| Name | lhotse JSON |
| Version |
1.22.0
JSON |
| download |
| home_page | |
| Summary | Data preparation for speech processing models training. |
| upload_time | 2024-03-07 19:27:30 |
| maintainer | |
| docs_url | None |
| author | The Lhotse Development Team |
| requires_python | >=3.8.0 |
| license | Apache-2.0 License |
| keywords |
|
| VCS |
|
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
|
| coveralls test coverage |
No coveralls.
|
<div align="center">
<img src="https://raw.githubusercontent.com/lhotse-speech/lhotse/master/docs/logo.png" width=376>
[](https://badge.fury.io/py/lhotse)
[](https://pypi.org/project/lhotse/)
[](https://pepy.tech/project/lhotse)
[](https://actions-badge.atrox.dev/pzelasko/lhotse/goto?ref=master)
[](https://lhotse.readthedocs.io/en/latest/?badge=latest)
[](https://codecov.io/gh/lhotse-speech/lhotse)
[](https://github.com/psf/black)
[](https://colab.research.google.com/github/lhotse-speech/lhotse-speech.github.io/blob/master/notebooks/lhotse-introduction.ipynb)
[](https://arxiv.org/abs/2110.12561)
</div>
# Lhotse
Lhotse is a Python library aiming to make speech and audio data preparation flexible and accessible to a wider community. Alongside [k2](https://github.com/k2-fsa/k2), it is a part of the next generation [Kaldi](https://github.com/kaldi-asr/kaldi) speech processing library.
## Tutorial presentations and materials
- (_Interspeech 2023_) Tutorial notebook [](https://colab.research.google.com/drive/1obZjUuVwks3A4oFX3gXFtPOM2LtrPQfL?usp=sharing)
- (_Interspeech 2023_) [Tutorial slides](https://livejohnshopkins-my.sharepoint.com/:p:/g/personal/mwiesne2_jh_edu/EYqRDl8cIr5BsVDxi1MOW5EBUpdqh10WFkzqixPIFM63hg?e=u3lrmL)
- (_Interspeech 2021_) [Recorded lecture (3h)](https://www.youtube.com/watch?v=y6CJLFQlmhc&pp=ygUgaW50ZXJzcGVlY2ggMjAyMSBsaG90c2UgdHV0b3JpYWw%3D)
## About
### Main goals
- Attract a wider community to speech processing tasks with a **Python-centric design**.
- Accommodate experienced Kaldi users with an **expressive command-line interface**.
- Provide **standard data preparation recipes** for commonly used corpora.
- Provide **PyTorch Dataset classes** for speech and audio related tasks.
- Flexible data preparation for model training with the notion of **audio cuts**.
- **Efficiency**, especially in terms of I/O bandwidth and storage capacity.
### Tutorials
We currently have the following tutorials available in `examples` directory:
- Basic complete Lhotse workflow [](https://colab.research.google.com/github/lhotse-speech/lhotse/blob/master/examples/00-basic-workflow.ipynb)
- Transforming data with Cuts [](https://colab.research.google.com/github/lhotse-speech/lhotse/blob/master/examples/01-cut-python-api.ipynb)
- WebDataset integration [](https://colab.research.google.com/github/lhotse-speech/lhotse/blob/master/examples/02-webdataset-integration.ipynb)
- How to combine multiple datasets [](https://colab.research.google.com/github/lhotse-speech/lhotse/blob/master/examples/03-combining-datasets.ipynb)
- Lhotse Shar: storage format optimized for sequential I/O and modularity [](https://colab.research.google.com/github/lhotse-speech/lhotse/blob/master/examples/04-lhotse-shar.ipynb)
### Examples of use
Check out the following links to see how Lhotse is being put to use:
- [Icefall recipes](https://github.com/k2-fsa/icefall): where k2 and Lhotse meet.
- Minimal ESPnet+Lhotse example: [](https://colab.research.google.com/drive/1HKSYPsWx_HoCdrnLpaPdYj5zwlPsM3NH)
### Main ideas
Like Kaldi, Lhotse provides standard data preparation recipes, but extends that with a seamless PyTorch integration
through task-specific Dataset classes. The data and meta-data are represented in human-readable text manifests and
exposed to the user through convenient Python classes.
Lhotse introduces the notion of audio cuts, designed to ease the training data construction with operations such as
mixing, truncation and padding that are performed on-the-fly to minimize the amount of storage required. Data
augmentation and feature extraction are supported both in pre-computed mode, with highly-compressed feature matrices
stored on disk, and on-the-fly mode that computes the transformations upon request. Additionally, Lhotse introduces
feature-space cut mixing to make the best of both worlds.
