transformer using batch data loader

examples/aishell/s0/local/data.sh

@@ -66,7 +66,6 @@ if [ ${stage} -le 2 ] && [ ${stop_stage} -ge 2 ]; then
     for dataset in train dev test; do
     {
         python3 ${MAIN_ROOT}/utils/format_data.py \
-                --feat_type "raw" \
                 --cmvn_path "data/mean_std.json" \
                 --unit_type "char" \
                 --vocab_path="data/vocab.txt" \

examples/aishell/s1/local/data.sh

@@ -67,7 +67,6 @@ if [ ${stage} -le 2 ] && [ ${stop_stage} -ge 2 ]; then
     for dataset in train dev test; do
     {
         python3 ${MAIN_ROOT}/utils/format_data.py \
-            --feat_type "raw" \
             --cmvn_path "data/mean_std.json" \
             --unit_type "char" \
             --vocab_path="data/vocab.txt" \

examples/callcenter/s1/local/data.sh

@@ -55,7 +55,6 @@ if [ ${stage} -le 2 ] && [ ${stop_stage} -ge 2 ]; then
     for dataset in train dev test; do
     {
         python3 ${MAIN_ROOT}/utils/format_data.py \
-            --feat_type "raw" \
             --cmvn_path "data/mean_std.json" \
             --unit_type "char" \
             --vocab_path="data/vocab.txt" \

examples/dataset/librispeech/librispeech.py

@@ -89,25 +89,28 @@ def create_manifest(data_dir, manifest_path):
             text_filepath = os.path.join(subfolder, text_filelist[0])
             for line in io.open(text_filepath, encoding="utf8"):
                 segments = line.strip().split()
+                n_token = len(segments[1:])
                 text = ' '.join(segments[1:]).lower()
 
                 audio_filepath = os.path.abspath(
                     os.path.join(subfolder, segments[0] + '.flac'))
                 audio_data, samplerate = soundfile.read(audio_filepath)
                 duration = float(len(audio_data)) / samplerate
+
+                utt = os.path.splitext(os.path.basename(audio_filepath))[0]
+                utt2spk = '-'.join(utt.split('-')[:2])
+
                 json_lines.append(
                     json.dumps({
-                        'utt':
-                        os.path.splitext(os.path.basename(audio_filepath))[0],
-                        'feat':
-                        audio_filepath,
+                        'utt': utt,
+                        'utt2spk': utt2spk,
+                        'feat': audio_filepath,
                         'feat_shape': (duration, ),  # second
-                        'text':
-                        text
+                        'text': text,
                     }))
 
                 total_sec += duration
-                total_text += len(text)
+                total_text += n_token
                 total_num += 1
 
     with codecs.open(manifest_path, 'w', 'utf-8') as out_file:

examples/librispeech/s0/local/data.sh

@@ -81,7 +81,6 @@ if [ ${stage} -le 2 ] && [ ${stop_stage} -ge 2 ]; then
     for set in train dev test dev-clean dev-other test-clean test-other; do
     {
         python3 ${MAIN_ROOT}/utils/format_data.py \
-        --feat_type "raw" \
         --cmvn_path "data/mean_std.json" \
         --unit_type ${unit_type} \
         --vocab_path="data/vocab.txt" \

examples/librispeech/s1/local/data.sh

@@ -88,7 +88,6 @@ if [ ${stage} -le 2 ] && [ ${stop_stage} -ge 2 ]; then
     for set in train dev test dev-clean dev-other test-clean test-other; do
     {
         python3 ${MAIN_ROOT}/utils/format_data.py \
-        --feat_type "raw" \
         --cmvn_path "data/mean_std.json" \
         --unit_type "spm" \
         --spm_model_prefix ${bpeprefix} \

examples/other/1xt2x/aishell/local/data.sh

@@ -50,7 +50,6 @@ if [ ${stage} -le 2 ] && [ ${stop_stage} -ge 2 ]; then
     for dataset in train dev test; do
     {
         python3 ${MAIN_ROOT}/utils/format_data.py \
-                --feat_type "raw" \
                 --cmvn_path "data/mean_std.npz" \
                 --unit_type "char" \
                 --vocab_path="data/vocab.txt" \

