NeMo/nemo/collections/asr/data/audio_to_text_dataset.py

# Copyright (c) 2020, 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 typing import Any, List, Optional, Union

import torch
from omegaconf import DictConfig, open_dict
from omegaconf.listconfig import ListConfig
from pytorch_lightning.callbacks import BasePredictionWriter
from torch.utils.data import ChainDataset

from nemo.collections.asr.data import audio_to_text, audio_to_text_dali
from nemo.utils import logging


def inject_dataloader_value_from_model_config(model_cfg: dict, dataloader_cfg: DictConfig, key: str):
    """
    Extracts the label set provided at the top level of the model, and propagates it to the dataloader
    config.

    Args:
        model_cfg: A DictConfig representing the model's config.
        dataloader_cfg: A DictConfig representing the individual data loader
        key: A str value representing a key in the model_cfg whose value will be propagated to the
            dataloader config.
    """
    if key not in model_cfg:
        logging.info(
            f"Model level config does not container `{key}`, please explicitly provide `{key}` to the dataloaders."
        )
        return

    if not isinstance(dataloader_cfg, DictConfig):
        dataloader_cfg = DictConfig(dataloader_cfg)

    # If key exists in the data loader config (either set explicitly or as a placeholder (via None))
    if key in dataloader_cfg:
        # Dataloader `labels` is provided and is non-null
        if dataloader_cfg[key] is not None and model_cfg[key] != dataloader_cfg[key]:
            # Model level `labels` dont match Dataloader level `labels`
            logging.warning(
                f'`{key}` is explicitly provided to the data loader, and is different from '
                f'the `{key}` provided at the model level config.\n'
                f'If this is incorrect, please set the dataloader\'s `{key}` to None.'
            )

        else:
            # Dataloader `key` is None or values match
            # Propagate from model level `key` (even if they match)
            with open_dict(dataloader_cfg):
                dataloader_cfg[key] = model_cfg[key]

    else:
        # If key key doesnt even exist in dataloader_cfg, inject it explicitly
        with open_dict(dataloader_cfg):
            dataloader_cfg[key] = model_cfg[key]


def get_char_dataset(config: dict, augmentor: Optional['AudioAugmentor'] = None) -> audio_to_text.AudioToCharDataset:
    """
    Instantiates a Character Encoding based AudioToCharDataset.

    Args:
        config: Config of the AudioToCharDataset.
        augmentor: Optional AudioAugmentor object for augmentations on audio data.

    Returns:
        An instance of AudioToCharDataset.
    """
    if 'labels' not in config:
        logging.warning(f"dataset does not have explicitly defined labels")

    dataset = audio_to_text.AudioToCharDataset(
        manifest_filepath=config['manifest_filepath'],
        labels=config.get('labels', None),
        sample_rate=config['sample_rate'],
        int_values=config.get('int_values', False),
        augmentor=augmentor,
        max_duration=config.get('max_duration', None),
        min_duration=config.get('min_duration', None),
        max_utts=config.get('max_utts', 0),
        blank_index=config.get('blank_index', -1),
        unk_index=config.get('unk_index', -1),
        normalize=config.get('normalize_transcripts', False),
        trim=config.get('trim_silence', False),
        parser=config.get('parser', 'en'),
        return_sample_id=config.get('return_sample_id', False),
    )
    return dataset


def get_bpe_dataset(
    config: dict, tokenizer: 'TokenizerSpec', augmentor: Optional['AudioAugmentor'] = None
) -> audio_to_text.AudioToBPEDataset:
    """
    Instantiates a Byte Pair Encoding / Word Piece Encoding based AudioToBPEDataset.

    Args:
        config: Config of the AudioToBPEDataset.
        tokenizer: An instance of a TokenizerSpec object.
        augmentor: Optional AudioAugmentor object for augmentations on audio data.

    Returns:
        An instance of AudioToBPEDataset.
    """
    dataset = audio_to_text.AudioToBPEDataset(
        manifest_filepath=config['manifest_filepath'],
        tokenizer=tokenizer,
        sample_rate=config['sample_rate'],
        int_values=config.get('int_values', False),
        augmentor=augmentor,
        max_duration=config.get('max_duration', None),
        min_duration=config.get('min_duration', None),
        max_utts=config.get('max_utts', 0),
        trim=config.get('trim_silence', False),
        use_start_end_token=config.get('use_start_end_token', True),
        return_sample_id=config.get('return_sample_id', False),
    )
    return dataset


def get_tarred_dataset(
    config: dict,
    shuffle_n: int,
    global_rank: int,
    world_size: int,
    tokenizer: Optional['TokenizerSpec'] = None,
    augmentor: Optional['AudioAugmentor'] = None,
) -> Union[audio_to_text.TarredAudioToBPEDataset, audio_to_text.TarredAudioToCharDataset]:
    """
    Instantiates a Word Piece/BPE Encoding based TarredAudioToBPEDataset or a char based TarredAudioToCharDataset.