## Installation
Lhotse supports Python version 3.7 and later.
### Pip
Lhotse is available on PyPI:
pip install lhotse
To install the latest, unreleased version, do:
pip install git+https://github.com/lhotse-speech/lhotse
### Development installation
For development installation, you can fork/clone the GitHub repo and install with pip:
git clone https://github.com/lhotse-speech/lhotse
cd lhotse
pip install -e '.[dev]'
pre-commit install # installs pre-commit hooks with style checks
# Running unit tests
pytest test
# Running linter checks
pre-commit run
This is an editable installation (`-e` option), meaning that your changes to the source code are automatically
reflected when importing lhotse (no re-install needed). The `[dev]` part means you're installing extra dependencies
that are used to run tests, build documentation or launch jupyter notebooks.
### Environment variables
Lhotse uses several environment variables to customize it's behavior. They are as follows:
- `LHOTSE_REQUIRE_TORCHAUDIO` - when it's set and not any of `1|True|true|yes`, we'll not check for torchaudio being installed and remove it from the requirements. It will disable many functionalities of Lhotse but the basic capabilities will remain (including reading audio with `soundfile`).
- `LHOTSE_AUDIO_DURATION_MISMATCH_TOLERANCE` - used when we load audio from a file and receive a different number of samples than declared in `Recording.num_samples`. This is sometimes necessary because different codecs (or even different versions of the same codec) may use different padding when decoding compressed audio. Typically values up to 0.1, or even 0.3 (second) are still reasonable, and anything beyond that indicates a serious issue.
- `LHOTSE_AUDIO_BACKEND` - may be set to any of the values returned from CLI `lhotse list-audio-backends` to override the default behavior of trial-and-error and always use a specific audio backend.
- `LHOTSE_AUDIO_LOADING_EXCEPTION_VERBOSE` - when set to `1` we'll emit full exception stack traces when every available audio backend fails to load a given file (they might be very large).
- `LHOTSE_DILL_ENABLED` - when it's set to `1|True|true|yes`, we will enable `dill`-based serialization of `CutSet` and `Sampler` across processes (it's disabled by default even when `dill` is installed).
- `LHOTSE_LEGACY_OPUS_LOADING` - (`=1`) reverts to a legacy OPUS loading mechanism that triggered a new ffmpeg subprocess for each OPUS file.
- `LHOTSE_PREPARING_RELEASE` - used internally by developers when releasing a new version of Lhotse.
- `TORCHAUDIO_USE_BACKEND_DISPATCHER` - when set to `1` and torchaudio version is below 2.1, we'll enable the experimental ffmpeg backend of torchaudio.
- `RANK`, `WORLD_SIZE`, `WORKER`, and `NUM_WORKERS` are internally used to inform Lhotse Shar dataloading subprocesses.
- `READTHEDOCS` is internally used for documentation builds.
### Optional dependencies
**Other pip packages.** You can leverage optional features of Lhotse by installing the relevant supporting package like this: `pip install lhotse[package_name]`. The supported optional packages include:
- `pip install lhotse[kaldi]` for a maximal feature set related to Kaldi compatibility. It includes libraries such as `kaldi_native_io` (a more efficient variant of `kaldi_io`) and `kaldifeat` that port some of Kaldi functionality into Python.
- `pip install lhotse[orjson]` for up to 50% faster reading of JSONL manifests.
- `pip install lhotse[webdataset]`. We support "compiling" your data into WebDataset tarball format for more effective IO. You can still interact with the data as if it was a regular lazy CutSet. To learn more, check out the following tutorial: [](https://colab.research.google.com/github/lhotse-speech/lhotse/blob/master/examples/02-webdataset-integration.ipynb)
- `pip install h5py` if you want to extract speech features and store them as HDF5 arrays.
- `pip install dill`. When `dill` is installed, we'll use it to pickle CutSet that uses a lambda function in calls such as `.map` or `.filter`. This is helpful in PyTorch DataLoader with `num_jobs>0`. Without `dill`, depending on your environment, you'll see an exception or a hanging script.
- `pip install smart_open` to read and write manifests and data in any location supported by `smart_open` (e.g. cloud, http).
- `pip install opensmile` for feature extraction using the OpenSmile toolkit's Python wrapper.
