add pretrained.py

egs/grid/AVSR/audionet_ctc_asr/pretrained.py

@@ -0,0 +1,274 @@
+#!/usr/bin/env python3
+# Copyright      2021  Xiaomi Corp.        (authors: Fangjun Kuang,
+#                                                    Wei Kang
+#                                                    Mingshuang Luo)
+#
+# See ../../../../LICENSE for clarification regarding multiple authors
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+
+import argparse
+import logging
+from typing import List
+
+import k2
+import kaldifeat
+import torch
+import torchaudio
+from model import AudioNet
+
+from icefall.decode import (
+    get_lattice,
+    one_best_decoding,
+    rescore_with_whole_lattice,
+)
+from icefall.utils import AttributeDict, get_texts
+
+
+def get_parser():
+    parser = argparse.ArgumentParser(
+        formatter_class=argparse.ArgumentDefaultsHelpFormatter
+    )
+
+    parser.add_argument(
+        "--checkpoint",
+        type=str,
+        required=True,
+        help="Path to the checkpoint. "
+        "The checkpoint is assumed to be saved by "
+        "icefall.checkpoint.save_checkpoint().",
+    )
+
+    parser.add_argument(
+        "--words-file",
+        type=str,
+        required=True,
+        help="Path to words.txt",
+    )
+
+    parser.add_argument(
+        "--HLG", type=str, required=True, help="Path to HLG.pt."
+    )
+
+    parser.add_argument(
+        "--method",
+        type=str,
+        default="1best",
+        help="""Decoding method.
+        Possible values are:
+        (1) 1best - Use the best path as decoding output. Only
+            the transformer encoder output is used for decoding.
+            We call it HLG decoding.
+        (2) whole-lattice-rescoring - Use an LM to rescore the
+            decoding lattice and then use 1best to decode the
+            rescored lattice.
+            We call it HLG decoding + n-gram LM rescoring.
+        """,
+    )
+
+    parser.add_argument(
+        "--G",
+        type=str,
+        help="""An LM for rescoring.
+        Used only when method is
+        whole-lattice-rescoring.
+        It's usually a 4-gram LM.
+        """,
+    )
+
+    parser.add_argument(
+        "--ngram-lm-scale",
+        type=float,
+        default=0.1,
+        help="""
+        Used only when method is whole-lattice-rescoring.
+        It specifies the scale for n-gram LM scores.
+        (Note: You need to tune it on a dataset.)
+        """,
+    )
+
+    parser.add_argument(
+        "sound_files",
+        type=str,
+        nargs="+",
+        help="The input sound file(s) to transcribe. "
+        "Supported formats are those supported by torchaudio.load(). "
+        "For example, wav and flac are supported. "
+        "The sample rate has to be 16kHz.",
+    )
+
+    return parser
+
+
+def get_params() -> AttributeDict:
+    params = AttributeDict(
+        {
+            "feature_dim": 80,
+            "subsampling_factor": 3,
+            "num_classes": 28,
+            "sample_rate": 16000,
+            "search_beam": 20,
+            "output_beam": 5,
+            "min_active_states": 30,
+            "max_active_states": 10000,
+            "use_double_scores": True,
+        }
+    )
+    return params
+
+
+def read_sound_files(
+    filenames: List[str], expected_sample_rate: float
+) -> List[torch.Tensor]:
+    """Read a list of sound files into a list 1-D float32 torch tensors.
+    Args:
+      filenames:
+        A list of sound filenames.
+      expected_sample_rate:
+        The expected sample rate of the sound files.
+    Returns:
+      Return a list of 1-D float32 torch tensors.
+    """
+    ans = []
+    for f in filenames:
+        wave, sample_rate = torchaudio.load(f)
+        # We use only the first channel
+        ans.append(wave[0])
+    return ans
+
+
+def main():
+    parser = get_parser()
+    args = parser.parse_args()
+
+    params = get_params()
+    params.update(vars(args))
