Merge branch 'develop' into ctc_grad_norm

.gitignore

@@ -11,6 +11,10 @@
 *.npz
 *.done
 *.whl
+*.egg-info
+build
+
+docs/build/
 
 tools/venv
 tools/kenlm

.readthedocs.yml

+# .readthedocs.yml
+# Read the Docs configuration file
+# See https://docs.readthedocs.io/en/stable/config-file/v2.html for details
+
+# Required
+version: 2
+
+# Build documentation in the docs/ directory with Sphinx
+sphinx:
+  configuration: docs/src/conf.py
+
+# Build documentation with MkDocs
+#mkdocs:
+#  configuration: mkdocs.yml
+
+# Optionally build your docs in additional formats such as PDF
+formats: []
+
+# Optionally set the version of Python and requirements required to build your docs
+python:
+  version: 3.7
+  install:
+    - method: pip
+      path: .
+      extra_requirements:
+        - doc
+    
+    - requirements: docs/requirements.txt
+
+

README.md

-# PaddlePaddle Speech to Any toolkit
+# PaddlePaddle Speech toolkit
 
 ![License](https://img.shields.io/badge/license-Apache%202-red.svg)
 ![python version](https://img.shields.io/badge/python-3.7+-orange.svg)
@@ -9,7 +9,7 @@
 
 ## Features
 
- See [feature list](docs/src/feature_list.md) for more information.
+ See [feature list](docs/source/asr/feature_list.md) for more information.
 
 ## Setup
 
@@ -18,20 +18,20 @@ All tested under:
 * python>=3.7
 * paddlepaddle==2.1.2
 
-Please see [install](docs/src/install.md).
+Please see [install](docs/source/asr/install.md).
 
 ## Getting Started
 
-Please see [Getting Started](docs/src/getting_started.md) and [tiny egs](examples/tiny/s0/README.md).
+Please see [Getting Started](docs/source/asr/getting_started.md) and [tiny egs](examples/tiny/s0/README.md).
 
 
 ## More Information  
 
-* [Data Prepration](docs/src/data_preparation.md)  
-* [Data Augmentation](docs/src/augmentation.md)  
-* [Ngram LM](docs/src/ngram_lm.md)  
-* [Benchmark](docs/src/benchmark.md)  
-* [Relased Model](docs/src/released_model.md)  
+* [Data Prepration](docs/source/asr/data_preparation.md)  
+* [Data Augmentation](docs/source/asr/augmentation.md)  
+* [Ngram LM](docs/source/asr/ngram_lm.md)  
+* [Benchmark](docs/source/asr/benchmark.md)  
+* [Relased Model](docs/source/asr/released_model.md)  
 
 
 ## Questions and Help
@@ -45,4 +45,4 @@ DeepSpeech is provided under the [Apache-2.0 License](./LICENSE).
 
 ## Acknowledgement
 
-We depends on many open source repos. See [References](docs/src/reference.md) for more information.
+We depends on many open source repos. See [References](docs/source/asr/reference.md) for more information.

deepspeech/__init__.py

@@ -355,6 +355,8 @@ if not hasattr(paddle.Tensor, 'tolist'):
     setattr(paddle.Tensor, 'tolist', tolist)
 
 
+
+########### hcak paddle.nn.functional #############
 # hack loss
 def ctc_loss(logits,
              labels,
@@ -389,3 +391,152 @@ logger.debug(
     "override ctc_loss of paddle.nn.functional if exists, remove this when fixed!"
 )
 F.ctc_loss = ctc_loss
+
+
+########### hcak paddle.nn #############
+from paddle.nn import Layer
+from typing import Optional
+from typing import Mapping
+from typing import Iterable
+from typing import Tuple
+from typing import Iterator
+from collections import OrderedDict, abc as container_abcs
+
+class LayerDict(paddle.nn.Layer):
+    r"""Holds submodules in a dictionary.
+
+    :class:`~paddle.nn.LayerDict` can be indexed like a regular Python dictionary,
+    but modules it contains are properly registered, and will be visible by all
+    :class:`~paddle.nn.Layer` methods.
+
+    :class:`~paddle.nn.LayerDict` is an **ordered** dictionary that respects
+
+    * the order of insertion, and
+
+    * in :meth:`~paddle.nn.LayerDict.update`, the order of the merged
+      ``OrderedDict``, ``dict`` (started from Python 3.6) or another
+      :class:`~paddle.nn.LayerDict` (the argument to
+      :meth:`~paddle.nn.LayerDict.update`).
+
+    Note that :meth:`~paddle.nn.LayerDict.update` with other unordered mapping
+    types (e.g., Python's plain ``dict`` before Python version 3.6) does not
+    preserve the order of the merged mapping.
+
+    Args:
+        modules (iterable, optional): a mapping (dictionary) of (string: module)
+            or an iterable of key-value pairs of type (string, module)
+
+    Example::
+
+        class MyModule(nn.Layer):
+            def __init__(self):
+                super(MyModule, self).__init__()
+                self.choices = nn.LayerDict({
+                        'conv': nn.Conv2d(10, 10, 3),
+                        'pool': nn.MaxPool2d(3)
+                })
+                self.activations = nn.LayerDict([
+                        ['lrelu', nn.LeakyReLU()],
+                        ['prelu', nn.PReLU()]
+                ])
+
+            def forward(self, x, choice, act):
+                x = self.choices[choice](x)
+                x = self.activations[act](x)
+                return x
+    """
+
+    def __init__(self, modules: Optional[Mapping[str, Layer]] = None) -> None:
+        super(LayerDict, self).__init__()
+        if modules is not None:
+            self.update(modules)
+
+    def __getitem__(self, key: str) -> Layer:
+        return self._modules[key]
+
+    def __setitem__(self, key: str, module: Layer) -> None:
+        self.add_module(key, module)
+
+    def __delitem__(self, key: str) -> None:
+        del self._modules[key]
+
+    def __len__(self) -> int:
+        return len(self._modules)
+
+    def __iter__(self) -> Iterator[str]:
+        return iter(self._modules)
+
+    def __contains__(self, key: str) -> bool:
+        return key in self._modules
+
+    def clear(self) -> None:
+        """Remove all items from the LayerDict.
+        """
+        self._modules.clear()
+
+    def pop(self, key: str) -> Layer:
+        r"""Remove key from the LayerDict and return its module.
+
+        Args:
+            key (string): key to pop from the LayerDict
+        """
+        v = self[key]
+        del self[key]
+        return v
+
+    def keys(self) -> Iterable[str]:
+        r"""Return an iterable of the LayerDict keys.
+        """
+        return self._modules.keys()
+
+    def items(self) -> Iterable[Tuple[str, Layer]]:
+        r"""Return an iterable of the LayerDict key/value pairs.
+        """
+        return self._modules.items()
+
+    def values(self) -> Iterable[Layer]:
+        r"""Return an iterable of the LayerDict values.
+        """
+        return self._modules.values()
+
+    def update(self, modules: Mapping[str, Layer]) -> None:
+        r"""Update the :class:`~paddle.nn.LayerDict` with the key-value pairs from a
+        mapping or an iterable, overwriting existing keys.
+
+        .. note::
+            If :attr:`modules` is an ``OrderedDict``, a :class:`~paddle.nn.LayerDict`, or
+            an iterable of key-value pairs, the order of new elements in it is preserved.
+
+        Args:
+            modules (iterable): a mapping (dictionary) from string to :class:`~paddle.nn.Layer`,
+                or an iterable of key-value pairs of type (string, :class:`~paddle.nn.Layer`)
+        """
+        if not isinstance(modules, container_abcs.Iterable):
+            raise TypeError("LayerDict.update should be called with an "
+                            "iterable of key/value pairs, but got " +
+                            type(modules).__name__)
+
+        if isinstance(modules, (OrderedDict, LayerDict, container_abcs.Mapping)):
+            for key, module in modules.items():
+                self[key] = module
+        else:
+            # modules here can be a list with two items
+            for j, m in enumerate(modules):
+                if not isinstance(m, container_abcs.Iterable):
+                    raise TypeError("LayerDict update sequence element "
+                                    "#" + str(j) + " should be Iterable; is" +
+                                    type(m).__name__)
+                if not len(m) == 2:
+                    raise ValueError("LayerDict update sequence element "
+                                     "#" + str(j) + " has length " + str(len(m)) +
+                                     "; 2 is required")
+                # modules can be Mapping (what it's typed at), or a list: [(name1, module1), (name2, module2)]
+                # that's too cumbersome to type correctly with overloads, so we add an ignore here
+                self[m[0]] = m[1]  # type: ignore[assignment]
+
+    # remove forward alltogether to fallback on Module's _forward_unimplemented
+
+if not hasattr(paddle.nn, 'LayerDict'):
+    logger.debug(
+        "register user LayerDict to paddle.nn, remove this when fixed!")
+    setattr(paddle.nn, 'LayerDict', LayerDict)

deepspeech/decoders/ctcdecoder/swig/ctc_greedy_decoder.cpp

@@ -53,7 +53,8 @@ std::string ctc_greedy_decoder(
     std::string best_path_result;
     for (size_t i = 0; i < idx_vec.size(); ++i) {
         if (idx_vec[i] != blank_id) {
-            best_path_result += vocabulary[idx_vec[i]];
+            std::string ch = vocabulary[idx_vec[i]];
+            best_path_result += (ch == kSPACE) ? tSPACE : ch;
         }
     }
     return best_path_result;

deepspeech/decoders/ctcdecoder/swig/decoder_utils.cpp

@@ -74,7 +74,8 @@ std::vector<std::pair<double, std::string>> get_beam_search_result(
         // convert index to string
         std::string output_str;
         for (size_t j = 0; j < output.size(); j++) {
-            output_str += vocabulary[output[j]];
+            std::string ch = vocabulary[output[j]];
+            output_str += (ch == kSPACE) ? tSPACE : ch;
         }
         std::pair<double, std::string> output_pair(
             -space_prefixes[i]->approx_ctc, output_str);

deepspeech/decoders/ctcdecoder/swig/decoder_utils.h

@@ -21,6 +21,7 @@
 #include "path_trie.h"
 
 const std::string kSPACE = "<space>";
+const std::string tSPACE = " ";
 const float NUM_FLT_INF = std::numeric_limits<float>::max();
 const float NUM_FLT_MIN = std::numeric_limits<float>::min();
 

deepspeech/decoders/ctcdecoder/swig/setup.sh

@@ -10,7 +10,7 @@ fi
 
 if [ ! -d openfst-1.6.3 ]; then
     echo "Download and extract openfst ..."
-    wget http://www.openfst.org/twiki/pub/FST/FstDownload/openfst-1.6.3.tar.gz
+    wget http://www.openfst.org/twiki/pub/FST/FstDownload/openfst-1.6.3.tar.gz --no-check-certificate
     tar -xzvf openfst-1.6.3.tar.gz
     echo -e "\n"
 fi

deepspeech/decoders/scores/ctc.py → deepspeech/decoders/scorers/ctc.py

@@ -15,8 +15,8 @@
 import numpy as np
 import paddle
 
-from .ctc_prefix_score import CTCPrefixScore
-from .ctc_prefix_score import CTCPrefixScorePD
+from .ctc_prefix_score import CTCPrefixScorer
+from .ctc_prefix_score import CTCPrefixScorerPD
 from .scorer_interface import BatchPartialScorerInterface
 
 

deepspeech/decoders/scores/ctc_prefix_score.py → deepspeech/decoders/scorers/ctc_prefix_score.py

@@ -6,7 +6,7 @@ import paddle
 import six
 
 
-class CTCPrefixScorePD():
+class CTCPrefixScorerPD():
     """Batch processing of CTCPrefixScore
 
     which is based on Algorithm 2 in WATANABE et al.
@@ -267,7 +267,7 @@ class CTCPrefixScorePD():
             return (r_prev_new, s_prev, f_min_prev, f_max_prev)
 
 
-class CTCPrefixScore():
+class CTCPrefixScorer():
     """Compute CTC label sequence scores
 
     which is based on Algorithm 2 in WATANABE et al.

deepspeech/decoders/scores/score_interface.py → deepspeech/decoders/scorers/score_interface.py

@@ -145,9 +145,11 @@ class PartialScorerInterface(ScorerInterface):
     and receives pre-pruned next tokens to score because it is too heavy to score
     all the tokens.
 
+    Score sub-set of tokens, not all.
+
     Examples:
          * Prefix search for connectionist-temporal-classification models
-             * :class:`espnet.nets.scorers.ctc.CTCPrefixScorer`
+             * :class:`decoders.scorers.ctc.CTCPrefixScorer`
 
     """
 

deepspeech/exps/deepspeech2/bin/export.py

@@ -20,6 +20,7 @@ from deepspeech.utils.utility import print_arguments
 
 def main_sp(config, args):
     exp = Tester(config, args)
+    with exp.eval():
         exp.setup()
         exp.run_export()
 

deepspeech/exps/deepspeech2/bin/test.py

@@ -20,6 +20,7 @@ from deepspeech.utils.utility import print_arguments
 
 def main_sp(config, args):
     exp = Tester(config, args)
+    with exp.eval():
         exp.setup()
         exp.run_test()
 

deepspeech/exps/deepspeech2/bin/test_export.py

@@ -20,6 +20,7 @@ from deepspeech.utils.utility import print_arguments
 
 def main_sp(config, args):
     exp = ExportTester(config, args)
+    with exp.eval():
         exp.setup()
         exp.run_test()
 

deepspeech/exps/deepspeech2/bin/test_hub.py

@@ -56,11 +56,6 @@ class DeepSpeech2Tester_hub():
             cutoff_prob=cfg.cutoff_prob,
             cutoff_top_n=cfg.cutoff_top_n,
             num_processes=cfg.num_proc_bsearch)
-        #replace the '<space>' with ' '
-        result_transcripts = [
-            self._text_featurizer.detokenize(sentence)
-            for sentence in result_transcripts
-        ]
 
         return result_transcripts
 

deepspeech/exps/deepspeech2/model.py

@@ -341,11 +341,6 @@ class DeepSpeech2Tester(DeepSpeech2Trainer):
             cutoff_prob=cfg.cutoff_prob,
             cutoff_top_n=cfg.cutoff_top_n,
             num_processes=cfg.num_proc_bsearch)
-        #replace the <space> with ' '
-        result_transcripts = [
-            self._text_featurizer.detokenize(sentence)
-            for sentence in result_transcripts
-        ]
 
         self.autolog.times.stamp()
         self.autolog.times.stamp()

deepspeech/exps/u2/bin/alignment.py

@@ -20,6 +20,7 @@ from deepspeech.utils.utility import print_arguments
 
 def main_sp(config, args):
     exp = Tester(config, args)
+    with exp.eval():
         exp.setup()
         exp.run_align()
 

deepspeech/exps/u2/bin/export.py

@@ -20,6 +20,7 @@ from deepspeech.utils.utility import print_arguments
 
 def main_sp(config, args):
     exp = Tester(config, args)
+    with exp.eval():
         exp.setup()
         exp.run_export()
 

deepspeech/exps/u2/bin/test.py

@@ -24,6 +24,7 @@ from deepspeech.utils.utility import print_arguments
 
 def main_sp(config, args):
     exp = Tester(config, args)
+    with exp.eval():
         exp.setup()
         exp.run_test()
 

