to/your/code.py
+
+    (You can use `gdb` to debug the code. Please consider compiling
+    a debug version of KNF.).
+
+    If you are unable to fix it, please open an issue at:
+
+      https://github.com/csukuangfj/kaldi-native-fbank/issues/new
+    )";
+    fprintf(stderr, "\n");
+    if (level_ == FATAL) {
+      std::string stack_trace = GetStackTrace();
+      if (!stack_trace.empty()) {
+        fprintf(stderr, "\n\n%s\n", stack_trace.c_str());
+      }
+
+      fflush(nullptr);
+
+#ifndef __ANDROID_API__
+      if (EnableAbort()) {
+        // NOTE: abort() will terminate the program immediately without
+        // printing the Python stack backtrace.
+        abort();
+      }
+
+      throw std::runtime_error(kErrMsg);
+#else
+      abort();
+#endif
+    }
+  }
+
+  const Logger &operator<<(bool b) const {
+    if (cur_level_ <= level_) {
+      fprintf(stderr, b ? "true" : "false");
+    }
+    return *this;
+  }
+
+  const Logger &operator<<(int8_t i) const {
+    if (cur_level_ <= level_) fprintf(stderr, "%d", i);
+    return *this;
+  }
+
+  const Logger &operator<<(const char *s) const {
+    if (cur_level_ <= level_) fprintf(stderr, "%s", s);
+    return *this;
+  }
+
+  const Logger &operator<<(int32_t i) const {
+    if (cur_level_ <= level_) fprintf(stderr, "%d", i);
+    return *this;
+  }
+
+  const Logger &operator<<(uint32_t i) const {
+    if (cur_level_ <= level_) fprintf(stderr, "%u", i);
+    return *this;
+  }
+
+  const Logger &operator<<(uint64_t i) const {
+    if (cur_level_ <= level_)
+      fprintf(stderr, "%llu", (long long unsigned int)i);  // NOLINT
+    return *this;
+  }
+
+  const Logger &operator<<(int64_t i) const {
+    if (cur_level_ <= level_)
+      fprintf(stderr, "%lli", (long long int)i);  // NOLINT
+    return *this;
+  }
+
+  const Logger &operator<<(float f) const {
+    if (cur_level_ <= level_) fprintf(stderr, "%f", f);
+    return *this;
+  }
+
+  const Logger &operator<<(double d) const {
+    if (cur_level_ <= level_) fprintf(stderr, "%f", d);
+    return *this;
+  }
+
+  template <typename T>
+  const Logger &operator<<(const T &t) const {
+    // require T overloads operator<<
+    std::ostringstream os;
+    os << t;
+    return *this << os.str().c_str();
+  }
+
+  // specialization to fix compile error: `stringstream << nullptr` is ambiguous
+  const Logger &operator<<(const std::nullptr_t &null) const {
+    if (cur_level_ <= level_) *this << "(null)";
+    return *this;
+  }
+
+ private:
+  const char *filename_;
+  const char *func_name_;
+  uint32_t line_num_;
+  LogLevel level_;
+  LogLevel cur_level_;
+};
+#endif  // KNF_ENABLE_CHECK
+
+class Voidifier {
+ public:
+#if KNF_ENABLE_CHECK
+  void operator&(const Logger &) const {}
+#endif
+};
+#if !defined(KNF_ENABLE_CHECK)
+template <typename T>
+const Voidifier &operator<<(const Voidifier &v, T &&) {
+  return v;
+}
+#endif
+
+}  // namespace knf
+
+#define KNF_STATIC_ASSERT(x) static_assert(x, "")
+
+#ifdef KNF_ENABLE_CHECK
+
+#if defined(__clang__) || defined(__GNUC__) || defined(__GNUG__) || \
+    defined(__PRETTY_FUNCTION__)
+// for clang and GCC
+#define KNF_FUNC __PRETTY_FUNCTION__
+#else
+// for other compilers
+#define KNF_FUNC __func__
+#endif
+
+#define KNF_CHECK(x)                                                  \
+  (x) ? (void)0                                                       \
+      : ::knf::Voidifier() &                                          \
+            ::knf::Logger(__FILE__, KNF_FUNC, __LINE__, ::knf::FATAL) \
+                << "Check failed: " << #x << " "
+
+// WARNING: x and y may be evaluated multiple times, but this happens only
+// when the check fails. Since the program aborts if it fails, we don't think
+// the extra evaluation of x and y matters.
+//
+// CAUTION: we recommend the following use case:
+//
+//      auto x = Foo();
+//      auto y = Bar();
+//      KNF_CHECK_EQ(x, y) << "Some message";
+//
+//  And please avoid
+//
+//      KNF_CHECK_EQ(Foo(), Bar());
+//
+//  if `Foo()` or `Bar()` causes some side effects, e.g., changing some
+//  local static variables or global variables.
+#define _KNF_CHECK_OP(x, y, op)                                              \
+  ((x)op(y)) ? (void)0                                                       \
+             : ::knf::Voidifier() &                                          \
+                   ::knf::Logger(__FILE__, KNF_FUNC, __LINE__, ::knf::FATAL) \
+                       << "Check failed: " << #x << " " << #op << " " << #y  \
+                       << " (" << (x) << " vs. " << (y) << ") "
+
+#define KNF_CHECK_EQ(x, y) _KNF_CHECK_OP(x, y, ==)
+#define KNF_CHECK_NE(x, y) _KNF_CHECK_OP(x, y, !=)
+#define KNF_CHECK_LT(x, y) _KNF_CHECK_OP(x, y, <)
+#define KNF_CHECK_LE(x, y) _KNF_CHECK_OP(x, y, <=)
+#define KNF_CHECK_GT(x, y) _KNF_CHECK_OP(x, y, >)
+#define KNF_CHECK_GE(x, y) _KNF_CHECK_OP(x, y, >=)
+
+#define KNF_LOG(x) ::knf::Logger(__FILE__, KNF_FUNC, __LINE__, ::knf::x)
+
+// ------------------------------------------------------------
+//       For debug check
+// ------------------------------------------------------------
+// If you define the macro "-D NDEBUG" while compiling kaldi-native-fbank,
+// the following macros are in fact empty and does nothing.
+
+#define KNF_DCHECK(x) ::knf::kDisableDebug ? (void)0 : KNF_CHECK(x)
+
+#define KNF_DCHECK_EQ(x, y) ::knf::kDisableDebug ? (void)0 : KNF_CHECK_EQ(x, y)
+
+#define KNF_DCHECK_NE(x, y) ::knf::kDisableDebug ? (void)0 : KNF_CHECK_NE(x, y)
+
+#define KNF_DCHECK_LT(x, y) ::knf::kDisableDebug ? (void)0 : KNF_CHECK_LT(x, y)
+
+#define KNF_DCHECK_LE(x, y) ::knf::kDisableDebug ? (void)0 : KNF_CHECK_LE(x, y)
+
+#define KNF_DCHECK_GT(x, y) ::knf::kDisableDebug ? (void)0 : KNF_CHECK_GT(x, y)
+
+#define KNF_DCHECK_GE(x, y) ::knf::kDisableDebug ? (void)0 : KNF_CHECK_GE(x, y)
+
+#define KNF_DLOG(x) \
+  ::knf::kDisableDebug ? (void)0 : ::knf::Voidifier() & KNF_LOG(x)
+
+#else
+
+#define KNF_CHECK(x) ::knf::Voidifier()
+#define KNF_LOG(x) ::knf::Voidifier()
+
+#define KNF_CHECK_EQ(x, y) ::knf::Voidifier()
+#define KNF_CHECK_NE(x, y) ::knf::Voidifier()
+#define KNF_CHECK_LT(x, y) ::knf::Voidifier()
+#define KNF_CHECK_LE(x, y) ::knf::Voidifier()
+#define KNF_CHECK_GT(x, y) ::knf::Voidifier()
+#define KNF_CHECK_GE(x, y) ::knf::Voidifier()
+
+#define KNF_DCHECK(x) ::knf::Voidifier()
+#define KNF_DLOG(x) ::knf::Voidifier()
+#define KNF_DCHECK_EQ(x, y) ::knf::Voidifier()
+#define KNF_DCHECK_NE(x, y) ::knf::Voidifier()
+#define KNF_DCHECK_LT(x, y) ::knf::Voidifier()
+#define KNF_DCHECK_LE(x, y) ::knf::Voidifier()
+#define KNF_DCHECK_GT(x, y) ::knf::Voidifier()
+#define KNF_DCHECK_GE(x, y) ::knf::Voidifier()
+
+#endif  // KNF_CHECK_NE
+
+#endif  // KALDI_NATIVE_FBANK_CSRC_LOG_H_

