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10 changes: 9 additions & 1 deletion CHANGELOG.md
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# Changelog

## `develop` branch
## Version 3.1.1 (2023-12-01)

### TL;DR

Providing `num_speakers` to [`pyannote/speaker-diarization-3.1`](https://hf.co/pyannote/speaker-diarization-3.1) now [works as expected](https://github.com/pyannote/pyannote-audio/issues/1567).

### Fixes

- fix(pipeline): fix support for setting `num_speakers` in [`pyannote/speaker-diarization-3.1`](https://hf.co/pyannote/speaker-diarization-3.1) pipeline

## Version 3.1.0 (2023-11-16)

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128 changes: 128 additions & 0 deletions CODE_OF_CONDUCT.md
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# Contributor Covenant Code of Conduct

## Our Pledge

We as members, contributors, and leaders pledge to make participation in our
community a harassment-free experience for everyone, regardless of age, body
size, visible or invisible disability, ethnicity, sex characteristics, gender
identity and expression, level of experience, education, socio-economic status,
nationality, personal appearance, race, religion, or sexual identity
and orientation.

We pledge to act and interact in ways that contribute to an open, welcoming,
diverse, inclusive, and healthy community.

## Our Standards

Examples of behavior that contributes to a positive environment for our
community include:

* Demonstrating empathy and kindness toward other people
* Being respectful of differing opinions, viewpoints, and experiences
* Giving and gracefully accepting constructive feedback
* Accepting responsibility and apologizing to those affected by our mistakes,
and learning from the experience
* Focusing on what is best not just for us as individuals, but for the
overall community

Examples of unacceptable behavior include:

* The use of sexualized language or imagery, and sexual attention or
advances of any kind
* Trolling, insulting or derogatory comments, and personal or political attacks
* Public or private harassment
* Publishing others' private information, such as a physical or email
address, without their explicit permission
* Other conduct which could reasonably be considered inappropriate in a
professional setting

## Enforcement Responsibilities

Community leaders are responsible for clarifying and enforcing our standards of
acceptable behavior and will take appropriate and fair corrective action in
response to any behavior that they deem inappropriate, threatening, offensive,
or harmful.

Community leaders have the right and responsibility to remove, edit, or reject
comments, commits, code, wiki edits, issues, and other contributions that are
not aligned to this Code of Conduct, and will communicate reasons for moderation
decisions when appropriate.

## Scope

This Code of Conduct applies within all community spaces, and also applies when
an individual is officially representing the community in public spaces.
Examples of representing our community include using an official e-mail address,
posting via an official social media account, or acting as an appointed
representative at an online or offline event.

## Enforcement

Instances of abusive, harassing, or otherwise unacceptable behavior may be
reported to the community leaders responsible for enforcement at
herve.bredin@irit.fr.
All complaints will be reviewed and investigated promptly and fairly.

All community leaders are obligated to respect the privacy and security of the
reporter of any incident.

## Enforcement Guidelines

Community leaders will follow these Community Impact Guidelines in determining
the consequences for any action they deem in violation of this Code of Conduct:

### 1. Correction

**Community Impact**: Use of inappropriate language or other behavior deemed
unprofessional or unwelcome in the community.

**Consequence**: A private, written warning from community leaders, providing
clarity around the nature of the violation and an explanation of why the
behavior was inappropriate. A public apology may be requested.

### 2. Warning

**Community Impact**: A violation through a single incident or series
of actions.

**Consequence**: A warning with consequences for continued behavior. No
interaction with the people involved, including unsolicited interaction with
those enforcing the Code of Conduct, for a specified period of time. This
includes avoiding interactions in community spaces as well as external channels
like social media. Violating these terms may lead to a temporary or
permanent ban.

### 3. Temporary Ban

**Community Impact**: A serious violation of community standards, including
sustained inappropriate behavior.

**Consequence**: A temporary ban from any sort of interaction or public
communication with the community for a specified period of time. No public or
private interaction with the people involved, including unsolicited interaction
with those enforcing the Code of Conduct, is allowed during this period.
Violating these terms may lead to a permanent ban.

### 4. Permanent Ban

**Community Impact**: Demonstrating a pattern of violation of community
standards, including sustained inappropriate behavior, harassment of an
individual, or aggression toward or disparagement of classes of individuals.

**Consequence**: A permanent ban from any sort of public interaction within
the community.

## Attribution

This Code of Conduct is adapted from the [Contributor Covenant][homepage],
version 2.0, available at
https://www.contributor-covenant.org/version/2/0/code_of_conduct.html.

Community Impact Guidelines were inspired by [Mozilla's code of conduct
enforcement ladder](https://github.com/mozilla/diversity).

