Upload 3 files

model.py

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+#! /usr/bin/env python
+# -*- coding: utf-8 -*-
+
+# Copyright 2023 Imperial College London (Pingchuan Ma)
+# Apache 2.0  (http://www.apache.org/licenses/LICENSE-2.0)
+
+import os
+import json
+import torch
+import argparse
+import numpy as np
+
+from espnet.asr.asr_utils import torch_load
+from espnet.asr.asr_utils import get_model_conf
+from espnet.asr.asr_utils import add_results_to_json
+from espnet.nets.batch_beam_search import BatchBeamSearch
+from espnet.nets.lm_interface import dynamic_import_lm
+from espnet.nets.scorers.length_bonus import LengthBonus
+from espnet.nets.pytorch_backend.e2e_asr_transformer import E2E
+
+
+class AVSR(torch.nn.Module):
+    def __init__(self, modality, model_path, model_conf, rnnlm=None, rnnlm_conf=None,
+        penalty=0., ctc_weight=0.1, lm_weight=0., beam_size=40, device="cuda:0"):
+        super(AVSR, self).__init__()
+        self.device = device
+
+        if modality == "audiovisual":
+            from espnet.nets.pytorch_backend.e2e_asr_transformer_av import E2E
+        else:
+            from espnet.nets.pytorch_backend.e2e_asr_transformer import E2E
+
+        with open(model_conf, "rb") as f:
+            confs = json.load(f)
+        args = confs if isinstance(confs, dict) else confs[2]
+        self.train_args = argparse.Namespace(**args)
+
+        labels_type = getattr(self.train_args, "labels_type", "char")
+        if labels_type == "char":
+            self.token_list = self.train_args.char_list
+        elif labels_type == "unigram5000":
+            file_path = os.path.join(os.path.dirname(__file__), "tokens", "unigram5000_units.txt")
+            self.token_list = ['<blank>'] + [word.split()[0] for word in open(file_path).read().splitlines()] + ['<eos>']
+        self.odim = len(self.token_list)
+
+        self.model = E2E(self.odim, self.train_args)
+        self.model.load_state_dict(torch.load(model_path, map_location=lambda storage, loc: storage))
+        self.model.to(device=self.device).eval()
+
+        self.beam_search = get_beam_search_decoder(self.model, self.token_list, rnnlm, rnnlm_conf, penalty, ctc_weight, lm_weight, beam_size)
+        self.beam_search.to(device=self.device).eval()
+        
+    def infer(self, data):
+        with torch.no_grad():
+            if isinstance(data, tuple):
+                enc_feats = self.model.encode(data[0].to(self.device), data[1].to(self.device))
+            else:
+                enc_feats = self.model.encode(data.to(self.device))
+            nbest_hyps = self.beam_search(enc_feats)
+            nbest_hyps = [h.asdict() for h in nbest_hyps[: min(len(nbest_hyps), 1)]]
+            transcription = add_results_to_json(nbest_hyps, self.token_list)
+            transcription = transcription.replace("▁", " ").strip()
+        return transcription.replace("<eos>", "")
+
+
+def get_beam_search_decoder(model, token_list, rnnlm=None, rnnlm_conf=None, penalty=0, ctc_weight=0.1, lm_weight=0., beam_size=40):
+    sos = model.odim - 1
+    eos = model.odim - 1
+    scorers = model.scorers()
+
+    if not rnnlm:
+        lm = None
+    else:
+        lm_args = get_model_conf(rnnlm, rnnlm_conf)
+        lm_model_module = getattr(lm_args, "model_module", "default")
+        lm_class = dynamic_import_lm(lm_model_module, lm_args.backend)
+        lm = lm_class(len(token_list), lm_args)
+        torch_load(rnnlm, lm)
+        lm.eval()
