tiny change, not important

deepspeech/__init__.py

@@ -355,7 +355,6 @@ if not hasattr(paddle.Tensor, 'tolist'):
         "register user tolist to paddle.Tensor, remove this when fixed!")
     setattr(paddle.Tensor, 'tolist', tolist)
 
-
 ########### hack paddle.nn #############
 from paddle.nn import Layer
 from typing import Optional
@@ -506,5 +505,3 @@ 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/recog.py

@@ -12,12 +12,8 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 """V2 backend for `asr_recog.py` using py:class:`decoders.beam_search.BeamSearch`."""
-import json
-from pathlib import Path
-
 import jsonlines
 import paddle
-import yaml
 from yacs.config import CfgNode
 
 from .beam_search import BatchBeamSearch

deepspeech/decoders/recog_bin.py

@@ -21,8 +21,6 @@ from distutils.util import strtobool
 import configargparse
 import numpy as np
 
-from .recog import recog_v2
-
 
 def get_parser():
     """Get default arguments."""

deepspeech/models/asr_interface.py

@@ -18,7 +18,7 @@ from deepspeech.utils.dynamic_import import dynamic_import
 
 
 class ASRInterface:
-    """ASR Interface for ESPnet model implementation."""
+    """ASR Interface model implementation."""
 
     @staticmethod
     def add_arguments(parser):
@@ -103,14 +103,14 @@ class ASRInterface:
     @property
     def attention_plot_class(self):
         """Get attention plot class."""
-        from espnet.asr.asr_utils import PlotAttentionReport
+        from deepspeech.training.extensions.plot import PlotAttentionReport
 
         return PlotAttentionReport
 
     @property
     def ctc_plot_class(self):
         """Get CTC plot class."""
-        from espnet.asr.asr_utils import PlotCTCReport
+        from deepspeech.training.extensions.plot import PlotCTCReport
 
         return PlotCTCReport
 

deepspeech/models/lm/__init__.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.

deepspeech/models/lm_interface.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.
 """Language model interface."""
-
 import argparse
 
 from deepspeech.decoders.scorers.scorer_interface import ScorerInterface
 from deepspeech.utils.dynamic_import import dynamic_import
 
+
 class LMInterface(ScorerInterface):
-    """LM Interface for ESPnet model implementation."""
+    """LM Interface model implementation."""
 
     @staticmethod
     def add_arguments(parser):
@@ -52,6 +65,7 @@ predefined_lms = {
     "transformer": "deepspeech.models.lm.transformer:TransformerLM",
 }
 
+
 def dynamic_import_lm(module):
     """Import LM class dynamically.
 
@@ -63,7 +77,6 @@ def dynamic_import_lm(module):
 
     """
     model_class = dynamic_import(module, predefined_lms)
-    assert issubclass(
-        model_class, LMInterface
-    ), f"{module} does not implement LMInterface"
+    assert issubclass(model_class,
+                      LMInterface), f"{module} does not implement LMInterface"
     return model_class

deepspeech/models/st_interface.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.
+"""ST Interface module."""
+from .asr_interface import ASRInterface
+from deepspeech.utils.dynamic_import import dynamic_import
+
+
+class STInterface(ASRInterface):
+    """ST Interface model implementation.
+
+    NOTE: This class is inherited from ASRInterface to enable joint translation
+    and recognition when performing multi-task learning with the ASR task.
+
+    """
+
+    def translate(self,
+                  x,
+                  trans_args,
+                  char_list=None,
+                  rnnlm=None,
+                  ensemble_models=[]):
+        """Recognize x for evaluation.
+
+        :param ndarray x: input acouctic feature (B, T, D) or (T, D)
+        :param namespace trans_args: argment namespace contraining options
+        :param list char_list: list of characters
+        :param paddle.nn.Layer rnnlm: language model module
+        :return: N-best decoding results
+        :rtype: list
+        """
+        raise NotImplementedError("translate method is not implemented")
+
+    def translate_batch(self, x, trans_args, char_list=None, rnnlm=None):
+        """Beam search implementation for batch.
+
+        :param paddle.Tensor x: encoder hidden state sequences (B, Tmax, Henc)
+        :param namespace trans_args: argument namespace containing options
+        :param list char_list: list of characters
+        :param paddle.nn.Layer rnnlm: language model module
+        :return: N-best decoding results
+        :rtype: list
+        """
+        raise NotImplementedError("Batch decoding is not supported yet.")
+
+
+predefined_st = {
+    "transformer": "deepspeech.models.u2_st:U2STModel",
+}
+
+
+def dynamic_import_st(module):
+    """Import ST models dynamically.
+
+    Args:
+        module (str): module_name:class_name or alias in `predefined_st`
+
+    Returns:
+        type: ST class
+
+    """
+    model_class = dynamic_import(module, predefined_st)
+    assert issubclass(model_class,
+                      STInterface), f"{module} does not implement STInterface"
+    return model_class

deepspeech/models/u2_st/__init__.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 .u2_st import U2STInferModel
+from .u2_st import U2STModel

deepspeech/modules/ctc.py

@@ -11,9 +11,10 @@
 # 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 Union
+
 import paddle
 from paddle import nn
-from typing import Union
 from paddle.nn import functional as F
 from typeguard import check_argument_types
 

deepspeech/modules/embedding.py

@@ -22,7 +22,10 @@ from deepspeech.utils.log import Log
 
 logger = Log(__name__).getlog()
 
-__all__ = ["NoPositionalEncoding", "PositionalEncoding", "RelPositionalEncoding"]
+__all__ = [
+    "NoPositionalEncoding", "PositionalEncoding", "RelPositionalEncoding"
+]
+
 
 class NoPositionalEncoding(nn.Layer):
     def __init__(self,

deepspeech/modules/encoder.py

@@ -103,7 +103,7 @@ class BaseEncoder(nn.Layer):
             pos_enc_class = PositionalEncoding
         elif pos_enc_layer_type == "rel_pos":
             pos_enc_class = RelPositionalEncoding
-        elif pos_enc_layer_type is "no_pos":
+        elif pos_enc_layer_type == "no_pos":
             pos_enc_class = NoPositionalEncoding
         else:
             raise ValueError("unknown pos_enc_layer: " + pos_enc_layer_type)

deepspeech/modules/subsampling.py

@@ -60,8 +60,8 @@ class LinearNoSubsampling(BaseSubsampling):
         self.out = nn.Sequential(
             nn.Linear(idim, odim),
             nn.LayerNorm(odim, epsilon=1e-12),
-            nn.Dropout(dropout_rate), 
-            nn.ReLU(),)
+            nn.Dropout(dropout_rate),
+            nn.ReLU(), )
         self.right_context = 0
         self.subsampling_rate = 1
 
@@ -83,10 +83,12 @@ class LinearNoSubsampling(BaseSubsampling):
         x, pos_emb = self.pos_enc(x, offset)
         return x, pos_emb, x_mask
 
+
 class Conv2dSubsampling(BaseSubsampling):
     def __init__(self, *args, **kwargs):
         super().__init__(*args, **kwargs)
 
+
 class Conv2dSubsampling4(Conv2dSubsampling):
     """Convolutional 2D subsampling (to 1/4 length)."""
 

deepspeech/training/extensions/plot.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 copy
+import os
+
+import numpy as np
+
+from . import extension
+
+
+class PlotAttentionReport(extension.Extension):
+    """Plot attention reporter.
+
+    Args:
+        att_vis_fn (espnet.nets.*_backend.e2e_asr.E2E.calculate_all_attentions):
+            Function of attention visualization.
+        data (list[tuple(str, dict[str, list[Any]])]): List json utt key items.
+        outdir (str): Directory to save figures.
+        converter (espnet.asr.*_backend.asr.CustomConverter):
+            Function to convert data.
+        device (int | torch.device): Device.
+        reverse (bool): If True, input and output length are reversed.
+        ikey (str): Key to access input
+            (for ASR/ST ikey="input", for MT ikey="output".)
+        iaxis (int): Dimension to access input
+            (for ASR/ST iaxis=0, for MT iaxis=1.)
+        okey (str): Key to access output
+            (for ASR/ST okey="input", MT okay="output".)
+        oaxis (int): Dimension to access output
+            (for ASR/ST oaxis=0, for MT oaxis=0.)
+        subsampling_factor (int): subsampling factor in encoder
+
+    """
+
+    def __init__(
+            self,
+            att_vis_fn,
+            data,
+            outdir,
+            converter,
+            transform,
+            device,
+            reverse=False,
+            ikey="input",
+            iaxis=0,
+            okey="output",
+            oaxis=0,
+            subsampling_factor=1, ):
+        self.att_vis_fn = att_vis_fn
+        self.data = copy.deepcopy(data)
+        self.data_dict = {k: v for k, v in copy.deepcopy(data)}
+        # key is utterance ID
+        self.outdir = outdir
+        self.converter = converter
+        self.transform = transform
+        self.device = device
+        self.reverse = reverse
+        self.ikey = ikey
+        self.iaxis = iaxis
+        self.okey = okey
+        self.oaxis = oaxis
+        self.factor = subsampling_factor
+        if not os.path.exists(self.outdir):
+            os.makedirs(self.outdir)
+
+    def __call__(self, trainer):
+        """Plot and save image file of att_ws matrix."""
+        att_ws, uttid_list = self.get_attention_weights()
+        if isinstance(att_ws, list):  # multi-encoder case
+            num_encs = len(att_ws) - 1
+            # atts
+            for i in range(num_encs):
+                for idx, att_w in enumerate(att_ws[i]):
+                    filename = "%s/%s.ep.{.updater.epoch}.att%d.png" % (
+                        self.outdir, uttid_list[idx], i + 1, )
+                    att_w = self.trim_attention_weight(uttid_list[idx], att_w)
+                    np_filename = "%s/%s.ep.{.updater.epoch}.att%d.npy" % (
+                        self.outdir, uttid_list[idx], i + 1, )
+                    np.save(np_filename.format(trainer), att_w)
+                    self._plot_and_save_attention(att_w,
+                                                  filename.format(trainer))
+            # han
+            for idx, att_w in enumerate(att_ws[num_encs]):
+                filename = "%s/%s.ep.{.updater.epoch}.han.png" % (
+                    self.outdir, uttid_list[idx], )
+                att_w = self.trim_attention_weight(uttid_list[idx], att_w)
+                np_filename = "%s/%s.ep.{.updater.epoch}.han.npy" % (
+                    self.outdir, uttid_list[idx], )
+                np.save(np_filename.format(trainer), att_w)
+                self._plot_and_save_attention(
+                    att_w, filename.format(trainer), han_mode=True)
+        else:
+            for idx, att_w in enumerate(att_ws):
+                filename = "%s/%s.ep.{.updater.epoch}.png" % (self.outdir,
+                                                              uttid_list[idx], )
+                att_w = self.trim_attention_weight(uttid_list[idx], att_w)
+                np_filename = "%s/%s.ep.{.updater.epoch}.npy" % (
+                    self.outdir, uttid_list[idx], )
+                np.save(np_filename.format(trainer), att_w)
+                self._plot_and_save_attention(att_w, filename.format(trainer))
+
+    def log_attentions(self, logger, step):
+        """Add image files of att_ws matrix to the tensorboard."""
+        att_ws, uttid_list = self.get_attention_weights()
+        if isinstance(att_ws, list):  # multi-encoder case
+            num_encs = len(att_ws) - 1
+            # atts
+            for i in range(num_encs):
+                for idx, att_w in enumerate(att_ws[i]):
+                    att_w = self.trim_attention_weight(uttid_list[idx], att_w)
+                    plot = self.draw_attention_plot(att_w)
+                    logger.add_figure(
+                        "%s_att%d" % (uttid_list[idx], i + 1),
+                        plot.gcf(),
+                        step, )
+            # han
+            for idx, att_w in enumerate(att_ws[num_encs]):
+                att_w = self.trim_attention_weight(uttid_list[idx], att_w)
+                plot = self.draw_han_plot(att_w)
+                logger.add_figure(
+                    "%s_han" % (uttid_list[idx]),
+                    plot.gcf(),
+                    step, )
+        else:
+            for idx, att_w in enumerate(att_ws):