examples/other/1xt2x/baidu_en8k/local/data.sh

@@ -65,7 +65,6 @@ if [ ${stage} -le 2 ] && [ ${stop_stage} -ge 2 ]; then
     for set in train dev test dev-clean dev-other test-clean test-other; do
     {
         python3 ${MAIN_ROOT}/utils/format_data.py \
-        --feat_type "raw" \
         --cmvn_path "data/mean_std.npz" \
         --unit_type ${unit_type} \
         --vocab_path="data/vocab.txt" \

examples/other/1xt2x/librispeech/local/data.sh

@@ -63,7 +63,6 @@ if [ ${stage} -le 2 ] && [ ${stop_stage} -ge 2 ]; then
     for set in train dev test dev-clean dev-other test-clean test-other; do
     {
         python3 ${MAIN_ROOT}/utils/format_data.py \
-        --feat_type "raw" \
         --cmvn_path "data/mean_std.npz" \
         --unit_type ${unit_type} \
         --vocab_path="data/vocab.txt" \

examples/ted_en_zh/t0/local/data.sh

@@ -89,7 +89,6 @@ if [ ${stage} -le 2 ] && [ ${stop_stage} -ge 2 ]; then
     for set in train dev test; do
     {
         python3 ${MAIN_ROOT}/utils/format_triplet_data.py \
-        --feat_type "raw" \
         --cmvn_path "data/mean_std.json" \
         --unit_type "spm" \
         --spm_model_prefix ${bpeprefix} \

examples/timit/s1/local/data.sh

@@ -66,7 +66,6 @@ if [ ${stage} -le 2 ] && [ ${stop_stage} -ge 2 ]; then
     for set in train dev test; do
     {
         python3 ${MAIN_ROOT}/utils/format_data.py \
-        --feat_type "raw" \
         --cmvn_path "data/mean_std.json" \
         --unit_type ${unit_type} \
         --vocab_path="data/vocab.txt" \

examples/tiny/s0/local/data.sh

@@ -63,7 +63,6 @@ fi
 if [ ${stage} -le 2 ] && [ ${stop_stage} -ge 2 ]; then
     # format manifest with tokenids, vocab size
     python3 ${MAIN_ROOT}/utils/format_data.py \
-    --feat_type "raw" \
     --cmvn_path "data/mean_std.json" \
     --unit_type ${unit_type} \
     --vocab_path="data/vocab.txt" \

examples/tiny/s1/conf/chunk_confermer.yaml

@@ -15,7 +15,7 @@ collator:
   vocab_filepath: data/vocab.txt 
   unit_type: 'spm'
   spm_model_prefix: 'data/bpe_unigram_200'
-  augmentation_config: conf/augmentation.json
+  augmentation_config: conf/preprocess.yaml
   batch_size: 4
   raw_wav: True  # use raw_wav or kaldi feature
   spectrum_type: fbank #linear, mfcc, fbank

examples/tiny/s1/conf/chunk_transformer.yaml

@@ -15,7 +15,7 @@ collator:
   vocab_filepath: data/vocab.txt 
   unit_type: 'spm'
   spm_model_prefix: 'data/bpe_unigram_200'
-  augmentation_config: conf/augmentation.json
+  augmentation_config: conf/preprocess.yaml
   batch_size: 4
   raw_wav: True  # use raw_wav or kaldi feature
   spectrum_type: fbank #linear, mfcc, fbank

examples/tiny/s1/conf/conformer.yaml

@@ -15,7 +15,7 @@ collator:
   vocab_filepath: data/vocab.txt 
   unit_type: 'spm'
   spm_model_prefix: 'data/bpe_unigram_200'
-  augmentation_config: conf/augmentation.json
+  augmentation_config: conf/preprocess.yaml
   batch_size: 4
   raw_wav: True  # use raw_wav or kaldi feature
   spectrum_type: fbank #linear, mfcc, fbank

examples/tiny/s1/conf/preprocess.yaml

@@ -0,0 +1,27 @@
+process:
+  # extract kaldi fbank from PCM
+  - type: "fbank_kaldi"
+    fs: 16000
+    n_mels: 80
+    n_shift: 160
+    win_length: 400
+    dither: true
+  # these three processes are a.k.a. SpecAugument
+  - type: "time_warp"
+    max_time_warp: 5
+    inplace: true
+    mode: "PIL"
+  - type: "freq_mask"
+    F: 30
+    n_mask: 2
+    inplace: true
+    replace_with_zero: false
+  - type: "time_mask"
+    T: 40
+    n_mask: 2
+    inplace: true
+    replace_with_zero: false
+
+
+
+

