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TS-Exported SpeakerNet model has vastly different outputs than non-exported model.
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**Describe the bug** I have exported the pre-trained SpeakerNet model into TorchScript. With the same data input, I am calling this exported model and its original non-exported model but the results are vastly different. **Steps/Code to reproduce bug** ``` processed_signal = ... # torch.Size([1, 64, 416]) processed_signal_len = ... # torch.Size([1]) # Load original pre-trained model. from nemo.collections.asr.models import EncDecSpeakerLabelModel original_encoder = EncDecSpeakerLabelModel.from_pretrained( model_name="speakerverification_speakernet" ) original_encoder.eval() # Export pre-trained model to TorchScript. original_encoder.export('exported_encoder.ts') # Original model's inference. original_logits, original_embs = original_encoder.forward_for_export(processed_signal, processed_signal_len) # Exported model's inference. exported_encoder = torch.jit.load('exported_encoder.ts') exported_logits, exported_embs = exported_encoder.forward(processed_signal=processed_signal, processed_signal_len=processed_signal_len) ``` However, I can see that `original_embs` and `exported_embs` is vastly different. This is very confusing to me. **Expected behavior** I would expect that the original model's and exported model's prediction to be the same (or at least very close to same). **Environment overview (please complete the following information)** Environment location is GCP. Method of install is from pip. **Environment details** If NVIDIA docker image is used you don't need to specify these. Otherwise, please provide: - OS version = Debian GNU/Linux - PyTorch version = 1.11.0+cu113 - Python version = 3.7.12
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# What does this PR do ? Add a one line overview of what this PR aims to accomplish. **Collection**: [Note which collection this PR will affect] # Changelog - Add specific line by line info of high level changes in this PR. # Usage * You can potentially add a usage example below ```python # Add a code snippet demonstrating how to use this ``` # Before your PR is "Ready for review" **Pre checks**: - [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) - [ ] Did you write any new necessary tests? - [ ] Did you add or update any necessary documentation? - [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc) - [ ] Reviewer: Does the PR have correct import guards for all optional libraries? **PR Type**: - [ ] New Feature - [ ] Bugfix - [ ] Documentation If you haven't finished some of the above items you can still open "Draft" PR. ## Who can review? Anyone in the community is free to review the PR once the checks have passed. [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas. # Additional Information * Related to # (issue)
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Flashlight Decoder for Nemo
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# What does this PR do ? Adds Flashlight decoding to Nemo **Collection**: ASR # Changelog - Added flashlight_decoder.py to collections/asr/modules - Changed collections/asr/metrics/wer.py and wer_bpe.py to support new setter methods for ctc_decoding object - Changed collections/asr/parts/submodules/ctc_beam_decoding.py to include all of the new logic for Flashlight decoding which uses the new beam search API - Added script to scripts/asr_language_modeling/ngram_lm which creates Flashlight format lexicon files from KenLM arpa files # Usage Works for lexicon and lexicon-free flashlight decoding ```python import torch from nemo.collections.asr.models import ASRModel from nemo.collections.asr.metrics.wer import CTCDecodingConfig device = torch.device('cuda') asr_model = ASRModel.restore_from(...).to(device) files = ['/path/to/audio1.wav', '/path/to/audio2.wav'] # Lexicon-based decoding_cfg = CTCDecodingConfig() decoding_cfg.strategy = "flashlight" decoding_cfg.beam.search_type = "flashlight" decoding_cfg.beam.kenlm_path = '/path/to/kenlm.bin' decoding_cfg.beam.flashlight_cfg.lexicon_path='/path/to/lexicon.lexicon' decoding_cfg.beam.beam_size = 32 decoding_cfg.beam.beam_alpha = 0.2 decoding_cfg.beam.beam_beta = 0.2 decoding_cfg.beam.flashlight_cfg.beam_size_token = 32 decoding_cfg.beam.flashlight_cfg.beam_threshold = 25.0 # Lexicon-free (for char based languages like Japanese) decoding_cfg = CTCDecodingConfig() decoding_cfg.strategy = "flashlight" decoding_cfg.beam.search_type = "flashlight" decoding_cfg.beam.kenlm_path = '/path/to/kenlm.bin' decoding_cfg.beam.flashlight_cfg.lexicon_path=None decoding_cfg.beam.beam_size = 32 decoding_cfg.beam.beam_alpha = 0.2 decoding_cfg.beam.beam_beta = 0.2 decoding_cfg.beam.flashlight_cfg.beam_size_token = 32 decoding_cfg.beam.flashlight_cfg.beam_threshold = 25.0 decoding_cfg.beam.flashlight_cfg.unit_lm = True asr_model.change_decoding_strategy(decoding_cfg) transcripts = asr_model.transcribe(paths2audio_files=files, batch_size=2, return_hypotheses=False, logprobs=False) ``` # Before your PR is "Ready for review" **Pre checks**: - [X] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) - [X] Did you write any new necessary tests? - [X] Did you add or update any necessary documentation? - [X] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc) - [X] Reviewer: Does the PR have correct import guards for all optional libraries? **PR Type**: - [X] New Feature - [ ] Bugfix - [ ] Documentation If you haven't finished some of the above items you can still open "Draft" PR. ## Who can review? Anyone in the community is free to review the PR once the checks have passed. [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas. # Additional Information * Related to # (issue)
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Update numpy requirements from 1.21 to 1.22
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Seeking to overcome this issue UserWarning: Failed to initialize NumPy: module compiled against API version 0xf but this version of numpy is 0xe based on https://iq.opengenus.org/module-compiled-against-api-version-0x10-but-this-version-of-numpy-is-0xe/ Note: this warning actually causes a crash later one when trying to import things from numpy typing # What does this PR do ? Add a one line overview of what this PR aims to accomplish. **Collection**: NLP, ASR # Changelog - Add specific line by line info of high level changes in this PR. # Usage * You can potentially add a usage example below ```python # Add a code snippet demonstrating how to use this ``` # Before your PR is "Ready for review" **Pre checks**: - [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) - [ ] Did you write any new necessary tests? - [ ] Did you add or update any necessary documentation? - [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc) - [ ] Reviewer: Does the PR have correct import guards for all optional libraries? **PR Type**: - [ ] New Feature - [x] Bugfix - [ ] Documentation If you haven't finished some of the above items you can still open "Draft" PR. ## Who can review? Anyone in the community is free to review the PR once the checks have passed. [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas. # Additional Information * Related to # (issue)
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Update radtts' infer path
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# What does this PR do ? Update radtts' infer path. **Collection**: TTS # Changelog - Update radtts' infer path
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fix token id bug
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fixed the tokenizer bug, add `bos_id` to the tabular tokenizer
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Finetune the pretrained nemo model
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python speech_to_text_ctc_bpe.py --config-path=“/path/to/confiig/file” --config-name=“conformer_ctc_bpe_v13” trainer.max_epochs=50 +init_from_nemo_model=“path/to/stt_hi_conformer_ctc_medium.nemo” Getting error as shown below: from checkpoint, the shape in current model is torch.Size([512]). size mismatch for encoder.layers.17.norm_self_att.weight: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([512]). size mismatch for encoder.layers.17.norm_self_att.bias: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([512]). size mismatch for encoder.layers.17.self_attn.pos_bias_u: copying a param with shape torch.Size([4, 64]) from checkpoint, the shape in current model is torch.Size([8, 64]). size mismatch for encoder.layers.17.self_attn.pos_bias_v: copying a param with shape torch.Size([4, 64]) from checkpoint, the shape in current model is torch.Size([8, 64]). size mismatch for encoder.layers.17.self_attn.linear_q.weight: copying a param with shape torch.Size([256, 256]) from checkpoint, the shape in current model is torch.Size([512, 512]). size mismatch for encoder.layers.17.self_attn.linear_q.bias: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([512]). size mismatch for encoder.layers.17.self_attn.linear_k.weight: copying a param with shape torch.Size([256, 256]) from checkpoint, the shape in current model is torch.Size([512, 512]). size mismatch for encoder.layers.17.self_attn.linear_k.bias: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([512]). size mismatch for encoder.layers.17.self_attn.linear_v.weight: copying a param with shape torch.Size([256, 256]) from checkpoint, the shape in current model is torch.Size([512, 512]). size mismatch for encoder.layers.17.self_attn.linear_v.bias: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([512]). size mismatch for encoder.layers.17.self_attn.linear_out.weight: copying a param with shape torch.Size([256, 256]) from checkpoint, the shape in current model is torch.Size([512, 512]). size mismatch for encoder.layers.17.self_attn.linear_out.bias: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([512]). size mismatch for encoder.layers.17.self_attn.linear_pos.weight: copying a param with shape torch.Size([256, 256]) from checkpoint, the shape in current model is torch.Size([512, 512]). size mismatch for encoder.layers.17.norm_feed_forward2.weight: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([512]). size mismatch for encoder.layers.17.norm_feed_forward2.bias: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([512]). size mismatch for encoder.layers.17.feed_forward2.linear1.weight: copying a param with shape torch.Size([1024, 256]) from checkpoint, the shape in current model is torch.Size([2048, 512]). size mismatch for encoder.layers.17.feed_forward2.linear1.bias: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([2048]). size mismatch for encoder.layers.17.feed_forward2.linear2.weight: copying a param with shape torch.Size([256, 1024]) from checkpoint, the shape in current model is torch.Size([512, 2048]). size mismatch for encoder.layers.17.feed_forward2.linear2.bias: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([512]). size mismatch for encoder.layers.17.norm_out.weight: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([512]). size mismatch for encoder.layers.17.norm_out.bias: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([512]). size mismatch for decoder.decoder_layers.0.weight: copying a param with shape torch.Size([129, 256, 1]) from checkpoint, the shape in current model is torch.Size([129, 512, 1]). Can you please help out with the error? <img width="947" alt="Screenshot 2023-01-11 at 12 34 53 PM" src="https://user-images.githubusercontent.com/42312456/211971875-1df94c67-82a3-4223-8a6e-8501e1d677c9.png">
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Update numpy requirements from 1.21 to 1.22
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Seeking to overcome this issue UserWarning: Failed to initialize NumPy: module compiled against API version 0xf but this version of numpy is 0xe based on https://iq.opengenus.org/module-compiled-against-api-version-0x10-but-this-version-of-numpy-is-0xe/ Note: this warning actually causes a crash later one when trying to import things from numpy typing # What does this PR do ? Add a one line overview of what this PR aims to accomplish. **Collection**: NLP, ASR # Changelog - Add specific line by line info of high level changes in this PR. # Usage * You can potentially add a usage example below ```python # Add a code snippet demonstrating how to use this ``` # Before your PR is "Ready for review" **Pre checks**: - [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) - [ ] Did you write any new necessary tests? - [ ] Did you add or update any necessary documentation? - [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc) - [ ] Reviewer: Does the PR have correct import guards for all optional libraries? **PR Type**: - [ ] New Feature - [x] Bugfix - [ ] Documentation If you haven't finished some of the above items you can still open "Draft" PR. ## Who can review? Anyone in the community is free to review the PR once the checks have passed. [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas. # Additional Information * Related to # (issue)
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[TTS][ZH] bugfix import jieba errors.