[homepage]: https://www.contributor-covenant.org

For answers to common questions about this code of conduct, see the FAQ at
https://www.contributor-covenant.org/faq. Translations are available at
https://www.contributor-covenant.org/translations.
69 changes: 35 additions & 34 deletions README.md
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Using `pyannote.audio` open-source toolkit in production?
Using `pyannote.audio` open-source toolkit in production?
Make the most of it thanks to our [consulting services](https://herve.niderb.fr/consulting.html).

# `pyannote.audio` speaker diarization toolkit
Expand All @@ -9,19 +9,17 @@ Make the most of it thanks to our [consulting services](https://herve.niderb.fr/
<a href="https://www.youtube.com/watch?v=37R_R82lfwA"><img src="https://img.youtube.com/vi/37R_R82lfwA/0.jpg"></a>
</p>


## TL;DR

1. Install [`pyannote.audio`](https://github.com/pyannote/pyannote-audio) `3.0` with `pip install pyannote.audio`
1. Install [`pyannote.audio`](https://github.com/pyannote/pyannote-audio) with `pip install pyannote.audio`
2. Accept [`pyannote/segmentation-3.0`](https://hf.co/pyannote/segmentation-3.0) user conditions
3. Accept [`pyannote/speaker-diarization-3.0`](https://hf.co/pyannote/speaker-diarization-3.0) user conditions
3. Accept [`pyannote/speaker-diarization-3.1`](https://hf.co/pyannote/speaker-diarization-3.1) user conditions
4. Create access token at [`hf.co/settings/tokens`](https://hf.co/settings/tokens).


```python
from pyannote.audio import Pipeline
pipeline = Pipeline.from_pretrained(
"pyannote/speaker-diarization-3.0",
"pyannote/speaker-diarization-3.1",
use_auth_token="HUGGINGFACE_ACCESS_TOKEN_GOES_HERE")

# send pipeline to GPU (when available)
Expand All @@ -47,50 +45,53 @@ for turn, _, speaker in diarization.itertracks(yield_label=True):
- :snake: Python-first API
- :zap: multi-GPU training with [pytorch-lightning](https://pytorchlightning.ai/)


## Documentation

- [Changelog](CHANGELOG.md)
- [Frequently asked questions](FAQ.md)
- Models
- Available tasks explained
- [Applying a pretrained model](tutorials/applying_a_model.ipynb)
- [Training, fine-tuning, and transfer learning](tutorials/training_a_model.ipynb)
- Available tasks explained
- [Applying a pretrained model](tutorials/applying_a_model.ipynb)
- [Training, fine-tuning, and transfer learning](tutorials/training_a_model.ipynb)
- Pipelines
- Available pipelines explained
- [Applying a pretrained pipeline](tutorials/applying_a_pipeline.ipynb)
- [Adapting a pretrained pipeline to your own data](tutorials/adapting_pretrained_pipeline.ipynb)
- [Training a pipeline](tutorials/voice_activity_detection.ipynb)
- Available pipelines explained
- [Applying a pretrained pipeline](tutorials/applying_a_pipeline.ipynb)
- [Adapting a pretrained pipeline to your own data](tutorials/adapting_pretrained_pipeline.ipynb)
- [Training a pipeline](tutorials/voice_activity_detection.ipynb)
- Contributing
- [Adding a new model](tutorials/add_your_own_model.ipynb)
- [Adding a new task](tutorials/add_your_own_task.ipynb)
- Adding a new pipeline
- Sharing pretrained models and pipelines
- [Adding a new model](tutorials/add_your_own_model.ipynb)
- [Adding a new task](tutorials/add_your_own_task.ipynb)
- Adding a new pipeline
- Sharing pretrained models and pipelines
- Blog
- 2022-12-02 > ["How I reached 1st place at Ego4D 2022, 1st place at Albayzin 2022, and 6th place at VoxSRC 2022 speaker diarization challenges"](tutorials/adapting_pretrained_pipeline.ipynb)
- 2022-10-23 > ["One speaker segmentation model to rule them all"](https://herve.niderb.fr/fastpages/2022/10/23/One-speaker-segmentation-model-to-rule-them-all)
- 2021-08-05 > ["Streaming voice activity detection with pyannote.audio"](https://herve.niderb.fr/fastpages/2021/08/05/Streaming-voice-activity-detection-with-pyannote.html)
- 2022-12-02 > ["How I reached 1st place at Ego4D 2022, 1st place at Albayzin 2022, and 6th place at VoxSRC 2022 speaker diarization challenges"](tutorials/adapting_pretrained_pipeline.ipynb)
