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I followed the notebook "Adapting pyannote.audio 2.1 pretrained speaker diarization pipeline to your own data" to adapt the Pyannote.audio 2.1 Speaker Diarization Pipeline to my own data.
I am using the pyannote.database structure to load my data, and the audio recordings are short snippets, with the shortest ones being one second in length.
However, when I try to fine-tune the hyperparameters using the following code: iterations = optimizer.tune_iter(dev_set, show_progress=False)
best_loss = 1.0
for i, iteration in enumerate(iterations):
print(f"Best segmentation threshold so far: {iteration['params']['segmentation']['threshold']}")
if i > 20: break # 50 iterations should give slightly better results
I encounter the following error: Traceback (most recent call last): File "/opt/miniconda3/envs/Asr2/lib/python3.9/site-packages/optuna/study/_optimize.py", line 200, in _run_trial value_or_values = func(trial) File "path/to/optimizer.py", line 210, in objective output = pipeline(input) File "/opt/miniconda3/envs/Asr2/lib/python3.9/site-packages/pyannote/audio/core/pipeline.py", line 238, in call return self.apply(file, **kwargs) File "/opt/miniconda3/envs/Asr2/lib/python3.9/site-packages/pyannote/audio/pipelines/speaker_diarization.py", line 494, in apply hardclusters, = self.clustering( File "/opt/miniconda3/envs/Asr2/lib/python3.9/site-packages/pyannote/audio/pipelines/clustering.py", line 612, in call oracle_segmentations = oracle_segmentation(file, window, frames=frames) File "/opt/miniconda3/envs/Asr2/lib/python3.9/site-packages/pyannote/audio/pipelines/utils/oracle.py", line 105, in oracle_segmentation return SlidingWindowFeature(np.float32(np.stack(segmentations)), window) File "<array_function internals>", line 180, in stack File "/opt/miniconda3/envs/Asr2/lib/python3.9/site-packages/numpy/core/shape_base.py", line 422, in stack raise ValueError('need at least one array to stack') ValueError: need at least one array to stack
Any suggestions on how to fix this error would be greatly appreciated.
The text was updated successfully, but these errors were encountered:
I followed the notebook "Adapting pyannote.audio 2.1 pretrained speaker diarization pipeline to your own data" to adapt the Pyannote.audio 2.1 Speaker Diarization Pipeline to my own data.
I am using the pyannote.database structure to load my data, and the audio recordings are short snippets, with the shortest ones being one second in length.
However, when I try to fine-tune the hyperparameters using the following code:
iterations
= optimizer.tune_iter(dev_set, show_progress=False)best_loss = 1.0
for i, iteration in enumerate(iterations):
print(f"Best segmentation threshold so far: {iteration['params']['segmentation']['threshold']}")
if i > 20: break # 50 iterations should give slightly better
results
I encounter the following error:
Traceback (most recent call last): File "/opt/miniconda3/envs/Asr2/lib/python3.9/site-packages/optuna/study/_optimize.py", line 200, in _run_trial value_or_values = func(trial) File "path/to/optimizer.py", line 210, in objective output = pipeline(input) File "/opt/miniconda3/envs/Asr2/lib/python3.9/site-packages/pyannote/audio/core/pipeline.py", line 238, in call return self.apply(file, **kwargs) File "/opt/miniconda3/envs/Asr2/lib/python3.9/site-packages/pyannote/audio/pipelines/speaker_diarization.py", line 494, in apply hardclusters, = self.clustering( File "/opt/miniconda3/envs/Asr2/lib/python3.9/site-packages/pyannote/audio/pipelines/clustering.py", line 612, in call oracle_segmentations = oracle_segmentation(file, window, frames=frames) File "/opt/miniconda3/envs/Asr2/lib/python3.9/site-packages/pyannote/audio/pipelines/utils/oracle.py", line 105, in oracle_segmentation return SlidingWindowFeature(np.float32(np.stack(segmentations)), window) File "<array_function internals>", line 180, in stack File "/opt/miniconda3/envs/Asr2/lib/python3.9/site-packages/numpy/core/shape_base.py", line 422, in stack raise ValueError('need at least one array to stack') ValueError: need at least one array to stack
Any suggestions on how to fix this error would be greatly appreciated.
The text was updated successfully, but these errors were encountered: