tutorials/applying_a_pipeline.ipynb

{"cells":[{"cell_type":"markdown","metadata":{},"source":["**<a href=\"https://colab.research.google.com/github/pyannote/pyannote-audio/blob/develop/tutorials/applying_a_pipeline.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>**"]},{"cell_type":"markdown","metadata":{"id":"uiNbotxCeq4b"},"source":["# Applying a pretrained pipeline\n","\n","In this tutorial, you will learn how to apply `pyannote.audio` pipelines on an audio file.\n","\n","A pipeline takes an audio file as input and returns a labeled temporal segmentation of the audio file.\n","\n","More precisely, it usually applies a pretrained model (= neural network) on the audio file, post-processes the output of the model, and returns its output as a [`pyannote.core.Annotation`](http://pyannote.github.io/pyannote-core/structure.html#annotation) instance. It should become clearer as you keep reading..."]},{"cell_type":"markdown","metadata":{"id":"z_4VUpZxesS2"},"source":["## Tutorial setup"]},{"cell_type":"markdown","metadata":{"id":"gbonpHYte3FC"},"source":["### `Google Colab` setup\n"]},{"cell_type":"markdown","metadata":{"id":"hxhhcj8Fe7v7"},"source":["If you are running this tutorial on `Colab`, execute the following commands in order to setup `Colab` environment. These commands will install `pyannote.audio`, and download resources used in this tutorial."]},{"cell_type":"code","execution_count":4,"metadata":{"executionInfo":{"elapsed":17283,"status":"ok","timestamp":1704808539241,"user":{"displayName":"Clément PAGES","userId":"11757386314069785178"},"user_tz":-60},"id":"NFdANMKsfD1p"},"outputs":[],"source":["!pip install -qq pyannote.audio==3.1.1\n","!pip install -qq ipython==7.34.0\n","!wget -q \"https://github.com/pyannote/pyannote-audio/raw/develop/tutorials/assets/sample.wav\"\n","!wget -q \"https://github.com/pyannote/pyannote-audio/raw/develop/tutorials/assets/sample.rttm\"\n","!wget -q -P ./assets/ \"https://github.com/pyannote/pyannote-audio/blob/develop/tutorials/assets/download-model.png\"\n","!wget -q -P ./assets/ \"https://github.com/pyannote/pyannote-audio/blob/develop/tutorials/assets/download-pipeline.png\""]},{"cell_type":"markdown","metadata":{"id":"PScZ6o_9gkfo"},"source":["⚠ Restart the runtime (Runtime > Restart session)."]},{"cell_type":"code","execution_count":13,"metadata":{"executionInfo":{"elapsed":458,"status":"ok","timestamp":1704809048938,"user":{"displayName":"Clément PAGES","userId":"11757386314069785178"},"user_tz":-60},"id":"ZU_QIx1UhcV6"},"outputs":[],"source":["AUDIO_FILE = \"sample.wav\"\n","REFERENCE = \"sample.rttm\""]},{"cell_type":"markdown","metadata":{"id":"d-qKbc9shI6o"},"source":["### Non `Google Colab` setup"]},{"cell_type":"markdown","metadata":{"id":"0KEh-IGqho6F"},"source":["If you are not using Colab, clone `pyannote.audio` [GitHub repository](https://github.com/pyannote/pyannote-audio) and update ROOT_DIR accordingly"]},{"cell_type":"code","execution_count":10,"metadata":{"id":"DuoohiL6hOiJ"},"outputs":[],"source":["ROOT_DIR = \"<path-to-pyannote-github-repo>/pyannote-audio\"\n","AUDIO_FILE = f\"{ROOT_DIR}/tutorials/assets/sample.wav\"\n","REFERENCE = f\"{ROOT_DIR}/tutorials/assets/sample.rttm\""]},{"cell_type":"markdown","metadata":{"id":"yA9JsY84eq4e"},"source":["## Loading pipeline from 🤗 hub\n","\n","A bunch of pretrained pipelines are available on [🤗 Huggingface model hub](https://hf.co/models?other=pyannote-audio-pipeline) and can be listed by looking for the [`pyannote-audio-pipeline`](https://hf.co/models?other=pyannote-audio-pipeline) tag."]