speechx/speechx/kaldi-native-fbank/csrc/mel-computations.cc

@@ -0,0 +1,256 @@
+/**
+ * Copyright (c)  2022  Xiaomi Corporation (authors: Fangjun Kuang)
+ *
+ * See LICENSE for clarification regarding multiple authors
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+// This file is copied/modified from kaldi/src/feat/mel-computations.cc
+
+#include "kaldi-native-fbank/csrc/mel-computations.h"
+
+#include <algorithm>
+#include <sstream>
+
+#include "kaldi-native-fbank/csrc/feature-window.h"
+
+namespace knf {
+
+std::ostream &operator<<(std::ostream &os, const MelBanksOptions &opts) {
+  os << opts.ToString();
+  return os;
+}
+
+float MelBanks::VtlnWarpFreq(
+    float vtln_low_cutoff,  // upper+lower frequency cutoffs for VTLN.
+    float vtln_high_cutoff,
+    float low_freq,  // upper+lower frequency cutoffs in mel computation
+    float high_freq, float vtln_warp_factor, float freq) {
+  /// This computes a VTLN warping function that is not the same as HTK's one,
+  /// but has similar inputs (this function has the advantage of never producing
+  /// empty bins).
+
+  /// This function computes a warp function F(freq), defined between low_freq
+  /// and high_freq inclusive, with the following properties:
+  ///  F(low_freq) == low_freq
+  ///  F(high_freq) == high_freq
+  /// The function is continuous and piecewise linear with two inflection
+  ///   points.
+  /// The lower inflection point (measured in terms of the unwarped
+  ///  frequency) is at frequency l, determined as described below.
+  /// The higher inflection point is at a frequency h, determined as
+  ///   described below.
+  /// If l <= f <= h, then F(f) = f/vtln_warp_factor.
+  /// If the higher inflection point (measured in terms of the unwarped
+  ///   frequency) is at h, then max(h, F(h)) == vtln_high_cutoff.
+  ///   Since (by the last point) F(h) == h/vtln_warp_factor, then
+  ///   max(h, h/vtln_warp_factor) == vtln_high_cutoff, so
+  ///   h = vtln_high_cutoff / max(1, 1/vtln_warp_factor).
+  ///     = vtln_high_cutoff * min(1, vtln_warp_factor).
+  /// If the lower inflection point (measured in terms of the unwarped
+  ///   frequency) is at l, then min(l, F(l)) == vtln_low_cutoff
+  ///   This implies that l = vtln_low_cutoff / min(1, 1/vtln_warp_factor)
+  ///                       = vtln_low_cutoff * max(1, vtln_warp_factor)
+
+  if (freq < low_freq || freq > high_freq)
+    return freq;  // in case this gets called
+  // for out-of-range frequencies, just return the freq.
+
+  KNF_CHECK_GT(vtln_low_cutoff, low_freq);
+  KNF_CHECK_LT(vtln_high_cutoff, high_freq);
+
+  float one = 1.0f;
+  float l = vtln_low_cutoff * std::max(one, vtln_warp_factor);
+  float h = vtln_high_cutoff * std::min(one, vtln_warp_factor);
+  float scale = 1.0f / vtln_warp_factor;
+  float Fl = scale * l;  // F(l);
+  float Fh = scale * h;  // F(h);
+  KNF_CHECK(l > low_freq && h < high_freq);
+  // slope of left part of the 3-piece linear function
+  float scale_left = (Fl - low_freq) / (l - low_freq);
+  // [slope of center part is just "scale"]
+
+  // slope of right part of the 3-piece linear function
+  float scale_right = (high_freq - Fh) / (high_freq - h);
+
+  if (freq < l) {
+    return low_freq + scale_left * (freq - low_freq);
+  } else if (freq < h) {
+    return scale * freq;
+  } else {  // freq >= h
+    return high_freq + scale_right * (freq - high_freq);
+  }
+}
+
+float MelBanks::VtlnWarpMelFreq(
+    float vtln_low_cutoff,  // upper+lower frequency cutoffs for VTLN.
+    float vtln_high_cutoff,
+    float low_freq,  // upper+lower frequency cutoffs in mel computation
+    float high_freq, float vtln_warp_factor, float mel_freq) {
+  return MelScale(VtlnWarpFreq(vtln_low_cutoff, vtln_high_cutoff, low_freq,
+                               high_freq, vtln_warp_factor,
+                               InverseMelScale(mel_freq)));
+}
+
+MelBanks::MelBanks(const MelBanksOptions &opts,
+                   const FrameExtractionOptions &frame_opts,
+                   float vtln_warp_factor)
+    : htk_mode_(opts.htk_mode) {
+  int32_t num_bins = opts.num_bins;
+  if (num_bins < 3) KNF_LOG(FATAL) << "Must have at least 3 mel bins";
+
+  float sample_freq = frame_opts.samp_freq;
+  int32_t window_length_padded = frame_opts.PaddedWindowSize();
+  KNF_CHECK_EQ(window_length_padded % 2, 0);
+
+  int32_t num_fft_bins = window_length_padded / 2;
+  float nyquist = 0.5f * sample_freq;
+
+  float low_freq = opts.low_freq, high_freq;
+  if (opts.high_freq > 0.0f)
+    high_freq = opts.high_freq;
+  else
+    high_freq = nyquist + opts.high_freq;
+
+  if (low_freq < 0.0f || low_freq >= nyquist || high_freq <= 0.0f ||
+      high_freq > nyquist || high_freq <= low_freq) {
