superb / superb.py
albertvillanova's picture
Remove deprecated tasks (#5)
c3e21b5 verified
# coding=utf-8
# Copyright 2021 The TensorFlow Datasets Authors and the HuggingFace Datasets Authors.
#
# 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.
# Lint as: python3
"""SUPERB: Speech processing Universal PERformance Benchmark."""
import csv
import glob
import os
import textwrap
from dataclasses import dataclass
import datasets
_CITATION = """\
@article{DBLP:journals/corr/abs-2105-01051,
author = {Shu{-}Wen Yang and
Po{-}Han Chi and
Yung{-}Sung Chuang and
Cheng{-}I Jeff Lai and
Kushal Lakhotia and
Yist Y. Lin and
Andy T. Liu and
Jiatong Shi and
Xuankai Chang and
Guan{-}Ting Lin and
Tzu{-}Hsien Huang and
Wei{-}Cheng Tseng and
Ko{-}tik Lee and
Da{-}Rong Liu and
Zili Huang and
Shuyan Dong and
Shang{-}Wen Li and
Shinji Watanabe and
Abdelrahman Mohamed and
Hung{-}yi Lee},
title = {{SUPERB:} Speech processing Universal PERformance Benchmark},
journal = {CoRR},
volume = {abs/2105.01051},
year = {2021},
url = {https://arxiv.org/abs/2105.01051},
archivePrefix = {arXiv},
eprint = {2105.01051},
timestamp = {Thu, 01 Jul 2021 13:30:22 +0200},
biburl = {https://dblp.org/rec/journals/corr/abs-2105-01051.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
"""
_DESCRIPTION = """\
Self-supervised learning (SSL) has proven vital for advancing research in
natural language processing (NLP) and computer vision (CV). The paradigm
pretrains a shared model on large volumes of unlabeled data and achieves
state-of-the-art (SOTA) for various tasks with minimal adaptation. However, the
speech processing community lacks a similar setup to systematically explore the
paradigm. To bridge this gap, we introduce Speech processing Universal
PERformance Benchmark (SUPERB). SUPERB is a leaderboard to benchmark the
performance of a shared model across a wide range of speech processing tasks
with minimal architecture changes and labeled data. Among multiple usages of the
shared model, we especially focus on extracting the representation learned from
SSL due to its preferable re-usability. We present a simple framework to solve
SUPERB tasks by learning task-specialized lightweight prediction heads on top of
the frozen shared model. Our results demonstrate that the framework is promising
as SSL representations show competitive generalizability and accessibility
across SUPERB tasks. We release SUPERB as a challenge with a leaderboard and a
benchmark toolkit to fuel the research in representation learning and general
speech processing.
Note that in order to limit the required storage for preparing this dataset, the
audio is stored in the .wav format and is not converted to a float32 array. To
convert the audio file to a float32 array, please make use of the `.map()`
function as follows:
```python
import soundfile as sf
def map_to_array(batch):
speech_array, _ = sf.read(batch["file"])
batch["speech"] = speech_array
return batch
dataset = dataset.map(map_to_array, remove_columns=["file"])
```
"""
class SuperbConfig(datasets.BuilderConfig):
"""BuilderConfig for Superb."""
def __init__(
self,
features,
url,
data_url=None,
supervised_keys=None,
**kwargs,
):
super().__init__(version=datasets.Version("1.9.0", ""), **kwargs)
self.features = features
self.data_url = data_url
self.url = url
self.supervised_keys = supervised_keys
class Superb(datasets.GeneratorBasedBuilder):
"""Superb dataset."""
BUILDER_CONFIGS = [
SuperbConfig(
name="asr",
description=textwrap.dedent(
"""\
ASR transcribes utterances into words. While PR analyzes the
improvement in modeling phonetics, ASR reflects the significance of
the improvement in a real-world scenario. LibriSpeech
train-clean-100/dev-clean/test-clean subsets are used for
training/validation/testing. The evaluation metric is word error
rate (WER)."""
),
features=datasets.Features(
{
"file": datasets.Value("string"),
"audio": datasets.Audio(sampling_rate=16_000),
"text": datasets.Value("string"),
"speaker_id": datasets.Value("int64"),
"chapter_id": datasets.Value("int64"),
"id": datasets.Value("string"),
}
),
supervised_keys=("file", "text"),
url="http://www.openslr.org/12",
data_url="http://www.openslr.org/resources/12/",
),
SuperbConfig(
name="ks",
description=textwrap.dedent(
"""\
Keyword Spotting (KS) detects preregistered keywords by classifying utterances into a predefined set of
words. The task is usually performed on-device for the fast response time. Thus, accuracy, model size, and
inference time are all crucial. SUPERB uses the widely used Speech Commands dataset v1.0 for the task.