deepspeech/exps/u2/bin/test_hub.py

+# 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.
+"""Evaluation for U2 model."""
+import cProfile
+import os
+import sys
+
+import paddle
+import soundfile
+
+from deepspeech.exps.u2.config import get_cfg_defaults
+from deepspeech.frontend.featurizer.text_featurizer import TextFeaturizer
+from deepspeech.io.collator import SpeechCollator
+from deepspeech.models.u2 import U2Model
+from deepspeech.training.cli import default_argument_parser
+from deepspeech.training.trainer import Trainer
+from deepspeech.utils import layer_tools
+from deepspeech.utils import mp_tools
+from deepspeech.utils.log import Log
+from deepspeech.utils.utility import print_arguments
+from deepspeech.utils.utility import UpdateConfig
+logger = Log(__name__).getlog()
+
+# TODO(hui zhang): dynamic load
+
+
+class U2Tester_Hub(Trainer):
+    def __init__(self, config, args):
+        # super().__init__(config, args)
+        self.args = args
+        self.config = config
+        self.audio_file = args.audio_file
+        self.collate_fn_test = SpeechCollator.from_config(config)
+        self._text_featurizer = TextFeaturizer(
+            unit_type=config.collator.unit_type,
+            vocab_filepath=None,
+            spm_model_prefix=config.collator.spm_model_prefix)
+
+    def setup_model(self):
+        config = self.config
+        model_conf = config.model
+
+        with UpdateConfig(model_conf):
+            model_conf.input_dim = self.collate_fn_test.feature_size
+            model_conf.output_dim = self.collate_fn_test.vocab_size
+
+        model = U2Model.from_config(model_conf)
+
+        if self.parallel:
+            model = paddle.DataParallel(model)
+
+        logger.info(f"{model}")
+        layer_tools.print_params(model, logger.info)
+
+        self.model = model
+        logger.info("Setup model")
+
+    @mp_tools.rank_zero_only
+    @paddle.no_grad()
+    def test(self):
+        self.model.eval()
+        cfg = self.config.decoding
+        audio_file = self.audio_file
+        collate_fn_test = self.collate_fn_test
+        audio, _ = collate_fn_test.process_utterance(
+            audio_file=audio_file, transcript="Hello")
+        audio_len = audio.shape[0]
+        audio = paddle.to_tensor(audio, dtype='float32')
+        audio_len = paddle.to_tensor(audio_len)
+        audio = paddle.unsqueeze(audio, axis=0)
+        vocab_list = collate_fn_test.vocab_list
+
+        text_feature = self.collate_fn_test.text_feature
+        result_transcripts = self.model.decode(
+            audio,
+            audio_len,
+            text_feature=text_feature,
+            decoding_method=cfg.decoding_method,
+            lang_model_path=cfg.lang_model_path,
+            beam_alpha=cfg.alpha,
+            beam_beta=cfg.beta,
+            beam_size=cfg.beam_size,
+            cutoff_prob=cfg.cutoff_prob,
+            cutoff_top_n=cfg.cutoff_top_n,
+            num_processes=cfg.num_proc_bsearch,
+            ctc_weight=cfg.ctc_weight,
+            decoding_chunk_size=cfg.decoding_chunk_size,
+            num_decoding_left_chunks=cfg.num_decoding_left_chunks,
+            simulate_streaming=cfg.simulate_streaming)
+        logger.info("The result_transcripts: " + result_transcripts[0][0])
+
+    def run_test(self):
+        self.resume()
+        try:
+            self.test()
+        except KeyboardInterrupt:
+            sys.exit(-1)
+
+    def setup(self):
+        """Setup the experiment.
+        """
+        paddle.set_device('gpu' if self.args.nprocs > 0 else 'cpu')
+
+        #self.setup_output_dir()
+        #self.setup_checkpointer()
+
+        #self.setup_dataloader()
+        self.setup_model()
+
+        self.iteration = 0
+        self.epoch = 0
+
+    def resume(self):
+        """Resume from the checkpoint at checkpoints in the output
+        directory or load a specified checkpoint.
+        """
+        params_path = self.args.checkpoint_path + ".pdparams"
+        model_dict = paddle.load(params_path)
+        self.model.set_state_dict(model_dict)
+
+
+def check(audio_file):
+    logger.info("checking the audio file format......")
+    try:
+        sig, sample_rate = soundfile.read(audio_file)
+    except Exception as e:
+        logger.error(str(e))
+        logger.error(
+            "can not open the wav file, please check the audio file format")
+        sys.exit(-1)
+    logger.info("The sample rate is %d" % sample_rate)
+    assert (sample_rate == 16000)
+    logger.info("The audio file format is right")
+
+
+def main_sp(config, args):
+    exp = U2Tester_Hub(config, args)
+    with exp.eval():
+        exp.setup()
+        exp.run_test()
+
+
+def main(config, args):
+    main_sp(config, args)
+
+
+if __name__ == "__main__":
+    parser = default_argument_parser()
+    # save asr result to
+    parser.add_argument(
+        "--result_file", type=str, help="path of save the asr result")
+    parser.add_argument(
+        "--audio_file", type=str, help="path of the input audio file")
+    args = parser.parse_args()
+    print_arguments(args, globals())
+
+    if not os.path.isfile(args.audio_file):
+        print("Please input the right audio file path")
+        sys.exit(-1)
+    check(args.audio_file)
+    # https://yaml.org/type/float.html
+    config = get_cfg_defaults()
+    if args.config:
+        config.merge_from_file(args.config)
+    if args.opts:
+        config.merge_from_list(args.opts)
+    config.freeze()
+    print(config)
+    if args.dump_config:
+        with open(args.dump_config, 'w') as f:
+            print(config, file=f)
+
+    # Setting for profiling
+    pr = cProfile.Profile()
+    pr.runcall(main, config, args)
+    pr.dump_stats('test.profile')

deepspeech/exps/u2/model.py

@@ -444,7 +444,7 @@ class U2Tester(U2Trainer):
         start_time = time.time()
         text_feature = self.test_loader.collate_fn.text_feature
         target_transcripts = self.ordid2token(texts, texts_len)
-        result_transcripts = self.model.decode(
+        result_transcripts, result_tokenids = self.model.decode(
             audio,
             audio_len,
             text_feature=text_feature,
@@ -462,14 +462,19 @@ class U2Tester(U2Trainer):
             simulate_streaming=cfg.simulate_streaming)
         decode_time = time.time() - start_time
 
-        for utt, target, result in zip(utts, target_transcripts,
-                                       result_transcripts):
+        for utt, target, result, rec_tids in zip(
+                utts, target_transcripts, result_transcripts, result_tokenids):
             errors, len_ref = errors_func(target, result)
             errors_sum += errors
             len_refs += len_ref
             num_ins += 1
             if fout:
-                fout.write({"utt": utt, "ref": target, "hyp": result})
+                fout.write({
+                    "utt": utt,
+                    "refs": [target],
+                    "hyps": [result],
+                    "hyps_tokenid": [rec_tids],
+                })
             logger.info(f"Utt: {utt}")
             logger.info(f"Ref: {target}")
             logger.info(f"Hyp: {result}")

deepspeech/exps/u2/trainer.py

@@ -86,7 +86,8 @@ class U2Trainer(Trainer):
             batch_size=config.collator.batch_size,
             shuffle=False,
             drop_last=False,
-            collate_fn=collate_fn_dev)
+            collate_fn=collate_fn_dev,
+            num_workers=config.collator.num_workers, )
 
         # test dataset, return raw text
         config.data.manifest = config.data.test_manifest

deepspeech/exps/u2_kaldi/bin/test.py

@@ -29,8 +29,8 @@ model_test_alias = {
 def main_sp(config, args):
     class_obj = dynamic_import(args.model_name, model_test_alias)
     exp = class_obj(config, args)
+    with exp.eval():
         exp.setup()
-
         if args.run_mode == 'test':
             exp.run_test()
         elif args.run_mode == 'export':

deepspeech/exps/u2_kaldi/model.py

@@ -265,7 +265,7 @@ class U2Trainer(Trainer):
             batch_frames_out=0,
             batch_frames_inout=0,
             preprocess_conf=None,
-            n_iter_processes=1,
+            n_iter_processes=config.collator.num_workers,
             subsampling_factor=1,
             num_encs=1)
 
@@ -390,6 +390,10 @@ 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)
 
     def id2token(self, texts, texts_len, text_feature):
         """ ord() id to chr() chr """
@@ -413,15 +417,11 @@ 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 = TextFeaturizer(
-            unit_type=self.config.collator.unit_type,
-            vocab_filepath=self.config.collator.vocab_filepath,
-            spm_model_prefix=self.config.collator.spm_model_prefix)
-        target_transcripts = self.id2token(texts, texts_len, text_feature)
-        result_transcripts = self.model.decode(
+        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,
@@ -436,14 +436,19 @@ class U2Tester(U2Trainer):
             simulate_streaming=cfg.simulate_streaming)
         decode_time = time.time() - start_time
 
-        for utt, target, result in zip(utts, target_transcripts,
-                                       result_transcripts):
+        for i, (utt, target, result, rec_tids) in enumerate(zip(
+                utts, target_transcripts, result_transcripts, result_tokenids)):
             errors, len_ref = errors_func(target, result)
             errors_sum += errors
             len_refs += len_ref
             num_ins += 1
             if fout:
-                fout.write({"utt": utt, "ref": target, "hyp": result})
+                fout.write({
+                    "utt": utt,
+                    "refs": [target],
+                    "hyps": [result],
+                    "hyps_tokenid": [rec_tids],
+                })
             logger.info(f"Utt: {utt}")
             logger.info(f"Ref: {target}")
             logger.info(f"Hyp: {result}")

deepspeech/exps/u2_st/bin/export.py

@@ -20,6 +20,7 @@ from deepspeech.utils.utility import print_arguments
 
 def main_sp(config, args):
     exp = Tester(config, args)
+    with exp.eval():
         exp.setup()
         exp.run_export()
 

deepspeech/exps/u2_st/bin/test.py

@@ -24,6 +24,7 @@ from deepspeech.utils.utility import print_arguments
 
 def main_sp(config, args):
     exp = Tester(config, args)
+    with exp.eval():
         exp.setup()
         exp.run_test()
 

deepspeech/frontend/featurizer/text_featurizer.py

@@ -140,7 +140,7 @@ class TextFeaturizer():
         Returns:
            str: text string.
         """
-        tokens = tokens.replace(SPACE, " ")
+        tokens = [t.replace(SPACE, " ") for t in tokens ]
         return "".join(tokens)
 
     def word_tokenize(self, text):

deepspeech/io/batchfy.py

@@ -354,7 +354,7 @@ def make_batchset(
     :param int batch_frames_out: maximum number of output frames in a minibatch.
     :param int batch_frames_out: maximum number of input+output frames in a minibatch.
     :param str count: strategy to count maximum size of batch.
-        For choices, see espnet.asr.batchfy.BATCH_COUNT_CHOICES
+        For choices, see io.batchfy.BATCH_COUNT_CHOICES
 
     :param int max_length_in: maximum length of input to decide adaptive batch size
     :param int max_length_out: maximum length of output to decide adaptive batch size

deepspeech/io/collator.py

@@ -32,7 +32,7 @@ __all__ = ["SpeechCollator", "TripletSpeechCollator"]
 logger = Log(__name__).getlog()
 
 
-def tokenids(text, keep_transcription_text):
+def _tokenids(text, keep_transcription_text):
     # for training text is token ids 
     tokens = text  # token ids
 
@@ -93,6 +93,8 @@ class SpeechCollatorBase():
         a user-defined shape) within one batch.
         """
         self.keep_transcription_text = keep_transcription_text
+        self.train_mode = not keep_transcription_text
+
         self.stride_ms = stride_ms
         self.window_ms = window_ms
         self.feat_dim = feat_dim
@@ -192,6 +194,7 @@ class SpeechCollatorBase():
         texts = []
         text_lens = []
         utts = []
+        tids = []  # tokenids
 
         for idx, item in enumerate(batch):
             utts.append(item['utt'])
@@ -203,7 +206,7 @@ class SpeechCollatorBase():
             audios.append(audio)  # [T, D]
             audio_lens.append(audio.shape[0])
 
-            tokens = tokenids(text, self.keep_transcription_text)
+            tokens = _tokenids(text, self.keep_transcription_text)
             texts.append(tokens)
             text_lens.append(tokens.shape[0])
 
@@ -351,7 +354,7 @@ class TripletSpeechCollator(SpeechCollator):
 
             tokens = [[], []]
             for idx, text in enumerate([translation, transcription]):
-                tokens[idx] = tokenids(text, self.keep_transcription_text)
+                tokens[idx] = _tokenids(text, self.keep_transcription_text)
 
             translation_text.append(tokens[0])
             translation_text_lens.append(tokens[0].shape[0])

deepspeech/io/dataloader.py

@@ -142,6 +142,15 @@ class BatchDataLoader():
             collate_fn=batch_collate,
             num_workers=self.n_iter_processes, )
 
+    def __len__(self):
+        return len(self.dataloader)
+
+    def __iter__(self):
+        return self.dataloader.__iter__()
+
+    def __call__(self):
+        return self.__iter__()
+
     def __repr__(self):
         echo = f"<{self.__class__.__module__}.{self.__class__.__name__} object at {hex(id(self))}> "
         echo += f"train_mode: {self.train_mode}, "
@@ -159,12 +168,3 @@ class BatchDataLoader():
         echo += f"num_workers: {self.n_iter_processes}, "
         echo += f"file: {self.json_file}"
         return echo
-
-    def __len__(self):
-        return len(self.dataloader)
-
-    def __iter__(self):
-        return self.dataloader.__iter__()
-
-    def __call__(self):
-        return self.__iter__()

deepspeech/models/ds2/deepspeech2.py

@@ -219,10 +219,10 @@ class DeepSpeech2Model(nn.Layer):
             The model built from pretrained result.
         """
         model = cls(
-            #feat_size=dataloader.collate_fn.feature_size,
-            feat_size=dataloader.dataset.feature_size,
-            #dict_size=dataloader.collate_fn.vocab_size,
-            dict_size=dataloader.dataset.vocab_size,
+            feat_size=dataloader.collate_fn.feature_size,
+            #feat_size=dataloader.dataset.feature_size,
+            dict_size=dataloader.collate_fn.vocab_size,
+            #dict_size=dataloader.dataset.vocab_size,
             num_conv_layers=config.model.num_conv_layers,
             num_rnn_layers=config.model.num_rnn_layers,
             rnn_size=config.model.rnn_layer_size,

deepspeech/models/u2/u2.py

@@ -809,7 +809,8 @@ class U2BaseModel(nn.Layer):
             raise ValueError(f"Not support decoding method: {decoding_method}")
 
         res = [text_feature.defeaturize(hyp) for hyp in hyps]
-        return res
+        res_tokenids = [hyp for hyp in hyps]
+        return res, res_tokenids
 
 
 class U2Model(U2BaseModel):

deepspeech/modules/ctc.py

@@ -23,9 +23,9 @@ from deepspeech.utils.log import Log
 logger = Log(__name__).getlog()
 
 try:
-    from deepspeech.decoders.swig_wrapper import ctc_beam_search_decoder_batch  # noqa: F401
-    from deepspeech.decoders.swig_wrapper import ctc_greedy_decoder  # noqa: F401
-    from deepspeech.decoders.swig_wrapper import Scorer  # noqa: F401
+    from deepspeech.decoders.ctcdecoder.swig_wrapper import ctc_beam_search_decoder_batch  # noqa: F401
+    from deepspeech.decoders.ctcdecoder.swig_wrapper import ctc_greedy_decoder  # noqa: F401
+    from deepspeech.decoders.ctcdecoder.swig_wrapper import Scorer  # noqa: F401
 except Exception as e:
     logger.info("ctcdecoder not installed!")
 

deepspeech/utils/utility.py

@@ -42,7 +42,7 @@ def all_version():
         "paddle_commit": paddle.version.commit,
         "soundfile": soundfile.__version__,
     }
-    logger.info(f"Deps Module Version:{pformat(vers.items())}")
+    logger.info(f"Deps Module Version:{pformat(list(vers.items()))}")
 
 
 @contextmanager

docs/Makefile

+# Minimal makefile for Sphinx documentation
+#
+
+# You can set these variables from the command line, and also
+# from the environment for the first two.
+SPHINXOPTS    ?=
+SPHINXBUILD   ?= sphinx-build
+SOURCEDIR     = source
+BUILDDIR      = build
+
+# Put it first so that "make" without argument is like "make help".
+help:
+	@$(SPHINXBUILD) -M help "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O)
+
+.PHONY: help Makefile
+
+# Catch-all target: route all unknown targets to Sphinx using the new
+# "make mode" option.  $(O) is meant as a shortcut for $(SPHINXOPTS).
+%: Makefile
+	@$(SPHINXBUILD) -M $@ "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O)

docs/make.bat

+@ECHO OFF
+
+pushd %~dp0
+
+REM Command file for Sphinx documentation
+
+if "%SPHINXBUILD%" == "" (
+	set SPHINXBUILD=sphinx-build
+)
+set SOURCEDIR=source
+set BUILDDIR=build
+
+if "%1" == "" goto help
+
+%SPHINXBUILD% >NUL 2>NUL
+if errorlevel 9009 (
+	echo.
+	echo.The 'sphinx-build' command was not found. Make sure you have Sphinx
+	echo.installed, then set the SPHINXBUILD environment variable to point
+	echo.to the full path of the 'sphinx-build' executable. Alternatively you
+	echo.may add the Sphinx directory to PATH.
+	echo.
+	echo.If you don't have Sphinx installed, grab it from
+	echo.http://sphinx-doc.org/
+	exit /b 1
+)
+
+%SPHINXBUILD% -M %1 %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% %O%
+goto end
+
+:help
+%SPHINXBUILD% -M help %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% %O%
+
+:end
+popd

docs/src/deepspeech_architecture.md → docs/source/asr/deepspeech_architecture.md

@@ -14,10 +14,11 @@ In addition, the training process and the testing process are also introduced.
 The arcitecture of the model is shown in Fig.1.
 