+                att_w = self.trim_attention_weight(uttid_list[idx], att_w)
+                plot = self.draw_attention_plot(att_w)
+                logger.add_figure("%s" % (uttid_list[idx]), plot.gcf(), step)
+
+    def get_attention_weights(self):
+        """Return attention weights.
+
+        Returns:
+            numpy.ndarray: attention weights. float. Its shape would be
+                differ from backend.
+                * pytorch-> 1) multi-head case => (B, H, Lmax, Tmax), 2)
+                    other case => (B, Lmax, Tmax).
+                * chainer-> (B, Lmax, Tmax)
+
+        """
+        return_batch, uttid_list = self.transform(self.data, return_uttid=True)
+        batch = self.converter([return_batch], self.device)
+        if isinstance(batch, tuple):
+            att_ws = self.att_vis_fn(*batch)
+        else:
+            att_ws = self.att_vis_fn(**batch)
+        return att_ws, uttid_list
+
+    def trim_attention_weight(self, uttid, att_w):
+        """Transform attention matrix with regard to self.reverse."""
+        if self.reverse:
+            enc_key, enc_axis = self.okey, self.oaxis
+            dec_key, dec_axis = self.ikey, self.iaxis
+        else:
+            enc_key, enc_axis = self.ikey, self.iaxis
+            dec_key, dec_axis = self.okey, self.oaxis
+        dec_len = int(self.data_dict[uttid][dec_key][dec_axis]["shape"][0])
+        enc_len = int(self.data_dict[uttid][enc_key][enc_axis]["shape"][0])
+        if self.factor > 1:
+            enc_len //= self.factor
+        if len(att_w.shape) == 3:
+            att_w = att_w[:, :dec_len, :enc_len]
+        else:
+            att_w = att_w[:dec_len, :enc_len]
+        return att_w
+
+    def draw_attention_plot(self, att_w):
+        """Plot the att_w matrix.
+
+        Returns:
+            matplotlib.pyplot: pyplot object with attention matrix image.
+
+        """
+        import matplotlib
+
+        matplotlib.use("Agg")
+        import matplotlib.pyplot as plt
+
+        plt.clf()
+        att_w = att_w.astype(np.float32)
+        if len(att_w.shape) == 3:
+            for h, aw in enumerate(att_w, 1):
+                plt.subplot(1, len(att_w), h)
+                plt.imshow(aw, aspect="auto")
+                plt.xlabel("Encoder Index")
+                plt.ylabel("Decoder Index")
+        else:
+            plt.imshow(att_w, aspect="auto")
+            plt.xlabel("Encoder Index")
+            plt.ylabel("Decoder Index")
+        plt.tight_layout()
+        return plt
+
+    def draw_han_plot(self, att_w):
+        """Plot the att_w matrix for hierarchical attention.
+
+        Returns:
+            matplotlib.pyplot: pyplot object with attention matrix image.
+
+        """
+        import matplotlib
+
+        matplotlib.use("Agg")
+        import matplotlib.pyplot as plt
+
+        plt.clf()
+        if len(att_w.shape) == 3:
+            for h, aw in enumerate(att_w, 1):
+                legends = []
+                plt.subplot(1, len(att_w), h)
+                for i in range(aw.shape[1]):
+                    plt.plot(aw[:, i])
+                    legends.append("Att{}".format(i))
+                plt.ylim([0, 1.0])
+                plt.xlim([0, aw.shape[0]])
+                plt.grid(True)
+                plt.ylabel("Attention Weight")
+                plt.xlabel("Decoder Index")
+                plt.legend(legends)
+        else:
+            legends = []
+            for i in range(att_w.shape[1]):
+                plt.plot(att_w[:, i])
+                legends.append("Att{}".format(i))
+            plt.ylim([0, 1.0])
+            plt.xlim([0, att_w.shape[0]])
+            plt.grid(True)
+            plt.ylabel("Attention Weight")
+            plt.xlabel("Decoder Index")
+            plt.legend(legends)
+        plt.tight_layout()
+        return plt
+
+    def _plot_and_save_attention(self, att_w, filename, han_mode=False):
+        if han_mode:
+            plt = self.draw_han_plot(att_w)
+        else:
+            plt = self.draw_attention_plot(att_w)
+        plt.savefig(filename)
+        plt.close()
+
+
+class PlotCTCReport(extension.Extension):
+    """Plot CTC reporter.
+
+    Args:
+        ctc_vis_fn (espnet.nets.*_backend.e2e_asr.E2E.calculate_all_ctc_probs):
+            Function of CTC visualization.
+        data (list[tuple(str, dict[str, list[Any]])]): List json utt key items.
+        outdir (str): Directory to save figures.
+        converter (espnet.asr.*_backend.asr.CustomConverter):
+            Function to convert data.
+        device (int | torch.device): Device.
+        reverse (bool): If True, input and output length are reversed.
+        ikey (str): Key to access input
+            (for ASR/ST ikey="input", for MT ikey="output".)
+        iaxis (int): Dimension to access input
+            (for ASR/ST iaxis=0, for MT iaxis=1.)
+        okey (str): Key to access output
+            (for ASR/ST okey="input", MT okay="output".)
+        oaxis (int): Dimension to access output
+            (for ASR/ST oaxis=0, for MT oaxis=0.)
+        subsampling_factor (int): subsampling factor in encoder
+
+    """
+
+    def __init__(
+            self,
+            ctc_vis_fn,
+            data,
+            outdir,
+            converter,
+            transform,
+            device,