+    logging.info(f"{params}")
+
+    device = torch.device("cpu")
+    if torch.cuda.is_available():
+        device = torch.device("cuda", 0)
+
+    logging.info(f"device: {device}")
+
+    logging.info("Creating model")
+    model = AudioNet(
+        num_features=params.feature_dim,
+        num_classes=params.num_classes,
+        subsampling_factor=params.subsampling_factor,
+    )
+
+    checkpoint = torch.load(args.checkpoint, map_location="cpu")
+    model.load_state_dict(checkpoint["model"])
+    model.to(device)
+    model.eval()
+
+    logging.info(f"Loading HLG from {params.HLG}")
+    HLG = k2.Fsa.from_dict(torch.load(params.HLG, map_location="cpu"))
+    HLG = HLG.to(device)
+    if not hasattr(HLG, "lm_scores"):
+        # For whole-lattice-rescoring and attention-decoder
+        HLG.lm_scores = HLG.scores.clone()
+
+    if params.method == "whole-lattice-rescoring":
+        logging.info(f"Loading G from {params.G}")
+        G = k2.Fsa.from_dict(torch.load(params.G, map_location="cpu"))
+        # Add epsilon self-loops to G as we will compose
+        # it with the whole lattice later
+        G = G.to(device)
+        G = k2.add_epsilon_self_loops(G)
+        G = k2.arc_sort(G)
+        G.lm_scores = G.scores.clone()
+
+    logging.info("Constructing Fbank computer")
+    opts = kaldifeat.FbankOptions()
+    opts.device = device
+    opts.frame_opts.dither = 0
+    opts.frame_opts.snip_edges = False
+    opts.frame_opts.samp_freq = params.sample_rate
+    opts.mel_opts.num_bins = params.feature_dim
+
+    fbank = kaldifeat.Fbank(opts)
+
+    logging.info(f"Reading sound files: {params.sound_files}")
+    waves = read_sound_files(
+        filenames=params.sound_files, expected_sample_rate=params.sample_rate
+    )
+    waves = [w.to(device) for w in waves]
+
+    logging.info("Decoding started")
+    features = fbank(waves)
+
+    features_new = torch.zeros(len(features), 480, params.feature_dim).to(
+        device
+    )
+    for i in range(len(features)):
+        length = features[i].shape[0]
+        features_new[i][:length] = features[i]
+
+    with torch.no_grad():
+        nnet_output = model(features_new.permute(0, 2, 1))
+        # nnet_output is (N, T, C)
+
+    batch_size = nnet_output.shape[0]
+    supervision_segments = torch.tensor(
+        [[i, 0, nnet_output.shape[1]] for i in range(batch_size)],
+        dtype=torch.int32,
+    )
+
+    lattice = get_lattice(
+        nnet_output=nnet_output,
+        decoding_graph=HLG,
+        supervision_segments=supervision_segments,
+        search_beam=params.search_beam,
+        output_beam=params.output_beam,
+        min_active_states=params.min_active_states,
+        max_active_states=params.max_active_states,
+        subsampling_factor=params.subsampling_factor,
+    )
+
+    if params.method == "1best":
+        logging.info("Use HLG decoding")
+        best_path = one_best_decoding(
+            lattice=lattice, use_double_scores=params.use_double_scores
+        )
+    elif params.method == "whole-lattice-rescoring":
+        logging.info("Use HLG decoding + LM rescoring")
+        best_path_dict = rescore_with_whole_lattice(
+            lattice=lattice,
+            G_with_epsilon_loops=G,
+            lm_scale_list=[params.ngram_lm_scale],
+        )
+        best_path = next(iter(best_path_dict.values()))
+
+    hyps = get_texts(best_path)
+    word_sym_table = k2.SymbolTable.from_file(params.words_file)
+    hyps = [[word_sym_table[i] for i in ids] for ids in hyps]
+
+    s = "\n"
+    for filename, hyp in zip(params.sound_files, hyps):
+        words = " ".join(hyp)
+        s += f"{filename}:\n{words}\n\n"
+    logging.info(s)
+
+    logging.info("Decoding Done")
+
+
+if __name__ == "__main__":
+    formatter = (
+        "%(asctime)s %(levelname)s [%(filename)s:%(lineno)d] %(message)s"
+    )
+
+    logging.basicConfig(format=formatter, level=logging.INFO)
+    main()

