Hey Buddy!
Collection
Hey Buddy! is an open-source library for training wake word models (a.k.a audio keyword spotters) and deploying them to the browser.
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Hey Buddy! is a library for training wake word models (a.k.a audio keyword spotters) and deploying them to the browser for real-time use on CPU or GPU.
Using a wake-word as a gating mechanism for voice-enabled web applications carries numerous benefits, including reduced power consumption, improved privacy, and enhanced performance in noisy environments over speech-to-text systems.
Designed for usability and flexibility, Hey Buddy! features the following:
- Available with a simple
pip install heybuddy - Use a single command to train a model, no downloads or data gathering required
- Models are resilient to noisy environments and hurried speech
- Verified for commercial use - uses open data and libraries with commercially-viable licenses (see below for specifics)
- Process an arbitrary number of Wake Word models at once, with plenty of frame budget left over
- Voice-command-oriented JavaScript module provides a recording of the audio from the beginning of the wake word to the end of speech, ready to be dispatched for downstream processing
- Callback interface for more advanced integrations
Demo
A live demo space is available here. As this runs entirely on-device, this will not consume any HuggingFace credits.
Installation
The Python library only works on Linux at the moment, due to piper-phonemize only working on Linux.
The easiest way to install and use Hey Buddy is with Anaconda/Miniconda and the maintained environment file like so:
wget https://raw.githubusercontent.com/painebenjamin/hey-buddy/refs/heads/main/environment.yml
conda env create -f environment.yml
conda activate heybuddy
pip install heybuddy[gpu]
If you already have a working CUDA environment, you can just skip to pip install heybuddy. You will need to install piper-phonemize separately in this case, see the rhasspy/piper-phonemizer releases page on GitHub to find the latest wheel for your python version - for example, the environment file uses python 3.10, so it installs https://github.com/rhasspy/piper-phonemize/releases/download/v1.1.0/piper_phonemize-1.1.0-cp310-cp310-manylinux_2_28_x86_64.whl.
Training Models
Training a model is as simple as executing heybuddy train:
heybuddy train "hello world"
This will:
- Generate 100,000 training speech samples of the wake phrase using Piper TTS
- Generate 100,000 adversarial training speech samples of phonetically-similar phrases using Piper TTS
- Augment the wake word text with common follow-up words for conversational or command-oriented speech
- Augment generated speech samples using sound effects and music in varying ratios of signal-to-noise
- Reverberate these speech samples using varying impule responses (IRs)
- Undergo any combination of additional distortions including white noise, pitch shifting, and more.
- Extract speech embeddings from both sets of samples, storing as memory-mapped
numpyarrays for quick access during training - Repeat the above process is repeated to generate testing samples and validation* samples
- Download the precalculated training and validation datasets if not already downloaded
- Train the model in an automated fashion in three stages
- Save the model's checkpoint for conversion to ONNX or resuming training at a later time.
* Validation is slightly different from testing in that the positive samples are not augmented with noise, and thus serve as a 'best-case' speech validation that should be close to 100% accurate. Validation, additionally, does not include adversarial samples.
Finally, a convenient script is available for converting the .pt checkpoint to a .onnx model for the web:
heybuddy convert checkpoints/hello_world_final.pt
You can now serve this model on the web. See the JavaScript API documentation below.
Example Generated Audio
When generating new datasets, an example .wav file will be generated in your current working directory for you to listen to. Here are some examples of these:
| Text | TTS Output | Augmented TTS Output | Adversarial (Sound-Alike) TTS Output | Augmented Adversarial TTS Output |
|---|---|---|---|---|
| Buddy | ||||
| Hey Buddy | ||||
| Hi Buddy | ||||
| Yo Buddy | ||||
| Sup Buddy | ||||
| Okay Buddy |
Precalculated Datasets
Training and validation datasets are automatically downloaded when using the command-line interface with default options. See here for more information about these datasets and how you can make your own.
If you have space limitations, reference the table below for command-line flags that can help reduce size.
| Flag | Size | Default |
|---|---|---|
--training-full-default-dataset |
72 GB | Yes |
--training-large-default-dataset |
46 GB | No |
--training-medium-default-dataset |
25 GB | No |
--training-no-default-dataset |
0 | No |
Note when using no default dataset, you will need to provide your own dataset with --training-dataset <path>. See the link above for how to create your own.