 <p align="center">
-<img src="../images/ds2onlineModel.png" width=800>
+<img src="../../images/ds2onlineModel.png" width=800>
 <br/>Fig.1 The Arcitecture of deepspeech2 online model
 </p>
 
+
 ### Data Preparation
 #### Vocabulary
 For English data, the vocabulary dictionary is composed of 26 English characters with " ' ", space, \<blank\> and \<eos\>. The \<blank\> represents the blank label in CTC, the \<unk\> represents the unknown character and the \<eos\> represents the start and the end characters. For mandarin, the vocabulary dictionary is composed of chinese characters statisticed from the training set and three additional characters are added. The added characters are \<blank\>, \<unk\> and \<eos\>.  For both English and mandarin data, we set the default indexs that \<blank\>=0, \<unk\>=1 and \<eos\>= last index.
@@ -130,7 +131,7 @@ By using the command above, the training process can be started. There are 5 sta
 Using the command below, you can test the deepspeech2 online model.
  ```
  bash run.sh --stage 3 --stop_stage 5 --model_type online --conf_path conf/deepspeech2_online.yaml
-```
+ ```
 The detail commands are:
 ```
 conf_path=conf/deepspeech2_online.yaml
@@ -152,7 +153,7 @@ if [ ${stage} -le 5 ] && [ ${stop_stage} -ge 5 ]; then
     # test export ckpt avg_n
     CUDA_VISIBLE_DEVICES=0 ./local/test_export.sh ${conf_path} exp/${ckpt}/checkpoints/${avg_ckpt}.jit ${model_type}|| exit -1
 fi
- ```
+```
 After the training process, we use stage 3,4,5 for testing process. The stage 3 is for testing the model generated in the stage 2 and provided the CER index of the test set. The stage 4 is for transforming the model from dynamic graph to static graph by using "paddle.jit" library. The stage 5 is for testing the model in static graph.
 
 
@@ -161,12 +162,13 @@ The deepspeech2 offline model is similarity to the deepspeech2 online model. The
 
 The arcitecture of the model is shown in Fig.2.
 <p align="center">
-<img src="../images/ds2offlineModel.png" width=800>
+<img src="../../images/ds2offlineModel.png" width=800>
 <br/>Fig.2 The Arcitecture of deepspeech2 offline model
 </p>
 
 
 
+
 For data preparation and decoder, the deepspeech2 offline model is same with the deepspeech2 online model.
 
 The code of encoder and decoder for deepspeech2 offline model is in:
@@ -182,7 +184,7 @@ For training and testing, the "model_type" and the "conf_path" must be set.
 # Training offline
 cd examples/aishell/s0
 bash run.sh --stage 0 --stop_stage 2 --model_type offline --conf_path conf/deepspeech2.yaml
-```
+ ```
 ```
 # Testing offline
 cd examples/aishell/s0

docs/source/conf.py

+# Copyright (c) 2020 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.
+# Configuration file for the Sphinx documentation builder.
+#
+# This file only contains a selection of the most common options. For a full
+# list see the documentation:
+# https://www.sphinx-doc.org/en/master/usage/configuration.html
+# -- Path setup --------------------------------------------------------------
+# If extensions (or modules to document with autodoc) are in another directory,
+# add these directories to sys.path here. If the directory is relative to the
+# documentation root, use os.path.abspath to make it absolute, like shown here.
+import recommonmark.parser
+import sphinx_rtd_theme
+
+# -- Project information -----------------------------------------------------
+
+project = 'paddle speech'
+copyright = '2021, Deepspeech-developers'
+author = 'Deepspeech-developers'
+
+# The full version, including alpha/beta/rc tags
+release = '2.1'
+
+# -- General configuration ---------------------------------------------------
+source_parsers = {
+    '.md': recommonmark.parser.CommonMarkParser,
+}
+source_suffix = ['.rst', '.md']
+
+master_doc = 'index'
+pygments_style = 'sphinx'
+# Add any Sphinx extension module names here, as strings. They can be
+# extensions coming with Sphinx (named 'sphinx.ext.*') or your custom
+# ones.
+extensions = [
+    'sphinx.ext.autodoc',
+    'sphinx.ext.viewcode',
+    'sphinx_rtd_theme',
+    'sphinx.ext.mathjax',
+    'sphinx.ext.autosummary',
+    'numpydoc',
+    'myst_parser',
+]
+
+# Add any paths that contain templates here, relative to this directory.
+templates_path = ['_templates']
+
+# List of patterns, relative to source directory, that match files and
+# directories to ignore when looking for source files.
+# This pattern also affects html_static_path and html_extra_path.
+exclude_patterns = [
+    '_build',
+]
+# -- Options for HTML output -------------------------------------------------
+
+# The theme to use for HTML and HTML Help pages.  See the documentation for
+# a list of builtin themes.
+#
+
+html_theme = 'sphinx_rtd_theme'
+html_theme_path = [sphinx_rtd_theme.get_html_theme_path()]
+smartquotes = False
+
+# Add any paths that contain custom static files (such as style sheets) here,
+# relative to this directory. They are copied after the builtin static files,
+# so a file named "default.css" will overwrite the builtin "default.css".
+html_static_path = ['_static']
+
+# -- Extension configuration -------------------------------------------------
+# numpydoc_show_class_members = False

docs/source/index.rst

+Welcome to paddle Deepspeech documentation !
+==============================================
+
+**Deepspeech** is a Speech toolkits implemented by paddlepaddle.
+
+
+Contents
+--------
+
+.. toctree::
+   :maxdepth: 1
+   :caption: Introduction
+   
+   asr/deepspeech_architecture 
+
+
+.. toctree::
+   :maxdepth: 1
+   :caption: Getting_started
+
+   asr/install
+   asr/getting_started
+
+
+.. toctree::
+   :maxdepth: 1
+   :caption: More Information
+
+   asr/data_preparation
+   asr/augmentation
+   asr/feature_list
+   asr/ngram_lm  
+
+
+.. toctree::
+   :maxdepth: 1
+   :caption: Released_model
+
+   asr/released_model
+
+
+.. toctree::
+   :maxdepth: 1
+   :caption: Acknowledgement
+
+   asr/reference
+

docs/source/tts/README.md

+# Parakeet
+Parakeet aims to provide a flexible, efficient and state-of-the-art text-to-speech toolkit for the open-source community. It is built on PaddlePaddle dynamic graph and includes many influential TTS models.  
+
+<div align="center">
+  <img src="../../images/logo.png" width=300 /> <br>
+</div>
+
+
+## News  <img src="../../images/news_icon.png" width="40"/>
+- Oct-12-2021, Refector examples code.
+- Oct-12-2021, Parallel WaveGAN with LJSpeech. Check [examples/GANVocoder/parallelwave_gan/ljspeech](./examples/GANVocoder/parallelwave_gan/ljspeech).
+- Oct-12-2021, FastSpeech2/FastPitch with LJSpeech. Check [examples/fastspeech2/ljspeech](./examples/fastspeech2/ljspeech).
+- Sep-14-2021, Reconstruction of TransformerTTS. Check [examples/transformer_tts/ljspeech](./examples/transformer_tts/ljspeech).
+- Aug-31-2021, Chinese Text Frontend. Check [examples/text_frontend](./examples/text_frontend).
+- Aug-23-2021, FastSpeech2/FastPitch with AISHELL-3. Check [examples/fastspeech2/aishell3](./examples/fastspeech2/aishell3).
+- Aug-03-2021, FastSpeech2/FastPitch with CSMSC. Check [examples/fastspeech2/baker](./examples/fastspeech2/baker).
+- Jul-19-2021, SpeedySpeech with CSMSC. Check [examples/speedyspeech/baker](./examples/speedyspeech/baker).
+- Jul-01-2021, Parallel WaveGAN with CSMSC. Check [examples/GANVocoder/parallelwave_gan/baker](./examples/GANVocoder/parallelwave_gan/baker).
+- Jul-01-2021, Montreal-Forced-Aligner. Check  [examples/use_mfa](./examples/use_mfa).
+- May-07-2021, Voice Cloning in Chinese. Check [examples/tacotron2_aishell3](./examples/tacotron2_aishell3).
+
+## Overview
+
+In order to facilitate exploiting the existing TTS models directly and developing the new ones, Parakeet selects typical models and provides their reference implementations in PaddlePaddle. Further more, Parakeet abstracts the TTS pipeline and standardizes the procedure of data preprocessing, common modules sharing, model configuration, and the process of training and synthesis. The models supported here include Text FrontEnd, end-to-end Acoustic models and Vocoders:
+
+- Text FrontEnd
+  - Rule based Chinese frontend.
+
+- Acoustic Models
+  - [【FastSpeech2】FastSpeech 2: Fast and High-Quality End-to-End Text to Speech](https://arxiv.org/abs/2006.04558)
+  - [【SpeedySpeech】SpeedySpeech: Efficient Neural Speech Synthesis](https://arxiv.org/abs/2008.03802)
+  - [【Transformer TTS】Neural Speech Synthesis with Transformer Network](https://arxiv.org/abs/1809.08895)
+  - [【Tacotron2】Natural TTS Synthesis by Conditioning WaveNet on Mel Spectrogram Predictions](https://arxiv.org/abs/1712.05884)
+- Vocoders
+  - [【Parallel WaveGAN】Parallel WaveGAN: A fast waveform generation model based on generative adversarial networks with multi-resolution spectrogram](https://arxiv.org/abs/1910.11480)
+  - [【WaveFlow】WaveFlow: A Compact Flow-based Model for Raw Audio](https://arxiv.org/abs/1912.01219)
+- Voice Cloning
+  - [Transfer Learning from Speaker Verification to Multispeaker Text-To-Speech Synthesis](https://arxiv.org/pdf/1806.04558v4.pdf)
+  - [【GE2E】Generalized End-to-End Loss for Speaker Verification](https://arxiv.org/abs/1710.10467)
+
+## Setup
+It's difficult to install some dependent libraries for this repo in Windows system, we recommend that you **DO NOT** use Windows system, please use `Linux`.
+
+Make sure the library `libsndfile1` is installed, e.g., on Ubuntu.
+
+```bash
+sudo apt-get install libsndfile1
+```
+### Install PaddlePaddle
+See [install](https://www.paddlepaddle.org.cn/install/quick) for more details. This repo requires PaddlePaddle **2.1.2** or above.
+
+### Install Parakeet
+
+```bash
+git clone https://github.com/PaddlePaddle/Parakeet
+cd Parakeet
+pip install -e .
+```
+
+If some python dependent packages cannot be installed successfully, you can run the following script first.
+(replace `python3.6` with your own python version)
+```bash
+sudo apt install -y python3.6-dev
+```
+
+See [install](https://paddle-parakeet.readthedocs.io/en/latest/install.html) for more details.
+
+## Examples
+Entries to the introduction, and the launch of training and synthsis for different example models:
+
+- [>>> Chinese Text Frontend](./examples/text_frontend)
+- [>>> FastSpeech2/FastPitch](./examples/fastspeech2)
+- [>>> Montreal-Forced-Aligner](./examples/use_mfa)
+- [>>> Parallel WaveGAN](./examples/GANVocoder/parallelwave_gan)
+- [>>> SpeedySpeech](./examples/speedyspeech)
+- [>>> Tacotron2_AISHELL3](./examples/tacotron2_aishell3)
+- [>>> GE2E](./examples/ge2e)
+- [>>> WaveFlow](./examples/waveflow)
+- [>>> TransformerTTS](./examples/transformer_tts)
+- [>>> Tacotron2](./examples/tacotron2)
+
+## Audio samples
+### TTS models (Acoustic Model + Neural Vocoder)
+Check our [website](https://paddleparakeet.readthedocs.io/en/latest/demo.html) for audio sampels.
+
+## Released Model
+
+### Acoustic Model
+
+#### FastSpeech2/FastPitch
+1. [fastspeech2_nosil_baker_ckpt_0.4.zip](https://paddlespeech.bj.bcebos.com/Parakeet/fastspeech2_nosil_baker_ckpt_0.4.zip)
+2. [fastspeech2_nosil_aishell3_ckpt_0.4.zip](https://paddlespeech.bj.bcebos.com/Parakeet/fastspeech2_nosil_aishell3_ckpt_0.4.zip)
+3. [fastspeech2_nosil_ljspeech_ckpt_0.5.zip](https://paddlespeech.bj.bcebos.com/Parakeet/fastspeech2_nosil_ljspeech_ckpt_0.5.zip)
+
+#### SpeedySpeech
+1. [speedyspeech_nosil_baker_ckpt_0.5.zip](https://paddlespeech.bj.bcebos.com/Parakeet/speedyspeech_nosil_baker_ckpt_0.5.zip)
+
+#### TransformerTTS
+
+1. [transformer_tts_ljspeech_ckpt_0.4.zip](https://paddlespeech.bj.bcebos.com/Parakeet/transformer_tts_ljspeech_ckpt_0.4.zip)
+
+#### Tacotron2
+
+1. [tacotron2_ljspeech_ckpt_0.3.zip](https://paddlespeech.bj.bcebos.com/Parakeet/tacotron2_ljspeech_ckpt_0.3.zip)
+2. [tacotron2_ljspeech_ckpt_0.3_alternative.zip](https://paddlespeech.bj.bcebos.com/Parakeet/tacotron2_ljspeech_ckpt_0.3_alternative.zip)
+
+### Vocoder
+
+#### WaveFlow
+
+1. [waveflow_ljspeech_ckpt_0.3.zip](https://paddlespeech.bj.bcebos.com/Parakeet/waveflow_ljspeech_ckpt_0.3.zip)
+
+#### Parallel WaveGAN
+
+1. [pwg_baker_ckpt_0.4.zip](https://paddlespeech.bj.bcebos.com/Parakeet/pwg_baker_ckpt_0.4.zip)
+2. [pwg_ljspeech_ckpt_0.5.zip](https://paddlespeech.bj.bcebos.com/Parakeet/pwg_ljspeech_ckpt_0.5.zip)
+
+### Voice Cloning
+
+#### Tacotron2_AISHELL3
+
+1. [tacotron2_aishell3_ckpt_0.3.zip](https://paddlespeech.bj.bcebos.com/Parakeet/tacotron2_aishell3_ckpt_0.3.zip)
+
+#### GE2E
+
+1. [ge2e_ckpt_0.3.zip](https://paddlespeech.bj.bcebos.com/Parakeet/ge2e_ckpt_0.3.zip)
+
+## License
+
+Parakeet is provided under the [Apache-2.0 license](LICENSE).

