from abc import ABC
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from abc import abstractmethod
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from pathlib import Path
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from typing import Collection
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from typing import Dict
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from typing import Iterable
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from typing import List
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from typing import Union
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import numpy as np
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import scipy.signal
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import soundfile
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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.text.build_tokenizer import build_tokenizer
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from funasr.text.cleaner import TextCleaner
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from funasr.text.token_id_converter import TokenIDConverter
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class AbsPreprocessor(ABC):
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def __init__(self, train: bool):
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self.train = train
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@abstractmethod
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def __call__(
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self, uid: str, data: Dict[str, Union[str, np.ndarray]]
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) -> Dict[str, np.ndarray]:
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raise NotImplementedError
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def framing(
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x,
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frame_length: int = 512,
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frame_shift: int = 256,
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centered: bool = True,
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padded: bool = True,
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):
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if x.size == 0:
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raise ValueError("Input array size is zero")
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if frame_length < 1:
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raise ValueError("frame_length must be a positive integer")
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if frame_length > x.shape[-1]:
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raise ValueError("frame_length is greater than input length")
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if 0 >= frame_shift:
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raise ValueError("frame_shift must be greater than 0")
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if centered:
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pad_shape = [(0, 0) for _ in range(x.ndim - 1)] + [
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(frame_length // 2, frame_length // 2)
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]
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x = np.pad(x, pad_shape, mode="constant", constant_values=0)
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if padded:
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# Pad to integer number of windowed segments
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# I.e make x.shape[-1] = frame_length + (nseg-1)*nstep,
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# with integer nseg
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nadd = (-(x.shape[-1] - frame_length) % frame_shift) % frame_length
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pad_shape = [(0, 0) for _ in range(x.ndim - 1)] + [(0, nadd)]
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x = np.pad(x, pad_shape, mode="constant", constant_values=0)
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# Created strided array of data segments
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if frame_length == 1 and frame_length == frame_shift:
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result = x[..., None]
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else:
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shape = x.shape[:-1] + (
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(x.shape[-1] - frame_length) // frame_shift + 1,
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frame_length,
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)
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strides = x.strides[:-1] + (frame_shift * x.strides[-1], x.strides[-1])
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result = np.lib.stride_tricks.as_strided(x, shape=shape, strides=strides)
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return result
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def detect_non_silence(
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x: np.ndarray,
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threshold: float = 0.01,
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frame_length: int = 1024,
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frame_shift: int = 512,
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window: str = "boxcar",
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) -> np.ndarray:
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"""Power based voice activity detection.
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Args:
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x: (Channel, Time)
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>>> x = np.random.randn(1000)
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>>> detect = detect_non_silence(x)
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>>> assert x.shape == detect.shape
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>>> assert detect.dtype == np.bool
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"""
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if x.shape[-1] < frame_length:
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return np.full(x.shape, fill_value=True, dtype=np.bool)
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if x.dtype.kind == "i":
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x = x.astype(np.float64)
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# framed_w: (C, T, F)
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framed_w = framing(
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x,
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frame_length=frame_length,
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frame_shift=frame_shift,
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centered=False,
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padded=True,
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)
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framed_w *= scipy.signal.get_window(window, frame_length).astype(framed_w.dtype)
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# power: (C, T)
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power = (framed_w**2).mean(axis=-1)
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# mean_power: (C, 1)
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mean_power = np.mean(power, axis=-1, keepdims=True)
