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tutorials/community/offline_usage_speaker_diarization.ipynb
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| { | ||
| "cells": [ | ||
| { | ||
| "cell_type": "markdown", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "# Offline Speaker Diarization (speaker-diarization-3.1)\n", | ||
| "\n", | ||
| "This notebooks gives a short introduction how to use the [speaker-diarization-3.1](https://huggingface.co/pyannote/speaker-diarization-3.1) pipeline with local models.\n", | ||
| "\n", | ||
| "In order to use local models, you first need to download them from huggingface and place them in a local folder. \n", | ||
| "Then you need to create a local config file, similar to the one in HF, but with local model paths.\n", | ||
| "\n", | ||
| "❗ **Naming of the model files is REALLY important! See end of notebook for details.** ❗\n", | ||
| "\n", | ||
| "## Get the models\n", | ||
| "\n", | ||
| "1. Install the `pyannote-audio` package: `!pip install pyannote.audio`\n", | ||
| "2. Create a huggingface account https://huggingface.co/join\n", | ||
| "3. Accept [pyannote/segmentation-3.0](https://hf.co/pyannote/segmentation-3.0) user conditions\n", | ||
| "4. Create a local folder `models`, place all downloaded files there\n", | ||
| " 1. [wespeaker-voxceleb-resnet34-LM](https://huggingface.co/pyannote/wespeaker-voxceleb-resnet34-LM/blob/main/pytorch_model.bin), to be placed in `models/pyannote_model_wespeaker-voxceleb-resnet34-LM.bin`\n", | ||
| " 2. [segmentation-3.0](https://huggingface.co/pyannote/segmentation-3.0/blob/main/pytorch_model.bin), to be placed in `models/pyannote_model_segmentation-3.0.bin`\n", | ||
| "\n", | ||
| "Running `ls models` should show the following files:\n", | ||
| "```\n", | ||
| "pyannote_model_segmentation-3.0.bin (5.7M)\n", | ||
| "pyannote_model_wespeaker-voxceleb-resnet34-LM.bin (26MB)\n", | ||
| "```\n", | ||
| "\n", | ||
| "❗ **make sure the 'wespeaker-voxceleb-resnet34-LM' model is named 'pyannote_model_wespeaker-voxceleb-resnet34-LM.bin'** ❗" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "markdown", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "## Config for local models\n", | ||
| "\n", | ||
| "Create a local config, similar to the one in HF: [speaker-diarization-3.1/blob/main/config.yaml](https://huggingface.co/pyannote/speaker-diarization-3.1/blob/main/config.yaml), but with local model paths\n", | ||
| "\n", | ||
| "Contents of `models/pyannote_diarization_config.yaml`:\n", | ||
| "\n", | ||
| "```yaml\n", | ||
| "version: 3.1.0\n", | ||
| "\n", | ||
| "pipeline:\n", | ||
| " name: pyannote.audio.pipelines.SpeakerDiarization\n", | ||
| " params:\n", | ||
| " clustering: AgglomerativeClustering\n", | ||
| " # embedding: pyannote/wespeaker-voxceleb-resnet34-LM # if you want to use the HF model\n", | ||
| " embedding: models/pyannote_model_wespeaker-voxceleb-resnet34-LM.bin # if you want to use the local model\n", | ||
| " embedding_batch_size: 32\n", | ||
| " embedding_exclude_overlap: true\n", | ||
| " # segmentation: pyannote/segmentation-3.0 # if you want to use the HF model\n", | ||
| " segmentation: models/pyannote_model_segmentation-3.0.bin # if you want to use the local model\n", | ||
| " segmentation_batch_size: 32\n", | ||
| "\n", | ||
| "params:\n", | ||
| " clustering:\n", | ||
| " method: centroid\n", | ||
| " min_cluster_size: 12\n", | ||
| " threshold: 0.7045654963945799\n", | ||
| " segmentation:\n", | ||
| " min_duration_off: 0.0\n", | ||
| "```" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "markdown", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "## Loading the local pipeline\n", | ||
| "\n", | ||
| "**Hint**: The paths in the config are relative to the current working directory, not relative to the config file.\n", | ||
| "If you want to start your notebook/script from a different directory, you can use `os.chdir` temporarily, to 'emulate' config-relative paths.\n" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "from pathlib import Path\n", | ||