Augmentation Datasets
These are the default augmentation datasets. These were selected for their commercial availability and lack of copyleft restrictions so your trained wake-word models are your own. If you substitute another dataset, make sure you have the required license to use that data for AI training.
- benjamin-paine/mit-impulse-response-survey-16khz (CC-BY)
- benjamin-paine/freesound-laion-640k-commercial-16khz-full (CC0, CC-BY, CC-Sampling)
- benjamin-paine/free-music-archive-commercial-16khz-full (CC0, CC-BY, CC-Sampling+, FMA Sound Recording Common Law, Free Art License, Public Domain)
When using the command-line, pass --augmentation-dataset-streaming to stream datasets instead of downloading them. This is not recommended for performance.
To disable using these datasets, pass --augmentation-no-default-background-dataset and --augmentation-no-default-impulse-dataset. You should pass your own datasets in the format <username>/<repo>, for loading via load_dataset
CLI Options
Usage: heybuddy train [OPTIONS] PHRASE
Trains a wake word detection model.
Options:
--additional-phrase TEXT Additional phrases to use for training.
--wandb-entity TEXT W&B entity to use for logging.
--layer-dim INTEGER Dimension of the linear layers to use for
the model. [default: 96]
--num-layers INTEGER The number of perceptron blocks. [default:
2]
--num-heads INTEGER The number of attention heads to use when
using the transformer model. [default: 1]
--steps INTEGER Number of optimization steps to take.
[default: 12500]
--stages INTEGER Number of training stages. [default: 3]
--learning-rate FLOAT Learning rate for the optimizer. [default:
0.001]
--high-loss-threshold FLOAT Threshold for high loss values (e.g. with
the default 0.001, a value is high-loss if
it's supposed to be 0 and is higher than
0.001 or supposed to be 1 and is lower than
0.999) [default: 0.0001]
--target-false-positive-rate FLOAT
Target false positive rate for the model.
[default: 0.5]
--dynamic-negative-weight / --no-dynamic-negative-weight
Dynamically adjust the negative weight based
on target false positive rate at each
validation step (instead of in-between
stages.) [default: dynamic-negative-weight]
--negative-weight FLOAT Negative weight for the loss function.
[default: 1.0]
--training-full-default-dataset
Use the full precalculated default training
set. [default: full]
--training-large-default-dataset
Use the large precalculated default training
set. [default: full]
--training-medium-default-dataset
Use the medium precalculated default
training set. [default: full]
--training-no-default-dataset Do not use a precalculated default training
set. [default: full]
--training-dataset FILE Use a custom precalculated training set.
--augment-phrase-prob FLOAT Probability of augmenting the phrase.
[default: 0.75]
--augment-phrase-default-words / --augment-phrase-no-default-words
Use the default words for augmentation.
[default: augment-phrase-default-words]
--augment-phrase-word TEXT Custom words to use for augmentation.
--augmentation-default-background-dataset / --augmentation-no-default-background-dataset
Use the default background dataset for
augmentation. [default: augmentation-
default-background-dataset]
--augmentation-background-dataset TEXT
Use a custom background dataset for
augmentation.
--augmentation-default-impulse-dataset / --augmentation-no-default-impulse-dataset
Use the default impulse dataset for
augmentation. [default: augmentation-
default-impulse-dataset]
--augmentation-impulse-dataset TEXT
Use a custom impulse dataset for
augmentation.