examples/tiny/s1/conf/transformer.yaml

@@ -15,7 +15,7 @@ collator:
   vocab_filepath: data/vocab.txt 
   unit_type: 'spm'
   spm_model_prefix: 'data/bpe_unigram_200'
-  augmentation_config: conf/augmentation.json
+  augmentation_config: conf/preprocess.yaml
   batch_size: 4
   raw_wav: True  # use raw_wav or kaldi feature
   spectrum_type: fbank #linear, mfcc, fbank

examples/tiny/s1/local/data.sh

@@ -69,7 +69,6 @@ fi
 if [ ${stage} -le 2 ] && [ ${stop_stage} -ge 2 ]; then
     # format manifest with tokenids, vocab size
     python3 ${MAIN_ROOT}/utils/format_data.py \
-    --feat_type "raw" \
     --cmvn_path "data/mean_std.json" \
     --unit_type "spm" \
     --spm_model_prefix ${bpeprefix} \

paddlespeech/s2t/exps/u2/model.py

@@ -27,7 +27,9 @@ from paddle import distributed as dist
 from paddle.io import DataLoader
 from yacs.config import CfgNode
 
+from paddlespeech.s2t.frontend.featurizer import TextFeaturizer
 from paddlespeech.s2t.io.collator import SpeechCollator
+from paddlespeech.s2t.io.dataloader import BatchDataLoader
 from paddlespeech.s2t.io.dataset import ManifestDataset
 from paddlespeech.s2t.io.sampler import SortagradBatchSampler
 from paddlespeech.s2t.io.sampler import SortagradDistributedBatchSampler
@@ -247,92 +249,103 @@ class U2Trainer(Trainer):
 
     def setup_dataloader(self):
         config = self.config.clone()
-        config.defrost()
-        config.collator.keep_transcription_text = False
 
