# Copyright (c) 2025, 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. # flake8: noqa # pylint: skip-file import copy import io import math import os from typing import Dict, List, Optional, Union import numpy as np import torch from megatron.core import parallel_state from omegaconf import DictConfig, ListConfig, open_dict from nemo.collections.asr.data.audio_to_text import ( VALID_FILE_FORMATS, cache_datastore_manifests, expand_sharded_filepaths, shard_manifests_if_needed, ) from nemo.collections.asr.data.audio_to_text_dataset import convert_to_config_list, get_chain_dataset from nemo.collections.asr.parts.preprocessing.features import WaveformFeaturizer from nemo.collections.asr.parts.preprocessing.segment import ChannelSelectorType from nemo.collections.common.data.blendable_dataset import BlendableDataset from nemo.collections.common.data.dataset import ConcatDataset from nemo.collections.common.parts.preprocessing import collections from nemo.collections.multimodal.speech_llm.parts.utils.data_utils import ( TextProcessing, build_loss_mask, ceil_to_nearest, get_num_samples_from_files, maybe_cast_to_list, ) from nemo.core.classes import Dataset, IterableDataset from nemo.utils import logging, logging_mode from nemo.utils import webdataset as wds from nemo.utils.distributed import webdataset_split_by_workers __all__ = [ 'AudioTextDataset', 'TarredAudioTextDataset', 'get_tarred_audio_text_dataset_from_config', 'get_audio_text_dataset_from_config', ] def _audio_collate_fn(audio_signals, audio_lengths): """collate batch of audio sig, audio len, tokens, tokens len Args: audio_signals: List[Tensor] audio_lengths: List[Tensor] """ max_audio_len = 0 has_audio = audio_lengths[0] is not None if has_audio: max_audio_len = max(audio_lengths).item() audio_signals_padded = [] for sig, sig_len in zip(audio_signals, audio_lengths): if has_audio: sig_len = sig_len.item() if sig_len < max_audio_len: pad = (0, max_audio_len - sig_len) sig = torch.nn.functional.pad(sig, pad) audio_signals_padded.append(sig) if has_audio: audio_signals_padded = torch.stack(audio_signals_padded) audio_lengths = torch.stack(audio_lengths) else: audio_signals_padded, audio_lengths = None, None return audio_signals_padded, audio_lengths def _collate_item(item: Union[torch.Tensor, np.ndarray, List], max_length: int, pad_id: int = 0): # function copied from nemo/collections/nlp/data/language_modelling/megatron/gpt_sft_dataset.py item = maybe_cast_to_list(item) # max_length = max([len(x) for x in item]) if item else 0 # here [0] should be tokenizer.pad_id item = [x + [pad_id] * (max_length - len(x)) for x in item] return item def _speechllm_audio_text_collate_fn( batch: Dict, tokens_to_generate: int, pad_to_max_length: bool, max_seq_length: int, text_pad_id: int, ): sample_ids = [x["idx"] for x in batch] sample_ids = torch.tensor(sample_ids, dtype=torch.int32) audio_signal = [x["audio_signal"] for x in batch] audio_lengths = [x["audio_length"] for x in batch] audio_signal, audio_lengths = _audio_collate_fn(audio_signal, audio_lengths) input_ids = [item['input_ids'][:-1] for item in batch] labels = [item['input_ids'][1:] for item in batch] contexts = [item['context_ids'] for item in batch] context_lengths = torch.LongTensor([item['context_length'] for item in batch]) answers = [item['answer_ids'] for item in batch] loss_mask = [build_loss_mask(item)[1:] for item in batch] max_length = max([len(x) for x in input_ids]) + tokens_to_generate # increase max length to nearest multiple of 4 or 8 if pad_to_max_length: max_length = max_seq_length else: max_length = min(max_seq_length, ceil_to_nearest(max_length, 8)) assert max_length <= max_seq_length position_ids = [list(range(max_length)) for _ in batch] position_ids = torch.LongTensor(position_ids) input_ids = torch.LongTensor(_collate_item(input_ids, max_length=max_length, pad_id=text_pad_id)) input_length = torch.LongTensor([len(x) for x in input_ids]) labels = torch.LongTensor(_collate_item(labels, max_length=max_length, pad_id=text_pad_id)) loss_mask = torch.LongTensor(_collate_item(loss_mask, max_length=max_length, pad_id=0)) contexts = torch.LongTensor(_collate_item(contexts, max_length=max_length, pad_id=text_pad_id)) answers = torch.LongTensor(_collate_item(answers, max_length=max_length, pad_id=text_pad_id)) batch = { 'sample_ids': sample_ids, 'audio_signal': audio_signal, 'audio_signal_length': audio_lengths, 'tokens': input_ids, 'tokens_length': input_length, 'labels': labels, 'loss_mask': loss_mask, 'position_ids': position_ids, 'contexts': contexts, 