--augmentation-dataset-streaming / --augmentation-dataset-no-streaming
Stream the augmentation datasets, instead of
downloading first. [default: augmentation-
dataset-no-streaming]
--augmentation-seven-band-prob FLOAT
Probability of applying the seven band
equalization augmentation. [default: 0.25]
--augmentation-seven-band-gain-db FLOAT
Gain in decibels for the seven band
equalization augmentation. [default: 6.0]
--augmentation-tanh-distortion-prob FLOAT
Probability of applying the tanh distortion
augmentation. [default: 0.25]
--augmentation-tanh-distortion-min FLOAT
Minimum value for the tanh distortion
augmentation. [default: 0.0001]
--augmentation-tanh-distortion-max FLOAT
Maximum value for the tanh distortion
augmentation. [default: 0.1]
--augmentation-pitch-shift-prob FLOAT
Probability of applying the pitch shift
augmentation. [default: 0.25]
--augmentation-pitch-shift-semitones INTEGER
Number of semitones to shift the pitch for
the pitch shift augmentation. [default: 3]
--augmentation-band-stop-prob FLOAT
Probability of applying the band stop filter
augmentation. [default: 0.25]
--augmentation-colored-noise-prob FLOAT
Probability of applying the colored noise
augmentation. [default: 0.25]
--augmentation-colored-noise-min-snr-db FLOAT
Minimum signal-to-noise ratio for the
colored noise augmentation. [default: 10.0]
--augmentation-colored-noise-max-snr-db FLOAT
Maximum signal-to-noise ratio for the
colored noise augmentation. [default: 30.0]
--augmentation-colored-noise-min-f-decay FLOAT
Minimum frequency decay for the colored
noise augmentation. [default: -1.0]
--augmentation-colored-noise-max-f-decay FLOAT
Maximum frequency decay for the colored
noise augmentation. [default: 2.0]
--augmentation-background-noise-prob FLOAT
Probability of applying the background noise
augmentation. [default: 0.75]
--augmentation-background-noise-min-snr-db FLOAT
Minimum signal-to-noise ratio for the
background noise augmentation. [default:
-10.0]
--augmentation-background-noise-max-snr-db FLOAT
Maximum signal-to-noise ratio for the
background noise augmentation. [default:
15.0]
--augmentation-gain-prob FLOAT Probability of applying the gain
augmentation. [default: 1.0]
--augmentation-reverb-prob FLOAT
Probability of applying the reverb
augmentation. [default: 0.75]
--logging-steps INTEGER How often to log step details. [default: 1]
--validation-steps INTEGER How often to validate the model. [default:
250]
--checkpoint-steps INTEGER How often to save the model. [default:
5000]
--positive-samples INTEGER Number of positive samples to use for
training. Will synthetically generate more
when needed. [default: 100000]
--adversarial-samples INTEGER Number of adversarial samples to use for
training. Will synthetically generate more
when needed. [default: 100000]
--adversarial-phrases INTEGER Number of adversarial phrases to use for
training. Will synthetically generate more
when needed. [default: 250]
--adversarial-phrase-custom TEXT
Custom adversarial phrases to use for
training.
--positive-batch-size INTEGER The number of positive samples to include in
each batch during training. [default: 50]
--negative-batch-size INTEGER The number of negative samples to include in
each batch during training. [default: 1000]
--adversarial-batch-size INTEGER
The number of adversarial samples to include
in each batch during training. [default:
50]
--num-batch-threads INTEGER The number of threads to spawn for creating
training batches. [default: 12]
--validation-positive-batch-size INTEGER
The number of positive samples to include in
each batch during validation. [default: 50]
--validation-negative-batch-size INTEGER
The number of negative samples to include in
each batch during validation. [default:
1000]
--validation-samples INTEGER The number of samples to use for validation.
Will synthetically generate more when
needed. [default: 25000]
--validation-num-batch-threads INTEGER
The number of threads to spawn for creating
validation batches. [default: 1]
--validation-default-dataset / --validation-no-default-dataset
Use the default validation dataset.
[default: validation-default-dataset]
--validation-dataset FILE Use a custom precalculated validation set.
--testing-positive-samples INTEGER
The number of positive samples to use for
testing. Will synthetically generate more
when needed. [default: 25000]
--testing-adversarial-samples INTEGER
The number of adversarial samples to use for
testing. Will synthetically generate more
when needed. [default: 25000]
--testing-positive-batch-size INTEGER
The number of positive samples to include in
each batch during testing. Default matches
the size used during training.
--testing-adversarial-batch-size INTEGER
The number of adversarial samples to include
in each batch during testing. Default
matches the size used during training.