+        if self.train:
             # train/valid dataset, return token ids
-        config.data.manifest = config.data.train_manifest
-        train_dataset = ManifestDataset.from_config(config)
-
-        config.data.manifest = config.data.dev_manifest
-        dev_dataset = ManifestDataset.from_config(config)
-
-        collate_fn_train = SpeechCollator.from_config(config)
-
-        config.collator.augmentation_config = ""
-        collate_fn_dev = SpeechCollator.from_config(config)
-
-        if self.parallel:
-            batch_sampler = SortagradDistributedBatchSampler(
-                train_dataset,
-                batch_size=config.collator.batch_size,
-                num_replicas=None,
-                rank=None,
-                shuffle=True,
-                drop_last=True,
-                sortagrad=config.collator.sortagrad,
-                shuffle_method=config.collator.shuffle_method)
-        else:
-            batch_sampler = SortagradBatchSampler(
-                train_dataset,
-                shuffle=True,
+            self.train_loader = BatchDataLoader(
+                json_file=config.data.train_manifest,
+                train_mode=True,
+                sortagrad=False,
                 batch_size=config.collator.batch_size,
-                drop_last=True,
-                sortagrad=config.collator.sortagrad,
-                shuffle_method=config.collator.shuffle_method)
-        self.train_loader = DataLoader(
-            train_dataset,
-            batch_sampler=batch_sampler,
-            collate_fn=collate_fn_train,
-            num_workers=config.collator.num_workers, )
-        self.valid_loader = DataLoader(
-            dev_dataset,
+                maxlen_in=float('inf'),
+                maxlen_out=float('inf'),
+                minibatches=0,
+                mini_batch_size=self.args.nprocs,
+                batch_count='auto',
+                batch_bins=0,
+                batch_frames_in=0,
+                batch_frames_out=0,
+                batch_frames_inout=0,
+                preprocess_conf=config.collator.
+                augmentation_config,  # aug will be off when train_mode=False
+                n_iter_processes=config.collator.num_workers,
+                subsampling_factor=1,
+                num_encs=1)
+
+            self.valid_loader = BatchDataLoader(
+                json_file=config.data.dev_manifest,
+                train_mode=False,
+                sortagrad=False,
                 batch_size=config.collator.batch_size,
-            shuffle=False,
-            drop_last=False,
-            collate_fn=collate_fn_dev,
-            num_workers=config.collator.num_workers, )
-
+                maxlen_in=float('inf'),
+                maxlen_out=float('inf'),
+                minibatches=0,
+                mini_batch_size=self.args.nprocs,
+                batch_count='auto',
+                batch_bins=0,
+                batch_frames_in=0,
+                batch_frames_out=0,
+                batch_frames_inout=0,
+                preprocess_conf=config.collator.
+                augmentation_config,  # aug will be off when train_mode=False
+                n_iter_processes=config.collator.num_workers,
+                subsampling_factor=1,
+                num_encs=1)
+            logger.info("Setup train/valid Dataloader!")
+        else:
             # test dataset, return raw text
-        config.data.manifest = config.data.test_manifest
-        # filter test examples, will cause less examples, but no mismatch with training
-        # and can use large batch size , save training time, so filter test egs now.
-        config.data.min_input_len = 0.0  # second
-        config.data.max_input_len = float('inf')  # second
-        config.data.min_output_len = 0.0  # tokens
-        config.data.max_output_len = float('inf')  # tokens
-        config.data.min_output_input_ratio = 0.00
-        config.data.max_output_input_ratio = float('inf')
-
-        test_dataset = ManifestDataset.from_config(config)
-        # return text ord id
-        config.collator.keep_transcription_text = True
-        config.collator.augmentation_config = ""
-        self.test_loader = DataLoader(
-            test_dataset,
+            self.test_loader = BatchDataLoader(
+                json_file=config.data.test_manifest,
+                train_mode=False,
+                sortagrad=False,
                 batch_size=config.decoding.batch_size,
-            shuffle=False,
-            drop_last=False,
-            collate_fn=SpeechCollator.from_config(config),
-            num_workers=config.collator.num_workers, )
-        # return text token id
-        config.collator.keep_transcription_text = False
-        self.align_loader = DataLoader(
-            test_dataset,
+                maxlen_in=float('inf'),
+                maxlen_out=float('inf'),
+                minibatches=0,
+                mini_batch_size=1,
+                batch_count='auto',
+                batch_bins=0,
+                batch_frames_in=0,
+                batch_frames_out=0,
+                batch_frames_inout=0,
+                preprocess_conf=config.collator.
+                augmentation_config,  # aug will be off when train_mode=False
+                n_iter_processes=1,
+                subsampling_factor=1,
+                num_encs=1)
+
+            self.align_loader = BatchDataLoader(
+                json_file=config.data.test_manifest,
+                train_mode=False,
+                sortagrad=False,
                 batch_size=config.decoding.batch_size,
-            shuffle=False,
-            drop_last=False,
-            collate_fn=SpeechCollator.from_config(config),
-            num_workers=config.collator.num_workers, )
-        logger.info("Setup train/valid/test/align Dataloader!")
+                maxlen_in=float('inf'),
+                maxlen_out=float('inf'),
+                minibatches=0,
+                mini_batch_size=1,
+                batch_count='auto',
+                batch_bins=0,
+                batch_frames_in=0,
+                batch_frames_out=0,
+                batch_frames_inout=0,
+                preprocess_conf=config.collator.
+                augmentation_config,  # aug will be off when train_mode=False
+                n_iter_processes=1,
+                subsampling_factor=1,
+                num_encs=1)
+            logger.info("Setup test/align Dataloader!")
 
     def setup_model(self):
         config = self.config
         model_conf = config.model
 
         with UpdateConfig(model_conf):
-            model_conf.input_dim = self.train_loader.collate_fn.feature_size
-            model_conf.output_dim = self.train_loader.collate_fn.vocab_size
+            if self.train:
+                model_conf.input_dim = self.train_loader.feat_dim
+                model_conf.output_dim = self.train_loader.vocab_size
+            else:
+                model_conf.input_dim = self.test_loader.feat_dim
+                model_conf.output_dim = self.test_loader.vocab_size
 
         model = U2Model.from_config(model_conf)
 
@@ -341,6 +354,11 @@ class U2Trainer(Trainer):
 
         logger.info(f"{model}")
         layer_tools.print_params(model, logger.info)
+        self.model = model
+        logger.info("Setup model!")
+
+        if not self.train:
+            return
 
         train_config = config.training
         optim_type = train_config.optim
@@ -381,10 +399,9 @@ class U2Trainer(Trainer):
         optimzer_args = optimizer_args(config, model.parameters(), lr_scheduler)
         optimizer = OptimizerFactory.from_args(optim_type, optimzer_args)
 
