Duplicate from google/speech_commands

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README.md

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+---
+annotations_creators:
+- other
+language_creators:
+- crowdsourced
+language:
+- en
+license:
+- cc-by-4.0
+multilinguality:
+- monolingual
+size_categories:
+- 100K<n<1M
+- 10K<n<100K
+source_datasets:
+- original
+task_categories:
+- audio-classification
+task_ids:
+- keyword-spotting
+pretty_name: SpeechCommands
+dataset_info:
+- config_name: v0.01
+  features:
+  - name: file
+    dtype: string
+  - name: audio
+    dtype:
+      audio:
+        sampling_rate: 16000
+  - name: label
+    dtype:
+      class_label:
+        names:
+          '0': 'yes'
+          '1': 'no'
+          '2': up
+          '3': down
+          '4': left
+          '5': right
+          '6': 'on'
+          '7': 'off'
+          '8': stop
+          '9': go
+          '10': zero
+          '11': one
+          '12': two
+          '13': three
+          '14': four
+          '15': five
+          '16': six
+          '17': seven
+          '18': eight
+          '19': nine
+          '20': bed
+          '21': bird
+          '22': cat
+          '23': dog
+          '24': happy
+          '25': house
+          '26': marvin
+          '27': sheila
+          '28': tree
+          '29': wow
+          '30': _silence_
+  - name: is_unknown
+    dtype: bool
+  - name: speaker_id
+    dtype: string
+  - name: utterance_id
+    dtype: int8
+  splits:
+  - name: train
+    num_bytes: 1626283624
+    num_examples: 51093
+  - name: validation
+    num_bytes: 217204539
+    num_examples: 6799
+  - name: test
+    num_bytes: 98979965
+    num_examples: 3081
+  download_size: 1454702755
+  dataset_size: 1942468128
+- config_name: v0.02
+  features:
+  - name: file
+    dtype: string
+  - name: audio
+    dtype:
+      audio:
+        sampling_rate: 16000
+  - name: label
+    dtype:
+      class_label:
+        names:
+          '0': 'yes'
+          '1': 'no'
+          '2': up
+          '3': down
+          '4': left
+          '5': right
+          '6': 'on'
+          '7': 'off'
+          '8': stop
+          '9': go
+          '10': zero
+          '11': one
+          '12': two
+          '13': three
+          '14': four
+          '15': five
+          '16': six
+          '17': seven
+          '18': eight
+          '19': nine
+          '20': bed
+          '21': bird
+          '22': cat
+          '23': dog
+          '24': happy
+          '25': house
+          '26': marvin
+          '27': sheila
+          '28': tree
+          '29': wow
+          '30': backward
+          '31': forward
+          '32': follow
+          '33': learn
+          '34': visual
+          '35': _silence_
+  - name: is_unknown
+    dtype: bool
+  - name: speaker_id
+    dtype: string
+  - name: utterance_id
+    dtype: int8
+  splits:
+  - name: train
+    num_bytes: 2684381672
+    num_examples: 84848
+  - name: validation
+    num_bytes: 316435178
+    num_examples: 9982
+  - name: test
+    num_bytes: 157096106
+    num_examples: 4890
+  download_size: 2285975869
+  dataset_size: 3157912956
+config_names:
+- v0.01
+- v0.02
+---
+
+# Dataset Card for SpeechCommands
+
+## Table of Contents
+- [Table of Contents](#table-of-contents)
+- [Dataset Description](#dataset-description)
+  - [Dataset Summary](#dataset-summary)
+  - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
+  - [Languages](#languages)
+- [Dataset Structure](#dataset-structure)
+  - [Data Instances](#data-instances)
+  - [Data Fields](#data-fields)
+  - [Data Splits](#data-splits)
+- [Dataset Creation](#dataset-creation)
+  - [Curation Rationale](#curation-rationale)
+  - [Source Data](#source-data)
+  - [Annotations](#annotations)
+  - [Personal and Sensitive Information](#personal-and-sensitive-information)
+- [Considerations for Using the Data](#considerations-for-using-the-data)
+  - [Social Impact of Dataset](#social-impact-of-dataset)
+  - [Discussion of Biases](#discussion-of-biases)
+  - [Other Known Limitations](#other-known-limitations)
+- [Additional Information](#additional-information)
+  - [Dataset Curators](#dataset-curators)
+  - [Licensing Information](#licensing-information)
+  - [Citation Information](#citation-information)
+  - [Contributions](#contributions)
+
+## Dataset Description
+
+- **Homepage:** https://www.tensorflow.org/datasets/catalog/speech_commands
+- **Repository:** [More Information Needed]
+- **Paper:** [Speech Commands: A Dataset for Limited-Vocabulary Speech Recognition](https://arxiv.org/pdf/1804.03209.pdf)
+- **Leaderboard:** [More Information Needed]
+- **Point of Contact:** Pete Warden, petewarden@google.com
+
+### Dataset Summary
+
+This is a set of one-second .wav audio files, each containing a single spoken
+English word or background noise. These words are from a small set of commands, and are spoken by a
+variety of different speakers. This data set is designed to help train simple
+machine learning models. It is covered in more detail at [https://arxiv.org/abs/1804.03209](https://arxiv.org/abs/1804.03209).
+
+Version 0.01 of the data set (configuration `"v0.01"`) was released on August 3rd 2017 and contains
+64,727 audio files. 
+
+Version 0.02 of the data set (configuration `"v0.02"`) was released on April 11th 2018 and 
+contains 105,829 audio files.
+
+
+### Supported Tasks and Leaderboards
+
+* `keyword-spotting`: the dataset can be used to train and evaluate keyword
+spotting systems. The task is to detect 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.
+
+### Languages
+
+The language data in SpeechCommands is in English (BCP-47 `en`).
+
+## Dataset Structure
+
+### Data Instances
+
+Example of a core word (`"label"` is a word, `"is_unknown"` is `False`):
+```python
+{
+  "file": "no/7846fd85_nohash_0.wav", 
+  "audio": {
+    "path": "no/7846fd85_nohash_0.wav", 
+    "array": array([ -0.00021362, -0.00027466, -0.00036621, ...,  0.00079346,
+          0.00091553,  0.00079346]), 
+    "sampling_rate": 16000
+    },
+  "label": 1,  # "no"
+  "is_unknown": False,
+  "speaker_id": "7846fd85",
+  "utterance_id": 0
+}
+```
+
+Example of an auxiliary word (`"label"` is a word, `"is_unknown"` is `True`)
+```python
+{
+  "file": "tree/8b775397_nohash_0.wav", 
+  "audio": {
+    "path": "tree/8b775397_nohash_0.wav", 
+    "array": array([ -0.00854492, -0.01339722, -0.02026367, ...,  0.00274658,
+          0.00335693,  0.0005188]), 
+    "sampling_rate": 16000
+    },
+  "label": 28,  # "tree"
+  "is_unknown": True,
+  "speaker_id": "1b88bf70",
+  "utterance_id": 0
+}
+```
+
+Example of background noise (`_silence_`) class:
+
+```python
+{
+  "file": "_silence_/doing_the_dishes.wav", 
+  "audio": {
+    "path": "_silence_/doing_the_dishes.wav", 
+    "array": array([ 0.        ,  0.        ,  0.        , ..., -0.00592041,
+         -0.00405884, -0.00253296]), 
+    "sampling_rate": 16000
+    }, 
+  "label": 30,  # "_silence_"
+  "is_unknown": False,
+  "speaker_id": "None",
+  "utterance_id": 0  # doesn't make sense here
+}
+```
+
+### Data Fields
+
+* `file`: relative audio filename inside the original archive. 
+* `audio`: dictionary containing a relative audio filename, 
+a decoded audio array, and the sampling rate. Note that when accessing 
+the audio column: `dataset[0]["audio"]` the audio is automatically decoded 
+and resampled to `dataset.features["audio"].sampling_rate`. 
