"""
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Author: Speech Lab, Alibaba Group, China
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"""
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import argparse
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import logging
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import os
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from pathlib import Path
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from typing import Callable
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from typing import Collection
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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 numpy as np
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import torch
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import yaml
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from typeguard import check_argument_types
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from typeguard import check_return_type
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from funasr.datasets.collate_fn import CommonCollateFn
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from funasr.datasets.preprocessor import CommonPreprocessor
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from funasr.layers.abs_normalize import AbsNormalize
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from funasr.layers.global_mvn import GlobalMVN
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from funasr.layers.utterance_mvn import UtteranceMVN
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from funasr.models.e2e_asr import ESPnetASRModel
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from funasr.models.decoder.abs_decoder import AbsDecoder
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from funasr.models.encoder.abs_encoder import AbsEncoder
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from funasr.models.encoder.rnn_encoder import RNNEncoder
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from funasr.models.encoder.resnet34_encoder import ResNet34, ResNet34_SP_L2Reg
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from funasr.models.pooling.statistic_pooling import StatisticPooling
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from funasr.models.decoder.sv_decoder import DenseDecoder
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from funasr.models.e2e_sv import ESPnetSVModel
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from funasr.models.frontend.abs_frontend import AbsFrontend
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from funasr.models.frontend.default import DefaultFrontend
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from funasr.models.frontend.fused import FusedFrontends
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from funasr.models.frontend.s3prl import S3prlFrontend
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from funasr.models.frontend.windowing import SlidingWindow
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from funasr.models.postencoder.abs_postencoder import AbsPostEncoder
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from funasr.models.postencoder.hugging_face_transformers_postencoder import (
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HuggingFaceTransformersPostEncoder, # noqa: H301
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)
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from funasr.models.preencoder.abs_preencoder import AbsPreEncoder
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from funasr.models.preencoder.linear import LinearProjection
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from funasr.models.preencoder.sinc import LightweightSincConvs
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from funasr.models.specaug.abs_specaug import AbsSpecAug
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from funasr.models.specaug.specaug import SpecAug
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from funasr.tasks.abs_task import AbsTask
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from funasr.torch_utils.initialize import initialize
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from funasr.train.abs_espnet_model import AbsESPnetModel
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from funasr.train.class_choices import ClassChoices
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from funasr.train.trainer import Trainer
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from funasr.utils.types import float_or_none
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from funasr.utils.types import int_or_none
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from funasr.utils.types import str2bool
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from funasr.utils.types import str_or_none
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from funasr.models.frontend.wav_frontend import WavFrontend
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frontend_choices = ClassChoices(
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name="frontend",
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classes=dict(
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default=DefaultFrontend,
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sliding_window=SlidingWindow,
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s3prl=S3prlFrontend,
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fused=FusedFrontends,
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wav_frontend=WavFrontend,
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),
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type_check=AbsFrontend,
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default="default",
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)
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specaug_choices = ClassChoices(
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name="specaug",
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classes=dict(
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specaug=SpecAug,
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),
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type_check=AbsSpecAug,
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default=None,
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optional=True,
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)
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normalize_choices = ClassChoices(
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"normalize",
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classes=dict(
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global_mvn=GlobalMVN,
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utterance_mvn=UtteranceMVN,
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),
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type_check=AbsNormalize,
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default=None,
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optional=True,
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)
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model_choices = ClassChoices(
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"model",
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classes=dict(
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espnet=ESPnetSVModel,
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),
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type_check=AbsESPnetModel,
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default="espnet",
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)
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preencoder_choices = ClassChoices(
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name="preencoder",
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classes=dict(
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sinc=LightweightSincConvs,
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linear=LinearProjection,
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),
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type_check=AbsPreEncoder,
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default=None,
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optional=True,
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)
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encoder_choices = ClassChoices(
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"encoder",
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classes=dict(
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resnet34=ResNet34,
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resnet34_sp_l2reg=ResNet34_SP_L2Reg,
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rnn=RNNEncoder,
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),
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type_check=AbsEncoder,
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default="resnet34",
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)
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postencoder_choices = ClassChoices(
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name="postencoder",
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classes=dict(
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hugging_face_transformers=HuggingFaceTransformersPostEncoder,
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),
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type_check=AbsPostEncoder,
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default=None,
