diff --git a/CHANGELOG.md b/CHANGELOG.md index 346d8ad26..777f41f38 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -1,6 +1,14 @@ # 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) diff --git a/CODE_OF_CONDUCT.md b/CODE_OF_CONDUCT.md new file mode 100644 index 000000000..b53ae3b44 --- /dev/null +++ b/CODE_OF_CONDUCT.md @@ -0,0 +1,128 @@ +# 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. diff --git a/README.md b/README.md index d63cd26f8..a82a2488f 100644 --- a/README.md +++ b/README.md @@ -1,4 +1,4 @@ -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 @@ -9,19 +9,17 @@ Make the most of it thanks to our [consulting services](https://herve.niderb.fr/

- ## 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) @@ -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) | Premium | -| ---------------------- | ---- | ---- | ------ | ------ | --------- | -| 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 diff --git a/doc/requirements.txt b/doc/requirements.txt index a0b596dbc..5377da241 100644 --- a/doc/requirements.txt +++ b/doc/requirements.txt @@ -1,4 +1,4 @@ -ipython==7.16.3 +ipython==8.10.0 recommonmark -Sphinx==2.2.2 +Sphinx==3.0.4 sphinx_rtd_theme==0.4.3 diff --git a/pyannote/audio/pipelines/clustering.py b/pyannote/audio/pipelines/clustering.py index b63ab214f..80098ea24 100644 --- a/pyannote/audio/pipelines/clustering.py +++ b/pyannote/audio/pipelines/clustering.py @@ -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) @@ -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, diff --git a/tutorials/intro.ipynb b/tutorials/intro.ipynb index 75344267a..572ea2f6d 100644 --- a/tutorials/intro.ipynb +++ b/tutorials/intro.ipynb @@ -1,3888 +1,1065 @@ { - "cells": [ - { - "cell_type": "markdown", - "metadata": { - "id": "9-KmdPlBYnp6" - }, - "source": [ - "\"Open" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "1Fs2d8otYnp7" - }, - "source": [ - "[`pyannote.audio`](https://github.com/pyannote/pyannote-audio) is an open-source toolkit written in Python for **speaker diarization**. \n", - "\n", - "Based on [`PyTorch`](https://pytorch.org) machine learning framework, it provides a set of trainable end-to-end neural building blocks that can be combined and jointly optimized to build speaker diarization pipelines. \n", - "\n", - "`pyannote.audio` also comes with pretrained [models](https://huggingface.co/models?other=pyannote-audio-model) and [pipelines](https://huggingface.co/models?other=pyannote-audio-pipeline) covering a wide range of domains for voice activity detection, speaker segmentation, overlapped speech detection, speaker embedding reaching state-of-the-art performance for most of them. \n", - "\n", - "**This notebook will teach you how to apply those pretrained pipelines on your own data.**\n", - "\n", - "Make sure you run it using a GPU (or it might otherwise be slow...)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "tckHJKZnYnp7" - }, - "source": [ - "## Installation" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "ai082p4HYnp7", - "outputId": "bb673846-8b58-4743-cea2-6c6270632d7f" - }, - "outputs": [], - "source": [ - "!pip install -qq pyannote.audio==3.0.1\n", - "!pip install -qq ipython==7.34.0" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "qggK-7VBYnp8" - }, - "source": [ - "# Visualization with `pyannote.core`\n", - "\n", - "For the purpose of this notebook, we will download and use an audio file coming from the [AMI corpus](http://groups.inf.ed.ac.uk/ami/corpus/), which contains a conversation between 4 people in a meeting room." - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": { - "id": "uJWoQiJgYnp8" - }, - "outputs": [], - "source": [ - "!wget -q http://groups.inf.ed.ac.uk/ami/AMICorpusMirror/amicorpus/ES2004a/audio/ES2004a.Mix-Headset.wav\n", - "DEMO_FILE = {'uri': 'ES2004a.Mix-Headset', 'audio': 'ES2004a.Mix-Headset.wav'}" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "EPIapoCJYnp8" - }, - "source": [ - "Because AMI is a benchmarking dataset, it comes with manual annotations (a.k.a *groundtruth*). \n", - "Let us load and visualize the expected output of the speaker diarization pipeline.