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add pretrained.py
This commit is contained in:
Mingshuang Luo
parent
2b5881f4d9
commit
283bd126c5
274
egs/grid/AVSR/audionet_ctc_asr/pretrained.py
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274
egs/grid/AVSR/audionet_ctc_asr/pretrained.py
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@ -0,0 +1,274 @@
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#!/usr/bin/env python3
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# Copyright 2021 Xiaomi Corp. (authors: Fangjun Kuang,
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# Wei Kang
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# Mingshuang Luo)
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#
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# See ../../../../LICENSE for clarification regarding multiple authors
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import argparse
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import logging
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from typing import List
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import k2
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import kaldifeat
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import torch
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import torchaudio
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from model import AudioNet
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from icefall.decode import (
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get_lattice,
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one_best_decoding,
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rescore_with_whole_lattice,
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)
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from icefall.utils import AttributeDict, get_texts
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def get_parser():
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parser = argparse.ArgumentParser(
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formatter_class=argparse.ArgumentDefaultsHelpFormatter
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)
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parser.add_argument(
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"--checkpoint",
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type=str,
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required=True,
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help="Path to the checkpoint. "
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"The checkpoint is assumed to be saved by "
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"icefall.checkpoint.save_checkpoint().",
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)
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parser.add_argument(
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"--words-file",
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type=str,
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required=True,
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help="Path to words.txt",
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)
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parser.add_argument(
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"--HLG", type=str, required=True, help="Path to HLG.pt."
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)
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parser.add_argument(
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"--method",
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type=str,
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default="1best",
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help="""Decoding method.
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Possible values are:
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(1) 1best - Use the best path as decoding output. Only
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the transformer encoder output is used for decoding.
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We call it HLG decoding.
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(2) whole-lattice-rescoring - Use an LM to rescore the
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decoding lattice and then use 1best to decode the
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rescored lattice.
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We call it HLG decoding + n-gram LM rescoring.
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""",
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)
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parser.add_argument(
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"--G",
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type=str,
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help="""An LM for rescoring.
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Used only when method is
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whole-lattice-rescoring.
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It's usually a 4-gram LM.
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""",
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)
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parser.add_argument(
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"--ngram-lm-scale",
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type=float,
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default=0.1,
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help="""
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Used only when method is whole-lattice-rescoring.
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It specifies the scale for n-gram LM scores.
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(Note: You need to tune it on a dataset.)
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""",
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)
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parser.add_argument(
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"sound_files",
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type=str,
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nargs="+",
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help="The input sound file(s) to transcribe. "
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"Supported formats are those supported by torchaudio.load(). "
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"For example, wav and flac are supported. "
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"The sample rate has to be 16kHz.",
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)
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return parser
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def get_params() -> AttributeDict:
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params = AttributeDict(
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{
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"feature_dim": 80,
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"subsampling_factor": 3,
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"num_classes": 28,
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"sample_rate": 16000,
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"search_beam": 20,
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"output_beam": 5,
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"min_active_states": 30,
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"max_active_states": 10000,
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"use_double_scores": True,
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}
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)
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return params
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def read_sound_files(
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filenames: List[str], expected_sample_rate: float
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) -> List[torch.Tensor]:
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"""Read a list of sound files into a list 1-D float32 torch tensors.
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Args:
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filenames:
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A list of sound filenames.
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expected_sample_rate:
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The expected sample rate of the sound files.
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Returns:
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Return a list of 1-D float32 torch tensors.