egs/grid/AVSR/combinenet_ctc_avsr/pretrained.py

@@ -0,0 +1,270 @@
+#!/usr/bin/env python3
+# Copyright      2021  Xiaomi Corp.        (authors: Fangjun Kuang,
+#                                                    Wei Kang
+#                                                    Mingshuang Luo)
+#
+# See ../../../../LICENSE for clarification regarding multiple authors
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+
+import argparse
+import cv2
+import logging
+import numpy as np
+import os
+
+import k2
+import kaldifeat
+import torch
+import torchaudio
+from model import TdnnLstm
+
+from icefall.decode import (
+    get_lattice,
+    one_best_decoding,
+    rescore_with_whole_lattice,
+)
+from icefall.utils import AttributeDict, get_texts
+
+
+def get_parser():
+    parser = argparse.ArgumentParser(
+        formatter_class=argparse.ArgumentDefaultsHelpFormatter
+    )
+
+    parser.add_argument(
+        "--checkpoint",
+        type=str,
+        required=True,
+        help="Path to the checkpoint. "
+        "The checkpoint is assumed to be saved by "
+        "icefall.checkpoint.save_checkpoint().",
+    )
+
+    parser.add_argument(
+        "--words-file",
+        type=str,
+        required=True,
+        help="Path to words.txt",
+    )
+
+    parser.add_argument(
+        "--HLG", type=str, required=True, help="Path to HLG.pt."
+    )
+
+    parser.add_argument(
+        "--method",
+        type=str,
+        default="1best",
+        help="""Decoding method.
+        Possible values are:
+        (1) 1best - Use the best path as decoding output. Only
+            the transformer encoder output is used for decoding.
+            We call it HLG decoding.
+        (2) whole-lattice-rescoring - Use an LM to rescore the
+            decoding lattice and then use 1best to decode the
+            rescored lattice.
+            We call it HLG decoding + n-gram LM rescoring.
+        """,
+    )
+
+    parser.add_argument(
+        "--G",
+        type=str,
+        help="""An LM for rescoring.
+        Used only when method is
+        whole-lattice-rescoring.
+        It's usually a 4-gram LM.
+        """,
+    )
+
+    parser.add_argument(
+        "--ngram-lm-scale",
+        type=float,
+        default=0.1,
+        help="""
+        Used only when method is whole-lattice-rescoring.
+        It specifies the scale for n-gram LM scores.
+        (Note: You need to tune it on a dataset.)
+        """,
+    )
+
+    parser.add_argument(
+        "--lipframes-dirs",
+        type=str,
+        nargs="+",
+        help="The input visual file(s) to transcribe. "
+        "Supported formats are those supported by cv2.imread(). "
+        "The frames sample rate is 25fps.",
+    )
+
+    return parser
+
+
+def get_params() -> AttributeDict:
+    params = AttributeDict(
+        {
+            "num_classes": 28,
+            "search_beam": 20,
+            "output_beam": 5,
+            "min_active_states": 30,
+            "max_active_states": 10000,
+            "use_double_scores": True,
+        }
+    )
+    return params
+
+
+def main():
+    parser = get_parser()
+    args = parser.parse_args()
+
+    params = get_params()
+    params.update(vars(args))
+    logging.info(f"{params}")
+
+    device = torch.device("cpu")
+    if torch.cuda.is_available():
+        device = torch.device("cuda", 0)
+
+    logging.info(f"device: {device}")
+
+    logging.info("Creating model")
+    model = TdnnLstm(num_features=80, num_classes=28, subsampling_factor=3)
+
+    checkpoint = torch.load(args.checkpoint, map_location="cpu")
+    model.load_state_dict(checkpoint["model"])
+    model.to(device)
+    model.eval()
+
+    logging.info(f"Loading HLG from {params.HLG}")
+    HLG = k2.Fsa.from_dict(torch.load(params.HLG, map_location="cpu"))
+    HLG = HLG.to(device)
+    if not hasattr(HLG, "lm_scores"):
+        # For whole-lattice-rescoring and attention-decoder
+        HLG.lm_scores = HLG.scores.clone()