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": { - "id": "Mmm0Q22JYnp8" - }, - "outputs": [], - "source": [ - "!wget -q https://raw.githubusercontent.com/pyannote/AMI-diarization-setup/main/only_words/rttms/test/ES2004a.rttm" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 233 - }, - "id": "ToqCwl_FYnp9", - "outputId": "a1d9631f-b198-44d1-ff6d-ec304125a9f4" - }, - "outputs": [ + "cells": [ { - "data": { - "image/png": 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- "text/plain": [ - "" + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "view-in-github" + }, + "source": [ + "\"Open" ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# load groundtruth\n", - "from pyannote.database.util import load_rttm\n", - "_, groundtruth = load_rttm('ES2004a.rttm').popitem()\n", - "\n", - "# visualize groundtruth\n", - "groundtruth" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "p_R9T9Y5Ynp9" - }, - "source": [ - "For the rest of this notebook, we will only listen to and visualize a one-minute long excerpt of the file (but will process the whole file anyway)." - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 230 }, - "id": "bAHza4Y1Ynp-", - "outputId": "c4cc2369-bfe4-4ac2-bb71-37602e7c7a8a" - }, - "outputs": [ { - "data": { - "image/png": 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- "text/plain": [ - "" + "cell_type": "markdown", + "metadata": { + "id": "9-KmdPlBYnp6" + }, + "source": [ + "\"Open" ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from pyannote.core import Segment, notebook\n", - "# make notebook visualization zoom on 600s < t < 660s time range\n", - "EXCERPT = Segment(600, 660)\n", - "notebook.crop = EXCERPT\n", - "\n", - "# visualize excerpt groundtruth\n", - "groundtruth" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "L3FQXT5FYnp-" - }, - "source": [ - "This nice visualization is brought to you by [`pyannote.core`](http://pyannote.github.io/pyannote-core/) and basically indicates when each speaker speaks. " - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 62 }, - "id": "rDhZ3bXEYnp-", - "outputId": "a82efe4e-2f9c-48bd-94fb-c62af3a3cb43" - }, - "outputs": [ { - "data": { - "text/html": [ - "\n", - " \n", - " " - ], - "text/plain": [ - "" + "cell_type": "markdown", + "metadata": { + "id": "1Fs2d8otYnp7" + }, + "source": [ + "[`pyannote.audio`](https://github.com/pyannote/pyannote-audio) is an open-source toolkit written in Python for **speaker diarization**.\n", + "\n", + "Based on [`PyTorch`](https://pytorch.org) machine learning framework, it provides a set of trainable end-to-end neural building blocks that can be combined and jointly optimized to build speaker diarization pipelines.\n", + "\n", + "`pyannote.audio` also comes with pretrained [models](https://huggingface.co/models?other=pyannote-audio-model) and [pipelines](https://huggingface.co/models?other=pyannote-audio-pipeline) covering a wide range of domains for voice activity detection, speaker segmentation, overlapped speech detection, speaker embedding reaching state-of-the-art performance for most of them.\n", + "\n", + "**This notebook will teach you how to apply those pretrained pipelines on your own data.**\n", + "\n", + "Make sure you run it using a GPU (or it might otherwise be slow...)" ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from pyannote.audio import Audio \n", - "from IPython.display import Audio as IPythonAudio\n", - "waveform, sr = Audio(mono=\"downmix\").crop(DEMO_FILE, EXCERPT)\n", - "IPythonAudio(waveform.flatten(), rate=sr)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "hkzox7QIYnp_" - }, - "source": [ - "# Processing your own audio file (optional)\n", - "\n", - "In case you just want to go ahead with the demo file, skip this section entirely.\n", - "\n", - "In case you want to try processing your own audio file, proceed with running this section. It will offer you to upload an audio file (preferably a `wav` file but all formats supported by [`SoundFile`](https://pysoundfile.readthedocs.io/en/latest/) should work just fine)." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "3hmFmLzFYnp_" - }, - "source": [ - "## Upload audio file" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "xC05jFO_Ynp_", - "outputId": "c5502632-56ae-4adb-8bdc-112deedc8893" - }, - "outputs": [], - "source": [ - "import google.colab\n", - "own_file, _ = google.colab.files.upload().popitem()\n", - "OWN_FILE = {'audio': own_file}\n", - "notebook.reset()\n", - "\n", - "# load audio waveform and play it\n", - "waveform, sample_rate = Audio(mono=\"downmix\")(OWN_FILE)\n", - "IPythonAudio(data=waveform.squeeze(), rate=sample_rate, autoplay=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "ctw4nLaPYnp_" - }, - "source": [ - "Simply replace `DEMO_FILE` by `OWN_FILE` in the rest of the notebook.\n", - "\n", - "Note, however, that unless you provide a groundtruth annotation in the next cell, you will (obviously) not be able to visualize groundtruth annotation nor evaluate the performance of the diarization pipeline quantitatively" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "x9AQgDzFYnp_" - }, - "source": [ - "## Upload groundtruth (optional)\n", - "\n", - "The groundtruth file is expected to use the RTTM format, with one line per speech turn with the following convention:\n", - "\n", - "```\n", - "SPEAKER {file_name} 1 {start_time} {duration} {speaker_name} \n", - "```" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "iZaFudpDYnp_", - "outputId": "981274fa-e654-4091-c838-91c81f921e5d" - }, - "outputs": [ + }, { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " Upload widget is only available when the cell has been executed in the\n", - " current browser session. Please rerun this cell to enable.\n", - " \n", - " " - ], - "text/plain": [ - "" + "cell_type": "markdown", + "metadata": { + "id": "tckHJKZnYnp7" + }, + "source": [ + "## Installation" ] - }, - "metadata": {}, - "output_type": "display_data" }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "Saving sample.rttm to sample.rttm\n" - ] + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "ai082p4HYnp7" + }, + "outputs": [], + "source": [ + "!pip install -qq pyannote.audio==3.1.1\n", + "!pip install -qq ipython==7.34.0" + ] }, { - "data": { - "image/png": "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", - "text/plain": [ - "" + "cell_type": "markdown", + "metadata": { + "id": "qggK-7VBYnp8" + }, + "source": [ + "# Visualization with `pyannote.core`\n", + "\n", + "For the purpose of this notebook, we will download and use an audio file coming from the [AMI corpus](http://groups.inf.ed.ac.uk/ami/corpus/), which contains a conversation between 4 people in a meeting room." ] - }, - "execution_count": null, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "groundtruth_rttm, _ = google.colab.files.upload().popitem()\n", - "groundtruths = load_rttm(groundtruth_rttm)\n", - "if OWN_FILE['audio'] in groundtruths:\n", - " groundtruth = groundtruths[OWN_FILE['audio']]\n", - "else:\n", - " _, groundtruth = groundtruths.popitem()\n", - "groundtruth" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "5MclWK2GYnp_" - }, - "source": [ - "# Speaker diarization with `pyannote.pipeline`\n", - "\n", - "We are about to run a full speaker diarization pipeline, that includes speaker segmentation, speaker embedding, and a final clustering step. **Brace yourself!**\n", - "\n", - "To load the speaker diarization pipeline, \n", - "\n", - "* accept the user conditions on [hf.co/pyannote/speaker-diarization-3.0](https://hf.co/pyannote/speaker-diarization-3.0)\n", - "* accept the user conditions on [hf.co/pyannote/segmentation-3.0](https://hf.co/pyannote/segmentation-3.0)\n", - "* login using `notebook_login` below" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 301, - "referenced_widgets": [ - "c8731777ce834e58a76a295076200cfc", - "859b12a6d95b4c6f987791ca848122b9", - "94756148d2e94a93ae233baba20af683", - "ba18cded436e486da34882d821d8f1eb", - "99898e6ee64a46bd832af112e79b58b7", - "79184c8c2a6f4b7493bb7f6983f18a09", - "ea95ffd922c0455d957120f034e541f8", - "13525aa369a9410a83343952ab511f3c", - "b2be65e192384c948fb8987d4cfca505", - "333b42ca7aa44788b1c22724eb11bcc3", - "0e382d66f09f4958a40baa7ab83c4ccb", - "6a45ce374e2e47ba9457d02e02522748", - "765485a1d3f941d28b79782dcffbf401", - "3499ef4dd9f243d9bef00b396e78ed69" - ] }, - "id": "r5u7VMb-YnqB", - "outputId": "c714a997-d4f8-417a-e5ad-0a4924333859" - }, - "outputs": [ { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "6e56329c30c0441c8d45df3975e75a76", - "version_major": 2, - "version_minor": 0 + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "uJWoQiJgYnp8" }, - "text/plain": [ - "VBox(children=(HTML(value='
" + "cell_type": "markdown", + "metadata": { + "id": "EPIapoCJYnp8" + }, + "source": [ + "Because AMI is a benchmarking dataset, it comes with manual annotations (a.k.a *groundtruth*). \n", + "Let us load and visualize the expected output of the speaker diarization pipeline.\n" ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "diarization" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "DLhErS6wYnqB" - }, - "source": [ - "# Evaluation with `pyannote.metrics`\n", - "\n", - "Because groundtruth is available, we can evaluate the quality of the diarization pipeline by computing the [diarization error rate](http://pyannote.github.io/pyannote-metrics/reference.html#diarization)." - ] - }, - { - "cell_type": "code", - "execution_count": 35, - "metadata": { - "id": "vNHQRTUIYnqB" - }, - "outputs": [], - "source": [ - "from pyannote.metrics.diarization import DiarizationErrorRate\n", - "metric = DiarizationErrorRate()\n", - "der = metric(groundtruth, diarization)" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" }, - "id": "9d0vKQ0fYnqB", - "outputId": "9a664753-cd84-4211-9153-d33e929bb252" - }, - "outputs": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "diarization error rate = 19.8%\n" - ] - } - ], - "source": [ - "print(f'diarization error rate = {100 * der:.1f}%')" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "Xz5QJV9nYnqB" - }, - "source": [ - "This implementation of diarization error rate is brought to you by [`pyannote.metrics`](http://pyannote.github.io/pyannote-metrics/).\n", - "\n", - "It can also be used to improve visualization by find the optimal one-to-one mapping between groundtruth and hypothesized speakers." - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 230 + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "Mmm0Q22JYnp8" + }, + "outputs": [], + "source": [ + "!wget -q https://raw.githubusercontent.com/pyannote/AMI-diarization-setup/main/only_words/rttms/test/ES2004a.rttm" + ] }, - "id": "xMLf4mrYYnqB", - "outputId": "ed08bcc8-24c6-439c-a244-3a673ff480b0" - }, - "outputs": [ { - "data": { - "image/png": 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+ "text/plain": [ + "" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# load groundtruth\n", + "from pyannote.database.util import load_rttm\n", + "_, groundtruth = load_rttm('ES2004a.rttm').popitem()\n", + "\n", + "# visualize groundtruth\n", + "groundtruth" ] - }, - "execution_count": 19, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "mapping = metric.optimal_mapping(groundtruth, diarization)\n", - "diarization.rename_labels(mapping=mapping)" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 230 }, - "id": "Z0ewsLlQYnqB", - "outputId": "8a8cd040-ee1d-48f7-d4be-eef9e08e9e55" - }, - "outputs": [ { - "data": { - "image/png": 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"@jupyter-widgets/base", - "_view_module_version": "1.2.0", - "_view_name": "LayoutView", - "align_content": null, - "align_items": null, - "align_self": null, - "border": null, - "bottom": null, - "display": null, - "flex": null, - "flex_flow": null, - "grid_area": null, - "grid_auto_columns": null, - "grid_auto_flow": null, - "grid_auto_rows": null, - "grid_column": null, - "grid_gap": null, - "grid_row": null, - "grid_template_areas": null, - "grid_template_columns": null, - "grid_template_rows": null, - "height": null, - "justify_content": null, - "justify_items": null, - "left": null, - "margin": null, - "max_height": null, - "max_width": null, - "min_height": null, - "min_width": null, - "object_fit": null, - "object_position": null, - "order": null, - "overflow": null, - "overflow_x": null, - "overflow_y": null, - "padding": null, - "right": null, - "top": null, - "visibility": null, - "width": null - } + "source": [ + "from pyannote.core import Segment, notebook\n", + "# make notebook visualization zoom on 600s < t < 660s time range\n", + "EXCERPT = Segment(600, 660)\n", + "notebook.crop = EXCERPT\n", + "\n", + "# visualize excerpt