+            reverse=False,
+            ikey="input",
+            iaxis=0,
+            okey="output",
+            oaxis=0,
+            subsampling_factor=1, ):
+        self.ctc_vis_fn = ctc_vis_fn
+        self.data = copy.deepcopy(data)
+        self.data_dict = {k: v for k, v in copy.deepcopy(data)}
+        # key is utterance ID
+        self.outdir = outdir
+        self.converter = converter
+        self.transform = transform
+        self.device = device
+        self.reverse = reverse
+        self.ikey = ikey
+        self.iaxis = iaxis
+        self.okey = okey
+        self.oaxis = oaxis
+        self.factor = subsampling_factor
+        if not os.path.exists(self.outdir):
+            os.makedirs(self.outdir)
+
+    def __call__(self, trainer):
+        """Plot and save image file of ctc prob."""
+        ctc_probs, uttid_list = self.get_ctc_probs()
+        if isinstance(ctc_probs, list):  # multi-encoder case
+            num_encs = len(ctc_probs) - 1
+            for i in range(num_encs):
+                for idx, ctc_prob in enumerate(ctc_probs[i]):
+                    filename = "%s/%s.ep.{.updater.epoch}.ctc%d.png" % (
+                        self.outdir, uttid_list[idx], i + 1, )
+                    ctc_prob = self.trim_ctc_prob(uttid_list[idx], ctc_prob)
+                    np_filename = "%s/%s.ep.{.updater.epoch}.ctc%d.npy" % (
+                        self.outdir, uttid_list[idx], i + 1, )
+                    np.save(np_filename.format(trainer), ctc_prob)
+                    self._plot_and_save_ctc(ctc_prob, filename.format(trainer))
+        else:
+            for idx, ctc_prob in enumerate(ctc_probs):
+                filename = "%s/%s.ep.{.updater.epoch}.png" % (self.outdir,
+                                                              uttid_list[idx], )
+                ctc_prob = self.trim_ctc_prob(uttid_list[idx], ctc_prob)
+                np_filename = "%s/%s.ep.{.updater.epoch}.npy" % (
+                    self.outdir, uttid_list[idx], )
+                np.save(np_filename.format(trainer), ctc_prob)
+                self._plot_and_save_ctc(ctc_prob, filename.format(trainer))
+
+    def log_ctc_probs(self, logger, step):
+        """Add image files of ctc probs to the tensorboard."""
+        ctc_probs, uttid_list = self.get_ctc_probs()
+        if isinstance(ctc_probs, list):  # multi-encoder case
+            num_encs = len(ctc_probs) - 1
+            for i in range(num_encs):
+                for idx, ctc_prob in enumerate(ctc_probs[i]):
+                    ctc_prob = self.trim_ctc_prob(uttid_list[idx], ctc_prob)
+                    plot = self.draw_ctc_plot(ctc_prob)
+                    logger.add_figure(
+                        "%s_ctc%d" % (uttid_list[idx], i + 1),
+                        plot.gcf(),
+                        step, )
+        else:
+            for idx, ctc_prob in enumerate(ctc_probs):
+                ctc_prob = self.trim_ctc_prob(uttid_list[idx], ctc_prob)
+                plot = self.draw_ctc_plot(ctc_prob)
+                logger.add_figure("%s" % (uttid_list[idx]), plot.gcf(), step)
+
+    def get_ctc_probs(self):
+        """Return CTC probs.
+
+        Returns:
+            numpy.ndarray: CTC probs. float. Its shape would be
+                differ from backend. (B, Tmax, vocab).
+
+        """
+        return_batch, uttid_list = self.transform(self.data, return_uttid=True)
+        batch = self.converter([return_batch], self.device)
+        if isinstance(batch, tuple):
+            probs = self.ctc_vis_fn(*batch)
+        else:
+            probs = self.ctc_vis_fn(**batch)
+        return probs, uttid_list
+
+    def trim_ctc_prob(self, uttid, prob):
+        """Trim CTC posteriors accoding to input lengths."""
+        enc_len = int(self.data_dict[uttid][self.ikey][self.iaxis]["shape"][0])
+        if self.factor > 1:
+            enc_len //= self.factor
+        prob = prob[:enc_len]
+        return prob
+
+    def draw_ctc_plot(self, ctc_prob):
+        """Plot the ctc_prob matrix.
+
+        Returns:
+            matplotlib.pyplot: pyplot object with CTC prob matrix image.
+
+        """
+        import matplotlib
+
+        matplotlib.use("Agg")
+        import matplotlib.pyplot as plt
+
+        ctc_prob = ctc_prob.astype(np.float32)
+
+        plt.clf()
+        topk_ids = np.argsort(ctc_prob, axis=1)
+        n_frames, vocab = ctc_prob.shape
+        times_probs = np.arange(n_frames)
+
+        plt.figure(figsize=(20, 8))
+
+        # NOTE: index 0 is reserved for blank
+        for idx in set(topk_ids.reshape(-1).tolist()):
+            if idx == 0:
+                plt.plot(
+                    times_probs,
+                    ctc_prob[:, 0],
+                    ":",
+                    label="<blank>",
+                    color="grey")
+            else:
+                plt.plot(times_probs, ctc_prob[:, idx])
+        plt.xlabel(u"Input [frame]", fontsize=12)
+        plt.ylabel("Posteriors", fontsize=12)
+        plt.xticks(list(range(0, int(n_frames) + 1, 10)))
+        plt.yticks(list(range(0, 2, 1)))
+        plt.tight_layout()
+        return plt
+
+    def _plot_and_save_ctc(self, ctc_prob, filename):
+        plt = self.draw_ctc_plot(ctc_prob)
+        plt.savefig(filename)
+        plt.close()

deepspeech/training/triggers/__init__.py

@@ -11,18 +11,3 @@
 # 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 .interval_trigger import IntervalTrigger
-
-
-def never_fail_trigger(trainer):
-    return False
-
-
-def get_trigger(trigger):
-    if trigger is None:
-        return never_fail_trigger
-    if callable(trigger):
-        return trigger
-    else:
-        trigger = IntervalTrigger(*trigger)
-        return trigger

deepspeech/training/triggers/compare_value_trigger.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 ..reporter import DictSummary
+from .utils import get_trigger
+
+
+class CompareValueTrigger():
+    """Trigger invoked when key value getting bigger or lower than before.
+
+    Args:
+        key (str) : Key of value.
+        compare_fn ((float, float) -> bool) : Function to compare the values.
+        trigger (tuple(int, str)) : Trigger that decide the comparison interval.
+
+    """
+
+    def __init__(self, key, compare_fn, trigger=(1, "epoch")):
+        self._key = key
+        self._best_value = None
+        self._interval_trigger = get_trigger(trigger)
+        self._init_summary()
+        self._compare_fn = compare_fn
+
+    def __call__(self, trainer):
+        """Get value related to the key and compare with current value."""
+        observation = trainer.observation
+        summary = self._summary
+        key = self._key
+        if key in observation:
+            summary.add({key: observation[key]})
+
+        if not self._interval_trigger(trainer):
+            return False
+
+        stats = summary.compute_mean()
+        value = float(stats[key])  # copy to CPU
+        self._init_summary()
+
+        if self._best_value is None:
+            # initialize best value
+            self._best_value = value
+            return False
+        elif self._compare_fn(self._best_value, value):
+            return True
+        else:
+            self._best_value = value
+            return False
+
+    def _init_summary(self):
+        self._summary = DictSummary()

deepspeech/training/triggers/time_trigger.py

@@ -30,3 +30,12 @@ class TimeTrigger():
             return True
         else:
             return False
+
+    def state_dict(self):
+        state_dict = {
+            "next_time": self._next_time,
+        }
+        return state_dict
+
+    def set_state_dict(self, state_dict):
+        self._next_time = state_dict['next_time']

deepspeech/training/triggers/utils.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 .interval_trigger import IntervalTrigger
+
+
+def never_fail_trigger(trainer):
+    return False
+
+
+def get_trigger(trigger):
+    if trigger is None:
+        return never_fail_trigger
+    if callable(trigger):
+        return trigger
+    else:
+        trigger = IntervalTrigger(*trigger)
+        return trigger

deepspeech/utils/asr_utils.py

@@ -12,6 +12,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 import json
+
 import numpy as np
 
