Offline loading of pipeline ('NoneType' object has no attribute 'eval') #1319
Comments
Accept user conditions of Both Models And add the user token during downloading the model from pre-trianed |
The whole reason to do offline loading is that you do not need a token for every user of the software. I can't expect that every user will create a huggingface account etc. |
pipeline:
name: pyannote.audio.pipelines.SpeakerDiarization
params:
clustering: AgglomerativeClustering
embedding: your/path/to/speechbrain/spkrec-ecapa-voxceleb # Folder, must contains `speechbrain` keyword.
embedding_batch_size: 32
embedding_exclude_overlap: true
segmentation: your/path/to/pyannote/segmentation/[email protected] # File
segmentation_batch_size: 32
params:
clustering:
method: centroid
min_cluster_size: 15
threshold: 0.7153814381597874
segmentation:
min_duration_off: 0.5817029604921046
threshold: 0.4442333667381752
self.classifier_ = SpeechBrain_EncoderClassifier.from_hparams(
source=self.embedding,
savedir=self.embedding if Path(self.embedding).exists() else f"{CACHE_DIR}/speechbrain",
run_opts={"device": self.device},
use_auth_token=use_auth_token,
)
from pyannote.audio import Pipeline
pipeline = Pipeline.from_pretrained("your/path/to/pyannote/speaker-diarization/config.yaml") |
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thanks. but still no luck. I placed the spkrec-ecapa-voxceleb stuff besides the pipeline config and changed the config accordingly: pipeline:
name: pyannote.audio.pipelines.SpeakerDiarization
params:
clustering: AgglomerativeClustering
embedding: speechbrain/spkrec-ecapa-voxceleb
embedding_batch_size: 32
embedding_exclude_overlap: true
segmentation: pytorch_model.bin
segmentation_batch_size: 32
params:
clustering:
method: centroid
min_cluster_size: 15
threshold: 0.7153814381597874
segmentation:
min_duration_off: 0.5817029604921046
threshold: 0.4442333667381752and changed the speaker_verification.py like you mentioned. |
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You need to download from speechbrain/spkrec-ecapa-voxceleb to the local classifier.ckpt |
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Hello @hbredin , Would a PR containing the following point be accepted for offline model loading ?
The modified line is |
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I'd gladly have a look at a PR facilitating the offline use of pyannote. |
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I will take a look and submit a PR. |
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The tutorial doesn't work? Because I'm getting the same error when running it: Error: |
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Hi @hbredin I am also facing similar issue with offline usage with VAD config. Code Error: |
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Hello. Are there plans to make the offline use of speaker-diarization-3.0 pipeline work? I tried above suggestions to no avail. |
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Also, feel free to make a PR improving the documentation! |
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except that this is exactly what i tried some time ago without success. Would have to try it again to see if it works now or if i was just missing something else in detail. So yes. an updated documentation would help a lot if someone gets this to work and would update it. |
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issue is still there as of today, the model is not found for some reason and a none value is returned, can someone look into this please. |
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I did not see this issue when proposing the respective PR: #1682 please check if the new tutorial addresses these issues: https://github.com/pyannote/pyannote-audio/blob/develop/tutorials/community/offline_usage_speaker_diarization.ipynb |
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I solved this by accepting both conditions as mentioned in the readme |
按照你的方法，还是报错huggingface_hub.utils._validators.HFValidationError: Repo id must be in the form 'repo_name' or 'namespace/repo_name' |
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it's my config.yaml version: 3.1.0
pipeline:
name: pyannote.audio.pipelines.SpeakerDiarization
params:
clustering: AgglomerativeClustering
#embedding: pyannote/wespeaker-voxceleb-resnet34-LM
embedding: /path/to/models/hbredin/wespeaker-voxceleb-resnet34-LM/speaker-embedding.onnx
embedding_batch_size: 32
embedding_exclude_overlap: true
#segmentation: pyannote/segmentation-3.0
segmentation: /path/to/models/pyannote/segmentation-3.0/pytorch_model.bin
segmentation_batch_size: 32
params:
clustering:
method: centroid
min_cluster_size: 12
threshold: 0.7045654963945799
segmentation:
min_duration_off: 0.0 |
I am not sure if i am missing something. I followed the documentation in how to load a pipeline for speaker diarization offline.
i followed this description:
https://github.com/pyannote/pyannote-audio/blob/develop/tutorials/applying_a_pipeline.ipynb
But used the config.yaml from https://huggingface.co/pyannote/speaker-diarization instead of the VAD that is used in the offline use section since i want to use speaker diarization and not voice activity detection. (thats a bit confusing since at the top of that description, speaker diarization is used.)
i try to load it like this:
but i get the following error:
The text was updated successfully, but these errors were encountered: