DeepLearningExamples/PyTorch/SpeechRecognition/QuartzNet/common/dataset.py

# Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import json
from pathlib import Path

import numpy as np

import torch
from torch.utils.data import Dataset, DataLoader
from torch.utils.data.distributed import DistributedSampler

from .audio import (audio_from_file, AudioSegment, GainPerturbation,
                    ShiftPerturbation, SpeedPerturbation)
from .text import _clean_text, punctuation_map


def normalize_string(s, labels, punct_map):
    """Normalizes string.

    Example:
        'call me at 8:00 pm!' -> 'call me at eight zero pm'
    """
    labels = set(labels)
    try:
        text = _clean_text(s, ["english_cleaners"], punct_map).strip()
        return ''.join([tok for tok in text if all(t in labels for t in tok)])
    except:
        print(f"WARNING: Normalizing failed: {s}")
        return None


class FilelistDataset(Dataset):
    def __init__(self, filelist_fpath):
        self.samples = [line.strip() for line in open(filelist_fpath, 'r')]

    def __len__(self):
        return len(self.samples)

    def __getitem__(self, index):
        audio, audio_len = audio_from_file(self.samples[index])
        return (audio.squeeze(0), audio_len, torch.LongTensor([0]),
                torch.LongTensor([0]))


class SingleAudioDataset(FilelistDataset):
    def __init__(self, audio_fpath):
        self.samples = [audio_fpath]


class AudioDataset(Dataset):
    def __init__(self, data_dir, manifest_fpaths, labels,
                 sample_rate=16000, min_duration=0.1, max_duration=float("inf"),
                 pad_to_max_duration=False, max_utts=0, normalize_transcripts=True,
                 sort_by_duration=False, trim_silence=False,
                 speed_perturbation=None, gain_perturbation=None,
                 shift_perturbation=None, ignore_offline_speed_perturbation=False):
        """Loads audio, transcript and durations listed in a .json file.

        Args:
            data_dir: absolute path to dataset folder
            manifest_filepath: relative path from dataset folder
                to manifest json as described above. Can be coma-separated paths.
            labels (str): all possible output symbols
            min_duration (int): skip audio shorter than threshold
            max_duration (int): skip audio longer than threshold
            pad_to_max_duration (bool): pad all sequences to max_duration
            max_utts (int): limit number of utterances
            normalize_transcripts (bool): normalize transcript text
            sort_by_duration (bool): sort sequences by increasing duration
            trim_silence (bool): trim leading and trailing silence from audio
            ignore_offline_speed_perturbation (bool): use precomputed speed perturbation

        Returns:
            tuple of Tensors
        """
        self.data_dir = data_dir
        self.labels = labels
        self.labels_map = dict([(labels[i], i) for i in range(len(labels))])
        self.punctuation_map = punctuation_map(labels)
        self.blank_index = len(labels)

        self.pad_to_max_duration = pad_to_max_duration

        self.sort_by_duration = sort_by_duration
        self.max_utts = max_utts
        self.normalize_transcripts = normalize_transcripts
        self.ignore_offline_speed_perturbation = ignore_offline_speed_perturbation

        self.min_duration = min_duration
        self.max_duration = max_duration
        self.trim_silence = trim_silence
        self.sample_rate = sample_rate

        perturbations = []
        if speed_perturbation is not None:
            perturbations.append(SpeedPerturbation(**speed_perturbation))
        if gain_perturbation is not None:
            perturbations.append(GainPerturbation(**gain_perturbation))
        if shift_perturbation is not None:
            perturbations.append(ShiftPerturbation(**shift_perturbation))
        self.perturbations = perturbations

        self.max_duration = max_duration

        self.samples = []
        self.duration = 0.0
        self.duration_filtered = 0.0

        for fpath in manifest_fpaths:
            self._load_json_manifest(fpath)

        if sort_by_duration:
            self.samples = sorted(self.samples, key=lambda s: s['duration'])