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if np.all(mean_power == 0):
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return np.full(x.shape, fill_value=True, dtype=np.bool)
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# detect_frames: (C, T)
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detect_frames = power / mean_power > threshold
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# detects: (C, T, F)
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detects = np.broadcast_to(
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detect_frames[..., None], detect_frames.shape + (frame_shift,)
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)
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# detects: (C, TF)
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detects = detects.reshape(*detect_frames.shape[:-1], -1)
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# detects: (C, TF)
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return np.pad(
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detects,
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[(0, 0)] * (x.ndim - 1) + [(0, x.shape[-1] - detects.shape[-1])],
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mode="edge",
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)
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class CommonPreprocessor(AbsPreprocessor):
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def __init__(
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self,
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train: bool,
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token_type: str = None,
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token_list: Union[Path, str, Iterable[str]] = None,
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bpemodel: Union[Path, str, Iterable[str]] = None,
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text_cleaner: Collection[str] = None,
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g2p_type: str = None,
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unk_symbol: str = "<unk>",
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space_symbol: str = "<space>",
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non_linguistic_symbols: Union[Path, str, Iterable[str]] = None,
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delimiter: str = None,
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rir_scp: str = None,
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rir_apply_prob: float = 1.0,
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noise_scp: str = None,
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noise_apply_prob: float = 1.0,
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noise_db_range: str = "3_10",
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speech_volume_normalize: float = None,
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speech_name: str = "speech",
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text_name: str = "text",
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split_with_space: bool = False,
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):
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super().__init__(train)
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self.train = train
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self.speech_name = speech_name
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self.text_name = text_name
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self.speech_volume_normalize = speech_volume_normalize
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self.rir_apply_prob = rir_apply_prob
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self.noise_apply_prob = noise_apply_prob
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self.split_with_space = split_with_space
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if token_type is not None:
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if token_list is None:
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raise ValueError("token_list is required if token_type is not None")
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self.text_cleaner = TextCleaner(text_cleaner)
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self.tokenizer = build_tokenizer(
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token_type=token_type,
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bpemodel=bpemodel,
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delimiter=delimiter,
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space_symbol=space_symbol,
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non_linguistic_symbols=non_linguistic_symbols,
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g2p_type=g2p_type,
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)
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self.token_id_converter = TokenIDConverter(
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token_list=token_list,
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unk_symbol=unk_symbol,
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)
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else:
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self.text_cleaner = None
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self.tokenizer = None
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self.token_id_converter = None
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if train and rir_scp is not None:
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self.rirs = []
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with open(rir_scp, "r", encoding="utf-8") as f:
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for line in f:
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sps = line.strip().split(None, 1)
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if len(sps) == 1:
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self.rirs.append(sps[0])
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else:
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self.rirs.append(sps[1])
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else:
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self.rirs = None
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if train and noise_scp is not None:
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self.noises = []
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with open(noise_scp, "r", encoding="utf-8") as f:
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for line in f:
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sps = line.strip().split(None, 1)
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if len(sps) == 1:
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self.noises.append(sps[0])
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else:
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self.noises.append(sps[1])
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sps = noise_db_range.split("_")
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if len(sps) == 1:
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self.noise_db_low, self.noise_db_high = float(sps[0])
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elif len(sps) == 2:
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self.noise_db_low, self.noise_db_high = float(sps[0]), float(sps[1])
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else:
