For model details, please refer to the main WaveGlow model page. This version contains a model that has 88 million parameters.
Trained or fine-tuned NeMo models (with the file extenstion
.nemo) can be converted to Riva models (with the file extension
.riva) and then deployed. Here is a pre-trained WaveGlow Speech Synthesis Riva model, again containing 88 million parameters.
This model is trained on LJSpeech sampled at 22050Hz, and has been tested on generating female English voices with an American accent.
No performance information available at this time.
This model can be automatically loaded from NGC.
NOTE: In order to generate audio, you also need a spectrogram generator from NeMo. This example uses the FastPitch model.
# Load spectrogram generator from nemo.collections.tts.models import FastPitchModel spec_generator = FastPitchModel.from_pretrained("tts_en_fastpitch") # Load WaveGlow from nemo.collections.tts.models import WaveGlowModel vocoder = WaveGlowModel.from_pretrained(model_name="tts_waveglow_88m") # Generate audio import soundfile as sf parsed = spec_generator.parse("You can type your sentence here to get nemo to produce speech.") spectrogram = spec_generator.generate_spectrogram(tokens=parsed) audio = vocoder.convert_spectrogram_to_audio(spec=spectrogram) # Save the audio to disk in a file called speech.wav sf.write("speech.wav", audio.to('cpu').numpy(), 22050)
This model accepts batches of mel spectrograms.
This model outputs audio at 22050Hz.
There are no known limitations at this time.
1.0.0 (current): An updated version of melgan that standardizes mel spectrogram generation across NeMo models.
1.0.0rc1: The original version that was released with NeMo 1.0.0.rc1
WaveGlow paper: https://arxiv.org/abs/1811.00002