+Decoding and resampling of a large number of audios might take a significant 
+amount of time. Thus, it is important to first query the sample index before 
+the `"audio"` column, i.e. `dataset[0]["audio"]` should always be preferred 
+over `dataset["audio"][0]`.
+* `label`: either word pronounced in an audio sample or background noise (`_silence_`) class.
+Note that it's an integer value corresponding to the class name.
+* `is_unknown`: if a word is auxiliary. Equals to `False` if a word is a core word or `_silence_`, 
+`True` if a word is an auxiliary word. 
+* `speaker_id`: unique id of a speaker. Equals to `None` if label is `_silence_`.
+* `utterance_id`: incremental id of a word utterance within the same speaker. 
+
+### Data Splits
+
+The dataset has two versions (= configurations): `"v0.01"` and `"v0.02"`. `"v0.02"` 
+contains more words (see section [Source Data](#source-data) for more details).
+
+|       | train | validation | test |
+|-----  |------:|-----------:|-----:|
+| v0.01 | 51093 |       6799 | 3081 |
+| v0.02 | 84848 |       9982 | 4890 |
+
+Note that in train and validation sets examples of `_silence_` class are longer than 1 second.
+You can use the following code to sample 1-second examples from the longer ones:
+
+```python
+def sample_noise(example):
+    # Use this function to extract random 1 sec slices of each _silence_ utterance,
+    # e.g. inside `torch.utils.data.Dataset.__getitem__()`
+    from random import randint
+
+    if example["label"] == "_silence_":
+        random_offset = randint(0, len(example["speech"]) - example["sample_rate"] - 1)
+        example["speech"] = example["speech"][random_offset : random_offset + example["sample_rate"]]
+
+    return example
+```
+
+## Dataset Creation
+
+### Curation Rationale
+
+The primary goal of the dataset is to provide a way to build and test small 
+models that can detect a single word from a set of target words and differentiate it 
+from background noise or unrelated speech with as few false positives as possible. 
+
+### Source Data
+
+#### Initial Data Collection and Normalization
+
+The audio files were collected using crowdsourcing, see
+[aiyprojects.withgoogle.com/open_speech_recording](https://github.com/petewarden/extract_loudest_section)
+for some of the open source audio collection code that was used. The goal was to gather examples of
+people speaking single-word commands, rather than conversational sentences, so
+they were prompted for individual words over the course of a five minute
+session. 
+
+In version 0.01 thirty different words were recoded: "Yes", "No", "Up", "Down", "Left",
+"Right", "On", "Off", "Stop", "Go", "Zero", "One", "Two", "Three", "Four", "Five", "Six", "Seven", "Eight", "Nine", 
+"Bed", "Bird", "Cat", "Dog", "Happy", "House", "Marvin", "Sheila", "Tree", "Wow". 
+
+
+In version 0.02 more words were added: "Backward", "Forward", "Follow", "Learn", "Visual".
+
+In both versions, ten of them are used as commands by convention: "Yes", "No", "Up", "Down", "Left",
+"Right", "On", "Off", "Stop", "Go". Other words are considered to be auxiliary (in current implementation 
+it is marked by `True` value of `"is_unknown"` feature). Their function is to teach a model to distinguish core words 
+from unrecognized ones.  
+
+The `_silence_` label contains a set of longer audio clips that are either recordings or 
+a mathematical simulation of noise.
+
+#### Who are the source language producers?
+
+The audio files were collected using crowdsourcing.
+
+### Annotations
+
+#### Annotation process
+
+Labels are the list of words prepared in advances.
+Speakers were prompted for individual words over the course of a five minute
+session. 
+
+#### Who are the annotators?
+
+[More Information Needed]
+
+### Personal and Sensitive Information
+
+The dataset consists of people who have donated their voice online.  You agree to not attempt to determine the identity of speakers in this dataset.
