Diarization | IndexError: shape mismatch: indexing tensors could not be broadcast together
Hi.
Let us test on the wav file you provided.
This is a new type of error we have never encountered.
It apprears `p_value` value in [this line ](https://github.com/NVIDIA/NeMo/blob/0fb851c8ed91f181c5550dcdce1e39f5891ec3b2/nemo/collections/asr/parts/utils/offline_clustering.py#L332) is causing this error.

I will follow your settings and check what is causing this error.  @tango4j any updates on this issue? This issue is stale because it has been open for 30 days with no activity. Remove stale label or comment or this will be closed in 7 days. This issue was closed because it has been inactive for 7 days since being marked as stale.
**Describe the bug**

When running diarization on a specific file I'm getting:
`IndexError: shape mismatch: indexing tensors could not be broadcast together with shapes [7557001], [7559750]`

The same pipeline/config/setup etc has worked for a couple of thousand other files but now on this (and a few more files) I'm getting this issue suddenly.

**Steps/Code to reproduce bug**

Shared a Google Colab of how one can reproduce the error, the steps are also listed below.

[Shared Google Colab](https://colab.research.google.com/drive/1e3RigN5Rg-lGhBhg7kfSunoT5nR8kMtp?usp=sharing)
[notebook ipynb](http://tinyurl.com/bdssr2r4)

Steps:

Pre-steps: 
Upload `issue_file.wav`, `config.yaml` and `speech_timestamps.rttm` to runtime (e.g. Colab)
Above mentioned files can be obtained:

`issue_file.wav` : [Audio file](http://tinyurl.com/2eynkewf)
`config.yaml`: [diarization config](http://tinyurl.com/3enesyur)
`speech_timestamps.rttm`: [speech timestamps rttm](http://tinyurl.com/35jwuut5)


Let me know if anything's missing or unclear.


1. 

```
!apt-get update && apt-get install -y libsndfile1 ffmpeg
!pip install nemo_toolkit['asr']
```

2. 

```
import os
import torch
import yaml
import json
from omegaconf import OmegaConf
from nemo.collections.asr.models import ClusteringDiarizer
```
3.

```
def diarize(workdir: str, rttm_filepath):

    manifest_path  = os.path.join(workdir, "manifest.json")
    output_dir     = os.path.join(workdir, "output")

    manifest =  {
        'audio_filepath': '/content/issue_file.wav',
        'offset': 0,
        'duration': None,
        'label': 'infer',
        'text': '-',
        'num_speakers': None,
        'rttm_filepath': rttm_filepath,
        'uem_filepath': None,
    }

    with open('/content/config.yaml', "r") as config_file:
        config_dict = yaml.load(config_file, Loader=yaml.FullLoader)
    
    config = OmegaConf.create(config_dict['diarizer'])
    config.device = "cuda:0" if torch.cuda.is_available() else "cpu"
    config.diarizer.manifest_filepath = manifest_path
    config.diarizer.oracle_vad = True
    config.diarizer.speaker_embeddings.model_path = 'titanet_large'
    config.diarizer.out_dir = output_dir


    with open(manifest_path, "w") as manifest_file:
        json.dump(manifest, manifest_file)

    model = ClusteringDiarizer(cfg=config)
    model.diarize()
```
4.
```
diarize('/content/', '/content/speech_timestamps.rttm')
```