+
+    scorers["lm"] = lm
+    scorers["length_bonus"] = LengthBonus(len(token_list))
+    weights = dict(
+        decoder=1.0 - ctc_weight,
+        ctc=ctc_weight,
+        lm=lm_weight,
+        length_bonus=penalty,
+    )
+
+    return BatchBeamSearch(
+        beam_size=beam_size,
+        vocab_size=len(token_list),
+        weights=weights,
+        scorers=scorers,
+        sos=sos,
+        eos=eos,
+        token_list=token_list,
+        pre_beam_score_key=None if ctc_weight == 1.0 else "decoder",
+    )

pipeline.py

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+#! /usr/bin/env python
+# -*- coding: utf-8 -*-
+
+# Copyright 2023 Imperial College London (Pingchuan Ma)
+# Apache 2.0  (http://www.apache.org/licenses/LICENSE-2.0)
+
+import os
+import torch
+import pickle
+from configparser import ConfigParser
+
+from pipelines.model import AVSR
+from pipelines.data.data_module import AVSRDataLoader
+
+
+class InferencePipeline(torch.nn.Module):
+    def __init__(self, config_filename, detector="retinaface", face_track=False, device="cuda:0"):
+        super(InferencePipeline, self).__init__()
+        assert os.path.isfile(config_filename), f"config_filename: {config_filename} does not exist."
+
+        config = ConfigParser()
+        config.read(config_filename)
+
+        # modality configuration
+        modality = config.get("input", "modality")
+
+        self.modality = modality
+        # data configuration
+        input_v_fps = config.getfloat("input", "v_fps")
+        model_v_fps = config.getfloat("model", "v_fps")
+
+        # model configuration
+        model_path = config.get("model","model_path")
+        model_conf = config.get("model","model_conf")
+
+        # language model configuration
+        rnnlm = config.get("model", "rnnlm")
+        rnnlm_conf = config.get("model", "rnnlm_conf")
+        penalty = config.getfloat("decode", "penalty")
+        ctc_weight = config.getfloat("decode", "ctc_weight")
+        lm_weight = config.getfloat("decode", "lm_weight")
+        beam_size = config.getint("decode", "beam_size")
+
+        self.dataloader = AVSRDataLoader(modality, speed_rate=input_v_fps/model_v_fps, detector=detector)
+        self.model = AVSR(modality, model_path, model_conf, rnnlm, rnnlm_conf, penalty, ctc_weight, lm_weight, beam_size, device)
+        if face_track and self.modality in ["video", "audiovisual"]:
+            if detector == "mediapipe":
+                from pipelines.detectors.mediapipe.detector import LandmarksDetector
+                self.landmarks_detector = LandmarksDetector()
+            if detector == "retinaface":
+                from pipelines.detectors.retinaface.detector import LandmarksDetector
+                self.landmarks_detector = LandmarksDetector(device="cuda:0")
+        else:
+            self.landmarks_detector = None
+
+
+    def process_landmarks(self, data_filename, landmarks_filename):
+        if self.modality == "audio":
+            return None
+        if self.modality in ["video", "audiovisual"]:
+            if isinstance(landmarks_filename, str):
+                landmarks = pickle.load(open(landmarks_filename, "rb"))
+            else:
+                landmarks = self.landmarks_detector(data_filename)
+            return landmarks
+
+
+    def forward(self, data_filename, landmarks_filename=None):
+        assert os.path.isfile(data_filename), f"data_filename: {data_filename} does not exist."