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There was a bug which hits errors below. This PR fixed the error by importing `jieba` correctly. ``` 21 Error executing job with overrides: ['++dataloader_params.num_workers=48', 'manifest_filepath=/manifest/train_manifest.json', 'sup_data_path=/sup_data'] 22 Traceback (most recent call last): 23 File "/usr/local/lib/python3.8/dist-packages/hydra/_internal/instantiate/_instantiate2.py", line 92, in _call_target 24 return _target_(*args, **kwargs) 25 File "/code/nemo/collections/tts/torch/data.py", line 241, in __init__ 26 file_info["text_tokens"] = self.text_tokenizer(file_info["normalized_text"]) 27 File "/code/nemo/collections/common/tokenizers/text_to_speech/tts_tokenizers.py", line 74, in __call__ 28 return self.encode(text) 29 File "/code/nemo/collections/common/tokenizers/text_to_speech/tts_tokenizers.py", line 723, in encode 30 g2p_text = self.g2p(text) # TODO: handle infer 31 File "/code/nemo_text_processing/g2p/modules.py", line 617, in __call__ 32 words_list = list(jieba.cut(text)) 33 NameError: name 'jieba' is not defined 34 35 The above exception was the direct cause of the following exception: 36 37 Traceback (most recent call last): 38 File "scripts/dataset_processing/tts/extract_sup_data.py", line 83, in <module> 39 main() # noqa pylint: disable=no-value-for-parameter 40 File "/code/nemo/core/config/hydra_runner.py", line 105, in wrapper 41 _run_hydra( 42 File "/usr/local/lib/python3.8/dist-packages/hydra/_internal/utils.py", line 389, in _run_hydra 43 _run_app( 44 File "/usr/local/lib/python3.8/dist-packages/hydra/_internal/utils.py", line 452, in _run_app 45 run_and_report( 46 File "/usr/local/lib/python3.8/dist-packages/hydra/_internal/utils.py", line 216, in run_and_report 47 raise ex 48 File "/usr/local/lib/python3.8/dist-packages/hydra/_internal/utils.py", line 213, in run_and_report 49 return func() 50 File "/usr/local/lib/python3.8/dist-packages/hydra/_internal/utils.py", line 453, in <lambda> 51 lambda: hydra.run( 52 File "/usr/local/lib/python3.8/dist-packages/hydra/_internal/hydra.py", line 132, in run 53 _ = ret.return_value 54 File "/usr/local/lib/python3.8/dist-packages/hydra/core/utils.py", line 260, in return_value 55 raise self._return_value 56 File "/usr/local/lib/python3.8/dist-packages/hydra/core/utils.py", line 186, in run_job 57 ret.return_value = task_function(task_cfg) 58 File "scripts/dataset_processing/tts/extract_sup_data.py", line 70, in main 59 dataset = instantiate(cfg.dataset) 60 File "/usr/local/lib/python3.8/dist-packages/hydra/_internal/instantiate/_instantiate2.py", line 222, in instantiate 61 return instantiate_node( 62 File "/usr/local/lib/python3.8/dist-packages/hydra/_internal/instantiate/_instantiate2.py", line 339, in instantiate_node 63 return _call_target(_target_, partial, args, kwargs, full_key) 64 File "/usr/local/lib/python3.8/dist-packages/hydra/_internal/instantiate/_instantiate2.py", line 97, in _call_target 65 raise InstantiationException(msg) from e 66 hydra.errors.InstantiationException: Error in call to target 'nemo.collections.tts.torch.data.TTSDataset': 67 NameError("name 'jieba' is not defined") 68 full_key: dataset ``` Signed-off-by: Xuesong Yang <[email protected]> # What does this PR do ? Add a one line overview of what this PR aims to accomplish. **Collection**: [Note which collection this PR will affect] # Changelog - Add specific line by line info of high level changes in this PR. # Usage * You can potentially add a usage example below ```python # Add a code snippet demonstrating how to use this ``` # Before your PR is "Ready for review" **Pre checks**: - [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) - [ ] Did you write any new necessary tests? - [ ] Did you add or update any necessary documentation? - [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc) - [ ] Reviewer: Does the PR have correct import guards for all optional libraries? **PR Type**: - [ ] New Feature - [ ] Bugfix - [ ] Documentation If you haven't finished some of the above items you can still open "Draft" PR. ## Who can review? Anyone in the community is free to review the PR once the checks have passed. [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas. # Additional Information * Related to # (issue)
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# What does this PR do ? Add a one line overview of what this PR aims to accomplish. **Collection**: [Note which collection this PR will affect] # Changelog - updating biased gelu - minor updates to mimic nemo nmt # Before your PR is "Ready for review" **Pre checks**: - [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) - [ ] Did you write any new necessary tests? - [ ] Did you add or update any necessary documentation? - [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc) - [ ] Reviewer: Does the PR have correct import guards for all optional libraries? **PR Type**: - [ ] New Feature - [x] Bugfix - [ ] Documentation If you haven't finished some of the above items you can still open "Draft" PR. ## Who can review? Anyone in the community is free to review the PR once the checks have passed. [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas. # Additional Information * Related to # (issue)
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Signed-off-by: smajumdar <[email protected]> # What does this PR do ? Update torchaudio dependency in tutorials to avoid issue with torch.stft signature. **Collection**: [ASR] # Changelog - Update tutorial dependency for torchaudio # Before your PR is "Ready for review" **Pre checks**: - [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) - [ ] Did you write any new necessary tests? - [ ] Did you add or update any necessary documentation? - [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc) - [ ] Reviewer: Does the PR have correct import guards for all optional libraries? **PR Type**: - [ ] New Feature - [x] Bugfix - [ ] Documentation
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Signed-off-by: smajumdar <[email protected]> # What does this PR do ? Update torchaudio dependency in tutorials to avoid issue with torch.stft signature. **Collection**: [ASR] # Changelog - Update tutorial dependency for torchaudio # Before your PR is "Ready for review" **Pre checks**: - [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) - [ ] Did you write any new necessary tests? - [ ] Did you add or update any necessary documentation? - [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc) - [ ] Reviewer: Does the PR have correct import guards for all optional libraries? **PR Type**: - [ ] New Feature - [x] Bugfix - [ ] Documentation
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set max_steps for lr decay through config
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# What does this PR do ? Allows setting lr decay steps through config that could be different from the trainer.max_steps **Collection**: [Note which collection this PR will affect] # Changelog - Add specific line by line info of high level changes in this PR. # Usage * You can potentially add a usage example below ```python # Add a code snippet demonstrating how to use this ``` # Before your PR is "Ready for review" **Pre checks**: - [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) - [ ] Did you write any new necessary tests? - [ ] Did you add or update any necessary documentation? - [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc) - [ ] Reviewer: Does the PR have correct import guards for all optional libraries? **PR Type**: - [ ] New Feature - [ ] Bugfix - [ ] Documentation If you haven't finished some of the above items you can still open "Draft" PR. ## Who can review? Anyone in the community is free to review the PR once the checks have passed. [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas. # Additional Information * Related to # (issue)
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DALI fails in mulit-node setup
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**Describe the bug** In a multi-node setup, DALI pipelines' fail because data is only visible on one node. **Steps/Code to reproduce bug** 1. Download the data on the main node (rank 0), place it in `/scratch/audio-data` 2. Launch training 3. DALI raises error on other nodes : `[/opt/dali/dali/operators/reader/loader/nemo_asr_loader.cc:80] Assert on "fstream" failed: Could not open NEMO ASR manifest file: "/scratch/audio-data/manifest.json` **Expected behavior** If `ddp` is enabled, DALI should run only on rank 0 node and load the data from there. **Environment overview (please complete the following information)** - Environment location: AWS (HPC) - Method of NeMo install: `pip install git+https://github.com/NVIDIA/NeMo.git@9bc1220132cca7ba71550105d98dd12cfc7fefbe#egg=nemo_toolkit[asr]` **Environment details** If NVIDIA docker image is used you don't need to specify these. Otherwise, please provide: - PyTorch version 1.13.1 - Python version: 3.8.10 **Additional context** Any other suggestions on how to setup multi-node training are welcome.
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Port Riva's mel cepstral distortion w/ dynamic time warping notebook
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# What does this PR do ? Ports the Riva Mel Cepstral Distortion - Dynamic Time Warping notebook (nvidia-riva/tutorials#96) to NeMo. HTML version here for ease of viewing: https://drive.google.com/file/d/1E7bk95HQXZE_LyCfryr1r9LEKPzm8J9D/view?usp=sharing **Collection**: TTS # Changelog - Minor changes to ensure the notebook runs both locally and in Colab (tarred the example data, changed the way the audio is displayed) - Additional docstrings and function explanations - Minor text updates **PR Type**: - [x] New Feature - [ ] Bugfix - [x] Documentation If you haven't finished some of the above items you can still open "Draft" PR.
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fix token id bug
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fixed the tokenizer bug, add `bos_id` to the tabular tokenizer
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[TTS][ZH] bugfix import jieba errors.
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There was a bug which hits errors below. This PR fixed the error by importing `jieba` correctly. ``` 21 Error executing job with overrides: ['++dataloader_params.num_workers=48', 'manifest_filepath=/manifest/train_manifest.json', 'sup_data_path=/sup_data'] 22 Traceback (most recent call last): 23 File "/usr/local/lib/python3.8/dist-packages/hydra/_internal/instantiate/_instantiate2.py", line 92, in _call_target 24 return _target_(*args, **kwargs) 25 File "/code/nemo/collections/tts/torch/data.py", line 241, in __init__ 26 file_info["text_tokens"] = self.text_tokenizer(file_info["normalized_text"]) 27 File "/code/nemo/collections/common/tokenizers/text_to_speech/tts_tokenizers.py", line 74, in __call__ 28 return self.encode(text) 29 File "/code/nemo/collections/common/tokenizers/text_to_speech/tts_tokenizers.py", line 723, in encode 30 g2p_text = self.g2p(text) # TODO: handle infer 31 File "/code/nemo_text_processing/g2p/modules.py", line 617, in __call__ 32 words_list = list(jieba.cut(text)) 33 NameError: name 'jieba' is not defined 34 35 The above exception was the direct cause of the following exception: 36 37 Traceback (most recent call last): 38 File "scripts/dataset_processing/tts/extract_sup_data.py", line 83, in <module> 39 main() # noqa pylint: disable=no-value-for-parameter 40 File "/code/nemo/core/config/hydra_runner.py", line 105, in wrapper 41 _run_hydra( 42 File "/usr/local/lib/python3.8/dist-packages/hydra/_internal/utils.py", line 389, in _run_hydra 43 _run_app( 44 File "/usr/local/lib/python3.8/dist-packages/hydra/_internal/utils.py", line 452, in _run_app 45 run_and_report( 46 File "/usr/local/lib/python3.8/dist-packages/hydra/_internal/utils.py", line 216, in run_and_report 47 raise ex 48 File "/usr/local/lib/python3.8/dist-packages/hydra/_internal/utils.py", line 213, in run_and_report 49 return func() 50 File "/usr/local/lib/python3.8/dist-packages/hydra/_internal/utils.py", line 453, in <lambda> 51 lambda: hydra.run( 52 File "/usr/local/lib/python3.8/dist-packages/hydra/_internal/hydra.py", line 132, in run 53 _ = ret.return_value 54 File "/usr/local/lib/python3.8/dist-packages/hydra/core/utils.py", line 260, in return_value 55 raise self._return_value 56 File "/usr/local/lib/python3.8/dist-packages/hydra/core/utils.py", line 186, in run_job 57 ret.return_value = task_function(task_cfg) 58 File "scripts/dataset_processing/tts/extract_sup_data.py", line 70, in main 59 dataset = instantiate(cfg.dataset) 60 File "/usr/local/lib/python3.8/dist-packages/hydra/_internal/instantiate/_instantiate2.py", line 222, in instantiate 61 return instantiate_node( 62 File "/usr/local/lib/python3.8/dist-packages/hydra/_internal/instantiate/_instantiate2.py", line 339, in instantiate_node 63 return _call_target(_target_, partial, args, kwargs, full_key) 64 File "/usr/local/lib/python3.8/dist-packages/hydra/_internal/instantiate/_instantiate2.py", line 97, in _call_target 65 raise InstantiationException(msg) from e 66 hydra.errors.InstantiationException: Error in call to target 'nemo.collections.tts.torch.data.TTSDataset': 67 NameError("name 'jieba' is not defined") 68 full_key: dataset ``` Signed-off-by: Xuesong Yang <[email protected]> # What does this PR do ? Add a one line overview of what this PR aims to accomplish. **Collection**: [Note which collection this PR will affect] # Changelog - Add specific line by line info of high level changes in this PR. # Usage * You can potentially add a usage example below ```python # Add a code snippet demonstrating how to use this ``` # Before your PR is "Ready for review" **Pre checks**: - [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) - [ ] Did you write any new necessary tests? - [ ] Did you add or update any necessary documentation? - [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc) - [ ] Reviewer: Does the PR have correct import guards for all optional libraries? **PR Type**: - [ ] New Feature - [ ] Bugfix - [ ] Documentation If you haven't finished some of the above items you can still open "Draft" PR. ## Who can review? Anyone in the community is free to review the PR once the checks have passed. [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas. # Additional Information * Related to # (issue)
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Docs g2p update
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# What does this PR do ? Add a one line overview of what this PR aims to accomplish. **Collection**: [Note which collection this PR will affect] # Changelog - Add specific line by line info of high level changes in this PR. # Usage * You can potentially add a usage example below ```python # Add a code snippet demonstrating how to use this ``` # Before your PR is "Ready for review" **Pre checks**: - [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) - [ ] Did you write any new necessary tests? - [ ] Did you add or update any necessary documentation? - [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc) - [ ] Reviewer: Does the PR have correct import guards for all optional libraries? **PR Type**: - [ ] New Feature - [ X] Bugfix - [X ] Documentation If you haven't finished some of the above items you can still open "Draft" PR. ## Who can review? Anyone in the community is free to review the PR once the checks have passed. [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas. # Additional Information * Related to # (issue)
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[ASR] Separate Audio-to-Text (BPE, Char) dataset construction
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Signed-off-by: Vladimir Bataev <[email protected]> # What does this PR do ? Separate Audio-to-Text Char and BPE dataset construction: - this allows to use datasets for further mixing (e.g. in hybrid ASR-TTS models #5659) - removes duplicated code from CTC, RNNT (Char and BPE) models **Collection**: [ASR] # Changelog - Add specific line by line info of high level changes in this PR. # Usage * You can potentially add a usage example below ```python from nemo.collections.asr.data.audio_to_text_dataset import get_audio_to_text_bpe_dataset_from_config dataset = get_audio_to_text_bpe_dataset_from_config(...) # construct BPE dataset from parameters ``` # Before your PR is "Ready for review" **Pre checks**: - [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) - [ ] Did you write any new necessary tests? - [ ] Did you add or update any necessary documentation? - [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc) - [ ] Reviewer: Does the PR have correct import guards for all optional libraries? **PR Type**: - [x] New Feature - [ ] Bugfix - [ ] Documentation If you haven't finished some of the above items you can still open "Draft" PR. ## Who can review? Anyone in the community is free to review the PR once the checks have passed. [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas. # Additional Information * Related to # (issue)
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adding back tar script for decoder dataset for duplex
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Signed-off-by: Yang Zhang <[email protected]> # What does this PR do ? adding back create_tarred_dataset.py for duplex TN **Collection**: nemol_nlp # Changelog - adding back create_tarred_dataset.py for duplex TN
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Add Esperanto ASR example