+
+    if params.method == "whole-lattice-rescoring":
+        logging.info(f"Loading G from {params.G}")
+        G = k2.Fsa.from_dict(torch.load(params.G, map_location="cpu"))
+        # Add epsilon self-loops to G as we will compose
+        # it with the whole lattice later
+        G = G.to(device)
+        G = k2.add_epsilon_self_loops(G)
+        G = k2.arc_sort(G)
+        G.lm_scores = G.scores.clone()
+
+    logging.info("Loading lip roi frames and audio wav files")
+    aud = []
+    vid = []
+
+    opts = kaldifeat.FbankOptions()
+    opts.device = device
+    opts.frame_opts.dither = 0
+    opts.frame_opts.snip_edges = False
+    opts.frame_opts.samp_freq = 16000
+    opts.mel_opts.num_bins = 80
+    fbank = kaldifeat.Fbank(opts)
+
+    for sample_dir in params.lipframes_dirs:
+        wave, sr = torchaudio.load(
+            sample_dir.replace("lip", "audio_25k").replace(
+                "video/mpg_6000/", ""
+            )
+            + ".wav"
+        )
+        wave = wave[0]
+        aud.append(fbank(wave))
+
+        files = os.listdir(sample_dir)
+        files = list(filter(lambda file: file.find(".jpg") != -1, files))
+        files = sorted(files, key=lambda file: int(os.path.splitext(file)[0]))
+        array = [cv2.imread(os.path.join(sample_dir, file)) for file in files]
+        array = list(filter(lambda im: im is not None, array))
+        array = [
+            cv2.resize(im, (128, 64), interpolation=cv2.INTER_LANCZOS4)
+            for im in array
+        ]
+        array = np.stack(array, axis=0).astype(np.float32)
+        vid.append(array)
+
+    L, H, W, C = vid[0].shape
+    features_v = torch.zeros(len(vid), 75, H, W, C).to(device)
+    for i in range(len(vid)):
+        length = vid[i].shape[0]
+        features_v[i][:length] = torch.FloatTensor(vid[i]).to(device)
+
+    features_a = torch.zeros(len(aud), 450, 80).to(device)
+    for i in range(len(aud)):
+        length = aud[i].shape[0]
+        features_a[i][:length] = torch.FloatTensor(aud[i]).to(device)
+
+    logging.info("Decoding started")
+    with torch.no_grad():
+        nnet_output = model(
+            features_v.permute(0, 4, 1, 2, 3) / 255.0,
+            features_a.permute(0, 2, 1),
+        )
+        # nnet_output is (N, T, C)
+
+    batch_size = nnet_output.shape[0]
+    supervision_segments = torch.tensor(
+        [[i, 0, nnet_output.shape[1]] for i in range(batch_size)],
+        dtype=torch.int32,
+    )
+
+    lattice = get_lattice(
+        nnet_output=nnet_output,
+        decoding_graph=HLG,
+        supervision_segments=supervision_segments,
+        search_beam=params.search_beam,
+        output_beam=params.output_beam,
+        min_active_states=params.min_active_states,
+        max_active_states=params.max_active_states,
+    )
+
+    if params.method == "1best":
+        logging.info("Use HLG decoding")
+        best_path = one_best_decoding(
+            lattice=lattice, use_double_scores=params.use_double_scores
+        )
+    elif params.method == "whole-lattice-rescoring":
+        logging.info("Use HLG decoding + LM rescoring")
+        best_path_dict = rescore_with_whole_lattice(
+            lattice=lattice,
+            G_with_epsilon_loops=G,
+            lm_scale_list=[params.ngram_lm_scale],
+        )
+        best_path = next(iter(best_path_dict.values()))
+
+    hyps = get_texts(best_path)
+    word_sym_table = k2.SymbolTable.from_file(params.words_file)
+    hyps = [[word_sym_table[i] for i in ids] for ids in hyps]
+
+    s = "\n"
+    for filename, hyp in zip(params.lipframes_dirs, hyps):
+        words = " ".join(hyp)
+        s += f"{filename}:\n{words}\n\n"
+    logging.info(s)
+
+    logging.info("Decoding Done")
+
+
+if __name__ == "__main__":
+    formatter = (
+        "%(asctime)s %(levelname)s [%(filename)s:%(lineno)d] %(message)s"
+    )
+
+    logging.basicConfig(format=formatter, level=logging.INFO)
+    main()

