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| | | import random |
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| | | import numpy as np |
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| | | import time |
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| | | # from funasr.layers.abs_normalize import AbsNormalize |
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| | | from funasr.losses.label_smoothing_loss import ( |
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| | | LabelSmoothingLoss, # noqa: H301 |
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| | | ) |
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| | | # from funasr.models.decoder.abs_decoder import AbsDecoder |
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| | | # from funasr.models.e2e_asr_common import ErrorCalculator |
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| | | # from funasr.models.encoder.abs_encoder import AbsEncoder |
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| | | # from funasr.models.frontend.abs_frontend import AbsFrontend |
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| | | # from funasr.frontends.abs_frontend import AbsFrontend |
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| | | # from funasr.models.postencoder.abs_postencoder import AbsPostEncoder |
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| | | from funasr.models.predictor.cif import mae_loss |
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| | | from funasr.models.paraformer.cif_predictor import mae_loss |
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| | | # from funasr.models.preencoder.abs_preencoder import AbsPreEncoder |
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| | | # from funasr.models.specaug.abs_specaug import AbsSpecAug |
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| | | from funasr.models.transformer.add_sos_eos import add_sos_eos |
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| | | from funasr.models.transformer.utils.add_sos_eos import add_sos_eos |
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| | | from funasr.models.transformer.utils.nets_utils import make_pad_mask, pad_list |
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| | | from funasr.models.transformer.utils.nets_utils import th_accuracy |
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| | | from funasr.metrics.compute_acc import th_accuracy |
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| | | from funasr.train_utils.device_funcs import force_gatherable |
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| | | # from funasr.models.base_model import FunASRModel |
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| | | # from funasr.models.predictor.cif import CifPredictorV3 |
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| | | # from funasr.models.paraformer.cif_predictor import CifPredictorV3 |
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| | | from funasr.models.paraformer.search import Hypothesis |
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| | | |
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| | | from funasr.models.model_class_factory import * |
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| | | @contextmanager |
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| | | def autocast(enabled=True): |
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| | | yield |
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| | | from funasr.datasets.fun_datasets.load_audio_extract_fbank import load_audio, extract_fbank |
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| | | from funasr.datasets.audio_datasets.load_audio_extract_fbank import load_audio, extract_fbank |
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| | | from funasr.utils import postprocess_utils |
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| | | from funasr.utils.datadir_writer import DatadirWriter |
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| | | from funasr.models.transformer.utils.nets_utils import get_transducer_task_io |
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| | |
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| | | super().__init__() |
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| | | |
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| | | if frontend is not None: |
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| | | frontend_class = frontend_choices.get_class(frontend) |
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| | | frontend_class = frontend_classes.get_class(frontend) |
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| | | frontend = frontend_class(**frontend_conf) |
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| | | if specaug is not None: |
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| | | specaug_class = specaug_choices.get_class(specaug) |
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| | | specaug_class = specaug_classes.get_class(specaug) |
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| | | specaug = specaug_class(**specaug_conf) |
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| | | if normalize is not None: |
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| | | normalize_class = normalize_choices.get_class(normalize) |
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| | | normalize_class = normalize_classes.get_class(normalize) |
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| | | normalize = normalize_class(**normalize_conf) |
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| | | encoder_class = encoder_choices.get_class(encoder) |
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| | | encoder_class = encoder_classes.get_class(encoder) |
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| | | encoder = encoder_class(input_size=input_size, **encoder_conf) |
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| | | encoder_output_size = encoder.output_size() |
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| | | |
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| | | decoder_class = decoder_choices.get_class(decoder) |
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| | | decoder_class = decoder_classes.get_class(decoder) |
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| | | decoder = decoder_class( |
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| | | vocab_size=vocab_size, |
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| | | encoder_output_size=encoder_output_size, |
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| | |
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| | | ) |
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| | | decoder_output_size = decoder.output_size |
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| | | |
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| | | joint_network_class = joint_network_choices.get_class(decoder) |
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| | | joint_network_class = joint_network_classes.get_class(decoder) |
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| | | joint_network = joint_network_class( |
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| | | vocab_size, |
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| | | encoder_output_size, |
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| | | audio_sample_list = load_audio(data_in, fs=self.frontend.fs, audio_fs=kwargs.get("fs", 16000)) |
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| | | time2 = time.perf_counter() |
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| | | meta_data["load_data"] = f"{time2 - time1:0.3f}" |
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| | | speech, speech_lengths = extract_fbank(audio_sample_list, date_type=kwargs.get("date_type", "sound"), frontend=self.frontend) |
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| | | speech, speech_lengths = extract_fbank(audio_sample_list, data_type=kwargs.get("data_type", "sound"), frontend=self.frontend) |
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| | | time3 = time.perf_counter() |
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| | | meta_data["extract_feat"] = f"{time3 - time2:0.3f}" |
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| | | meta_data["batch_data_time"] = speech_lengths.sum().item() * self.frontend.frame_shift * self.frontend.lfr_n / 1000 |
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