--testing-num-batch-threads INTEGER
The number of threads to spawn for creating
testing batches. [default: 1]
--resume / --no-resume Resume training from the last checkpoint.
[default: no-resume]
--debug / --no-debug Enable debug logging. [default: no-debug]
--help Show this message and exit.
Evaluating Models
A number of evaluation metrics are recorded during training. The best way to access this data is to use Weights & Biases, then pass your entity (username or team name) to the training script via --wandb-entity <name>.
Inference
JavaScript API
Hey Buddy! is distributed as two JavaScript files that should be made available through any implementing web application:
hey-buddy.min.js(90 kB) which provides the HeyBuddy API, andhey-buddy-worklet.js(1 kB) which implements the Audio Worklet interface which will be imported by the browser using the AudioWorklet API.
You need to make the ONNX runtime available prior to importing the HeyBuddy JS API. The easiest way to do this is to use a JS CDN, like so:
<!-- ONNX Runtime -->
<script src="https://cdn.jsdelivr.net/npm/[email protected]/dist/ort.min.js"></script>
<!-- Hey Buddy API -->
<script src="/path/to/my/website/hey-buddy.min.js"></script>
<!-- Your Code -->
<script>...</script>
You will also need to host the onnx file created by heybuddy convert (see above.)
Your implementation code could look something like this:
const options = {
record: true, // disable when not needed to save memory
modelPath: ["/models/my-model.onnx", "/models/my-other-model.onnx"], // Also accepts single strings
// workletUrl: /path/to/my/website/hey-buddy-worklet.js // Only needed if not at the same path as the library code
};
const heyBuddy = new HeyBuddy(options);
// do something with a recording
heyBuddy.onRecording(async (audio) => {
// audio is a Float32Array of samples
});
// do something every frame
heyBuddy.onProcessed((result) => {
/**
* this is triggered every frame with:
* {
* listening: bool,
* recording: bool,
* speech: {
* probability: 0.0 <= float <= 1.0,
* active: bool
* },
* wakeWords: {
* $modelNameWithoutExtension: {
* probability: 0.0 <= float <= 1.0,
* active: bool
* }
* }
* }
*/
});
Python API
To use the torch model directly, you can do the following.
from heybuddy import WakeWord
audio = "/path/to/audio.wav" # OR flac OR mp3 OR numpy array/tensor (int16 or float32) OR list of the same
model = WakeWord.from_file("/path/to/model.pt")
model.to("cuda") # optional
predictions = model.predict(
audio,
threshold=0.5, # you can experiment with different thresholds
return_scores=False, # when true, return the prediction scores instead of bools. default false.
)
first_audio_has_wakeword = predictions[0] # bool by default
A threaded API is also available for ease-of-use and to minimize performance impact.
from heybuddy import WakeWordModelThread
audio = "/path/to/audio.wav" # OR flac OR mp3 OR numpy array/tensor (int16 or float32) OR list of the same
thread = WakeWordModelThread(
"/path/to/model.pt", # or onnx
device_id=None, # set to GPU index to use CUDA for torch or onnx, otherwise runs on CPU
threshold=0.5, # you can experiment with different thresholds
return_scores=False, # when true, return the prediction scores instead of bools. default false.
)
thread.put(audio)
predictions = thread.get( # same arguments as queue.Queue.get()
block=True,
timeout=None
)
first_audio_has_wakeword = predictions[0] # bool by default
Errata
These are some potentially useful JavaScript snippets for working with raw audio samples.
/**
* Play audio samples using the Web Audio API.
* @param {Float32Array} audioSamples - The audio samples to play.
* @param {number} sampleRate - The sample rate of the audio samples.
*/
function playAudioSamples(audioSamples, sampleRate = 16000) {
// Create an AudioContext
const audioContext = new (window.AudioContext || window.webkitAudioContext)();
// Create an AudioBuffer
const audioBuffer = audioContext.createBuffer(
1, // number of channels
audioSamples.length, // length of the buffer in samples
sampleRate // sample rate (samples per second)
);
// Fill the AudioBuffer with the Float32Array of audio samples
audioBuffer.getChannelData(0).set(audioSamples);
// Create a BufferSource node
const source = audioContext.createBufferSource();
source.buffer = audioBuffer;
// Connect the source to the AudioContext's destination (the speakers)
source.connect(audioContext.destination);
// Start playback
source.start();
};
/**
* Turns floating-point audio samples to a Wave blob.