The dataset consists of ten classes of keywords, a class for silence, and an unknown class to include the
false positive. The evaluation metric is accuracy (ACC)"""
),
features=datasets.Features(
{
"file": datasets.Value("string"),
"audio": datasets.Audio(sampling_rate=16_000),
"label": datasets.ClassLabel(
names=[
"yes",
"no",
"up",
"down",
"left",
"right",
"on",
"off",
"stop",
"go",
"_silence_",
"_unknown_",
]
),
}
),
supervised_keys=("file", "label"),
url="https://www.tensorflow.org/datasets/catalog/speech_commands",
data_url="http://download.tensorflow.org/data/{filename}",
),
SuperbConfig(
name="ic",
description=textwrap.dedent(
"""\
Intent Classification (IC) classifies utterances into predefined classes to determine the intent of
speakers. SUPERB uses the Fluent Speech Commands dataset, where each utterance is tagged with three intent
labels: action, object, and location. The evaluation metric is accuracy (ACC)."""
),
features=datasets.Features(
{
"file": datasets.Value("string"),
"audio": datasets.Audio(sampling_rate=16_000),
"speaker_id": datasets.Value("string"),
"text": datasets.Value("string"),
"action": datasets.ClassLabel(
names=["activate", "bring", "change language", "deactivate", "decrease", "increase"]
),
"object": datasets.ClassLabel(
names=[
"Chinese",
"English",
"German",
"Korean",
"heat",
"juice",
"lamp",
"lights",
"music",
"newspaper",
"none",
"shoes",
"socks",
"volume",
]
),
"location": datasets.ClassLabel(names=["bedroom", "kitchen", "none", "washroom"]),
}
),
supervised_keys=None,
url="https://fluent.ai/fluent-speech-commands-a-dataset-for-spoken-language-understanding-research/",
data_url="http://fluent.ai:2052/jf8398hf30f0381738rucj3828chfdnchs.tar.gz",
),
SuperbConfig(
name="si",
description=textwrap.dedent(
"""\
Speaker Identification (SI) classifies each utterance for its speaker identity as a multi-class
classification, where speakers are in the same predefined set for both training and testing. The widely
used VoxCeleb1 dataset is adopted, and the evaluation metric is accuracy (ACC)."""
),
features=datasets.Features(
{
"file": datasets.Value("string"),
"audio": datasets.Audio(sampling_rate=16_000),
# VoxCeleb1 contains 1251 speaker IDs in range ["id10001",..."id11251"]
"label": datasets.ClassLabel(names=[f"id{i + 10001}" for i in range(1251)]),
}
),
supervised_keys=("file", "label"),
url="https://www.robots.ox.ac.uk/~vgg/data/voxceleb/vox1.html",
),
SuperbConfig(
name="sd",
description=textwrap.dedent(
"""\
Speaker Diarization (SD) predicts `who is speaking when` for each timestamp, and multiple speakers can
speak simultaneously. The model has to encode rich speaker characteristics for each frame and should be
able to represent mixtures of signals. [LibriMix] is adopted where LibriSpeech
train-clean-100/dev-clean/test-clean are used to generate mixtures for training/validation/testing.
We focus on the two-speaker scenario as the first step. The time-coded speaker labels were generated using
alignments from Kaldi LibriSpeech ASR model. The evaluation metric is diarization error rate (DER)."""
),
features=datasets.Features(
{
"record_id": datasets.Value("string"),
"file": datasets.Value("string"),
"audio": datasets.Audio(sampling_rate=16_000),
"start": datasets.Value("int64"),
"end": datasets.Value("int64"),
"speakers": [
{
"speaker_id": datasets.Value("string"),
"start": datasets.Value("int64"),
"end": datasets.Value("int64"),
}
],
}
), # TODO
supervised_keys=None, # TODO
url="https://github.com/ftshijt/LibriMix",
data_url="https://huggingface.co/datasets/superb/superb-data/resolve/main/sd/{split}/{filename}",
),
SuperbConfig(
name="er",
description=textwrap.dedent(
"""\
Emotion Recognition (ER) predicts an emotion class for each utterance. The most widely used ER dataset
IEMOCAP is adopted, and we follow the conventional evaluation protocol: we drop the unbalanced emotion
classes to leave the final four classes with a similar amount of data points and cross-validate on five
folds of the standard splits. The evaluation metric is accuracy (ACC)."""