groundtruth\n", + "groundtruth" + ] }, - "27f6f437c5264368bc2c679942ad1e53": { - "model_module": "@jupyter-widgets/base", - "model_module_version": "1.2.0", - "model_name": "LayoutModel", - "state": { - "_model_module": "@jupyter-widgets/base", - "_model_module_version": "1.2.0", - "_model_name": "LayoutModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "1.2.0", - "_view_name": "LayoutView", - "align_content": null, - "align_items": null, - "align_self": null, - "border": null, - "bottom": null, - "display": null, - "flex": null, - "flex_flow": null, - "grid_area": null, - "grid_auto_columns": null, - "grid_auto_flow": null, - "grid_auto_rows": null, - "grid_column": null, - "grid_gap": null, - "grid_row": null, - "grid_template_areas": null, - "grid_template_columns": null, - "grid_template_rows": null, - "height": null, - "justify_content": null, - "justify_items": null, - "left": null, - "margin": null, - "max_height": null, - "max_width": null, - "min_height": null, - "min_width": null, - "object_fit": null, - "object_position": null, - "order": null, - "overflow": null, - "overflow_x": null, - "overflow_y": null, - "padding": null, - "right": null, - "top": null, - "visibility": null, - "width": null - } + { + "cell_type": "markdown", + "metadata": { + "id": "L3FQXT5FYnp-" + }, + "source": [ + "This nice visualization is brought to you by [`pyannote.core`](http://pyannote.github.io/pyannote-core/) and basically indicates when each speaker speaks." + ] }, - "28004251b0e44a6c9dfa7ce1b30dcb18": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "HBoxModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "HBoxModel", - 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"min_width": null, - "object_fit": null, - "object_position": null, - "order": null, - "overflow": null, - "overflow_x": null, - "overflow_y": null, - "padding": null, - "right": null, - "top": null, - "visibility": null, - "width": null - } + "source": [ + "from pyannote.audio import Audio\n", + "from IPython.display import Audio as IPythonAudio\n", + "waveform, sr = Audio(mono=\"downmix\").crop(DEMO_FILE, EXCERPT)\n", + "IPythonAudio(waveform.flatten(), rate=sr)" + ] }, - "3499ef4dd9f243d9bef00b396e78ed69": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "DescriptionStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "DescriptionStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "1.2.0", - "_view_name": "StyleView", - "description_width": "" - } + { + "cell_type": "markdown", + "metadata": { + "id": "hkzox7QIYnp_" + }, + "source": [ + "# Processing your own audio file (optional)\n", + "\n", + "In case you just want to go ahead with the demo file, skip this section entirely.\n", + "\n", + "In case you want to try processing your own audio file, proceed with running this section. It will offer you to upload an audio file (preferably a `wav` file but all formats supported by [`SoundFile`](https://pysoundfile.readthedocs.io/en/latest/) should work just fine)." + ] }, - "358c3a67f8b54c4c899e095611fa116b": { - "model_module": "@jupyter-widgets/base", - "model_module_version": "1.2.0", - "model_name": "LayoutModel", - "state": { - "_model_module": "@jupyter-widgets/base", - "_model_module_version": "1.2.0", - "_model_name": "LayoutModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "1.2.0", - "_view_name": "LayoutView", - "align_content": null, - "align_items": null, - "align_self": null, - "border": null, - "bottom": null, - "display": null, - "flex": null, - "flex_flow": null, - "grid_area": null, - "grid_auto_columns": null, - "grid_auto_flow": null, - "grid_auto_rows": null, - "grid_column": null, - "grid_gap": null, - "grid_row": null, - "grid_template_areas": null, - "grid_template_columns": null, - "grid_template_rows": null, - 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"layout": "IPY_MODEL_0adb304bf90f4079a4031caea1cfb924", - "placeholder": "​", - "style": "IPY_MODEL_40021e0b59fe4e1e9bac351dbec57c6c", - "value": "Downloading: 100%" - } + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "xC05jFO_Ynp_" + }, + "outputs": [], + "source": [ + "import google.colab\n", + "own_file, _ = google.colab.files.upload().popitem()\n", + "OWN_FILE = {'audio': own_file}\n", + "notebook.reset()\n", + "\n", + "# load audio waveform and play it\n", + "waveform, sample_rate = Audio(mono=\"downmix\")(OWN_FILE)\n", + "IPythonAudio(data=waveform.squeeze(), rate=sample_rate, autoplay=True)" + ] }, - "3bd33a372aad4c438f64d73c97f14c6a": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "DescriptionStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "DescriptionStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "1.2.0", - "_view_name": "StyleView", - "description_width": "" - } + { + "cell_type": "markdown", + "metadata": { + "id": "ctw4nLaPYnp_" + }, + "source": [ + "Simply replace `DEMO_FILE` by `OWN_FILE` in the rest of the notebook.