* @param {Float32Array} audioSamples - The audio samples to play.
* @param {number} sampleRate - The sample rate of the audio samples.
* @param {number} numChannels - The number of channels in the audio. Defaults to 1 (mono).
* @return {Blob} A blob of type `audio/wav`
*/
function samplesToBlob(audioSamples, sampleRate = 16000, numChannels = 1) {
// Helper to write a string to the DataView
const writeString = (view, offset, string) => {
for (let i = 0; i < string.length; i++) {
view.setUint8(offset + i, string.charCodeAt(i));
}
};
// Helper to convert Float32Array to Int16Array (16-bit PCM)
const floatTo16BitPCM = (output, offset, input) => {
for (let i = 0; i < input.length; i++, offset += 2) {
let s = Math.max(-1, Math.min(1, input[i])); // Clamping to [-1, 1]
output.setInt16(offset, s < 0 ? s * 0x8000 : s * 0x7FFF, true); // Convert to 16-bit PCM
}
};
const byteRate = sampleRate * numChannels * 2; // 16-bit PCM = 2 bytes per sample
// Calculate sizes
const blockAlign = numChannels * 2; // 2 bytes per sample for 16-bit audio
const wavHeaderSize = 44;
const dataLength = audioSamples.length * numChannels * 2; // 16-bit PCM data length
const buffer = new ArrayBuffer(wavHeaderSize + dataLength);
const view = new DataView(buffer);
// Write WAV file headers
writeString(view, 0, 'RIFF'); // ChunkID
view.setUint32(4, 36 + dataLength, true); // ChunkSize
writeString(view, 8, 'WAVE'); // Format
writeString(view, 12, 'fmt '); // Subchunk1ID
view.setUint32(16, 16, true); // Subchunk1Size (PCM = 16)
view.setUint16(20, 1, true); // AudioFormat (PCM = 1)
view.setUint16(22, numChannels, true); // NumChannels
view.setUint32(24, sampleRate, true); // SampleRate
view.setUint32(28, byteRate, true); // ByteRate
view.setUint16(32, blockAlign, true); // BlockAlign
view.setUint16(34, 16, true); // BitsPerSample (16-bit PCM)
writeString(view, 36, 'data'); // Subchunk2ID
view.setUint32(40, dataLength, true); // Subchunk2Size
// Convert the Float32Array audio samples to 16-bit PCM and write them to the DataView
floatTo16BitPCM(view, wavHeaderSize, audioSamples);
// Create and return the Blob
return new Blob([view], { type: 'audio/wav' });
}
/**
* Renders a blob to an audio element with controls.
* Use `appendChild(result)` to add to the document or a node.
* @param {Blob} audioBlob - A blob with a valid audio type.
* @see samplesToBlob
*/
function blobToAudio(audioBlob) {
// Create data URL
const url = URL.createObjectURL(audioBlob);
// Create and configure audio element
const audio = document.createElement("audio");
audio.controls = true;
audio.src = url;
return audio;
}
/**
* Downloads a blob as a file.
* @param {Blob} blob - A blob with a type that can be converted to an object URL.
* @param {string} filename - The file name after downloading.
*/
function downloadBlob(blob, filename) {
// Create data URL
const url = URL.createObjectURL(blob);
// Create and configure link element
const link = document.createElement("a");
link.href = url;
link.setAttribute("download", filename);
link.style.display = "none";
// Add the link to the page, click, then remove
document.body.appendChild(link);
window.requestAnimationFrame(() => {
link.dispatchEvent(new MouseEvent("click"));
document.body.removeChild(link);
});
}
License
- HeyBuddy source code and pretrained models are released under the Apache License 2.0.
- Pre-calculated training and validation datasets are released under CC-BY 4.0. See the dataset card for individual licensing information.