| "from pyannote.audio import Pipeline\n", | ||
| "\n", | ||
| "def load_pipeline_from_pretrained(path_to_config: str | Path) -> Pipeline:\n", | ||
| " path_to_config = Path(path_to_config)\n", | ||
| "\n", | ||
| " print(f\"Loading pyannote pipeline from {path_to_config}...\")\n", | ||
| " # the paths in the config are relative to the current working directory\n", | ||
| " # so we need to change the working directory to the model path\n", | ||
| " # and then change it back\n", | ||
| "\n", | ||
| " cwd = Path.cwd().resolve() # store current working directory\n", | ||
| "\n", | ||
| " # first .parent is the folder of the config, second .parent is the folder containing the 'models' folder\n", | ||
| " cd_to = path_to_config.parent.parent.resolve()\n", | ||
| "\n", | ||
| " print(f\"Changing working directory to {cd_to}\")\n", | ||
| " os.chdir(cd_to)\n", | ||
| "\n", | ||
| " pipeline = Pipeline.from_pretrained(path_to_config)\n", | ||
| "\n", | ||
| " print(f\"Changing working directory back to {cwd}\")\n", | ||
| " os.chdir(cwd)\n", | ||
| "\n", | ||
| " return pipeline\n", | ||
| "\n", | ||
| "PATH_TO_CONFIG = \"path/to/your/pyannote_diarization_config.yaml\"\n", | ||
| "pipeline = load_pipeline_from_pretrained(PATH_TO_CONFIG)" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "markdown", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "## Notes on file naming (pyannote-audio 3.1.1)\n", | ||
| "\n", | ||
| "Pyannote uses some internal logic to determine the model type.\n", | ||
| "\n", | ||
| "The funtion `def PretrainedSpeakerEmbedding(...` in (speaker_verification.py)[https://github.com/pyannote/pyannote-audio/blob/develop/pyannote/audio/pipelines/speaker_verification.py#L712] uses the the file path of the model to infer the model type.\n", | ||
| "\n", | ||
| "```python\n", | ||
| "def PretrainedSpeakerEmbedding(\n", | ||
| " embedding: PipelineModel,\n", | ||
| " device: torch.device = None,\n", | ||
| " use_auth_token: Union[Text, None] = None,\n", | ||
| "):\n", | ||
| " #...\n", | ||
| " if isinstance(embedding, str) and \"pyannote\" in embedding:\n", | ||
| " return PyannoteAudioPretrainedSpeakerEmbedding(\n", | ||
| " embedding, device=device, use_auth_token=use_auth_token\n", | ||
| " )\n", | ||
| "\n", | ||
| " elif isinstance(embedding, str) and \"speechbrain\" in embedding:\n", | ||
| " return SpeechBrainPretrainedSpeakerEmbedding(\n", | ||
| " embedding, device=device, use_auth_token=use_auth_token\n", | ||
| " )\n", | ||
| "\n", | ||
| " elif isinstance(embedding, str) and \"nvidia\" in embedding:\n", | ||
| " return NeMoPretrainedSpeakerEmbedding(embedding, device=device)\n", | ||
| "\n", | ||
| " elif isinstance(embedding, str) and \"wespeaker\" in embedding:\n", | ||
| " return ONNXWeSpeakerPretrainedSpeakerEmbedding(embedding, device=device) # <-- this is called, but the wespeaker-voxceleb-resnet34-LM is not an ONNX model\n", | ||
| "\n", | ||
| " else:\n", | ||
| " # fallback to pyannote in case we are loading a local model\n", | ||
| " return PyannoteAudioPretrainedSpeakerEmbedding(\n", | ||
| " embedding, device=device, use_auth_token=use_auth_token\n", | ||
| " )\n", | ||
| "```\n", | ||
| "\n", | ||
| "The [wespeaker-voxceleb-resnet34-LM](https://huggingface.co/pyannote/wespeaker-voxceleb-resnet34-LM/blob/main/pytorch_model.bin) model is not an ONNX model, but a `PyannoteAudioPretrainedSpeakerEmbedding`. So if `wespeaker` is in the file name, the code will infer the model type incorrectly. If `pyannote` is somewhere in the file name, the model type will be inferred correctly, as the first if statement will be true..." | ||
| ] | ||
| } | ||
| ], | ||
| "metadata": { | ||
| "kernelspec": { | ||
| "display_name": "Python 3", | ||
| "language": "python", | ||
| "name": "python3" | ||
| }, | ||
| "language_info": { | ||
| "name": "python", | ||
| "version": "3.11.7" | ||
| } | ||
| }, | ||
| "nbformat": 4, | ||
| "nbformat_minor": 2 | ||
| } |