+    KNF_LOG(FATAL) << "Bad values in options: low-freq " << low_freq
+                   << " and high-freq " << high_freq << " vs. nyquist "
+                   << nyquist;
+  }
+
+  float fft_bin_width = sample_freq / window_length_padded;
+  // fft-bin width [think of it as Nyquist-freq / half-window-length]
+
+  float mel_low_freq = MelScale(low_freq);
+  float mel_high_freq = MelScale(high_freq);
+
+  debug_ = opts.debug_mel;
+
+  // divide by num_bins+1 in next line because of end-effects where the bins
+  // spread out to the sides.
+  float mel_freq_delta = (mel_high_freq - mel_low_freq) / (num_bins + 1);
+
+  float vtln_low = opts.vtln_low, vtln_high = opts.vtln_high;
+  if (vtln_high < 0.0f) {
+    vtln_high += nyquist;
+  }
+
+  if (vtln_warp_factor != 1.0f &&
+      (vtln_low < 0.0f || vtln_low <= low_freq || vtln_low >= high_freq ||
+       vtln_high <= 0.0f || vtln_high >= high_freq || vtln_high <= vtln_low)) {
+    KNF_LOG(FATAL) << "Bad values in options: vtln-low " << vtln_low
+                   << " and vtln-high " << vtln_high << ", versus "
+                   << "low-freq " << low_freq << " and high-freq " << high_freq;
+  }
+
+  bins_.resize(num_bins);
+  center_freqs_.resize(num_bins);
+
+  for (int32_t bin = 0; bin < num_bins; ++bin) {
+    float left_mel = mel_low_freq + bin * mel_freq_delta,
+          center_mel = mel_low_freq + (bin + 1) * mel_freq_delta,
+          right_mel = mel_low_freq + (bin + 2) * mel_freq_delta;
+
+    if (vtln_warp_factor != 1.0f) {
+      left_mel = VtlnWarpMelFreq(vtln_low, vtln_high, low_freq, high_freq,
+                                 vtln_warp_factor, left_mel);
+      center_mel = VtlnWarpMelFreq(vtln_low, vtln_high, low_freq, high_freq,
+                                   vtln_warp_factor, center_mel);
+      right_mel = VtlnWarpMelFreq(vtln_low, vtln_high, low_freq, high_freq,
+                                  vtln_warp_factor, right_mel);
+    }
+    center_freqs_[bin] = InverseMelScale(center_mel);
+
+    // this_bin will be a vector of coefficients that is only
+    // nonzero where this mel bin is active.
+    std::vector<float> this_bin(num_fft_bins);
+
+    int32_t first_index = -1, last_index = -1;
+    for (int32_t i = 0; i < num_fft_bins; ++i) {
+      float freq = (fft_bin_width * i);  // Center frequency of this fft
+                                         // bin.
+      float mel = MelScale(freq);
+      if (mel > left_mel && mel < right_mel) {
+        float weight;
+        if (mel <= center_mel)
+          weight = (mel - left_mel) / (center_mel - left_mel);
+        else
+          weight = (right_mel - mel) / (right_mel - center_mel);
+        this_bin[i] = weight;
+        if (first_index == -1) first_index = i;
+        last_index = i;
+      }
+    }
+    KNF_CHECK(first_index != -1 && last_index >= first_index &&
+              "You may have set num_mel_bins too large.");
+
+    bins_[bin].first = first_index;
+    int32_t size = last_index + 1 - first_index;
+    bins_[bin].second.insert(bins_[bin].second.end(),
+                             this_bin.begin() + first_index,
+                             this_bin.begin() + first_index + size);
+
+    // Replicate a bug in HTK, for testing purposes.
+    if (opts.htk_mode && bin == 0 && mel_low_freq != 0.0f) {
+      bins_[bin].second[0] = 0.0;
+    }
+  }  // for (int32_t bin = 0; bin < num_bins; ++bin) {
+
+  if (debug_) {
+    std::ostringstream os;
+    for (size_t i = 0; i < bins_.size(); i++) {
+      os << "bin " << i << ", offset = " << bins_[i].first << ", vec = ";
+      for (auto k : bins_[i].second) os << k << ", ";
+      os << "\n";
+    }
+    KNF_LOG(INFO) << os.str();
+  }
+}
+
+// "power_spectrum" contains fft energies.
+void MelBanks::Compute(const float *power_spectrum,
+                       float *mel_energies_out) const {
+  int32_t num_bins = bins_.size();
+
+  for (int32_t i = 0; i < num_bins; i++) {
+    int32_t offset = bins_[i].first;
+    const auto &v = bins_[i].second;
+    float energy = 0;
+    for (int32_t k = 0; k != v.size(); ++k) {
+      energy += v[k] * power_spectrum[k + offset];
+    }
+
+    // HTK-like flooring- for testing purposes (we prefer dither)
+    if (htk_mode_ && energy < 1.0) {
+      energy = 1.0;
+    }
+
+    mel_energies_out[i] = energy;
+
+    // The following assert was added due to a problem with OpenBlas that
+    // we had at one point (it was a bug in that library).  Just to detect
+    // it early.
+    KNF_CHECK_EQ(energy, energy);  // check that energy is not nan
+  }
+
+  if (debug_) {
+    fprintf(stderr, "MEL BANKS:\n");
+    for (int32_t i = 0; i < num_bins; i++)
+      fprintf(stderr, " %f", mel_energies_out[i]);
+    fprintf(stderr, "\n");
+  }
+}
+
+}  // namespace knf