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optional=True,
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)
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pooling_choices = ClassChoices(
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name="pooling_type",
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classes=dict(
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statistic=StatisticPooling,
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),
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type_check=torch.nn.Module,
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default="statistic",
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)
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decoder_choices = ClassChoices(
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"decoder",
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classes=dict(
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dense=DenseDecoder,
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),
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type_check=AbsDecoder,
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default="dense",
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)
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class SVTask(AbsTask):
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# If you need more than one optimizers, change this value
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num_optimizers: int = 1
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# Add variable objects configurations
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class_choices_list = [
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# --frontend and --frontend_conf
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frontend_choices,
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# --specaug and --specaug_conf
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specaug_choices,
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# --normalize and --normalize_conf
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normalize_choices,
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# --model and --model_conf
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model_choices,
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# --preencoder and --preencoder_conf
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preencoder_choices,
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# --encoder and --encoder_conf
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encoder_choices,
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# --postencoder and --postencoder_conf
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postencoder_choices,
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# --pooling and --pooling_conf
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pooling_choices,
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# --decoder and --decoder_conf
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decoder_choices,
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]
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# If you need to modify train() or eval() procedures, change Trainer class here
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trainer = Trainer
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@classmethod
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def add_task_arguments(cls, parser: argparse.ArgumentParser):
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group = parser.add_argument_group(description="Task related")
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# NOTE(kamo): add_arguments(..., required=True) can't be used
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# to provide --print_config mode. Instead of it, do as
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required = parser.get_default("required")
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required += ["token_list"]
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group.add_argument(
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"--token_list",
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type=str_or_none,
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default=None,
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help="A text mapping int-id to speaker name",
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)
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group.add_argument(
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"--init",
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type=lambda x: str_or_none(x.lower()),
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default=None,
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help="The initialization method",
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choices=[
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"chainer",
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"xavier_uniform",
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"xavier_normal",
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"kaiming_uniform",
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"kaiming_normal",
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None,
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],
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)
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group.add_argument(
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"--input_size",
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type=int_or_none,
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default=None,
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help="The number of input dimension of the feature",
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)
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group = parser.add_argument_group(description="Preprocess related")
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group.add_argument(
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"--use_preprocessor",
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type=str2bool,
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default=True,
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help="Apply preprocessing to data or not",
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)
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parser.add_argument(
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"--cleaner",
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type=str_or_none,
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choices=[None, "tacotron", "jaconv", "vietnamese"],
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default=None,
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help="Apply text cleaning",
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)
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parser.add_argument(
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"--speech_volume_normalize",
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type=float_or_none,
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default=None,
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help="Scale the maximum amplitude to the given value.",
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)
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parser.add_argument(
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"--rir_scp",
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type=str_or_none,
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default=None,
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help="The file path of rir scp file.",
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)
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parser.add_argument(
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"--rir_apply_prob",
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type=float,
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default=1.0,
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help="THe probability for applying RIR convolution.",
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)
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parser.add_argument(
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"--noise_scp",
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type=str_or_none,
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default=None,
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help="The file path of noise scp file.",
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)
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parser.add_argument(
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"--noise_apply_prob",
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type=float,
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default=1.0,
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help="The probability applying Noise adding.",
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)
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parser.add_argument(
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"--noise_db_range",
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type=str,
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default="13_15",
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help="The range of noise decibel level.",
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)
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for class_choices in cls.class_choices_list:
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# Append --<name> and --<name>_conf.