This fails:

```
[<ipython-input-7-4afd3b7bf15c>](https://localhost:8080/#) in diarize(workdir, rttm_filepath)
     30 
     31     model = ClusteringDiarizer(cfg=config)
---> 32     model.diarize()

[/usr/local/lib/python3.10/dist-packages/nemo/collections/asr/models/clustering_diarizer.py](https://localhost:8080/#) in diarize(self, paths2audio_files, batch_size)
    454 
    455         # Clustering
--> 456         all_reference, all_hypothesis = perform_clustering(
    457             embs_and_timestamps=embs_and_timestamps,
    458             AUDIO_RTTM_MAP=self.AUDIO_RTTM_MAP,

[/usr/local/lib/python3.10/dist-packages/nemo/collections/asr/parts/utils/speaker_utils.py](https://localhost:8080/#) in perform_clustering(embs_and_timestamps, AUDIO_RTTM_MAP, out_rttm_dir, clustering_params, device, verbose)
    483         base_scale_idx = uniq_embs_and_timestamps['multiscale_segment_counts'].shape[0] - 1
    484 
--> 485         cluster_labels = speaker_clustering.forward_infer(
    486             embeddings_in_scales=uniq_embs_and_timestamps['embeddings'],
    487             timestamps_in_scales=uniq_embs_and_timestamps['timestamps'],

[/usr/local/lib/python3.10/dist-packages/nemo/collections/asr/parts/utils/longform_clustering.py](https://localhost:8080/#) in forward_infer(self, embeddings_in_scales, timestamps_in_scales, multiscale_segment_counts, multiscale_weights, oracle_num_speakers, max_rp_threshold, max_num_speakers, enhanced_count_thres, sparse_search_volume, fixed_thres, chunk_cluster_count, embeddings_per_chunk)
    407             )
    408         else:
--> 409             cluster_labels = self.speaker_clustering.forward_infer(
    410                 embeddings_in_scales=embeddings_in_scales,
    411                 timestamps_in_scales=timestamps_in_scales,

[/usr/local/lib/python3.10/dist-packages/nemo/collections/asr/parts/utils/offline_clustering.py](https://localhost:8080/#) in forward_infer(self, embeddings_in_scales, timestamps_in_scales, multiscale_segment_counts, multiscale_weights, oracle_num_speakers, max_num_speakers, max_rp_threshold, enhanced_count_thres, sparse_search_volume, fixed_thres, kmeans_random_trials)
   1376         )
   1377 
-> 1378         return self.forward_unit_infer(
   1379             mat=mat,
   1380             oracle_num_speakers=oracle_num_speakers,

[/usr/local/lib/python3.10/dist-packages/nemo/collections/asr/parts/utils/offline_clustering.py](https://localhost:8080/#) in forward_unit_infer(self, mat, oracle_num_speakers, max_num_speakers, max_rp_threshold, sparse_search_volume, est_num_of_spk_enhanced, fixed_thres, kmeans_random_trials)
   1225         if mat.shape[0] > self.min_samples_for_nmesc:
   1226             est_num_of_spk, p_hat_value = nmesc.forward()
-> 1227             affinity_mat = getAffinityGraphMat(mat, p_hat_value)
   1228         else:
   1229             nmesc.fixed_thres = max_rp_threshold

[/usr/local/lib/python3.10/dist-packages/nemo/collections/asr/parts/utils/offline_clustering.py](https://localhost:8080/#) in getAffinityGraphMat(affinity_mat_raw, p_value)
    349     symmetrize the binarized graph matrix.
    350     """
--> 351     X = affinity_mat_raw if p_value <= 0 else getKneighborsConnections(affinity_mat_raw, p_value)
    352     symm_affinity_mat = 0.5 * (X + X.T)
    353     return symm_affinity_mat

[/usr/local/lib/python3.10/dist-packages/nemo/collections/asr/parts/utils/offline_clustering.py](https://localhost:8080/#) in getKneighborsConnections(affinity_mat, p_value, mask_method)
    332     indices_col = torch.arange(dim[1]).repeat(p_value, 1).T.flatten()
    333     if mask_method == 'binary' or mask_method is None:
--> 334         binarized_affinity_mat[indices_row, indices_col] = (
    335             torch.ones(indices_row.shape[0]).to(affinity_mat.device).half()
    336         )

IndexError: shape mismatch: indexing tensors could not be broadcast together with shapes [7557001], [7559750]
```

**Expected behavior**

Diarization shouldn't fail on a seemingly non-corrupt audio-file. This config has been tested multiple times before on other files without any issues.

**Environment overview (please complete the following information)**

 - Environment location: Google Colab ( Freshly created )
 - Method of NeMo install:
```
 !apt-get update && apt-get install -y libsndfile1 ffmpeg
!pip install nemo_toolkit['asr']
```

**Environment details**

```

PRETTY_NAME="Ubuntu 22.04.3 LTS"
NAME="Ubuntu"
VERSION_ID="22.04"
VERSION="22.04.3 LTS (Jammy Jellyfish)"
VERSION_CODENAME=jammy
ID=ubuntu
ID_LIKE=debian
HOME_URL="https://www.ubuntu.com/"
SUPPORT_URL="https://help.ubuntu.com/"
BUG_REPORT_URL="https://bugs.launchpad.net/ubuntu/"
PRIVACY_POLICY_URL="https://www.ubuntu.com/legal/terms-and-policies/privacy-policy"
UBUNTU_CODENAME=jammy
=============
2.1.0+cu121
=============
Python 3.10.12
```