'context_lengths': context_lengths, 'answers': answers, 'max_length': torch.LongTensor(max_length), 'metadata': [x['metadata'] for x in batch], } return batch def _speechllm_multi_audio_text_collate_fn( batch: Dict, tokens_to_generate: int, pad_to_max_length: bool, max_seq_length: int, text_pad_id: int, ): """Collate function for multi audio case.""" context_start_idx = [item['context_start_idx'] for item in batch] audio_signals = [x["audio_signal"] for x in batch] audio_lengths = [x["audio_length"] for x in batch] num_audios = [len(x) for x in audio_signals] # put all audios from all samples in one batch audio_signals_merged = [item for audio_list in audio_signals for item in audio_list] audio_lengths_merged = [item for length_list in audio_lengths for item in length_list] audio_signals_merged, audio_lengths_merged = _audio_collate_fn(audio_signals_merged, audio_lengths_merged) for i in range(len(batch)): # create dummy audio_signal and audio_length for _speechllm_audio_text_collate_fn() batch[i]["audio_signal"] = audio_signals[i][0] batch[i]["audio_length"] = audio_lengths[i][0] batch = _speechllm_audio_text_collate_fn(batch, tokens_to_generate, pad_to_max_length, max_seq_length, text_pad_id) # add multi audio specific fields batch['context_start_idx'] = list(context_start_idx) batch['num_audios'] = torch.LongTensor(num_audios) batch['audio_signal'] = audio_signals_merged batch['audio_signal_length'] = audio_lengths_merged return batch class AudioTextDataset(Dataset): """ Dataset that loads tensors via a json file containing paths to audio files, transcripts, and durations (in seconds). Each new line is a different sample. Example below: .. code-block:: json {"audio_filepath": "1.wav", "duration": 1.12, "question": "what is the capital of France?", "answer": "Paris"} {"audio_filepath": "2.wav", "duration": 2.15, "question": "what is the capital of Italy?", "answer": "Rome"} Args: manifest_filepath: Path to manifest json as described above. Can be comma-separated paths. text_processor: TextProcessing object sample_rate (int): Sample rate to resample loaded audio to int_values (bool): If true, load samples as 32-bit integers. Defauts to False. augmentor (nemo.collections.asr.parts.perturb.AudioAugmentor): AudioAugmentor to augment loaded audio max_duration: If audio exceeds this length, do not include in dataset min_duration: If audio is less than this length, do not include in dataset max_utts: Limit number of utterances trim: whether or not to trim silence. Defaults to False channel_selector (int | Iterable[int] | str): select a single channel or a subset of channels from multi-channel audio. If set to `'average'`, it performs averaging across channels. Disabled if set to `None`. Defaults to `None`. Uses zero-based indexing. :note: below args are NLP-specific max_seq_length (int): maximum sequence length for each dataset examples. Examples will either be truncated to fit this length or dropped if they cannot be truncated. min_seq_length (int): min length of each data example in the dataset. Data examples will be dropped if they do not meet the min length requirements. tokens_to_generate (int): max num of tokens to generate in a single pass. Defaults to 128. pad_to_max_length (bool): If True, pad all samples to the max_seq_length. If False, pad to the max length context_key: Key to use for the context in your JSONL file answer_key: Key to use for the label in your JSONL file context_file: Optional[Union[List[str], str]] = None, if provided, will use this file to load random questions from, if question is not in manifest. """ def __init__( self, manifest_filepath: str, text_processor: TextProcessing, sample_rate: int, int_values: bool = False, augmentor: 'nemo.collections.asr.parts.perturb.AudioAugmentor' = None, max_duration: Optional[int] = None, min_duration: Optional[int] = None, max_utts: int = 0, trim: bool = False, channel_selector: Optional[ChannelSelectorType] = None, max_seq_length: int = 1024, min_seq_length: int = 1, tokens_to_generate: int = 128, pad_to_max_length: bool = False, max_num_samples: Optional[int] = None, index_by_file_id: bool = False, context_key: str = 'context', answer_key: str = 'answer', context_file: Optional[Union[List[str], str]] = None, ): super().