},{"cell_type":"code","execution_count":2,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":2323,"status":"ok","timestamp":1704808592263,"user":{"displayName":"Clément PAGES","userId":"11757386314069785178"},"user_tz":-60},"id":"eAXGgYt8eq4f","outputId":"86772355-3bf0-42e5-c478-2d35a80853e9"},"outputs":[{"data":{"text/plain":["['pyannote/overlapped-speech-detection',\n"," 'pyannote/speaker-diarization',\n"," 'pyannote/speaker-segmentation',\n"," 'pyannote/voice-activity-detection',\n"," 'pyannote/speaker-diarization-3.0',\n"," 'pyannote/speaker-diarization-3.1']"]},"execution_count":2,"metadata":{},"output_type":"execute_result"}],"source":["from huggingface_hub import HfApi\n","available_pipelines = [p.modelId for p in HfApi().list_models(filter=\"pyannote-audio-pipeline\")]\n","list(filter(lambda p: p.startswith(\"pyannote/\"), available_pipelines))"]},{"cell_type":"markdown","metadata":{"id":"fry3qMrJeq4h"},"source":["Official [pyannote.audio](https://github.com/pyannote/pyannote-audio) pipelines (i.e. those under the [`pyannote` organization](https://hf.co/pyannote) umbrella) are open-source, but gated. It means that you have to first accept users conditions on their respective Huggingface page to access the pretrained weights and hyper-parameters. Despite this initial process, those pipelines can perfectly be downloaded for later offline use: keep reading this tutorial until the end to learn how to do that.\n","\n","For instance, to load the speaker diarization pipeline used in this tutorial, you have to visit [hf.co/pyannote/speaker-diarization](https://hf.co/pyannote/speaker-diarization), accept the terms, visit [hf.co/pyannote/segmentation](https://hf.co/pyannote/segmentation) (used internally by the speaker diarization pipeline), accept the terms, and log in using `notebook_login` below:"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":145,"referenced_widgets":["6fe26270a8794dea85b9ac8dedde6353","f9969c7acd834359bd67e83c31b82c0b","ef6be001daaa465598bdf20c304bae0b","a8e61b300a324911b1d3babf6b1ebac1","45c2352ba41b46288c8ba74c661e1f85","78f428e1d8ac4c78bd84d208a78b1fe5","9d7b5e47af9448c98a2a434f02b54633","253408a6cab947c7b9ae40a14e255328","a435c85128f148cb80152d03825710e2","90533da4fc1341a28bc247bc9fbbbe9e","6556790d1997431a8476447297e3f595","5253489738b249f69b30f1c84884a95a","3f500b1182a945c3b8b546b1bd04f98e","c3b3069a93994e55b993cab04c066f17","f77186e2976d4a7daa798fd9f0f7b4cb","7ffa4930e0a7421e9464abff87bbde7f","1dd6bfb824ba4919ba392e0d4cd5ac82","7c23cd10c573438c840148ba1afaa5f3","bc4d3f45eaf04cccb7a655ef87d7a927","36f60aa99ea84b739f066c6ee216f159","5033834cc66f49efaa16b240245ec1d3","0b127e22a6de4339aecc1e76a6719d7f","8a7afaf126ac49dcaf24d64c2d918086","1b5f49211db54348924a05482ca7379d","1f3be36a57074214a5f92e08165e41be","275642719b694328ad3b0fb06b53e42a","ae378b480ed747438bcbe147a7e8d277","b22a1d21940e4995b72642b003ae1301","58e423b86bf349aa89fc81a2b0d0f697","a3ebe0f0e6e44522a2545db0ed47af62","dc9c065286e544df921e5bc4a51faf64","16244ab1dc76493d9fafad80a9b65700"]},"executionInfo":{"elapsed":418,"status":"ok","timestamp":1704808601143,"user":{"displayName":"Clément PAGES","userId":"11757386314069785178"},"user_tz":-60},"id":"IdXJumYveq4i","outputId":"cb87e106-b30f-4ec8-e726-d797ca98698b"},"outputs":[],"source":["from huggingface_hub import notebook_login\n","notebook_login()"]},{"cell_type":"markdown","metadata":{"id":"ym2671Fweq4j"},"source":["Once authenticated, you can load the pipeline (and the internal models)..."]