docs/source/tts/basic_usage.md

+# Basic Usage
+This section shows how to use pretrained models provided by parakeet and make inference with them.
+
+Pretrained models in v0.4 are provided in a archive. Extract it to get a folder like this:
+```
+checkpoint_name/
+├──default.yaml
+├──snapshot_iter_76000.pdz
+├──speech_stats.npy
+└──phone_id_map.txt
+```
+`default.yaml` stores the config used to train the model.
+`snapshot_iter_N.pdz` is the chechpoint file, where `N` is the steps it has been trained.
+`*_stats.npy` is the stats file of feature if  it has been normalized before training.
+`phone_id_map.txt` is the map of  phonemes to phoneme_ids.
+
+The example code below shows how to use the models for prediction.
+## Acoustic Models (text to spectrogram)
+The code below show how to use a `FastSpeech2` model.  After loading the pretrained model, use it and normalizer object to construct a prediction object，then use fastspeech2_inferencet(phone_ids) to generate spectrograms, which can be further used to synthesize raw audio with a vocoder.
+
+```python
+from pathlib import Path
+import numpy as np
+import paddle
+import yaml
+from yacs.config import CfgNode
+from parakeet.models.fastspeech2 import FastSpeech2
+from parakeet.models.fastspeech2 import FastSpeech2Inference
+from parakeet.modules.normalizer import ZScore
+# Parakeet/examples/fastspeech2/baker/frontend.py
+from frontend import Frontend
+
+# load the pretrained model
+checkpoint_dir = Path("fastspeech2_nosil_baker_ckpt_0.4")
+with open(checkpoint_dir / "phone_id_map.txt", "r") as f:
+    phn_id = [line.strip().split() for line in f.readlines()]
+vocab_size = len(phn_id)
+with open(checkpoint_dir / "default.yaml") as f:
+    fastspeech2_config = CfgNode(yaml.safe_load(f))
+odim = fastspeech2_config.n_mels
+model = FastSpeech2(
+    idim=vocab_size, odim=odim, **fastspeech2_config["model"])
+model.set_state_dict(
+    paddle.load(args.fastspeech2_checkpoint)["main_params"])
+model.eval()
+
+# load stats file
+stat = np.load(checkpoint_dir / "speech_stats.npy")
+mu, std = stat
+mu = paddle.to_tensor(mu)
+std = paddle.to_tensor(std)
+fastspeech2_normalizer = ZScore(mu, std)
+
+# construct a prediction object
+fastspeech2_inference = FastSpeech2Inference(fastspeech2_normalizer, model)
+
+# load Chinese Frontend
+frontend = Frontend(checkpoint_dir / "phone_id_map.txt")
+
+# text to spectrogram
+sentence = "你好吗？"
+input_ids = frontend.get_input_ids(sentence, merge_sentences=True)
+phone_ids = input_ids["phone_ids"]
+flags = 0
+# The output of Chinese text frontend is segmented
+for part_phone_ids in phone_ids:
+    with paddle.no_grad():
+        temp_mel = fastspeech2_inference(part_phone_ids)
+        if flags == 0:
+            mel = temp_mel
+            flags = 1
+        else:
+            mel = paddle.concat([mel, temp_mel])
+```
+
+## Vocoder (spectrogram to wave)
+The code below show how to use a  ` Parallel WaveGAN` model. Like the example above, after loading the pretrained model, use it and normalizer object to construct a prediction object，then use pwg_inference(mel) to generate  raw audio (in wav format).
+
+```python
+from pathlib import Path
+import numpy as np
+import paddle
+import soundfile as sf
+import yaml
+from yacs.config import CfgNode
+from parakeet.models.parallel_wavegan import PWGGenerator
+from parakeet.models.parallel_wavegan import PWGInference
+from parakeet.modules.normalizer import ZScore
+
+# load the pretrained model
+checkpoint_dir = Path("parallel_wavegan_baker_ckpt_0.4")
+with open(checkpoint_dir / "pwg_default.yaml") as f:
+    pwg_config = CfgNode(yaml.safe_load(f))
+vocoder = PWGGenerator(**pwg_config["generator_params"])
+vocoder.set_state_dict(paddle.load(args.pwg_params))
+vocoder.remove_weight_norm()
+vocoder.eval()
+
+# load stats file
+stat = np.load(checkpoint_dir / "pwg_stats.npy")
+mu, std = stat
+mu = paddle.to_tensor(mu)
+std = paddle.to_tensor(std)
+pwg_normalizer = ZScore(mu, std)
+
+# construct a prediction object
+pwg_inference = PWGInference(pwg_normalizer, vocoder)
+
+# spectrogram to wave
+wav = pwg_inference(mel)
+sf.write(
+        audio_path,
+        wav.numpy(),
+        samplerate=fastspeech2_config.fs)
+```

docs/source/tts/cn_text_frontend.md

+# Chinese Rule Based Text Frontend
+TTS system mainly includes three modules: `text frontend`, `Acoustic model` and `Vocoder`. We provide a complete Chinese text frontend module in Parakeet, see exapmle in `Parakeet/examples/text_frontend/`.
+
+A text frontend module mainly includes:
+ - Text Segmentation
+ - Text Normalization (TN)
+ - Word Segmentation (mainly in Chinese)
+ - Part-of-Speech
+ - Prosody
+ - G2P (Grapheme-to-Phoneme, include Polyphone and Tone Sandhi, etc.)
+ - Linguistic Features/Charactors/Phonemes
+
+```text
+• text: 90 后为中华人民共和国成立 70 周年准备了大礼
+• Text Normalization: 九零后为中华人民共和国成立七十周年准备了大礼
+• Word Segmentation: 九零后/为/中华人民/共和国/成立/七十/周年/准备/了/大礼
+• G2P:
+    jiu3 ling2 hou4 wei4 zhong1 hua2 ren2 min2 gong4 he2 guo2 ...
+• Prosody (prosodic words #1, prosodic phrases #2, intonation phrases #3, sentence #4):
+    九零后#1为中华人民#1共和国#2成立七十周年#3准备了大礼#4
+```
+
+Among them, Text Normalization and G2P are the most important modules. We mainly introduce them here.
+
+## Text Normalization
+### Supported NSW (Non-Standard-Word) Normalization
+
+|NSW type|raw|normalized|
+|:--|:-|:-|
+|serial number|电影中梁朝伟扮演的陈永仁的编号27149|电影中梁朝伟扮演的陈永仁的编号二七一四九|
+|cardinal|这块黄金重达324.75克<br>我们班的最高总分为583分|这块黄金重达三百二十四点七五克<br>我们班的最高总分为五百八十三分|
+|numeric range |12\~23<br>-1.5\~2|十二到二十三<br>负一点五到二|
+|date|她出生于86年8月18日，她弟弟出生于1995年3月1日|她出生于八六年八月十八日， 她弟弟出生于一九九五年三月一日|
+|time|等会请在12:05请通知我|等会请在十二点零五分请通知我
+|temperature|今天的最低气温达到-10°C|今天的最低气温达到零下十度
+|fraction|现场有7/12的观众投出了赞成票|现场有十二分之七的观众投出了赞成票|
+|percentage|明天有62％的概率降雨|明天有百分之六十二的概率降雨|
+|money|随便来几个价格12块5，34.5元，20.1万|随便来几个价格十二块五，三十四点五元，二十点一万|
+|telephone|这是固话0421-33441122<br>这是手机+86 18544139121|这是固话零四二一三三四四一一二二<br>这是手机八六一八五四四一三九一二一|
+
+
+## Grapheme-to-Phoneme
+In Chinese, G2P is a very complex module, which mainly includes **polyphone**  and **tone sandhi**.
+
+We use [g2pM](https://github.com/kakaobrain/g2pM) and [pypinyin](https://github.com/mozillazg/python-pinyin)  as the defalut g2p tools. They can solve the problem of polyphone to a certain extent. In the future, we intend to use a trainable language model (for example, [BERT](https://arxiv.org/abs/1810.04805)) for polyphone.
+
+However, g2pM and pypinyin do not perform well in tone sandhi, we use rules to solve this problem, which requires relevant linguistic knowledge.
+
+The **tone sandhi** in Chinese mainly include:
+
+ - soft tone sandhi (轻声变调)
+ - "一" "不" tone sandhi ("一" "不" 变调)
+ - three tone sandhi  (三声变调)
+
+For ease of understanding, we list the tone sandhi rules in Chinese here
+### 1. 轻声变调
+|  |cases  |
+|:--|:-|
+| 语气助词“吧、呢、啊”等 | 吃吧、走吗、去呢、跑啊 |
+| 结构助词：“的、地、得”| 我的书、慢慢地走、跑得很快等 |
+|有的轻声音节和非轻声音节构成对比区别意义 |买卖：一指生意；二指买和卖。 <br/> 地道：一指纯粹、真正；二指地下通道。<br> 大意：一指没有注意；二指主要的意思。 <br/>  东西：一指各种事物；二指东面与西面。<br>  言语：一指所说的话；二指开口，招呼。<br/>运气：一指一种锻炼的方法。二指幸运。<br> |
+|名词的后缀：“们、子、头”|你们、房子、石头 |
+|名词或动词的第二个重叠音节 | 奶奶、姐姐、爸爸、试试、看看、说说、问问 |
+|名词后面表示方位的：“上、下、里” |桌上、地下、院里  |
+| 动态助词：“了、着、过” | 走了、看着、去过|
+| 作宾语的人称代词：“我、你、他” | 找我、请你、麻烦他。 |
+| 约定俗成 | 匀称、盘算、枇杷、篱笆、活泼、玄乎。狐狸、学生、拾掇、麻烦、蛤蟆、石榴。玫瑰、凉快、萝卜、朋友、奴才、云彩。脑袋、老爷、老婆、嘴巴、指头、指甲。委屈、喇叭、讲究、打发、打听、喜欢。点心、伙计、打扮、哑巴、女婿、首饰。自在、吓唬、力气、漂亮、队伍、地方。痛快、念叨、笑语、丈夫、志气、钥匙。月亮、正经、位置、秀气、上司、悟性。告示、动静、热闹、屁股、阔气、意思。等 |
+
+
+### 2. "一" "不" 变调
+#### "一" 变调
+|  | 是否变调 | cases|
+|:--|:-|:-|
+| 单独念 | 否 | 第一、一楼|
+| 序数 |否  | |
+| 用在语句末尾 | 否 | |
+| 去声前变阳平（四声前变二声） |  | 一栋yí dòng、一段yí duàn、一律yí lǜ、一路yí lù|
+| 非去声前变去声（非四声前变四声） |  | 阴平（一声）<br>一发yì fā 、一端yì duān、一天yì tiān、一忽yì hū<br>阳平（二声）<br>一叠yì dié 、一同yì tóng 、一头yì tóu 、一条yì tiáo<br>上声（三声）<br>一统yì tǒng、一体yì tǐ、一览yì lǎn、一口yì kǒu|
+|轻读，当“一”嵌在重叠式的动词之间  |  | 听一听 tīng yi tīng|
+
+#### "不" 变调
+|  | 是否变调 | cases|
+|:--|:-|:-|
+|单独念|否  | |
+| 用在语句末尾| 否  | 我不|
+|去声前变阳平（四声前变成二声）  |  | 不怕bú pà、不妙bú miào、不犯bú fàn、不忿bú fèn|
+| 轻读，不”夹在重叠动词或重叠形容词之间、夹在动词和补语之间 |  |懂不懂 dǒng bu dǒng 、看不清 kàn bu qīng |
+
+
+### 3. 三声变调
+|  | 子类别| 如何变调|cases|
+|:--|:-|:-|:-|
+|单独念 |  | 否|  |
+|句末 |  | 否|  |
+|在句中停顿并没被后音节影响  |  |否 |  |
+|三声+三声  |  | 二声+三声|保险、保养、党委、尽管、老板、本领、引导、古老、敏感、鼓舞、永远、语法、口语、岛屿、保姆、远景、北海、首长、母语 |
+| 三个三声相连| 双音节+单音节（“双单格”结构）| 前两个变二声|演讲稿、跑马场、展览馆、管理组、水彩笔、蒙古语、选取法、古典舞、虎骨酒、洗脸水、草稿纸|
+|  | 单音节+双音节（“单双格”结构）|第二个变二声|史小姐、党小组、好小伙、跑百米、纸老虎、李厂长、老保姆、冷处理、很友好、小雨伞|
+|  | 单音节+单音节+单音节（“单三格”结构）| 前两个变二声| 软懒散、稳准狠|
+| 更多三声音节相连时|  | 按语意与若干二字组成三字组，然后按以上变调规律处理|岂有 / 此理。<br>请你 / 给我 / 打点儿 / 洗脸水。<br>手表厂 / 有五种 /好产品。|
+
+## References
+
+ - [chinese_text_normalization](https://github.com/speechio/chinese_text_normalization)
+ - [声调篇｜这些“一、不”变调规律，你不得不知](https://zhuanlan.zhihu.com/p/36156170)
+ - [TTS前端模块中的普通话变调规则](https://zhuanlan.zhihu.com/p/65091429)
+ - [轻声和变调](https://wenku.baidu.com/view/ad2016d94693daef5ef73db1.html)
+ - [必读轻声词语表546条](http://www.chaziwang.com/article-view-504.html)

docs/source/tts/index.rst

+.. parakeet documentation master file, created by
+   sphinx-quickstart on Fri Sep 10 14:22:24 2021.
+   You can adapt this file completely to your liking, but it should at least
+   contain the root `toctree` directive.
+
+Parakeet 
+====================================
+
+``parakeet`` is a deep learning based text-to-speech toolkit built upon ``paddlepaddle`` framework. It aims to provide a flexible, efficient and state-of-the-art text-to-speech toolkit for the open-source community. It includes many influential TTS models proposed by `Baidu Research <http://research.baidu.com>`_ and other research groups. 
+
+``parakeet`` mainly consists of components below.
+
+#. Implementation of models and commonly used neural network layers.
+#. Dataset abstraction and common data preprocessing pipelines.
+#. Ready-to-run experiments.
+
+.. toctree::
+   :maxdepth: 1
+   :caption: Introduction
+
+   introduction
+
+.. toctree::
+   :maxdepth: 1
+   :caption: Getting started
+
+   install
+   basic_usage
+   advanced_usage
+   cn_text_frontend
+   released_models
+
+.. toctree::
+   :maxdepth: 1
+   :caption: Demos
+   
+   demo
+   
+
+Indices and tables
+==================
+
+* :ref:`genindex`
+* :ref:`modindex`
+* :ref:`search`

docs/source/tts/install.md

+# Installation
+## Install PaddlePaddle
+Parakeet requires PaddlePaddle as its backend. Note that 2.1.2 or newer versions of paddle is required.
+
+Since paddlepaddle has multiple packages depending on the device (cpu or gpu) and the dependency libraries, it is recommended to install a proper package of paddlepaddle with respect to the device and dependency library versons via `pip`.
+
+Installing paddlepaddle with conda or build paddlepaddle from source is also supported. Please refer to [PaddlePaddle installation](https://www.paddlepaddle.org.cn/install/quick?docurl=/documentation/docs/zh/install/pip/linux-pip.html) for more details.
+
+Example instruction to install paddlepaddle via pip is listed below.
+
+### PaddlePaddle with GPU
+```python
+# CUDA10.1 的 PaddlePaddle
+python -m pip install paddlepaddle-gpu==2.1.2.post101 -f https://www.paddlepaddle.org.cn/whl/linux/mkl/avx/stable.html
+# CUDA10.2 的 PaddlePaddle
+python -m pip install paddlepaddle-gpu -i https://mirror.baidu.com/pypi/simple
+# CUDA11.0 的 PaddlePaddle
+python -m pip install paddlepaddle-gpu==2.1.2.post110 -f https://www.paddlepaddle.org.cn/whl/linux/mkl/avx/stable.html
+# CUDA11.2 的 PaddlePaddle
+python -m pip install paddlepaddle-gpu==2.1.2.post112 -f https://www.paddlepaddle.org.cn/whl/linux/mkl/avx/stable.html
+```
+### PaddlePaddle with CPU
+```python
+python -m pip install paddlepaddle==2.1.2 -i https://mirror.baidu.com/pypi/simple
+```
+## Install libsndfile
+Experimemts in parakeet often involve audio and spectrum processing, thus `librosa` and `soundfile` are required. `soundfile` requires a extra C library `libsndfile`, which is not always handled by pip.
+
+For Windows and Mac users, `libsndfile` is also installed when installing `soundfile` via pip, but for Linux users, installing `libsndfile` via system package manager is required. Example commands for popular distributions are listed below.
+```bash
+# ubuntu, debian
+sudo apt-get install libsndfile1
+# centos, fedora
+sudo yum install libsndfile
+# openSUSE
+sudo zypper in libsndfile
+```
+For any problem with installtion of soundfile, please refer to [SoundFile](https://pypi.org/project/SoundFile/).
+## Install Parakeet
+There are two ways to install parakeet according to the purpose of using it.
+
+ 1. If you want to run experiments provided by parakeet or add new models and experiments, it is recommended to clone the project from github (Parakeet), and install it in editable mode.
+       ```python
+       git clone https://github.com/PaddlePaddle/Parakeet
+       cd Parakeet
+       pip install -e .
+       ```

docs/source/tts/introduction.md

+# Parakeet - PAddle PARAllel text-to-speech toolKIT
+
+## What is Parakeet?
+Parakeet is a deep learning based text-to-speech toolkit built upon paddlepaddle framework. It aims to provide a flexible, efficient and state-of-the-art text-to-speech toolkit for the open-source community. It includes many influential TTS models proposed by Baidu Research and other research groups.
+
+## What can Parakeet do?
+Parakeet mainly consists of components below:
+- Implementation of models and commonly used neural network layers.
+- Dataset abstraction and common data preprocessing pipelines.
+- Ready-to-run experiments.
+
+Parakeet provides you with a complete TTS pipeline, including:
+- Text FrontEnd
+    - Rule based Chinese frontend.
+- Acoustic Models
+    - FastSpeech2
+    - SpeedySpeech
+    - TransformerTTS
+    - Tacotron2
+- Vocoders
+    - Parallel WaveGAN
+    - WaveFlow
+- Voice Cloning
+    - Transfer Learning from Speaker Verification to Multispeaker Text-To-Speech Synthesis
+    - GE2E
+
+Parakeet helps you to train TTS models with simple commands.

docs/source/tts/released_models.md

+# Released Models
+TTS system mainly includes three modules: `text frontend`, `Acoustic model` and `Vocoder`. We introduce a rule based Chinese text frontend in [cn_text_frontend.md](./cn_text_frontend.md). Here, we will introduce acoustic models and vocoders, which are trainable models.
+
+The main processes of TTS include:
+1. Convert the original text into characters/phonemes, through `text frontend` module.
+2. Convert characters/phonemes into acoustic features , such as linear spectrogram, mel spectrogram, LPC features, etc. through `Acoustic models`.
+3. Convert acoustic features into waveforms through `Vocoders`.
+
+A simple text frontend module can be implemented by rules. Acoustic models and vocoders need to be trained. The models provided by Parakeet are acoustic models and vocoders.
+
+## Acoustic Models
+### Modeling Objectives of Acoustic Models
+Modeling the mapping relationship between text sequences and speech features：
+```text
+text X = {x1,...,xM}
+specch Y = {y1,...yN}
+```
+Modeling Objectives:
+```text
+Ω = argmax p(Y|X,Ω)
+```
+### Modeling process of Acoustic Models
+At present, there are two mainstream acoustic model structures.
+
+- Frame level acoustic model:
+   - Duration model (M Tokens - > N Frames).
+   - Acoustic decoder (N Frames - > N Frames).
+
+<div align="left">
+  <img src="https://paddlespeech.bj.bcebos.com/Parakeet/docs/images/frame_level_am.png" width=500 /> <br>
+</div>
+
+- Sequence to sequence acoustic model:
+    - M Tokens - > N Frames.
+
+<div align="left">
+  <img src="https://paddlespeech.bj.bcebos.com/Parakeet/docs/images/seq2seq_am.png" width=500 /> <br>
+</div>
+
+### Tacotron2
+ [Tacotron](https://arxiv.org/abs/1703.10135)  is the first end-to-end acoustic model based on deep learning, and it is also the most widely used acoustic model.
+
+[Tacotron2](https://arxiv.org/abs/1712.05884) is the Improvement of Tacotron.
+#### Tacotron
+**Features of Tacotron:**
+- Encoder.
+   - CBHG.
+   - Input: character sequence.
+- Decoder.
+    - Global soft attention.
+    - unidirectional RNN.
+    - Autoregressive teacher force training (input real speech feature).
+    - Multi frame prediction.
+    - CBHG postprocess.
+    - Vocoder: Griffin-Lim.
+<div align="left">
+  <img src="https://paddlespeech.bj.bcebos.com/Parakeet/docs/images/tacotron.png" width=700 /> <br>
+</div>
+
+**Advantage of Tacotron:**
+- No need for complex text frontend analysis modules.
+- No need for additional duration model.
+- Greatly simplifies the acoustic model construction process and reduces the dependence of speech synthesis tasks on domain knowledge.
+
+**Disadvantages of Tacotron:**
+- The CBHG  is complex and the amount of parameters is relatively large.
+- Global soft attention.
+- Poor stability for speech synthesis tasks.
+- In training, the less the number of speech frames predicted at each moment, the more difficult it is to train.
+-  Phase problem in Griffin-Lim casues speech distortion during wave reconstruction.
+- The autoregressive decoder cannot be stopped during the generation process.
+
+#### Tacotron2
+**Features of Tacotron2:**
+- Reduction of parameters.
+   - CBHG -> PostNet (3 Conv layers + BLSTM or 5 Conv layers).
+   - remove Attention RNN.
+- Speech distortion caused by Griffin-Lim.
+    - WaveNet.
+- Improvements of PostNet.
+   - CBHG -> 5 Conv layers.
+   -  The input and output of the PostNet calculate `L2` loss with real Mel spectrogram.
+   - Residual connection.
+- Bad stop in autoregressive decoder.
+   - Predict whether it should stop at each moment of decoding (stop token).
+   - Set a threshold to determine whether to stop generating when decoding.
+- Stability of attention.
+   - Location-aware attention.
+   - The alignment matrix of previous time is considered at the step `t` of decoder.
+
+<div align="left">
+  <img src="https://paddlespeech.bj.bcebos.com/Parakeet/docs/images/tacotron2.png" width=500 /> <br>
+</div>
+
+You can find Parakeet's tacotron2 example at `Parakeet/examples/tacotron2`.
+
+### TransformerTTS
+**Disadvantages of the Tacotrons:**
+- Encodr and decoder are relatively weak at global information modeling
+   - Vanishing gradient of RNN.
+   - Fixed-length context modeling problem in CNN kernel.
+- Training is relatively inefficient.
+- The attention is not robust enough and the stability is poor.
+
+Transformer TTS is a combination of Tacotron2 and Transformer.
+
+#### Transformer
+ [Transformer](https://arxiv.org/abs/1706.03762) is a seq2seq model based entirely on attention mechanism.
+
+**Features of Transformer:**
+- Encoder.
+    - `N` blocks based on self-attention mechanism.
+    - Positional Encoding.
+- Decoder.
+    - `N` blocks based on self-attention mechanism.
+    - Add Mask to the self-attention in blocks to cover up the information after `t` step.
+    - Attentions between encoder and decoder.
+    - Positional Encoding.
+
+<div align="left">
+  <img src="https://paddlespeech.bj.bcebos.com/Parakeet/docs/images/transformer.png" width=500 /> <br>
+</div>
+
+#### Transformer TTS
+Transformer TTS is a seq2seq acoustic model based on Transformer and Tacotron2.
+
+**Motivations：**
+- RNNs in Tacotron2  make the inefficiency of training.
+- Vanishing gradient of RNN makes the model's ability to model long-term contexts weak.
+- Self-attention doesn't contain any recursive structure which can be trained in parallel.
+- Self-attention can model global context information well.
+
+**Features of Transformer TTS:**
+- Add conv based PreNet in encoder and decoder.
+- Stop Token in decoder controls when to stop autoregressive generation.
+- Add PostNet after decoder to improve the quality of synthetic speech.
+- Scaled position encoding.
+    - Uniform scale position encoding may have a negative impact on input or output sequences.
+
+<div align="left">
+  <img src="https://paddlespeech.bj.bcebos.com/Parakeet/docs/images/transformer_tts.png" width=500 /> <br>
+</div>
+
+**Disadvantages of Transformer TTS:**
+- The ability of position encoding for timing information is still relatively weak.
+- The ability to perceive local information is weak, and local information is more related to pronunciation.
+- Stability is worse than Tacotron2.
+
+You can find Parakeet's Transformer TTS example at `Parakeet/examples/transformer_tts`.
+
+
+### FastSpeech2
+**Disadvantage of seq2seq models:**
+- In the seq2seq model based on attention, no matter how to improve the attention mechanism, it's difficult to avoid generation errors in the decoding stage.
+
+Frame level acoustic models use duration models to determine the pronunciation duration of phonemes, and the frame level mapping does not have the uncertainty of sequence generation.
+
+In seq2saq models, the concept of duration models is used as the alignment module of two sequences to replace attention, which can avoid the uncertainty in attention, and significantly improve the stability of the seq2saq models.
+
+#### FastSpeech
+Instead of using the encoder-attention-decoder based architecture as adopted by most seq2seq based autoregressive and non-autoregressive generation, [FastSpeech](https://arxiv.org/abs/1905.09263) is  a novel feed-forward structure, which can generate a target mel spectrogram sequence in parallel.
+
+**Features of FastSpeech:**
+- Encoder: based on Transformer.
+- Change `FFN` to `CNN` in self-attention.
+    -  Model local dependency.
+- Length regulator.
+    - Use real phoneme durations to expand output frame of encoder during training.
+- Non autoregressive decode.
+    -  Improve generation efficiency.
+
+**Length predictor:**
+- Pretrain a TransformerTTS model.
+- Get alignment matrix of train data.
+- Caculate the phoneme durations according to the probability of the alignment matrix.
+- Use the output of encoder to predict the phoneme durations and calculate the MSE loss.
+- Use real phoneme durations to expand output frame of encoder during training.
+- Use phoneme durations predicted by the duration model to expand the frame during prediction.
+    - Attentrion can not control phoneme durations. The explicit duration modeling can control durations through duration coefficient (duration coefficient is `1` during training).
+
+**Advantages of non-autoregressive decoder:**
+- The built-in duration model of the seq2seq model has converted the input length `M` to the output length `N`.
+- The length of output is known, `stop token` is no longer used, avoiding the problem of being unable to stop.
+• Can be generated in parallel (decoding time is less affected by sequence length)
+
+<div align="left">
+  <img src="https://paddlespeech.bj.bcebos.com/Parakeet/docs/images/fastspeech.png" width=800 /> <br>
+</div>
+
+#### FastPitch
+[FastPitch](https://arxiv.org/abs/2006.06873) follows FastSpeech. A single pitch value is predicted for every temporal location, which improves the overall quality of synthesized speech.
+
+<div align="left">
+  <img src="https://paddlespeech.bj.bcebos.com/Parakeet/docs/images/fastpitch.png" width=500 /> <br>
+</div>
+
+#### FastSpeech2
+**Disadvantages of FastSpeech:**
+- The teacher-student distillation pipeline is complicated and time-consuming.
+- The duration extracted from the teacher model is not accurate enough.
+- The target mel spectrograms distilled from teacher model suffer from information loss due to data simplification.
+
+[FastSpeech2](https://arxiv.org/abs/2006.04558)  addresses the issues in FastSpeech and better solves the one-to-many mapping problem in TTS.
+
+**Features of FastSpeech2:**
+- Directly training the model with ground-truth target instead of the simplified output from teacher.
+- Introducing more variation information of speech as conditional inputs, extract `duration`, `pitch` and `energy` from speech waveform and directly take them as conditional inputs in training and use predicted values in inference.
+
+FastSpeech2 is similar to FastPitch but introduces more variation information of speech.
+
+<div align="left">
+  <img src="https://paddlespeech.bj.bcebos.com/Parakeet/docs/images/fastspeech2.png" width=800 /> <br>
+</div>
+
+You can find Parakeet's FastSpeech2/FastPitch example at `Parakeet/examples/fastspeech2`, We use token-averaged pitch and energy values introduced in FastPitch rather than frame level ones in FastSpeech2.
+
+### SpeedySpeech
+[SpeedySpeech](https://arxiv.org/abs/2008.03802) simplify the teacher-student architecture of FastSpeech and provide a fast and stable training procedure.
+
+**Features of SpeedySpeech:**
+- Use a simpler, smaller and faster-to-train convolutional teacher model ([Deepvoice3](https://arxiv.org/abs/1710.07654) and [DCTTS](https://arxiv.org/abs/1710.08969)) with a single attention layer instead of Transformer used in FastSpeech.  
+- Show that self-attention layers in the student network are not needed for high-quality speech synthesis.
+- Describe a simple data augmentation technique that can be used early in the training to make the teacher network robust to sequential error propagation.
+
+<div align="left">
+  <img src="https://paddlespeech.bj.bcebos.com/Parakeet/docs/images/speedyspeech.png" width=500 /> <br>
+</div>
+
+You can find Parakeet's SpeedySpeech example at `Parakeet/examples/speedyspeech/baker`.
+
+## Vocoders
+In speech synthesis, the main task of the vocoder is to convert the spectral parameters predicted by the acoustic model into the final speech waveform.
+
+Taking into account the short-term change frequency of the waveform, the acoustic model usually avoids direct modeling of the speech waveform, but firstly models the spectral features extracted from the speech waveform, and then reconstructs the waveform by the decoding part of the vocoder.
+
+A vocoder usually consists of a pair of encoders and decoders for speech analysis and synthesis. The encoder estimate the parameters, and then the decoder restores the speech.
+
+Vocoders based on neural networks usually is speech synthesis, which learns the mapping relationship from spectral features to waveforms through training data.
+
+### Categories of neural vocodes
+- Autoregression
+    - WaveNet
+    - WaveRNN
+    - LPCNet
+
+- Flow
+    - **WaveFlow**
+    - WaveGlow
+    - FloWaveNet
+    - Parallel WaveNet
+- GAN
+    - WaveGAN
+    - **Parallel WaveGAN**
+    - MelGAN
+    - HiFi-GAN
+- VAE
+    - Wave-VAE
+- Diffusion
+    - WaveGrad
+    - DiffWave
+
+**Motivations of GAN-based vocoders:**
+- Modeling speech signal by estimating probability distribution usually has high requirements for the expression ability of the model itself. In addition, specific assumptions need to be made about the distribution of waveforms.
+- Although autoregressive neural vocoders can obtain high-quality synthetic speech, such models usually have a **slow generation speed**.
+- The training of inverse autoregressive flow vocoders is complex, and they also require the modeling capability of long term context information.
+- Vocoders based on Bipartite Transformation converge slowly and are complex.
+- GAN-based vocoders don't need to make assumptions about the speech distribution, and train through adversarial learning.
+
+Here, we introduce a Flow-based vocoder WaveFlow and a GAN-based vocoder Parallel WaveGAN.
+
+### WaveFlow
+ [WaveFlow](https://arxiv.org/abs/1912.01219) is proposed by Baidu Research.
+
+**Features of WaveFlow:**
+- It can synthesize 22.05 kHz high-fidelity speech around 40x faster than real-time on a Nvidia V100 GPU without engineered inference kernels, which is faster than [WaveGlow](https://github.com/NVIDIA/waveglow) and serveral orders of magnitude faster than WaveNet.
+- It is a small-footprint flow-based model for raw audio. It has only 5.9M parameters, which is 15x smalller than WaveGlow (87.9M).
+- It is directly trained with maximum likelihood without probability density distillation and auxiliary losses as used in [Parallel WaveNet](https://arxiv.org/abs/1711.10433) and [ClariNet](https://openreview.net/pdf?id=HklY120cYm), which simplifies the training pipeline and reduces the cost of development.
+
+You can find Parakeet's WaveFlow example at `Parakeet/examples/waveflow`.
+
+### Parallel WaveGAN
+[Parallel WaveGAN](https://arxiv.org/abs/1910.11480) trains a non-autoregressive WaveNet variant as a generator in a GAN based training method.
+
+**Features of Parallel WaveGAN:**
+
+- Use non-causal convolution instead of causal convolution.
+- The input is random Gaussian white noise.
+- The model is non-autoregressive both in training and prediction, which is fast
+-  Multi-resolution STFT loss.
+
+<div align="left">
+  <img src="https://paddlespeech.bj.bcebos.com/Parakeet/docs/images/pwg.png" width=600 /> <br>
+</div>
+
+You can find Parakeet's Parallel WaveGAN example at `Parakeet/examples/parallelwave_gan/baker`.