),
features=datasets.Features(
{
"file": datasets.Value("string"),
"audio": datasets.Audio(sampling_rate=16_000),
"label": datasets.ClassLabel(names=["neu", "hap", "ang", "sad"]),
}
),
supervised_keys=("file", "label"),
url="https://sail.usc.edu/iemocap/",
),
]
@property
def manual_download_instructions(self):
if self.config.name == "si":
return textwrap.dedent(
"""
Please download the VoxCeleb dataset using the following script,
which should create `VoxCeleb1/wav/id*` directories for both train and test speakers`:
```
mkdir VoxCeleb1
cd VoxCeleb1
wget https://thor.robots.ox.ac.uk/~vgg/data/voxceleb/vox1a/vox1_dev_wav_partaa
wget https://thor.robots.ox.ac.uk/~vgg/data/voxceleb/vox1a/vox1_dev_wav_partab
wget https://thor.robots.ox.ac.uk/~vgg/data/voxceleb/vox1a/vox1_dev_wav_partac
wget https://thor.robots.ox.ac.uk/~vgg/data/voxceleb/vox1a/vox1_dev_wav_partad
cat vox1_dev* > vox1_dev_wav.zip
unzip vox1_dev_wav.zip
wget https://thor.robots.ox.ac.uk/~vgg/data/voxceleb/vox1a/vox1_test_wav.zip
unzip vox1_test_wav.zip
# download the official SUPERB train-dev-test split
wget https://raw.githubusercontent.com/s3prl/s3prl/master/s3prl/downstream/voxceleb1/veri_test_class.txt
```"""
)
elif self.config.name == "er":
return textwrap.dedent(
"""
Please download the IEMOCAP dataset after submitting the request form here:
https://sail.usc.edu/iemocap/iemocap_release.htm
Having downloaded the dataset you can extract it with `tar -xvzf IEMOCAP_full_release.tar.gz`
which should create a folder called `IEMOCAP_full_release`
"""
)
return None
def _info(self):
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=self.config.features,
supervised_keys=self.config.supervised_keys,
homepage=self.config.url,
citation=_CITATION,
)
def _split_generators(self, dl_manager):
if self.config.name == "asr":
_DL_URLS = {
"dev": self.config.data_url + "dev-clean.tar.gz",
"test": self.config.data_url + "test-clean.tar.gz",
"train": self.config.data_url + "train-clean-100.tar.gz",
}
archive_path = dl_manager.download_and_extract(_DL_URLS)
return [
datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"archive_path": archive_path["train"]}),
datasets.SplitGenerator(
name=datasets.Split.VALIDATION, gen_kwargs={"archive_path": archive_path["dev"]}
),
datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"archive_path": archive_path["test"]}),
]
elif self.config.name == "ks":
_DL_URLS = {
"train_val_test": self.config.data_url.format(filename="speech_commands_v0.01.tar.gz"),
"test": self.config.data_url.format(filename="speech_commands_test_set_v0.01.tar.gz"),
}
archive_path = dl_manager.download_and_extract(_DL_URLS)
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={"archive_path": archive_path["train_val_test"], "split": "train"},
),
datasets.SplitGenerator(
name=datasets.Split.VALIDATION,
gen_kwargs={"archive_path": archive_path["train_val_test"], "split": "val"},
),
datasets.SplitGenerator(
name=datasets.Split.TEST, gen_kwargs={"archive_path": archive_path["test"], "split": "test"}
),
]
elif self.config.name == "ic":
archive_path = dl_manager.download_and_extract(self.config.data_url)
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={"archive_path": archive_path, "split": "train"},
),
datasets.SplitGenerator(
name=datasets.Split.VALIDATION,
gen_kwargs={"archive_path": archive_path, "split": "valid"},
),
datasets.SplitGenerator(
name=datasets.Split.TEST, gen_kwargs={"archive_path": archive_path, "split": "test"}
),
]
elif self.config.name == "si":
manual_dir = os.path.abspath(os.path.expanduser(dl_manager.manual_dir))
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={"archive_path": manual_dir, "split": 1},
),
datasets.SplitGenerator(
name=datasets.Split.VALIDATION,
gen_kwargs={"archive_path": manual_dir, "split": 2},
),
datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"archive_path": manual_dir, "split": 3}),
]
elif self.config.name == "sd":
splits = ["train", "dev", "test"]
_DL_URLS = {
split: {
filename: self.config.data_url.format(split=split, filename=filename)
for filename in ["reco2dur", "segments", "utt2spk", "wav.zip"]
}
for split in splits
}
archive_path = dl_manager.download_and_extract(_DL_URLS)
return [
datasets.SplitGenerator(
name=datasets.NamedSplit(split), gen_kwargs={"archive_path": archive_path[split], "split": split}
)
for split in splits
]
elif self.config.name == "er":
manual_dir = os.path.abspath(os.path.expanduser(dl_manager.manual_dir))
return [
datasets.SplitGenerator(
name=f"session{i}",
gen_kwargs={"archive_path": manual_dir, "split": i},
)
for i in range(1, 6)
]
def _generate_examples(self, archive_path, split=None):
"""Generate examples."""
