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"""Class containing utils for the ASR system."""
import os
from enum import Enum
from typing import TypedDict
import numpy as np
import torch
import torchaudio
from tokenizers import Tokenizer
from torch.utils.data import Dataset
from swr2_asr.tokenizer import CharTokenizer, TokenizerType
train_audio_transforms = torch.nn.Sequential(
torchaudio.transforms.MelSpectrogram(sample_rate=16000, n_mels=128),
torchaudio.transforms.FrequencyMasking(freq_mask_param=30),
torchaudio.transforms.TimeMasking(time_mask_param=100),
)
# create enum specifiying dataset splits
class MLSSplit(str, Enum):
"""Enum specifying dataset as they are defined in the
Multilingual LibriSpeech dataset"""
train = "train"
test = "test"
dev = "dev"
class Split(str, Enum):
"""Extending the MLSSplit class to allow for a custom validatio split"""
train = "train"
valid = "valid"
test = "test"
dev = "dev"
def split_to_mls_split(split: Split) -> MLSSplit:
"""Converts the custom split to a MLSSplit"""
if split == Split.valid:
return MLSSplit.train
else:
return split # type: ignore
class Sample(TypedDict):
"""Type for a sample in the dataset"""
waveform: torch.Tensor
spectrogram: torch.Tensor
input_length: int
utterance: torch.Tensor
utterance_length: int
sample_rate: int
speaker_id: str
book_id: str
chapter_id: str
class MLSDataset(Dataset):
"""Custom Dataset for reading Multilingual LibriSpeech
Attributes:
dataset_path (str):
path to the dataset
language (str):
language of the dataset
split (Split):
split of the dataset
mls_split (MLSSplit):
split of the dataset as defined in the Multilingual LibriSpeech dataset
dataset_lookup (list):
list of dicts containing the speakerid, bookid, chapterid and utterance
directory structure:
<dataset_path>
├── <language>
│ ├── train
│ │ ├── transcripts.txt
│ │ └── audio
│ │ └── <speakerid>
│ │ └── <bookid>
│ │ └── <speakerid>_<bookid>_<chapterid>.opus / .flac
each line in transcripts.txt has the following format:
<speakerid>_<bookid>_<chapterid> <utterance>
"""
def __init__(self, dataset_path: str, language: str, split: Split, download: bool):
"""Initializes the dataset"""
self.dataset_path = dataset_path
self.language = language
self.file_ext = ".opus" if "opus" in language else ".flac"
self.mls_split: MLSSplit = split_to_mls_split(split) # split path on disk
self.split: Split = split # split used internally
self.dataset_lookup = []
self.tokenizer: type[TokenizerType]
self._handle_download_dataset(download)
self._validate_local_directory()
transcripts_path = os.path.join(dataset_path, language, self.mls_split, "transcripts.txt")
with open(transcripts_path, "r", encoding="utf-8") as script_file:
# read all lines in transcripts.txt
transcripts = script_file.readlines()
# split each line into (<speakerid>_<bookid>_<chapterid>, <utterance>)
transcripts = [line.strip().split("\t", 1) for line in transcripts] # type: ignore
utterances = [utterance.strip() for _, utterance in transcripts] # type: ignore
identifier = [identifier.strip() for identifier, _ in transcripts] # type: ignore
identifier = [path.split("_") for path in identifier]
if self.split == Split.valid:
np.random.seed(42)
indices = np.random.choice(len(utterances), int(len(utterances) * 0.2))
utterances = [utterances[i] for i in indices]
identifier = [identifier[i] for i in indices]
elif self.split == Split.train:
np.random.seed(42)
indices = np.random.choice(len(utterances), int(len(utterances) * 0.8))
utterances = [utterances[i] for i in indices]
identifier = [identifier[i] for i in indices]
self.dataset_lookup = [
{
"speakerid": path[0],
"bookid": path[1],
"chapterid": path[2],
"utterance": utterance,
}
for path, utterance in zip(identifier, utterances, strict=False)
]
def set_tokenizer(self, tokenizer: type[TokenizerType]):
