Source code for nnmnkwii.datasets.voice_statistics

from os import listdir
from os.path import exists, isdir, join, splitext

import numpy as np
from nnmnkwii.datasets import FileDataSource

available_speakers = ["fujitou", "tsuchiya", "uemura", "hiroshiba"]
available_emotions = ["angry", "happy", "normal"]

def _get_dir(speaker, emotion):
    return "{}_{}".format(speaker, emotion)

[docs]class TranscriptionDataSource(FileDataSource): """Transcription data source for VoiceStatistics dataset Users are expected to inherit the class and implement ``collect_features`` method, which defines how features are computed given a transcription. Args: data_root (str): Data root. column (str): ``sentense``, ``yomi`` or ``monophone``. max_files (int): Total number of files to be collected. Atributes: transcriptions (list): Transcriptions. """ column_map = {"sentence_id": 0, "sentence": 1, "yomi": 2, "monophone": 3} def __init__(self, data_root, column="sentence", max_files=None): path = join(data_root, "balance_sentences.txt") if not exists(path): raise RuntimeError( 'balance_sentences.txt doesn\'t exist at "{}"'.format(path) ) self.transcriptions = [] self.max_files = max_files if column not in self.column_map: raise ValueError( "Not supported column {}. It should be one of 'sentense'," " 'yomi' or 'monophone'.".format(column) ) with open(path) as f: for line in f: # header if line.startswith("sentence_id"): continue v = line.split("\t")[self.column_map[column]].strip() self.transcriptions.append(v) assert len(self.transcriptions) == 100
[docs] def collect_files(self): """Collect text transcriptions. .. warning:: Note that it returns list of transcriptions (str), not file paths. Returns: list: List of text transcription. """ if self.max_files is None: return self.transcriptions else: return self.transcriptions[: self.max_files]
[docs]class WavFileDataSource(FileDataSource): """Wav file data source for Voice-statistics dataset. The data source collects wav files from voice-statistics. Users are expected to inherit the class and implement ``collect_features`` method, which defines how features are computed given a wav file path. Args: data_root (str): Data root speakers (list): List of speakers to load. Supported names of speaker are ``fujitou``, ``tsuchiya`` and ``uemura``. labelmap (dict[optional]): Dict of speaker labels. If None, it's assigned as incrementally (i.e., 0, 1, 2) for specified speakers. max_files (int): Total number of files to be collected. emotions (list): List of emotions we use. Supported names of emotions are ``angry``, ``happy`` and ``normal``. Attributes: labels (numpy.ndarray): List of speaker identifiers determined by labelmap. Stored in ``collect_files``. """ def __init__( self, data_root, speakers, labelmap=None, max_files=None, emotions=None ): if emotions is None: emotions = ["normal"] for speaker in speakers: if speaker not in available_speakers: raise ValueError( "Unknown speaker '{}'. It should be one of {}".format( speaker, available_speakers ) ) for emotion in emotions: if emotion not in available_emotions: raise ValueError( "Unknown emotion '{}'. It should be one of {}".format( emotion, available_emotions ) ) self.data_root = data_root self.speakers = speakers self.emotions = emotions if labelmap is None: labelmap = {} for idx, speaker in enumerate(speakers): labelmap[speaker] = idx self.labelmap = labelmap self.labels = None self.max_files = max_files
[docs] def collect_files(self): """Collect wav files for specific speakers. Returns: list: List of collected wav files. """ paths = [] labels = [] if self.max_files is None: max_files_per_dir = None else: max_files_per_dir = ( self.max_files // len(self.emotions) // len(self.speakers) ) for speaker in self.speakers: dirs = list( map(lambda x: join(self.data_root, _get_dir(speaker, x)), self.emotions) ) files = [] for d in dirs: if not isdir(d): raise RuntimeError("{} doesn't exist.".format(d)) fs = [join(d, f) for f in listdir(d)] fs = list(filter(lambda x: splitext(x)[1] == ".wav", fs)) fs = sorted(fs) fs = fs[:max_files_per_dir] files.extend(fs) for f in files: paths.append(f) labels.append(self.labelmap[speaker]) self.labels = np.array(labels, dtype=np.int32) return paths
# For compat, remove this after v0.1.0 VoiceStatisticsWavFileDataSource = WavFileDataSource