egs/grid/AVSR/visualnet2_ctc_vsr/decode.py

@@ -32,8 +32,7 @@ import torch.nn as nn
 from torch.utils.data import DataLoader
 from local.dataset_visual import dataset_visual
 
-# from model import LipNet
+from model import VisualNet2
-from model import visual_frontend
 
 from icefall.checkpoint import average_checkpoints, load_checkpoint
 from icefall.decode import (
@@ -131,7 +130,7 @@ def get_parser():
 def get_params() -> AttributeDict:
     params = AttributeDict(
         {
-            "exp_dir": Path("visualnet_ctc_vsr2/exp"),
+            "exp_dir": Path("visualnet2_ctc_vsr/exp"),
             "lang_dir": Path("data/lang_character"),
             "lm_dir": Path("data/lm"),
             "search_beam": 20,
@@ -388,6 +387,7 @@ def main():
     logging.info(params)
 
     lexicon = Lexicon(params.lang_dir)
+    max_token_id = max(lexicon.tokens)
 
     device = torch.device("cpu")
     if torch.cuda.is_available():
@@ -441,7 +441,7 @@ def main():
     else:
         G = None
 
-    model = visual_frontend()
+    model = VisualNet2(num_classes=max_token_id + 1)
     if params.avg == 1:
         load_checkpoint(f"{params.exp_dir}/epoch-{params.epoch}.pt", model)
     else:

egs/grid/AVSR/visualnet2_ctc_vsr/model.py

@@ -115,9 +115,10 @@ class ResNet(nn.Module):
 
 
 class VisualNet2(nn.Module):
-    def __init__(self, inputDim=512):
+    def __init__(self, num_classes):
         super(VisualNet2, self).__init__()
-        self.inputDim = inputDim
+        self.num_classes = num_classes
+        self.inputDim = 512
         self.conv3d = nn.Conv3d(
             3,
             64,
@@ -143,7 +144,7 @@ class VisualNet2(nn.Module):
         self.dropout = nn.Dropout(p=0.5)
 
