Source code for lhotse.dataset.unsupervised

import math
from typing import Any, Dict, List, Optional

import numpy as np
import torch
from import IterableDataset

from lhotse import RecordingSet, Seconds, compute_num_samples, validate
from import torchaudio_supports_ffmpeg
from import suppress_audio_loading_errors
from lhotse.augmentation import AugmentFn
from lhotse.cut import CutSet
from lhotse.dataset.collation import collate_audio, collate_features, collate_matrices
from lhotse.features import FeatureExtractor
from lhotse.features.kaldi.layers import _get_strided_batch_streaming

[docs] class UnsupervisedDataset( """ Dataset that contains no supervision - it only provides the features extracted from recordings. .. code-block:: { 'features': (B x T x F) tensor 'features_lens': (B, ) tensor } """
[docs] def __init__(self) -> None: super().__init__()
def __getitem__(self, cuts: CutSet) -> Dict[str, Any]: self._validate(cuts) features, features_lens = collate_features(cuts) return { "cuts": cuts, "features": features, "features_lens": features_lens, } def _validate(self, cuts: CutSet) -> None: validate(cuts) assert all(cut.has_features for cut in cuts)
[docs] class UnsupervisedWaveformDataset(UnsupervisedDataset): """ A variant of UnsupervisedDataset that provides waveform samples instead of features. The output is a tensor of shape (C, T), with C being the number of channels and T the number of audio samples. In this implementation, there will always be a single channel. Returns: .. code-block:: { 'audio': (B x NumSamples) float tensor 'audio_lens': (B, ) int tensor } """
[docs] def __init__(self, collate: bool = True) -> None: super().__init__() self.collate = collate
def __getitem__(self, cuts: CutSet) -> Dict[str, Any]: if self.collate: audio, audio_lens = collate_audio(cuts) return { "cuts": cuts, "audio": audio, "audio_lens": audio_lens, } else: remain_cuts = [] remain_audios = [] for c in cuts: with suppress_audio_loading_errors(): remain_audios.append(c.load_audio()) remain_cuts.append(c) return {"cuts": CutSet.from_cuts(remain_cuts), "audio": remain_audios} def _validate(self, cuts: CutSet) -> None: validate(cuts) assert all(cut.has_recording for cut in cuts)
[docs] class DynamicUnsupervisedDataset(UnsupervisedDataset): """ An example dataset that shows how to use on-the-fly feature extraction in Lhotse. It accepts two additional inputs - a FeatureExtractor and an optional WavAugmenter for time-domain data augmentation.. The output is approximately the same as that of the ``UnsupervisedDataset`` - there might be slight differences for ``MixedCut``s, because this dataset mixes them in the time domain, and ``UnsupervisedDataset`` does that in the feature domain. Cuts that are not mixed will yield identical results in both dataset classes. """
[docs] def __init__( self, feature_extractor: FeatureExtractor, augment_fn: Optional[AugmentFn] = None, ): super().__init__() self.feature_extractor = feature_extractor self.augment_fn = augment_fn
def __getitem__(self, cuts: CutSet) -> torch.Tensor: self._validate(cuts) def generate_cut(cuts: CutSet): for cut in cuts: with suppress_audio_loading_errors(): yield cut.compute_features( extractor=self.feature_extractor, augment_fn=self.augment_fn, ) features = collate_matrices(generate_cut(cuts)) return features def _validate(self, cuts: CutSet) -> None: validate(cuts) assert all(cut.has_recording for cut in cuts)
[docs] class RecordingChunkIterableDataset(IterableDataset): """ This dataset iterates over chunks of a recording, for each recording provided. It supports setting a chunk_shift < chunk_size to run model predictions on overlapping audio chunks. The format of yielded items is the following:: { "recording_id": str "begin_time": tensor with dtype=float32 shape=(1,) "end_time": tensor with dtype=float32 shape=(1,) "audio": tensor with dtype=float32 shape=(chunk_size_in_samples,) } Unlike most other datasets in Lhotse, this dataset does not yield batched items, and should be used like the following:: >>> recordings = RecordingSet.from_file("my-recordings.jsonl.gz") ... dataset = RecordingChunkIterableDataset(recordings, chunk_size=30.0, chunk_shift=25.0) ... dloader = ... dataset, ... batch_size=32, ... collate_fn=audio_chunk_collate, ... worker_init_fn=audio_chunk_worker_init_fn, ... ) """
[docs] def __init__( self, recordings: RecordingSet, chunk_size: Seconds, chunk_shift: Seconds ) -> None: self.recordings = list(recordings) self.chunk_size = chunk_size self.chunk_shift = chunk_shift self.start = 0 self.end = len(self.recordings) self.validate()
[docs] def validate(self) -> None: for r in self.recordings: assert ( len(r.sources) == 1 ), f"We currently don't support multi-source audio in this dataset (got {len(r.sources)} sources in recording {})." assert ( r.sources[0].type == "file" ), f"We currently only support 'file' AudioSource type in this dataset (got: {r.sources[0].type} in recording {})." assert ( r.num_channels == 1 ), f"We currently only support single-channel audio in this dataset (got {r.num_channels} channels in recording {})."
def __iter__(self): import soundfile as sf for r in self.recordings[self.start : self.end]: chunk_size = compute_num_samples(self.chunk_size, r.sampling_rate) chunk_overlap = compute_num_samples( self.chunk_size - self.chunk_shift, r.sampling_rate ) begin_time = 0 end_time = self.chunk_size with sf.SoundFile(r.sources[0].source, "rb") as stream: for chunk in stream.blocks( chunk_size, overlap=chunk_overlap, dtype=np.float32 ): chunk = torch.as_tensor(chunk) yield { "recording_id":, "begin_time": torch.as_tensor(begin_time, dtype=torch.float32), "end_time": torch.as_tensor(end_time, dtype=torch.float32), "audio": chunk, } begin_time += self.chunk_shift end_time = begin_time + self.chunk_size
[docs] def audio_chunk_collate(batch: List[Dict]): from import default_collate audios = [d.pop("audio") for d in batch] out = default_collate(batch) maxlen = max(a.shape[0] for a in audios) audio = torch.zeros((len(audios), maxlen)) for i, a in enumerate(audios): audio[i, : a.shape[0]] = torch.as_tensor(a) out["audio"] = audio return out
[docs] def audio_chunk_worker_init_fn(worker_id): worker_info = dataset = worker_info.dataset # the dataset copy in this worker process overall_start = dataset.start overall_end = dataset.end # configure the dataset to only process the split workload per_worker = int( math.ceil((overall_end - overall_start) / float(worker_info.num_workers)) ) worker_id = dataset.start = overall_start + worker_id * per_worker dataset.end = min(dataset.start + per_worker, overall_end)