-        self.model = model
         self.optimizer = optimizer
         self.lr_scheduler = lr_scheduler
-        logger.info("Setup model/optimizer/lr_scheduler!")
+        logger.info("Setup optimizer/lr_scheduler!")
 
 
 class U2Tester(U2Trainer):
@@ -419,14 +436,19 @@ class U2Tester(U2Trainer):
 
     def __init__(self, config, args):
         super().__init__(config, args)
+        self.text_feature = TextFeaturizer(
+            unit_type=self.config.collator.unit_type,
+            vocab_filepath=self.config.collator.vocab_filepath,
+            spm_model_prefix=self.config.collator.spm_model_prefix)
+        self.vocab_list = self.text_feature.vocab_list
 
-    def ordid2token(self, texts, texts_len):
+    def id2token(self, texts, texts_len, text_feature):
         """ ord() id to chr() chr """
         trans = []
         for text, n in zip(texts, texts_len):
             n = n.numpy().item()
             ids = text[:n]
-            trans.append(''.join([chr(i) for i in ids]))
+            trans.append(text_feature.defeaturize(ids.numpy().tolist()))
         return trans
 
     def compute_metrics(self,
@@ -442,12 +464,11 @@ class U2Tester(U2Trainer):
         error_rate_func = error_rate.cer if cfg.error_rate_type == 'cer' else error_rate.wer
 
         start_time = time.time()
-        text_feature = self.test_loader.collate_fn.text_feature
-        target_transcripts = self.ordid2token(texts, texts_len)
+        target_transcripts = self.id2token(texts, texts_len, self.text_feature)
         result_transcripts, result_tokenids = self.model.decode(
             audio,
             audio_len,
-            text_feature=text_feature,
+            text_feature=self.text_feature,
             decoding_method=cfg.decoding_method,
             lang_model_path=cfg.lang_model_path,
             beam_alpha=cfg.alpha,
@@ -497,7 +518,7 @@ class U2Tester(U2Trainer):
         self.model.eval()
         logger.info(f"Test Total Examples: {len(self.test_loader.dataset)}")
 
-        stride_ms = self.test_loader.collate_fn.stride_ms
+        stride_ms = self.config.collator.stride_ms
         error_rate_type = None
         errors_sum, len_refs, num_ins = 0.0, 0, 0
         num_frames = 0.0
@@ -556,8 +577,8 @@ class U2Tester(U2Trainer):
     def align(self):
         ctc_utils.ctc_align(
             self.model, self.align_loader, self.config.decoding.batch_size,
-            self.align_loader.collate_fn.stride_ms,
-            self.align_loader.collate_fn.vocab_list, self.args.result_file)
+            self.config.collator.stride_ms,
+            self.vocab_list, self.args.result_file)
 
     def load_inferspec(self):
         """infer model and input spec.

paddlespeech/s2t/exps/u2_kaldi/model.py

@@ -392,6 +392,7 @@ class U2Tester(U2Trainer):
             unit_type=self.config.collator.unit_type,
             vocab_filepath=self.config.collator.vocab_filepath,
             spm_model_prefix=self.config.collator.spm_model_prefix)
+        self.vocab_list = self.text_feature.vocab_list
 
     def id2token(self, texts, texts_len, text_feature):
         """ ord() id to chr() chr """
@@ -529,8 +530,8 @@ class U2Tester(U2Trainer):
     def align(self):
         ctc_utils.ctc_align(
             self.model, self.align_loader, self.config.decoding.batch_size,
-            self.align_loader.collate_fn.stride_ms,
-            self.align_loader.collate_fn.vocab_list, self.args.result_file)
+            self.config.collator.stride_ms,
+            self.vocab_list, self.args.result_file)
 
     def load_inferspec(self):
         """infer model and input spec.

paddlespeech/s2t/io/dataset.py

@@ -207,34 +207,16 @@ class AudioDataset(Dataset):
         if sort:
             data = sorted(data, key=lambda x: x["feat_shape"][0])
         if raw_wav:
-            assert data[0]['feat'].split(':')[0].splitext()[-1] not in ('.ark',
-                                                                        '.scp')
-            data = map(lambda x: (float(x['feat_shape'][0]) * 1000 / stride_ms))
+            path_suffix = data[0]['feat'].split(':')[0].splitext()[-1]
+            assert path_suffix not in ('.ark', '.scp')
+            # m second to n frame
+            data = list(
+                map(lambda x: (float(x['feat_shape'][0]) * 1000 / stride_ms),
+                    data))
 
         self.input_dim = data[0]['feat_shape'][1]
         self.output_dim = data[0]['token_shape'][1]
 