Citations and Acknowledgements
- David Scripka, OpenWakeWord (Apache 2.0 License)
- Lin, Kilgour, Roblek, Sharifi et. Al, Speech Embeddings (Apache 2.0 License)
- Rhasspy and the Open Home Foundation, Piper TTS (MIT License)
- Silero Team, SileroVAD (MIT License)
- Siebert, Adelman and Git, npy-apppend-array
@misc{lin2020trainingkeywordspotterslimited,
title={Training Keyword Spotters with Limited and Synthesized Speech Data},
author={James Lin and Kevin Kilgour and Dominik Roblek and Matthew Sharifi},
year={2020},
eprint={2002.01322},
archivePrefix={arXiv},
primaryClass={eess.AS},
url={https://arxiv.org/abs/2002.01322},
}
@inproceedings{50492,
title={Improving Automatic Speech Recognition with Neural Embeddings},
author={Christopher Li and Diamantino A. Caseiro and Leonid Velikovich and Pat Rondon and Petar S. Aleksic and Xavier L Velez},
year={2021},
address={111 8th AveNew York, NY 10011}
}
@misc{Silero VAD,
author = {Silero Team},
title = {Silero VAD: pre-trained enterprise-grade Voice Activity Detector (VAD), Number Detector and Language Classifier},
year = {2021},
publisher = {GitHub},
journal = {GitHub repository},
howpublished = {\url{https://github.com/snakers4/silero-vad}},
commit = {insert_some_commit_here},
email = {[email protected]}
}
@inproceedings{fma_dataset,
title = {{FMA}: A Dataset for Music Analysis},
author = {Defferrard, Micha\"el and Benzi, Kirell and Vandergheynst, Pierre and Bresson, Xavier},
booktitle = {18th International Society for Music Information Retrieval Conference (ISMIR)},
year = {2017},
archiveprefix = {arXiv},
eprint = {1612.01840},
url = {https://arxiv.org/abs/1612.01840},
}
@inproceedings{fma_challenge,
title = {Learning to Recognize Musical Genre from Audio},
subtitle = {Challenge Overview},
author = {Defferrard, Micha\"el and Mohanty, Sharada P. and Carroll, Sean F. and Salath\'e, Marcel},
booktitle = {The 2018 Web Conference Companion},
year = {2018},
publisher = {ACM Press},
isbn = {9781450356404},
doi = {10.1145/3184558.3192310},
archiveprefix = {arXiv},
eprint = {1803.05337},
url = {https://arxiv.org/abs/1803.05337},
}
@article{
doi:10.1073/pnas.1612524113,
author = {James Traer and Josh H. McDermott},
title = {Statistics of natural reverberation enable perceptual separation of sound and space},
journal = {Proceedings of the National Academy of Sciences},
volume = {113},
number = {48},
pages = {E7856-E7865},
year = {2016},
doi = {10.1073/pnas.1612524113},
URL = {https://www.pnas.org/doi/abs/10.1073/pnas.1612524113},
eprint = {https://www.pnas.org/doi/pdf/10.1073/pnas.1612524113},
abstract = {Sounds produced in the world reflect off surrounding surfaces on their way to our ears. Known as reverberation, these reflections distort sound but provide information about the world around us. We asked whether reverberation exhibits statistical regularities that listeners use to separate its effects from those of a sound’s source. We conducted a large-scale statistical analysis of real-world acoustics, revealing strong regularities of reverberation in natural scenes. We found that human listeners can estimate the contributions of the source and the environment from reverberant sound, but that they depend critically on whether environmental acoustics conform to the observed statistical regularities. The results suggest a separation process constrained by knowledge of environmental acoustics that is internalized over development or evolution. In everyday listening, sound reaches our ears directly from a source as well as indirectly via reflections known as reverberation. Reverberation profoundly distorts the sound from a source, yet humans can both identify sound sources and distinguish environments from the resulting sound, via mechanisms that remain unclear. The core computational challenge is that the acoustic signatures of the source and environment are combined in a single signal received by the ear. Here we ask whether our recognition of sound sources and spaces reflects an ability to separate their