speechx/speechx/kaldi-native-fbank/csrc/mel-computations.h

@@ -0,0 +1,115 @@
+/**
+ * Copyright (c)  2022  Xiaomi Corporation (authors: Fangjun Kuang)
+ *
+ * See LICENSE for clarification regarding multiple authors
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+// This file is copied/modified from kaldi/src/feat/mel-computations.h
+#ifndef KALDI_NATIVE_FBANK_CSRC_MEL_COMPUTATIONS_H_
+#define KALDI_NATIVE_FBANK_CSRC_MEL_COMPUTATIONS_H_
+
+#include <cmath>
+#include <string>
+
+#include "kaldi-native-fbank/csrc/feature-window.h"
+
+namespace knf {
+
+struct MelBanksOptions {
+  int32_t num_bins = 25;  // e.g. 25; number of triangular bins
+  float low_freq = 20;    // e.g. 20; lower frequency cutoff
+
+  // an upper frequency cutoff; 0 -> no cutoff, negative
+  // ->added to the Nyquist frequency to get the cutoff.
+  float high_freq = 0;
+
+  float vtln_low = 100;  // vtln lower cutoff of warping function.
+
+  // vtln upper cutoff of warping function: if negative, added
+  // to the Nyquist frequency to get the cutoff.
+  float vtln_high = -500;
+
+  bool debug_mel = false;
+  // htk_mode is a "hidden" config, it does not show up on command line.
+  // Enables more exact compatibility with HTK, for testing purposes.  Affects
+  // mel-energy flooring and reproduces a bug in HTK.
+  bool htk_mode = false;
+
+  std::string ToString() const {
+    std::ostringstream os;
+    os << "num_bins: " << num_bins << "\n";
+    os << "low_freq: " << low_freq << "\n";
+    os << "high_freq: " << high_freq << "\n";
+    os << "vtln_low: " << vtln_low << "\n";
+    os << "vtln_high: " << vtln_high << "\n";
+    os << "debug_mel: " << debug_mel << "\n";
+    os << "htk_mode: " << htk_mode << "\n";
+    return os.str();
+  }
+};
+
+std::ostream &operator<<(std::ostream &os, const MelBanksOptions &opts);
+
+class MelBanks {
+ public:
+  static inline float InverseMelScale(float mel_freq) {
+    return 700.0f * (expf(mel_freq / 1127.0f) - 1.0f);
+  }
+
+  static inline float MelScale(float freq) {
+    return 1127.0f * logf(1.0f + freq / 700.0f);
+  }
+
+  static float VtlnWarpFreq(
+      float vtln_low_cutoff,
+      float vtln_high_cutoff,  // discontinuities in warp func
+      float low_freq,
+      float high_freq,  // upper+lower frequency cutoffs in
+      // the mel computation
+      float vtln_warp_factor, float freq);
+
+  static float VtlnWarpMelFreq(float vtln_low_cutoff, float vtln_high_cutoff,
+                               float low_freq, float high_freq,
+                               float vtln_warp_factor, float mel_freq);
+
+  // TODO(fangjun): Remove vtln_warp_factor
+  MelBanks(const MelBanksOptions &opts,
+           const FrameExtractionOptions &frame_opts, float vtln_warp_factor);
+
+  /// Compute Mel energies (note: not log energies).
+  /// At input, "fft_energies" contains the FFT energies (not log).
+  ///
+  /// @param fft_energies 1-D array of size num_fft_bins/2+1
+  /// @param mel_energies_out  1-D array of size num_mel_bins
+  void Compute(const float *fft_energies, float *mel_energies_out) const;
+
+  int32_t NumBins() const { return bins_.size(); }
+
+ private:
+  // center frequencies of bins, numbered from 0 ... num_bins-1.
+  // Needed by GetCenterFreqs().
+  std::vector<float> center_freqs_;
+
+  // the "bins_" vector is a vector, one for each bin, of a pair:
+  // (the first nonzero fft-bin), (the vector of weights).
+  std::vector<std::pair<int32_t, std::vector<float>>> bins_;
+
+  // TODO(fangjun): Remove debug_ and htk_mode_
+  bool debug_;
+  bool htk_mode_;
+};
+
+}  // namespace knf
+
+#endif  // KALDI_NATIVE_FBANK_CSRC_MEL_COMPUTATIONS_H_