+
+## Considerations for Using the Data
+
+### Social Impact of Dataset
+
+[More Information Needed]
+
+### Discussion of Biases
+
+[More Information Needed]
+
+### Other Known Limitations
+
+[More Information Needed]
+
+## Additional Information
+
+### Dataset Curators
+
+[More Information Needed]
+
+### Licensing Information
+
+Creative Commons BY 4.0 License ((CC-BY-4.0)[https://creativecommons.org/licenses/by/4.0/legalcode]).
+
+### Citation Information
+
+```
+@article{speechcommandsv2,
+   author = { {Warden}, P.},
+    title = "{Speech Commands: A Dataset for Limited-Vocabulary Speech Recognition}",
+  journal = {ArXiv e-prints},
+  archivePrefix = "arXiv",
+  eprint = {1804.03209},
+  primaryClass = "cs.CL",
+  keywords = {Computer Science - Computation and Language, Computer Science - Human-Computer Interaction},
+    year = 2018,
+    month = apr,
+    url = {https://arxiv.org/abs/1804.03209},
+}
+```
+### Contributions
+
+Thanks to [@polinaeterna](https://github.com/polinaeterna) for adding this dataset.

speech_commands.py

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+# coding=utf-8
+# Copyright 2021 The HuggingFace Datasets Authors and the current dataset script contributor.
+#
+# 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.
+
+"""Speech Commands, an audio dataset of spoken words designed to help train and evaluate keyword spotting systems. """
+
+
+import textwrap
+
+import datasets
+
+
+_CITATION = """
+@article{speechcommandsv2,
+   author = { {Warden}, P.},
+    title = "{Speech Commands: A Dataset for Limited-Vocabulary Speech Recognition}",
+  journal = {ArXiv e-prints},
+  archivePrefix = "arXiv",
+  eprint = {1804.03209},
+  primaryClass = "cs.CL",
+  keywords = {Computer Science - Computation and Language, Computer Science - Human-Computer Interaction},
+    year = 2018,
+    month = apr,
+    url = {https://arxiv.org/abs/1804.03209},
+}
+"""
+
+_DESCRIPTION = """
+This is a set of one-second .wav audio files, each containing a single spoken
+English word or background noise. These words are from a small set of commands, and are spoken by a
+variety of different speakers. This data set is designed to help train simple
+machine learning models. This dataset is covered in more detail at
+[https://arxiv.org/abs/1804.03209](https://arxiv.org/abs/1804.03209).
+
+Version 0.01 of the data set (configuration `"v0.01"`) was released on August 3rd 2017 and contains
+64,727 audio files.
+
+In version 0.01 thirty different words were recoded: "Yes", "No", "Up", "Down", "Left",
+"Right", "On", "Off", "Stop", "Go", "Zero", "One", "Two", "Three", "Four", "Five", "Six", "Seven", "Eight", "Nine",
+"Bed", "Bird", "Cat", "Dog", "Happy", "House", "Marvin", "Sheila", "Tree", "Wow".
+
+
+In version 0.02 more words were added: "Backward", "Forward", "Follow", "Learn", "Visual".
+
+In both versions, ten of them are used as commands by convention: "Yes", "No", "Up", "Down", "Left",
+"Right", "On", "Off", "Stop", "Go". Other words are considered to be auxiliary (in current implementation
+it is marked by `True` value of `"is_unknown"` feature). Their function is to teach a model to distinguish core words
+from unrecognized ones.
+
+The `_silence_` class contains a set of longer audio clips that are either recordings or
+a mathematical simulation of noise.