\n", + "\n", + "Note, however, that unless you provide a groundtruth annotation in the next cell, you will (obviously) not be able to visualize groundtruth annotation nor evaluate the performance of the diarization pipeline quantitatively" + ] }, - "40021e0b59fe4e1e9bac351dbec57c6c": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "DescriptionStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "DescriptionStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "1.2.0", - "_view_name": "StyleView", - "description_width": "" - } + { + "cell_type": "markdown", + "metadata": { + "id": "x9AQgDzFYnp_" + }, + "source": [ + "## Upload groundtruth (optional)\n", + "\n", + "The groundtruth file is expected to use the RTTM format, with one line per speech turn with the following convention:\n", + "\n", + "```\n", + "SPEAKER {file_name} 1 {start_time} {duration} {speaker_name} \n", + "```" + ] }, - 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Please rerun this cell to enable.\n", + " \n", + " " + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saving sample.rttm to sample.rttm\n" + ] + }, + { + "data": { + "image/png": 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and a final clustering step. **Brace yourself!**\n", + "\n", + "To load the speaker diarization pipeline,\n", + "\n", + "* accept the user conditions on [hf.co/pyannote/speaker-diarization-3.1](https://hf.co/pyannote/speaker-diarization-3.1)\n", + "* accept the user conditions on [hf.co/pyannote/segmentation-3.0](https://hf.co/pyannote/segmentation-3.0)\n", + "* login using `notebook_login` below" + ] }, - "41eb32a6fef141ff9cc3ce6e4d771822": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "HBoxModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "HBoxModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "1.5.0", - "_view_name": "HBoxView", - "box_style": "", - "children": [ - "IPY_MODEL_0d10fb0edc9144b1a1fc1f2c9e322410", - "IPY_MODEL_32accb0adfa24c62a75c15c8ec88df8c", - 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- "right": null, - "top": null, - "visibility": null, - "width": null - } + "source": [ + "print(f'diarization error rate = {100 * der:.1f}%')" + ] }, - "c9974003727a401797953ef2885db5a2": { - "model_module": "@jupyter-widgets/base", - "model_module_version": "1.2.0", - "model_name": "LayoutModel", - "state": { - "_model_module": "@jupyter-widgets/base", - "_model_module_version": "1.2.0", - "_model_name": "LayoutModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "1.2.0", - "_view_name": "LayoutView", - "align_content": null, - "align_items": null, - "align_self": null, - "border": null, - "bottom": null, - "display": null, - "flex": null, - "flex_flow": null, - "grid_area": null, - "grid_auto_columns": null, - "grid_auto_flow": null, - "grid_auto_rows": null, - "grid_column": null, - "grid_gap": null, - "grid_row": null, - "grid_template_areas": null, - "grid_template_columns": null, - "grid_template_rows": null, - "height": null, - 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+ "text/plain": [ + "" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } ], - "layout": "IPY_MODEL_2d7a0b901d7044d5b1f273a3e9bea560" - } - }, - "d13ba6030aff42bca48c72ff071c44c0": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "FloatProgressModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "FloatProgressModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "1.5.0", - "_view_name": "ProgressView", - "bar_style": "success", - "description": "", - "description_tooltip": null, - "layout": "IPY_MODEL_c8e0c9a60ef34d2caee9d55a3c21c3d4", - "max": 5534328, - "min": 0, - "orientation": "horizontal", - "style": "IPY_MODEL_764aa53d75324d73ab06936c52fd8fc8", - "value": 5534328 - } + "source": [ + "mapping = metric.optimal_mapping(groundtruth, diarization)\n", + 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