examples/aishell/README.md

 # ASR
 
-* s0 for deepspeech2 offline
-* s1 for u2
+* s0 for deepspeech2
+* s1 for u2/transformer/conformer
+
+## Data
+
+| Data Subset         | Duration in Seconds   |
+| ------------------- | --------------------- |
+| data/manifest.train | 1.23 ~ 14.53125       |
+| data/manifest.dev   | 1.645 ~ 12.533        |
+| data/manifest.test  | 1.859125 ~ 14.6999375 |

examples/aishell/s0/README.md

 # Aishell-1
 
-## Data
-| Data Subset | Duration in Seconds |
-| data/manifest.train |  1.23 ~ 14.53125 |
-| data/manifest.dev  | 1.645 ~ 12.533 |  
-| data/manifest.test | 1.859125 ~ 14.6999375 |
-
 ## Deepspeech2
 
 | Model | Params | Release | Config | Test set | Loss | CER |  

examples/aishell/s0/local/data.sh

@@ -26,22 +26,6 @@ if [ ${stage} -le -1 ] && [ ${stop_stage} -ge -1 ]; then
 fi
 
 if [ ${stage} -le 0 ] && [ ${stop_stage} -ge 0 ]; then
-    # download data, generate manifests
-    # build vocabulary
-    python3 ${MAIN_ROOT}/utils/build_vocab.py \
-    --unit_type="char" \
-    --count_threshold=0 \
-    --vocab_path="data/vocab.txt" \
-    --manifest_paths "data/manifest.train.raw" "data/manifest.dev.raw"
-
-    if [ $? -ne 0 ]; then
-        echo "Build vocabulary failed. Terminated."
-        exit 1
-    fi
-fi
-
-
-if [ ${stage} -le 1 ] && [ ${stop_stage} -ge 1 ]; then
     # compute mean and stddev for normalizer
     num_workers=$(nproc)
     python3 ${MAIN_ROOT}/utils/compute_mean_std.py \
@@ -62,6 +46,20 @@ if [ ${stage} -le 1 ] && [ ${stop_stage} -ge 1 ]; then
     fi
 fi
 
+if [ ${stage} -le 1 ] && [ ${stop_stage} -ge 1 ]; then
+    # download data, generate manifests
+    # build vocabulary
+    python3 ${MAIN_ROOT}/utils/build_vocab.py \
+    --unit_type="char" \
+    --count_threshold=0 \
+    --vocab_path="data/vocab.txt" \
+    --manifest_paths "data/manifest.train.raw" "data/manifest.dev.raw"
+
+    if [ $? -ne 0 ]; then
+        echo "Build vocabulary failed. Terminated."
+        exit 1
+    fi
+fi
 
 if [ ${stage} -le 2 ] && [ ${stop_stage} -ge 2 ]; then
     # format manifest with tokenids, vocab size

examples/aishell/s1/README.md

@@ -11,6 +11,7 @@
 
 
 ## Chunk Conformer
+Need set `decoding.decoding_chunk_size=16` when decoding.
 
 | Model | Params | Config | Augmentation| Test set | Decode method | Chunk Size & Left Chunks | Loss | WER |  
 | --- | --- | --- | --- | --- | --- | --- | --- | --- |  
@@ -18,10 +19,3 @@
 | conformer | 47.06M | conf/chunk_conformer.yaml | spec_aug + shift | test | ctc_greedy_search | 16, -1 | - | 0.070806 |  
 | conformer | 47.06M | conf/chunk_conformer.yaml | spec_aug + shift | test | ctc_prefix_beam_search | 16, -1 | - | 0.070739 |  
 | conformer | 47.06M | conf/chunk_conformer.yaml | spec_aug + shift | test | attention_rescoring | 16, -1 |  - | 0.059400 |  
-
-
-## Transformer
-
-| Model | Params | Config | Augmentation| Test set | Decode method | Loss | WER |  
-| --- | --- | --- | --- | --- | --- | --- | ---|  
-| transformer | - | conf/transformer.yaml | spec_aug + shift | test | attention | - | - |  

examples/aishell/s1/conf/augmentation.json

@@ -19,17 +19,17 @@
   {
     "type": "specaug",
     "params": {
+      "W": 0,
+      "warp_mode": "PIL",
       "F": 10,
-      "T": 50,
       "n_freq_masks": 2,
+      "T": 50,
       "n_time_masks": 2,
       "p": 1.0,
-      "W": 80,
       "adaptive_number_ratio": 0,
       "adaptive_size_ratio": 0,
       "max_n_time_masks": 20,
-      "replace_with_zero": true,
-      "warp_mode": "PIL"
+      "replace_with_zero": true
     },
     "prob": 1.0
   }

examples/aishell/s1/local/data.sh

@@ -26,22 +26,6 @@ if [ ${stage} -le -1 ] && [ ${stop_stage} -ge -1 ]; then
 fi
 
 if [ ${stage} -le 0 ] && [ ${stop_stage} -ge 0 ]; then
-    # download data, generate manifests
-    # build vocabulary
-    python3 ${MAIN_ROOT}/utils/build_vocab.py \
-    --unit_type="char" \
-    --count_threshold=0 \
-    --vocab_path="data/vocab.txt" \
-    --manifest_paths "data/manifest.train.raw"
-
-    if [ $? -ne 0 ]; then
-        echo "Build vocabulary failed. Terminated."
-        exit 1
-    fi
-fi
-
-
-if [ ${stage} -le 1 ] && [ ${stop_stage} -ge 1 ]; then
     # compute mean and stddev for normalizer
     num_workers=$(nproc)
     python3 ${MAIN_ROOT}/utils/compute_mean_std.py \
@@ -63,6 +47,20 @@ if [ ${stage} -le 1 ] && [ ${stop_stage} -ge 1 ]; then
     fi
 fi
 
+if [ ${stage} -le 1 ] && [ ${stop_stage} -ge 1 ]; then
+    # download data, generate manifests
+    # build vocabulary
+    python3 ${MAIN_ROOT}/utils/build_vocab.py \
+    --unit_type="char" \
+    --count_threshold=0 \
+    --vocab_path="data/vocab.txt" \
+    --manifest_paths "data/manifest.train.raw"
+
+    if [ $? -ne 0 ]; then
+        echo "Build vocabulary failed. Terminated."
+        exit 1
+    fi
+fi
 
 if [ ${stage} -le 2 ] && [ ${stop_stage} -ge 2 ]; then
     # format manifest with tokenids, vocab size

examples/aishell/s1/local/test_hub.sh

+#!/bin/bash
+
+if [ $# != 3 ];then
+    echo "usage: ${0} config_path ckpt_path_prefix audio_file"
+    exit -1
+fi
+
+ngpu=$(echo $CUDA_VISIBLE_DEVICES | awk -F "," '{print NF}')
+echo "using $ngpu gpus..."
+
+config_path=$1
+ckpt_prefix=$2
+audio_file=$3
+
+chunk_mode=false
+if [[ ${config_path} =~ ^.*chunk_.*yaml$ ]];then
+    chunk_mode=true
+fi
+
+# download language model
+#bash local/download_lm_ch.sh
+#if [ $? -ne 0 ]; then
+#    exit 1
+#fi
+
+
+
+for type in  attention_rescoring; do
+    echo "decoding ${type}"
+    batch_size=1
+    output_dir=${ckpt_prefix}
+    mkdir -p ${output_dir}
+    python3 -u ${BIN_DIR}/test_hub.py \
+    --nproc ${ngpu} \
+    --config ${config_path} \
+    --result_file ${output_dir}/${type}.rsl \
+    --checkpoint_path ${ckpt_prefix} \
+    --opts decoding.decoding_method ${type} \
+    --opts decoding.batch_size ${batch_size} \
+    --audio_file ${audio_file}
+
+    if [ $? -ne 0 ]; then
+        echo "Failed in evaluation!"
+        exit 1
+    fi
+done
+exit 0

examples/aishell/s1/path.sh

@@ -25,5 +25,5 @@ export PATH=${PATH}:${SRILM}/bin:${SRILM}/bin/i686-m64
 export KALDI_ROOT=${MAIN_ROOT}/tools/kaldi
 [ -f $KALDI_ROOT/tools/env.sh ] && . $KALDI_ROOT/tools/env.sh
 export PATH=$PWD/utils/:$KALDI_ROOT/tools/openfst/bin:$PWD:$PATH
-[ ! -f $KALDI_ROOT/tools/config/common_path.sh ] && echo >&2 "The standard file $KALDI_ROOT/tools/config/common_path.sh is not present -> Exit!"
-. $KALDI_ROOT/tools/config/common_path.sh || true
+[ ! -f $KALDI_ROOT/tools/config/common_path.sh ] && echo >&2 "The standard file $KALDI_ROOT/tools/config/common_path.sh is not present, can not using Kaldi!"
+[ -f $KALDI_ROOT/tools/config/common_path.sh ] && . $KALDI_ROOT/tools/config/common_path.sh

examples/aishell/s1/run.sh

 #!/bin/bash
-set -e
 source path.sh
+set -e
 
 stage=0
 stop_stage=100
@@ -13,6 +13,8 @@ avg_ckpt=avg_${avg_num}
 ckpt=$(basename ${conf_path} | awk -F'.' '{print $1}')
 echo "checkpoint name ${ckpt}"
 
+audio_file="data/tmp.wav"
+
 if [ ${stage} -le 0 ] && [ ${stop_stage} -ge 0 ]; then
     # prepare data
     bash ./local/data.sh || exit -1
@@ -48,3 +50,8 @@ fi
     # train lm and build TLG
     ./local/tlg.sh --corpus aishell --lmtype srilm
  fi
+
+if [ ${stage} -le 7 ] && [ ${stop_stage} -ge 7 ]; then
+    # test a single .wav file
+    CUDA_VISIBLE_DEVICES=3 ./local/test_hub.sh ${conf_path} exp/${ckpt}/checkpoints/${avg_ckpt} ${audio_file} || exit -1
+fi