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Signed-off-by: andrusenkoau <[email protected]> # What does this PR do ? Adds ASR example for training Esperanto Conformer-CTC-large model. **Collection**: ASR # Changelog - Adds Esperanto example to docs/source/asr/examples/ # Before your PR is "Ready for review" **Pre checks**: - [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) - [ ] Did you write any new necessary tests? - [ ] Did you add or update any necessary documentation? - [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc) - [ ] Reviewer: Does the PR have correct import guards for all optional libraries? **PR Type**: - [ ] New Feature - [ ] Bugfix - [x] Documentation
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Signed-off-by: Boris Fomitchev <[email protected]> Mask/length vector was ignored in bottleneck layer before. **Collection**: [Note which collection this PR will affect] TTS # Changelog - Add specific line by line info of high level changes in this PR. # Usage * You can potentially add a usage example below ```python # Add a code snippet demonstrating how to use this ``` # Before your PR is "Ready for review" **Pre checks**: - [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) - [ ] Did you write any new necessary tests? - [ ] Did you add or update any necessary documentation? - [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc) - [ ] Reviewer: Does the PR have correct import guards for all optional libraries? **PR Type**: - [ ] New Feature - [ ] Bugfix - [ ] Documentation If you haven't finished some of the above items you can still open "Draft" PR. ## Who can review? Anyone in the community is free to review the PR once the checks have passed. [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas. # Additional Information * Related to # (issue)
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Fix wrong label mapping in batch_inference for label_model
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# What does this PR do ? Fix groundtruth label mapping in `batch_inference` in `label_models.py` and add test. **Collection**: ASR # Changelog - The groundtruth label `batch_inference` was mapped wrongly. Fixed it - Add test for `batch_inference` # Usage * You can potentially add a usage example below ```python from nemo.collections.asr.models import EncDecSpeakerLabelModel model = EncDecSpeakerLabelModel.from_pretrained('langid_ambernet') embs, logits, gt_labels, mapped_labels = model.batch_inference(<path_to_manifest>) ``` # Before your PR is "Ready for review" **Pre checks**: - [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) - [x] Did you write any new necessary tests? - [x] Did you add or update any necessary documentation? - [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc) - [ ] Reviewer: Does the PR have correct import guards for all optional libraries? **PR Type**: - [ ] New Feature - [x] Bugfix - [ ] Documentation If you haven't finished some of the above items you can still open "Draft" PR. ## Who can review? Anyone in the community is free to review the PR once the checks have passed. [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas. # Additional Information * Related to https://github.com/NVIDIA/NeMo/pull/5278
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Megatron export triton update
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# What does this PR do ? Add a one line overview of what this PR aims to accomplish. **Collection**: [Note which collection this PR will affect] # Changelog - updating biased gelu - minor updates to mimic nemo nmt # Before your PR is "Ready for review" **Pre checks**: - [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) - [ ] Did you write any new necessary tests? - [ ] Did you add or update any necessary documentation? - [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc) - [ ] Reviewer: Does the PR have correct import guards for all optional libraries? **PR Type**: - [ ] New Feature - [x] Bugfix - [ ] Documentation If you haven't finished some of the above items you can still open "Draft" PR. ## Who can review? Anyone in the community is free to review the PR once the checks have passed. [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas. # Additional Information * Related to # (issue)
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Missing "create_tarred_dataset.py" script for Duplex Text Normalization
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The script for creation **TarredDataset** described in the [Duplex Text Normalization Doc](https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/stable/nlp/text_normalization/nn_text_normalization.html) is missing. According to the documentation it should be located: `examples/nlp/duplex_text_normalization/create_tarred_dataset.py`
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[ASR][Test] Enable test for cache audio with a single worker
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Signed-off-by: Ante Jukić <[email protected]> # What does this PR do ? Enable a test which was disabled in #5615. **Collection**: ASR tests # Changelog - Enabled the test using a single worker # Before your PR is "Ready for review" **Pre checks**: - [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) - [ ] Did you write any new necessary tests? - [ ] Did you add or update any necessary documentation? - [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc) - [ ] Reviewer: Does the PR have correct import guards for all optional libraries? **PR Type**: - [ ] New Feature - [x] Bugfix - [ ] Documentation If you haven't finished some of the above items you can still open "Draft" PR. ## Who can review? Anyone in the community is free to review the PR once the checks have passed. [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas. # Additional Information * Related to #5607 and #5615
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Update Tacotron2 NGC checkpoint load to latest version
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Signed-off-by: Jocelyn Huang <[email protected]> # What does this PR do ? Second version of #5748, applied to r1.15.0 this time. Tacotron2's `list_available_models()` was still pointing to a previous version, so it would load that instead of the newest one. **PR Type**: - [ ] New Feature - [x] Bugfix - [x] Documentation # Additional Information * First noticed in #5714
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[Fix] ConformerEncoder forward when length is None
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Signed-off-by: Ante Jukić <[email protected]> # What does this PR do ? This PR fixes a bug in `forward` of `ConformerEncoder` when `length is None` **Collection**: ASR # Changelog - Fixed initialization of `length` with `new_full` # Usage Currently, the following ``` import torch from nemo.collections.asr.modules.conformer_encoder import ConformerEncoder # create layer conformer_encoder = ConformerEncoder(feat_in=10, n_layers=1, d_model=16) # run out, out_length = conformer_encoder(audio_signal=torch.rand(10, 9, 32), length=None) ``` results in a `TypeError`: ``` Traceback (most recent call last): File "<stdin>", line 1, in <module> File "${PATH}/miniforge3/envs/nemo_env/lib/python3.9/site-packages/torch/nn/modules/module.py", line 1482, in _call_impl return forward_call(*args, **kwargs) File "${NEMO}/nemo/core/classes/common.py", line 1087, in __call__ outputs = wrapped(*args, **kwargs) File "${NEMO}/nemo/collections/asr/modules/conformer_encoder.py", line 416, in forward length = audio_signal.new_full( TypeError: new_full(): argument 'size' (position 1) must be tuple of ints, not int ``` # Before your PR is "Ready for review" **Pre checks**: - [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) - [ ] Did you write any new necessary tests? - [ ] Did you add or update any necessary documentation? - [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc) - [ ] Reviewer: Does the PR have correct import guards for all optional libraries? **PR Type**: - [ ] New Feature - [x] Bugfix - [ ] Documentation If you haven't finished some of the above items you can still open "Draft" PR. ## Who can review? Anyone in the community is free to review the PR once the checks have passed. [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas. # Additional Information * Related to # (issue)
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Update Tacotron2 NGC checkpoint load to latest version
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Signed-off-by: Jocelyn Huang <[email protected]> # What does this PR do ? Second version of #5748, applied to r1.15.0 this time. Tacotron2's `list_available_models()` was still pointing to a previous version, so it would load that instead of the newest one. **PR Type**: - [ ] New Feature - [x] Bugfix - [x] Documentation # Additional Information * First noticed in #5714
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TypeError: _run_hydra() missing 1 required positional argument: 'args'
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While training tacotron2, I end up with the following error. ``` Traceback (most recent call last): File "tacotron2.py", line 44, in <module> main() # noqa pylint: disable=no-value-for-parameter File "/usr/local/lib/python3.8/dist-packages/nemo/core/config/hydra_runner.py", line 98, in wrapper _run_hydra( TypeError: _run_hydra() missing 1 required positional argument: 'args' [ ] ``` Just before that, I fixed the following issue using this [solution](https://github.com/NVIDIA/NeMo/issues/3915): ``` noqa pylint: disable=no-value-for-parameter File "/usr/local/lib/python3.7/dist-packages/nemo/core/config/hydra_runner.py", line 103, in wrapper strict=None, TypeError: _run_hydra() got an unexpected keyword argument 'strict' ``` After removing the argument `strict=None`, I get the above error: `TypeError: _run_hydra() missing 1 required positional argument: 'args'`. How to fix it? Thank you!
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Fix EMA topk checkpoint deletion
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# What does this PR do ? Fixes case where we need to also delete the EMA checkpoints from previous runs. **Collection**: Common # Before your PR is "Ready for review" **Pre checks**: - [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) - [ ] Did you write any new necessary tests? - [ ] Did you add or update any necessary documentation? - [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc) - [ ] Reviewer: Does the PR have correct import guards for all optional libraries? **PR Type**: - [ ] New Feature - [x] Bugfix - [ ] Documentation
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# What does this PR do ? Adds simple documentation for EMA. Also fixes the docs on main due to an incorrect import in the EMA class. **Collection**: Common # Changelog - Add EMA documentation # Before your PR is "Ready for review" **Pre checks**: - [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) - [ ] Did you write any new necessary tests? - [x] Did you add or update any necessary documentation? - [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc) - [ ] Reviewer: Does the PR have correct import guards for all optional libraries? **PR Type**: - [ ] New Feature - [ ] Bugfix - [x] Documentation
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Signed-off-by: andrusenkoau <[email protected]> # What does this PR do ? Adds ASR example for training Esperanto Conformer-CTC-large model. **Collection**: ASR # Changelog - Adds Esperanto example to docs/source/asr/examples/ # Before your PR is "Ready for review" **Pre checks**: - [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) - [ ] Did you write any new necessary tests? - [ ] Did you add or update any necessary documentation? - [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc) - [ ] Reviewer: Does the PR have correct import guards for all optional libraries? **PR Type**: - [ ] New Feature - [ ] Bugfix - [x] Documentation
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GPU memory leak in nemo asr_model.transcribe()
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**Describe the bug** GPU memory usage increases over the time ![Screenshot from 2023-01-08 23-10-52](https://user-images.githubusercontent.com/104126711/211239138-45bc947c-1910-459f-bd87-ced91070c1cd.png) **Steps/Code to reproduce bug** You can reproduce this bug using this notebook [Colab Notebook](https://colab.research.google.com/drive/1PqW0MqVyzCJ53fM1uwGrNvEcsmJN8Q6R?usp=sharing) You can take any random WAV audio file and make sure you run it for 40 min to 1 hour. The Memory error should reproduce like mine. **Expected behavior** Memory usage aways should same after 1st batch processing complete **Environment overview (please complete the following information)** Google Colab
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Errors when running first cell in 00_NeMo_Primer in Google Colab
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**Describe the bug** When running the first cell in tutorials/00_NeMo_Primer.ipynb on Google Colab, I get the following errors: ``` ERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts. tensorflow 2.9.2 requires protobuf<3.20,>=3.9.2, but you have protobuf 3.20.3 which is incompatible. tensorflow 2.9.2 requires tensorboard<2.10,>=2.9, but you have tensorboard 2.11.0 which is incompatible. ``` I then get FileNotFound exceptions when running later cells trying to import NeMo collections **Steps/Code to reproduce bug** 1. Click link on https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/stable/starthere/tutorials.html for NeMo Fundamentals 2. Switch Runtime to use GPU Hardware Accelerator 3. Run first cell **Expected behavior** Expected dependencies to import without issue and to be able to go through the rest of the tutorial **Environment overview (please complete the following information)** - Environment location: Collab - Method of NeMo install: pip install (specified in notebook with the commands: ```BRANCH = 'r1.14.0' !python -m pip install git+https://github.com/NVIDIA/NeMo.git@$BRANCH#egg=nemo_toolkit[all]``` **Environment details** - Python version 3.8.16 **Additional context** Seems NeMo wants protobuf<4,>=3.20.2, but tensorflow 2.9.2 wants < 3.20. Same for tensorboard.
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[TTS][DE] refine grapheme-based tokenizer and fastpitch training recipe on thorsten's neutral datasets.
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# What does this PR do ? ``` 1. refine `GermanCharsTokenizer` to support only graphemes as inputs by removing sentence-level phoneme representation; 2. refine `GermanCharsTokenizer` to preserve mixed cases from the original input graphemes; 3. add a new Thorsten's 22.10 dataset; 4. revise the thorsten's voice neutral datasets preparation script to support two versions of thorsten's voice datasets in a single script. 5. succeeded in running through model training. 6. fixed some typos. ``` **TODO in separate PRs** ``` 1. update NGC URL with new model ckpts. 2. update the tutorial accordingly. 3. train grapheme-based models on HUI-ACG datasets and update NGC releases. ``` **Collection**: [Note which collection this PR will affect] # Changelog - Add specific line by line info of high level changes in this PR. # Usage * You can potentially add a usage example below ```python # Add a code snippet demonstrating how to use this ``` # Before your PR is "Ready for review" **Pre checks**: - [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) - [ ] Did you write any new necessary tests? - [ ] Did you add or update any necessary documentation? - [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc) - [ ] Reviewer: Does the PR have correct import guards for all optional libraries? **PR Type**: - [ ] New Feature - [ ] Bugfix - [ ] Documentation If you haven't finished some of the above items you can still open "Draft" PR. ## Who can review? Anyone in the community is free to review the PR once the checks have passed. [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas. # Additional Information * Related to # (issue): https://github.com/NVIDIA/NeMo/issues/5400
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Set the stream position to 0 for pydub
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# What does this PR do ? This PR sets the stream position to 0 to use pydub as fallback if SoundFile fails to read an audio file. When using tarred datasets, `audio_file` is a file-like object, not a path-like object. In this situation, to use pydub as fallback, we need to set the stream position of `audio_file` to 0 since SoundFile have advanced the position while reading it. **Collection**: ASR # Changelog - Add specific line by line info of high level changes in this PR. # Usage * You can potentially add a usage example below ```python # Add a code snippet demonstrating how to use this ``` # Before your PR is "Ready for review" **Pre checks**: - [X] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) - [ ] Did you write any new necessary tests? - [ ] Did you add or update any necessary documentation? - [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc) - [ ] Reviewer: Does the PR have correct import guards for all optional libraries? **PR Type**: - [ ] New Feature - [X] Bugfix - [ ] Documentation If you haven't finished some of the above items you can still open "Draft" PR. ## Who can review? Anyone in the community is free to review the PR once the checks have passed. [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas. # Additional Information * Related to # (issue)
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Pretraining argument for tokenizer
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" ${model.language_model.pretrained_model_name} # tokenizer that inherits from TokenizerSpec" AttributeError: 'BERTLMModel' object has no attribute 'tokenizer'
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Expose conditional log prob method for enc-dec models.