__init__() self.text_processor = text_processor self.max_seq_length = max_seq_length self.min_seq_length = min_seq_length self.tokens_to_generate = tokens_to_generate self.pad_to_max_length = pad_to_max_length if isinstance(manifest_filepath, str): manifest_filepath = manifest_filepath.split(",") # If necessary, cache manifests and audio from object store cache_datastore_manifests(manifest_filepaths=manifest_filepath, cache_audio=True) self.collection = collections.SpeechLLMAudioTextCollection( manifests_files=manifest_filepath, min_duration=min_duration, max_duration=max_duration, max_number=max_utts, index_by_file_id=index_by_file_id, max_num_samples=max_num_samples, context_file=context_file, context_key=context_key, answer_key=answer_key, ) self.featurizer = WaveformFeaturizer(sample_rate=sample_rate, int_values=int_values, augmentor=augmentor) self.trim = trim self.channel_selector = channel_selector def get_manifest_sample(self, sample_id): """ return the manifest item of the given index """ return self.collection[sample_id] def __getitem__(self, index): output = {"idx": index} sample = self.collection[index] offset = sample.offset if offset is None: offset = 0 if sample.audio_file is not None: features = self.featurizer.process( sample.audio_file, offset=offset, duration=sample.duration, trim=self.trim, orig_sr=sample.orig_sr, channel_selector=self.channel_selector, ) f, fl = features, torch.tensor(features.shape[0]).long() output["audio_signal"] = f output["audio_length"] = fl else: # dummy features output["audio_signal"] = torch.zeros([80]) # accomodates normalize_batch output["audio_length"] = torch.tensor(80) text_data = self.text_processor(context=sample.context, output=sample.answer) output.update(text_data) output['metadata'] = { 'audio_filepath': sample.audio_file, 'offset': offset, 'duration': sample.duration, } return output def __len__(self): return len(self.collection) def _collate_fn(self, batch): return _speechllm_audio_text_collate_fn( batch=batch, tokens_to_generate=self.tokens_to_generate, pad_to_max_length=self.pad_to_max_length, max_seq_length=self.max_seq_length, text_pad_id=self.text_processor.pad_id, ) def collate_fn(self, batch): # override collate_fn to skip type checking return self._collate_fn(batch) class MultiAudioTextDataset(AudioTextDataset): """ Dataset for having multi audios per sample, for example in few-shot in-context learning. To use this dataset, you need to specify the `audio_locator` field in the dataset config, and use that to specify the locations of the audio files in your manifest. In this case, the `audio_filepath` field in the manifest is a list of audio filepaths, and the `duration` field is a list of durations, one for each audio file. The `offset` field is optional, and if not specified, it is assumed to be 0.0. The `offset` field is also a list of offsets if specified. Example manifest item for audio_locator='|audio|': { "audio_filepath": ["1.wav","2.wav","3.wav"], "duration": [1.05,1.05,2.0], "answer": "this was her dream as nearly as she could recall it", "question": "Following are examples of speech audios and their transcriptions. Example 1: audio is |audio|, transcription is 'I have a dream'. Example 2: audio is |audio|, transcription is ' I don't have a dream'. Given the following audio |audio|, transcribe the audio into words." } """ def __init__( self, *args, **kwargs, ): super().__init__(*args, **kwargs) def _collate_fn(self, batch): return _speechllm_multi_audio_text_collate_fn( batch=batch, tokens_to_generate=self.tokens_to_generate, pad_to_max_length=self.pad_to_max_length, max_seq_length=self.max_seq_length, text_pad_id=self.text_processor.pad_id, ) def __getitem__(self, index): output = {"idx": index} sample = self.collection[index] offsets = sample.offset if sample.offset else 0.0 durations = sample.duration if sample.duration else 0.0 num_audios = 0 output["audio_signal"] = [] output["audio_length"] = [] if sample.audio_file is not None: audio_list = sample.audio_file if isinstance(sample.audio_file, str): audio_list = [sample.audio_file] if not isinstance(audio_list, list): raise ValueError( f"The field `audio_file` must be either a str or a list of str,", f"but got type {type(sample.audio_file)} instead", ) num_audios = len(audio_list) if isinstance(durations, list) and len(durations) != num_audios: raise ValueError( f"The number of durations ({len(durations)}) must match the number of audio clips ({num_audios})" ) if isinstance(offsets, list) and len(offsets) != num_audios: raise ValueError( f"The number of offsets ({len(offsets)}) must match the number of audio clips ({num_audios})" ) for i, audio_file in enumerate(audio_list): duration = durations[i] if isinstance(durations, list) else 0 offset = offsets[i] if isinstance(offsets, list) else 0 features = self.featurizer.process( audio_file, offset=offset, duration=duration, trim=self.trim, orig_sr=sample.orig_sr, channel_selector=self.channel_selector, ) f, fl = features, torch.tensor(features.shape[0]).long() output["audio_signal"].append(f) output["audio_length"].append(fl) else: # dummy features output["audio_signal"] = [torch.zeros([8])] # accomodates normalize_batch output["audio_length"] = [torch.tensor(8)] text_data = self.text_processor(context=sample.context, output=sample.answer) if isinstance(output["audio_signal"], list) and len(output["audio_signal"]) + 1 != len( text_data['context_start_idx'] ): raise ValueError( f"The number of text segments ({len(text_data['context_start_idx'])})", f"must be one more than number of audios ({len(output['audio_signal'])})", ) output.update(text_data) output['metadata'] = { 'audio_filepath': sample.audio_file, 'offset': offsets, 'duration': sample.duration, } return output class TarredAudioFilter: """ filter function for tarred audio files, skip entry if not in manifest """ def __init__(self, collection, iterator): self.iterator = iterator self.collection = collection def __iter__(self): return self def __next__(self): while True: audio_bytes, audio_filename = next(self.iterator) file_id, _ = os.path.splitext(os.path.basename(audio_filename)) if file_id in self.collection.mapping: return audio_bytes, audio_filename else: logging.warning(f"key not in manifest: {file_id}", mode=logging_mode.ONCE) class TarredAudioLoopOffsets: """ Loop over tarred audio files """ def __init__(self, collection, iterator): self.iterator = iterator self.collection = collection self.current_fn = None self.current_bytes = None self.offset_id = 0 def __iter__(self): return self def __next__(self): if self.current_fn is None: self.current_bytes, self.current_fn = next(self.iterator) self.offset_id = 0 else: offset_list = self.collection.mapping[self.current_fn] if len(offset_list) == self.offset_id + 1: self.current_bytes, self.current_fn = next(self.iterator) self.offset_id = 0 else: self.offset_id += 1 return self.current_bytes, self.current_fn, self.offset_id class TarredAudioTextDataset(IterableDataset): """ A similar Dataset to the AudioTextDataset, but which loads tarred audio files. Accepts a single comma-separated JSON manifest file (in the same style as for the AudioTextDataset), as well as the path(s) to the tarball(s) containing the wav files. Each line of the manifest should contain the information for one audio file, including at least the transcript and name of the audio file within the tarball. Valid formats for the audio_tar_filepaths argument include: (1) a single string that can be brace-expanded, e.g. 'path/to/audio.tar' or 'path/to/audio_{1..100}.tar.gz', or (2) a list of file paths that will not be brace-expanded, e.g. ['audio_1.tar', 'audio_2.tar', ...]. Note: For brace expansion in (1), there may be cases where `{x..y}` syntax can't be used due to shell. This occurs most commonly inside SLURM scripts. Therefore we provide a few equivalent replacements. Supported opening braces - { <=> (, [, < and the special tag _OP_. Supported closing braces - } <=> ), ], > and the special tag _CL_. For SLURM based tasks, we suggest the use of the special tags for ease of use. See the WebDataset documentation for more information about accepted data and input formats. If using multiple workers the number of shards should be divisible by world_size to ensure an even split among workers. If it is not divisible, logging will give a warning but training will proceed. In addition, if using mutiprocessing, each shard MUST HAVE THE SAME NUMBER OF ENTRIES after filtering is applied. We currently do not check for this, but your program may hang if the shards are uneven! Additionally, please note that the len() of this DataLayer is assumed to be the length of the manifest after filtering. An incorrect manifest length may lead to some DataLoader issues down the line. Args: audio_tar_filepaths: Either a list of audio tarball filepaths, or a string (can be brace-expandable). manifest_filepath (str): Path to the manifest. text_processor: TextProcessing object, sample_rate (int): Sample rate to resample loaded audio to int_values (bool): If true, load samples as 32-bit integers. Defauts to False. augmentor (nemo.collections.asr.parts.perturb.AudioAugmentor): An AudioAugmentor object used to augment loaded audio shuffle_n (int): How many samples to look ahead and load to be shuffled. See WebDataset documentation for more details. Defaults to 0. min_duration (float): Dataset parameter. All training files which have a duration less than min_duration are dropped. Note: Duration is read from the manifest JSON. Defaults to 0.1. max_duration (float): Dataset parameter. All training files which have a duration more than max_duration are dropped. Note: Duration is read from the manifest JSON. Defaults to None. blank_index (int): Blank character index, defaults to -1. unk_index (int): Unknown character index, defaults to -1. normalize (bool): Dataset parameter. Whether to use automatic text cleaning. It is highly recommended to manually clean text for best results. Defaults to True. trim (bool): Whether to use trim silence from beginning and end of audio signal using librosa.effects.trim(). Defaults to False. bos_id (id): Dataset parameter. Beginning of string symbol id used for seq2seq models. Defaults to None. eos_id (id): Dataset parameter. End of string symbol id used for seq2seq models. Defaults to None. pad_id (id): Token used to pad when collating samples in batches. If this is None, pads using 0s. Defaults to None. shard_strategy (str): Tarred dataset shard distribution strategy chosen as a str value during ddp. - `scatter`: The default shard strategy applied by WebDataset, where each node gets a unique set of shards, which are permanently pre-allocated and never changed at runtime. - `replicate`: Optional shard strategy, where each node gets all of the set of shards available in the tarred dataset, which are permanently pre-allocated and never changed at runtime. The benefit of replication is that it allows each node to sample data points from the entire dataset independently of other nodes, and reduces dependence on value of `shuffle_n`. :warning: Replicated strategy allows every node to sample the entire set of available tarfiles, and therefore more than one node may sample the same tarfile, and even sample the same data points! As such, there is no assured guarantee that all samples in the dataset will be sampled at least once during 1 epoch. Scattered strategy, on the other hand, on specific occasions (when the number of shards is not divisible with ``world_size``), will not sample the entire dataset. For these reasons it is not advisable to use tarred datasets as validation or test datasets. shard_manifests (bool): Whether or not to try / shard manifests. Defaults to False. global_rank (int): Worker rank, used for partitioning shards. Defaults to 0. world_size (int): Total number of processes, used for partitioning shards. Defaults to 0. :note: Below args are NLP-specific max_seq_length (int): maximum sequence length for each dataset examples. Examples will either be truncated to fit this length or dropped if they cannot be truncated. min_seq_length (int): min length of each data example in the dataset. Data examples will be dropped if they do not meet the min length requirements. tokens_to_generate (int): maximum tokens to generate in a single pass. Defaults to 128. context_key: Key to use for the context in your JSONL file answer_key: Key to use for the label in your JSONL file context_file: Optional[Union[List[str], str]] = None, if provided, will use this file to load random questions from, if question is not in manifest. """ def __init__( self, audio_tar_filepaths: Union[str, List[str]], manifest_filepath: str, text_processor: TextProcessing, sample_rate: int, int_values: bool = False, augmentor: Optional['nemo.collections.asr.parts.perturb.AudioAugmentor'] = None, shuffle_n: int = 0, min_duration: Optional[float] = None, max_duration: Optional[float] = None, trim: bool = False, shard_strategy: str = "scatter", shard_manifests: bool = False, global_rank: int = 0, world_size: int = 0, max_seq_length: int = 1024, min_seq_length: int = 1, tokens_to_generate: int = 128, pad_to_max_length: bool = False, context_key: str = 'context', answer_key: str = 'answer', context_file: Optional[Union[List[str], str]] = None, ): super().