speechx/speechx/kaldi-native-fbank/csrc/online-feature.cc

@@ -0,0 +1,161 @@
+/**
+ * Copyright (c)  2022  Xiaomi Corporation (authors: Fangjun Kuang)
+ *
+ * See LICENSE for clarification regarding multiple authors
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+// The content in this file is copied/modified from
+// This file is copied/modified from kaldi/src/feat/online-feature.cc
+
+#include "kaldi-native-fbank/csrc/online-feature.h"
+
+#include "kaldi-native-fbank/csrc/feature-window.h"
+#include "kaldi-native-fbank/csrc/log.h"
+
+namespace knf {
+
+RecyclingVector::RecyclingVector(int32_t items_to_hold)
+    : items_to_hold_(items_to_hold == 0 ? -1 : items_to_hold),
+      first_available_index_(0) {}
+
+const float *RecyclingVector::At(int32_t index) const {
+  if (index < first_available_index_) {
+    KNF_LOG(FATAL) << "Attempted to retrieve feature vector that was "
+                      "already removed by the RecyclingVector (index = "
+                   << index << "; "
+                   << "first_available_index = " << first_available_index_
+                   << "; "
+                   << "size = " << Size() << ")";
+  }
+  // 'at' does size checking.
+  return items_.at(index - first_available_index_).data();
+}
+
+void RecyclingVector::PushBack(std::vector<float> item) {
+  // Note: -1 is a larger number when treated as unsigned
+  if (items_.size() == static_cast<size_t>(items_to_hold_)) {
+    items_.pop_front();
+    ++first_available_index_;
+  }
+  items_.push_back(std::move(item));
+}
+
+int32_t RecyclingVector::Size() const {
+  return first_available_index_ + static_cast<int32_t>(items_.size());
+}
+
+template <class C>
+OnlineGenericBaseFeature<C>::OnlineGenericBaseFeature(
+    const typename C::Options &opts)
+    : computer_(opts),
+      window_function_(computer_.GetFrameOptions()),
+      features_(opts.frame_opts.max_feature_vectors),
+      input_finished_(false),
+      waveform_offset_(0) {
+  // RE the following assert: search for ONLINE_IVECTOR_LIMIT in
+  // online-ivector-feature.cc.
+  // Casting to uint32, an unsigned type, means that -1 would be treated
+  // as `very large`.
+  KNF_CHECK(static_cast<uint32_t>(opts.frame_opts.max_feature_vectors) > 200);
+}
+
+template <class C>
+void OnlineGenericBaseFeature<C>::AcceptWaveform(float sampling_rate,
+                                                 const float *waveform,
+                                                 int32_t n) {
+  if (n == 0) {
+    return;  // Nothing to do.
+  }
+
+  if (input_finished_) {
+    KNF_LOG(FATAL) << "AcceptWaveform called after InputFinished() was called.";
+  }
+
+  KNF_CHECK_EQ(sampling_rate, computer_.GetFrameOptions().samp_freq);
+
+  waveform_remainder_.insert(waveform_remainder_.end(), waveform, waveform + n);
+
+  ComputeFeatures();
+}
+
+template <class C>
+void OnlineGenericBaseFeature<C>::InputFinished() {
+  input_finished_ = true;
+  ComputeFeatures();
+}
+
+template <class C>
+void OnlineGenericBaseFeature<C>::ComputeFeatures() {
+  const FrameExtractionOptions &frame_opts = computer_.GetFrameOptions();
+
+  int64_t num_samples_total = waveform_offset_ + waveform_remainder_.size();
+
+  int32_t num_frames_old = features_.Size();
+
+  int32_t num_frames_new =
+      NumFrames(num_samples_total, frame_opts, input_finished_);
+
+  KNF_CHECK_GE(num_frames_new, num_frames_old);
+
+  // note: this online feature-extraction code does not support VTLN.
+  float vtln_warp = 1.0;
+
+  std::vector<float> window;
+  bool need_raw_log_energy = computer_.NeedRawLogEnergy();
+
+  for (int32_t frame = num_frames_old; frame < num_frames_new; ++frame) {
+    std::fill(window.begin(), window.end(), 0);
+    float raw_log_energy = 0.0;
+    ExtractWindow(waveform_offset_, waveform_remainder_, frame, frame_opts,
+                  window_function_, &window,
+                  need_raw_log_energy ? &raw_log_energy : nullptr);
+
+    std::vector<float> this_feature(computer_.Dim());
+
+    computer_.Compute(raw_log_energy, vtln_warp, &window, this_feature.data());
+    features_.PushBack(std::move(this_feature));
+  }
+
+  // OK, we will now discard any portion of the signal that will not be
+  // necessary to compute frames in the future.
+  int64_t first_sample_of_next_frame =
+      FirstSampleOfFrame(num_frames_new, frame_opts);
+
+  int32_t samples_to_discard = first_sample_of_next_frame - waveform_offset_;
+
+  if (samples_to_discard > 0) {
+    // discard the leftmost part of the waveform that we no longer need.
+    int32_t new_num_samples =
+        static_cast<int32_t>(waveform_remainder_.size()) - samples_to_discard;
+
+    if (new_num_samples <= 0) {
+      // odd, but we'll try to handle it.
+      waveform_offset_ += waveform_remainder_.size();
+      waveform_remainder_.resize(0);
+    } else {
+      std::vector<float> new_remainder(new_num_samples);
+
+      std::copy(waveform_remainder_.begin() + samples_to_discard,
+                waveform_remainder_.end(), new_remainder.begin());
+      waveform_offset_ += samples_to_discard;
+
+      waveform_remainder_.swap(new_remainder);
+    }
+  }
+}
+
+template class OnlineGenericBaseFeature<FbankComputer>;
+
+}  // namespace knf