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":266,"referenced_widgets":["5a2a95f41f014663b5d131635844e3c8","f8df65e5ad6444648681230f415b8d08","17e14569a3f74cbf991b582c27fdf280","4e0ca7f084804d30a89cb9dc439b6cdc","d304f698c6f34f3692548267ca0aba47","3f69734124f7449da0efffbbfd1153fe","686b58e0ccee468f938ea5659f3a9623","e98b1f7ec5e04e748069340fa2fcec2d","616f35eccdd34bcdb5c9f700103ff538","185457147fe44751a1c6e7ae6ad23570","b02b253086e34249ae1d896a75a08960","0b191fb0d6db4f1a98a641dc50445f1e","4a589ea883de4409b7a8275127747fa7","b5f4829158cf4a5c8350966d8eacbf60","7cab72ad0ace45d58d6954021082ace7","5440ac4cffa0438c99c0daeca27a4087","0d0dfc2576784812b6abf673752fa812","84e416e01cfa4a79b0294a44cfd77f32","b33021db4313476ab8bcb85bb1b190d7","6d5431955c09424a96e825aa9bba1b62","54eacc5604f64f658617cde596a728dd","1749a0e7fc6847849ab40ac7f764b699","ff048516fdba44a48a69c51a82aa7aaa","775611277f1948a5ab95641c4e92ff2e","c955f8144b46451493474e02726b5deb","c9eeb26bd5e049f89205e494d54e3818","d4d428f0b0c44756a128adae747716d2","70d672feec1c452aa49a40a3b8c1a4f0","e8b3188ee4934ab4becbc431fc4ccb0b","754a843fd634446b8d246998eaded1fa","1e53e26668aa4f8dabdd6973b79b5b9f","cf5ae82d36384a728d8ca893cc27392b","5cc77ca8ba304ad88342ed6ced6c6a4a","6412ef268e11484f8bbcdb510f66ee21","a987408c2f994329a9a80208b22e3c52","9446f61936c4421d9610464984890353","a80c0fb6a1254bb9a00741c1346b9d9a","b55e95f31216480fa6c81bd1b57ad729","517a1b1faec045f7847ca84f0538f4e1","008a47e4d1174b188e75232fed325398","30864d7b4d074994ba643c73875a67b8","b852998433e24f7fad1fe27e249085f9","46ac2d0bfedc47c8836e8dde0fc91662","58b140d0453a4a219bc64e0152cc5c5e","3bb60a8db7c0406f8debdc8dd88bcdb4","bf644ae3e2894787bb416740749200cd","0fccc53081a34d58a46912d1ceae0533","39cd6fe37b6f4476b6032c29f026d570","87a0e712c0b045449b98ee368c62fff2","2be7c1f5fe32462f8d1cd7ea57cfbf3a","d6135ce9cfaa4f8496a70cee4f1e89a7","00ac431507a54c0d9a41244013b1c0f3","6f318bc3e900434a9134a79e4057f666","7580595b498a4c50b748af9df67132e2","3f1d750804a54d78a6abff2623441360"]},"executionInfo":{"elapsed":30028,"status":"ok","timestamp":1704808674003,"user":{"displayName":"Clément PAGES","userId":"11757386314069785178"},"user_tz":-60},"id":"1NihzPUPeq4k","outputId":"04275648-23ef-4453-9bf4-8d8dfadd99e0"},"outputs":[],"source":["from pyannote.audio import Pipeline\n","pipeline = Pipeline.from_pretrained(\"pyannote/speaker-diarization-3.1\", use_auth_token=True)"]},{"cell_type":"markdown","metadata":{"id":"cZhtRXAHeq4l"},"source":["## Processing a file from disk"]},{"cell_type":"markdown","metadata":{"id":"X7hQRbzeeq4m"},"source":["... and apply it to an audio file.  \n","\n","The pipeline will automatically use GPUs when available.\n","On CPU it might take a long while (up to 10x RT)."]},{"cell_type":"code","execution_count":9,"metadata":{"executionInfo":{"elapsed":82100,"status":"ok","timestamp":1704808982782,"user":{"displayName":"Clément PAGES","userId":"11757386314069785178"},"user_tz":-60},"id":"digxFLaueq4n"},"outputs":[],"source":["dia = pipeline(AUDIO_FILE)"]},{"cell_type":"markdown","metadata":{"id":"9WTsQVjjeq4o"},"source":["## Visualizing the output\n","\n","Most pipelines return a [`pyannote.core.Annotation`](http://pyannote.github.io/pyannote-core/structure.html#annotation) instance..."]},{"cell_type":"code","execution_count":10,"metadata":{"executionInfo":{"elapsed":1079,"status":"ok","timestamp":1704809008347,"user":{"displayName":"Clément PAGES","userId":"11757386314069785178"},"user_tz":-60},"id":"Ch7SaA4-eq4p"},"outputs":[],"source":["from pyannote.core import Annotation\n","assert isinstance(dia, Annotation)"]},{"cell_type":"markdown","metadata":{"id":"T4BsOhmXeq4p"},"source":["... whose [API](https://pyannote.github.io/pyannote-core/structure.html#annotation) you can use to print the result:"]},{"cell_type":"code","execution_count":11,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":610,"status":"ok","timestamp":1704809011691,"user":{"displayName":"Clément