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Signed-off-by: MaximumEntropy <[email protected]> # What does this PR do ? Exposes a method `log_prob()` to get the conditional log prob `p(y|x)` for enc-dec models. **Collection**: NLP # Changelog - Add specific line by line info of high level changes in this PR. # Usage * You can potentially add a usage example below ```python # Add a code snippet demonstrating how to use this ``` # Before your PR is "Ready for review" **Pre checks**: - [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) - [ ] Did you write any new necessary tests? - [ ] Did you add or update any necessary documentation? - [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc) - [ ] Reviewer: Does the PR have correct import guards for all optional libraries? **PR Type**: - [ ] New Feature - [ ] Bugfix - [ ] Documentation If you haven't finished some of the above items you can still open "Draft" PR. ## Who can review? Anyone in the community is free to review the PR once the checks have passed. [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas. # Additional Information * Related to # (issue)
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# What does this PR do ? adds explicit random seed init for all prompt learning and adapter learning based methods. Add a one line overview of what this PR aims to accomplish. **Collection**: [Note which collection this PR will affect] # Changelog - Add specific line by line info of high level changes in this PR. # Usage * You can potentially add a usage example below ```python # Add a code snippet demonstrating how to use this ``` # Before your PR is "Ready for review" **Pre checks**: - [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) - [ ] Did you write any new necessary tests? - [ ] Did you add or update any necessary documentation? - [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc) - [ ] Reviewer: Does the PR have correct import guards for all optional libraries? **PR Type**: - [ ] New Feature - [ ] Bugfix - [ ] Documentation If you haven't finished some of the above items you can still open "Draft" PR. ## Who can review? Anyone in the community is free to review the PR once the checks have passed. [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas. # Additional Information * Related to # (issue)
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Update Tacotron2's list_available_models()
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Signed-off-by: Jocelyn Huang <[email protected]> # What does this PR do ? Tacotron2's `list_available_models()` was still pointing to a previous version, so it would load that instead of the newest one. **Collection**: TTS **PR Type**: - [ ] New Feature - [x] Bugfix - [ ] Documentation # Additional Information * As noticed in #5714
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**Describe the bug** All files present in manifest contains silence, aborting next steps. When I run the diarization with an audio file which contains no speech, it terminates the program and throws an error **Steps/Code to reproduce bug** Run diarization on any audio wav file which contains no speech/audio **Expected behavior** Rather than program terminating, I would like a blank output and a blank rttm file. Or how do I circumvent the "raise ValueError" in the script?
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[TTS] Update Spanish TTS model to 1.15
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Signed-off-by: Ryan <[email protected]> # What does this PR do ? Update FastPitch Spanish config from 1.14.0 to 1.15.0, which contains IPA support and improvements from speaker level pitch normalization. https://catalog.ngc.nvidia.com/orgs/nvidia/teams/nemo/models/tts_es_multispeaker_fastpitchhifigan/version **Collection**: [TTS] # Before your PR is "Ready for review" **Pre checks**: - [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) - [ ] Did you write any new necessary tests? - [ ] Did you add or update any necessary documentation? - [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc) - [ ] Reviewer: Does the PR have correct import guards for all optional libraries? **PR Type**: - [x] New Feature - [ ] Bugfix - [ ] Documentation
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typo and link fixed
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Signed-off-by: ekmb <[email protected]> # What does this PR do ? Add a one line overview of what this PR aims to accomplish. **Collection**: [Note which collection this PR will affect] # Changelog - Add specific line by line info of high level changes in this PR. # Usage * You can potentially add a usage example below ```python # Add a code snippet demonstrating how to use this ``` # Before your PR is "Ready for review" **Pre checks**: - [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) - [ ] Did you write any new necessary tests? - [ ] Did you add or update any necessary documentation? - [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc) - [ ] Reviewer: Does the PR have correct import guards for all optional libraries? **PR Type**: - [ ] New Feature - [ X] Bugfix - [ ] Documentation If you haven't finished some of the above items you can still open "Draft" PR. ## Who can review? Anyone in the community is free to review the PR once the checks have passed. [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas. # Additional Information * Related to # (issue)
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Fixed a minor typo in primer tutorial. Signed-off-by: schaltung <[email protected]> # What does this PR do ? Fixed a minor typo: "White" <-- "While". **Collection**: None # Changelog - Single-word typo fix in the text markup of the jupyter notebook. # Usage * You can potentially add a usage example below # Before your PR is "Ready for review" **Pre checks**: - [] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) - [ ] Did you write any new necessary tests? - [x] Did you add or update any necessary documentation? - [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc) - [ ] Reviewer: Does the PR have correct import guards for all optional libraries? **PR Type**: - [ ] New Feature - [] Bugfix - [x] Documentation If you haven't finished some of the above items you can still open "Draft" PR. ## Who can review? Anyone in the community is free to review the PR once the checks have passed. [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas. # Additional Information * Related to # (issue)
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Remove ending newlines when encoding strings w/ sentencepiece tokenizer
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Signed-off-by: Patrick Simianer <[email protected]> # What does this PR do ? SentencePiece models can be generated in a number of different ways in terms of handling whitespace. An important parameter is `normalization_rule_name`: Setting this to "identity" will cause newline characters `\n` to remain in the string when encoding. In the default setting (`nmt_nfkc`) any `\n` would be replaced by a single space. Since `remove_extra_whitespaces` is also on by default this'll cause it just to be removed. The problem with the `identity` setting in conjunction with NeMo is that NeMo presents the input strings for preprocessing with the final `\n` character included, and since sentencepiece itself won't add it to the vocabulary (https://github.com/google/sentencepiece/issues/804), this will cause an unknown token at the end of every training example. A minimal example to reproduce this behavior is: Train SentencePiece model: ``` #!/bin/bash ../sentencepiece_upstream/build/src/spm_train \ --input ../europarl-v7.de-en.en,../europarl-v7.de-en.de \ --input_sentence_size 9999 \ --model_prefix "bpe.joint" \ --model_type "bpe" \ --pad_id 3 \ --pad_piece "<pad>" \ --normalization_rule_name "identity" ``` ``` #!/usr/bin/env bash CUDA_VISIBLE_DEVICES= python ../nemo/examples/nlp/machine_translation/create_tarred_parallel_dataset.py \ --shared_tokenizer \ --encoder_tokenizer_name sentencepiece \ --decoder_tokenizer_name sentencepiece \ --encoder_tokenizer_bpe_dropout 0.0 \ --decoder_tokenizer_bpe_dropout 0.0 \ --src_fname ../europarl-v7.de-en.en \ --tgt_fname ../europarl-v7.de-en.de \ --out_dir preproc_dir \ --max_seq_length 512 \ --min_seq_length 1 \ --tokens_in_batch 8192 \ --lines_per_dataset_fragment 1000000 \ --num_batches_per_tarfile 100 \ --encoder_tokenizer_model bpe.joint.model \ --decoder_tokenizer_model bpe.joint.model \ --n_preproc_jobs 32 \ --clean ``` Observe that every training example has a unknown token (id 0) right before EOS (id 2): ``` > cd preproc_dir > tar xfv parallel.batches.tokens.8192.0.tar > cd preproc_dir > python >>> import pickle >>> pickle.load(open("fragment-0-batch-1.pkl", "rb"))["src"][0] [ 1 1336 0 2] ``` As a minimal fix this PR just removes the final newline character before encoding w/ sentencepiece's methods. (The other tokenization option, yttm, will by default remove any trailing whitespace and there's also not setting to change that. So there won't be any issues like that.) **Collection**: `common` # Changelog - Remove ending newlines when encoding strings w/ sentencepiece tokenizer # Usage * See above. # Before your PR is "Ready for review" **Pre checks**: - [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) - [ ] Did you write any new necessary tests? - [ ] Did you add or update any necessary documentation? - [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc) - [ ] Reviewer: Does the PR have correct import guards for all optional libraries? **PR Type**: - [ ] New Feature - [x] Bugfix - [ ] Documentation ## Who can review? Anyone in the community is free to review the PR once the checks have passed. [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas. # Additional Information
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Mismatch in the dimensions length of log probabilities of Nemo ASR transcribe function and Vocab size
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Hi All, I am working on pyctcdecode integration with Nemo ASR models. However, there is a mismatch in the dimensions of the log probabilities matrix output by Nemo ASR transcribe function and the length of vocabulary size. Because of this I am unable to proceed further. Any suggestions, will be very helpful. asr_model1 = nemo_asr.models.EncDecCTCModelBPE.from_pretrained(model_name="stt_en_conformer_ctc_medium") print(len(asr_model1.decoder.vocabulary)) ---> this outputs "1024" logits = asr_model1.transcribe(["/data/manju/tamil/data/segWav/tvs_test_9976337304-in-Speaker_1-11.wav"], logprobs=True)[0] print(logits.shape) --> This outputs " (36, 1025)". Here, it should have produced "1024" in place of "1025" Pls suggest @titu1994 @jbalam-nv . Thanks
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Don't add output directory twice when creating shared sentencepiece tokenizer
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Signed-off-by: Patrick Simianer <[email protected]> # What does this PR do ? As the title says this is a small fix: The output dir was already added to `encoder_tokenizer_model` on line 789 in the same file. **Collection**: NLP # Changelog - Bugfix for creating shared sentencepiece tokenizer. # Usage The error can be triggered by running preprocessing with NeMo for an MT data set: ``` #!/bin/bash python ../nemo/examples/nlp/machine_translation/enc_dec_nmt.py \ -cn aayn_base \ do_training=false \ model.preproc_out_dir=./preproc_dir/ \ model.train_ds.use_tarred_dataset=true \ model.train_ds.lines_per_dataset_fragment=1000000 \ model.train_ds.num_batches_per_tarfile=200 \ model.train_ds.src_file_name=../europarl-v7.de-en.en \ model.train_ds.tgt_file_name=../europarl-v7.de-en.de \ model.validation_ds.src_file_name=../valid.en \ model.validation_ds.tgt_file_name=../valid.de \ model.encoder_tokenizer.vocab_size=32000 \ model.decoder_tokenizer.vocab_size=32000 \ model.encoder_tokenizer.library=sentencepiece \ model.encoder_tokenizer.training_sample_size=9999 \ model.decoder_tokenizer.library=sentencepiece \ model.decoder_tokenizer.training_sample_size=9999 \ ~model.test_ds \ trainer.accelerator='cpu' \ +trainer.fast_dev_run=true \ exp_manager=null ``` # Before your PR is "Ready for review" **Pre checks**: - [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) - [ ] Did you write any new necessary tests? - [ ] Did you add or update any necessary documentation? - [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc) - [ ] Reviewer: Does the PR have correct import guards for all optional libraries? **PR Type**: - [ ] New Feature - [x] Bugfix - [ ] Documentation ## Who can review? Anyone in the community is free to review the PR once the checks have passed. [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas. # Additional Information
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# What does this PR do ? Allows DLLogger to work with types of non-builtin containers. # Before your PR is "Ready for review" **Pre checks**: - [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) - [ ] Did you write any new necessary tests? - [ ] Did you add or update any necessary documentation? - [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc) - [ ] Reviewer: Does the PR have correct import guards for all optional libraries? **PR Type**: - [ ] New Feature - [x] Bugfix - [ ] Documentation ## Who can review? Anyone in the community is free to review the PR once the checks have passed. [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
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# What does this PR do ? Adds ASR example for training Esperanto Conformer-CTC-large model. **Collection**: ASR # Changelog - Adds Esperanto example to docs/source/asr/examples/ # Before your PR is "Ready for review" **Pre checks**: - [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) - [ ] Did you write any new necessary tests? - [ ] Did you add or update any necessary documentation? - [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc) - [ ] Reviewer: Does the PR have correct import guards for all optional libraries? **PR Type**: - [ ] New Feature - [ ] Bugfix - [x] Documentation
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# What does this PR do ? Opening to show provenance, closing immediately. **Collection**: [Note which collection this PR will affect] # Changelog - Add specific line by line info of high level changes in this PR. # Usage * You can potentially add a usage example below ```python # Add a code snippet demonstrating how to use this ``` # Before your PR is "Ready for review" **Pre checks**: - [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) - [ ] Did you write any new necessary tests? - [ ] Did you add or update any necessary documentation? - [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc) - [ ] Reviewer: Does the PR have correct import guards for all optional libraries? **PR Type**: - [ ] New Feature - [ ] Bugfix - [ ] Documentation If you haven't finished some of the above items you can still open "Draft" PR. ## Who can review? Anyone in the community is free to review the PR once the checks have passed. [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas. # Additional Information * Related to # (issue)
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# What does this PR do ? Sometimes in CausalConv1D.update_cache input_x_keep ends up having no frames (ie size of [M, N, 0]). Make sure that the we have at least one frame. **Collection**: asr # Changelog - Add specific line by line info of high level changes in this PR. # Usage * You can potentially add a usage example below ```python # Add a code snippet demonstrating how to use this ``` # Before your PR is "Ready for review" **Pre checks**: - [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) - [ ] Did you write any new necessary tests? - [ ] Did you add or update any necessary documentation? - [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc) - [ ] Reviewer: Does the PR have correct import guards for all optional libraries? **PR Type**: - [ ] New Feature - [x] Bugfix - [ ] Documentation If you haven't finished some of the above items you can still open "Draft" PR. ## Who can review? Anyone in the community is free to review the PR once the checks have passed. [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas. @VahidooX # Additional Information To reproduce the original issue in main run: ``` #!/bin/bash python examples/asr/asr_cache_aware_streaming/speech_to_text_cache_aware_streaming_infer.py \ --asr_model=stt_en_conformer_ctc_small \ --chunk_size=100 \ --shift_size=50 \ --left_chunks=2 \ --online_normalization \ --manifest_file=/datasets/ls_test_other/transcripts.local.json \ --batch_size=16 \ --compare_vs_offline \ --use_amp \ --debug_mode ``` Error output: ``` ... Traceback (most recent call last): File "examples/asr/asr_cache_aware_streaming/speech_to_text_cache_aware_streaming_infer.py", line 393, in <module> main() File "examples/asr/asr_cache_aware_streaming/speech_to_text_cache_aware_streaming_infer.py", line 349, in main streaming_tran, offline_tran = perform_streaming( File "examples/asr/asr_cache_aware_streaming/speech_to_text_cache_aware_streaming_infer.py", line 154, in perform_streaming ) = asr_model.conformer_stream_step( File "/home/grclark/code/NeMo.git/streaming-conformer/nemo/collections/asr/parts/mixins/mixins.py", line 475, in conformer_stream_step (encoded, encoded_len, cache_last_channel_next, cache_last_time_next) = self.encoder.cache_aware_stream_step( File "/home/grclark/code/NeMo.git/streaming-conformer/nemo/collections/asr/parts/mixins/streaming.py", line 61, in cache_aware_stream_step encoder_output = self( File "/usr/local/lib/python3.8/dist-packages/torch/nn/modules/module.py", line 1190, in _call_impl return forward_call(*input, **kwargs) File "/home/grclark/code/NeMo.git/streaming-conformer/nemo/core/classes/common.py", line 1087, in __call__ outputs = wrapped(*args, **kwargs) File "/home/grclark/code/NeMo.git/streaming-conformer/nemo/collections/asr/modules/conformer_encoder.py", line 471, in forward audio_signal = layer( File "/usr/local/lib/python3.8/dist-packages/torch/nn/modules/module.py", line 1190, in _call_impl return forward_call(*input, **kwargs) File "/home/grclark/code/NeMo.git/streaming-conformer/nemo/collections/asr/parts/submodules/conformer_modules.py", line 191, in forward x = self.conv(x, pad_mask=pad_mask, cache=cache_last_time, cache_next=cache_last_time_next) File "/usr/local/lib/python3.8/dist-packages/torch/nn/modules/module.py", line 1190, in _call_impl return forward_call(*input, **kwargs) File "/home/grclark/code/NeMo.git/streaming-conformer/nemo/collections/asr/parts/submodules/conformer_modules.py", line 350, in forward x = self.depthwise_conv(x, cache=cache, cache_next=cache_next) File "/usr/local/lib/python3.8/dist-packages/torch/nn/modules/module.py", line 1190, in _call_impl return forward_call(*input, **kwargs) File "/home/grclark/code/NeMo.git/streaming-conformer/nemo/collections/asr/parts/submodules/causal_convs.py", line 162, in forward x = self.update_cache(x, cache=cache, cache_next=cache_next) File "/home/grclark/code/NeMo.git/streaming-conformer/nemo/collections/asr/parts/submodules/causal_convs.py", line 158, in update_cache cache_next[self._cache_id, :, :, -cache_keep_size:] = input_x_kept[:, :, -cache_keep_size:] RuntimeError: The expanded size of the tensor (1) must match the existing size (0) at non-singleton dimension 2. Target sizes: [16, 176, 1]. Tensor sizes: [16, 176, 0] ```
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Signed-off-by: Patrick Simianer <[email protected]> # What does this PR do ? In https://github.com/NVIDIA/NeMo/pull/5052 I forgot to add the `keep_initializers_as_inputs` arg to the `_export` method. **Collection**: core # Changelog - Simple fix. # Usage * See https://github.com/NVIDIA/NeMo/pull/5052 # Before your PR is "Ready for review" **Pre checks**: - [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) - [ ] Did you write any new necessary tests? - [ ] Did you add or update any necessary documentation? - [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc) - [ ] Reviewer: Does the PR have correct import guards for all optional libraries? **PR Type**: - [ ] New Feature - [x] Bugfix - [ ] Documentation ## Who can review? Anyone in the community is free to review the PR once the checks have passed. [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas. # Additional Information * Related to #5052
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Signed-off-by: Jocelyn Huang <[email protected]> # What does this PR do ? Updates the remaining call to `torch.stft()` to use `return_complex=True` due to the deprecation of the `return_complex=False` option. I've done a quick grep through the NeMo repo, this should be the only other place where `torch.stft()` (or `torch.istft()`) is called. **Collection**: ASR # Changelog - Changed the preprocessing lambda to use `return_complex=True`, which means `torch.stft()` will always return a complex tensor - Removed the conditional for `view_as_real` since it will always need to be called now **PR Type**: - [ ] New Feature - [x] Bugfix - [ ] Documentation # Additional Information * See #5729 for more information (and the TTS fixes)
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Signed-off-by: Jocelyn Huang <[email protected]> # What does this PR do ? Fixes errors due to deprecation of `return_complex=False` in `torch.stft()` in version 1.14. Most of our calls implicitly use `return_complex=False` (was the default), which returns a tensor of shape [B, N, T, 2] where the last dimension captures the real and imaginary parts of the tensor. However, moving forward, pytorch is deprecating this in favor of the behavior of `return_complex=True`, which returns a complex tensor of shape [B, N, T]. This bugfix explicitly sets `return_complex=True` as required by v1.14, and then converts the output of `torch.stft()` back to [B, N, T, 2] using `torch.view_as_real()`. Similarly, `torch.istft()` now wants a complex tensor input; this is fixed by calling `torch.view_as_complex()` on our original input. **Collection**: TTS # Changelog - `torch.stft()` calls explicitly set `return_complex=True` and then convert output where necessary - Added `torch.view_as_complex()` to input of `torch.istft()` **PR Type**: - [ ] New Feature - [x] Bugfix - [ ] Documentation
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# What does this PR do ? A tool to thoroughly evaluate an ASR model. **Collection**: ASR # Changelog - Simple step to evaluate a model in all three modes currently supported by NeMo: offline, chunked, and offline_by_chunked. - On-the-fly data augmentation (silence, noise, etc.,) for ASR robustness evaluation. - Investigate the model's performance by detailed insertion, deletion, and substitution error rates for each and all samples. - Evaluate models' reliability on different target groups such as gender, and audio length if metadata is presented. # Usage ``` python asr_evaluator.py \ engine.pretrained_name="stt_en_conformer_transducer_large" \ engine.inference_mode.mode="offline" \ engine.test_ds.augmentor.noise.manifest_path=<manifest file for noise data> ``` # Before your PR is "Ready for review" **Pre checks**: - [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) - [x] Did you write any new necessary tests? - [x] Did you add or update any necessary documentation? - [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc) - [ ] Reviewer: Does the PR have correct import guards for all optional libraries? **PR Type**: - [x] New Feature - [ ] Bugfix - [ ] Documentation ## Who can review? Anyone in the community is free to review the PR once the checks have passed. [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas. # Additional Information Note because the PR is getting bigger, on-the-fly augmentation for buffered inference mode would be included in another PR soon.
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# What does this PR do ? When using `shard_strategy='scatter'` this PR loads only a subset of lines from the manifest file. This may reduce the time to process a manifest file by an order of magnitude. Opening as a draft to get feedback if there are any underlying assumptions which may be broken by this change. **Collection**: ASR # Changelog | File | Change | | ---------------- | -------- | | `manifest.py::item_iter` | Load only a subset of lines if `shard_strategy == 'scatter'` | | `collections.py:: ASRAudioText ` | forward `shard_strategy`, `global_rank` and `world_size` to `manifest.item_iter` | | `collections.py:: AudioText ` | Use rank and world size to restore the original data list length for `shard_strategy='scatter'` | | `audio_to_text.py` | forward `shard_strategy`, `global_rank` and `world_size` to `collections.ASRAudioText` | | `utils,py` | Added a function to get number of lines from a text file | | `test_utils.py` | Added unit test for the utility function added above | # Before your PR is "Ready for review" **Pre checks**: - [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) - [x] Did you write any new necessary tests? - [ ] Did you add or update any necessary documentation? - [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc) - [ ] Reviewer: Does the PR have correct import guards for all optional libraries? **PR Type**: - [ ] New Feature - [x] Bugfix - [ ] Documentation If you haven't finished some of the above items you can still open "Draft" PR. ## Who can review? Anyone in the community is free to review the PR once the checks have passed. [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas. # Additional Information * Related to # (issue)
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2023-01-03T17:25:55
2023-01-06T08:53:46
2023-01-06T04:48:57
CONTRIBUTOR
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**Describe the bug** In `nemo/collections/nlp/modules/common/transformer/transformer_modules.py` a hard coded `padding_idx` is set (value 0): https://github.com/NVIDIA/NeMo/blob/main/nemo/collections/nlp/modules/common/transformer/transformer_modules.py#L112 According to the [documentation](https://pytorch.org/docs/stable/generated/torch.nn.Embedding.html) of `nn.Embedding` this will set the respective column to zeros on initialization. Given that the padding may be at different indices for different models (i.e. tokenizers) this should be set accordingly, and not be hard coded to index 0. I'm also not sure if the statement from the documentation holds true for NeMo trained models: > [...] the embedding vector at padding_idx is not updated during training, [...] as the pertained ende model (`nmt_en_de_transformer12x2.nemo`) has non-zero weights at index 0: ``` >>> checkpoint = torch.load("model_weights.ckpt", map_location="cpu") >>> checkpoint["decoder._embedding.token_embedding.weight"][0] tensor([-0.0071, -0.0716, 0.0123, ..., 0.0321, 0.0360, 0.0308]) ``` I could also reproduce this with a custom trained model. **Steps/Code to reproduce bug** 1. Observe that `padding_idx` is set to a hard coded value. 2. Observe that none of the columns of the embeddings are all zeros in the pertained MTEncDec models. **Expected behavior** 1. `padding_idx` gets set according to the index of `<pad>` in the respective tokenizer. 2. The column at `padding_idx` does not get trained/updated during training. **Environment overview (please complete the following information)** - Any **Environment details** - Any **Additional context** - Potential fix: https://github.com/NVIDIA/NeMo/pull/5543
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Don't print exp_manager warning when max_steps == -1
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2023-01-03T16:00:10
2023-01-03T22:50:16
2023-01-03T22:50:16
CONTRIBUTOR
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# What does this PR do ? Doesn't print a warning when max_steps == -1 and val monitoring. For example, when we just provide max_epochs we don't want to see that warning. ``` [NeMo W 2023-01-03 15:47:31 exp_manager:988] The checkpoint callback was told to monitor a validation value and trainer's max_steps was set to -1. Please ensure that max_steps will run for at least 1 epochs to ensure that checkpointing will not error out. ``` # Before your PR is "Ready for review" **Pre checks**: - [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) - [ ] Did you write any new necessary tests? - [ ] Did you add or update any necessary documentation? - [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc) - [ ] Reviewer: Does the PR have correct import guards for all optional libraries? **PR Type**: - [ ] New Feature - [x] Bugfix - [ ] Documentation ## Who can review? Anyone in the community is free to review the PR once the checks have passed. [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
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2023-01-03T11:05:25
2023-01-05T19:27:57
2023-01-05T19:27:49
COLLABORATOR
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# What does this PR do ? Closes #5631 **Collection**: Common # Changelog - Delete EMA checkpoints when normal checkpoints are deleted. # Before your PR is "Ready for review" **Pre checks**: - [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) - [x] Did you write any new necessary tests? - [x] Did you add or update any necessary documentation? - [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc) - [ ] Reviewer: Does the PR have correct import guards for all optional libraries? **PR Type**: - [ ] New Feature - [x] Bugfix - [ ] Documentation
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Error converting p-tuned model for inferencing
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**Describe the bug** I am trying to p-tune a megatron_gpt_345m model using the steps and the notebook mentioned in the NeMo repo [here](https://github.com/NVIDIA/NeMo/blob/main/tutorials/nlp/Multitask_Prompt_and_PTuning.ipynb). I am able to p-tune the model for my tasks, however when I try to convert it to [FasterTransformer](https://github.com/NVIDIA/FasterTransformer) format following the steps mentioned [here](https://developer.nvidia.com/blog/deploying-a-1-3b-gpt-3-model-with-nvidia-nemo-megatron/), I get a `KeyError`. ``` WARNING: Your kernel does not support swap limit capabilities or the cgroup is not mounted. Memory limited without swap. ============ == BigNLP == ============ NVIDIA Release 22.08.01 (build 44509868) Container image Copyright (c) 2022, NVIDIA CORPORATION & AFFILIATES. All rights reserved. Copyright (c) 2019-2022, NVIDIA CORPORATION & AFFILIATES. All rights reserved. Copyright (c) 2014-2022 Facebook Inc. Copyright (c) 2011-2014 Idiap Research Institute (Ronan Collobert) Copyright (c) 2012-2014 Deepmind Technologies (Koray Kavukcuoglu) Copyright (c) 2011-2012 NEC Laboratories America (Koray Kavukcuoglu) Copyright (c) 2011-2013 NYU (Clement Farabet) Copyright (c) 2006-2010 NEC Laboratories America (Ronan Collobert, Leon Bottou, Iain Melvin, Jason Weston) Copyright (c) 2006 Idiap Research Institute (Samy Bengio) Copyright (c) 2001-2004 Idiap Research Institute (Ronan Collobert, Samy Bengio, Johnny Mariethoz) Copyright (c) 2015 Google Inc. Copyright (c) 2015 Yangqing Jia Copyright (c) 2013-2016 The Caffe contributors All rights reserved. Various files include modifications (c) NVIDIA CORPORATION & AFFILIATES. All rights reserved. This container image and its contents are governed by the NVIDIA Deep Learning Container License. By pulling and using the container, you accept the terms and conditions of this license: https://developer.nvidia.com/ngc/nvidia-deep-learning-container-license NOTE: Mellanox network driver detected, but NVIDIA peer memory driver not detected. Multi-node communication performance may be reduced. 2023-01-03 09:32:00,492 __main__ [INFO] Spent 0:00:00.002772 (h:m:s) to unpack NeMo archive =============== Argument =============== saved_dir: /model_repository/p_tuned_gpt in_file: /checkpoints/multitask_p_tuned_gpt.nemo prompt_in_file: None prompt_saved_dir: None infer_gpu_num: 1 fused_qkv: 1 processes: 16 weight_data_type: fp16 load_checkpoints_to_cpu: 0 vocab_path: None merges_path: None tokenizer_model_path: None verbose: False ======================================== Traceback (most recent call last): File "FasterTransformer/examples/pytorch/gpt/utils/nemo_ckpt_convert.py", line 656, in <module> main() File "FasterTransformer/examples/pytorch/gpt/utils/nemo_ckpt_convert.py", line 629, in main convert_checkpoint(unpacked_checkpoint_dir, args) File "FasterTransformer/examples/pytorch/gpt/utils/nemo_ckpt_convert.py", line 280, in convert_checkpoint val = model_00.get("state_dict", model_00)["model.language_model.embedding.position_embeddings.weight"] KeyError: 'model.language_model.embedding.position_embeddings.weight' ``` **Steps/Code to reproduce bug** Here's the complete docker command for converting the p-tuned `.nemo` model ``` docker run --rm \ --gpus '"device=0"' \ --shm-size=16GB \ --cpus 16 --memory 32g \ -v /opt/NeMo/additional_data:/checkpoints \ -v /opt/NeMo/additional_data/output:/model_repository \ --network dbr0 \ nvcr.io/ea-bignlp/bignlp-training:22.08.01-py3 \ bash -c 'export PYTHONPATH=/opt/bignlp/FasterTransformer:${PYTHONPATH} && \ cd /opt/bignlp && \ python3 FasterTransformer/examples/pytorch/gpt/utils/nemo_ckpt_convert.py \ --in-file /checkpoints/multitask_p_tuned_gpt.nemo \ --infer-gpu-num 1 \ --saved-dir /model_repository/p_tuned_gpt \ --weight-data-type fp16 \ --load-checkpoints-to-cpu 0 && \ python3 /opt/bignlp/bignlp-scripts/bignlp/collections/export_scripts/prepare_triton_model_config.py \ --model-train-name p_tuned_gpt \ --template-path /opt/bignlp/fastertransformer_backend/all_models/gpt/fastertransformer/config.pbtxt \ --ft-checkpoint /model_repository/p_tuned_gpt/1-gpu \ --config-path /model_repository/p_tuned_gpt/config.pbtxt \ --max-batch-size 256 \ --pipeline-model-parallel-size 1 \ --tensor-model-parallel-size 1 \ --data-type bf16' ``` **Expected behavior** The converted is generated and stored in the output directory mounted **Environment overview (please complete the following information)** Here is the docker pull command `docker pull nvcr.io/ea-bignlp/bignlp-training:22.08.01-py3`