+        landmarks = self.process_landmarks(data_filename, landmarks_filename)
+        data = self.dataloader.load_data(data_filename, landmarks)
+        transcript = self.model.infer(data)
+        return transcript

unigram5000_units.txt

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+▁CONDITION 1343
+▁CONDITIONS 1344
+▁CONDUCT 1345
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+▁CONSUMING 1383
+▁CONSUMPTION 1384
+▁CONTACT 1385
+▁CONTAIN 1386
+▁CONTEMPORARY 1387
+▁CONTENT 1388
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+▁CONTINENT 1391
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+▁CONTRACT 1395
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+▁CONVERT 1409
+▁CONVICT 1410
+▁CONVINCE 1411
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+▁COOK 1414
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+▁EVENTUALLY 1944
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+▁EXACTLY 1961
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+▁EXCEPT 1969
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+▁EXCHANGE 1972
+▁EXCITED 1973
+▁EXCITEMENT 1974
+▁EXCITING 1975
+▁EXCLUSIVE 1976
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+▁EXECUTE 1978
+▁EXECUTIVE 1979
+▁EXERCISE 1980
+▁EXHAUSTED 1981
+▁EXHIBIT 1982
+▁EXIST 1983
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+▁EXISTING 1985
+▁EXPAND 1986
+▁EXPECT 1987
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+▁EXPERIENCE 1993
+▁EXPERIENCED 1994
+▁EXPERIENCES 1995
+▁EXPERIENCING 1996
+▁EXPERIMENT 1997
+▁EXPERIMENTS 1998
+▁EXPERT 1999
+▁EXPERTISE 2000
+▁EXPERTS 2001
+▁EXPLAIN 2002
+▁EXPLAINED 2003
+▁EXPLANATION 2004
+▁EXPLICIT 2005
+▁EXPLODE 2006
+▁EXPLOIT 2007
+▁EXPLORATION 2008
+▁EXPLORE 2009
+▁EXPLORING 2010
+▁EXPLOSION 2011
+▁EXPONENTIAL 2012
+▁EXPOSED 2013
+▁EXPOSURE 2014
+▁EXPRESS 2015
+▁EXPRESSION 2016
+▁EXTEND 2017
+▁EXTENSION 2018
+▁EXTENSIVE 2019
+▁EXTENT 2020
+▁EXTERNAL 2021
+▁EXTINCT 2022
+▁EXTINCTION 2023
+▁EXTRA 2024
+▁EXTRACT 2025
+▁EXTRAORDINARY 2026
+▁EXTREME 2027
+▁EXTREMELY 2028
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+▁EYE 2031
+▁EYES 2032
+▁F 2033
+▁FA 2034
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+▁PRESSURE 3652
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+▁PROSTHETIC 3726
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+▁PROTOCOL 3732
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+▁PSYCHOLOGIST 3741
+▁PSYCHOLOGY 3742