         # fc
-        self.linear = nn.Linear(1024, 28)
+        self.linear = nn.Linear(1024, self.num_classes)
 
         # initialize
         self._initialize_weights()

egs/grid/AVSR/visualnet2_ctc_vsr/pretrained.py

@@ -0,0 +1,243 @@
+#!/usr/bin/env python3
+# Copyright      2021  Xiaomi Corp.        (authors: Fangjun Kuang,
+#                                                    Wei Kang
+#                                                    Mingshuang Luo)
+#
+# See ../../../../LICENSE for clarification regarding multiple authors
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+
+import argparse
+import cv2
+import logging
+import numpy as np
+import os
+
+import k2
+import torch
+from model import VisualNet2
+
+from icefall.decode import (
+    get_lattice,
+    one_best_decoding,
+    rescore_with_whole_lattice,
+)
+from icefall.utils import AttributeDict, get_texts
+
+
+def get_parser():
+    parser = argparse.ArgumentParser(
+        formatter_class=argparse.ArgumentDefaultsHelpFormatter
+    )
+
+    parser.add_argument(
+        "--checkpoint",
+        type=str,
+        required=True,
+        help="Path to the checkpoint. "
+        "The checkpoint is assumed to be saved by "
+        "icefall.checkpoint.save_checkpoint().",
+    )
+
+    parser.add_argument(
+        "--words-file",
+        type=str,
+        required=True,
+        help="Path to words.txt",
+    )
+
+    parser.add_argument(
+        "--HLG", type=str, required=True, help="Path to HLG.pt."
+    )
+
+    parser.add_argument(
+        "--method",
+        type=str,
+        default="1best",
+        help="""Decoding method.
+        Possible values are:
+        (1) 1best - Use the best path as decoding output. Only
+            the transformer encoder output is used for decoding.
+            We call it HLG decoding.
+        (2) whole-lattice-rescoring - Use an LM to rescore the
+            decoding lattice and then use 1best to decode the
+            rescored lattice.
+            We call it HLG decoding + n-gram LM rescoring.
+        """,
+    )
+
+    parser.add_argument(
+        "--G",
+        type=str,
+        help="""An LM for rescoring.
+        Used only when method is
+        whole-lattice-rescoring.
+        It's usually a 4-gram LM.
+        """,
+    )
+
+    parser.add_argument(
+        "--ngram-lm-scale",
+        type=float,
+        default=0.1,
+        help="""
+        Used only when method is whole-lattice-rescoring.
+        It specifies the scale for n-gram LM scores.
+        (Note: You need to tune it on a dataset.)
+        """,
+    )
+
+    parser.add_argument(
+        "--lipframes-dirs",
+        type=str,
+        nargs="+",
+        help="The input visual file(s) to transcribe. "
+        "Supported formats are those supported by cv2.imread(). "
+        "The frames sample rate is 25fps.",
+    )
+
+    return parser
+
+
+def get_params() -> AttributeDict:
+    params = AttributeDict(
+        {
+            "num_classes": 28,
+            "search_beam": 20,
+            "output_beam": 5,
+            "min_active_states": 30,
+            "max_active_states": 10000,
+            "use_double_scores": True,
+        }
+    )
+    return params
+
+
+def main():
+    parser = get_parser()
+    args = parser.parse_args()
+
+    params = get_params()
+    params.update(vars(args))
+    logging.info(f"{params}")
+
+    device = torch.device("cpu")
+    if torch.cuda.is_available():
+        device = torch.device("cuda", 0)
+
+    logging.info(f"device: {device}")
+
+    logging.info("Creating model")
+    model = VisualNet2(num_classes=params.num_classes)
+
+    checkpoint = torch.load(args.checkpoint, map_location="cpu")
+    model.load_state_dict(checkpoint["model"])
+    model.to(device)
+    model.eval()
+
+    logging.info(f"Loading HLG from {params.HLG}")
+    HLG = k2.Fsa.from_dict(torch.load(params.HLG, map_location="cpu"))
+    HLG = HLG.to(device)
+    if not hasattr(HLG, "lm_scores"):
+        # For whole-lattice-rescoring and attention-decoder
+        HLG.lm_scores = HLG.scores.clone()
+
+    if params.method == "whole-lattice-rescoring":
+        logging.info(f"Loading G from {params.G}")
+        G = k2.Fsa.from_dict(torch.load(params.G, map_location="cpu"))
+        # Add epsilon self-loops to G as we will compose
+        # it with the whole lattice later
+        G = G.to(device)
+        G = k2.add_epsilon_self_loops(G)
+        G = k2.arc_sort(G)
+        G.lm_scores = G.scores.clone()
+
+    logging.info("Loading lip roi frames")
+
+    vid = []
+    for sample_dir in params.lipframes_dirs:
+        files = os.listdir(sample_dir)
+        files = list(filter(lambda file: file.find(".jpg") != -1, files))
+        files = sorted(files, key=lambda file: int(os.path.splitext(file)[0]))
+        array = [cv2.imread(os.path.join(sample_dir, file)) for file in files]
+        array = list(filter(lambda im: im is not None, array))
+        array = [
+            cv2.resize(im, (128, 64), interpolation=cv2.INTER_LANCZOS4)
+            for im in array
+        ]
+        array = np.stack(array, axis=0).astype(np.float32)
+        vid.append(array)
+
+    _, H, W, C = vid[0].shape
+    features = torch.zeros(len(vid), 75, H, W, C).to(device)
+    for i in range(len(vid)):
+        length = vid[i].shape[0]
+        features[i][:length] = torch.FloatTensor(vid[i]).to(device)
+
+    logging.info("Decoding started")
+    features = features / 255.0
+    with torch.no_grad():
+        nnet_output = model(features.permute(0, 4, 1, 2, 3))
+        # nnet_output is (N, T, C)
+
+    batch_size = nnet_output.shape[0]
+    supervision_segments = torch.tensor(
+        [[i, 0, nnet_output.shape[1]] for i in range(batch_size)],
+        dtype=torch.int32,
+    )
+
+    lattice = get_lattice(
+        nnet_output=nnet_output,
+        decoding_graph=HLG,
+        supervision_segments=supervision_segments,
+        search_beam=params.search_beam,
+        output_beam=params.output_beam,
+        min_active_states=params.min_active_states,
+        max_active_states=params.max_active_states,
+    )
+
+    if params.method == "1best":
+        logging.info("Use HLG decoding")
+        best_path = one_best_decoding(
+            lattice=lattice, use_double_scores=params.use_double_scores
+        )
+    elif params.method == "whole-lattice-rescoring":
+        logging.info("Use HLG decoding + LM rescoring")
+        best_path_dict = rescore_with_whole_lattice(
+            lattice=lattice,
+            G_with_epsilon_loops=G,
+            lm_scale_list=[params.ngram_lm_scale],
+        )
+        best_path = next(iter(best_path_dict.values()))
+
+    hyps = get_texts(best_path)
+    word_sym_table = k2.SymbolTable.from_file(params.words_file)
+    hyps = [[word_sym_table[i] for i in ids] for ids in hyps]
+
+    s = "\n"
+    for filename, hyp in zip(params.lipframes_dirs, hyps):
+        words = " ".join(hyp)
+        s += f"{filename}:\n{words}\n\n"
+    logging.info(s)
+
+    logging.info("Decoding Done")
+
+
+if __name__ == "__main__":
+    formatter = (
+        "%(asctime)s %(levelname)s [%(filename)s:%(lineno)d] %(message)s"
+    )
+
+    logging.basicConfig(format=formatter, level=logging.INFO)
+    main()