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Signed-off-by: nithinraok <[email protected]> # What does this PR do ? Fix torchmetrics version, as latest version has issues with pytorch Dataloader loading time. **Collection**: ASR # Changelog - pin torchmetrics version to 0.10.3 - revert accuracy API to 0.10.3 from 0.11.0 - revert Speaker Jenkins run # Before your PR is "Ready for review" **Pre checks**: - [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) - [ ] Did you write any new necessary tests? - [ ] Did you add or update any necessary documentation? - [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc) - [ ] Reviewer: Does the PR have correct import guards for all optional libraries? **PR Type**: - [ ] New Feature - [x] Bugfix - [ ] Documentation
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Signed-off-by: MaximumEntropy <[email protected]> # What does this PR do ? Adds args that can configure whether to add bos/eos to the encoder and to replace bos with the pad token for HF converted T5 checkpoints. **Collection**: NLP # Changelog - Add specific line by line info of high level changes in this PR. # Usage * You can potentially add a usage example below ```python # Add a code snippet demonstrating how to use this ``` # Before your PR is "Ready for review" **Pre checks**: - [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) - [ ] Did you write any new necessary tests? - [ ] Did you add or update any necessary documentation? - [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc) - [ ] Reviewer: Does the PR have correct import guards for all optional libraries? **PR Type**: - [ ] New Feature - [ ] Bugfix - [ ] Documentation If you haven't finished some of the above items you can still open "Draft" PR. ## Who can review? Anyone in the community is free to review the PR once the checks have passed. [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas. # Additional Information * Related to # (issue)
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Hi. I have trained univnet model and I faced 2 issues. 1. model has memory leak 2. high response time I attempt to convert model to onnx but I got this error: `torch.onnx.errors.SymbolicValueError: Unsupported: ONNX export of operator Unfold, input size not accessible. Please feel free to request support or submit a pull request on PyTorch GitHub: https://github.com/pytorch/pytorch/issues [Caused by the value '381 defined in (%381 : Float(*, *, *, strides=[65600, 2050, 1], requires_grad=0, device=cuda:0) = onnx::Pad[mode="constant"](%output, %379, %380), scope: nemo.collections.tts.models.univnet.UnivNetModel::/nemo.collections.tts.modules.univnet_modules.Generator::generator/nemo.collections.tts.modules.univnet_modules.LVCBlock::res_stack.0 ` do you have any idea to fix this problem?Is fixing the memory leak problem on your plan? @redoctopus
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# What does this PR do ? As with #5715 creating to show provenance + close **Collection**: [Note which collection this PR will affect] # Changelog - Add specific line by line info of high level changes in this PR. # Usage * You can potentially add a usage example below ```python # Add a code snippet demonstrating how to use this ``` # Before your PR is "Ready for review" **Pre checks**: - [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) - [ ] Did you write any new necessary tests? - [ ] Did you add or update any necessary documentation? - [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc) - [ ] Reviewer: Does the PR have correct import guards for all optional libraries? **PR Type**: - [ ] New Feature - [ ] Bugfix - [ ] Documentation If you haven't finished some of the above items you can still open "Draft" PR. ## Who can review? Anyone in the community is free to review the PR once the checks have passed. [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas. # Additional Information * Related to # (issue)
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# What does this PR do ? As with #5715 creating to show provenance + close **Collection**: [Note which collection this PR will affect] # Changelog - Add specific line by line info of high level changes in this PR. # Usage * You can potentially add a usage example below ```python # Add a code snippet demonstrating how to use this ``` # Before your PR is "Ready for review" **Pre checks**: - [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) - [ ] Did you write any new necessary tests? - [ ] Did you add or update any necessary documentation? - [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc) - [ ] Reviewer: Does the PR have correct import guards for all optional libraries? **PR Type**: - [ ] New Feature - [ ] Bugfix - [ ] Documentation If you haven't finished some of the above items you can still open "Draft" PR. ## Who can review? Anyone in the community is free to review the PR once the checks have passed. [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas. # Additional Information * Related to # (issue)
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# What does this PR do ? I've just seen that the text processing module has moved; this is messy, includes pieces that ought to be in other places, and just generally needs a cleanup. The main point of making the pull request is to show that I didn't just pluck this stuff from thin air. **Collection**: [Note which collection this PR will affect] # Changelog - Add specific line by line info of high level changes in this PR. # Usage * You can potentially add a usage example below ```python # Add a code snippet demonstrating how to use this ``` # Before your PR is "Ready for review" **Pre checks**: - [X] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) - [ ] Did you write any new necessary tests? - [ ] Did you add or update any necessary documentation? - [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc) - [ ] Reviewer: Does the PR have correct import guards for all optional libraries? **PR Type**: - [X] New Feature - [ ] Bugfix - [ ] Documentation If you haven't finished some of the above items you can still open "Draft" PR. ## Who can review? Anyone in the community is free to review the PR once the checks have passed. [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas. # Additional Information * Related to # (issue)
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Hi, Nemo Developer: Happy new year first. **Describe the bug** I am trying to have a trial on voice clone. So I am trying to finetune a TTS model based on a test person's voice clips. Have successfully realized this using that FastPitch notebook. I am stuck on Tacotron2 finetune trial. **Steps/Code to reproduce bug** I am trying to run that tacotron2_finetune.py for the training. As my audio clips are 22050hz, I modified the three parameters in the tacotron2_44100.yaml following the 22050hz original tacotron2.yaml training config file: n_window_size: 1024 n_window_stride: 256 n_fft: 1024 Other configs untouched. **Expected behavior** I am running it as: `!(python tacotron2_finetune.py --config-name=tacotron2_22050.yaml \ train_dataset=./TestFileList.json \ validation_datasets=./TestFileList.json \ exp_manager.exp_dir=./ljspeech_to_test_no_mixing \ +init_from_nemo_model=./tts_en_tacotron2.nemo \ trainer.max_epochs=20 \ trainer.check_val_every_n_epoch=5 \ model.train_ds.dataloader_params.batch_size=24 model.validation_ds.dataloader_params.batch_size=24 \ )` So I am mainly adding the pretrained model of the original pretrained tacotron2 model. The output is shown as below: `[NeMo I 2022-12-29 23:33:49 features:267] PADDING: 16 [NeMo I 2022-12-29 23:33:49 save_restore_connector:243] Model Tacotron2Model was successfully restored from /media/xyzhou/extDisk2t1/DeepFake_Audio/Nemo_Clone/tts_nemo/tts_en_tacotron2.nemo. Error executing job with overrides: ['train_dataset=./EricFileList.json', 'validation_datasets=./EricFileList.json', 'exp_manager.exp_dir=./ljspeech_to_eric_no_mixing', '+init_from_nemo_model=./tts_en_tacotron2.nemo', 'trainer.max_epochs=20', 'trainer.check_val_every_n_epoch=5', 'model.train_ds.dataloader_params.batch_size=24', 'model.validation_ds.dataloader_params.batch_size=24'] Traceback (most recent call last): File "tacotron2_finetune.py", line 35, in main model.maybe_init_from_pretrained_checkpoint(cfg=cfg) File "/home/xyzhou/anaconda3/envs/nemo/lib/python3.8/site-packages/lightning_utilities/core/rank_zero.py", line 24, in wrapped_fn return fn(*args, **kwargs) File "/home/xyzhou/anaconda3/envs/nemo/lib/python3.8/site-packages/nemo/core/classes/modelPT.py", line 1066, in maybe_init_from_pretrained_checkpoint self.load_state_dict(restored_model.state_dict(), strict=False) File "/home/xyzhou/anaconda3/envs/nemo/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1671, in load_state_dict raise RuntimeError('Error(s) in loading state_dict for {}:\n\t{}'.format( RuntimeError: Error(s) in loading state_dict for Tacotron2Model: size mismatch for text_embedding.weight: copying a param with shape torch.Size([69, 512]) from checkpoint, the shape in current model is torch.Size([114, 512]).` The the main error shown is the final part: RuntimeError('Error(s) in loading state_dict for {}:\n\t{}'.format( RuntimeError: Error(s) in loading state_dict for Tacotron2Model: size mismatch for text_embedding.weight: copying a param with shape torch.Size([69, 512]) from checkpoint, the shape in current model is torch.Size([114, 512]). Not sure how to deal with this. Thank you!
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stt_zh_citrinet_1024_gamma_0_25 will not be found in ASRDecoderTimeStamps
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Hi Nemo developers, **Describe the bug** I tried to run tutorials/speaker_tasks/ASR_with_SpeakerDiarization.ipynb with chinese data. When I changed the model from `cfg.diarizer.asr.model_path = 'stt_en_conformer_ctc_large'` to `stt_zh_citrinet_1024_gamma_0_25`, the model could not be found correctly because of the logic in https://github.com/NVIDIA/NeMo/blob/58d6765929d7c13ab933640d74630493957b1dfb/nemo/collections/asr/parts/utils/decoder_timestamps_utils.py#L331-L356. According to current logic, as `stt_zh_citrinet_1024_gamma_0_25` contains citrinet it will go to https://github.com/NVIDIA/NeMo/blob/58d6765929d7c13ab933640d74630493957b1dfb/nemo/collections/asr/parts/utils/decoder_timestamps_utils.py#L350-L356 the `self.encdec_class` will be `EncDecCTCModelBPE`. However the `stt_zh_citrinet_1024_gamma_0_25` is listed here. https://github.com/NVIDIA/NeMo/blob/58d6765929d7c13ab933640d74630493957b1dfb/nemo/collections/asr/models/ctc_models.py#L818-L822 Belongs to `EncDecCTCModel`. I create a PR(https://github.com/NVIDIA/NeMo/pull/5712) that will not solve the issue but could express my point more clearly. BRs, Qxxxx
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Fix zh_citrinet not found
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# What does this PR do ? Add a one line overview of what this PR aims to accomplish. **Collection**: [Note which collection this PR will affect] # Changelog - Add specific line by line info of high level changes in this PR. # Usage * You can potentially add a usage example below ```python # Add a code snippet demonstrating how to use this ``` # Before your PR is "Ready for review" **Pre checks**: - [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) - [ ] Did you write any new necessary tests? - [ ] Did you add or update any necessary documentation? - [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc) - [ ] Reviewer: Does the PR have correct import guards for all optional libraries? **PR Type**: - [ ] New Feature - [ ] Bugfix - [ ] Documentation If you haven't finished some of the above items you can still open "Draft" PR. ## Who can review? Anyone in the community is free to review the PR once the checks have passed. [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas. # Additional Information * Related to # (issue)
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Update libraries on Tutorial - Cython, gcc, g++
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Hi. I'm new on this field and I was going to follow [this tutorial](https://github.com/NVIDIA/NeMo/blob/main/tutorials/asr/ASR_with_NeMo.ipynb) step by step. I just made new environment using conda on my linux server and stuck with issue below. <img width="800" alt="image" src="https://user-images.githubusercontent.com/49643709/209797058-49fa20b2-21bc-476b-929c-a2479512ac36.png"> I installed cython and stuck again like below. <img width="800" alt="image" src="https://user-images.githubusercontent.com/49643709/209798306-f7250507-2031-4e57-9b9d-d6f6642f7964.png"> I run `apt-get install gcc` and stuck again with issue below. <img width="800" alt="image" src="https://user-images.githubusercontent.com/49643709/209798768-a3cedfc7-a34c-411a-a13a-e16ed907c311.png"> I run `apt-get install g++` and install just works well. For newbie like me, it would be more great to prevent someone is getting stuck at installation. So how about adding this commands? ``` pip install cython apt-get install gcc apt-get install g++ ```
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**Describe the bug** According to the article by nvidia published [here](https://developer.nvidia.com/blog/deploying-a-1-3b-gpt-3-model-with-nvidia-nemo-megatron/), mentions the use of a couple of docker images ``` nvcr.io/ea-bignlp/bignlp-training:22.08.01-py3 nvcr.io/ea-bignlp/bignlp-inference:22.08-py3 ``` in order to use or inference on LLM. But while pulling the images I am encountering the access denied error. **This is still the condition even after successfully logging in to nvcr.io** **Steps/Code to reproduce bug** docker login nvcr.io Username: $oauthtoken Password: <**your SECRET KEY**> ``` docker pull nvcr.io/ea-bignlp/bignlp-training:22.08.01-py3 docker pull nvcr.io/ea-bignlp/bignlp-inference:22.08-py3 ``` **Expected behavior** The docker should start pulling the images.
