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config as dict feat(metric): add detection, precision, and recall diarization metrics (pyannote#1808) fix: ensure round number of frames fix: update pipeline parameters in separation tutorial (pyannote#1829) setup: update torch_audiomentations, pyannote.database, pyannote.metrics and typer dependencies BREAKING: fix `cache_dir` support, rename `use_auth_token` to `token`, and other changes feat: add segmentation error rate (pyannote#1836) doc: improve HF-related docstring feat: add CLI to apply (and benchmark) pretrained pipelines binarize annotation optimizations pass checkpoint as a dict Squashed commit of the following: commit 2ccac02 Author: benniekiss <63211101+benniekiss@users.noreply.github.com> Date: Wed Jan 29 17:48:15 2025 -0500 pass checkpoint as a dict Squashed commit of the following: commit 0f3b2fe Author: benniekiss <63211101+benniekiss@users.noreply.github.com> Date: Wed Jan 29 17:58:10 2025 -0500 dont rename tracks when generating annotation * let the user decide how to rename tracks, if necessary * reduces a costly step for long audios commit 649c060 Author: benniekiss <63211101+benniekiss@users.noreply.github.com> Date: Wed Sep 11 10:31:20 2024 -0400 conditionally use optimized method pass checkpoint as a dict cleanup
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| #!/usr/bin/env python | ||
| # encoding: utf-8 | ||
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| # MIT License | ||
| # | ||
| # Copyright (c) 2024- CNRS | ||
| # | ||
| # Permission is hereby granted, free of charge, to any person obtaining a copy | ||
| # of this software and associated documentation files (the "Software"), to deal | ||
| # in the Software without restriction, including without limitation the rights | ||
| # to use, copy, modify, merge, publish, distribute, sublicense, and/or sell | ||
| # copies of the Software, and to permit persons to whom the Software is | ||
| # furnished to do so, subject to the following conditions: | ||
| # | ||
| # The above copyright notice and this permission notice shall be included in all | ||
| # copies or substantial portions of the Software. | ||
| # | ||
| # THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR | ||
| # IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, | ||
| # FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE | ||
| # AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER | ||
| # LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, | ||
| # OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE | ||
| # SOFTWARE. | ||
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| import sys | ||
| from contextlib import nullcontext | ||
| from enum import Enum | ||
| from pathlib import Path | ||
| from typing import Optional | ||
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| import pyannote.database | ||
| import torch | ||
| import typer | ||
| from pyannote.core import Annotation | ||
| from typing_extensions import Annotated | ||
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| from pyannote.audio import Pipeline | ||
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| class Subset(str, Enum): | ||
| train = "train" | ||
| development = "development" | ||
| test = "test" | ||
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| class Device(str, Enum): | ||
| CPU = "cpu" | ||
| CUDA = "cuda" | ||
| MPS = "mps" | ||
| AUTO = "auto" | ||
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| def parse_device(device: Device) -> torch.device: | ||
| if device == Device.AUTO: | ||
| if torch.cuda.is_available(): | ||
| device = Device.CUDA | ||
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| elif torch.backends.mps.is_available(): | ||
| device = Device.MPS | ||
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| else: | ||
| device = Device.CPU | ||
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| return torch.device(device.value) | ||
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| app = typer.Typer() | ||
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| # TODO: add option to download pretrained pipeline for later use without internet | ||
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| @app.command("apply") | ||
| def apply( | ||
| pipeline: Annotated[ | ||
| str, | ||
| typer.Argument( | ||
| help="Pretrained pipeline (e.g. pyannote/speaker-diarization-3.1)" | ||
| ), | ||
| ], | ||
| audio: Annotated[ | ||
| Path, | ||
| typer.Argument( | ||
