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renwei2024

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nemo-github-issues

like 0

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[ "jenkins" ]

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[ "Hi.\r\nLet us test on the wav file you provided.\r\nThis is a new type of error we have never encountered.\r\nIt 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.\r\n\r\nI 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." ]

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[ "@athitten @aklife97 anything else that needs to be addressed here? ", "This PR is stale because it has been open for 14 days with no activity. Remove stale label or comment or update or this will be closed in 7 days.", "@titu1994 anything else that needs to be resolved here?", "jenkins", "@athitten for final approval, it looks ok to me ", "This PR is stale because it has been open for 14 days with no activity. Remove stale label or comment or update or this will be closed in 7 days.", "@athitten status?", "This PR is stale because it has been open for 14 days with no activity. Remove stale label or comment or update or this will be closed in 7 days.", "ping", "This PR is stale because it has been open for 14 days with no activity. Remove stale label or comment or update or this will be closed in 7 days.", "This PR was closed because it has been inactive for 7 days since being marked as stale.", "@itzsimpl sorry this completely fell through the cracks. My apologies. Since the feature branch is in your personal forked version of NeMo, can I rebase your feature branch to latest main? There's a minor conflict that needs to be addressed for merging this PR.", "Fixed, let me know if you need anything else.", "> Fixed, let me know if you need anything else.\r\n\r\nThank you, we need to wait for the CI to pass. I have started the CI", "@titu1994 do you want to take another pass over the PR and approve it since its a nemo core PR ? Sorry this was opened few months ago, but fell through the cracks from my end.", "This PR is stale because it has been open for 14 days with no activity. Remove stale label or comment or update or this will be closed in 7 days.", "@athitten @titu1994 anything else that needs to be done here?", "This PR is stale because it has been open for 14 days with no activity. Remove stale label or comment or update or this will be closed in 7 days.", "This PR was closed because it has been inactive for 7 days since being marked as stale." ]

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[ "Audio was stereo, my mistake" ]

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[ "jenkins", "closing as this is now addressed in #7988" ]

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[ "This PR is stale because it has been open for 14 days with no activity. Remove stale label or comment or update or this will be closed in 7 days.", "This PR was closed because it has been inactive for 7 days since being marked as stale." ]

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[ "jenkins" ]

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[ "jenkins", "jenkins", "jenkins", "jenkins", "jenkins", "jenkins", "jenkins", "jenkins", "jenkins", "jenkins", "jenkins", "jenkins", "jenkins", "jenkins" ]

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[ "This PR is stale because it has been open for 14 days with no activity. Remove stale label or comment or update or this will be closed in 7 days.", "This PR was closed because it has been inactive for 7 days since being marked as stale." ]

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[ "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." ]

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[ "jenkins", "jenkins", "jenkins" ]

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[ "This PR is stale because it has been open for 14 days with no activity. Remove stale label or comment or update or this will be closed in 7 days.", "This PR was closed because it has been inactive for 7 days since being marked as stale." ]

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[ "jenkins" ]

