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@@ -0,0 +1,826 @@
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+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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+
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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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+
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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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+
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+
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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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+
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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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+
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+
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+def forward_segment(text, dic):
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+ word_list = []
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+ i = 0
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+ while i < len(text):
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+ longest_word = text[i]
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+ for j in range(i + 1, len(text) + 1):
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+ word = text[i:j]
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+ if word in dic:
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+ if len(word) > len(longest_word):
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+ longest_word = word
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+ word_list.append(longest_word)
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+ i += len(longest_word)
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+ return word_list
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+
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+
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+def seg_tokenize(txt, seg_dict):
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+ out_txt = ""
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+ for word in txt:
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+ if word in seg_dict:
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+ out_txt += seg_dict[word] + " "
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+ else:
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+ out_txt += "<unk>" + " "
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+ return out_txt.strip().split()
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+
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+
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+def seg_tokenize_wo_pattern(txt, seg_dict):
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+ out_txt = ""
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+ for word in txt:
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+ if word in seg_dict:
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+ out_txt += seg_dict[word] + " "
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+ else:
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+ out_txt += "<unk>" + " "
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+ return out_txt.strip().split()
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+
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+
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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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+
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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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+
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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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+
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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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+
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+
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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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+
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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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+
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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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+
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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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+ seg_dict_file: str = None,
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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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+ self.seg_dict = None
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+ if seg_dict_file is not None:
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+ self.seg_dict = {}
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+ with open(seg_dict_file) as f:
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+ lines = f.readlines()
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+ for line in lines:
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+ s = line.strip().split()
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+ key = s[0]
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+ value = s[1:]
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+ self.seg_dict[key] = " ".join(value)
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+
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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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+
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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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+
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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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+
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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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+
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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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+
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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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+
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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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+
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+ # rir: (Nmic, Time)
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+ rir = rir.T
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+
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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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+
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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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+
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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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+
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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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+
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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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+
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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]:
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+ if self.text_name in data and self.tokenizer is not None:
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+ 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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+ if self.seg_dict is not None:
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+ tokens = forward_segment("".join(tokens), self.seg_dict)
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+ tokens = seg_tokenize(tokens, self.seg_dict)
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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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+
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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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+ assert check_argument_types()
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+
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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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+
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+## FIXME
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+class LMPreprocessor(CommonPreprocessor):
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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,
|
|
|
+ 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: str = "text",
|
|
|
+ split_with_space: bool = False,
|
|
|
+ seg_dict_file: str = None,
|
|
|
+ ):
|
|
|
+ super().__init__(train,
|
|
|
+ token_type,
|
|
|
+ token_list,
|
|
|
+ bpemodel,
|
|
|
+ text_cleaner,
|
|
|
+ g2p_type,
|
|
|
+ unk_symbol,
|
|
|
+ space_symbol,
|
|
|
+ non_linguistic_symbols,
|
|
|
+ delimiter,
|
|
|
+ rir_scp,
|
|
|
+ rir_apply_prob,
|
|
|
+ noise_scp,
|
|
|
+ noise_apply_prob,
|
|
|
+ noise_db_range,
|
|
|
+ speech_volume_normalize,
|
|
|
+ speech_name,
|
|
|
+ text_name,
|
|
|
+ split_with_space,
|
|
|
+ seg_dict_file,
|
|
|
+ )
|
|
|
+
|
|
|
+ def _text_process(
|
|
|
+ self, data: Dict[str, Union[str, np.ndarray]]
|
|
|
+ ) -> Dict[str, np.ndarray]:
|
|
|
+ if self.text_name in data and self.tokenizer is not None:
|
|
|
+ text = data[self.text_name]
|
|
|
+ text = self.text_cleaner(text)
|
|
|
+ if self.split_with_space:
|
|
|
+ tokens = text.strip().split(" ")
|
|
|
+ if self.seg_dict is not None:
|
|
|
+ tokens = seg_tokenize_wo_pattern(tokens, self.seg_dict)
|
|
|
+ else:
|
|
|
+ tokens = self.tokenizer.text2tokens(text)
|
|
|
+ text_ints = self.token_id_converter.tokens2ids(tokens)
|
|
|
+ data[self.text_name] = np.array(text_ints, dtype=np.int64)
|
|
|
+ assert check_return_type(data)
|
|
|
+ return data
|
|
|
+
|
|
|
+
|
|
|
+class CommonPreprocessor_multi(AbsPreprocessor):
|
|
|
+ def __init__(
|
|
|
+ self,
|
|
|
+ train: bool,
|
|
|
+ token_type: str = None,
|
|
|
+ token_list: Union[Path, str, Iterable[str]] = None,
|
|
|
+ bpemodel: 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,
|
|
|
+ speech_name: str = "speech",
|
|
|
+ text_name: List[str] = ["text"],
|
|
|
+ ):
|
|
|
+ super().__init__(train)
|
|
|
+ self.train = train
|
|
|
+ self.speech_name = speech_name
|
|
|
+ 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")
|
|
|
+ self.text_cleaner = TextCleaner(text_cleaner)
|
|
|
+
|
|
|
+ self.tokenizer = build_tokenizer(
|
|
|
+ token_type=token_type,
|
|
|
+ bpemodel=bpemodel,
|
|
|
+ delimiter=delimiter,
|
|
|
+ space_symbol=space_symbol,
|
|
|
+ non_linguistic_symbols=non_linguistic_symbols,
|
|
|
+ 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]
|
|
|
+ text = self.text_cleaner(text)
|
|
|
+ tokens = self.tokenizer.text2tokens(text)
|
|
|
+ text_ints = self.token_id_converter.tokens2ids(tokens)
|
|
|
+ data[text_n] = np.array(text_ints, dtype=np.int64)
|
|
|
+ assert check_return_type(data)
|
|
|
+ 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
|
|
|
+
|
|
|
+ data = self._text_process(data)
|
|
|
+ return data
|
|
|
+
|
|
|
+
|
|
|
+class MutliTokenizerCommonPreprocessor(CommonPreprocessor):
|
|
|
+ def __init__(
|
|
|
+ self,
|
|
|
+ train: bool,
|
|
|
+ token_type: List[str] = [None],
|
|
|
+ token_list: List[Union[Path, str, Iterable[str]]] = [None],
|
|
|
+ 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
|
|
|
+
|
|
|
+
|
|
|
+class CodeMixTokenizerCommonPreprocessor(CommonPreprocessor):
|
|
|
+ def __init__(
|
|
|
+ self,
|
|
|
+ train: bool,
|
|
|
+ token_type: str = None,
|
|
|
+ token_list: Union[Path, str, Iterable[str]] = None,
|
|
|
+ bpemodel: 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: str = "text",
|
|
|
+ split_text_name: str = "split_text",
|
|
|
+ split_with_space: bool = False,
|
|
|
+ seg_dict_file: str = None,
|
|
|
+ ):
|
|
|
+ super().__init__(
|
|
|
+ train=train,
|
|
|
+ # Force to use word.
