nvidia
/

diar_sortformer_4spk-v1

Audio Classification

speaker-diarization

speaker-recognition

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taejinp commited on about 1 month ago

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@@ -209,7 +209,7 @@ The model can be trained using this [example script](https://github.com/NVIDIA/N
 To evaluate Sortformer diarizer and save diarization results in RTTM format, use the inference [example script](https://github.com/NVIDIA/NeMo/blob/main/examples/speaker_tasks/diarization/neural_diarizer/e2e_diarize_speech.py):
 ```bash
-python [NEMO_GIT_FOLDER]/examples/speaker_tasks/diarization/neural_diarizer/e2e_diarize_speech.py
  model_path="/path/to/diar_sortformer_4spk-v1.nemo" \
  manifest_filepath="/path/to/multispeaker_manifest_with_reference_rttms.json" \
  collar=COLLAR \
@@ -218,7 +218,7 @@ python [NEMO_GIT_FOLDER]/examples/speaker_tasks/diarization/neural_diarizer/e2e_
 You can provide the post-processing YAML configs from [`post_processing` folder](https://github.com/NVIDIA/NeMo/tree/main/examples/speaker_tasks/diarization/conf/post_processing) to reproduce the optimized post-processing algorithm for each development dataset:
 ```bash
-python [NEMO_GIT_FOLDER]/examples/speaker_tasks/diarization/neural_diarizer/e2e_diarize_speech.py \
  model_path="/path/to/diar_sortformer_4spk-v1.nemo" \
  manifest_filepath="/path/to/multispeaker_manifest_with_reference_rttms.json" \
  collar=COLLAR \

 To evaluate Sortformer diarizer and save diarization results in RTTM format, use the inference [example script](https://github.com/NVIDIA/NeMo/blob/main/examples/speaker_tasks/diarization/neural_diarizer/e2e_diarize_speech.py):
 ```bash
+python ${NEMO_GIT_FOLDER}/examples/speaker_tasks/diarization/neural_diarizer/e2e_diarize_speech.py
  model_path="/path/to/diar_sortformer_4spk-v1.nemo" \
  manifest_filepath="/path/to/multispeaker_manifest_with_reference_rttms.json" \
  collar=COLLAR \
 You can provide the post-processing YAML configs from [`post_processing` folder](https://github.com/NVIDIA/NeMo/tree/main/examples/speaker_tasks/diarization/conf/post_processing) to reproduce the optimized post-processing algorithm for each development dataset:
 ```bash
+python ${NEMO_GIT_FOLDER}/examples/speaker_tasks/diarization/neural_diarizer/e2e_diarize_speech.py \
  model_path="/path/to/diar_sortformer_4spk-v1.nemo" \
  manifest_filepath="/path/to/multispeaker_manifest_with_reference_rttms.json" \
  collar=COLLAR \