**sph2pipe.** For reading older LDC SPHERE (.sph) audio files that are compressed with codecs unsupported by ffmpeg and sox, please run:
# CLI
lhotse install-sph2pipe
# Python
from lhotse.tools import install_sph2pipe
install_sph2pipe()
It will download it to `~/.lhotse/tools`, compile it, and auto-register in `PATH`. The program should be automatically detected and used by Lhotse.
## Examples
We have example recipes showing how to prepare data and load it in Python as a PyTorch `Dataset`.
They are located in the `examples` directory.
A short snippet to show how Lhotse can make audio data preparation quick and easy:
```python
from torch.utils.data import DataLoader
from lhotse import CutSet, Fbank
from lhotse.dataset import VadDataset, SimpleCutSampler
from lhotse.recipes import prepare_switchboard
# Prepare data manifests from a raw corpus distribution.
# The RecordingSet describes the metadata about audio recordings;
# the sampling rate, number of channels, duration, etc.
# The SupervisionSet describes metadata about supervision segments:
# the transcript, speaker, language, and so on.
swbd = prepare_switchboard('/export/corpora3/LDC/LDC97S62')
# CutSet is the workhorse of Lhotse, allowing for flexible data manipulation.
# We create 5-second cuts by traversing SWBD recordings in windows.
# No audio data is actually loaded into memory or stored to disk at this point.
cuts = CutSet.from_manifests(
recordings=swbd['recordings'],
supervisions=swbd['supervisions']
).cut_into_windows(duration=5)
# We compute the log-Mel filter energies and store them on disk;
# Then, we pad the cuts to 5 seconds to ensure all cuts are of equal length,
# as the last window in each recording might have a shorter duration.
# The padding will be performed once the features are loaded into memory.
cuts = cuts.compute_and_store_features(
extractor=Fbank(),
storage_path='feats',
num_jobs=8
).pad(duration=5.0)
# Construct a Pytorch Dataset class for Voice Activity Detection task:
dataset = VadDataset()
sampler = SimpleCutSampler(cuts, max_duration=300)
dataloader = DataLoader(dataset, sampler=sampler, batch_size=None)
batch = next(iter(dataloader))
```
The `VadDataset` will yield a batch with pairs of feature and supervision tensors such as the following - the speech
starts roughly at the first second (100 frames):
Raw data
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"description": "<div align=\"center\">\n<img src=\"https://raw.githubusercontent.com/lhotse-speech/lhotse/master/docs/logo.png\" width=376>\n\n[](https://badge.fury.io/py/lhotse)\n[](https://pypi.org/project/lhotse/)\n[](https://pepy.tech/project/lhotse)\n[](https://actions-badge.atrox.dev/pzelasko/lhotse/goto?ref=master)\n[](https://lhotse.readthedocs.io/en/latest/?badge=latest)\n[](https://codecov.io/gh/lhotse-speech/lhotse)\n[](https://github.com/psf/black)\n[](https://colab.research.google.com/github/lhotse-speech/lhotse-speech.github.io/blob/master/notebooks/lhotse-introduction.ipynb)\n[](https://arxiv.org/abs/2110.12561)\n\n</div>\n\n\n# Lhotse\n\nLhotse is a Python library aiming to make speech and audio data preparation flexible and accessible to a wider community. Alongside [k2](https://github.com/k2-fsa/k2), it is a part of the next generation [Kaldi](https://github.com/kaldi-asr/kaldi) speech processing library.\n\n## Tutorial presentations and materials\n\n- (_Interspeech 2023_) Tutorial notebook [](https://colab.research.google.com/drive/1obZjUuVwks3A4oFX3gXFtPOM2LtrPQfL?usp=sharing)\n- (_Interspeech 2023_) [Tutorial slides](https://livejohnshopkins-my.sharepoint.com/:p:/g/personal/mwiesne2_jh_edu/EYqRDl8cIr5BsVDxi1MOW5EBUpdqh10WFkzqixPIFM63hg?e=u3lrmL)\n- (_Interspeech 2021_) [Recorded lecture (3h)](https://www.youtube.com/watch?v=y6CJLFQlmhc&pp=ygUgaW50ZXJzcGVlY2ggMjAyMSBsaG90c2UgdHV0b3JpYWw%3D)\n\n## About\n\n### Main goals\n\n- Attract a wider community to speech processing tasks with a **Python-centric design**.\n- Accommodate experienced Kaldi users with an **expressive command-line interface**.\n- Provide **standard data preparation recipes** for commonly used corpora.\n- Provide **PyTorch Dataset classes** for speech and audio related tasks.\n- Flexible data preparation for model training with the notion of **audio cuts**.\n- **Efficiency**, especially in terms of I/O bandwidth and storage capacity.