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[ "To confirm, you're using a fine-tuning script from NeMo right ? The one inside examples ?\r\n\r\n@VahidooX is there something up with the checkpoint ? The config seems ok. ", "Plus that's not exactly the write command for extract - the key is one of the modules inside of the actual model not the model name. But anyway we don't support inference or training with bare pt ckpt, only with NeMo files usually ", "Your model file looks like to be corrupted. Please download it and try again. Even training from scratch should work. In that issue, they used a very small batch size which is not easy to train an model with.", "@VahidooX I've tried to use script for inference this model on audio file and it works fine. It makes a good transcription on english language, but when i try to use it in training it still throws the mistake.", "@titu1994 I use script `speech_to_text_hybrid_rnnt_ctc_bpe.py` from your repo. It works fine for training from scratch (actually it doesn't converge but it at least does something), but it throws the mistake in case of fine tuning.\r\n\r\nFull code:\r\n```\r\nimport pytorch_lightning as pl\r\nfrom omegaconf import OmegaConf\r\n\r\nfrom nemo.collections.asr.models import EncDecHybridRNNTCTCBPEModel\r\nfrom nemo.core.config import hydra_runner\r\nfrom nemo.utils import logging\r\nfrom nemo.utils.exp_manager import exp_manager\r\n\r\n\r\n@hydra_runner(\r\n config_path=\"./conf\", config_name=\"fastconformer_hybrid_transducer_ctc_bpe_streaming.yaml\"\r\n)\r\ndef main(cfg):\r\n logging.info(f'Hydra config: {OmegaConf.to_yaml(cfg)}')\r\n\r\n trainer = pl.Trainer(**cfg.trainer)\r\n exp_manager(trainer, cfg.get(\"exp_manager\", None))\r\n asr_model = EncDecHybridRNNTCTCBPEModel(cfg=cfg.model, trainer=trainer)\r\n\r\n # Initialize the weights of the model from another model, if provided via config\r\n asr_model.maybe_init_from_pretrained_checkpoint(cfg)\r\n\r\n trainer.fit(asr_model)\r\n\r\n if hasattr(cfg.model, 'test_ds') and cfg.model.test_ds.manifest_filepath is not None:\r\n if asr_model.prepare_test(trainer):\r\n trainer.test(asr_model)\r\n\r\n\r\nif __name__ == '__main__':\r\n main() # noqa pylint: disable=no-value-for-parameter\r\n\r\n```\r\n\r\nMay be I should use something else?", "@traidn, maybe this is not an official way, but you can try \r\n```\r\nstate_dict = torch.load(\"model_weights.ckpt\", map_location=device)\r\nasr_model.load_state_dict(state_dict)\r\n```\r\n\r\nyou can get `model_weights.ckpt` if you unpack the nemo checkpoint with `tar xvf`", "@bene-ges Yeah, it allows to get weight, but unfortunately it gives me error \r\n```\r\nreturn checkpoint[\"pytorch-lightning_version\"]\r\nKeyError: 'pytorch-lightning_version'\r\n```\r\nin the start of trainng.\r\n", "@traidn - maybe you can try to load weights and then train like you did from scratch? Without resuming from checkpoint", "@bene-ges Thanks for idea, but it still gives keyerror with pytorch `pytorch-lightning_version` when I load state dict. Even if I leave field \"resume_from_checkpoint\" empty.", "I am going to try this model next week to make sure it is not a bug. Have you tried the latest nemo release or one of the old releases to convert and train the model?", "@VahidooX That will be appreciated. I use nemo-toolkit 1.21.0 and pytorch 2.1.1.\r\n", "In the meantime, would you please try an older nemo version for both conversion and training?", "I tried a previous version. And it still doesn't work properly. Unfortunately I can't install more older version right now in my environment due to i have troubles with building wheels.", "@VahidooX I downgraded Nemo to version 1.20.0 (when the models STT En FastConformer Hybrid Large Streaming 1040ms (doesn't train too) and STT En FastConformer Hybrid Transducer-CTC Large Streaming Multi were presented). I download config file from branch r1.20.0. But it still throws the error with `KeyError: \"filename 'storages' not found\"` . And one more question. Does this two models (STT En FastConformer Hybrid Large Streaming 1040ms and STT En FastConformer Hybrid Transducer-CTC Large Streaming Multi) differ by only one line in the config - att_context_size? Because both modelcard link to the same [config](https://github.com/NVIDIA/NeMo/blob/r1.20.0/examples/asr/conf/fastconformer/hybrid_cache_aware_streaming/fastconformer_hybrid_transducer_ctc_bpe_streaming.yaml) file.", "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." ]