if self.config.name == "asr":
transcripts_glob = os.path.join(archive_path, "LibriSpeech", "*", "*", "*", "*.txt")
key = 0
for transcript_path in sorted(glob.glob(transcripts_glob)):
transcript_dir_path = os.path.dirname(transcript_path)
with open(transcript_path, "r", encoding="utf-8") as f:
for line in f:
line = line.strip()
id_, transcript = line.split(" ", 1)
audio_file = f"{id_}.flac"
speaker_id, chapter_id = [int(el) for el in id_.split("-")[:2]]
audio_path = os.path.join(transcript_dir_path, audio_file)
yield key, {
"id": id_,
"speaker_id": speaker_id,
"chapter_id": chapter_id,
"file": audio_path,
"audio": audio_path,
"text": transcript,
}
key += 1
elif self.config.name == "ks":
words = ["yes", "no", "up", "down", "left", "right", "on", "off", "stop", "go"]
splits = _split_ks_files(archive_path, split)
for key, audio_file in enumerate(sorted(splits[split])):
base_dir, file_name = os.path.split(audio_file)
_, word = os.path.split(base_dir)
if word in words:
label = word
elif word == "_silence_" or word == "_background_noise_":
label = "_silence_"
else:
label = "_unknown_"
yield key, {"file": audio_file, "audio": audio_file, "label": label}
elif self.config.name == "ic":
root_path = os.path.join(archive_path, "fluent_speech_commands_dataset")
csv_path = os.path.join(root_path, "data", f"{split}_data.csv")
with open(csv_path, encoding="utf-8") as csv_file:
csv_reader = csv.reader(csv_file, delimiter=",", skipinitialspace=True)
next(csv_reader)
for row in csv_reader:
key, file_path, speaker_id, text, action, object_, location = row
audio_path = os.path.join(root_path, file_path)
yield key, {
"file": audio_path,
"audio": audio_path,
"speaker_id": speaker_id,
"text": text,
"action": action,
"object": object_,
"location": location,
}
elif self.config.name == "si":
wav_path = os.path.join(archive_path, "wav")
splits_path = os.path.join(archive_path, "veri_test_class.txt")
with open(splits_path, "r", encoding="utf-8") as f:
for key, line in enumerate(f):
split_id, file_path = line.strip().split(" ")
if int(split_id) != split:
continue
speaker_id = file_path.split("/")[0]
audio_path = os.path.join(wav_path, file_path)
yield key, {
"file": audio_path,
"audio": audio_path,
"label": speaker_id,
}
elif self.config.name == "sd":
data = SdData(archive_path)
args = SdArgs()
chunk_indices = _generate_chunk_indices(data, args, split=split)
if split != "test":
for key, (rec, st, ed) in enumerate(chunk_indices):
speakers = _get_speakers(rec, data, args)
yield key, {
"record_id": rec,
"file": data.wavs[rec],
"audio": data.wavs[rec],
"start": st,
"end": ed,
"speakers": speakers,
}
else:
key = 0
for rec in chunk_indices:
for rec, st, ed in chunk_indices[rec]:
speakers = _get_speakers(rec, data, args)
yield key, {
"record_id": rec,
"file": data.wavs[rec],
"audio": data.wavs[rec],
"start": st,
"end": ed,
"speakers": speakers,
}
key += 1
elif self.config.name == "er":
root_path = os.path.join(archive_path, f"Session{split}")
wav_path = os.path.join(root_path, "sentences", "wav")
labels_path = os.path.join(root_path, "dialog", "EmoEvaluation", "*.txt")
emotions = ["neu", "hap", "ang", "sad", "exc"]
key = 0
for labels_file in sorted(glob.glob(labels_path)):
with open(labels_file, "r", encoding="utf-8") as f:
for line in f:
if line[0] != "[":
continue
_, filename, emo, _ = line.split("\t")
if emo not in emotions:
continue
wav_subdir = filename.rsplit("_", 1)[0]
filename = f"{filename}.wav"
audio_path = os.path.join(wav_path, wav_subdir, filename)
yield key, {
"file": audio_path,
"audio": audio_path,
"label": emo.replace("exc", "hap"),
}
key += 1
class SdData:
def __init__(self, data_dir):
"""Load sd data."""