\n\n### Tutorials\n\nWe currently have the following tutorials available in `examples` directory:\n- Basic complete Lhotse workflow [](https://colab.research.google.com/github/lhotse-speech/lhotse/blob/master/examples/00-basic-workflow.ipynb)\n- Transforming data with Cuts [](https://colab.research.google.com/github/lhotse-speech/lhotse/blob/master/examples/01-cut-python-api.ipynb)\n- WebDataset integration [](https://colab.research.google.com/github/lhotse-speech/lhotse/blob/master/examples/02-webdataset-integration.ipynb)\n- How to combine multiple datasets [](https://colab.research.google.com/github/lhotse-speech/lhotse/blob/master/examples/03-combining-datasets.ipynb)\n- Lhotse Shar: storage format optimized for sequential I/O and modularity [](https://colab.research.google.com/github/lhotse-speech/lhotse/blob/master/examples/04-lhotse-shar.ipynb)\n\n### Examples of use\n\nCheck out the following links to see how Lhotse is being put to use:\n- [Icefall recipes](https://github.com/k2-fsa/icefall): where k2 and Lhotse meet.\n- Minimal ESPnet+Lhotse example: [](https://colab.research.google.com/drive/1HKSYPsWx_HoCdrnLpaPdYj5zwlPsM3NH)\n\n### Main ideas\n\nLike Kaldi, Lhotse provides standard data preparation recipes, but extends that with a seamless PyTorch integration\nthrough task-specific Dataset classes. The data and meta-data are represented in human-readable text manifests and\nexposed to the user through convenient Python classes.\n\n\n\nLhotse introduces the notion of audio cuts, designed to ease the training data construction with operations such as\nmixing, truncation and padding that are performed on-the-fly to minimize the amount of storage required. Data\naugmentation and feature extraction are supported both in pre-computed mode, with highly-compressed feature matrices\nstored on disk, and on-the-fly mode that computes the transformations upon request. Additionally, Lhotse introduces\nfeature-space cut mixing to make the best of both worlds.\n\n\n\n## Installation\n\nLhotse supports Python version 3.7 and later.\n\n### Pip\n\nLhotse is available on PyPI:\n\n pip install lhotse\n\nTo install the latest, unreleased version, do:\n\n pip install git+https://github.com/lhotse-speech/lhotse\n\n### Development installation\n\nFor development installation, you can fork/clone the GitHub repo and install with pip:\n\n git clone https://github.com/lhotse-speech/lhotse\n cd lhotse\n pip install -e '.[dev]'\n pre-commit install # installs pre-commit hooks with style checks\n\n # Running unit tests\n pytest test\n\n # Running linter checks\n pre-commit run\n\nThis is an editable installation (`-e` option), meaning that your changes to the source code are automatically\nreflected when importing lhotse (no re-install needed). The `[dev]` part means you're installing extra dependencies\nthat are used to run tests, build documentation or launch jupyter notebooks.\n\n### Environment variables\n\nLhotse uses several environment variables to customize it's behavior. They are as follows:\n- `LHOTSE_REQUIRE_TORCHAUDIO` - when it's set and not any of `1|True|true|yes`, we'll not check for torchaudio being installed and remove it from the requirements. It will disable many functionalities of Lhotse but the basic capabilities will remain (including reading audio with `soundfile`).\n- `LHOTSE_AUDIO_DURATION_MISMATCH_TOLERANCE` - used when we load audio from a file and receive a different number of samples than declared in `Recording.num_samples`. This is sometimes necessary because different codecs (or even different versions of the same codec) may use different padding when decoding compressed audio. Typically values up to 0.1, or even 0.3 (second) are still reasonable, and anything beyond that indicates a serious issue.\n- `LHOTSE_AUDIO_BACKEND` - may be set to any of the values returned from CLI `lhotse list-audio-backends` to override the default behavior of trial-and-error and always use a specific audio backend.\n- `LHOTSE_AUDIO_LOADING_EXCEPTION_VERBOSE` - when set to `1` we'll emit full exception stack traces when every available audio backend fails to load a given file (they might be very large).\n- `LHOTSE_DILL_ENABLED` - when it's set to `1|True|true|yes`, we will enable `dill`-based serialization of `CutSet` and `Sampler` across processes (it's disabled by default even when `dill` is installed).\n- `LHOTSE_LEGACY_OPUS_LOADING` - (`=1`) reverts to a legacy OPUS loading mechanism that triggered a new ffmpeg subprocess for each OPUS file.