Overview

This is a checkpoint for the Waveglow model that was trained in NeMo on LJspeech for 1200 epochs.\nIt was trained with Apex/Amp optimization level O1, with 8 * 32GB V100, and with a batch size of 12 per GPU for a total batch size of 96.

It contains the checkpoints for the Waveglow Neural Modules and the yaml config file:

WaveGlowNM.pt

Documentation

Refer to documentation at https://github.com/NVIDIA/NeMo

Usage example: Please download both the NeMo Tacotron 2 and the NeMo WaveGlow checkpoints. Put the checkpoints into a checkpoint_dir, and run tts_infer.py (from NeMo's TTS examples).

python tts_infer.py --spec_model tacotron2 --spec_model_config=$checkpoint_dir/tacotron2.yaml --spec_model_load_dir=$checkpoint_dir --vocoder waveglow --vocoder_model_config=$checkpoint_dir/waveglow.yaml--vocoder_model_load_dir=$checkpoint_dir --eval_dataset=.json

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users logged in across NVIDIA domains.","api.ngc.nvidia.com","This cookie is managed by AWS and is used for load balancing.","bm_so","This cookie name is used to load the site as quickly as possible and to recognize bots using Akamai.","The __cf_bm cookie is a cookie necessary to support Cloudflare Bot Management, currently in private beta. 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