122 lines
4.0 KiB
Python
122 lines
4.0 KiB
Python
import glob
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import os
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import numpy as np
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from scipy.io import wavfile
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MATCHING_FNS = ("one_to_one", "one_to_many")
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class ProcessorFunctions:
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"""
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Preprocessing methods for different types of speech enhacement datasets.
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"""
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@staticmethod
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def one_to_one(clean_path, noisy_path):
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"""
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One clean audio can have only one noisy audio file
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"""
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matching_wavfiles = list()
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clean_filenames = [
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file.split("/")[-1]
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for file in glob.glob(os.path.join(clean_path, "*.wav"))
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]
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noisy_filenames = [
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file.split("/")[-1]
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for file in glob.glob(os.path.join(noisy_path, "*.wav"))
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]
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common_filenames = np.intersect1d(noisy_filenames, clean_filenames)
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for file_name in common_filenames:
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sr_clean, clean_file = wavfile.read(
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os.path.join(clean_path, file_name)
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)
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sr_noisy, noisy_file = wavfile.read(
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os.path.join(noisy_path, file_name)
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)
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if (clean_file.shape[-1] == noisy_file.shape[-1]) and (
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sr_clean == sr_noisy
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):
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matching_wavfiles.append(
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{
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"clean": os.path.join(clean_path, file_name),
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"noisy": os.path.join(noisy_path, file_name),
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"duration": clean_file.shape[-1] / sr_clean,
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}
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)
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return matching_wavfiles
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@staticmethod
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def one_to_many(clean_path, noisy_path):
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"""
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One clean audio have multiple noisy audio files
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"""
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matching_wavfiles = list()
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clean_filenames = [
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file.split("/")[-1]
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for file in glob.glob(os.path.join(clean_path, "*.wav"))
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]
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for clean_file in clean_filenames:
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noisy_filenames = glob.glob(
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os.path.join(noisy_path, f"*_{clean_file}")
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)
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for noisy_file in noisy_filenames:
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sr_clean, clean_wav = wavfile.read(
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os.path.join(clean_path, clean_file)
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)
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sr_noisy, noisy_wav = wavfile.read(noisy_file)
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if (clean_wav.shape[-1] == noisy_wav.shape[-1]) and (
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sr_clean == sr_noisy
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):
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matching_wavfiles.append(
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{
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"clean": os.path.join(clean_path, clean_file),
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"noisy": noisy_file,
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"duration": clean_wav.shape[-1] / sr_clean,
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}
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)
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return matching_wavfiles
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class Fileprocessor:
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def __init__(self, clean_dir, noisy_dir, matching_function=None):
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self.clean_dir = clean_dir
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self.noisy_dir = noisy_dir
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self.matching_function = matching_function
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@classmethod
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def from_name(cls, name: str, clean_dir, noisy_dir, matching_function=None):
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if matching_function is None:
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if name.lower() == "vctk":
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return cls(clean_dir, noisy_dir, ProcessorFunctions.one_to_one)
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elif name.lower() == "dns-2020":
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return cls(clean_dir, noisy_dir, ProcessorFunctions.one_to_many)
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else:
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raise ValueError(
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f"Invalid matching function, Please use valid matching function from {MATCHING_FNS}"
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)
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else:
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if matching_function not in MATCHING_FNS:
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raise ValueError(
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f"Invalid matching function! Avaialble options are {MATCHING_FNS}"
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)
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else:
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return cls(
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clean_dir,
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noisy_dir,
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getattr(ProcessorFunctions, matching_function),
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)
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def prepare_matching_dict(self):
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if self.matching_function is None:
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raise ValueError("Not a valid matching function")
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return self.matching_function(self.clean_dir, self.noisy_dir)
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