    def __getitem__(self, index):
        s = self.samples[index]
        rn_indx = np.random.randint(len(s['audio_filepath']))
        duration = s['audio_duration'][rn_indx] if 'audio_duration' in s else 0
        offset = s.get('offset', 0)

        segment = AudioSegment(
            s['audio_filepath'][rn_indx], target_sr=self.sample_rate,
            offset=offset, duration=duration, trim=self.trim_silence)

        for p in self.perturbations:
            p.maybe_apply(segment, self.sample_rate)

        segment = torch.FloatTensor(segment.samples)

        return (segment,
                torch.tensor(segment.shape[0]).int(),
                torch.tensor(s["transcript"]),
                torch.tensor(len(s["transcript"])).int())

    def __len__(self):
        return len(self.samples)

    def _load_json_manifest(self, fpath):
        for s in json.load(open(fpath, "r", encoding="utf-8")):

            if self.pad_to_max_duration and not self.ignore_offline_speed_perturbation:
                # require all perturbed samples to be < self.max_duration
                s_max_duration = max(f['duration'] for f in s['files'])
            else:
                # otherwise we allow perturbances to be > self.max_duration
                s_max_duration = s['original_duration']

            s['duration'] = s.pop('original_duration')
            if not (self.min_duration <= s_max_duration <= self.max_duration):
                self.duration_filtered += s['duration']
                continue

            # Prune and normalize according to transcript
            tr = (s.get('transcript', None) or
                  self.load_transcript(s['text_filepath']))

            if not isinstance(tr, str):
                print(f'WARNING: Skipped sample (transcript not a str): {tr}.')
                self.duration_filtered += s['duration']
                continue

            if self.normalize_transcripts:
                tr = normalize_string(tr, self.labels, self.punctuation_map)

            s["transcript"] = self.to_vocab_inds(tr)

            files = s.pop('files')
            if self.ignore_offline_speed_perturbation:
                files = [f for f in files if f['speed'] == 1.0]

            s['audio_duration'] = [f['duration'] for f in files]
            s['audio_filepath'] = [str(Path(self.data_dir, f['fname']))
                                   for f in files]
            self.samples.append(s)
            self.duration += s['duration']

            if self.max_utts > 0 and len(self.samples) >= self.max_utts:
                print(f'Reached max_utts={self.max_utts}. Finished parsing {fpath}.')
                break

    def load_transcript(self, transcript_path):
        with open(transcript_path, 'r', encoding="utf-8") as transcript_file:
            transcript = transcript_file.read().replace('\n', '')
        return transcript

    def to_vocab_inds(self, transcript):
        chars = [self.labels_map.get(x, self.blank_index) for x in list(transcript)]
        transcript = list(filter(lambda x: x != self.blank_index, chars))
        return transcript


def collate_fn(batch):
    bs = len(batch)
    max_len = lambda l, idx: max(el[idx].size(0) for el in l)
    audio = torch.zeros(bs, max_len(batch, 0))
    audio_lens = torch.zeros(bs, dtype=torch.int32)
    transcript = torch.zeros(bs, max_len(batch, 2))
    transcript_lens = torch.zeros(bs, dtype=torch.int32)

    for i, sample in enumerate(batch):
        audio[i].narrow(0, 0, sample[0].size(0)).copy_(sample[0])
        audio_lens[i] = sample[1]
        transcript[i].narrow(0, 0, sample[2].size(0)).copy_(sample[2])
        transcript_lens[i] = sample[3]
    return audio, audio_lens, transcript, transcript_lens


def get_data_loader(dataset, batch_size, multi_gpu=True, shuffle=True,
                    drop_last=True, num_workers=4):

    kw = {'dataset': dataset, 'collate_fn': collate_fn,
          'num_workers': num_workers, 'pin_memory': True}

    if multi_gpu:
        loader_shuffle = False
        sampler = DistributedSampler(dataset, shuffle=shuffle)
    else:
        loader_shuffle = shuffle
        sampler = None

    return DataLoader(batch_size=batch_size, drop_last=drop_last,
                      sampler=sampler, shuffle=loader_shuffle, **kw)