| help="Path to audio file", | ||
| exists=True, | ||
| file_okay=True, | ||
| readable=True, | ||
| ), | ||
| ], | ||
| into: Annotated[ | ||
| Path, | ||
| typer.Option( | ||
| help="Path to file where results are saved.", | ||
| exists=False, | ||
| dir_okay=False, | ||
| file_okay=True, | ||
| writable=True, | ||
| resolve_path=True, | ||
| ), | ||
| ] = None, | ||
| device: Annotated[ | ||
| Device, typer.Option(help="Accelerator to use (CPU, CUDA, MPS)") | ||
| ] = Device.AUTO, | ||
| ): | ||
| """ | ||
| Apply a pretrained PIPELINE to an AUDIO file | ||
| """ | ||
|
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| # load pretrained pipeline | ||
| pretrained_pipeline = Pipeline.from_pretrained(pipeline) | ||
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| # send pipeline to device | ||
| torch_device = parse_device(device) | ||
| pretrained_pipeline.to(torch_device) | ||
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| # apply pipeline to audio file | ||
| prediction: Annotation = pretrained_pipeline(audio) | ||
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| # save (or print) results | ||
| with open(into, "w") if into else nullcontext(sys.stdout) as rttm: | ||
| prediction.write_rttm(rttm) | ||
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| @app.command("benchmark") | ||
| def benchmark( | ||
| pipeline: Annotated[ | ||
| str, | ||
| typer.Argument( | ||
| help="Pretrained pipeline (e.g. pyannote/speaker-diarization-3.1)" | ||
| ), | ||
| ], | ||
| protocol: Annotated[ | ||
| str, | ||
| typer.Argument(help="Benchmarked protocol"), | ||
| ], | ||
| into: Annotated[ | ||
| Path, | ||
| typer.Argument( | ||
| help="Directory into which benchmark results are saved", | ||
| exists=True, | ||
| dir_okay=True, | ||
| file_okay=False, | ||
| writable=True, | ||
| resolve_path=True, | ||
| ), | ||
| ], | ||
| subset: Annotated[ | ||
| Subset, | ||
| typer.Option( | ||
| help="Benchmarked subset", | ||
| case_sensitive=False, | ||
| ), | ||
| ] = Subset.test, | ||
| device: Annotated[ | ||
| Device, typer.Option(help="Accelerator to use (CPU, CUDA, MPS)") | ||
| ] = Device.AUTO, | ||
| registry: Annotated[ | ||
| Optional[Path], | ||
| typer.Option( | ||
| help="Loaded registry", | ||
| exists=True, | ||
| dir_okay=False, | ||
| file_okay=True, | ||
| readable=True, | ||
| ), | ||
| ] = None, | ||
| ): | ||
| """ | ||
| Benchmark a pretrained PIPELINE | ||
| """ | ||
|
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| # load pretrained pipeline | ||
| pretrained_pipeline = Pipeline.from_pretrained(pipeline) | ||
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| # send pipeline to device | ||
| torch_device = parse_device(device) | ||
| pretrained_pipeline.to(torch_device) | ||
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| # load pipeline metric (when available) | ||
| try: | ||
| metric = pretrained_pipeline.get_metric() | ||
| except NotImplementedError: | ||
| metric = None | ||
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| # load protocol from (optional) registry | ||
| if registry: | ||
| pyannote.database.registry.load_database(registry) | ||
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| loaded_protocol = pyannote.database.registry.get_protocol( | ||
| protocol, {"audio": pyannote.database.FileFinder()} | ||
| ) | ||
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| with open(into / f"{protocol}.{subset.value}.rttm", "w") as rttm: | ||
| for file in getattr(loaded_protocol, subset.value)(): | ||
| prediction: Annotation = pretrained_pipeline(file) | ||
| prediction.write_rttm(rttm) | ||
| rttm.flush() | ||
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| if metric is None: | ||
| continue | ||
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| groundtruth = file.get("annotation", None) | ||
| if groundtruth is None: | ||
| continue | ||
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| annotated = file.get("annotated", None) | ||
| _ = metric(groundtruth, prediction, uem=annotated) | ||
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| if metric is None: | ||
| return | ||
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| with open(into / f"{protocol}.{subset.value}.txt", "w") as txt: | ||
| txt.write(str(metric)) | ||
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| print(str(metric)) | ||
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| if __name__ == "__main__": | ||
| app() |
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