self.segments = self._load_segments_rechash(data_dir["segments"])
self.utt2spk = self._load_utt2spk(data_dir["utt2spk"])
self.wavs = self._load_wav_zip(data_dir["wav.zip"])
self.reco2dur = self._load_reco2dur(data_dir["reco2dur"])
def _load_segments_rechash(self, segments_file):
"""Load segments file as dict with recid index."""
ret = {}
if not os.path.exists(segments_file):
return None
with open(segments_file, encoding="utf-8") as f:
for line in f:
utt, rec, st, et = line.strip().split()
if rec not in ret:
ret[rec] = []
ret[rec].append({"utt": utt, "st": float(st), "et": float(et)})
return ret
def _load_wav_zip(self, wav_zip):
"""Return dictionary { rec: wav_rxfilename }."""
wav_dir = os.path.join(wav_zip, "wav")
return {
os.path.splitext(filename)[0]: os.path.join(wav_dir, filename) for filename in sorted(os.listdir(wav_dir))
}
def _load_utt2spk(self, utt2spk_file):
"""Returns dictionary { uttid: spkid }."""
with open(utt2spk_file, encoding="utf-8") as f:
lines = [line.strip().split(None, 1) for line in f]
return {x[0]: x[1] for x in lines}
def _load_reco2dur(self, reco2dur_file):
"""Returns dictionary { recid: duration }."""
if not os.path.exists(reco2dur_file):
return None
with open(reco2dur_file, encoding="utf-8") as f:
lines = [line.strip().split(None, 1) for line in f]
return {x[0]: float(x[1]) for x in lines}
@dataclass
class SdArgs:
chunk_size: int = 2000
frame_shift: int = 160
subsampling: int = 1
label_delay: int = 0
num_speakers: int = 2
rate: int = 16000
use_last_samples: bool = True
def _generate_chunk_indices(data, args, split=None):
chunk_indices = [] if split != "test" else {}
# make chunk indices: filepath, start_frame, end_frame
for rec in data.wavs:
data_len = int(data.reco2dur[rec] * args.rate / args.frame_shift)
data_len = int(data_len / args.subsampling)
if split == "test":
chunk_indices[rec] = []
if split != "test":
for st, ed in _gen_frame_indices(
data_len,
args.chunk_size,
args.chunk_size,
args.use_last_samples,
label_delay=args.label_delay,
subsampling=args.subsampling,
):
chunk_indices.append((rec, st * args.subsampling, ed * args.subsampling))
else:
for st, ed in _gen_chunk_indices(data_len, args.chunk_size):
chunk_indices[rec].append((rec, st * args.subsampling, ed * args.subsampling))
return chunk_indices
def _count_frames(data_len, size, step):
# no padding at edges, last remaining samples are ignored
return int((data_len - size + step) / step)
def _gen_frame_indices(data_length, size=2000, step=2000, use_last_samples=False, label_delay=0, subsampling=1):
i = -1
for i in range(_count_frames(data_length, size, step)):
yield i * step, i * step + size
if use_last_samples and i * step + size < data_length:
if data_length - (i + 1) * step - subsampling * label_delay > 0:
yield (i + 1) * step, data_length
def _gen_chunk_indices(data_len, chunk_size):
step = chunk_size
start = 0
while start < data_len:
end = min(data_len, start + chunk_size)
yield start, end
start += step
def _get_speakers(rec, data, args):
return [
{
"speaker_id": data.utt2spk[segment["utt"]],
"start": round(segment["st"] * args.rate / args.frame_shift),
"end": round(segment["et"] * args.rate / args.frame_shift),
}
for segment in data.segments[rec]
]
def _split_ks_files(archive_path, split):
audio_path = os.path.join(archive_path, "**", "*.wav")
audio_paths = glob.glob(audio_path)
if split == "test":
# use all available files for the test archive
return {"test": audio_paths}
val_list_file = os.path.join(archive_path, "validation_list.txt")
test_list_file = os.path.join(archive_path, "testing_list.txt")
with open(val_list_file, encoding="utf-8") as f:
val_paths = f.read().strip().splitlines()
val_paths = [os.path.join(archive_path, p) for p in val_paths]
with open(test_list_file, encoding="utf-8") as f:
test_paths = f.read().strip().splitlines()
test_paths = [os.path.join(archive_path, p) for p in test_paths]
# the paths for the train set is just whichever paths that do not exist in
# either the test or validation splits
train_paths = list(set(audio_paths) - set(val_paths) - set(test_paths))
return {"train": train_paths, "val": val_paths}