update repo

嘉渊

2023-06-14 7d6177b43f1120182b833ae11a37d9105164306a

 funasr/bin/asr_infer.py

@@ -24,7 +24,7 @@
from packaging.version import parse as V
from typeguard import check_argument_types
from typeguard import check_return_type

from  funasr.build_utils.build_model_from_file import build_model_from_file
from funasr.models.e2e_asr_contextual_paraformer import NeatContextualParaformer
from funasr.models.e2e_asr_paraformer import BiCifParaformer, ContextualParaformer
from funasr.models.frontend.wav_frontend import WavFrontend, WavFrontendOnline
@@ -35,9 +35,7 @@
from funasr.modules.beam_search.beam_search_transducer import Hypothesis as HypothesisTransducer
from funasr.modules.scorers.ctc import CTCPrefixScorer
from funasr.modules.scorers.length_bonus import LengthBonus
from funasr.tasks.asr import ASRTask
from funasr.tasks.asr import frontend_choices
from funasr.tasks.lm import LMTask
from funasr.build_utils.build_asr_model import frontend_choices
from funasr.text.build_tokenizer import build_tokenizer
from funasr.text.token_id_converter import TokenIDConverter
from funasr.torch_utils.device_funcs import to_device
@@ -84,15 +82,14 @@

        # 1. Build ASR model
        scorers = {}
        asr_model, asr_train_args = ASRTask.build_model_from_file(
            asr_train_config, asr_model_file, cmvn_file, device
        asr_model, asr_train_args = build_model_from_file(
            asr_train_config, asr_model_file, cmvn_file, device, mode="asr"
        )
        frontend = None
        if asr_train_args.frontend is not None and asr_train_args.frontend_conf is not None:
            if asr_train_args.frontend == 'wav_frontend':
                frontend = WavFrontend(cmvn_file=cmvn_file, **asr_train_args.frontend_conf)
            else:
                from funasr.tasks.asr import frontend_choices
                frontend_class = frontend_choices.get_class(asr_train_args.frontend)
                frontend = frontend_class(**asr_train_args.frontend_conf).eval()

@@ -112,7 +109,7 @@

        # 2. Build Language model
        if lm_train_config is not None:
            lm, lm_train_args = LMTask.build_model_from_file(
            lm, lm_train_args = build_model_from_file(
                lm_train_config, lm_file, None, device
            )
            scorers["lm"] = lm.lm
@@ -295,9 +292,8 @@

        # 1. Build ASR model
        scorers = {}
        from funasr.tasks.asr import ASRTaskParaformer as ASRTask
        asr_model, asr_train_args = ASRTask.build_model_from_file(
            asr_train_config, asr_model_file, cmvn_file, device
        asr_model, asr_train_args = build_model_from_file(
            asr_train_config, asr_model_file, cmvn_file, device, mode="paraformer"
        )
        frontend = None
        if asr_train_args.frontend is not None and asr_train_args.frontend_conf is not None:
@@ -319,7 +315,7 @@

        # 2. Build Language model
        if lm_train_config is not None:
            lm, lm_train_args = LMTask.build_model_from_file(
            lm, lm_train_args = build_model_from_file(
                lm_train_config, lm_file, device
            )
            scorers["lm"] = lm.lm
@@ -616,9 +612,8 @@

        # 1. Build ASR model
        scorers = {}
        from funasr.tasks.asr import ASRTaskParaformer as ASRTask
        asr_model, asr_train_args = ASRTask.build_model_from_file(
            asr_train_config, asr_model_file, cmvn_file, device
        asr_model, asr_train_args = build_model_from_file(
            asr_train_config, asr_model_file, cmvn_file, device, mode="paraformer"
        )
        frontend = None
        if asr_train_args.frontend is not None and asr_train_args.frontend_conf is not None:
@@ -640,7 +635,7 @@

        # 2. Build Language model
        if lm_train_config is not None:
            lm, lm_train_args = LMTask.build_model_from_file(
            lm, lm_train_args = build_model_from_file(
                lm_train_config, lm_file, device
            )
            scorers["lm"] = lm.lm
@@ -873,9 +868,8 @@

        # 1. Build ASR model
        scorers = {}
        from funasr.tasks.asr import ASRTaskUniASR as ASRTask
        asr_model, asr_train_args = ASRTask.build_model_from_file(
            asr_train_config, asr_model_file, cmvn_file, device
        asr_model, asr_train_args = build_model_from_file(
            asr_train_config, asr_model_file, cmvn_file, device, mode="uniasr"
        )
        frontend = None
        if asr_train_args.frontend is not None and asr_train_args.frontend_conf is not None:
@@ -901,8 +895,8 @@

        # 2. Build Language model
        if lm_train_config is not None:
            lm, lm_train_args = LMTask.build_model_from_file(
                lm_train_config, lm_file, device
            lm, lm_train_args = build_model_from_file(
                lm_train_config, lm_file, device, "lm"
            )
            scorers["lm"] = lm.lm

@@ -1104,9 +1098,8 @@
        assert check_argument_types()

        # 1. Build ASR model
        from funasr.tasks.asr import ASRTaskMFCCA as ASRTask
        scorers = {}
        asr_model, asr_train_args = ASRTask.build_model_from_file(
        asr_model, asr_train_args = build_model_from_file(
            asr_train_config, asr_model_file, cmvn_file, device
        )

@@ -1126,7 +1119,7 @@

        # 2. Build Language model
        if lm_train_config is not None:
            lm, lm_train_args = LMTask.build_model_from_file(
            lm, lm_train_args = build_model_from_file(
                lm_train_config, lm_file, device
            )
            lm.to(device)
@@ -1315,8 +1308,7 @@
        super().__init__()

        assert check_argument_types()
        from funasr.tasks.asr import ASRTransducerTask
        asr_model, asr_train_args = ASRTransducerTask.build_model_from_file(
        asr_model, asr_train_args = build_model_from_file(
            asr_train_config, asr_model_file, cmvn_file, device
        )

@@ -1350,7 +1342,7 @@
            asr_model.to(dtype=getattr(torch, dtype)).eval()

        if lm_train_config is not None:
            lm, lm_train_args = LMTask.build_model_from_file(
            lm, lm_train_args = build_model_from_file(
                lm_train_config, lm_file, device
            )
            lm_scorer = lm.lm
@@ -1638,9 +1630,8 @@
        assert check_argument_types()

        # 1. Build ASR model
        from funasr.tasks.sa_asr import ASRTask
        scorers = {}
        asr_model, asr_train_args = ASRTask.build_model_from_file(
        asr_model, asr_train_args = build_model_from_file(
            asr_train_config, asr_model_file, cmvn_file, device
        )
        frontend = None
@@ -1667,7 +1658,7 @@

        # 2. Build Language model
        if lm_train_config is not None:
            lm, lm_train_args = LMTask.build_model_from_file(
            lm, lm_train_args = build_model_from_file(
                lm_train_config, lm_file, None, device
            )
            scorers["lm"] = lm.lm