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[ "jenkins", " Thanks Dmytro!", "Was this able to start training megatron_gpt model successfully? If so, what version of Megatron-LM was used? \r\nI see the commit in the base Dockerfile is 27cbe46714a50c43ed290f1b1472db8d2780c55c, which is on the core_r0.4.0 branch. \r\n\r\nBut the addition of GPTDatasetConfig in megatron.core.datasets.gpt_dataset only exists on main, and for reset_position_ids and etc only after this commit: https://github.com/NVIDIA/Megatron-LM/commit/8d8241a9cfdf5ad6c511c5303a2623185ee18c3c\r\n\r\nI'm wondering where eod_id comes from since it's passed as part of all kwargs into GPTDatasetConfig.\r\nBut I don't see eod_id existing in gpt_dataset in Megatron-LM https://github.com/NVIDIA/Megatron-LM/blame/4bd4e7426c8def582eb80ae53f296a803643d2f5/megatron/core/datasets/gpt_dataset.py#L33\r\n\r\nCurrently I'm having issues launching the job:\r\n```\r\n File \"lib/python3.10/site-packages/nemo/collections/nlp/models/language_modeling/megatron_gpt_model.py\", line 1223, in build_train_valid_test_datasets\r\n dataset_config = GPTDatasetConfig(**kwargs)\r\nTypeError: GPTDatasetConfig.__init__() got an unexpected keyword argument 'eod_id'\r\n```", "@ryxli I had a similar issue. My WAR was to disable warmup on synthetic data." ]

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[ "After renaming/refactoring + converting old ckpt to use new target model definition it works - merging this to https://github.com/NVIDIA/NeMo/tree/canary-2-dataset-refactor where I'll next rename/refactor the dataset class" ]

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[ "I do not know what am I missing, but I have the same issue.\r\n\r\nExecuting this command: `nemo2riva --key None --out hifigan.riva hifigan.nemo`\r\nResults in an error: \r\n`[NeMo I 2024-02-15 21:43:09 exportable:130] Successfully exported HifiGanModel to /tmp/tmppe74nxmm/model_graph.onnx\r\nTraceback (most recent call last):\r\n File \"/home/silent/.local/bin/nemo2riva\", line 8, in <module>\r\n sys.exit(nemo2riva())\r\n File \"/home/silent/.local/lib/python3.10/site-packages/nemo2riva/cli/nemo2riva.py\", line 49, in nemo2riva\r\n Nemo2Riva(args)\r\n File \"/home/silent/.local/lib/python3.10/site-packages/nemo2riva/convert.py\", line 87, in Nemo2Riva\r\n export_model(\r\n File \"/home/silent/.local/lib/python3.10/site-packages/nemo2riva/cookbook.py\", line 141, in export_model\r\n create_artifact(\r\n File \"/home/silent/.local/lib/python3.10/site-packages/nemo2riva/artifacts.py\", line 92, in create_artifact\r\n af.encrypt()\r\n File \"<frozen eff.core.file>\", line 128, in encrypt\r\nPermissionError: Cannot encrypt the artifact without encryption`", "Can you open an issue on https://github.com/nvidia-riva/nemo2riva? In the new issue, please provide the hifigan checkpoint that you're using." ]

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[ "jenkins", "jenkins", "jenkins", "Closed since Jenkins will be replaced with Github actions in the near future" ]

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[ "This PR is stale because it has been open for 14 days with no activity. Remove stale label or comment or update or this will be closed in 7 days.", "This PR was closed because it has been inactive for 7 days since being marked as stale." ]

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[ "jenkins\r\n", "jenkins", "jenkins", "jenkins" ]

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[ "Merging to `canary-2` for further development to unblock others -- we should address the rest of comments there." ]

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[ "This PR is stale because it has been open for 14 days with no activity. Remove stale label or comment or update or this will be closed in 7 days.", "This PR was closed because it has been inactive for 7 days since being marked as stale." ]