-        # with open(data_file, 'r') as f:
-        #     for line in f:
-        #         arr = line.strip().split('\t')
-        #         if len(arr) != 7:
-        #             continue
-        #         key = arr[0].split(':')[1]
-        #         tokenid = arr[5].split(':')[1]
-        #         output_dim = int(arr[6].split(':')[1].split(',')[1])
-        #         if raw_wav:
-        #             wav_path = ':'.join(arr[1].split(':')[1:])
-        #             duration = int(float(arr[2].split(':')[1]) * 1000 / 10)
-        #             data.append((key, wav_path, duration, tokenid))
-        #         else:
-        #             feat_ark = ':'.join(arr[1].split(':')[1:])
-        #             feat_info = arr[2].split(':')[1].split(',')
-        #             feat_dim = int(feat_info[1].strip())
-        #             num_frames = int(feat_info[0].strip())
-        #             data.append((key, feat_ark, num_frames, tokenid))
-        #             self.input_dim = feat_dim
-        #         self.output_dim = output_dim
-
         valid_data = []
         for i in range(len(data)):
             length = data[i]['feat_shape'][0]
@@ -242,17 +224,17 @@ class AudioDataset(Dataset):
             # remove too lang or too short utt for both input and output
             # to prevent from out of memory
             if length > max_length or length < min_length:
-                # logging.warn('ignore utterance {} feature {}'.format(
-                #     data[i][0], length))
                 pass
             elif token_length > token_max_length or token_length < token_min_length:
                 pass
             else:
                 valid_data.append(data[i])
+        logger.info(f"raw dataset len: {len(data)}")
         data = valid_data
+        num_data = len(data)
+        logger.info(f"dataset len after filter: {num_data}")
 
         self.minibatch = []
-        num_data = len(data)
         # Dynamic batch size
         if batch_type == 'dynamic':
             assert (max_frames_in_batch > 0)
@@ -277,7 +259,9 @@ class AudioDataset(Dataset):
                 cur = end
 
     def __len__(self):
+        """number of example(batch)"""
         return len(self.minibatch)
 
     def __getitem__(self, idx):
+        """batch example of idx"""
         return self.minibatch[idx]

paddlespeech/s2t/io/reader.py

@@ -18,8 +18,10 @@ import kaldiio
 import numpy as np
 import soundfile
 
-from paddlespeech.s2t.frontend.augmentor.augmentation import AugmentationPipeline as Transformation
+from .utility import feat_type
+from paddlespeech.s2t.transform.transformation import Transformation
 from paddlespeech.s2t.utils.log import Log
+# from paddlespeech.s2t.frontend.augmentor.augmentation import AugmentationPipeline as Transformation
 
 __all__ = ["LoadInputsAndTargets"]
 
@@ -322,20 +324,7 @@ class LoadInputsAndTargets():
                 "Not supported: loader_type={}".format(filetype))
 
     def file_type(self, filepath):
-        suffix = filepath.split(":")[0].split('.')[-1].lower()
-        if suffix == 'ark':
-            return 'mat'
-        elif suffix == 'scp':
-            return 'scp'
-        elif suffix == 'npy':
-            return 'npy'
-        elif suffix == 'npz':
-            return 'npz'
-        elif suffix in ['wav', 'flac']:
-            # PCM16
-            return 'sound'
-        else:
-            raise ValueError(f"Not support filetype: {suffix}")
+        return feat_type(filepath)
 
 
 class SoundHDF5File():

paddlespeech/s2t/io/utility.py

@@ -17,7 +17,7 @@ import numpy as np
 
 from paddlespeech.s2t.utils.log import Log
 
-__all__ = ["pad_list", "pad_sequence"]
+__all__ = ["pad_list", "pad_sequence", "feat_type"]
 
 logger = Log(__name__).getlog()
 