examples/aishell3/README.md

+# Aishell3
+
+* tts0 - fastspeech2
+* vc0 - tactron2 voice clone

examples/aishell3/vc0/local/tacotron2/README_cn.md

+## Tacotron2 + AISHELL-3 数据集训练语音克隆模型
+
+本实验的内容是利用 AISHELL-3 数据集和 Tacotron 2 模型进行语音克隆任务，使用的模型大体结构和论文 [Transfer Learning from Speaker Verification to Multispeaker Text-To-Speech Synthesis](https://arxiv.org/pdf/1806.04558.pdf) 相同。大致步骤如下：
+
+1. Speaker Encoder: 我们使用了一个 Speaker Verification 任务训练一个 speaker encoder。这部分任务所用的数据集和训练 Tacotron 2 的数据集不同，因为不需要 transcription 的缘故，我们使用了较多的训练数据，可以参考实现 [ge2e](../ge2e)。
+2. Synthesizer: 然后使用训练好的 speaker encoder 为 AISHELL-3 数据集中的每个句子生成对应的 utterance embedding. 这个 Embedding 作为 Tacotron 模型中的一个额外输入和 encoder outputs 拼接在一起。
+3. Vocoder: 我们使用的声码器是 WaveFlow，参考实验 [waveflow](../waveflow).
+
+## 数据处理
+
+### utterance embedding 的生成
+
+使用训练好的 speaker encoder 为 AISHELL-3 数据集中的每个句子生成对应的 utterance embedding. 以和音频文件夹同构的方式存储。存储格式是 `.npy` 文件。
+
+首先 cd 到 [ge2e](../ge2e) 文件夹。下载训练好的 [模型](https://paddlespeech.bj.bcebos.com/Parakeet/ge2e_ckpt_0.3.zip)，然后运行脚本生成每个句子的 utterance embedding.
+
+```bash
+python inference.py --input=<intput> --output=<output> --device="gpu" --checkpoint_path=<pretrained checkpoint>
+```
+
+其中 input 是只包含音频文件夹的文件。这里可以用 `~/datasets/aishell3/train/wav`，然后 output 是用于存储 utterance embed 的文件夹，这里可以用 `~/datasets/aishell3/train/embed`。Utterance embedding 会以和音频文件夹相同的文件结构存储，格式为 `.npy`.
+
+utterance embedding 的计算可能会用几个小时的时间，请耐心等待。
+
+### 音频处理
+
+因为 AISHELL-3 数据集前后有一些空白，静音片段，而且语音幅值很小，所以我们需要进行空白移除和音量规范化。空白移除可以简单的使用基于音量或者能量的方法，但是效果不是很好，对于不同的句子很难取到一个一致的阈值。我们使用的是先利用 Force Aligner 进行文本和语音的对齐。然后根据对齐结果截除空白。
+
+我们使用的工具是 Montreal Force Aligner 1.0. 因为 aishell 的标注包含拼音标注，所以我们提供给 Montreal Force Aligner 的是拼音 transcription 而不是汉字 transcription. 而且需要把其中的韵律标记(`$` 和 `%`)去除，并且处理成 Montreal Force Alinger 所需要的文件形式。和音频同名的文本文件，扩展名为 `.lab`.
+
+此外还需要准备词典文件。其中包含把拼音序列转换为 phone 序列的映射关系。在这里我们只做声母和韵母的切分，而声调则归为韵母的一部分。我们使用的[词典文件](./lexicon.txt)可以下载。
+
+准备好之后运行训练和对齐。首先下载 [Montreal Force Aligner 1.0](https://github.com/MontrealCorpusTools/Montreal-Forced-Aligner/releases/tag/v1.0.1).下载之后解压即可运行。cd 到其中的 bin 文件夹运行命令，即可进行训练和对齐。前三个命令行参数分别是音频文件夹的路径，词典路径和对齐文件输出路径。可以通过`-o` 传入训练得到的模型保存路径。
+
+```bash
+./mfa_train_and_align \
+  ~/datasets/aishell3/train/wav \
+  lexicon.txt \
+  ~/datasets/aishell3/train/alignment \
+  -o aishell3_model \
+  -v
+```
+
+因为训练和对齐的时间比较长。我们提供了对齐后的 [alignment 文件](https://paddlespeech.bj.bcebos.com/Parakeet/alignment_aishell3.tar.gz)，其中每个句子对应的文件为 `.TextGrid` 格式的文本。
+
+得到了对齐文件之后，可以运行 `process_wav.py` 脚本来处理音频。
+
+```bash
+python process_wav.py --input=<input> --output=<output> --alignment=<alignment>
+```
+
+默认 input, output, alignment 分别是 `~/datasets/aishell3/train/wav`, `~/datasets/aishell3/train/normalized_wav`, `~/datasets/aishell3/train/alignment`.
+
+处理结束后，会将处理好的音频保存在 `<output>` 文件夹中。
+
+### 转录文本处理
+
+把文本转换成为 phone 和 tone 的形式，并存储起来。值得注意的是，这里我们的处理和用于 montreal force aligner 的不一样。我们把声调分了出来。这是一个处理方式，当然也可以只做声母和韵母的切分。
+
+运行脚本处理转录文本。
+
+```bash
+python preprocess_transcription.py --input=<input> --output=<output>
+```
+
+默认的 input 是 `~/datasets/aishell3/train`，其中会包含 `label_train-set.txt` 文件，处理后的结果会 `metadata.yaml` 和 `metadata.pickle`. 前者是文本格式，方便查看，后者是二进制格式，方便直接读取。
+
+### mel 频谱提取
+
+对处理后的音频进行 mel 频谱的提取，并且以和音频文件夹同构的方式存储，存储格式是 `.npy` 文件。
+
+```python
+python extract_mel.py --input=<intput> --output=<output>
+```
+
+input 是处理后的音频所在的文件夹，output 是输出频谱的文件夹。
+
+## 训练
+
+运行脚本训练。
+
+```python
+python train.py --data=<data> --output=<output> --device="gpu"
+```
+
+我们的模型去掉了 tacotron2 模型中的 stop token prediction。因为实践中由于 stop token prediction 是一个正负样例比例极不平衡的问题，每个句子可能有几百帧对应负样例，只有一帧正样例，而且这个 stop token prediction 对音频静音的裁切十分敏感。我们转用 attention 的最高点到达 encoder 侧的最后一个符号为终止条件。
+
+另外，为了加速模型的收敛，我们加上了 guided attention loss, 诱导 encoder-decoder 之间的 alignment 更快地呈现对角线。
+
+可以使用 visualdl 查看训练过程的 log。
+
+```bash
+visualdl --logdir=<output> --host=$HOSTNAME
+```
+
+示例 training loss / validation loss 曲线如下。
+
+![train](./images/train.png)
+
+![valid](./images/valid.png)
+
+<img src="images/alignment-step2000.png" alt="alignment-step2000" style="zoom:50%;" />
+
+大约从训练 2000 步左右就从 validation 过程中产出的 alignement 中可以观察到模糊的对角线。随着训练步数增加，对角线会更加清晰。但因为 validation 也是以 teacher forcing 的方式进行的，所以要在真正的 auto regressive 合成中产出的 alignment 中观察到对角线，需要更长的时间。
+
+## 预训练模型
+
+预训练模型下载链接。[tacotron2_aishell3_ckpt_0.3.zip](https://paddlespeech.bj.bcebos.com/Parakeet/tacotron2_aishell3_ckpt_0.3.zip).
+
+## 使用
+
+本实验包含了一个简单的使用示例，用户可以替换作为参考的声音以及文本，用训练好的模型来合成语音。使用方式参考 [notebook](./voice_cloning.ipynb) 上的使用说明。

examples/aishell3/vc0/local/tacotron2/aishell3.py

+# 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 pickle
+from pathlib import Path
+
+import numpy as np
+from paddle.io import Dataset
+from parakeet.frontend import Vocab
+from parakeet.data import batch_text_id, batch_spec
+
+from preprocess_transcription import _phones, _tones
+
+voc_phones = Vocab(sorted(list(_phones)))
+print("vocab_phones:\n", voc_phones)
+voc_tones = Vocab(sorted(list(_tones)))
+print("vocab_tones:\n", voc_tones)
+
+
+class AiShell3(Dataset):
+    """Processed AiShell3 dataset."""
+
+    def __init__(self, root):
+        super().__init__()
+        self.root = Path(root).expanduser()
+        self.embed_dir = self.root / "embed"
+        self.mel_dir = self.root / "mel"
+
+        with open(self.root / "metadata.pickle", 'rb') as f:
+            self.records = pickle.load(f)
+
+    def __getitem__(self, index):
+        metadatum = self.records[index]
+        sentence_id = metadatum["sentence_id"]
+        speaker_id = sentence_id[:7]
+        phones = metadatum["phones"]
+        tones = metadatum["tones"]
+        phones = np.array(
+            [voc_phones.lookup(item) for item in phones], dtype=np.int64)
+        tones = np.array(
+            [voc_tones.lookup(item) for item in tones], dtype=np.int64)
+        mel = np.load(str(self.mel_dir / speaker_id / (sentence_id + ".npy")))
+        embed = np.load(
+            str(self.embed_dir / speaker_id / (sentence_id + ".npy")))
+        return phones, tones, mel, embed
+
+    def __len__(self):
+        return len(self.records)
+
+
+def collate_aishell3_examples(examples):
+    phones, tones, mel, embed = list(zip(*examples))
+
+    text_lengths = np.array([item.shape[0] for item in phones], dtype=np.int64)
+    spec_lengths = np.array([item.shape[1] for item in mel], dtype=np.int64)
+    T_dec = np.max(spec_lengths)
+    stop_tokens = (
+        np.arange(T_dec) >= np.expand_dims(spec_lengths, -1)).astype(np.float32)
+    phones, _ = batch_text_id(phones)
+    tones, _ = batch_text_id(tones)
+    mel, _ = batch_spec(mel)
+    mel = np.transpose(mel, (0, 2, 1))
+    embed = np.stack(embed)
+    # 7 fields
+    # (B, T), (B, T), (B, T, C), (B, C), (B,), (B,), (B, T)
+    return phones, tones, mel, embed, text_lengths, spec_lengths, stop_tokens
+
+
+if __name__ == "__main__":
+    dataset = AiShell3("~/datasets/aishell3/train")
+    example = dataset[0]
+
+    examples = [dataset[i] for i in range(10)]
+    batch = collate_aishell3_examples(examples)
+
+    for field in batch:
+        print(field.shape, field.dtype)

examples/aishell3/vc0/local/tacotron2/chinese_g2p.py

+# 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.
+
+from typing import List, Tuple
+from pypinyin import lazy_pinyin, Style
+from preprocess_transcription import split_syllable
+
+
+def convert_to_pinyin(text: str) -> List[str]:
+    """convert text into list of syllables, other characters that are not chinese, thus
+    cannot be converted to pinyin are splited.
+    """
+    syllables = lazy_pinyin(
+        text, style=Style.TONE3, neutral_tone_with_five=True)
+    return syllables
+
+
+def convert_sentence(text: str) -> List[Tuple[str]]:
+    """convert a sentence into two list: phones and tones"""
+    syllables = convert_to_pinyin(text)
+    phones = []
+    tones = []
+    for syllable in syllables:
+        p, t = split_syllable(syllable)
+        phones.extend(p)
+        tones.extend(t)
+
+    return phones, tones

examples/aishell3/vc0/local/tacotron2/config.py

+# Copyright (c) 2020 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.
+
+from yacs.config import CfgNode as CN
+
+_C = CN()
+_C.data = CN(
+    dict(
+        batch_size=32,  # batch size
+        valid_size=64,  # the first N examples are reserved for validation
+        sample_rate=22050,  # Hz, sample rate
+        n_fft=1024,  # fft frame size
+        win_length=1024,  # window size
+        hop_length=256,  # hop size between ajacent frame
+        fmax=8000,  # Hz, max frequency when converting to mel
+        fmin=0,  # Hz, min frequency when converting to mel
+        d_mels=80,  # mel bands
+        padding_idx=0,  # text embedding's padding index
+    ))
+
+_C.model = CN(
+    dict(
+        vocab_size=70,
+        n_tones=10,
+        reduction_factor=1,  # reduction factor
+        d_encoder=512,  # embedding & encoder's internal size
+        encoder_conv_layers=3,  # number of conv layer in tacotron2 encoder
+        encoder_kernel_size=5,  # kernel size of conv layers in tacotron2 encoder
+        d_prenet=256,  # hidden size of decoder prenet
+        # hidden size of the first rnn layer in tacotron2 decoder
+        d_attention_rnn=1024,
+        # hidden size of the second rnn layer in tacotron2 decoder
+        d_decoder_rnn=1024,
+        d_attention=128,  # hidden size of  decoder location linear layer
+        attention_filters=32,  # number of filter in decoder location conv layer
+        attention_kernel_size=31,  # kernel size of decoder location conv layer
+        d_postnet=512,  # hidden size of decoder postnet
+        postnet_kernel_size=5,  # kernel size of conv layers in postnet
+        postnet_conv_layers=5,  # number of conv layer in decoder postnet
+        p_encoder_dropout=0.5,  # droput probability in encoder
+        p_prenet_dropout=0.5,  # droput probability in decoder prenet
+
+        # droput probability of first rnn layer in decoder
+        p_attention_dropout=0.1,
+        # droput probability of second rnn layer in decoder
+        p_decoder_dropout=0.1,
+        p_postnet_dropout=0.5,  # droput probability in decoder postnet
+        guided_attention_loss_sigma=0.2,
+        d_global_condition=256,
+
+        # whether to use a classifier to predict stop probability
+        use_stop_token=False,
+        # whether to use guided attention loss in training
+        use_guided_attention_loss=True, ))
+
+_C.training = CN(
+    dict(
+        lr=1e-3,  # learning rate
+        weight_decay=1e-6,  # the coeff of weight decay
+        grad_clip_thresh=1.0,  # the clip norm of grad clip.
+        valid_interval=1000,  # validation
+        save_interval=1000,  # checkpoint
+        max_iteration=500000,  # max iteration to train
+    ))
+
+
+def get_cfg_defaults():
+    """Get a yacs CfgNode object with default values for my_project."""
+    # Return a clone so that the defaults will not be altered
+    # This is for the "local variable" use pattern
+    return _C.clone()

examples/aishell3/vc0/local/tacotron2/extract_mel.py

+# 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 multiprocessing as mp
+from functools import partial
+from pathlib import Path
+
+import numpy as np
+from parakeet.audio import AudioProcessor
+from parakeet.audio.spec_normalizer import NormalizerBase, LogMagnitude
+
+import tqdm
+
+from config import get_cfg_defaults
+
+
+def extract_mel(fname: Path,
+                input_dir: Path,
+                output_dir: Path,
+                p: AudioProcessor,
+                n: NormalizerBase):
+    relative_path = fname.relative_to(input_dir)
+    out_path = (output_dir / relative_path).with_suffix(".npy")
+    out_path.parent.mkdir(parents=True, exist_ok=True)
+    wav = p.read_wav(fname)
+    mel = p.mel_spectrogram(wav)
+    mel = n.transform(mel)
+    np.save(out_path, mel)
+
+
+def extract_mel_multispeaker(config, input_dir, output_dir, extension=".wav"):
+    input_dir = Path(input_dir).expanduser()
+    fnames = list(input_dir.rglob(f"*{extension}"))
+    output_dir = Path(output_dir).expanduser()
+    output_dir.mkdir(parents=True, exist_ok=True)
+
+    p = AudioProcessor(config.sample_rate, config.n_fft, config.win_length,
+                       config.hop_length, config.n_mels, config.fmin,
+                       config.fmax)
+    n = LogMagnitude(1e-5)
+
+    func = partial(
+        extract_mel, input_dir=input_dir, output_dir=output_dir, p=p, n=n)
+
+    with mp.Pool(16) as pool:
+        list(
+            tqdm.tqdm(
+                pool.imap(func, fnames), total=len(fnames), unit="utterance"))
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser(
+        description="Extract mel spectrogram from processed wav in AiShell3 training dataset."
+    )
+    parser.add_argument(
+        "--config",
+        type=str,
+        help="yaml config file to overwrite the default config")
+    parser.add_argument(
+        "--input",
+        type=str,
+        default="~/datasets/aishell3/train/normalized_wav",
+        help="path of the processed wav folder")
+    parser.add_argument(
+        "--output",
+        type=str,
+        default="~/datasets/aishell3/train/mel",
+        help="path of the folder to save mel spectrograms")
+    parser.add_argument(
+        "--opts",
+        nargs=argparse.REMAINDER,
+        help="options to overwrite --config file and the default config, passing in KEY VALUE pairs"
+    )
+    default_config = get_cfg_defaults()
+
+    args = parser.parse_args()
+    if args.config:
+        default_config.merge_from_file(args.config)
+    if args.opts:
+        default_config.merge_from_list(args.opts)
+    default_config.freeze()
+    audio_config = default_config.data
+
+    extract_mel_multispeaker(audio_config, args.input, args.output)