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Merge 1.14.0 main
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# What does this PR do ? Add a one line overview of what this PR aims to accomplish. **Collection**: [Note which collection this PR will affect] # Changelog - Add specific line by line info of high level changes in this PR. # Usage * You can potentially add a usage example below ```python # Add a code snippet demonstrating how to use this ``` # Before your PR is "Ready for review" **Pre checks**: - [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) - [ ] Did you write any new necessary tests? - [ ] Did you add or update any necessary documentation? - [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc) - [ ] Reviewer: Does the PR have correct import guards for all optional libraries? **PR Type**: - [ ] New Feature - [ ] Bugfix - [ ] Documentation If you haven't finished some of the above items you can still open "Draft" PR. ## Who can review? Anyone in the community is free to review the PR once the checks have passed. [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas. # Additional Information * Related to # (issue)
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Bump setuptools from 59.5.0 to 65.5.1 in /requirements
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Bumps [setuptools](https://github.com/pypa/setuptools) from 59.5.0 to 65.5.1. <details> <summary>Release notes</summary> <p><em>Sourced from <a href="https://github.com/pypa/setuptools/releases">setuptools's releases</a>.</em></p> <blockquote> <h2>v65.5.1</h2> <p>No release notes provided.</p> <h2>v65.5.0</h2> <p>No release notes provided.</p> <h2>v65.4.1</h2> <p>No release notes provided.</p> <h2>v65.4.0</h2> <p>No release notes provided.</p> <h2>v65.3.0</h2> <p>No release notes provided.</p> <h2>v65.2.0</h2> <p>No release notes provided.</p> <h2>v65.1.1</h2> <p>No release notes provided.</p> <h2>v65.1.0</h2> <p>No release notes provided.</p> <h2>v65.0.2</h2> <p>No release notes provided.</p> <h2>v65.0.1</h2> <p>No release notes provided.</p> <h2>v65.0.0</h2> <p>No release notes provided.</p> <h2>v64.0.3</h2> <p>No release notes provided.</p> <h2>v64.0.2</h2> <p>No release notes provided.</p> <h2>v64.0.1</h2> <p>No release notes provided.</p> <h2>v64.0.0</h2> <p>No release notes provided.</p> <h2>v63.4.3</h2> <p>No release notes provided.</p> <h2>v63.4.2</h2> <p>No release notes provided.</p> <!-- raw HTML omitted --> </blockquote> <p>... (truncated)</p> </details> <details> <summary>Changelog</summary> <p><em>Sourced from <a href="https://github.com/pypa/setuptools/blob/main/CHANGES.rst">setuptools's changelog</a>.</em></p> <blockquote> <h2>v65.5.1</h2> <p>Misc ^^^^</p> <ul> <li><a href="https://github-redirect.dependabot.com/pypa/setuptools/issues/3638">#3638</a>: Drop a test dependency on the <code>mock</code> package, always use :external+python:py:mod:<code>unittest.mock</code> -- by :user:<code>hroncok</code></li> <li><a href="https://github-redirect.dependabot.com/pypa/setuptools/issues/3659">#3659</a>: Fixed REDoS vector in package_index.</li> </ul> <h2>v65.5.0</h2> <p>Changes ^^^^^^^</p> <ul> <li><a href="https://github-redirect.dependabot.com/pypa/setuptools/issues/3624">#3624</a>: Fixed editable install for multi-module/no-package <code>src</code>-layout projects.</li> <li><a href="https://github-redirect.dependabot.com/pypa/setuptools/issues/3626">#3626</a>: Minor refactorings to support distutils using stdlib logging module.</li> </ul> <p>Documentation changes ^^^^^^^^^^^^^^^^^^^^^</p> <ul> <li><a href="https://github-redirect.dependabot.com/pypa/setuptools/issues/3419">#3419</a>: Updated the example version numbers to be compliant with PEP-440 on the &quot;Specifying Your Project’s Version&quot; page of the user guide.</li> </ul> <p>Misc ^^^^</p> <ul> <li><a href="https://github-redirect.dependabot.com/pypa/setuptools/issues/3569">#3569</a>: Improved information about conflicting entries in the current working directory and editable install (in documentation and as an informational warning).</li> <li><a href="https://github-redirect.dependabot.com/pypa/setuptools/issues/3576">#3576</a>: Updated version of <code>validate_pyproject</code>.</li> </ul> <h2>v65.4.1</h2> <p>Misc ^^^^</p> <ul> <li><a href="https://github-redirect.dependabot.com/pypa/setuptools/issues/3613">#3613</a>: Fixed encoding errors in <code>expand.StaticModule</code> when system default encoding doesn't match expectations for source files.</li> <li><a href="https://github-redirect.dependabot.com/pypa/setuptools/issues/3617">#3617</a>: Merge with pypa/distutils@6852b20 including fix for <a href="https://github-redirect.dependabot.com/pypa/distutils/issues/181">pypa/distutils#181</a>.</li> </ul> <h2>v65.4.0</h2> <p>Changes ^^^^^^^</p> <ul> <li><a href="https://github-redirect.dependabot.com/pypa/setuptools/issues/3609">#3609</a>: Merge with pypa/distutils@d82d926 including support for DIST_EXTRA_CONFIG in <a href="https://github-redirect.dependabot.com/pypa/distutils/issues/177">pypa/distutils#177</a>.</li> </ul> <p>v65.3.0</p> <!-- raw HTML omitted --> </blockquote> <p>... 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Fixes <a href="https://github-redirect.dependabot.com/pypa/setuptools/issues/3659">#3659</a>.</li> <li><a href="https://github.com/pypa/setuptools/commit/579134321d4d9397c886a5cb50cc26d0e3fa4279"><code>5791343</code></a> Add test capturing failed expectation. Ref <a href="https://github-redirect.dependabot.com/pypa/setuptools/issues/3659">#3659</a>.</li> <li><a href="https://github.com/pypa/setuptools/commit/1f97905bc40310ca454ff1ea3884f233b7dcc88c"><code>1f97905</code></a> ⚫ Fade to black.</li> <li><a href="https://github.com/pypa/setuptools/commit/6254567c6ae323bb8ce19a6930ae3cc5f7fb25cc"><code>6254567</code></a> Remove workaround for emacs.</li> <li><a href="https://github.com/pypa/setuptools/commit/729b180e926634930c21ccce5558780d42707763"><code>729b180</code></a> ⚫ Fade to black.</li> <li><a href="https://github.com/pypa/setuptools/commit/c068081a7234a0c5c322a9312654e7d0f4aaa8d1"><code>c068081</code></a> Typo corrections</li> <li><a href="https://github.com/pypa/setuptools/commit/f777a40ed9abf529906c2939f80a184a5ed035fa"><code>f777a40</code></a> Suppress deprecation warning in --rsyncdir. Workaround for <a href="https://github-redirect.dependabot.com/pypa/setuptools/issues/3655">#3655</a>.</li> <li>Additional commits viewable in <a href="https://github.com/pypa/setuptools/compare/v59.5.0...v65.5.1">compare view</a></li> </ul> </details> <br /> [![Dependabot compatibility score](https://dependabot-badges.githubapp.com/badges/compatibility_score?dependency-name=setuptools&package-manager=pip&previous-version=59.5.0&new-version=65.5.1)](https://docs.github.com/en/github/managing-security-vulnerabilities/about-dependabot-security-updates#about-compatibility-scores) Dependabot will resolve any conflicts with this PR as long as you don't alter it yourself. You can also trigger a rebase manually by commenting `@dependabot rebase`. 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Nemo with multiprocessing
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2023-01-02T09:03:59
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Hello developers. I have a lot of useful data and would like to retrieve embeddings and get diarization I thought to use a multiprocessing module to speed up the process. [This module](https://docs.python.org/3/library/multiprocessing.html) But I failed. If I make a processing pool and add tasks after a while processes stop, and it seems like processes lock themselves I tried to set up _set_start_method('spawn')_ and it didn't work out, the exception occured Is there a possibility to use Nemo with a multiprocessing module, has someone had such an experience? I'll appreciate any help!
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1,510,877,295
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5,702
The predicted output remains empty during training. Training loss is decreasing but validation loss is constant
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2022-12-26T11:37:51
2022-12-27T13:40:15
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**Describe the bug** I am training stt_en_conformer_ctc_large on my custom datset+librispeech dev and test splits. Currently, half of epoch 0 has completed but the best value of WER is 1 and logs show epty in predicted. I am a beginner and do not understand the ASR process fully so it must be something that I am doing wrong. Can someone help me?. I am tokenizing using SPE with a vocab size of 15000, and am using the default training script from ``` https://github.com/NVIDIA/NeMo/blob/main/examples/asr/asr_ctc/speech_to_text_ctc_bpe.py ``` and the config from ``` https://github.com/NVIDIA/NeMo/blob/main/examples/asr/conf/conformer/conformer_ctc_bpe.yaml ``` with some fields changed according to my need **Steps/Code to reproduce bug** Running the above training script with the config file which contains all the relevant path information. **Expected behavior** At least something should be predicted in logs **Environment overview (please complete the following information)** - Environment location: AWS - Method of NeMo install: [pip install nemo_toolkit["all"] **Environment details** If NVIDIA docker image is used you don't need to specify these. Otherwise, please provide: - OS version Ubuntu 20.04 - PyTorch version 11.7 - Python version 3.8 **Additional context** Add any other context about the problem here. Tesla T4
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No confidence score for megatron outputs?
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2022-12-26T07:47:27
2023-01-03T08:49:32
2023-01-03T08:49:31
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**The outputs form the megatron generations has no scoring method** For megatron models when `model.generate() `function is called, results returned doesn't contain any scores which could give the confidence of generation. **Here's a code snippet** ``` response = model.generate(inputs=test_examples, length_params=None) print(response.keys() ``` Is there a way that could validate these generations from model.
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I could not achieve to run eval_beamsearch_ngram.py script on my GPU.