\n- `LHOTSE_PREPARING_RELEASE` - used internally by developers when releasing a new version of Lhotse.\n- `TORCHAUDIO_USE_BACKEND_DISPATCHER` - when set to `1` and torchaudio version is below 2.1, we'll enable the experimental ffmpeg backend of torchaudio.\n- `RANK`, `WORLD_SIZE`, `WORKER`, and `NUM_WORKERS` are internally used to inform Lhotse Shar dataloading subprocesses.\n- `READTHEDOCS` is internally used for documentation builds.\n\n### Optional dependencies\n\n**Other pip packages.** You can leverage optional features of Lhotse by installing the relevant supporting package like this: `pip install lhotse[package_name]`. The supported optional packages include:\n- `pip install lhotse[kaldi]` for a maximal feature set related to Kaldi compatibility. It includes libraries such as `kaldi_native_io` (a more efficient variant of `kaldi_io`) and `kaldifeat` that port some of Kaldi functionality into Python.\n- `pip install lhotse[orjson]` for up to 50% faster reading of JSONL manifests.\n- `pip install lhotse[webdataset]`. We support \"compiling\" your data into WebDataset tarball format for more effective IO. You can still interact with the data as if it was a regular lazy CutSet. To learn more, check out the following tutorial: [](https://colab.research.google.com/github/lhotse-speech/lhotse/blob/master/examples/02-webdataset-integration.ipynb)\n- `pip install h5py` if you want to extract speech features and store them as HDF5 arrays.\n- `pip install dill`. When `dill` is installed, we'll use it to pickle CutSet that uses a lambda function in calls such as `.map` or `.filter`. This is helpful in PyTorch DataLoader with `num_jobs>0`. Without `dill`, depending on your environment, you'll see an exception or a hanging script.\n- `pip install smart_open` to read and write manifests and data in any location supported by `smart_open` (e.g. cloud, http).\n- `pip install opensmile` for feature extraction using the OpenSmile toolkit's Python wrapper.\n\n**sph2pipe.** For reading older LDC SPHERE (.sph) audio files that are compressed with codecs unsupported by ffmpeg and sox, please run:\n\n # CLI\n lhotse install-sph2pipe\n\n # Python\n from lhotse.tools import install_sph2pipe\n install_sph2pipe()\n\nIt will download it to `~/.lhotse/tools`, compile it, and auto-register in `PATH`. The program should be automatically detected and used by Lhotse.\n\n## Examples\n\nWe have example recipes showing how to prepare data and load it in Python as a PyTorch `Dataset`.\nThey are located in the `examples` directory.\n\nA short snippet to show how Lhotse can make audio data preparation quick and easy:\n\n```python\nfrom torch.utils.data import DataLoader\nfrom lhotse import CutSet, Fbank\nfrom lhotse.dataset import VadDataset, SimpleCutSampler\nfrom lhotse.recipes import prepare_switchboard\n\n# Prepare data manifests from a raw corpus distribution.\n# The RecordingSet describes the metadata about audio recordings;\n# the sampling rate, number of channels, duration, etc.\n# The SupervisionSet describes metadata about supervision segments:\n# the transcript, speaker, language, and so on.\nswbd = prepare_switchboard('/export/corpora3/LDC/LDC97S62')\n\n# CutSet is the workhorse of Lhotse, allowing for flexible data manipulation.\n# We create 5-second cuts by traversing SWBD recordings in windows.\n# No audio data is actually loaded into memory or stored to disk at this point.\ncuts = CutSet.from_manifests(\n recordings=swbd['recordings'],\n supervisions=swbd['supervisions']\n).cut_into_windows(duration=5)\n\n# We compute the log-Mel filter energies and store them on disk;\n# Then, we pad the cuts to 5 seconds to ensure all cuts are of equal length,\n# as the last window in each recording might have a shorter duration.\n# The padding will be performed once the features are loaded into memory.\ncuts = cuts.compute_and_store_features(\n extractor=Fbank(),\n storage_path='feats',\n num_jobs=8\n).pad(duration=5.0)\n\n# Construct a Pytorch Dataset class for Voice Activity Detection task:\ndataset = VadDataset()\nsampler = SimpleCutSampler(cuts, max_duration=300)\ndataloader = DataLoader(dataset, sampler=sampler, batch_size=None)\nbatch = next(iter(dataloader))\n```\n\nThe `VadDataset` will yield a batch with pairs of feature and supervision tensors such as the following - the speech\nstarts roughly at the first second (100 frames):\n\n\n",
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