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| | | #!/usr/bin/env python3 |
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| | | # -*- encoding: utf-8 -*- |
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| | | # Copyright FunASR (https://github.com/alibaba-damo-academy/FunASR). All Rights Reserved. |
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| | | # MIT License (https://opensource.org/licenses/MIT) |
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| | | import logging |
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| | | from typing import Dict |
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| | | from typing import List |
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| | | from typing import Optional |
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| | | from typing import Tuple |
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| | | from typing import Union |
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| | | import tempfile |
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| | | import codecs |
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| | | import requests |
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| | | import re |
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| | | import copy |
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| | | import torch |
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| | | import torch.nn as nn |
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| | | import random |
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| | | import numpy as np |
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| | | import time |
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| | | import torch |
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| | | import logging |
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| | | from contextlib import contextmanager |
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| | | from distutils.version import LooseVersion |
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| | | from typing import Dict, List, Optional, Tuple |
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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.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.paraformer.search import Hypothesis |
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| | | from funasr.utils.load_utils import load_audio_and_text_image_video, 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.utils.timestamp_tools import ts_prediction_lfr6_standard |
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| | | from funasr.register import tables |
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| | | from funasr.models.ctc.ctc import CTC |
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| | | from funasr.utils import postprocess_utils |
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| | | from funasr.metrics.compute_acc import th_accuracy |
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| | | from funasr.utils.datadir_writer import DatadirWriter |
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| | | from funasr.models.paraformer.model import Paraformer |
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| | | from funasr.models.paraformer.search import Hypothesis |
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| | | from funasr.train_utils.device_funcs import force_gatherable |
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| | | from funasr.models.transformer.utils.add_sos_eos import add_sos_eos |
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| | | from funasr.utils.timestamp_tools import ts_prediction_lfr6_standard |
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| | | from funasr.models.transformer.utils.nets_utils import make_pad_mask, pad_list |
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| | | from funasr.utils.load_utils import load_audio_text_image_video, extract_fbank |
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| | | if LooseVersion(torch.__version__) >= LooseVersion("1.6.0"): |
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| | | from torch.cuda.amp import autocast |
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| | | else: |
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| | | # Nothing to do if torch<1.6.0 |
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| | | @contextmanager |
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| | | def autocast(enabled=True): |
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| | | yield |
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| | | @tables.register("model_classes", "BiCifParaformer") |
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| | | class BiCifParaformer(Paraformer): |
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| | | return loss, stats, weight |
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| | | def generate(self, |
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| | | def inference(self, |
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| | | data_in, |
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| | | data_lengths=None, |
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| | | key: list = None, |
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| | | else: |
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| | | # extract fbank feats |
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| | | time1 = time.perf_counter() |
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| | | audio_sample_list = load_audio_and_text_image_video(data_in, fs=frontend.fs, audio_fs=kwargs.get("fs", 16000)) |
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| | | audio_sample_list = load_audio_text_image_video(data_in, fs=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, data_type=kwargs.get("data_type", "sound"), |
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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() * frontend.frame_shift * frontend.lfr_n / 1000 |
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| | | speech.to(device=kwargs["device"]), speech_lengths.to(device=kwargs["device"]) |
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| | | speech = speech.to(device=kwargs["device"]) |
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| | | speech_lengths = speech_lengths.to(device=kwargs["device"]) |
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| | | |
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| | | # Encoder |
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| | | encoder_out, encoder_out_lens = self.encode(speech, speech_lengths) |
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