 __all__ = ["label_smoothing_dist"]
@@ -33,8 +34,7 @@ def label_smoothing_dist(odim, lsm_type, transcript=None, blank=0):
 
     if lsm_type == "unigram":
         assert transcript is not None, (
-            "transcript is required for %s label smoothing" % lsm_type
-        )
+            "transcript is required for %s label smoothing" % lsm_type)
         labelcount = np.zeros(odim)
         for k, v in trans_json.items():
             ids = np.array([int(n) for n in v["output"][0]["tokenid"].split()])

deepspeech/utils/bleu_score.py

@@ -14,9 +14,9 @@
 """This module provides functions to calculate bleu score in different level.
 e.g. wer for word-level, cer for char-level.
 """
-import sacrebleu
 import nltk
 import numpy as np
+import sacrebleu
 
 __all__ = ['bleu', 'char_bleu', "ErrorCalculator"]
 
@@ -106,11 +106,14 @@ class ErrorCalculator():
             # NOTE: padding index (-1) in y_true is used to pad y_hat
             # because y_hats is not padded with -1
             seq_hat = [self.char_list[int(idx)] for idx in y_hat[:ymax]]
-            seq_true = [self.char_list[int(idx)] for idx in y_true if int(idx) != -1]
+            seq_true = [
+                self.char_list[int(idx)] for idx in y_true if int(idx) != -1
+            ]
             seq_hat_text = "".join(seq_hat).replace(self.space, " ")
             seq_hat_text = seq_hat_text.replace(self.pad, "")
             seq_true_text = "".join(seq_true).replace(self.space, " ")
             seqs_hat.append(seq_hat_text)
             seqs_true.append(seq_true_text)
-        bleu = nltk.bleu_score.corpus_bleu([[ref] for ref in seqs_true], seqs_hat)
+        bleu = nltk.bleu_score.corpus_bleu([[ref] for ref in seqs_true],
+                                           seqs_hat)
         return bleu * 100

deepspeech/utils/error_rate.py

@@ -14,11 +14,10 @@
 """This module provides functions to calculate error rate in different level.
 e.g. wer for word-level, cer for char-level.
 """
+from itertools import groupby
+
 import editdistance
 import numpy as np
-import logging
-import sys
-from itertools import groupby
 
 __all__ = ['word_errors', 'char_errors', 'wer', 'cer', "ErrorCalculator"]
 
@@ -225,9 +224,12 @@ class ErrorCalculator():
     :return:
     """
 
-    def __init__(
-        self, char_list, sym_space, sym_blank, report_cer=False, report_wer=False
-    ):
+    def __init__(self,
+                 char_list,
+                 sym_space,
+                 sym_blank,
+                 report_cer=False,
+                 report_wer=False):
         """Construct an ErrorCalculator object."""
         super().__init__()
 
@@ -317,7 +319,9 @@ class ErrorCalculator():
             ymax = eos_true[0] if len(eos_true) > 0 else len(y_true)
             # NOTE: padding index (-1) in y_true is used to pad y_hat
             seq_hat = [self.char_list[int(idx)] for idx in y_hat[:ymax]]
-            seq_true = [self.char_list[int(idx)] for idx in y_true if int(idx) != -1]
+            seq_true = [
+                self.char_list[int(idx)] for idx in y_true if int(idx) != -1
+            ]
             seq_hat_text = "".join(seq_hat).replace(self.space, " ")
             seq_hat_text = seq_hat_text.replace(self.blank, "")
             seq_true_text = "".join(seq_true).replace(self.space, " ")

setup.py

@@ -15,7 +15,6 @@ import contextlib
 import inspect
 import io
 import os
-import re
 import subprocess as sp
 import sys
 from pathlib import Path
@@ -84,7 +83,7 @@ def _post_install(install_lib_dir):
     tools_extrs_dir = HERE / 'tools/extras'
     with pushd(tools_extrs_dir):
         print(os.getcwd())
-        check_call(f"./install_autolog.sh")
+        check_call("./install_autolog.sh")
     print("autolog install.")
 
     # ctcdecoder

utils/json2trn.py

@@ -4,7 +4,6 @@
 #           2018 Xuankai Chang (Shanghai Jiao Tong University)
 #  Apache 2.0  (http://www.apache.org/licenses/LICENSE-2.0)
 import argparse
-import json
 import logging
 import sys