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# e.g. --encoder and --encoder_conf
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class_choices.add_arguments(group)
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@classmethod
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def build_collate_fn(
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cls, args: argparse.Namespace, train: bool
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) -> Callable[
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[Collection[Tuple[str, Dict[str, np.ndarray]]]],
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Tuple[List[str], Dict[str, torch.Tensor]],
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]:
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assert check_argument_types()
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# NOTE(kamo): int value = 0 is reserved by CTC-blank symbol
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return CommonCollateFn(float_pad_value=0.0, int_pad_value=-1)
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@classmethod
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def build_preprocess_fn(
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cls, args: argparse.Namespace, train: bool
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) -> Optional[Callable[[str, Dict[str, np.array]], Dict[str, np.ndarray]]]:
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assert check_argument_types()
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if args.use_preprocessor:
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retval = CommonPreprocessor(
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train=train,
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token_type=None,
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token_list=None,
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bpemodel=None,
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non_linguistic_symbols=None,
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text_cleaner=args.cleaner,
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g2p_type=None,
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# NOTE(kamo): Check attribute existence for backward compatibility
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rir_scp=args.rir_scp if hasattr(args, "rir_scp") else None,
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rir_apply_prob=args.rir_apply_prob
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if hasattr(args, "rir_apply_prob")
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else 1.0,
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noise_scp=args.noise_scp if hasattr(args, "noise_scp") else None,
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noise_apply_prob=args.noise_apply_prob
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if hasattr(args, "noise_apply_prob")
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else 1.0,
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noise_db_range=args.noise_db_range
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if hasattr(args, "noise_db_range")
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else "13_15",
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speech_volume_normalize=args.speech_volume_normalize
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if hasattr(args, "rir_scp")
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else None,
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)
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else:
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retval = None
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assert check_return_type(retval)
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return retval
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@classmethod
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def required_data_names(
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cls, train: bool = True, inference: bool = False
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) -> Tuple[str, ...]:
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if not inference:
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retval = ("speech", "text")
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else:
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# Recognition mode
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retval = ("speech",)
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return retval
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@classmethod
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def optional_data_names(
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cls, train: bool = True, inference: bool = False
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) -> Tuple[str, ...]:
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retval = ()
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if inference:
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retval = ("ref_speech",)
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assert check_return_type(retval)
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return retval
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@classmethod
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def build_model(cls, args: argparse.Namespace) -> ESPnetSVModel:
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assert check_argument_types()
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if isinstance(args.token_list, str):
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with open(args.token_list, encoding="utf-8") as f:
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token_list = [line.rstrip() for line in f]
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# Overwriting token_list to keep it as "portable".
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args.token_list = list(token_list)
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elif isinstance(args.token_list, (tuple, list)):
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token_list = list(args.token_list)
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else:
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raise RuntimeError("token_list must be str or list")
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vocab_size = len(token_list)
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logging.info(f"Speaker number: {vocab_size}")
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# 1. frontend
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if args.input_size is None:
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# Extract features in the model
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frontend_class = frontend_choices.get_class(args.frontend)
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frontend = frontend_class(**args.frontend_conf)
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input_size = frontend.output_size()
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else:
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# Give features from data-loader
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args.frontend = None
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args.frontend_conf = {}
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frontend = None
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input_size = args.input_size
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# 2. Data augmentation for spectrogram
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if args.specaug is not None:
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specaug_class = specaug_choices.get_class(args.specaug)
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specaug = specaug_class(**args.specaug_conf)
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else:
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specaug = None
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# 3. Normalization layer
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if args.normalize is not None:
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normalize_class = normalize_choices.get_class(args.normalize)
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normalize = normalize_class(**args.normalize_conf)
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else:
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normalize = None
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# 4. Pre-encoder input block
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# NOTE(kan-bayashi): Use getattr to keep the compatibility
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if getattr(args, "preencoder", None) is not None:
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preencoder_class = preencoder_choices.get_class(args.preencoder)
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preencoder = preencoder_class(**args.preencoder_conf)
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input_size = preencoder.output_size()
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else:
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preencoder = None
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# 5. Encoder
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encoder_class = encoder_choices.get_class(args.encoder)
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encoder = encoder_class(input_size=input_size, **args.encoder_conf)
|
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# 6. Post-encoder block
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# NOTE(kan-bayashi): Use getattr to keep the compatibility
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encoder_output_size = encoder.output_size()
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if getattr(args, "postencoder", None) is not None:
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postencoder_class = postencoder_choices.get_class(args.postencoder)
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postencoder = postencoder_class(
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input_size=encoder_output_size, **args.postencoder_conf
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)
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encoder_output_size = postencoder.output_size()
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else:
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postencoder = None
|
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# 7. Pooling layer
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pooling_class = pooling_choices.get_class(args.pooling_type)
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pooling_dim = (2, 3)
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eps = 1e-12
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if hasattr(args, "pooling_type_conf"):
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if "pooling_dim" in args.pooling_type_conf:
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pooling_dim = args.pooling_type_conf["pooling_dim"]
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if "eps" in args.pooling_type_conf:
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eps = args.pooling_type_conf["eps"]
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pooling_layer = pooling_class(
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pooling_dim=pooling_dim,
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eps=eps,
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)
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if args.pooling_type == "statistic":
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encoder_output_size *= 2
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# 8. Decoder
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decoder_class = decoder_choices.get_class(args.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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**args.decoder_conf,
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)
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# 7. Build model
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try:
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model_class = model_choices.get_class(args.model)
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except AttributeError:
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model_class = model_choices.get_class("espnet")
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model = model_class(
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vocab_size=vocab_size,
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token_list=token_list,
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frontend=frontend,
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specaug=specaug,
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normalize=normalize,
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preencoder=preencoder,
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encoder=encoder,
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postencoder=postencoder,
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pooling_layer=pooling_layer,
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decoder=decoder,
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**args.model_conf,
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)
|
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# FIXME(kamo): Should be done in model?