speechx/speechx/kaldi-native-fbank/csrc/online-feature.h

@@ -0,0 +1,141 @@
+/**
+ * Copyright (c)  2022  Xiaomi Corporation (authors: Fangjun Kuang)
+ *
+ * See LICENSE for clarification regarding multiple authors
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+// The content in this file is copied/modified from
+// This file is copied/modified from kaldi/src/feat/online-feature.h
+#ifndef KALDI_NATIVE_FBANK_CSRC_ONLINE_FEATURE_H_
+#define KALDI_NATIVE_FBANK_CSRC_ONLINE_FEATURE_H_
+
+#include <cstdint>
+#include <deque>
+
+#include "kaldi-native-fbank/csrc/feature-fbank.h"
+
+namespace knf {
+
+/// This class serves as a storage for feature vectors with an option to limit
+/// the memory usage by removing old elements. The deleted frames indices are
+/// "remembered" so that regardless of the MAX_ITEMS setting, the user always
+/// provides the indices as if no deletion was being performed.
+/// This is useful when processing very long recordings which would otherwise
+/// cause the memory to eventually blow up when the features are not being
+/// removed.
+class RecyclingVector {
+ public:
+  /// By default it does not remove any elements.
+  explicit RecyclingVector(int32_t items_to_hold = -1);
+
+  ~RecyclingVector() = default;
+  RecyclingVector(const RecyclingVector &) = delete;
+  RecyclingVector &operator=(const RecyclingVector &) = delete;
+
+  // The pointer is owned by RecyclingVector
+  // Users should not free it
+  const float *At(int32_t index) const;
+
+  void PushBack(std::vector<float> item);
+
+  /// This method returns the size as if no "recycling" had happened,
+  /// i.e. equivalent to the number of times the PushBack method has been
+  /// called.
+  int32_t Size() const;
+
+ private:
+  std::deque<std::vector<float>> items_;
+  int32_t items_to_hold_;
+  int32_t first_available_index_;
+};
+
+/// This is a templated class for online feature extraction;
+/// it's templated on a class like MfccComputer or PlpComputer
+/// that does the basic feature extraction.
+template <class C>
+class OnlineGenericBaseFeature {
+ public:
+  // Constructor from options class
+  explicit OnlineGenericBaseFeature(const typename C::Options &opts);
+
+  int32_t Dim() const { return computer_.Dim(); }
+
+  float FrameShiftInSeconds() const {
+    return computer_.GetFrameOptions().frame_shift_ms / 1000.0f;
+  }
+
+  int32_t NumFramesReady() const { return features_.Size(); }
+
+  // Note: IsLastFrame() will only ever return true if you have called
+  // InputFinished() (and this frame is the last frame).
+  bool IsLastFrame(int32_t frame) const {
+    return input_finished_ && frame == NumFramesReady() - 1;
+  }
+
+  const float *GetFrame(int32_t frame) const { return features_.At(frame); }
+
+  // This would be called from the application, when you get
+  // more wave data.  Note: the sampling_rate is only provided so
+  // the code can assert that it matches the sampling rate
+  // expected in the options.
+  //
+  // @param sampling_rate The sampling_rate of the input waveform
+  // @param waveform Pointer to a 1-D array of size n
+  // @param n Number of entries in waveform
+  void AcceptWaveform(float sampling_rate, const float *waveform, int32_t n);
+
+  // InputFinished() tells the class you won't be providing any
+  // more waveform.  This will help flush out the last frame or two
+  // of features, in the case where snip-edges == false; it also
+  // affects the return value of IsLastFrame().
+  void InputFinished();
+
+ private:
+  // This function computes any additional feature frames that it is possible to
+  // compute from 'waveform_remainder_', which at this point may contain more
+  // than just a remainder-sized quantity (because AcceptWaveform() appends to
+  // waveform_remainder_ before calling this function).  It adds these feature
+  // frames to features_, and shifts off any now-unneeded samples of input from
+  // waveform_remainder_ while incrementing waveform_offset_ by the same amount.
+  void ComputeFeatures();
+
+  C computer_;  // class that does the MFCC or PLP or filterbank computation
+
+  FeatureWindowFunction window_function_;
+
+  // features_ is the Mfcc or Plp or Fbank features that we have already
+  // computed.
+
+  RecyclingVector features_;
+
+  // True if the user has called "InputFinished()"
+  bool input_finished_;
+
+  // waveform_offset_ is the number of samples of waveform that we have
+  // already discarded, i.e. that were prior to 'waveform_remainder_'.
+  int64_t waveform_offset_;
+
+  // waveform_remainder_ is a short piece of waveform that we may need to keep
+  // after extracting all the whole frames we can (whatever length of feature
+  // will be required for the next phase of computation).
+  // It is a 1-D tensor
+  std::vector<float> waveform_remainder_;
+};
+
+using OnlineFbank = OnlineGenericBaseFeature<FbankComputer>;
+
+}  // namespace knf
+
+#endif  // KALDI_NATIVE_FBANK_CSRC_ONLINE_FEATURE_H_

speechx/speechx/kaldi-native-fbank/csrc/rfft.cc

@@ -0,0 +1,66 @@
+/**
+ * Copyright (c)  2022  Xiaomi Corporation (authors: Fangjun Kuang)
+ *
+ * See LICENSE for clarification regarding multiple authors
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "kaldi-native-fbank/csrc/rfft.h"
+
+#include <cmath>
+#include <vector>
+
+#include "kaldi-native-fbank/csrc/log.h"
+
+// see fftsg.c
+#ifdef __cplusplus
+extern "C" void rdft(int n, int isgn, double *a, int *ip, double *w);
+#else
+void rdft(int n, int isgn, double *a, int *ip, double *w);
+#endif
+
+namespace knf {
+class Rfft::RfftImpl {
+ public:
+  explicit RfftImpl(int32_t n) : n_(n), ip_(2 + std::sqrt(n / 2)), w_(n / 2) {
+    KNF_CHECK_EQ(n & (n - 1), 0);
+  }
+
+  void Compute(float *in_out) {
+    std::vector<double> d(in_out, in_out + n_);
+
+    Compute(d.data());
+
+    std::copy(d.begin(), d.end(), in_out);
+  }
+
+  void Compute(double *in_out) {
+    // 1 means forward fft
+    rdft(n_, 1, in_out, ip_.data(), w_.data());
+  }
+
+ private:
+  int32_t n_;
+  std::vector<int32_t> ip_;
+  std::vector<double> w_;
+};
+
+Rfft::Rfft(int32_t n) : impl_(std::make_unique<RfftImpl>(n)) {}
+
+Rfft::~Rfft() = default;
+
+void Rfft::Compute(float *in_out) { impl_->Compute(in_out); }
+void Rfft::Compute(double *in_out) { impl_->Compute(in_out); }
+
+}  // namespace knf

