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Fix links to speaker identification notebook #5260

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4 changes: 2 additions & 2 deletions examples/speaker_tasks/recognition/README.md
Original file line number Diff line number Diff line change
Expand Up @@ -29,14 +29,14 @@ For training ecapa_tdnn (channel-attention) model:
```bash
python speaker_reco.py --config_path='conf' --config_name='ecapa_tdnn.yaml'
```
For step by step tutorial see [notebook](https://github.com/NVIDIA/NeMo/blob/main/tutorials/speaker_tasks/Speaker_Recognition_Verification.ipynb).
For step by step tutorial see [notebook](https://github.com/NVIDIA/NeMo/blob/main/tutorials/speaker_tasks/Speaker_Identification_Verification.ipynb).

### Fine Tuning
For fine tuning on a pretrained .nemo speaker recognition model,
```bash
python speaker_reco_finetune.py --config_path='conf' --config_name='titanet-finetune.yaml'
```
for fine tuning tips see this [tutorial](https://github.com/NVIDIA/NeMo/blob/main/tutorials/speaker_tasks/Speaker_Recognition_Verification.ipynb)
for fine tuning tips see this [tutorial](https://github.com/NVIDIA/NeMo/blob/main/tutorials/speaker_tasks/Speaker_Identification_Verification.ipynb)

## Inference
We provide generic scripts for manifest file creation, embedding extraction, Voxceleb evaluation and speaker ID inference. Hence most of the steps would be common and differ slightly based on your end application.
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2 changes: 1 addition & 1 deletion examples/speaker_tasks/recognition/speaker_reco.py
Original file line number Diff line number Diff line change
Expand Up @@ -37,7 +37,7 @@
exp_manager.name=$EXP_NAME +exp_manager.use_datetime_version=False \
exp_manager.exp_dir='./speaker_exps'

See https://github.com/NVIDIA/NeMo/blob/main/tutorials/speaker_tasks/Speaker_Recognition_Verification.ipynb for notebook tutorial
See https://github.com/NVIDIA/NeMo/blob/main/tutorials/speaker_tasks/Speaker_Identification_Verification.ipynb for notebook tutorial

Optional: Use tarred dataset to speech up data loading.
Prepare ONE manifest that contains all training data you would like to include. Validation should use non-tarred dataset.
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