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Organizing the script folder (NVIDIA-NeMo#1844)
* fixed paths in tutorials. Signed-off-by: Vahid <vnoroozi@nvidia.com> * Updated. Signed-off-by: Vahid <vnoroozi@nvidia.com> * added nvidia copyright. Signed-off-by: Vahid <vnoroozi@nvidia.com> * fixed the paths. Signed-off-by: Vahid <vnoroozi@nvidia.com> * fixed the branch. Signed-off-by: Vahid <vnoroozi@nvidia.com>
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‎examples/asr/speech_to_label.py‎

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# Task 1: Speech Command
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## Preparing the dataset
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Use the `process_speech_commands_data.py` script under <NEMO_ROOT>/scripts in order to prepare the dataset.
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Use the `process_speech_commands_data.py` script under <NEMO_ROOT>/scripts/dataset_processing in order to prepare the dataset.
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```sh
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python <NEMO_ROOT>/scripts/process_speech_commands_data.py \
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python <NEMO_ROOT>/scripts/dataset_processing/process_speech_commands_data.py \
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--data_root=<absolute path to where the data should be stored> \
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--data_version=<either 1 or 2, indicating version of the dataset> \
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--class_split=<either "all" or "sub", indicates whether all 30/35 classes should be used, or the 10+2 split should be used> \
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# Task 2: Voice Activity Detection
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## Preparing the dataset
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Use the `process_vad_data.py` script under <NEMO_ROOT>/scripts in order to prepare the dataset.
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Use the `process_vad_data.py` script under <NEMO_ROOT>/scripts/dataset_processing in order to prepare the dataset.
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```sh
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python process_vad_data.py \
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Note that it's possible that tarred datasets impacts validation scores because it drop values in order to have same amount of files per tarfile;
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Scores might be off since some data is missing.
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Use the `convert_to_tarred_audio_dataset.py` script under <NEMO_ROOT>/scripts in order to prepare tarred audio dataset.
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Use the `convert_to_tarred_audio_dataset.py` script under <NEMO_ROOT>/scripts/speech_recognition in order to prepare tarred audio dataset.
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For details, please see TarredAudioToClassificationLabelDataset in <NEMO_ROOT>/nemo/collections/asr/data/audio_to_label.py
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python speech_to_label.py \

‎examples/asr/speech_to_text_bpe.py‎

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"""
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# Preparing the Tokenizer for the dataset
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Use the `process_asr_text_tokenizer.py` script under <NEMO_ROOT>/scripts in order to prepare the tokenizer.
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Use the `process_asr_text_tokenizer.py` script under <NEMO_ROOT>/scripts/tokenizers/ in order to prepare the tokenizer.
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```sh
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python <NEMO_ROOT>/scripts/process_asr_text_tokenizer.py \
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python <NEMO_ROOT>/scripts/tokenizers/process_asr_text_tokenizer.py \
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--manifest=<path to train manifest files, seperated by commas>
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OR
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--data_file=<path to text data, seperated by commas> \

‎examples/asr/speech_to_text_rnnt.py‎

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"""
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# Preparing the Tokenizer for the dataset
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Use the `process_asr_text_tokenizer.py` script under <NEMO_ROOT>/scripts in order to prepare the tokenizer.
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Use the `process_asr_text_tokenizer.py` script under <NEMO_ROOT>/scripts/tokenizers/ in order to prepare the tokenizer.
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```sh
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python <NEMO_ROOT>/scripts/process_asr_text_tokenizer.py \
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python <NEMO_ROOT>/scripts/tokenizers/process_asr_text_tokenizer.py \
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--manifest=<path to train manifest files, seperated by commas> \
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--data_root="<output directory>" \
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--vocab_size=<number of tokens in vocabulary> \

‎examples/asr/vad_infer.py‎

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1) shift the window of length time_length (e.g. 0.63s) by shift_length (e.g. 10ms) to generate the frame and use the prediction of the window to represent the label for the frame;
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[this script demonstrate how to do this approach]
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2) generate predictions with overlapping input segments. Then a smoothing filter is applied to decide the label for a frame spanned by multiple segments.
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[get frame level prediction by this script and use vad_overlap_posterior.py in NeMo/scripts/
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[get frame level prediction by this script and use vad_overlap_posterior.py in NeMo/scripts/voice_activity_detection
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One can also find posterior about converting frame level prediction
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to speech/no-speech segment in start and end times format in that script.]
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‎examples/speaker_recognition/speaker_reco.py‎

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Note that it's possible that tarred datasets impacts validation scores because it drop values in order to have same amount of files per tarfile;
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Scores might be off since some data is missing.
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Use the `convert_to_tarred_audio_dataset.py` script under <NEMO_ROOT>/scripts in order to prepare tarred audio dataset.
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Use the `convert_to_tarred_audio_dataset.py` script under <NEMO_ROOT>/speech_recognition/scripts in order to prepare tarred audio dataset.
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For details, please see TarredAudioToClassificationLabelDataset in <NEMO_ROOT>/nemo/collections/asr/data/audio_to_label.py
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"""
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scripts/rttm_to_manifest.py renamed to scripts/ speaker_recognition/rttm_to_manifest.py

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scripts/scp_to_manifest.py renamed to scripts/ speaker_recognition/scp_to_manifest.py

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scripts/process_asr_text_tokenizer.py renamed to scripts/ tokenizers/process_asr_text_tokenizer.py

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scripts/create_tarred_transformer_lm_dataset.py renamed to scripts/asr_language_modelling/create_tarred_transformer_lm_dataset.py

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scripts/install_ctc_decoders.sh renamed to scripts/asr_language_modelling/install_ctc_decoders.sh

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