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raise ValueError(
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"Format error: '{noise_db_range}' e.g. -3_4 -> [-3db,4db]"
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)
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else:
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self.noises = None
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def _speech_process(
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self, data: Dict[str, Union[str, np.ndarray]]
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) -> Dict[str, Union[str, np.ndarray]]:
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assert check_argument_types()
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if self.speech_name in data:
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if self.train and (self.rirs is not None or self.noises is not None):
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speech = data[self.speech_name]
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nsamples = len(speech)
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# speech: (Nmic, Time)
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if speech.ndim == 1:
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speech = speech[None, :]
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else:
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speech = speech.T
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# Calc power on non shlence region
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power = (speech[detect_non_silence(speech)] ** 2).mean()
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# 1. Convolve RIR
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if self.rirs is not None and self.rir_apply_prob >= np.random.random():
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rir_path = np.random.choice(self.rirs)
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if rir_path is not None:
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rir, _ = soundfile.read(
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rir_path, dtype=np.float64, always_2d=True
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)
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# rir: (Nmic, Time)
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rir = rir.T
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# speech: (Nmic, Time)
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# Note that this operation doesn't change the signal length
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speech = scipy.signal.convolve(speech, rir, mode="full")[
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:, : speech.shape[1]
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]
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# Reverse mean power to the original power
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power2 = (speech[detect_non_silence(speech)] ** 2).mean()
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speech = np.sqrt(power / max(power2, 1e-10)) * speech
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# 2. Add Noise
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if (
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self.noises is not None
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and self.noise_apply_prob >= np.random.random()
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):
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noise_path = np.random.choice(self.noises)
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if noise_path is not None:
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noise_db = np.random.uniform(
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self.noise_db_low, self.noise_db_high
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)
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with soundfile.SoundFile(noise_path) as f:
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if f.frames == nsamples:
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noise = f.read(dtype=np.float64, always_2d=True)
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elif f.frames < nsamples:
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offset = np.random.randint(0, nsamples - f.frames)
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# noise: (Time, Nmic)
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noise = f.read(dtype=np.float64, always_2d=True)
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# Repeat noise
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noise = np.pad(
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noise,
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[(offset, nsamples - f.frames - offset), (0, 0)],
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mode="wrap",
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)
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else:
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offset = np.random.randint(0, f.frames - nsamples)
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f.seek(offset)
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# noise: (Time, Nmic)
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noise = f.read(
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nsamples, dtype=np.float64, always_2d=True
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)
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if len(noise) != nsamples:
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raise RuntimeError(f"Something wrong: {noise_path}")
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# noise: (Nmic, Time)
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noise = noise.T
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noise_power = (noise**2).mean()
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scale = (
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10 ** (-noise_db / 20)
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* np.sqrt(power)
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/ np.sqrt(max(noise_power, 1e-10))
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)
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speech = speech + scale * noise
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speech = speech.T
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ma = np.max(np.abs(speech))
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if ma > 1.0:
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speech /= ma
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data[self.speech_name] = speech
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if self.speech_volume_normalize is not None:
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speech = data[self.speech_name]
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ma = np.max(np.abs(speech))
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data[self.speech_name] = speech * self.speech_volume_normalize / ma
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assert check_return_type(data)
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return data
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def _text_process(
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self, data: Dict[str, Union[str, np.ndarray]]
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) -> Dict[str, np.ndarray]:
|
if self.text_name in data and self.tokenizer is not None:
|
text = data[self.text_name]