"""Sets the tokenizer"""
self.tokenizer = tokenizer
self.calc_paddings()
def _handle_download_dataset(self, download: bool):
"""Download the dataset"""
if not os.path.exists(os.path.join(self.dataset_path, self.language)) and download:
os.makedirs(self.dataset_path)
url = f"https://dl.fbaipublicfiles.com/mls/{self.language}.tar.gz"
torch.hub.download_url_to_file(url, self.dataset_path)
elif not os.path.exists(os.path.join(self.dataset_path, self.language)) and not download:
raise ValueError("Dataset not found. Set download to True to download it")
def _validate_local_directory(self):
# check if dataset_path exists
if not os.path.exists(self.dataset_path):
raise ValueError("Dataset path does not exist")
if not os.path.exists(os.path.join(self.dataset_path, self.language)):
raise ValueError("Language not found in dataset")
if not os.path.exists(os.path.join(self.dataset_path, self.language, self.mls_split)):
raise ValueError("Split not found in dataset")
def calc_paddings(self):
"""Sets the maximum length of the spectrogram"""
# check if dataset has been loaded and tokenizer has been set
if not self.dataset_lookup:
raise ValueError("Dataset not loaded")
if not self.tokenizer:
raise ValueError("Tokenizer not set")
max_spec_length = 0
max_uterance_length = 0
for sample in self.dataset_lookup:
spec_length = sample["spectrogram"].shape[0]
if spec_length > max_spec_length:
max_spec_length = spec_length
utterance_length = sample["utterance"].shape[0]
if utterance_length > max_uterance_length:
max_uterance_length = utterance_length
self.max_spec_length = max_spec_length
self.max_utterance_length = max_uterance_length
def __len__(self):
"""Returns the length of the dataset"""
return len(self.dataset_lookup)
def __getitem__(self, idx: int) -> Sample:
"""One sample"""
# get the utterance
utterance = self.dataset_lookup[idx]["utterance"]
# get the audio file
audio_path = os.path.join(
self.dataset_path,
self.language,
self.mls_split,
"audio",
self.dataset_lookup[idx]["speakerid"],
self.dataset_lookup[idx]["bookid"],
"_".join(
[
self.dataset_lookup[idx]["speakerid"],
self.dataset_lookup[idx]["bookid"],
self.dataset_lookup[idx]["chapterid"],
]
)
+ self.file_ext,
)
waveform, sample_rate = torchaudio.load(audio_path) # type: ignore
# TODO: figure out if we have to resample or not
# TODO: pad correctly (manually)
spec = torchaudio.transforms.MelSpectrogram(sample_rate=16000, n_mels=128)(waveform).squeeze(0).transpose(0, 1)
print(f"spec.shape: {spec.shape}")
input_length = spec.shape[0] // 2
spec = (
torch.nn.functional.pad(spec, pad=(0, self.max_spec_length), mode="constant", value=0)
.unsqueeze(1)
.transpose(2, 3)
)
utterance_length = len(utterance)
self.tokenizer.enable_padding()
utterance = self.tokenizer.encode(
utterance,
).ids
utterance = torch.Tensor(utterance)
return Sample(
# TODO: add flag to only return spectrogram or waveform or both
waveform=waveform,
spectrogram=spec,
input_length=input_length,
utterance=utterance,
utterance_length=utterance_length,
sample_rate=sample_rate,
speaker_id=self.dataset_lookup[idx]["speakerid"],
book_id=self.dataset_lookup[idx]["bookid"],
chapter_id=self.dataset_lookup[idx]["chapterid"],
)
def download(self, dataset_path: str, language: str):
"""Download the dataset"""
os.makedirs(dataset_path)
url = f"https://dl.fbaipublicfiles.com/mls/{language}.tar.gz"
torch.hub.download_url_to_file(url, dataset_path)
if __name__ == "__main__":
dataset_path = "/Volumes/pherkel/SWR2-ASR"
language = "mls_german_opus"
split = Split.train
download = False
dataset = MLSDataset(dataset_path, language, split, download)
tok = Tokenizer.from_file("data/tokenizers/bpe_tokenizer_german_3000.json")
dataset.set_tokenizer(tok)
dataset.calc_paddings()
print(dataset[41]["spectrogram"].shape)
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