+
+"""
+
+_LICENSE = "Creative Commons BY 4.0 License"
+
+_URL = "https://www.tensorflow.org/datasets/catalog/speech_commands"
+
+_DL_URL = "https://s3.amazonaws.com/datasets.huggingface.co/SpeechCommands/{name}/{name}_{split}.tar.gz"
+
+WORDS = [
+    "yes",
+    "no",
+    "up",
+    "down",
+    "left",
+    "right",
+    "on",
+    "off",
+    "stop",
+    "go",
+]
+
+UNKNOWN_WORDS_V1 = [
+    "zero",
+    "one",
+    "two",
+    "three",
+    "four",
+    "five",
+    "six",
+    "seven",
+    "eight",
+    "nine",
+    "bed",
+    "bird",
+    "cat",
+    "dog",
+    "happy",
+    "house",
+    "marvin",
+    "sheila",
+    "tree",
+    "wow",
+]
+
+UNKNOWN_WORDS_V2 = UNKNOWN_WORDS_V1 + [
+    "backward",
+    "forward",
+    "follow",
+    "learn",
+    "visual",
+]
+
+SILENCE = "_silence_"  # background noise
+LABELS_V1 = WORDS + UNKNOWN_WORDS_V1 + [SILENCE]
+LABELS_V2 = WORDS + UNKNOWN_WORDS_V2 + [SILENCE]
+
+
+class SpeechCommandsConfig(datasets.BuilderConfig):
+    """BuilderConfig for SpeechCommands."""
+
+    def __init__(self, labels, **kwargs):
+        super(SpeechCommandsConfig, self).__init__(**kwargs)
+        self.labels = labels
+
+
+class SpeechCommands(datasets.GeneratorBasedBuilder):
+    BUILDER_CONFIGS = [
+        SpeechCommandsConfig(
+            name="v0.01",
+            description=textwrap.dedent(
+                """\
+                Version 0.01 of the SpeechCommands dataset. Contains 30 words
+                (20 of them are auxiliary) and background noise.
+                """
+            ),
+            labels=LABELS_V1,
+            version=datasets.Version("0.1.0"),
+        ),
+        SpeechCommandsConfig(
+            name="v0.02",
+            description=textwrap.dedent(
+                """\
+                Version 0.02 of the SpeechCommands dataset.
+                Contains 35 words (25 of them are auxiliary) and background noise.
+                """
+            ),
+            labels=LABELS_V2,
+            version=datasets.Version("0.2.0"),
+        ),
+    ]
+
+    def _info(self):
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=datasets.Features(
+                {
+                    "file": datasets.Value("string"),
+                    "audio": datasets.features.Audio(sampling_rate=16_000),
+                    "label": datasets.ClassLabel(names=self.config.labels),
+                    "is_unknown": datasets.Value("bool"),
+                    "speaker_id": datasets.Value("string"),
+                    "utterance_id": datasets.Value("int8"),
+                }
+            ),
+            homepage=_URL,
+            citation=_CITATION,
+            license=_LICENSE,
+            version=self.config.version,
+        )
+
+    def _split_generators(self, dl_manager):
+
+        archive_paths = dl_manager.download(
+            {
+                "train": _DL_URL.format(name=self.config.name, split="train"),
+                "validation": _DL_URL.format(name=self.config.name, split="validation"),
+                "test": _DL_URL.format(name=self.config.name, split="test"),
+            }
+        )
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    "archive": dl_manager.iter_archive(archive_paths["train"]),
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION,
+                gen_kwargs={
+                    "archive": dl_manager.iter_archive(archive_paths["validation"]),
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                gen_kwargs={
+                    "archive": dl_manager.iter_archive(archive_paths["test"]),
+                },
+            ),
+        ]
+
+    def _generate_examples(self, archive):
+        for path, file in archive:
+            if not path.endswith(".wav"):
+                continue
+
+            word, audio_filename = path.split("/")
+            is_unknown = False
+
+            if word == SILENCE:
+                speaker_id, utterance_id = None, 0
+
+            else:  # word is either in WORDS or unknown
+                if word not in WORDS:
+                    is_unknown = True
+                # an audio filename looks like `0bac8a71_nohash_0.wav`
+                speaker_id, _, utterance_id = audio_filename.split(".wav")[0].split("_")
+
+            yield path, {
+                "file": path,
+                "audio": {"path": path, "bytes": file.read()},
+                "label": word,
+                "is_unknown": is_unknown,
+                "speaker_id": speaker_id,
+                "utterance_id": utterance_id,
+            }