|
|
|
+ token_type="word",
|
|
|
+ token_list=token_list,
|
|
|
+ bpemodel=bpemodel,
|
|
|
+ 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,
|
|
|
+ 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,
|
|
|
+ split_with_space=split_with_space,
|
|
|
+ seg_dict_file=seg_dict_file,
|
|
|
+ )
|
|
|
+ # The data field name for split text.
|
|
|
+ self.split_text_name = split_text_name
|
|
|
+
|
|
|
+ @classmethod
|
|
|
+ def split_words(cls, text: str):
|
|
|
+ words = []
|
|
|
+ segs = text.split()
|
|
|
+ for seg in segs:
|
|
|
+ # There is no space in seg.
|
|
|
+ current_word = ""
|
|
|
+ for c in seg:
|
|
|
+ if len(c.encode()) == 1:
|
|
|
+ # This is an ASCII char.
|
|
|
+ current_word += c
|
|
|
+ else:
|
|
|
+ # This is a Chinese char.
|
|
|
+ if len(current_word) > 0:
|
|
|
+ words.append(current_word)
|
|
|
+ current_word = ""
|
|
|
+ words.append(c)
|
|
|
+ if len(current_word) > 0:
|
|
|
+ words.append(current_word)
|
|
|
+ return words
|
|
|
+
|
|
|
+ def __call__(
|
|
|
+ self, uid: str, data: Dict[str, Union[list, str, np.ndarray]]
|
|
|
+ ) -> Dict[str, Union[list, np.ndarray]]:
|
|
|
+ assert check_argument_types()
|
|
|
+ # Split words.
|
|
|
+ if isinstance(data[self.text_name], str):
|
|
|
+ split_text = self.split_words(data[self.text_name])
|
|
|
+ else:
|
|
|
+ split_text = data[self.text_name]
|
|
|
+ data[self.text_name] = " ".join(split_text)
|
|
|
+ data = self._speech_process(data)
|
|
|
+ data = self._text_process(data)
|
|
|
+ data[self.split_text_name] = split_text
|
|
|
+ return data
|
|
|
+
|
|
|
+ def pop_split_text_data(self, data: Dict[str, Union[str, np.ndarray]]):
|
|
|
+ result = data[self.split_text_name]
|
|
|
+ del data[self.split_text_name]
|
|
|
+ return result
|
|
|
+
|
|
|
+
|
|
|
+class PuncTrainTokenizerCommonPreprocessor(CommonPreprocessor):
|
|
|
+ def __init__(
|
|
|
+ self,
|
|
|
+ train: bool,
|
|
|
+ token_type: List[str] = [None],
|
|
|
+ token_list: List[Union[Path, str, Iterable[str]]] = [None],
|
|
|
+ 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"],
|
|
|
+ vad_name: str = "vad_indexes",
|
|
|
+ ):
|
|
|
+ # 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
|
|
|
+ self.vad_name = vad_name
|
|
|
+
|
|
|
+ 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)
|
|
|
+ if "vad:" in tokens[-1]:
|
|
|
+ vad = tokens[-1][4:]
|
|
|
+ tokens = tokens[:-1]
|
|
|
+ if len(vad) == 0:
|
|
|
+ vad = -1
|
|
|
+ else:
|
|
|
+ vad = int(vad)
|
|
|
+ data[self.vad_name] = np.array([vad], dtype=np.int64)
|
|
|
+ text_ints = self.token_id_converter[i].tokens2ids(tokens)
|
|
|
+ data[text_name] = np.array(text_ints, dtype=np.int64)
|
|
|
+
|
|
|
+
|
|
|
+def split_to_mini_sentence(words: list, word_limit: int = 20):
|
|
|
+ assert word_limit > 1
|
|
|
+ if len(words) <= word_limit:
|
|
|
+ return [words]
|
|
|
+ sentences = []
|
|
|
+ length = len(words)
|
|
|
+ sentence_len = length // word_limit
|
|
|
+ for i in range(sentence_len):
|
|
|
+ sentences.append(words[i * word_limit:(i + 1) * word_limit])
|
|
|
+ if length % word_limit > 0:
|
|
|
+ sentences.append(words[sentence_len * word_limit:])
|
|
|
+ return sentences
|
|
|
+
|
|
|
+
|
|
|
+def build_preprocess(args):
|
|
|
+ if args.task_name == "asr":
|
|
|
+ pass
|
|
|
+ else:
|
|
|
+ raise ValueError(f"Not supported task={args.task_name}")
|
speech_asr