@@ -85,3 +85,20 @@ def pad_sequence(sequences: List[np.ndarray],
             out_tensor[:length, i, ...] = tensor
 
     return out_tensor
+
+
+def feat_type(filepath):
+    suffix = filepath.split(":")[0].split('.')[-1].lower()
+    if suffix == 'ark':
+        return 'mat'
+    elif suffix == 'scp':
+        return 'scp'
+    elif suffix == 'npy':
+        return 'npy'
+    elif suffix == 'npz':
+        return 'npz'
+    elif suffix in ['wav', 'flac']:
+        # PCM16
+        return 'sound'
+    else:
+        raise ValueError(f"Not support filetype: {suffix}")

paddlespeech/s2t/transform/spectrogram.py

@@ -14,6 +14,7 @@
 # Modified from espnet(https://github.com/espnet/espnet)
 import librosa
 import numpy as np
+from python_speech_features import logfbank
 
 
 def stft(x,
@@ -304,3 +305,85 @@ class IStft():
             win_length=self.win_length,
             window=self.window,
             center=self.center, )
+
+
+class LogMelSpectrogramKaldi():
+    def __init__(
+            self,
+            fs=16000,
+            n_mels=80,
+            n_fft=512,  # fft point
+            n_shift=160,  # unit:sample, 10ms
+            win_length=400,  # unit:sample, 25ms
+            window="povey",
+            fmin=20,
+            fmax=None,
+            eps=1e-10,
+            dither=False):
+        self.fs = fs
+        self.n_mels = n_mels
+        self.n_fft = n_fft
+        if n_shift > win_length:
+            raise ValueError("Stride size must not be greater than "
+                             "window size.")
+        self.n_shift = n_shift / fs  # unit: ms
+        self.win_length = win_length / fs  # unit: ms
+
+        self.window = window
+        self.fmin = fmin
+        if fmax is None:
+            fmax_ = fmax if fmax else self.fs / 2
+        elif fmax > int(self.fs / 2):
+            raise ValueError("fmax must not be greater than half of "
+                             "sample rate.")
+        self.fmax = fmax_
+
+        self.eps = eps
+        self.remove_dc_offset = True
+        self.preemph = 0.97
+        self.dither = dither
+
+    def __repr__(self):
+        return ("{name}(fs={fs}, n_mels={n_mels}, n_fft={n_fft}, "
+                "n_shift={n_shift}, win_length={win_length}, window={window}, "
+                "fmin={fmin}, fmax={fmax}, eps={eps}))".format(
+                    name=self.__class__.__name__,
+                    fs=self.fs,
+                    n_mels=self.n_mels,
+                    n_fft=self.n_fft,
+                    n_shift=self.n_shift,
+                    win_length=self.win_length,
+                    window=self.window,
+                    fmin=self.fmin,
+                    fmax=self.fmax,
+                    eps=self.eps, ))
+
+    def __call__(self, x):
+        """
+
+        Args:
+            x (np.ndarray): shape (Ti,)
+
+        Raises:
+            ValueError: not support (Ti, C)
+
+        Returns:
+            np.ndarray: (T, D)
+        """
+        if x.ndim != 1:
+            raise ValueError("Not support x: [Time, Channel]")
+        if x.dtype == np.int16:
+            x = x / 2**(16 - 1)
+        return logfbank(
+            signal=x,
+            samplerate=self.fs,
+            winlen=self.win_length,  # unit ms
+            winstep=self.n_shift,  # unit ms
+            nfilt=self.n_mels,
+            nfft=self.n_fft,
+            lowfreq=self.fmin,
+            highfreq=self.fmax,
+            dither=self.dither,
+            remove_dc_offset=self.remove_dc_offset,
+            preemph=self.preemph,
+            wintype=self.window)

paddlespeech/s2t/transform/transformation.py

@@ -45,6 +45,7 @@ import_alias = dict(
     stft2fbank="paddlespeech.s2t.transform.spectrogram:Stft2LogMelSpectrogram",
     wpe="paddlespeech.s2t.transform.wpe:WPE",
     channel_selector="paddlespeech.s2t.transform.channel_selector:ChannelSelector",
+    fbank_kaldi="paddlespeech.s2t.transform.spectrogram:LogMelSpectrogramKaldi",
 )
 