    Args:
        config: Config of the TarredAudioToBPEDataset or TarredAudioToCharDataset.
        tokenizer: An instance of a TokenizerSpec object if BPE dataset is needed.
            Passsing None would return a char-based dataset.
        shuffle_n: How many samples to look ahead and load to be shuffled.
            See WebDataset documentation for more details.
        global_rank: Global rank of this device.
        world_size: Global world size in the training method.
        augmentor: Optional AudioAugmentor object for augmentations on audio data.

    Returns:
        An instance of TarredAudioToBPEDataset or TarredAudioToCharDataset.
    """
    tarred_audio_filepaths = config['tarred_audio_filepaths']
    manifest_filepaths = config['manifest_filepath']
    datasets = []
    tarred_audio_filepaths = convert_to_config_list(tarred_audio_filepaths)
    manifest_filepaths = convert_to_config_list(manifest_filepaths)

    if len(manifest_filepaths) != len(tarred_audio_filepaths):
        raise ValueError(f"manifest_filepaths and tarred_audio_filepaths need to have the same number of buckets.")

    if 'labels' not in config:
        logging.warning(f"dataset does not have explicitly defined labels")

    for dataset_idx, (tarred_audio_filepath, manifest_filepath) in enumerate(
        zip(tarred_audio_filepaths, manifest_filepaths)
    ):
        if len(tarred_audio_filepath) == 1:
            tarred_audio_filepath = tarred_audio_filepath[0]
        if tokenizer is None:
            dataset = audio_to_text.TarredAudioToCharDataset(
                audio_tar_filepaths=tarred_audio_filepath,
                manifest_filepath=manifest_filepath,
                labels=config.get('labels', None),
                sample_rate=config['sample_rate'],
                int_values=config.get('int_values', False),
                augmentor=augmentor,
                shuffle_n=shuffle_n,
                max_duration=config.get('max_duration', None),
                min_duration=config.get('min_duration', None),
                max_utts=config.get('max_utts', 0),
                blank_index=config.get('blank_index', -1),
                unk_index=config.get('unk_index', -1),
                normalize=config.get('normalize_transcripts', False),
                trim=config.get('trim_silence', False),
                parser=config.get('parser', 'en'),
                shard_strategy=config.get('tarred_shard_strategy', 'scatter'),
                global_rank=global_rank,
                world_size=world_size,
                return_sample_id=config.get('return_sample_id', False),
            )
        else:
            dataset = audio_to_text.TarredAudioToBPEDataset(
                audio_tar_filepaths=tarred_audio_filepath,
                manifest_filepath=manifest_filepath,
                tokenizer=tokenizer,
                sample_rate=config['sample_rate'],
                int_values=config.get('int_values', False),
                augmentor=augmentor,
                shuffle_n=shuffle_n,
                max_duration=config.get('max_duration', None),
                min_duration=config.get('min_duration', None),
                max_utts=config.get('max_utts', 0),
                trim=config.get('trim_silence', False),
                use_start_end_token=config.get('use_start_end_token', True),
                shard_strategy=config.get('tarred_shard_strategy', 'scatter'),
                global_rank=global_rank,
                world_size=world_size,
                return_sample_id=config.get('return_sample_id', False),
            )

        datasets.append(dataset)

    if len(datasets) > 1:
        return ChainDataset(datasets)
    else:
        return datasets[0]


def get_dali_char_dataset(
    config: dict,
    shuffle: bool,
    device_id: int,
    global_rank: int,
    world_size: int,
    preprocessor_cfg: Optional[DictConfig] = None,
) -> audio_to_text_dali.AudioToCharDALIDataset:
    """
    Instantiates a Character Encoding based AudioToCharDALIDataset.