effects and whether any such separation is enabled by statistical regularities of real-world reverberation. To first determine whether such statistical regularities exist, we measured impulse responses (IRs) of 271 spaces sampled from the distribution encountered by humans during daily life. The sampled spaces were diverse, but their IRs were tightly constrained, exhibiting exponential decay at frequency-dependent rates: Mid frequencies reverberated longest whereas higher and lower frequencies decayed more rapidly, presumably due to absorptive properties of materials and air. To test whether humans leverage these regularities, we manipulated IR decay characteristics in simulated reverberant audio. Listeners could discriminate sound sources and environments from these signals, but their abilities degraded when reverberation characteristics deviated from those of real-world environments. Subjectively, atypical IRs were mistaken for sound sources. The results suggest the brain separates sound into contributions from the source and the environment, constrained by a prior on natural reverberation. This separation process may contribute to robust recognition while providing information about spaces around us.}
}
@article{Pratap2020MLSAL,
title={MLS: A Large-Scale Multilingual Dataset for Speech Research},
author={Vineel Pratap and Qiantong Xu and Anuroop Sriram and Gabriel Synnaeve and Ronan Collobert},
journal={ArXiv},
year={2020},
volume={abs/2012.03411}
}
@inproceedings{commonvoice:2020,
author = {Ardila, R. and Branson, M. and Davis, K. and Henretty, M. and Kohler, M. and Meyer, J. and Morais, R. and Saunders, L. and Tyers, F. M. and Weber, G.},
title = {Common Voice: A Massively-Multilingual Speech Corpus},
booktitle = {Proceedings of the 12th Conference on Language Resources and Evaluation (LREC 2020)},
pages = {4211--4215},
year = 2020
}
@misc{wang2024globe,
title={GLOBE: A High-quality English Corpus with Global Accents for Zero-shot Speaker Adaptive Text-to-Speech},
author={Wenbin Wang and Yang Song and Sanjay Jha},
year={2024},
eprint={2406.14875},
archivePrefix={arXiv},
}
@article{Instruction Speech 2024,
title={Instruction Speech},
author={JanAI},
year=2024,
month=June},
url={https://huggingface.co/datasets/jan-hq/instruction-speech}
}
@inproceedings{wang-etal-2021-voxpopuli,
title = "{V}ox{P}opuli: A Large-Scale Multilingual Speech Corpus for Representation Learning, Semi-Supervised Learning and Interpretation",
author = "Wang, Changhan and
Riviere, Morgane and
Lee, Ann and
Wu, Anne and
Talnikar, Chaitanya and
Haziza, Daniel and
Williamson, Mary and
Pino, Juan and
Dupoux, Emmanuel",
booktitle = "Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)",
month = aug,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.acl-long.80",
pages = "993--1003",
}
@article{fleurs2022arxiv,
title = {FLEURS: Few-shot Learning Evaluation of Universal Representations of Speech},
author = {Conneau, Alexis and Ma, Min and Khanuja, Simran and Zhang, Yu and Axelrod, Vera and Dalmia, Siddharth and Riesa, Jason and Rivera, Clara and Bapna, Ankur},
journal={arXiv preprint arXiv:2205.12446},
url = {https://arxiv.org/abs/2205.12446},
year = {2022},
}
@misc{vansegbroeck2019dipcodinnerparty,
title={DiPCo -- Dinner Party Corpus},
author={Maarten Van Segbroeck and Ahmed Zaid and Ksenia Kutsenko and Cirenia Huerta and Tinh Nguyen and Xuewen Luo and Björn Hoffmeister and Jan Trmal and Maurizio Omologo and Roland Maas},
year={2019},
eprint={1909.13447},
archivePrefix={arXiv},
primaryClass={eess.AS},
url={https://arxiv.org/abs/1909.13447},
}
@software{siebert2023npy_append_array,
author = {Michael Siebert and Joshua Adelman and Yoav Git},
title = {xor2k/npy-append-array: 0.9.16},
version = {0.9.16},
doi = {10.5281/zenodo.13820984},
url = {https://github.com/xor2k/npy-append-array/tree/0.9.16},
year = {2023},
month = {feb},
day = {24},
abstract = {Create Numpy .npy files by appending on the growth axis},
license = {MIT},
orcid = {0000-0002-1369-6321},
type = {software},
}