speechx/speechx/kaldi-native-fbank/csrc/rfft.h

@@ -0,0 +1,56 @@
+/**
+ * Copyright (c)  2022  Xiaomi Corporation (authors: Fangjun Kuang)
+ *
+ * See LICENSE for clarification regarding multiple authors
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#ifndef KALDI_NATIVE_FBANK_CSRC_RFFT_H_
+#define KALDI_NATIVE_FBANK_CSRC_RFFT_H_
+
+#include <memory>
+
+namespace knf {
+
+// n-point Real discrete Fourier transform
+// where n is a power of 2. n >= 2
+//
+//  R[k] = sum_j=0^n-1 in[j]*cos(2*pi*j*k/n), 0<=k<=n/2
+//  I[k] = sum_j=0^n-1 in[j]*sin(2*pi*j*k/n), 0<k<n/2
+class Rfft {
+ public:
+  // @param n Number of fft bins. it should be a power of 2.
+  explicit Rfft(int32_t n);
+  ~Rfft();
+
+  /** @param in_out A 1-D array of size n.
+   *             On return:
+   *               in_out[0] = R[0]
+   *               in_out[1] = R[n/2]
+   *               for 1 < k < n/2,
+   *                 in_out[2*k] = R[k]
+   *                 in_out[2*k+1] = I[k]
+   *
+   */
+  void Compute(float *in_out);
+  void Compute(double *in_out);
+
+ private:
+  class RfftImpl;
+  std::unique_ptr<RfftImpl> impl_;
+};
+
+}  // namespace knf
+
+#endif  // KALDI_NATIVE_FBANK_CSRC_RFFT_H_

speechx/speechx/kaldi-native-fbank/csrc/test-log.cc

@@ -0,0 +1,69 @@
+/**
+ * Copyright      2022  Xiaomi Corporation (authors: Fangjun Kuang)
+ *
+ * See LICENSE for clarification regarding multiple authors
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "gtest/gtest.h"
+#include "kaldi-native-fbank/csrc/log.h"
+
+namespace knf {
+
+TEST(Log, TestLog) {
+  KNF_LOG(TRACE) << "this is a trace message";
+  KNF_LOG(DEBUG) << "this is a debug message";
+  KNF_LOG(INFO) << "this is an info message";
+  KNF_LOG(WARNING) << "this is a warning message";
+  KNF_LOG(ERROR) << "this is an error message";
+
+  ASSERT_THROW(KNF_LOG(FATAL) << "This will crash the program",
+               std::runtime_error);
+
+  // For debug build
+
+  KNF_DLOG(TRACE) << "this is a trace message for debug build";
+  KNF_DLOG(DEBUG) << "this is a trace message for debug build";
+  KNF_DLOG(INFO) << "this is a trace message for debug build";
+  KNF_DLOG(ERROR) << "this is an error message for debug build";
+  KNF_DLOG(WARNING) << "this is a trace message for debug build";
+
+#if !defined(NDEBUG)
+  ASSERT_THROW(KNF_DLOG(FATAL) << "this is a trace message for debug build",
+               std::runtime_error);
+#endif
+}
+
+TEST(Log, TestCheck) {
+  KNF_CHECK_EQ(1, 1) << "ok";
+  KNF_CHECK_LE(1, 3) << "ok";
+  KNF_CHECK_LT(1, 2) << "ok";
+  KNF_CHECK_GT(2, 1) << "ok";
+  KNF_CHECK_GE(2, 1) << "ok";
+
+  ASSERT_THROW(KNF_CHECK_EQ(2, 1) << "bad things happened", std::runtime_error);
+
+  // for debug build
+  KNF_DCHECK_EQ(1, 1) << "ok";
+  KNF_DCHECK_LE(1, 3) << "ok";
+  KNF_DCHECK_LT(1, 2) << "ok";
+  KNF_DCHECK_GT(2, 1) << "ok";
+  KNF_DCHECK_GE(2, 1) << "ok";
+
+#if !defined(NDEBUG)
+  ASSERT_THROW(KNF_CHECK_EQ(2, 1) << "bad things happened", std::runtime_error);
+#endif
+}
+
+}  // namespace knf

speechx/speechx/kaldi-native-fbank/csrc/test-online-fbank-my.cc

@@ -0,0 +1,86 @@
+/**
+ * Copyright (c)  2022  Xiaomi Corporation (authors: Fangjun Kuang)
+ *
+ * See LICENSE for clarification regarding multiple authors
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <iostream>
+
+#include "kaldi-native-fbank/csrc/online-feature.h"
+
+#include "kaldi/feat/wave-reader.h"
+#include "kaldi/util/kaldi-io.h"
+#include "kaldi/util/table-types.h"
+#include <fstream>
+
+int main(int argc, char* argv[]) {
+  kaldi::SequentialTableReader<kaldi::WaveHolder> wav_reader(
+        argv[1]);
+  kaldi::SequentialTableReader<kaldi::WaveInfoHolder> wav_info_reader(
+        argv[1]);
+
+  knf::FbankOptions opts;
+  opts.frame_opts.frame_length_ms = 32;
+  opts.frame_opts.frame_shift_ms = 10;
+  opts.mel_opts.num_bins = 64;
+  opts.frame_opts.dither = 0.0;
+
+  for (; !wav_reader.Done() && !wav_info_reader.Done();
+         wav_reader.Next(), wav_info_reader.Next()) {
+        const std::string& utt = wav_reader.Key();
+        const kaldi::WaveData& wave_data = wav_reader.Value();
+
+        const std::string& utt2 = wav_info_reader.Key();
+        const kaldi::WaveInfo& wave_info = wav_info_reader.Value();
+
+        // CHECK(utt == utt2)
+        //     << "wav reader and wav info reader using diff rspecifier!!!";
+        // LOG(INFO) << "utt: " << utt;
+        // LOG(INFO) << "samples: " << wave_info.SampleCount();
+        // LOG(INFO) << "dur: " << wave_info.Duration() << " sec";
+        // CHECK(wave_info.SampFreq() == FLAGS_sample_rate)
+        //     << "need " << FLAGS_sample_rate << " get " << wave_info.SampFreq();
+
+        // load first channel wav
+        int32 this_channel = 0;
+        kaldi::SubVector<kaldi::BaseFloat> waveform(wave_data.Data(),
+                                                    this_channel);
+        // compute feat chunk by chunk
+        int tot_samples = waveform.Dim();
+        int sample_offset = 0;
+        std::vector<kaldi::Vector<BaseFloat>> feats;
+        int feature_rows = 0;
+
+        for (int i = 0; i < tot_samples; i++){
+            waveform(i) = waveform(i) / 32768;
+        }
+
+        knf::OnlineFbank fbank(opts);
+        fbank.AcceptWaveform(16000, waveform.Data(), tot_samples);
+
+        std::ostringstream os;
+
+        int32_t n = fbank.NumFramesReady();
+        for (int32_t i = 0; i != n; ++i) {
+          const float *frame = fbank.GetFrame(i);
+          for (int32_t k = 0; k != opts.mel_opts.num_bins; ++k) {
+            // os << frame[k] << ", ";
+          }
+          // os << "\n";
+        }
+        // std::cout << os.str() << "\n";
+  }
+  return 0;
+}