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[ "using the docker container is highly recommended https://github.com/NVIDIA/NeMo?tab=readme-ov-file#docker-containers", "You are using the wrong version of python, you need to use python 3.10.12.", "For some reasons, I need to stick to python3.8.\r\nPrevious Nemo version installed and worked ok (I do not use anything related to megatron). Is the reported error (missing file) related to Python version?", "If you need just ASR, you can safely install nemo_toolkit[ASR]. However it may be time to update. 3.8 has been deprecated for over a year now, and ASR has also started using syntax from 3.10 for Lhotse datasets. So in the coming month or two even pip install nemo_toolkit[ASR] will no longer safely work on python ,3.8", "As to docker, it's actually not the primary path we recommend for local 1 GPU development. \r\n\r\nNeMo ASR at least is designed to be a pip installable package. Ofc, docker is very convenient when you just want to start working on clusters, and we too only use docker containers in those cases ", "This should be fixed with this PR: https://github.com/NVIDIA/NeMo/pull/8465\r\n\r\nPlease try again and close the bug if your issue is solved.", "thanks" ]

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[ "jenkins", "jenkins", "jenkins", "jenkins", "jenkins", "jenkins", "jenkins", "jenkins" ]

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[ "@cuichenx / @ericharper can you review?", "jenkins" ]

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[ "jenkins", "jenkins" ]

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[ "This PR is stale because it has been open for 14 days with no activity. Remove stale label or comment or update or this will be closed in 7 days.", "jenkins", "jenkins", "jenkins", "> What about model class file? Would it be added later?\r\n\r\nThis is trained using the same `audio_codec.py` model class.", "jenkins" ]

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[ "jenkins", "jenkins", "jenkins", "jenkins" ]

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[ "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." ]