    Args:
        config: Config of the AudioToCharDALIDataset.
        shuffle: Bool flag whether to shuffle the dataset.
        device_id: Index of the GPU to be used (local_rank). Only applicable when device == 'gpu'. Defaults to 0.
        global_rank: Global rank of this device.
        world_size: Global world size in the training method.
        augmentor: Optional AudioAugmentor object for augmentations on audio data.

    Returns:
        An instance of AudioToCharDALIDataset.
    """
    device = 'gpu' if torch.cuda.is_available() else 'cpu'
    dataset = audio_to_text_dali.AudioToCharDALIDataset(
        manifest_filepath=config['manifest_filepath'],
        device=device,
        batch_size=config['batch_size'],
        labels=config['labels'],
        sample_rate=config['sample_rate'],
        max_duration=config.get('max_duration', None),
        min_duration=config.get('min_duration', None),
        blank_index=config.get('blank_index', -1),
        unk_index=config.get('unk_index', -1),
        normalize=config.get('normalize_transcripts', False),
        trim=config.get('trim_silence', False),
        parser=config.get('parser', 'en'),
        shuffle=shuffle,
        device_id=device_id,
        global_rank=global_rank,
        world_size=world_size,
        preprocessor_cfg=preprocessor_cfg,
        return_sample_id=config.get('return_sample_id', False),
    )
    return dataset


def get_dali_bpe_dataset(
    config: dict,
    tokenizer,
    shuffle: bool,
    device_id: int,
    global_rank: int,
    world_size: int,
    preprocessor_cfg: Optional[DictConfig] = None,
) -> audio_to_text_dali.AudioToCharDALIDataset:
    """
    Instantiates a Subword Encoding based AudioToBPEDALIDataset.

    Args:
        config: Config of the AudioToBPEDALIDataset.
        tokenizer: An implementation of NeMo TokenizerSpec.
        shuffle: Bool flag whether to shuffle the dataset.
        device_id: Index of the GPU to be used (local_rank). Only applicable when device == 'gpu'. Defaults to 0.
        global_rank: Global rank of this device.
        world_size: Global world size in the training method.
        augmentor: Optional AudioAugmentor object for augmentations on audio data.

    Returns:
        An instance of AudioToCharDALIDataset.
    """
    device = 'gpu' if torch.cuda.is_available() else 'cpu'
    dataset = audio_to_text_dali.AudioToBPEDALIDataset(
        manifest_filepath=config['manifest_filepath'],
        tokenizer=tokenizer,
        device=device,
        batch_size=config['batch_size'],
        sample_rate=config['sample_rate'],
        max_duration=config.get('max_duration', None),
        min_duration=config.get('min_duration', None),
        trim=config.get('trim_silence', False),
        use_start_end_token=config.get('use_start_end_token', True),
        shuffle=shuffle,
        device_id=device_id,
        global_rank=global_rank,
        world_size=world_size,
        preprocessor_cfg=preprocessor_cfg,
        return_sample_id=config.get('return_sample_id', False),
    )
    return dataset


class ASRPredictionWriter(BasePredictionWriter):
    def __init__(self, dataset, output_file: str):
        super().__init__(write_interval="batch")
        self.outf = open(output_file, 'w')
        self.dataset = dataset
        self.samples_num = 0

    def write_on_batch_end(
        self,
        trainer,
        pl_module: 'LightningModule',
        prediction: Any,
        batch_indices: List[int],
        batch: Any,
        batch_idx: int,
        dataloader_idx: int,
    ):
        for sample_id, transcribed_text in prediction:
            item = {}
            sample = self.dataset.get_manifest_sample(sample_id)
            item["audio_filepath"] = sample.audio_file
            item["duration"] = sample.duration
            item["text"] = sample.text_raw
            item["pred_text"] = transcribed_text
            self.outf.write(json.dumps(item) + "\n")
            self.samples_num += 1
        return

    def close_output_file(self):
        self.outf.close()
        return self.samples_num


def convert_to_config_list(initial_list):
    if initial_list is None or initial_list == []:
        raise ValueError("manifest_filepaths and tarred_audio_filepaths must not be empty.")
    if not isinstance(initial_list, ListConfig):
        initial_list = ListConfig([initial_list])

    for list_idx, list_val in enumerate(initial_list):
        if type(list_val) != type(initial_list[0]):
            raise ValueError(
                "manifest_filepaths and tarred_audio_filepaths need to be a list of lists for bucketing or just a list of strings"
            )
    if type(initial_list[0]) is not ListConfig:
        initial_list = ListConfig([initial_list])
    return initial_list