__init__() self.text_processor = text_processor self.max_seq_length = max_seq_length self.min_seq_length = min_seq_length self.is_megatron_iterable = True self.shard_manifests = shard_manifests self.tokens_to_generate = tokens_to_generate self.pad_to_max_length = pad_to_max_length # Shard manifests if necessary and possible and then expand the paths manifest_filepath = shard_manifests_if_needed( shard_manifests=shard_manifests, shard_strategy=shard_strategy, manifest_filepaths=manifest_filepath, world_size=world_size, global_rank=global_rank, ) # If necessary, cache manifests from object store cache_datastore_manifests(manifest_filepaths=manifest_filepath) self.collection = collections.SpeechLLMAudioTextCollection( manifests_files=manifest_filepath, min_duration=min_duration, max_duration=max_duration, index_by_file_id=True, context_file=context_file, context_key=context_key, answer_key=answer_key, ) self.len = self._compute_len() self.featurizer = WaveformFeaturizer(sample_rate=sample_rate, int_values=int_values, augmentor=augmentor) self.trim = trim audio_tar_filepaths = expand_sharded_filepaths( sharded_filepaths=audio_tar_filepaths, shard_strategy=shard_strategy, world_size=world_size, global_rank=global_rank, ) # Put together WebDataset self._dataset = wds.WebDataset(urls=audio_tar_filepaths, nodesplitter=None) if shuffle_n == 0: logging.info("WebDataset will not shuffle files within the tar files.") # Put together WebDataset pipeline self._dataset = wds.DataPipeline( wds.SimpleShardList(urls=audio_tar_filepaths), webdataset_split_by_workers, wds.shuffle(shuffle_n), wds.tarfile_to_samples(), wds.rename(audio=VALID_FILE_FORMATS, key='__key__'), wds.to_tuple('audio', 'key'), self._filter, self._loop_offsets, wds.map(self._build_sample), ) def _filter(self, iterator): """This function is used to remove samples that have been filtered out by ASRAudioText already. Otherwise, we would get a KeyError as _build_sample attempts to find the manifest entry for a sample that was filtered out (e.g. for duration). Note that if using multi-GPU training, filtering may lead to an imbalance in samples in each shard, which may make your code hang as one process will finish before the other. """ return TarredAudioFilter(self.collection, iterator) def _loop_offsets(self, iterator): """This function is used to iterate through utterances with different offsets for each file.""" return TarredAudioLoopOffsets(self.collection, iterator) def _collate_fn(self, batch): return _speechllm_audio_text_collate_fn( batch=batch, tokens_to_generate=self.tokens_to_generate, pad_to_max_length=self.pad_to_max_length, max_seq_length=self.max_seq_length, text_pad_id=self.text_processor.pad_id, ) def collate_fn(self, batch): # override collate_fn to skip type checking return self._collate_fn(batch) def _build_sample(self, tup): """Builds the training sample by combining the data from the WebDataset with the manifest info.""" audio_bytes, audio_filename, offset_id = tup if audio_filename is not None: # Grab manifest entry from self.manifest_preprocessor.collection file_id, _ = os.path.splitext(os.path.basename(audio_filename)) manifest_idx = self.collection.mapping[file_id][offset_id] manifest_entry = self.collection[manifest_idx] # init output dict output = {"idx": manifest_idx} offset = manifest_entry.offset if offset is None: offset = 0 # Convert audio bytes to IO stream for processing (for SoundFile to read) audio_filestream = io.BytesIO(audio_bytes) features = self.featurizer.process( audio_filestream, offset=offset, duration=manifest_entry.duration, trim=self.trim, orig_sr=manifest_entry.orig_sr, ) audio_filestream.close() # Audio features output["audio_signal"] = features output["audio_length"] = torch.tensor(features.shape[0]).long() else: # dummy features output["audio_signal"] = torch.zeros([80]) # accomodates normalize_batch output["audio_length"] = torch.tensor(80) # Text features text_data = self.text_processor(context=manifest_entry.context, output=manifest_entry.answer) output.update(text_data) output['metadata'] = { 'audio_filepath': audio_filename, 'offset': offset, 'duration': manifest_entry.duration, } return output def get_manifest_sample(self, sample_id): """ return manifest item given the index """ return self.collection[sample_id] def __iter__(self): return self._dataset.__iter__() def _compute_len(self): # TODO: need to figure out why here needs to be divided by world_size, while in ASR we don't need to. if self.shard_manifests and torch.distributed.is_available() and torch.distributed.is_initialized(): my_len = torch.tensor(len(self.collection), dtype=torch.int32).cuda() torch.distributed.all_reduce(my_len) my_len = my_len.int() // parallel_state.get_data_parallel_world_size() logging.info(f'Sharded manifests: Total length: {my_len}') else: my_len = len(self.collection) // parallel_state.get_data_parallel_world_size() return my_len def __len__(self): return self.len def get_tarred_audio_text_dataset( config, text_processor, augmentor, global_rank=0, world_size=1, shuffle_n=0, ): """ Get tarred audio to text dataset """ 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) bucketing_weights = config.get('bucketing_weights', None) # For upsampling buckets if bucketing_weights: for idx, weight in enumerate(bucketing_weights): if not isinstance(weight, int) or weight <= 0: raise ValueError(f"bucket weights must be positive integers") if len(manifest_filepaths) != len(tarred_audio_filepaths): raise ValueError( f"manifest_filepaths (length={len(manifest_filepaths)}) and tarred_audio_filepaths", f"(length={len(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") if 'max_utts' in config: raise ValueError('"max_utts" parameter is not supported for tarred datasets') 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 len(manifest_filepath) == 1: manifest_filepath = manifest_filepath[0] dataset = TarredAudioTextDataset( audio_tar_filepaths=tarred_audio_filepath, manifest_filepath=manifest_filepath, text_processor=text_processor, 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), trim=config.get('trim_silence', False), shard_strategy=config.get('tarred_shard_strategy', 'scatter'), shard_manifests=config.get('shard_manifests', False), global_rank=global_rank, world_size=world_size, max_seq_length=config.max_seq_length, min_seq_length=config.min_seq_length, tokens_to_generate=config.get('tokens_to_generate', 0), pad_to_max_length=config.get('pad_to_max_length', False), context_key=config.get('context_key', 'context'), answer_key=config.get('answer_key', 'answer'), context_file=config.get('context_file', None), ) if bucketing_weights: [datasets.append(dataset) for _ in range(bucketing_weights[dataset_idx])] else: datasets.append(dataset) with open_dict(config): # patch for bucketing tarred datasets config['batch_size'] = config.get("micro_batch_size", 1) return get_chain_dataset(datasets=datasets, ds_config=config, rank=global_rank) def get_concat_tarred_audio_text_dataset( config, text_processor, augmentor, global_rank=0, world_size=1, shuffle_n=0, ): """ Get concat tarred audio to text dataset """ tarred_audio_filepaths = config['tarred_audio_filepaths'] manifest_filepaths = config['manifest_filepath'] datasets = [] for dataset_idx, (tarred_audio_filepath, manifest_filepath) in enumerate( zip(tarred_audio_filepaths, manifest_filepaths) ): conf = copy.deepcopy(config) conf['manifest_filepath'] = manifest_filepath conf['tarred_audio_filepaths'] = tarred_audio_filepath context_files = config.get('context_file', None) if isinstance(context_files, ListConfig) and len(context_files) == len(manifest_filepaths): conf['context_file'] = context_files[dataset_idx] else: conf['context_file'] = context_files dataset = get_tarred_audio_text_dataset( config=conf, text_processor=text_processor, shuffle_n=shuffle_n, global_rank=global_rank, world_size=world_size, augmentor=augmentor, ) datasets.append(dataset) concat_sampling_probabilities = config.get('concat_sampling_probabilities', None) if not isinstance(concat_sampling_probabilities, ListConfig) or len(concat_sampling_probabilities) != len( datasets ): logging.info( f"concat_sampling_probabilities is not provided or is not of the same size as datasets," f"using uniform sampling: concat_sampling_probabilities={concat_sampling_probabilities}" ) concat_sampling_probabilities = [1.0 / len(datasets)] * len(datasets) dataset = ConcatDataset( datasets, sampling_technique=config.get('concat_sampling_technique', 'temperature'), sampling_temperature=config.get('concat_sampling_temperature', 5), sampling_scale=config.get('concat_sampling_scale', 1), sampling_probabilities=concat_sampling_probabilities, shuffle=config.get('concat_shuffle', True), seed=config.get('concat_sampling_seed', None), global_rank=global_rank, world_size=world_size, ) return dataset def get_tarred_audio_text_dataset_from_config( config: DictConfig, text_processor: TextProcessing, augmentor, global_rank: int = 0, world_size: int = 1, ): """ Get tarred dataset from config """ is_concat = config.get('is_concat', False) if is_concat: if 'concat_sampling_technique' in config and config['concat_sampling_technique'] is None: logging.warning( f"Concat dataset requires `concat_sampling_technique` but it was not provided. Config: {config}" ) return None data_parallel_size = parallel_state.get_data_parallel_world_size() num_micro_batches = config.global_batch_size // (config.micro_batch_size * data_parallel_size) global_batch_size_on_this_data_parallel_rank = num_micro_batches * config.micro_batch_size shuffle = config['shuffle'] shuffle_n = config.get('shuffle_n', 4 * global_batch_size_on_this_data_parallel_rank) if shuffle else 0 if is_concat: dataset = get_concat_tarred_audio_text_dataset( config=config, text_processor=text_processor, augmentor=augmentor, shuffle_n=shuffle_n, global_rank=global_rank, world_size=world_size, ) else: dataset = get_tarred_audio_text_dataset( config=config, text_processor=text_processor, augmentor=augmentor, shuffle_n=shuffle_n, global_rank=global_rank, world_size=world_size, ) return dataset def get_audio_text_dataset_from_config( manifest_filepath: str, config: DictConfig, text_processor: TextProcessing, augmentor, is_train, ): """ Get non-tarred aduio to text dataset from config """ if isinstance(config.manifest_filepath, str): manifest_filepath = config.manifest_filepath.split(',') else: manifest_filepath = config.manifest_filepath data_cls = MultiAudioTextDataset if config.get('audio_locator', None) else AudioTextDataset logging.info(f"Using `{data_cls.__name__}` for dataset") datasets = [] if is_train: # Construct the data prefix list for `get_datasets_weights_and_num_samples()` # that is of the format [weight1,file_name1,weight2,file_name2,...] concat_sampling_probabilities = config.get('concat_sampling_probabilities', None) if concat_sampling_probabilities is None: concat_sampling_probabilities = [1.0 / len(manifest_filepath)] * len(manifest_filepath) elif len(config.get('concat_sampling_probabilities', None)) != len(manifest_filepath): raise ValueError( ( f"concat_sampling_probabilities must be of the same size as manifest_filepath.", f"Provided size {len(config.concat_sampling_probabilities)},", f"number of datasets {len(manifest_filepath)}", ) ) data_prefix = [] for weight, prefix in zip(concat_sampling_probabilities, manifest_filepath): data_prefix.append(weight) data_prefix.append(prefix) num_samples_per_dataset = get_num_samples_from_files(manifest_filepath) num_train_samples = [len(manifest_filepath) * max(num_samples_per_dataset)] _, _, num_train_samples_per_dataset = get_datasets_weights_and_num_samples(data_prefix, num_train_samples) num_train_samples_after_blend = sum([x[0] for x in num_train_samples_per_dataset]) # for dataset_idx, (file_path, num_samples) in enumerate(zip(manifest_filepath, num_train_samples_per_dataset)): for dataset_idx, file_path in enumerate(manifest_filepath): context_file = config.get('context_file', None) if isinstance(context_file, ListConfig) and len(context_file) == len(manifest_filepath): context_file = context_file[dataset_idx] dataset = data_cls( manifest_filepath=file_path, text_processor=text_processor, sample_rate=config.sample_rate, int_values=config.get('int_values', False), augmentor=augmentor, max_duration=getattr(config, 'max_duration', None), min_duration=getattr(config, 'min_duration', None), max_utts=getattr(config, 'max_utts', -1), trim=getattr(config, 'trim_silence', False), channel_selector=getattr(config, 'channel_selector', None), max_seq_length=config.max_seq_length, min_seq_length=config.min_seq_length, tokens_to_generate=config.get('tokens_to_generate', 0), pad_to_max_length=config.get('pad_to_max_length', False), context_key=config.get('context_key', 'context'), answer_key=config.get('answer_key', 'answer'), context_file=context_file, ) datasets.append(dataset) if is_train: dataset = BlendableDataset( datasets=datasets, weights=concat_sampling_probabilities, size=num_train_samples_after_blend ) return dataset else: return datasets def get_datasets_weights_and_num_samples(data_prefix, num_samples): # The data prefix should be in the format of: # weight-1, data-prefix-1, weight-2, data-prefix-2, .. assert len(data_prefix) % 2 == 0 num_datasets = len(data_prefix) // 2 weights = [0] * num_datasets prefixes = [0] * num_datasets for i in range(num_datasets): weights[i] = float(data_prefix[2 * i]) prefixes[i] = (data_prefix[2 * i + 1]).strip() # Normalize weights weight_sum = 0.0 for weight in weights: weight_sum += weight assert weight_sum > 0.0 weights = [weight / weight_sum for weight in weights] # Add 0.5% (the 1.005 factor) so in case the bleding dataset does # not uniformly distribute the number of samples, we still have # samples left to feed to the network. # TODO: check data leakage between train/val/test? datasets_train_valid_test_num_samples = [] for weight in weights: # Comes here when we have seperate train,test and validation datasets. if isinstance(num_samples, int): datasets_train_valid_test_num_samples.append(int(math.ceil(num_samples * weight * 1.005))) else: datasets_train_valid_test_num_samples.append([int(math.ceil(val * weight * 1.005)) for val in num_samples]) return prefixes, weights, datasets_train_valid_test_num_samples