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[ "@tttalshaul can you share more info on length of audio and size of embeddings in pkl file you saved? ", "Audio length is 3:43 hours after cutting silence (using VAD). \r\nembeddings:\r\nscale0 6.6M len 8920\r\nscale1 7.9M len 10704\r\nscale2 9.9M len 13385\r\nscale3 14M len 17846\r\nscale4 20M len 26772\r\nscale5 40M len 53548", "@nithinraok \r\nWhat tests can I make to understand the cause of this problem?\r\n\r\nI'm moving ClusteringDiarizer(cfg=cfg).to(device), and I see that device is cuda:0. \r\nI understand that this only effects vad and embeddings extraction, but clustering occurs in cpu RAM anyway?\r\n\r\nThank you", "@tango4j what is the maximum audio duration we currently support for single pass offline diarzation\r\n", "@tttalshaul \r\n\r\nSorry for the late reply.\r\nIf you have visible cuda devices, both embedding extractions and clustering will be happening on GPU RAM.\r\n\r\n```\r\nclustering: \r\n parameters:\r\n....\r\n chunk_cluster_count: 50 # Number of forced clusters (overclustering) per unit chunk in long-form audio clustering.\r\n embeddings_per_chunk: 10000 # Number of embeddings in each chunk for long-form audio clustering. Adjust based on GPU memory capacity. (default: 10000, approximately 40 mins of audio) \r\n```\r\nYou should **increase** the `embeddings_per_chunk` value to consume less amount of memory.\r\nAlso note that even if you use long form audio clustering, there is a limit of audio length.\r\nThus, you need to make a compromise between performance and length. \r\nIncreasing `embeddings_per_chunk` will consume less memory but could lead to deteriorated performance.\r\n\r\n", "tried now embeddings_per_chunk 50000, and also tried 500000. \r\ngpustat showed 0 GPU utilization after embedding, there was a small step of get_argmin_mat and then OOM.. (There wasn't enough time to see more steps). \r\n\r\nWhat can I do to debug it further (suppose I can't export the audio)?\r\nThank you. ", "Also, what is the supported limit of audio length with long form audio clustering? (Is is less than 4 hours?)\r\nIncreasing embeddings_per_chunk will consume less memory in CPU RAM? or in GPU vRAM? \r\nIn \"deteriorated performance\" you mean less good results? or that it will take greater time?", "@tango4j 🙏", "> gpustat showed 0 GPU utilization after embedding, there was a small step of get_argmin_mat and then OOM.. (There wasn't enough time to see more steps).\r\n\r\nFirst check `torch.cuda.is_available()` in your pytorch env.\r\nSecond,\r\n[diarizer_model = NeuralDiarizer(cfg).to(device)](https://github.com/NVIDIA/NeMo/blob/4e7293a1011918fd90a073bb86bf3e54c7c766f4/examples/speaker_tasks/diarization/neural_diarizer/multiscale_diar_decoder_infer.py#L32)\r\ncheck if this model variable is assigned to the device you want.\r\n\r\n> Also, what is the supported limit of audio length with long form audio clustering? (Is is less than 4 hours?)\r\n\r\nThis depends on your GPU memory size, portion of detected silence in your audio file (3) `embeddings_per_chunk` variable you set. This cannot be answered without running the sample. \r\nHowever, your script is loading embedding vectors to CPU RAM, so GPU RAM size is not even an issue. Please make sure everything is loaded on GPU RAM.\r\n\r\n> you mean less good results? or that it will take greater time?\r\n\r\nThis means speaker counting can be wrong (wrong number of speakers) and diarization accuracy can be dropped.", "@tango4j \r\ncuda.is_available() is True, device is cuda:0 (as I've mentioned). \r\nI'm using ClusteringDiarizer, is NeuralDiarizer more recommended? or might behave elsewise regarding doing all computations on GPU and not CPU?\r\n\r\nmy GPU is T4 with 14G vRAM. \r\nClusteringDiarizer model is moved to GPU, vad is running on GPU, embeddings are running on GPU. \r\nWhy would get_argmin_mat method use CPU RAM so highly? (or other steps of clustering?)\r\n\r\nWhat else can I do to make sure everything is loaded on GPU RAM?", "@tango4j any idea?", "@tttalshaul \r\nHi. Now I can see that the CPU-RAM that is piling up is speaker embedding vectors for clustering.\r\nThat speaker embedding vectors cannot be removed since it needs to be used for clustering process.\r\nThis can only fixed by saving the speaker embedding to your disk drive and load it again. (Since you are trying very long audio) - This feature should be implemented in the future.\r\n\r\nHowever, you have another quick solution:\r\nhttps://github.com/chimechallenge/C8DASR-Baseline-NeMo/tree/main/scripts/chime8\r\n\r\nNeMo team is supporting CHiME-8 Challenge and we are providing the baseline for everyone.\r\nThis version of diarization uses less amount of memory, so there is a chance that this version performs without OOM (although not guaranteed)\r\nIf you are urgent on this task, please try CHiME-8 baseline diarization.\r\n\r\n\r\n\r\n", "Hello @tango4j! Are you talking about `msdd_v2_models` or `msdd_v3_models` module? Does the new MSDD model provide the same DER?", "I have also faced a CUDA OOM on long audio samples. Maybe my solution might help you too? https://github.com/NVIDIA/NeMo/pull/8469\r\n\r\nSurprisingly, small changes in math helped not to hit the limit of GPU capacity.", "@dkurt \r\nThere are two types of OOM issues:\r\n`CPU-RAM OOM`: After extracting speaker embeddings with GPU, it needs to load off the vectors to CPU-RAM, cause GPU-RAM is \r\n heavily limited. However, if an audio file is even longer than the amount that your CPU-RAM can hold, CPU-RAM will be maxed out. One way to solve this is that you can write speaker embedding files on your disk drive than load again to avoid this. We do not implement this because it will slow down the process a lot.