 

utils/format_data.py

@@ -20,13 +20,13 @@ import json
 from paddlespeech.s2t.frontend.featurizer.text_featurizer import TextFeaturizer
 from paddlespeech.s2t.frontend.utility import load_cmvn
 from paddlespeech.s2t.frontend.utility import read_manifest
+from paddlespeech.s2t.io.utility import feat_type
 from paddlespeech.s2t.utils.utility import add_arguments
 from paddlespeech.s2t.utils.utility import print_arguments
 
 parser = argparse.ArgumentParser(description=__doc__)
 add_arg = functools.partial(add_arguments, argparser=parser)
 # yapf: disable
-add_arg('feat_type', str, "raw", "speech feature type, e.g. raw(wav, flac), mat(ark), scp")
 add_arg('cmvn_path',       str,
         'examples/librispeech/data/mean_std.json',
         "Filepath of cmvn.")
@@ -62,24 +62,64 @@ def main():
     vocab_size = text_feature.vocab_size
     print(f"Vocab size: {vocab_size}")
 
+    # josnline like this
+    # {
+    #   "input": [{"name": "input1", "shape": (100, 83), "feat": "xxx.ark:123"}],
+    #   "output": [{"name":"target1", "shape": (40, 5002), "text": "a b c de"}],
+    #   "utt2spk": "111-2222",
+    #   "utt": "111-2222-333"
+    # }
     count = 0
     for manifest_path in args.manifest_paths:
         manifest_jsons = read_manifest(manifest_path)
         for line_json in manifest_jsons:
+            output_json = {
+                "input": [],
+                "output": [],
+                'utt': line_json['utt'],
+                'utt2spk': line_json.get('utt2spk', 'global'),
+            }
+
+            # output
             line = line_json['text']
+            if isinstance(line, str):
+                # only one target
                 tokens = text_feature.tokenize(line)
                 tokenids = text_feature.featurize(line)
-            line_json['token'] = tokens
-            line_json['token_id'] = tokenids
-            line_json['token_shape'] = (len(tokenids), vocab_size)
+                output_json['output'].append({
+                    'name': 'traget1',
+                    'shape': (len(tokenids), vocab_size),
+                    'text': line,
+                    'token': ' '.join(tokens),
+                    'tokenid': ' '.join(map(str, tokenids)),
+                })
+            else:
+                # isinstance(line, list), multi target 
+                raise NotImplementedError("not support multi output now!")
+
+            # input
+            line = line_json['feat']
+            if isinstance(line, str):
+                # only one input
                 feat_shape = line_json['feat_shape']
                 assert isinstance(feat_shape, (list, tuple)), type(feat_shape)
-            if args.feat_type == 'raw':
+                filetype = feat_type(line)
+                if filetype == 'sound':
                     feat_shape.append(feat_dim)
-                line_json['filetype'] = 'sound'
                 else: # kaldi
                     raise NotImplementedError('no support kaldi feat now!')
-            fout.write(json.dumps(line_json) + '\n')
+
+                output_json['input'].append({
+                    "name": "input1",
+                    "shape": feat_shape,
+                    "feat": line,
+                    "filetype": filetype,
+                })
+            else:
+                # isinstance(line, list), multi input 
+                raise NotImplementedError("not support multi input now!")
+
+            fout.write(json.dumps(output_json) + '\n')
             count += 1
 
     print(f"Examples number: {count}")

utils/format_triplet_data.py

@@ -20,13 +20,13 @@ import json
 from paddlespeech.s2t.frontend.featurizer.text_featurizer import TextFeaturizer
 from paddlespeech.s2t.frontend.utility import load_cmvn
 from paddlespeech.s2t.frontend.utility import read_manifest
+from paddlespeech.s2t.io.utility import feat_type
 from paddlespeech.s2t.utils.utility import add_arguments
 from paddlespeech.s2t.utils.utility import print_arguments
 
 parser = argparse.ArgumentParser(description=__doc__)
 add_arg = functools.partial(add_arguments, argparser=parser)
 # yapf: disable
-add_arg('feat_type', str, "raw", "speech feature type, e.g. raw(wav, flac), kaldi")
 add_arg('cmvn_path',       str,
         'examples/librispeech/data/mean_std.json',
         "Filepath of cmvn.")
@@ -79,9 +79,11 @@ def main():
             line_json['token1'] = tokens
             line_json['token_id1'] = tokenids
             line_json['token_shape1'] = (len(tokenids), vocab_size)
+
             feat_shape = line_json['feat_shape']
             assert isinstance(feat_shape, (list, tuple)), type(feat_shape)
-            if args.feat_type == 'raw':
+            filetype = feat_type(line_json['feat'])
+            if filetype == 'sound':
                 feat_shape.append(feat_dim)
             else: # kaldi
                 raise NotImplementedError('no support kaldi feat now!')