PAGES","userId":"11757386314069785178"},"user_tz":-60},"id":"OzEPn1hqeq4p","outputId":"e61015ad-c0df-4890-acc6-f65c87d7c30d"},"outputs":[{"name":"stdout","output_type":"stream","text":[" 6.7  7.2 SPEAKER_01\n"," 7.2  7.2 SPEAKER_02\n"," 7.6  8.3 SPEAKER_01\n"," 8.3  9.9 SPEAKER_02\n"," 9.9 10.9 SPEAKER_01\n","10.5 14.7 SPEAKER_02\n","10.9 11.0 SPEAKER_00\n","14.3 17.9 SPEAKER_00\n","18.0 21.5 SPEAKER_02\n","18.2 18.4 SPEAKER_00\n","21.8 28.5 SPEAKER_00\n","27.9 30.0 SPEAKER_02\n"]}],"source":["for speech_turn, track, speaker in dia.itertracks(yield_label=True):\n","    print(f\"{speech_turn.start:4.1f} {speech_turn.end:4.1f} {speaker}\")"]},{"cell_type":"markdown","metadata":{"id":"PqiV2D2geq4q"},"source":["If you happen to be running this example in a _Jupyter notebook_, `dia` can be [visualized directly](http://pyannote.github.io/pyannote-core/visualization.html):"]},{"cell_type":"code","execution_count":12,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":220},"executionInfo":{"elapsed":841,"status":"ok","timestamp":1704809016036,"user":{"displayName":"Clément 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at 0x78f839fca470>"]},"execution_count":12,"metadata":{},"output_type":"execute_result"}],"source":["# we visualize [0, 30] time range\n","from pyannote.core import notebook, Segment\n","notebook.crop = Segment(0, 30)\n","dia"]},{"cell_type":"markdown","metadata":{"id":"TC_ZiDYoeq4s"},"source":["When available, the reference annotation can be visualized too, for comparison:"]},{"cell_type":"code","execution_count":14,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":220},"executionInfo":{"elapsed":1085,"status":"ok","timestamp":1704809060416,"user":{"displayName":"Clément 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at 0x78f9c9ae1840>"]},"execution_count":14,"metadata":{},"output_type":"execute_result"}],"source":["from pyannote.database.util import load_rttm\n","\n","reference = load_rttm(REFERENCE)[\"sample\"]\n","\n","# map hypothesized and reference speakers for visualization purposes\n","pipeline.optimal_mapping(dia, reference)"]},{"cell_type":"markdown","metadata":{"id":"rfTV3nSSeq4t"},"source":["## Processing a file from memory\n","\n","In case the audio file is not stored on disk, pipelines can also process audio provided as a `{\"waveform\": ..., \"sample_rate\": ...}` dictionary."]},{"cell_type":"code","execution_count":11,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":410,"status":"ok","timestamp":1704809066383,"user":{"displayName":"Clément PAGES","userId":"11757386314069785178"},"user_tz":-60},"id":"Nha4sg76eq4u","outputId":"69192d82-c240-47d1-fe01-d373c2b4e500"},"outputs":[{"name":"stdout","output_type":"stream","text":["type(waveform)=<class 'torch.Tensor'>\n","waveform.shape=torch.Size([1, 480000])\n","waveform.dtype=torch.float32\n"]}],"source":["import torchaudio\n","waveform, sample_rate = torchaudio.load(AUDIO_FILE)\n","\n","print(f\"{type(waveform)=}\")\n","print(f\"{waveform.shape=}\")\n","print(f\"{waveform.dtype=}\")\n","\n","audio_in_memory = {\"waveform\": waveform, \"sample_rate\": sample_rate}"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":388,"referenced_widgets":["514552137d7442368b39aaddce056bb8","f744ac29ccdd4d9d91050fabaed13e16","f18c80869ccc4ce1b2a215293d493e19","2c0b1ed212544e14b4934e036e8736d7","9988b4433d6e4c58a819a2e1b54f57b2","b8861f8cb72c4e65afac6519b7004170","668a49e70c2b4c8a9dcb534cb30714d6","9025d2b6c7b14162a692b98c3e86ec8a","6c361855da5e4eb78e311defb2ca2f2b","3be13f7f05da47b1ae3795ead9f2546a","07bb94f0dcda45a9a5e2d553fd239106","57ef34701b594a60a9fd269943b49888","1d051eb2d87e4671aa08039761c7899a","9537f195677745cba18618764b2290ab","4f0d63f098b0491097c6b72f3baee04f","f7be373f08c64c8bb5b6e070a7373cca","be8b64ce7f8247