- 2022-10-23 > ["One speaker segmentation model to rule them all"](https://herve.niderb.fr/fastpages/2022/10/23/One-speaker-segmentation-model-to-rule-them-all)
- 2021-08-05 > ["Streaming voice activity detection with pyannote.audio"](https://herve.niderb.fr/fastpages/2021/08/05/Streaming-voice-activity-detection-with-pyannote.html)
- Videos
- [Introduction to speaker diarization](https://umotion.univ-lemans.fr/video/9513-speech-segmentation-and-speaker-diarization/) / JSALT 2023 summer school / 90 min
- [Speaker segmentation model](https://www.youtube.com/watch?v=wDH2rvkjymY) / Interspeech 2021 / 3 min
- [First releaase of pyannote.audio](https://www.youtube.com/watch?v=37R_R82lfwA) / ICASSP 2020 / 8 min
- [First releaase of pyannote.audio](https://www.youtube.com/watch?v=37R_R82lfwA) / ICASSP 2020 / 8 min

## Benchmark

Out of the box, `pyannote.audio` speaker diarization [pipeline](https://hf.co/pyannote/speaker-diarization-3.0) v3.0 is expected to be much better (and faster) than v2.x.
Out of the box, `pyannote.audio` speaker diarization [pipeline](https://hf.co/pyannote/speaker-diarization-3.1) v3.1 is expected to be much better (and faster) than v2.x.
Those numbers are diarization error rates (in %):

| Dataset \ Version | v1.1 | v2.0 | [v2.1](https://hf.co/pyannote/speaker-diarization-2.1) | [v3.0](https://hf.co/pyannote/speaker-diarization-3.0) | <a href="mailto:herve-at-niderb-dot-fr?subject=Premium pyannote.audio pipeline&body=Looks like I got your attention! Drop me an email for more details. Hervé.">Premium</a> |
| ---------------------- | ---- | ---- | ------ | ------ | --------- |
| AISHELL-4 | - | 14.6 | 14.1 | 12.3 | 12.3 |
| AliMeeting (channel 1) | - | - | 27.4 | 24.3 | 19.4 |
| AMI (IHM) | 29.7 | 18.2 | 18.9 | 19.0 | 16.7 |
| AMI (SDM) | - | 29.0 | 27.1 | 22.2 | 20.1 |
| AVA-AVD | - | - | - | 49.1 | 42.7 |
| DIHARD 3 (full) | 29.2 | 21.0 | 26.9 | 21.7 | 17.0 |
| MSDWild | - | - | - | 24.6 | 20.4 |
| REPERE (phase2) | - | 12.6 | 8.2 | 7.8 | 7.8 |
| VoxConverse (v0.3) | 21.5 | 12.6 | 11.2 | 11.3 | 9.5 |
| Benchmark | [v2.1](https://hf.co/pyannote/speaker-diarization-2.1) | [v3.1](https://hf.co/pyannote/speaker-diarization-3.1) | [Premium](https://forms.gle/eKhn7H2zTa68sMMx8) |
| ---------------------- | ------------------------------------------------------ | ------------------------------------------------------ | ---------------------------------------------- |
| AISHELL-4 | 14.1 | 12.3 | 11.9 |
| AliMeeting (channel 1) | 27.4 | 24.5 | 22.5 |
| AMI (IHM) | 18.9 | 18.8 | 16.6 |
| AMI (SDM) | 27.1 | 22.6 | 20.9 |
| AVA-AVD | 66.3 | 50.0 | 39.8 |
| CALLHOME (part 2) | 31.6 | 28.4 | 22.2 |
| DIHARD 3 (full) | 26.9 | 21.4 | 17.2 |
| Ego4D (dev.) | 61.5 | 51.2 | 43.8 |
| MSDWild | 32.8 | 25.4 | 19.8 |
| REPERE (phase2) | 8.2 | 7.8 | 7.6 |
| VoxConverse (v0.3) | 11.2 | 11.2 | 9.4 |

[Diarization error rate](http://pyannote.github.io/pyannote-metrics/reference.html#diarization) (in %)

## Citations

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4 changes: 2 additions & 2 deletions doc/requirements.txt
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ipython==7.16.3
ipython==8.10.0
recommonmark
Sphinx==2.2.2
Sphinx==3.0.4
sphinx_rtd_theme==0.4.3
9 changes: 8 additions & 1 deletion pyannote/audio/pipelines/clustering.py
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Expand Up @@ -97,7 +97,13 @@ def filter_embeddings(
speaker_idx : (num_embeddings, ) array
"""

chunk_idx, speaker_idx = np.where(~np.any(np.isnan(embeddings), axis=2))
# whether speaker is active
active = np.sum(segmentations.data, axis=1) > 0
# whether speaker embedding extraction went fine
valid = ~np.any(np.isnan(embeddings), axis=2)

# indices of embeddings that are both active and valid
chunk_idx, speaker_idx = np.where(active * valid)

# sample max_num_embeddings embeddings
num_embeddings = len(chunk_idx)
Expand Down Expand Up @@ -240,6 +246,7 @@ def __call__(
)

num_embeddings, _ = train_embeddings.shape

num_clusters, min_clusters, max_clusters = self.set_num_clusters(
num_embeddings,
num_clusters=num_clusters,
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