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+▁RECOGNIZING 3849
+▁RECOMMEND 3850
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+▁RECYCLE 3857
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+▁REDESIGN 3860
+▁REDUC 3861
+▁REDUCE 3862
+▁REDUCTION 3863
+▁REEF 3864
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+▁REFRA 3870
+▁REFUGEE 3871
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+▁REGULATION 3882
+▁REHABILITATION 3883
+▁REINFORCE 3884
+▁REINVENT 3885
+▁REJECT 3886
+▁REJECTION 3887
+▁RELATE 3888
+▁RELATED 3889
+▁RELATION 3890
+▁RELATIONSHIP 3891
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+▁RELATIVE 3893
+▁RELATIVELY 3894
+▁RELATIVITY 3895
+▁RELAX 3896
+▁RELEASE 3897
+▁RELEASED 3898
+▁RELEVANT 3899
+▁RELI 3900
+▁RELIABLE 3901
+▁RELIEF 3902
+▁RELIGION 3903
+▁RELIGIOUS 3904
+▁RELY 3905
+▁REMAIN 3906
+▁REMARKABLE 3907
+▁REMARKABLY 3908
+▁REMEMBER 3909
+▁REMIND 3910
+▁REMOTE 3911
+▁REMOVE 3912
+▁REMOVING 3913
+▁RENAISSANCE 3914
+▁RENEWABLE 3915
+▁RENT 3916
+▁REPAIR 3917
+▁REPEAT 3918
+▁REPLACE 3919
+▁REPLICA 3920
+▁REPORT 3921
+▁REPRESENT 3922
+▁REPRESENTATION 3923
+▁REPRESENTATIVE 3924
+▁REPRODUCE 3925
+▁REPRODUCTION 3926
+▁REPRODUCTIVE 3927
+▁REPUBLIC 3928
+▁REPUTATION 3929
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+▁REQUIRE 3931
+▁REQUIRED 3932
+▁REQUIRES 3933
+▁RESCUE 3934
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+▁RESIDENT 3938
+▁RESILIENCE 3939
+▁RESILIENT 3940
+▁RESIST 3941
+▁RESISTANCE 3942
+▁RESOLUTION 3943
+▁RESOLVE 3944
+▁RESONATE 3945
+▁RESOURCE 3946
+▁RESOURCES 3947
+▁RESPECT 3948
+▁RESPOND 3949
+▁RESPONSE 3950
+▁RESPONSIBILITY 3951
+▁RESPONSIBLE 3952
+▁REST 3953
+▁RESTAURANT 3954
+▁RESTORE 3955
+▁RESTRICT 3956
+▁RESULT 3957
+▁RESULTS 3958
+▁RESUME 3959
+▁RETAIL 3960
+▁RETHINK 3961
+▁RETIRE 3962
+▁RETREAT 3963
+▁RETURN 3964
+▁REV 3965
+▁REVEAL 3966
+▁REVENGE 3967
+▁REVENUE 3968
+▁REVERSE 3969
+▁REVIEW 3970
+▁REVOLUTION 3971
+▁REWARD 3972
+▁RHYTHM 3973
+▁RI 3974
+▁RICE 3975
+▁RICH 3976
+▁RICHARD 3977
+▁RID 3978
+▁RIDE 3979
+▁RIDICULOUS 3980
+▁RIGHT 3981
+▁RIGHTS 3982
+▁RIGID 3983
+▁RING 3984
+▁RIPPLE 3985
+▁RISE 3986
+▁RISING 3987
+▁RISK 3988
+▁RISKS 3989
+▁RITUAL 3990
+▁RIVER 3991
+▁RO 3992
+▁ROAD 3993
+▁ROB 3994
+▁ROBERT 3995
+▁ROBOT 3996
+▁ROBOTIC 3997
+▁ROBOTS 3998
+▁ROBUST 3999
+▁ROCK 4000
+▁ROCKET 4001
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+▁ROLL 4003
+▁ROMAN 4004
+▁ROMANTIC 4005
+▁ROOF 4006
+▁ROOM 4007
+▁ROOT 4008
+▁ROSE 4009
+▁ROUGH 4010
+▁ROUGHLY 4011