speechx/speechx/kaldi-native-fbank/csrc/test-online-fbank.cc

@@ -0,0 +1,48 @@
+/**
+ * Copyright (c)  2022  Xiaomi Corporation (authors: Fangjun Kuang)
+ *
+ * See LICENSE for clarification regarding multiple authors
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <iostream>
+
+#include "kaldi-native-fbank/csrc/online-feature.h"
+
+int main() {
+  knf::FbankOptions opts;
+  opts.frame_opts.dither = 0;
+  opts.mel_opts.num_bins = 10;
+
+  knf::OnlineFbank fbank(opts);
+  for (int32_t i = 0; i < 1600; ++i) {
+    float s = (i * i - i / 2) / 32767.;
+    fbank.AcceptWaveform(16000, &s, 1);
+  }
+
+  std::ostringstream os;
+
+  int32_t n = fbank.NumFramesReady();
+  for (int32_t i = 0; i != n; ++i) {
+    const float *frame = fbank.GetFrame(i);
+    for (int32_t k = 0; k != opts.mel_opts.num_bins; ++k) {
+      os << frame[k] << ", ";
+    }
+    os << "\n";
+  }
+
+  std::cout << os.str() << "\n";
+
+  return 0;
+}

speechx/speechx/kaldi-native-fbank/csrc/test-online-feature.cc

@@ -0,0 +1,59 @@
+/**
+ * Copyright (c)  2022  Xiaomi Corporation (authors: Fangjun Kuang)
+ *
+ * See LICENSE for clarification regarding multiple authors
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "gtest/gtest.h"
+#include "kaldi-native-fbank/csrc/online-feature.h"
+namespace knf {
+
+TEST(RecyclingVector, TestUnlimited) {
+  RecyclingVector v(-1);
+  constexpr int32_t N = 100;
+  for (int32_t i = 0; i != N; ++i) {
+    std::unique_ptr<float[]> p(new float[3]{i, i + 1, i + 2});
+    v.PushBack(std::move(p));
+  }
+  ASSERT_EQ(v.Size(), N);
+
+  for (int32_t i = 0; i != N; ++i) {
+    const float *t = v.At(i);
+    for (int32_t k = 0; k != 3; ++k) {
+      EXPECT_EQ(t[k], (i + k));
+    }
+  }
+}
+
+TEST(RecyclingVector, Testlimited) {
+  constexpr int32_t K = 3;
+  constexpr int32_t N = 10;
+  RecyclingVector v(K);
+  for (int32_t i = 0; i != N; ++i) {
+    std::unique_ptr<float[]> p(new float[3]{i, i + 1, i + 2});
+    v.PushBack(std::move(p));
+  }
+
+  ASSERT_EQ(v.Size(), N);
+
+  for (int32_t i = N - K; i != N; ++i) {
+    const float *t = v.At(i);
+
+    for (int32_t k = 0; k != 3; ++k) {
+      EXPECT_EQ(t[k], (i + k));
+    }
+  }
+}
+}  // namespace knf

speechx/speechx/kaldi-native-fbank/csrc/test-rfft.cc

@@ -0,0 +1,52 @@
+/**
+ * Copyright      2022  Xiaomi Corporation (authors: Fangjun Kuang)
+ *
+ * See LICENSE for clarification regarding multiple authors
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "gtest/gtest.h"
+#include "kaldi-native-fbank/csrc/rfft.h"
+
+namespace knf {
+
+#if 0
+>>> import torch
+>>> a = torch.tensor([1., -1, 3, 8, 20, 6, 0, 2])
+>>> torch.fft.rfft(a)
+tensor([ 39.0000+0.0000j, -28.1924-2.2929j,  18.0000+5.0000j,  -9.8076+3.7071j,
+          9.0000+0.0000j])
+#endif
+
+TEST(Rfft, TestRfft) {
+  knf::Rfft fft(8);
+  for (int32_t i = 0; i != 10; ++i) {
+    std::vector<float> d = {1, -1, 3, 8, 20, 6, 0, 2};
+    fft.Compute(d.data());
+
+    EXPECT_EQ(d[0], 39);
+    EXPECT_EQ(d[1], 9);
+
+    EXPECT_NEAR(d[2], -28.1924, 1e-3);
+    EXPECT_NEAR(-d[3], -2.2929, 1e-3);
+
+    EXPECT_NEAR(d[4], 18, 1e-3);
+    EXPECT_NEAR(-d[5], 5, 1e-3);
+
+    EXPECT_NEAR(d[6], -9.8076, 1e-3);
+    EXPECT_NEAR(-d[7], 3.7071, 1e-3);
+  }
+}
+
+}  // namespace knf