\r\n`GPU-RAM OOM`: this happens affinity matrix grows too big since it has O(n^2). This can be mitigated by increasing the :\r\n`embeddings_per_chunk` to a bigger number to a certain degree. But this also has limit since GPU-RAM is not inifinite. Try increasing this parameter as I stated above. ", "@khaykingleb The MSDD-v2 model for Chime8 baseline will give you a different DER number for the same audio file, since these chime8 models are tuned for chime7 train/dev datasets.", "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.", "@tango4j Thank you, \r\nDo you have an example of how to use Chime8 diarization inference with custom input files? jupyter notebook or other example?\r\nI'll try that..", "I believe it has the same interface. For example, you can see `examples/speaker_tasks/diarization/neural_diarizer/multiscale_diar_decoder_v2_infer.py`. \r\n\r\nBut the running doesn't work even if you download `MSDD_v2_PALO_100ms_intrpl_3scales.nemo` from HuggingFace Hub and try `examples/speaker_tasks/diarization/conf/inference/diar_infer_msdd_v2.yaml` instead. \r\n\r\nI get the following error:\r\n```\r\nFile ~/experiments/C8DASR-Baseline-NeMo/nemo/collections/asr/models/msdd_v2_models.py:1132, in EncDecDiarLabelModel.forward_vad(self, audio_signal, audio_signal_length)\r\n 1130 def forward_vad(self, audio_signal, audio_signal_length):\r\n 1131 log_probs = self.msdd._vad_model(input_signal=audio_signal, input_signal_length=audio_signal_length)\r\n-> 1132 vad_probs = torch.softmax(log_probs, dim=-1)[:,:,1]\r\n 1133 vad_probs_frame = torch.repeat_interleave(vad_probs, self.subsample_vad, dim=1)\r\n 1134 return vad_probs_frame\r\n\r\nIndexError: too many indices for tensor of dimension 2\r\n```\r\n\r\nAnd if I fix it, I get another error. @tango4j, can we really use MSDD V2? ", "I've managed to run chime8 diarization somehow on a custom recording I've choosed but I've got not good results..\r\nI'm using currently the LongClusteringDiarizer from gabitza-tech fork.. it splits the file to small chunks and of course don't OOM, but I'm not sure that the speakers in each split match the other splits.. ", "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.", "Hey, I made a solution that worked for me. Previously the code never went to long form clustering because it was giving OOM at get_argmin_mat. Let me know if this works for you. I was able to process a 5 hour audio on g4.xlarge using this. [Merge-Request](https://github.com/NVIDIA/NeMo/pull/9114)", "Thanks for creating the PR, @tango4j could you please help to review the PR. ", "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.", "The issue still exists\r\nPlease view the [PR](https://github.com/NVIDIA/NeMo/pull/9114)", "@tango4j please add your comments/views for the PR. ", "@nithinraok \r\nI was not able to get the 4hour long audio samples yet. Once I get access to it, I will work on this PR. ", "@tango4j Have you got the 4hour audio samples? \r\nWhen do you think you will be available to approve the PR? \r\nIt's waiting for 3 months already.. \r\nThank you.", "Hey @tango4j @nithinraok Please view the [PR](https://github.com/NVIDIA/NeMo/pull/9114). Its been waiting for quite a while now" ]

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[ "jenkins", "jenkins", "This PR is stale because it has been open for 14 days with no activity. Remove stale label or comment or update or this will be closed in 7 days." ]

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[ "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." ]

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[ "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." ]

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[ "jenkins", "jenkins\r\n\r\n", "jenkins", "jenkins", "closed in favor of #8622 " ]

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[]

[ "jenkins" ]

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[ "Anyone willing to review? It's really a small change.\r\n\r\n@MaximumEntropy, @ericharper, @ekmb, @yzhang123, @VahidooX, @vladgets, @okuchaiev", "This PR is stale because it has been open for 14 days with no activity. Remove stale label or comment or update or this will be closed in 7 days.", "This PR was closed because it has been inactive for 7 days since being marked as stale." ]

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[ "Hi, any updates?", "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." ]

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[]

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[]

[ "jenkins", "@GNroy, @andrusenkoau FYI" ]

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[ "Streaming ASR at the moment does not support ngram", "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." ]

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[ "jenkins", "jenkins", "jenkins", "jenkins", "jenkins", "jenkins", "jenkins", "jenkins", "jenkins", "jenkins", "jenkins" ]