egs/grid/AVSR/visualnet2_ctc_vsr/train.py

@@ -503,13 +503,14 @@ def run(rank, world_size, args):
         tb_writer = None
 
     lexicon = Lexicon(params.lang_dir)
+    max_token_id = max(lexicon.tokens)
 
     device = torch.device("cpu")
     if torch.cuda.is_available():
         device = torch.device("cuda", rank)
 
     graph_compiler = CtcTrainingGraphCompiler(lexicon=lexicon, device=device)
-    model = VisualNet2()
+    model = VisualNet2(num_classes=max_token_id + 1)
 
     checkpoints = load_checkpoint_if_available(params=params, model=model)
 

egs/grid/AVSR/visualnet_ctc_vsr/pretrained.py

@@ -0,0 +1,243 @@
+#!/usr/bin/env python3
+# Copyright      2021  Xiaomi Corp.        (authors: Fangjun Kuang,
+#                                                    Wei Kang
+#                                                    Mingshuang Luo)
+#
+# See ../../../../LICENSE for clarification regarding multiple authors
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+
+import argparse
+import cv2
+import logging
+import numpy as np
+import os
+
+import k2
+import torch
+from model import VisualNet
+
+from icefall.decode import (
+    get_lattice,
+    one_best_decoding,
+    rescore_with_whole_lattice,
+)
+from icefall.utils import AttributeDict, get_texts
+
+
+def get_parser():
+    parser = argparse.ArgumentParser(
+        formatter_class=argparse.ArgumentDefaultsHelpFormatter
+    )
+
+    parser.add_argument(
+        "--checkpoint",
+        type=str,
+        required=True,
+        help="Path to the checkpoint. "
+        "The checkpoint is assumed to be saved by "
+        "icefall.checkpoint.save_checkpoint().",
+    )
+
+    parser.add_argument(
+        "--words-file",
+        type=str,
+        required=True,
+        help="Path to words.txt",
+    )
+
+    parser.add_argument(
+        "--HLG", type=str, required=True, help="Path to HLG.pt."
+    )
+
+    parser.add_argument(
+        "--method",
+        type=str,
+        default="1best",
+        help="""Decoding method.
+        Possible values are:
+        (1) 1best - Use the best path as decoding output. Only
+            the transformer encoder output is used for decoding.
+            We call it HLG decoding.
+        (2) whole-lattice-rescoring - Use an LM to rescore the
+            decoding lattice and then use 1best to decode the
+            rescored lattice.
+            We call it HLG decoding + n-gram LM rescoring.
+        """,
+    )
+
+    parser.add_argument(
+        "--G",
+        type=str,
+        help="""An LM for rescoring.
+        Used only when method is
+        whole-lattice-rescoring.
+        It's usually a 4-gram LM.
+        """,
+    )
+
+    parser.add_argument(
+        "--ngram-lm-scale",
+        type=float,
+        default=0.1,
+        help="""
+        Used only when method is whole-lattice-rescoring.
+        It specifies the scale for n-gram LM scores.
+        (Note: You need to tune it on a dataset.)
+        """,
+    )
+
+    parser.add_argument(
+        "--lipframes-dirs",
+        type=str,
+        nargs="+",
+        help="The input visual file(s) to transcribe. "
+        "Supported formats are those supported by cv2.imread(). "
+        "The frames sample rate is 25fps.",
+    )
+
+    return parser
+
+
+def get_params() -> AttributeDict:
+    params = AttributeDict(
+        {
+            "num_classes": 28,
+            "search_beam": 20,
+            "output_beam": 5,
+            "min_active_states": 30,
+            "max_active_states": 10000,
+            "use_double_scores": True,
+        }
+    )
+    return params
+
+
+def main():
+    parser = get_parser()
+    args = parser.parse_args()
+
+    params = get_params()
+    params.update(vars(args))
+    logging.info(f"{params}")
+
+    device = torch.device("cpu")