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"""
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ans = []
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for f in filenames:
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wave, sample_rate = torchaudio.load(f)
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# We use only the first channel
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ans.append(wave[0])
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return ans
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def main():
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parser = get_parser()
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args = parser.parse_args()
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params = get_params()
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params.update(vars(args))
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logging.info(f"{params}")
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device = torch.device("cpu")
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if torch.cuda.is_available():
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device = torch.device("cuda", 0)
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logging.info(f"device: {device}")
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logging.info("Creating model")
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model = AudioNet(
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num_features=params.feature_dim,
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num_classes=params.num_classes,
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subsampling_factor=params.subsampling_factor,
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)
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checkpoint = torch.load(args.checkpoint, map_location="cpu")
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model.load_state_dict(checkpoint["model"])
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model.to(device)
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model.eval()
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logging.info(f"Loading HLG from {params.HLG}")
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HLG = k2.Fsa.from_dict(torch.load(params.HLG, map_location="cpu"))
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HLG = HLG.to(device)
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if not hasattr(HLG, "lm_scores"):
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# For whole-lattice-rescoring and attention-decoder
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HLG.lm_scores = HLG.scores.clone()
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if params.method == "whole-lattice-rescoring":
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logging.info(f"Loading G from {params.G}")
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G = k2.Fsa.from_dict(torch.load(params.G, map_location="cpu"))
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# Add epsilon self-loops to G as we will compose
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# it with the whole lattice later
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G = G.to(device)
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G = k2.add_epsilon_self_loops(G)
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G = k2.arc_sort(G)
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G.lm_scores = G.scores.clone()
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logging.info("Constructing Fbank computer")
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opts = kaldifeat.FbankOptions()
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opts.device = device
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opts.frame_opts.dither = 0
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opts.frame_opts.snip_edges = False
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opts.frame_opts.samp_freq = params.sample_rate
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opts.mel_opts.num_bins = params.feature_dim
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fbank = kaldifeat.Fbank(opts)
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logging.info(f"Reading sound files: {params.sound_files}")
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waves = read_sound_files(
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filenames=params.sound_files, expected_sample_rate=params.sample_rate
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)
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waves = [w.to(device) for w in waves]
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logging.info("Decoding started")
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features = fbank(waves)
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features_new = torch.zeros(len(features), 480, params.feature_dim).to(
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device
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)
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for i in range(len(features)):
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length = features[i].shape[0]
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features_new[i][:length] = features[i]
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with torch.no_grad():
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nnet_output = model(features_new.permute(0, 2, 1))
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# nnet_output is (N, T, C)
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batch_size = nnet_output.shape[0]
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supervision_segments = torch.tensor(
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[[i, 0, nnet_output.shape[1]] for i in range(batch_size)],
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dtype=torch.int32,
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)
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lattice = get_lattice(
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nnet_output=nnet_output,
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decoding_graph=HLG,
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supervision_segments=supervision_segments,
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search_beam=params.search_beam,
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output_beam=params.output_beam,
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min_active_states=params.min_active_states,
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max_active_states=params.max_active_states,
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subsampling_factor=params.subsampling_factor,
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)
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if params.method == "1best":
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logging.info("Use HLG decoding")
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best_path = one_best_decoding(
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lattice=lattice, use_double_scores=params.use_double_scores
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)
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elif params.method == "whole-lattice-rescoring":
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logging.info("Use HLG decoding + LM rescoring")
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best_path_dict = rescore_with_whole_lattice(
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lattice=lattice,
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G_with_epsilon_loops=G,
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lm_scale_list=[params.ngram_lm_scale],
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)
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best_path = next(iter(best_path_dict.values()))
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hyps = get_texts(best_path)
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word_sym_table = k2.SymbolTable.from_file(params.words_file)
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hyps = [[word_sym_table[i] for i in ids] for ids in hyps]
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s = "\n"
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for filename, hyp in zip(params.sound_files, hyps):
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words = " ".join(hyp)
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s += f"{filename}:\n{words}\n\n"
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logging.info(s)
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logging.info("Decoding Done")
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if __name__ == "__main__":
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formatter = (
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"%(asctime)s %(levelname)s [%(filename)s:%(lineno)d] %(message)s"
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)
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logging.basicConfig(format=formatter, level=logging.INFO)
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main()
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270
egs/grid/AVSR/combinenet_ctc_avsr/pretrained.py
Normal file
270
egs/grid/AVSR/combinenet_ctc_avsr/pretrained.py
Normal file
@ -0,0 +1,270 @@
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#!/usr/bin/env python3
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# Copyright 2021 Xiaomi Corp. (authors: Fangjun Kuang,
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# Wei Kang
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# Mingshuang Luo)
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#
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# See ../../../../LICENSE for clarification regarding multiple authors
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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||||
#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import argparse
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import cv2
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import logging
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import numpy as np
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import os
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import k2
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import kaldifeat
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import torch
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import torchaudio
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from model import TdnnLstm
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from icefall.decode import (
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get_lattice,
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one_best_decoding,
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rescore_with_whole_lattice,
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)
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from icefall.utils import AttributeDict, get_texts
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def get_parser():
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parser = argparse.ArgumentParser(
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formatter_class=argparse.ArgumentDefaultsHelpFormatter
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)
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parser.add_argument(
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"--checkpoint",
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type=str,
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required=True,
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help="Path to the checkpoint. "
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"The checkpoint is assumed to be saved by "
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"icefall.checkpoint.save_checkpoint().",
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)
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parser.add_argument(
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"--words-file",
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type=str,
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required=True,
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help="Path to words.txt",
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)
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parser.add_argument(
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"--HLG", type=str, required=True, help="Path to HLG.pt."
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)
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parser.add_argument(
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"--method",
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type=str,
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default="1best",
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help="""Decoding method.
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Possible values are:
|
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(1) 1best - Use the best path as decoding output. Only
|
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the transformer encoder output is used for decoding.
|
||||
We call it HLG decoding.
|
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(2) whole-lattice-rescoring - Use an LM to rescore the
|
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decoding lattice and then use 1best to decode the
|
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rescored lattice.
|
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We call it HLG decoding + n-gram LM rescoring.
|
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""",
|
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)
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parser.add_argument(
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"--G",
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type=str,
|
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help="""An LM for rescoring.
|
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Used only when method is
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whole-lattice-rescoring.
|
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It's usually a 4-gram LM.
|
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""",
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)
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parser.add_argument(
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"--ngram-lm-scale",
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type=float,
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default=0.1,
|
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help="""
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Used only when method is whole-lattice-rescoring.
|
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It specifies the scale for n-gram LM scores.
|
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(Note: You need to tune it on a dataset.)