499c8bbcb62c5dd902","491c3dcba4474a52b860080fa7a627e7","4abbe0d8e0fa4e1b9d712743013b2d4e","661c7494ef2d47c794e6236e5f2c8978","6358920ecd46403a87cca46509465076","4f25b89365b24056b803119d415ac056","1d97d11a99454cc990cc5e374d0f8197","425146d4e0fb453486629d9d6af0a999","8fad50e0475341d9801e9f86e04e15c7","ea03f9526a7440a0a9a281ee877892fd","d1863b2c9ebc4efa9cac2dad7dfaf53a","8bda58a4c0c6430ca1470282ddbe8222","5d38b6ceec1844068f48da7bedf471fa","2a72c7660c2f498089c63c78eaa69fc8","05283cf0054d412c9a1ebfcc44bf41cb","b9d9acd287c345debdff1e72a85641af","91ca95844e084619b9ae01fe8176f875"]},"executionInfo":{"elapsed":12810,"status":"ok","timestamp":1704809082573,"user":{"displayName":"Clément PAGES","userId":"11757386314069785178"},"user_tz":-60},"id":"LvboMeeYeq4u","outputId":"225dd352-6367-4793-ae11-f8125d930c2b"},"outputs":[],"source":["vad = Pipeline.from_pretrained(\"pyannote/voice-activity-detection\", use_auth_token=True)"]},{"cell_type":"code","execution_count":12,"metadata":{},"outputs":[{"data":{"image/png":"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at 0x7fe31795faf0>"]},"execution_count":12,"metadata":{},"output_type":"execute_result"}],"source":["vad(audio_in_memory)"]},{"cell_type":"markdown","metadata":{"id":"LllNe0s3eq4u"},"source":["## Offline use\n","\n","Gating models and pipelines allows [me](https://herve.niderb.fr) to know a bit more about `pyannote.audio` user base and eventually help me write grant proposals to make `pyannote.audio` even better. Please fill this form as precisely as possible.\n","\n","For instance, before gating `pyannote/speaker-diarization`, I had no idea that so many people were relying on it in production. Hint: sponsors are more than welcome! maintaining open source libraries is time consuming.\n","\n","That being said: this whole authentication process does not prevent you from using official `pyannote.audio` models and pipelines offline (i.e. without going through the authentication process in every `docker run ...` or whatever you are using in production).\n","\n","* Step 1: download `config.yaml` of [`pyannote/voice-activity-detection`](https://hf.co/pyannote/voice-activity-detection) pipeline\n","\n","![](https://github.com/pyannote/pyannote-audio/blob/develop/tutorials/assets/download-pipeline.png?raw=1)\n","\n","* Step 2: download the `pytorch_model.bin` model\n","\n","![](https://github.com/pyannote/pyannote-audio/blob/develop/tutorials/assets/download-model.png?raw=1)\n","\n","* Step 3: edit `config.yaml` to point to the local model\n","\n","```diff\n","pipeline:\n","  name: pyannote.audio.pipelines.VoiceActivityDetection\n","  params:\n","-    segmentation: pyannote/segmentation@Interspeech2021\n","+    segmentation: pytorch_model.bin\n","\n","params:\n","  min_duration_off: 0.09791355693027545\n","  min_duration_on: 0.05537587440407595\n","  offset: 0.4806866463041527\n","  onset: 0.8104268538848918\n","```\n","\n","* Step 4: load the 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at 0x288d1e940>"]},"execution_count":21,"metadata":{},"output_type":"execute_result"}],"source":["# look ma: no hands!\n","offline_vad = Pipeline.from_pretrained(\"config.yaml\")\n","offline_vad(audio_in_memory)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"eepjEec8eq4w"},"outputs":[],"source":["# just checking output is the same\n","assert (vad(audio_in_memory) == offline_vad(audio_in_memory))"]}],"metadata":{"accelerator":"GPU","colab":{"gpuType":"T4","provenance":[{"file_id":"https://github.com/pyannote/pyannote-audio/blob/develop/tutorials/applying_a_pipeline.ipynb","timestamp":1704808199718}]},"kernelspec":{"display_name":"Python 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