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text = self.text_cleaner(text)
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if self.split_with_space:
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tokens = text.strip().split(" ")
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else:
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tokens = self.tokenizer.text2tokens(text)
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text_ints = self.token_id_converter.tokens2ids(tokens)
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data[self.text_name] = np.array(text_ints, dtype=np.int64)
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assert check_return_type(data)
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return data
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def __call__(
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self, uid: str, data: Dict[str, Union[str, np.ndarray]]
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) -> Dict[str, np.ndarray]:
|
assert check_argument_types()
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data = self._speech_process(data)
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data = self._text_process(data)
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return data
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|
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class CommonPreprocessor_multi(AbsPreprocessor):
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def __init__(
|
self,
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train: bool,
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token_type: str = None,
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token_list: Union[Path, str, Iterable[str]] = None,
|
bpemodel: Union[Path, str, Iterable[str]] = None,
|
text_cleaner: Collection[str] = None,
|
g2p_type: str = None,
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unk_symbol: str = "<unk>",
|
space_symbol: str = "<space>",
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non_linguistic_symbols: Union[Path, str, Iterable[str]] = None,
|
delimiter: str = None,
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speech_name: str = "speech",
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text_name: List[str] = ["text"],
|
):
|
super().__init__(train)
|
self.train = train
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self.speech_name = speech_name
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self.text_name = text_name
|
|
if token_type is not None:
|
if token_list is None:
|
raise ValueError("token_list is required if token_type is not None")
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self.text_cleaner = TextCleaner(text_cleaner)
|
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self.tokenizer = build_tokenizer(
|
token_type=token_type,
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bpemodel=bpemodel,
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delimiter=delimiter,
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space_symbol=space_symbol,
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non_linguistic_symbols=non_linguistic_symbols,
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g2p_type=g2p_type,
|
)
|
self.token_id_converter = TokenIDConverter(
|
token_list=token_list,
|
unk_symbol=unk_symbol,
|
)
|
else:
|
self.text_cleaner = None
|
self.tokenizer = None
|
self.token_id_converter = None
|
|
def _text_process(
|
self, data: Dict[str, Union[str, np.ndarray]]
|
) -> Dict[str, np.ndarray]:
|
for text_n in self.text_name:
|
if text_n in data and self.tokenizer is not None:
|
text = data[text_n]
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text = self.text_cleaner(text)
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tokens = self.tokenizer.text2tokens(text)
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text_ints = self.token_id_converter.tokens2ids(tokens)
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data[text_n] = np.array(text_ints, dtype=np.int64)
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assert check_return_type(data)
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return data
|
|
def __call__(
|
self, uid: str, data: Dict[str, Union[str, np.ndarray]]
|
) -> Dict[str, np.ndarray]:
|
assert check_argument_types()
|
|
if self.speech_name in data:
|
# Nothing now: candidates:
|
# - STFT
|
# - Fbank
|
# - CMVN
|
# - Data augmentation
|
pass
|
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data = self._text_process(data)
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return data
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|
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class MutliTokenizerCommonPreprocessor(CommonPreprocessor):
|
def __init__(
|
self,
|
train: bool,
|
token_type: List[str] = [None],
|
token_list: List[Union[Path, str, Iterable[str]]] = [None],
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bpemodel: List[Union[Path, str, Iterable[str]]] = [None],
|
text_cleaner: Collection[str] = None,
|
g2p_type: str = None,
|
unk_symbol: str = "<unk>",
|
space_symbol: str = "<space>",
|
non_linguistic_symbols: Union[Path, str, Iterable[str]] = None,
|
delimiter: str = None,
|
rir_scp: str = None,
|
rir_apply_prob: float = 1.0,
|
noise_scp: str = None,
|
noise_apply_prob: float = 1.0,
|
noise_db_range: str = "3_10",
|
speech_volume_normalize: float = None,
|
speech_name: str = "speech",
|
text_name: List[str] = ["text"],
|
):
|
# TODO(jiatong): sync with Kamo and Jing on interface for preprocessor
|
super().__init__(
|
train=train,
|
token_type=token_type[0],
|
token_list=token_list[0],
|
bpemodel=bpemodel[0],
|
text_cleaner=text_cleaner,
|
g2p_type=g2p_type,
|
unk_symbol=unk_symbol,
|
space_symbol=space_symbol,
|
non_linguistic_symbols=non_linguistic_symbols,
|
delimiter=delimiter,
|
speech_name=speech_name,
|
text_name=text_name[0],
|
rir_scp=rir_scp,
|
rir_apply_prob=rir_apply_prob,
|
noise_scp=noise_scp,
|
noise_apply_prob=noise_apply_prob,
|
noise_db_range=noise_db_range,
|
speech_volume_normalize=speech_volume_normalize,
|
)
|
|
assert (
|
len(token_type) == len(token_list) == len(bpemodel) == len(text_name)
|
), "token_type, token_list, bpemodel, or processing text_name mismatched"
|
self.num_tokenizer = len(token_type)
|
self.tokenizer = []
|
self.token_id_converter = []
|
|
for i in range(self.num_tokenizer):
|
if token_type[i] is not None:
|
if token_list[i] is None:
|
raise ValueError("token_list is required if token_type is not None")
|
|
self.tokenizer.append(
|
build_tokenizer(
|
token_type=token_type[i],
|
bpemodel=bpemodel[i],
|
delimiter=delimiter,
|
space_symbol=space_symbol,
|
non_linguistic_symbols=non_linguistic_symbols,
|
g2p_type=g2p_type,
|
)
|
)
|
self.token_id_converter.append(
|
TokenIDConverter(
|
token_list=token_list[i],
|
unk_symbol=unk_symbol,
|
)
|
)
|
else:
|
self.tokenizer.append(None)
|
self.token_id_converter.append(None)
|
|
self.text_cleaner = TextCleaner(text_cleaner)
|
self.text_name = text_name # override the text_name from CommonPreprocessor
|
|
def _text_process(
|
self, data: Dict[str, Union[str, np.ndarray]]
|
) -> Dict[str, np.ndarray]:
|
for i in range(self.num_tokenizer):
|
text_name = self.text_name[i]
|
if text_name in data and self.tokenizer[i] is not None:
|
text = data[text_name]
|
text = self.text_cleaner(text)
|
tokens = self.tokenizer[i].text2tokens(text)
|
text_ints = self.token_id_converter[i].tokens2ids(tokens)
|
data[text_name] = np.array(text_ints, dtype=np.int64)
|
assert check_return_type(data)
|
return data
|