examples/aishell3/vc0/local/tacotron2/preprocess_transcription.py

+# 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
+from pathlib import Path
+import re
+import pickle
+
+import yaml
+import tqdm
+
+zh_pattern = re.compile("[\u4e00-\u9fa5]")
+
+_tones = {'<pad>', '<s>', '</s>', '0', '1', '2', '3', '4', '5'}
+
+_pauses = {'%', '$'}
+
+_initials = {
+    'b',
+    'p',
+    'm',
+    'f',
+    'd',
+    't',
+    'n',
+    'l',
+    'g',
+    'k',
+    'h',
+    'j',
+    'q',
+    'x',
+    'zh',
+    'ch',
+    'sh',
+    'r',
+    'z',
+    'c',
+    's',
+}
+
+_finals = {
+    'ii',
+    'iii',
+    'a',
+    'o',
+    'e',
+    'ea',
+    'ai',
+    'ei',
+    'ao',
+    'ou',
+    'an',
+    'en',
+    'ang',
+    'eng',
+    'er',
+    'i',
+    'ia',
+    'io',
+    'ie',
+    'iai',
+    'iao',
+    'iou',
+    'ian',
+    'ien',
+    'iang',
+    'ieng',
+    'u',
+    'ua',
+    'uo',
+    'uai',
+    'uei',
+    'uan',
+    'uen',
+    'uang',
+    'ueng',
+    'v',
+    've',
+    'van',
+    'ven',
+    'veng',
+}
+
+_ernized_symbol = {'&r'}
+
+_specials = {'<pad>', '<unk>', '<s>', '</s>'}
+
+_phones = _initials | _finals | _ernized_symbol | _specials | _pauses
+
+
+def is_zh(word):
+    global zh_pattern
+    match = zh_pattern.search(word)
+    return match is not None
+
+
+def ernized(syllable):
+    return syllable[:2] != "er" and syllable[-2] == 'r'
+
+
+def convert(syllable):
+    # expansion of o -> uo
+    syllable = re.sub(r"([bpmf])o$", r"\1uo", syllable)
+    # syllable = syllable.replace("bo", "buo").replace("po", "puo").replace("mo", "muo").replace("fo", "fuo")
+    # expansion for iong, ong
+    syllable = syllable.replace("iong", "veng").replace("ong", "ueng")
+
+    # expansion for ing, in
+    syllable = syllable.replace("ing", "ieng").replace("in", "ien")
+
+    # expansion for un, ui, iu
+    syllable = syllable.replace("un", "uen").replace("ui",
+                                                     "uei").replace("iu", "iou")
+
+    # rule for variants of i
+    syllable = syllable.replace("zi", "zii").replace("ci", "cii").replace("si", "sii")\
+        .replace("zhi", "zhiii").replace("chi", "chiii").replace("shi", "shiii")\
+        .replace("ri", "riii")
+
+    # rule for y preceding i, u
+    syllable = syllable.replace("yi", "i").replace("yu", "v").replace("y", "i")
+
+    # rule for w
+    syllable = syllable.replace("wu", "u").replace("w", "u")
+
+    # rule for v following j, q, x
+    syllable = syllable.replace("ju", "jv").replace("qu",
+                                                    "qv").replace("xu", "xv")
+
+    return syllable
+
+
+def split_syllable(syllable: str):
+    """Split a syllable in pinyin into a list of phones and a list of tones.
+    Initials have no tone, represented by '0', while finals have tones from
+    '1,2,3,4,5'.
+
+    e.g.
+
+    zhang -> ['zh', 'ang'], ['0', '1']
+    """
+    if syllable in _pauses:
+        # syllable, tone
+        return [syllable], ['0']
+
+    tone = syllable[-1]
+    syllable = convert(syllable[:-1])
+
+    phones = []
+    tones = []
+
+    global _initials
+    if syllable[:2] in _initials:
+        phones.append(syllable[:2])
+        tones.append('0')
+        phones.append(syllable[2:])
+        tones.append(tone)
+    elif syllable[0] in _initials:
+        phones.append(syllable[0])
+        tones.append('0')
+        phones.append(syllable[1:])
+        tones.append(tone)
+    else:
+        phones.append(syllable)
+        tones.append(tone)
+    return phones, tones
+
+
+def load_aishell3_transcription(line: str):
+    sentence_id, pinyin, text = line.strip().split("|")
+    syllables = pinyin.strip().split()
+
+    results = []
+
+    for syllable in syllables:
+        if syllable in _pauses:
+            results.append(syllable)
+        elif not ernized(syllable):
+            results.append(syllable)
+        else:
+            results.append(syllable[:-2] + syllable[-1])
+            results.append('&r5')
+
+    phones = []
+    tones = []
+    for syllable in results:
+        p, t = split_syllable(syllable)
+        phones.extend(p)
+        tones.extend(t)
+    for p in phones:
+        assert p in _phones, p
+    return {
+        "sentence_id": sentence_id,
+        "text": text,
+        "syllables": results,
+        "phones": phones,
+        "tones": tones
+    }
+
+
+def process_aishell3(dataset_root, output_dir):
+    dataset_root = Path(dataset_root).expanduser()
+    output_dir = Path(output_dir).expanduser()
+    output_dir.mkdir(parents=True, exist_ok=True)
+
+    prosody_label_path = dataset_root / "label_train-set.txt"
+    with open(prosody_label_path, 'rt') as f:
+        lines = [line.strip() for line in f]
+
+    records = lines[5:]
+
+    processed_records = []
+    for record in tqdm.tqdm(records):
+        new_record = load_aishell3_transcription(record)
+        processed_records.append(new_record)
+        print(new_record)
+
+    with open(output_dir / "metadata.pickle", 'wb') as f:
+        pickle.dump(processed_records, f)
+
+    with open(output_dir / "metadata.yaml", 'wt', encoding="utf-8") as f:
+        yaml.safe_dump(
+            processed_records, f, default_flow_style=None, allow_unicode=True)
+
+    print("metadata done!")
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser(
+        description="Preprocess transcription of AiShell3 and save them in a compact file(yaml and pickle)."
+    )
+    parser.add_argument(
+        "--input",
+        type=str,
+        default="~/datasets/aishell3/train",
+        help="path of the training dataset,(contains a label_train-set.txt).")
+    parser.add_argument(
+        "--output",
+        type=str,
+        help="the directory to save the processed transcription."
+        "If not provided, it would be the same as the input.")
+    args = parser.parse_args()
+    if args.output is None:
+        args.output = args.input
+
+    process_aishell3(args.input, args.output)

examples/aishell3/vc0/local/tacotron2/process_wav.py

+# 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
+from pathlib import Path
+from multiprocessing import Pool
+from functools import partial
+
+import numpy as np
+import librosa
+import soundfile as sf
+from tqdm import tqdm
+from praatio import tgio
+
+
+def get_valid_part(fpath):
+    f = tgio.openTextgrid(fpath)
+
+    start = 0
+    phone_entry_list = f.tierDict['phones'].entryList
+    first_entry = phone_entry_list[0]
+    if first_entry.label == "sil":
+        start = first_entry.end
+
+    last_entry = phone_entry_list[-1]
+    if last_entry.label == "sp":
+        end = last_entry.start
+    else:
+        end = last_entry.end
+    return start, end
+
+
+def process_utterance(fpath, source_dir, target_dir, alignment_dir):
+    rel_path = fpath.relative_to(source_dir)
+    opath = target_dir / rel_path
+    apath = (alignment_dir / rel_path).with_suffix(".TextGrid")
+    opath.parent.mkdir(parents=True, exist_ok=True)
+
+    start, end = get_valid_part(apath)
+    wav, _ = librosa.load(fpath, sr=22050, offset=start, duration=end - start)
+    normalized_wav = wav / np.max(wav) * 0.999
+    sf.write(opath, normalized_wav, samplerate=22050, subtype='PCM_16')
+    # print(f"{fpath} => {opath}")
+
+
+def preprocess_aishell3(source_dir, target_dir, alignment_dir):
+    source_dir = Path(source_dir).expanduser()
+    target_dir = Path(target_dir).expanduser()
+    alignment_dir = Path(alignment_dir).expanduser()
+
+    wav_paths = list(source_dir.rglob("*.wav"))
+    print(f"there are {len(wav_paths)} audio files in total")
+    fx = partial(
+        process_utterance,
+        source_dir=source_dir,
+        target_dir=target_dir,
+        alignment_dir=alignment_dir)
+    with Pool(16) as p:
+        list(
+            tqdm(p.imap(fx, wav_paths), total=len(wav_paths), unit="utterance"))
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser(
+        description="Process audio in AiShell3, trim silence according to the alignment "
+        "files generated by MFA, and normalize volume by peak.")
+    parser.add_argument(
+        "--input",
+        type=str,
+        default="~/datasets/aishell3/train/wav",
+        help="path of the original audio folder in aishell3.")
+    parser.add_argument(
+        "--output",
+        type=str,
+        default="~/datasets/aishell3/train/normalized_wav",
+        help="path of the folder to save the processed audio files.")
+    parser.add_argument(
+        "--alignment",
+        type=str,
+        default="~/datasets/aishell3/train/alignment",
+        help="path of the alignment files.")
+    args = parser.parse_args()
+
+    preprocess_aishell3(args.input, args.output, args.alignment)

examples/aishell3/vc0/local/tacotron2/train.py

+# Copyright (c) 2020 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 time
+from pathlib import Path
+from collections import defaultdict
+
+import numpy as np
+from matplotlib import pyplot as plt
+
+import paddle
+from paddle import distributed as dist
+from paddle.io import DataLoader, DistributedBatchSampler
+
+from parakeet.data import dataset
+from parakeet.training.cli import default_argument_parser
+from parakeet.training.experiment import ExperimentBase
+from parakeet.utils import display, mp_tools
+from parakeet.models.tacotron2 import Tacotron2, Tacotron2Loss
+
+from config import get_cfg_defaults
+from aishell3 import AiShell3, collate_aishell3_examples
+
+
+class Experiment(ExperimentBase):
+    def compute_losses(self, inputs, outputs):
+        texts, tones, mel_targets, utterance_embeds, text_lens, output_lens, stop_tokens = inputs
+
+        mel_outputs = outputs["mel_output"]
+        mel_outputs_postnet = outputs["mel_outputs_postnet"]
+        alignments = outputs["alignments"]
+
+        losses = self.criterion(mel_outputs, mel_outputs_postnet, mel_targets,
+                                alignments, output_lens, text_lens)
+        return losses
+
+    def train_batch(self):
+        start = time.time()
+        batch = self.read_batch()
+        data_loader_time = time.time() - start
+
+        self.optimizer.clear_grad()
+        self.model.train()
+        texts, tones, mels, utterance_embeds, text_lens, output_lens, stop_tokens = batch
+        outputs = self.model(
+            texts,
+            text_lens,
+            mels,
+            output_lens,
+            tones=tones,
+            global_condition=utterance_embeds)
+        losses = self.compute_losses(batch, outputs)
+        loss = losses["loss"]
+        loss.backward()
+        self.optimizer.step()
+        iteration_time = time.time() - start
+
+        losses_np = {k: float(v) for k, v in losses.items()}
+        # logging
+        msg = "Rank: {}, ".format(dist.get_rank())
+        msg += "step: {}, ".format(self.iteration)
+        msg += "time: {:>.3f}s/{:>.3f}s, ".format(data_loader_time,
+                                                  iteration_time)
+        msg += ', '.join('{}: {:>.6f}'.format(k, v)
+                         for k, v in losses_np.items())
+        self.logger.info(msg)
+
+        if dist.get_rank() == 0:
+            for key, value in losses_np.items():
+                self.visualizer.add_scalar(f"train_loss/{key}", value,
+                                           self.iteration)
+
+    @mp_tools.rank_zero_only
+    @paddle.no_grad()
+    def valid(self):
+        valid_losses = defaultdict(list)
+        for i, batch in enumerate(self.valid_loader):
+            texts, tones, mels, utterance_embeds, text_lens, output_lens, stop_tokens = batch
+            outputs = self.model(
+                texts,
+                text_lens,
+                mels,
+                output_lens,
+                tones=tones,
+                global_condition=utterance_embeds)
+            losses = self.compute_losses(batch, outputs)
+            for key, value in losses.items():
+                valid_losses[key].append(float(value))
+
+            attention_weights = outputs["alignments"]
+            self.visualizer.add_figure(
+                f"valid_sentence_{i}_alignments",
+                display.plot_alignment(attention_weights[0].numpy().T),
+                self.iteration)
+            self.visualizer.add_figure(
+                f"valid_sentence_{i}_target_spectrogram",
+                display.plot_spectrogram(mels[0].numpy().T), self.iteration)
+            mel_pred = outputs['mel_outputs_postnet']
+            self.visualizer.add_figure(
+                f"valid_sentence_{i}_predicted_spectrogram",
+                display.plot_spectrogram(mel_pred[0].numpy().T), self.iteration)
+
+        # write visual log
+        valid_losses = {k: np.mean(v) for k, v in valid_losses.items()}
+
+        # logging
+        msg = "Valid: "
+        msg += "step: {}, ".format(self.iteration)
+        msg += ', '.join('{}: {:>.6f}'.format(k, v)
+                         for k, v in valid_losses.items())
+        self.logger.info(msg)
+
+        for key, value in valid_losses.items():
+            self.visualizer.add_scalar(f"valid/{key}", value, self.iteration)
+
+    @mp_tools.rank_zero_only
+    @paddle.no_grad()
+    def eval(self):
+        """Evaluation of Tacotron2 in autoregressive manner."""
+        self.model.eval()
+        mel_dir = Path(self.output_dir / ("eval_{}".format(self.iteration)))
+        mel_dir.mkdir(parents=True, exist_ok=True)
+        for i, batch in enumerate(self.test_loader):
+            texts, tones, mels, utterance_embeds, *_ = batch
+            outputs = self.model.infer(
+                texts, tones=tones, global_condition=utterance_embeds)
+
+            display.plot_alignment(outputs["alignments"][0].numpy().T)
+            plt.savefig(mel_dir / f"sentence_{i}.png")
+            plt.close()
+            np.save(mel_dir / f"sentence_{i}",
+                    outputs["mel_outputs_postnet"][0].numpy().T)
+            print(f"sentence_{i}")
+
+    def setup_model(self):
+        config = self.config
+        model = Tacotron2(
+            vocab_size=config.model.vocab_size,
+            n_tones=config.model.n_tones,
+            d_mels=config.data.d_mels,
+            d_encoder=config.model.d_encoder,
+            encoder_conv_layers=config.model.encoder_conv_layers,
+            encoder_kernel_size=config.model.encoder_kernel_size,
+            d_prenet=config.model.d_prenet,
+            d_attention_rnn=config.model.d_attention_rnn,
+            d_decoder_rnn=config.model.d_decoder_rnn,
+            attention_filters=config.model.attention_filters,
+            attention_kernel_size=config.model.attention_kernel_size,
+            d_attention=config.model.d_attention,
+            d_postnet=config.model.d_postnet,
+            postnet_kernel_size=config.model.postnet_kernel_size,
+            postnet_conv_layers=config.model.postnet_conv_layers,
+            reduction_factor=config.model.reduction_factor,
+            p_encoder_dropout=config.model.p_encoder_dropout,
+            p_prenet_dropout=config.model.p_prenet_dropout,
+            p_attention_dropout=config.model.p_attention_dropout,
+            p_decoder_dropout=config.model.p_decoder_dropout,
+            p_postnet_dropout=config.model.p_postnet_dropout,
+            d_global_condition=config.model.d_global_condition,
+            use_stop_token=config.model.use_stop_token, )
+
+        if self.parallel:
+            model = paddle.DataParallel(model)
+
+        grad_clip = paddle.nn.ClipGradByGlobalNorm(
+            config.training.grad_clip_thresh)
+        optimizer = paddle.optimizer.Adam(
+            learning_rate=config.training.lr,
+            parameters=model.parameters(),
+            weight_decay=paddle.regularizer.L2Decay(
+                config.training.weight_decay),
+            grad_clip=grad_clip)
+        criterion = Tacotron2Loss(
+            use_stop_token_loss=config.model.use_stop_token,
+            use_guided_attention_loss=config.model.use_guided_attention_loss,
+            sigma=config.model.guided_attention_loss_sigma)
+        self.model = model
+        self.optimizer = optimizer
+        self.criterion = criterion
+
+    def setup_dataloader(self):
+        args = self.args
+        config = self.config
+        ljspeech_dataset = AiShell3(args.data)
+
+        valid_set, train_set = dataset.split(ljspeech_dataset,
+                                             config.data.valid_size)
+        batch_fn = collate_aishell3_examples
+
+        if not self.parallel:
+            self.train_loader = DataLoader(
+                train_set,
+                batch_size=config.data.batch_size,
+                shuffle=True,
+                drop_last=True,
+                collate_fn=batch_fn)
+        else:
+            sampler = DistributedBatchSampler(
+                train_set,
+                batch_size=config.data.batch_size,
+                shuffle=True,
+                drop_last=True)
+            self.train_loader = DataLoader(
+                train_set, batch_sampler=sampler, collate_fn=batch_fn)
+
+        self.valid_loader = DataLoader(
+            valid_set,
+            batch_size=config.data.batch_size,
+            shuffle=False,
+            drop_last=False,
+            collate_fn=batch_fn)
+
+        self.test_loader = DataLoader(
+            valid_set,
+            batch_size=1,
+            shuffle=False,
+            drop_last=False,
+            collate_fn=batch_fn)
+
+
+def main_sp(config, args):
+    exp = Experiment(config, args)
+    exp.setup()
+    exp.resume_or_load()
+    if not args.test:
+        exp.run()
+    else:
+        exp.eval()
+
+
+def main(config, args):
+    if args.nprocs > 1 and args.device == "gpu":
+        dist.spawn(main_sp, args=(config, args), nprocs=args.nprocs)
+    else:
+        main_sp(config, args)
+
+
+if __name__ == "__main__":
+    config = get_cfg_defaults()
+    parser = default_argument_parser()
+    parser.add_argument("--test", action="store_true")
+    args = parser.parse_args()
+    if args.config:
+        config.merge_from_file(args.config)
+    if args.opts:
+        config.merge_from_list(args.opts)
+    config.freeze()
+    print(config)
+    print(args)
+
+    main(config, args)