+    if torch.cuda.is_available():
+        device = torch.device("cuda", 0)
+
+    logging.info(f"device: {device}")
+
+    logging.info("Creating model")
+    model = VisualNet(num_classes=params.num_classes)
+
+    checkpoint = torch.load(args.checkpoint, map_location="cpu")
+    model.load_state_dict(checkpoint["model"])
+    model.to(device)
+    model.eval()
+
+    logging.info(f"Loading HLG from {params.HLG}")
+    HLG = k2.Fsa.from_dict(torch.load(params.HLG, map_location="cpu"))
+    HLG = HLG.to(device)
+    if not hasattr(HLG, "lm_scores"):
+        # For whole-lattice-rescoring and attention-decoder
+        HLG.lm_scores = HLG.scores.clone()
+
+    if params.method == "whole-lattice-rescoring":
+        logging.info(f"Loading G from {params.G}")
+        G = k2.Fsa.from_dict(torch.load(params.G, map_location="cpu"))
+        # Add epsilon self-loops to G as we will compose
+        # it with the whole lattice later
+        G = G.to(device)
+        G = k2.add_epsilon_self_loops(G)
+        G = k2.arc_sort(G)
+        G.lm_scores = G.scores.clone()
+
+    logging.info("Loading lip roi frames")
+
+    vid = []
+    for sample_dir in params.lipframes_dirs:
+        files = os.listdir(sample_dir)
+        files = list(filter(lambda file: file.find(".jpg") != -1, files))
+        files = sorted(files, key=lambda file: int(os.path.splitext(file)[0]))
+        array = [cv2.imread(os.path.join(sample_dir, file)) for file in files]
+        array = list(filter(lambda im: im is not None, array))
+        array = [
+            cv2.resize(im, (128, 64), interpolation=cv2.INTER_LANCZOS4)
+            for im in array
+        ]
+        array = np.stack(array, axis=0).astype(np.float32)
+        vid.append(array)
+
+    _, H, W, C = vid[0].shape
+    features = torch.zeros(len(vid), 75, H, W, C).to(device)
+    for i in range(len(vid)):
+        length = vid[i].shape[0]
+        features[i][:length] = torch.FloatTensor(vid[i]).to(device)
+
+    logging.info("Decoding started")
+    features = features / 255.0
+    with torch.no_grad():
+        nnet_output = model(features.permute(0, 4, 1, 2, 3))
+        # nnet_output is (N, T, C)
+
+    batch_size = nnet_output.shape[0]
+    supervision_segments = torch.tensor(
+        [[i, 0, nnet_output.shape[1]] for i in range(batch_size)],
+        dtype=torch.int32,
+    )
+
+    lattice = get_lattice(
+        nnet_output=nnet_output,
+        decoding_graph=HLG,
+        supervision_segments=supervision_segments,
+        search_beam=params.search_beam,
+        output_beam=params.output_beam,
+        min_active_states=params.min_active_states,
+        max_active_states=params.max_active_states,
+    )
+
+    if params.method == "1best":
+        logging.info("Use HLG decoding")
+        best_path = one_best_decoding(
+            lattice=lattice, use_double_scores=params.use_double_scores
+        )
+    elif params.method == "whole-lattice-rescoring":
+        logging.info("Use HLG decoding + LM rescoring")
+        best_path_dict = rescore_with_whole_lattice(
+            lattice=lattice,
+            G_with_epsilon_loops=G,
+            lm_scale_list=[params.ngram_lm_scale],
+        )
+        best_path = next(iter(best_path_dict.values()))
+
+    hyps = get_texts(best_path)
+    word_sym_table = k2.SymbolTable.from_file(params.words_file)
+    hyps = [[word_sym_table[i] for i in ids] for ids in hyps]
+
+    s = "\n"
+    for filename, hyp in zip(params.lipframes_dirs, hyps):
+        words = " ".join(hyp)
+        s += f"{filename}:\n{words}\n\n"
+    logging.info(s)
+
+    logging.info("Decoding Done")
+
+
+if __name__ == "__main__":
+    formatter = (
+        "%(asctime)s %(levelname)s [%(filename)s:%(lineno)d] %(message)s"
+    )
+
+    logging.basicConfig(format=formatter, level=logging.INFO)
+    main()