+▁ROUND 4012
+▁ROUTE 4013
+▁ROUTINE 4014
+▁ROW 4015
+▁ROYAL 4016
+▁RU 4017
+▁RUBB 4018
+▁RUIN 4019
+▁RULE 4020
+▁RULES 4021
+▁RUN 4022
+▁RUNNING 4023
+▁RURAL 4024
+▁RUSH 4025
+▁RUSSIA 4026
+▁RUSSIAN 4027
+▁RWANDA 4028
+▁S 4029
+▁SA 4030
+▁SACRED 4031
+▁SACRIFICE 4032
+▁SAD 4033
+▁SAFE 4034
+▁SAFETY 4035
+▁SAID 4036
+▁SAIL 4037
+▁SAL 4038
+▁SAME 4039
+▁SAMPLE 4040
+▁SAN 4041
+▁SAND 4042
+▁SANDWICH 4043
+▁SARAH 4044
+▁SAT 4045
+▁SATELLITE 4046
+▁SATISFACTION 4047
+▁SATISFIED 4048
+▁SATISFY 4049
+▁SATURDAY 4050
+▁SAUDI 4051
+▁SAVE 4052
+▁SAVED 4053
+▁SAVING 4054
+▁SAW 4055
+▁SAY 4056
+▁SAYING 4057
+▁SAYS 4058
+▁SC 4059
+▁SCALE 4060
+▁SCAN 4061
+▁SCANN 4062
+▁SCAR 4063
+▁SCARED 4064
+▁SCARY 4065
+▁SCENARIO 4066
+▁SCENE 4067
+▁SCHEDULE 4068
+▁SCHIZOPHRENIA 4069
+▁SCHOLAR 4070
+▁SCHOLARSHIP 4071
+▁SCHOOL 4072
+▁SCHOOLS 4073
+▁SCIENCE 4074
+▁SCIENTIFIC 4075
+▁SCIENTIST 4076
+▁SCIENTISTS 4077
+▁SCORE 4078
+▁SCOTT 4079
+▁SCRAP 4080
+▁SCRATCH 4081
+▁SCREAM 4082
+▁SCREEN 4083
+▁SCREW 4084
+▁SCRIPT 4085
+▁SE 4086
+▁SEA 4087
+▁SEARCH 4088
+▁SEARCHING 4089
+▁SEASON 4090
+▁SEAT 4091
+▁SECOND 4092
+▁SECRET 4093
+▁SECTION 4094
+▁SECTOR 4095
+▁SECULAR 4096
+▁SECURE 4097
+▁SECURITY 4098
+▁SEE 4099
+▁SEEDS 4100
+▁SEEING 4101
+▁SEEK 4102
+▁SEEM 4103
+▁SEEMED 4104
+▁SEEMINGLY 4105
+▁SEEMS 4106
+▁SEEN 4107
+▁SELECT 4108
+▁SELF 4109
+▁SELL 4110
+▁SELLING 4111
+▁SELVES 4112
+▁SEMESTER 4113
+▁SEMI 4114
+▁SEND 4115
+▁SENIOR 4116
+▁SENSATION 4117
+▁SENSE 4118
+▁SENSITIVE 4119
+▁SENSOR 4120
+▁SENT 4121
+▁SENTENCE 4122
+▁SEPARATE 4123
+▁SEPARATION 4124
+▁SEPTEMBER 4125
+▁SEQUENCE 4126
+▁SERIES 4127
+▁SERIOUS 4128
+▁SERIOUSLY 4129
+▁SERVE 4130
+▁SERVICE 4131
+▁SERVICES 4132
+▁SERVING 4133
+▁SESSION 4134
+▁SET 4135
+▁SETTING 4136
+▁SETTLE 4137
+▁SEVEN 4138
+▁SEVERAL 4139
+▁SEVERE 4140
+▁SEX 4141
+▁SEXUAL 4142
+▁SEXUALITY 4143
+▁SH 4144
+▁SHA 4145
+▁SHADOW 4146
+▁SHAKE 4147
+▁SHAKESPEARE 4148
+▁SHAME 4149
+▁SHAPE 4150
+▁SHARE 4151
+▁SHARED 4152
+▁SHARING 4153
+▁SHARK 4154
+▁SHARP 4155
+▁SHE 4156
+▁SHELTER 4157
+▁SHI 4158
+▁SHIFT 4159
+▁SHIP 4160
+▁SHIRT 4161
+▁SHOCK 4162
+▁SHOES 4163
+▁SHOOT 4164
+▁SHOOTING 4165
+▁SHOP 4166
+▁SHOPPING 4167
+▁SHORE 4168
+▁SHORT 4169
+▁SHOT 4170
+▁SHOULD 4171
+▁SHOULDER 4172
+▁SHOULDN 4173
+▁SHOUT 4174
+▁SHOW 4175
+▁SHOWED 4176
+▁SHOWING 4177
+▁SHOWN 4178
+▁SHOWS 4179
+▁SHRINK 4180
+▁SHUT 4181
+▁SHY 4182
+▁SI 4183
+▁SICK 4184
+▁SIDE 4185
+▁SIGHT 4186
+▁SIGN 4187
+▁SIGNAL 4188
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