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2022-12-25T12:41:17
2023-01-02T17:30:30
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It is working on CPU but not working on my GPU. I tried Cuda 11.3, 11.8, 12.0 and Python 3.8, 3.9. It did not work. I got same error each time. This is how i run the code; `python eval_beamsearch_ngram.py --nemo_model_file /media/mesut/Depo1/models/49/49.nemo --kenlm_model_file /media/mesut/Depo1/models/tbmmV8/final.nemo --input_manifest /media/mesut/Depo1/works/NeMo/tools/asr_webapp/manifest_input.json --preds_output_folder out --beam_width 128 --beam_alpha 1 --beam_beta 0` Error is something like that; ``` Reading Manifest /media/mesut/Depo1/works/NeMo/tools/asr_webapp/manifest_input.json ...: 1it [00:00, 10131.17it/s] Transcribing: 0%| | 0/1 [00:00<?, ?it/s] Traceback (most recent call last): File "eval_beamsearch_ngram.py", line 362, in <module> main() File "eval_beamsearch_ngram.py", line 269, in main all_logits = asr_model.transcribe(audio_file_paths, batch_size=args.acoustic_batch_size, logprobs=True) File "/usr/local/lib/python3.8/dist-packages/torch/autograd/grad_mode.py", line 27, in decorate_context return func(*args, **kwargs) File "/app/NeMo/nemo/collections/asr/models/ctc_models.py", line 183, in transcribe logits, logits_len, greedy_predictions = self.forward( File "/app/NeMo/nemo/core/classes/common.py", line 1084, in __call__ outputs = wrapped(*args, **kwargs) File "/app/NeMo/nemo/collections/asr/models/ctc_models.py", line 536, in forward processed_signal, processed_signal_length = self.preprocessor( File "/usr/local/lib/python3.8/dist-packages/torch/nn/modules/module.py", line 1194, in _call_impl return forward_call(*input, **kwargs) File "/app/NeMo/nemo/core/classes/common.py", line 1084, in __call__ outputs = wrapped(*args, **kwargs) File "/usr/local/lib/python3.8/dist-packages/torch/autograd/grad_mode.py", line 27, in decorate_context return func(*args, **kwargs) File "/app/NeMo/nemo/collections/asr/modules/audio_preprocessing.py", line 85, in forward processed_signal, processed_length = self.get_features(input_signal, length) File "/app/NeMo/nemo/collections/asr/modules/audio_preprocessing.py", line 268, in get_features return self.featurizer(input_signal, length) File "/usr/local/lib/python3.8/dist-packages/torch/nn/modules/module.py", line 1194, in _call_impl return forward_call(*input, **kwargs) File "/usr/local/lib/python3.8/dist-packages/torch/autograd/grad_mode.py", line 27, in decorate_context return func(*args, **kwargs) File "/app/NeMo/nemo/collections/asr/parts/preprocessing/features.py", line 350, in forward x = self.stft(x) File "/app/NeMo/nemo/collections/asr/parts/preprocessing/features.py", line 242, in <lambda> self.stft = lambda x: torch.stft( File "/usr/local/lib/python3.8/dist-packages/torch/functional.py", line 632, in stft return _VF.stft(input, n_fft, hop_length, win_length, window, # type: ignore[attr-defined] RuntimeError: cuFFT error: CUFFT_INTERNAL_ERROR ``` Ubuntu 20.04 GPU: Gigabyte Windforce 4090
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ModuleNotFoundError: No module named 'nemo.utils.data_utils'
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**Describe the bug** Whenever I try to run the file for tokenization I get the above error. **Steps/Code to reproduce bug** Run the command ``` python ./scripts/tokenizers/process_asr_text_tokenizer.py --manifest=../code/manifests/train_clean_5.json --data_root=tokenized --vocab_size=2048 tokenizer=bpe --log ``` **Expected behavior** It should have tokenized the text manifest. **Environment overview (please complete the following information)** - Environment location: AWS - Method of NeMo install: pip install nemo_toolkit['all'] - If method of install is [Docker], provide `docker pull` & `docker run` commands used **Environment details** If NVIDIA docker image is used you don't need to specify these. Otherwise, please provide: - OS version Ubutnu 20 - PyTorch version 1.6 - Python version 3.8 **Additional context** Add any other context about the problem here. Example: GPU model
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asr报错
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**Describe the bug** 调用stt_zh_citrinet_512模型进行音频转文字操作 **Steps/Code to reproduce bug** asr_model = nemo_asr.models.EncDecCTCModel.from_pretrained(model_name="stt_zh_citrinet_512") mandarin_text=asr_model.transcribe(paths2audio_files=["./test.wav"]) print(mandarin_text)** **Expected behavior** 输出音频文本内容 **Environment overview (please complete the following information)** 安装方式为pip,命令为pip install nemo_toolkit['all'] -i https://pypi.doubanio.com/simple/ **Environment details** If NVIDIA docker image is used you don't need to specify these. Otherwise, please provide: - torch1.10.0+cu113 - Python 3.8 **Additional context** 报错ValueError: Input signal length=2 is too small to resample from 44100->16000 ![image](https://user-images.githubusercontent.com/39375578/209262117-6888b66e-d0b2-4af6-8782-451e325ddf76.png)
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temp disable speaker recognision CI test
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Signed-off-by: fayejf <[email protected]> # What does this PR do ? temporary disable speaker recognision CI test due to long duration (~20M) **Collection**: ASR # Changelog - Add specific line by line info of high level changes in this PR. # Usage * You can potentially add a usage example below ```python # Add a code snippet demonstrating how to use this ``` # Before your PR is "Ready for review" **Pre checks**: - [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) - [ ] Did you write any new necessary tests? - [ ] Did you add or update any necessary documentation? - [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc) - [ ] Reviewer: Does the PR have correct import guards for all optional libraries? **PR Type**: - [ ] New Feature - [ ] Bugfix - [ ] Documentation If you haven't finished some of the above items you can still open "Draft" PR. ## Who can review? Anyone in the community is free to review the PR once the checks have passed. [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas. # Additional Information * Related to # (issue)
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Error in downloading the financial_phrase_bank dataset
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**Describe the bug** I was trying to run "tutorials/nlp/Multitask_Prompt_and_PTuning.ipynb". But I got the following error for downloading the "financial_phrase_bank" dataset. https://www.researchgate.net/profile/Pekka_Malo/publication/251231364_FinancialPhraseBank-v1.0/data/0c96051eee4fb1d56e000000/FinancialPhraseBank-v1.0.zip Resolving [www.researchgate.net](http://www.researchgate.net/) ([www.researchgate.net](http://www.researchgate.net/))... 104.17.32.105, 104.17.33.105, 2606:4700::6811:2169, ... Connecting to [www.researchgate.net](http://www.researchgate.net/) ([www.researchgate.net)|104.17.32.105|:443](http://www.researchgate.net%29%7C104.17.32.105%7C:443/)... connected. HTTP request sent, awaiting response... 403 Forbidden 2022-12-21 22:47:57 ERROR 403: Forbidden. **Steps/Code to reproduce bug** I tried to download the dataset in both my local machine and Google Colab. But both cases failed. **Expected behavior** I expected the dataset to be downloaded without any error. **Environment overview (please complete the following information)** - Environment location: Colab, MacOS - Method of NeMo install: I uploaded the notebook on Colab - If method of install is [Docker], provide `docker pull` & `docker run` commands used **Environment details** If NVIDIA docker image is used you don't need to specify these. Otherwise, please provide: - OS version - PyTorch version - Python version **Additional context** Add any other context about the problem here. Example: GPU model
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Update to pytorch 22.12 container
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# What does this PR do ? Add a one line overview of what this PR aims to accomplish. **Collection**: [Note which collection this PR will affect] # Changelog - Add specific line by line info of high level changes in this PR. # Usage * You can potentially add a usage example below ```python # Add a code snippet demonstrating how to use this ``` # Before your PR is "Ready for review" **Pre checks**: - [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) - [ ] Did you write any new necessary tests? - [ ] Did you add or update any necessary documentation? - [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc) - [ ] Reviewer: Does the PR have correct import guards for all optional libraries? **PR Type**: - [ ] New Feature - [ ] Bugfix - [ ] Documentation If you haven't finished some of the above items you can still open "Draft" PR. ## Who can review? Anyone in the community is free to review the PR once the checks have passed. [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas. # Additional Information * Related to # (issue)
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[ASR] Mask-based dereverb algorithm
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# What does this PR do ? This PR adds a multichannel input, multichannel output mask-based dereverberation algorithm based on weighted prediction error (WPE). **Collection**: ASR # Changelog - New: Dereverberation algorithm implemented in `MaskBasedDereverbWPE` - Added unit tests for handling of convolution matrices - Input length is now optional for `AudioToSpectrogram` - Updated relevant unit tests # Usage An example script with a test vector is attached to this PR. ## Example script [pr_5693_example_script.zip](https://github.com/NVIDIA/NeMo/files/10281448/pr_5693_example_script.zip) ## Code example The algorithm can be used as follows: ```python import soundfile as sf import torch from nemo.collections.asr.modules.audio_modules import MaskBasedDereverbWPE from nemo.collections.asr.modules.audio_preprocessing import AudioToSpectrogram, SpectrogramToAudio from nemo.collections.asr.parts.preprocessing.segment import AudioSegment # Parameters fft_length = 512 hop_length = fft_length // 2 filter_length = 8 delay = 2 num_iterations = 3 # Load audio sample_rate = 16000 x = AudioSegment.from_file(filename, target_sr=sample_rate).samples.T # Add batch dimension, shape (B, C, T) x = x[None, ...] # Prepare analysis and synthesis transforms stft = AudioToSpectrogram(fft_length=fft_length, hop_length=hop_length) istft = SpectrogramToAudio(fft_length=fft_length, hop_length=hop_length) # Analysis transform X, _ = stft(input=torch.tensor(x)) # Prepare dereverb instance dereverb = MaskBasedDereverbWPE(filter_length=filter_length, prediction_delay=delay, num_iterations=num_iterations) # Processing Y, _ = dereverb(input=X, mask=None) # Synthesis transform: shape (B, C, T) y, _ = istft(input=Y) # Save audio sf.write('output/dereverb_output.wav', y.cpu().numpy()[0, ...].T, sample_rate) ``` ## Signal example Below we show spectrograms of the input signal and the processed output signal. Signals are available in the attached example. Only the first channel is shown. Input signal | Output signal :-------------------------:|:-------------------------: ![dereverb_input_ch0](https://user-images.githubusercontent.com/108555623/208999856-5dcc8f4c-219d-488e-a2f0-e75406194eb8.png) | ![dereverb_output_ch0](https://user-images.githubusercontent.com/108555623/208999901-bbf9329f-1ea6-4f45-909a-1c85479b9b4a.png) # Before your PR is "Ready for review" **Pre checks**: - [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) - [x] Did you write any new necessary tests? - [ ] Did you add or update any necessary documentation? - [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc) - [ ] Reviewer: Does the PR have correct import guards for all optional libraries? **PR Type**: - [x] New Feature - [ ] Bugfix - [ ] Documentation If you haven't finished some of the above items you can still open "Draft" PR. ## Who can review? Anyone in the community is free to review the PR once the checks have passed. [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas. # Additional Information * Related to # (issue)
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Fix for incorrect computation of batched alignment in transducers
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# What does this PR do ? The original code had a bug because of which whenever at least one batch element predicted non-blank symbol, all blank elements would duplicate the last prediction in their `.alignments` variable. Additionally, I've unified `_greedy_decode_blank_as_pad` and `_greedy_decode_masked` into a single function with an if check. The two methods had 99% of the same code except 5 lines or so, so imo it does not make sense to have them as fully separate code paths. Please let me know if for some reason we don't want to have this change. **Collection**: ASR # Changelog - Add specific line by line info of high level changes in this PR. # Usage * You can potentially add a usage example below ```python # Add a code snippet demonstrating how to use this ``` # Before your PR is "Ready for review" **Pre checks**: - [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) - [x] Did you write any new necessary tests? - [ ] Did you add or update any necessary documentation? - [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc) - [ ] Reviewer: Does the PR have correct import guards for all optional libraries? **PR Type**: - [ ] New Feature - [x] Bugfix - [ ] Documentation If you haven't finished some of the above items you can still open "Draft" PR. ## Who can review? Anyone in the community is free to review the PR once the checks have passed. [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas. # Additional Information * Related to # (issue)
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Fix missing torchelastic fixes for PTL 1.8
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Signed-off-by: MaximumEntropy <[email protected]> # What does this PR do ? Fixes missing torchelastic import **Collection**: NLP # Changelog - Add specific line by line info of high level changes in this PR. # Usage * You can potentially add a usage example below ```python # Add a code snippet demonstrating how to use this ``` # Before your PR is "Ready for review" **Pre checks**: - [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) - [ ] Did you write any new necessary tests? - [ ] Did you add or update any necessary documentation? - [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc) - [ ] Reviewer: Does the PR have correct import guards for all optional libraries? **PR Type**: - [ ] New Feature - [ ] Bugfix - [ ] Documentation If you haven't finished some of the above items you can still open "Draft" PR. ## Who can review? Anyone in the community is free to review the PR once the checks have passed. [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas. # Additional Information * Related to # (issue)
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Remove cell output from tutorial
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Signed-off-by: ericharper <[email protected]> # What does this PR do ? Add a one line overview of what this PR aims to accomplish. **Collection**: [Note which collection this PR will affect] # Changelog - Add specific line by line info of high level changes in this PR. # Usage * You can potentially add a usage example below ```python # Add a code snippet demonstrating how to use this ``` # Before your PR is "Ready for review" **Pre checks**: - [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) - [ ] Did you write any new necessary tests? - [ ] Did you add or update any necessary documentation? - [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc) - [ ] Reviewer: Does the PR have correct import guards for all optional libraries? **PR Type**: - [ ] New Feature - [ ] Bugfix - [ ] Documentation If you haven't finished some of the above items you can still open "Draft" PR. ## Who can review? Anyone in the community is free to review the PR once the checks have passed. [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas. # Additional Information * Related to # (issue)
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Signed-off-by: ericharper <[email protected]> # What does this PR do ? Add a one line overview of what this PR aims to accomplish. **Collection**: [Note which collection this PR will affect] # Changelog - Add specific line by line info of high level changes in this PR. # Usage * You can potentially add a usage example below ```python # Add a code snippet demonstrating how to use this ``` # Before your PR is "Ready for review" **Pre checks**: - [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) - [ ] Did you write any new necessary tests? - [ ] Did you add or update any necessary documentation? - [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc) - [ ] Reviewer: Does the PR have correct import guards for all optional libraries? **PR Type**: - [ ] New Feature - [ ] Bugfix - [ ] Documentation If you haven't finished some of the above items you can still open "Draft" PR. ## Who can review? Anyone in the community is free to review the PR once the checks have passed. [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas. # Additional Information * Related to # (issue)
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5,688
Trainer is required in EncDecRNNTModel
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2022-12-21T17:16:45
2022-12-22T08:19:09
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**Describe the bug** In the constructor of the class `EncDecRNNTModel`, the PTL trainer is a kwarg with default value `null`. However, in the `self.training_step` method, the method `self.log_dict` is called (line 725 in file `collections/asr/models/rnnt_models.py`), which raises an error if no PTL trainer object is present in the `EncDecRNNTModel` object. **Expected behavior** If no PTL trainer is passed to the `EncDecRNNTModel`, it should not call `self.log` and just return the logs. The fix is just an if statement before the `self.log_dict`
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Using nemo LM in other nemo training
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I want to use our own training nemo LM in other nemo training and I found only this reference [https://github.com/NVIDIA/NeMo/blob/4cd9b3449cbfedc671348fbabbe8e3a55fbd659d/examples/nlp/text_classification/conf/ptune_text_classification_config.yaml](url) that have nemo_path in language model config so I follow that(training text classification) but it doesn't work, it has to have model.tokenizer.tokenizer_name > [NeMo I 2022-12-20 16:02:14 exp_manager:563] TensorboardLogger has been set up > Traceback (most recent call last): > File "/workspace/data/bew/NeMo/examples/nlp/text_classification/text_classification_with_bert.py", line 117, in main > model = TextClassificationModel(cfg.model, trainer=trainer) > File "/opt/conda/lib/python3.8/site-packages/nemo/collections/nlp/models/text_classification/text_classification_model.py", line 54, in __init__ > self.setup_tokenizer(cfg.tokenizer) > File "/opt/conda/lib/python3.8/site-packages/nemo/collections/nlp/models/nlp_model.py", line 112, in setup_tokenizer > tokenizer_name=cfg.tokenizer_name, > omegaconf.errors.ConfigAttributeError: Key 'tokenizer_name' is not in struct > full_key: model.tokenizer.tokenizer_name > reference_type=Any > object_type=dict > > Set the environment variable HYDRA_FULL_ERROR=1 for a complete stack trace. If I mention model.tokenizer.tokenizer_name and model.language_model.pretrained_model_name then put model.language_model.nemo_file(path to our custom nemo model), the training look ok but it seems to use the model from huggingface. Is there a way to using custom LM model?
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Is it possible to use Hugging face XLSR pretrained model with Nemo?
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2022-12-21T04:01:11
2022-12-30T18:41:39
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Hi Is it possible to use Hugging face XLSR pretrained model with Nemo? Thanks. Pls suggest @titu1994 @jbalam-nv
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