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# 8. Initialize
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if args.init is not None:
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initialize(model, args.init)
|
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assert check_return_type(model)
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return model
|
|
# ~~~~~~~~~ The methods below are mainly used for inference ~~~~~~~~~
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@classmethod
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def build_model_from_file(
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cls,
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config_file: Union[Path, str] = None,
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model_file: Union[Path, str] = None,
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cmvn_file: Union[Path, str] = None,
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device: str = "cpu",
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):
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"""Build model from the files.
|
|
This method is used for inference or fine-tuning.
|
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Args:
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config_file: The yaml file saved when training.
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model_file: The model file saved when training.
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cmvn_file: The cmvn file for front-end
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device: Device type, "cpu", "cuda", or "cuda:N".
|
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"""
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assert check_argument_types()
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if config_file is None:
|
assert model_file is not None, (
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"The argument 'model_file' must be provided "
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"if the argument 'config_file' is not specified."
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)
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config_file = Path(model_file).parent / "config.yaml"
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else:
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config_file = Path(config_file)
|
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with config_file.open("r", encoding="utf-8") as f:
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args = yaml.safe_load(f)
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if cmvn_file is not None:
|
args["cmvn_file"] = cmvn_file
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args = argparse.Namespace(**args)
|
model = cls.build_model(args)
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if not isinstance(model, AbsESPnetModel):
|
raise RuntimeError(
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f"model must inherit {AbsESPnetModel.__name__}, but got {type(model)}"
|
)
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model.to(device)
|
model_dict = dict()
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model_name_pth = None
|
if model_file is not None:
|
logging.info("model_file is {}".format(model_file))
|
if device == "cuda":
|
device = f"cuda:{torch.cuda.current_device()}"
|
model_dir = os.path.dirname(model_file)
|
model_name = os.path.basename(model_file)
|
if "model.ckpt-" in model_name or ".bin" in model_name:
|
if ".bin" in model_name:
|
model_name_pth = os.path.join(model_dir, model_name.replace('.bin', '.pb'))
|
else:
|
model_name_pth = os.path.join(model_dir, "{}.pb".format(model_name))
|
if os.path.exists(model_name_pth):
|
logging.info("model_file is load from pth: {}".format(model_name_pth))
|
model_dict = torch.load(model_name_pth, map_location=device)
|
else:
|
model_dict = cls.convert_tf2torch(model, model_file)
|
model.load_state_dict(model_dict)
|
else:
|
model_dict = torch.load(model_file, map_location=device)
|
model.load_state_dict(model_dict)
|
if model_name_pth is not None and not os.path.exists(model_name_pth):
|
torch.save(model_dict, model_name_pth)
|
logging.info("model_file is saved to pth: {}".format(model_name_pth))
|
|
return model, args
|
|
@classmethod
|
def convert_tf2torch(
|
cls,
|
model,
|
ckpt,
|
):
|
logging.info("start convert tf model to torch model")
|
from funasr.modules.streaming_utils.load_fr_tf import load_tf_dict
|
var_dict_tf = load_tf_dict(ckpt)
|
var_dict_torch = model.state_dict()
|
var_dict_torch_update = dict()
|
# speech encoder
|
var_dict_torch_update_local = model.encoder.convert_tf2torch(var_dict_tf, var_dict_torch)
|
var_dict_torch_update.update(var_dict_torch_update_local)
|
# pooling layer
|
var_dict_torch_update_local = model.pooling_layer.convert_tf2torch(var_dict_tf, var_dict_torch)
|
var_dict_torch_update.update(var_dict_torch_update_local)
|
# decoder
|
var_dict_torch_update_local = model.decoder.convert_tf2torch(var_dict_tf, var_dict_torch)
|
var_dict_torch_update.update(var_dict_torch_update_local)
|
|
return var_dict_torch_update
|
