more interface, trigger, extension

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_interface.py

@@ -6,7 +6,7 @@ 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):

deepspeech/models/st_interface.py

@@ -0,0 +1,59 @@
+"""ST Interface module."""
+
+
+import argparse
+from deepspeech.utils.dynamic_import import dynamic_import
+from .asr_interface import ASRInterface
+
+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

@@ -0,0 +1,2 @@
+from .u2_st import U2STModel
+from .u2_st import U2STInferModel

deepspeech/training/extensions/plot.py

@@ -0,0 +1,437 @@
+
+import argparse
+import copy
+import json
+import os
+import shutil
+import tempfile
+import numpy as np
+
+
+from . import extension
+from ..updaters.trainer import Trainer
+
+
+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/extensions/visualizer.py

@@ -36,4 +36,4 @@ class VisualDL(extension.Extension):
             self.writer.add_scalar(k, v, step=trainer.updater.state.iteration)
 
     def finalize(self, trainer):
-        self.writer.close()
+        self.writer.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

@@ -0,0 +1,60 @@
+# 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 .utils import get_trigger
+from ..reporter import DictSummary
+
+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/limit_trigger.py

@@ -28,4 +28,4 @@ class LimitTrigger():
         state = trainer.updater.state
         index = getattr(state, self.unit)
         fire = index >= self.limit
-        return fire
+        return fire

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

@@ -0,0 +1,15 @@
+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