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""",
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)
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parser.add_argument(
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"--lipframes-dirs",
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type=str,
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nargs="+",
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help="The input visual file(s) to transcribe. "
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"Supported formats are those supported by cv2.imread(). "
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"The frames sample rate is 25fps.",
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)
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return parser
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def get_params() -> AttributeDict:
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params = AttributeDict(
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{
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"num_classes": 28,
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"search_beam": 20,
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"output_beam": 5,
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"min_active_states": 30,
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"max_active_states": 10000,
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"use_double_scores": True,
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}
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)
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return params
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def main():
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parser = get_parser()
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args = parser.parse_args()
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params = get_params()
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params.update(vars(args))
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logging.info(f"{params}")
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device = torch.device("cpu")
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if torch.cuda.is_available():
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device = torch.device("cuda", 0)
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logging.info(f"device: {device}")
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logging.info("Creating model")
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model = TdnnLstm(num_features=80, num_classes=28, subsampling_factor=3)
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checkpoint = torch.load(args.checkpoint, map_location="cpu")
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model.load_state_dict(checkpoint["model"])
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model.to(device)
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model.eval()
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logging.info(f"Loading HLG from {params.HLG}")
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HLG = k2.Fsa.from_dict(torch.load(params.HLG, map_location="cpu"))
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HLG = HLG.to(device)
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if not hasattr(HLG, "lm_scores"):
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# For whole-lattice-rescoring and attention-decoder
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HLG.lm_scores = HLG.scores.clone()
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if params.method == "whole-lattice-rescoring":
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logging.info(f"Loading G from {params.G}")
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G = k2.Fsa.from_dict(torch.load(params.G, map_location="cpu"))
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# Add epsilon self-loops to G as we will compose
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# it with the whole lattice later
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G = G.to(device)
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G = k2.add_epsilon_self_loops(G)
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G = k2.arc_sort(G)
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G.lm_scores = G.scores.clone()
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logging.info("Loading lip roi frames and audio wav files")
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aud = []
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vid = []
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opts = kaldifeat.FbankOptions()
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opts.device = device
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opts.frame_opts.dither = 0
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opts.frame_opts.snip_edges = False
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opts.frame_opts.samp_freq = 16000
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opts.mel_opts.num_bins = 80
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fbank = kaldifeat.Fbank(opts)
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for sample_dir in params.lipframes_dirs:
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wave, sr = torchaudio.load(
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sample_dir.replace("lip", "audio_25k").replace(
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"video/mpg_6000/", ""
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)
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+ ".wav"
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)
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wave = wave[0]
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aud.append(fbank(wave))
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files = os.listdir(sample_dir)
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files = list(filter(lambda file: file.find(".jpg") != -1, files))
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files = sorted(files, key=lambda file: int(os.path.splitext(file)[0]))
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array = [cv2.imread(os.path.join(sample_dir, file)) for file in files]
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array = list(filter(lambda im: im is not None, array))
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array = [
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cv2.resize(im, (128, 64), interpolation=cv2.INTER_LANCZOS4)
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for im in array
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]
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array = np.stack(array, axis=0).astype(np.float32)
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vid.append(array)
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L, H, W, C = vid[0].shape
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features_v = torch.zeros(len(vid), 75, H, W, C).to(device)
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for i in range(len(vid)):
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length = vid[i].shape[0]
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features_v[i][:length] = torch.FloatTensor(vid[i]).to(device)
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features_a = torch.zeros(len(aud), 450, 80).to(device)
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for i in range(len(aud)):
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length = aud[i].shape[0]
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features_a[i][:length] = torch.FloatTensor(aud[i]).to(device)
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logging.info("Decoding started")
|
||||
with torch.no_grad():
|
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nnet_output = model(
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features_v.permute(0, 4, 1, 2, 3) / 255.0,
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features_a.permute(0, 2, 1),
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)
|
||||
# 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()
|
||||
@ -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 visual_frontend
|
||||
from model import VisualNet2
|
||||
|
||||
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:
|
||||
|
||||
@ -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()
|
||||
|
||||
243
egs/grid/AVSR/visualnet2_ctc_vsr/pretrained.py
Normal file
243
egs/grid/AVSR/visualnet2_ctc_vsr/pretrained.py
Normal file
@ -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()
|
||||
@ -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)
|
||||
|
||||
|
||||
243
egs/grid/AVSR/visualnet_ctc_vsr/pretrained.py
Normal file
243
egs/grid/AVSR/visualnet_ctc_vsr/pretrained.py
Normal file
@ -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()
|
||||
Loading…
x
Reference in New Issue
Block a user