Model Overview

SqueezeWave is a Glow-based (alternatively flow-based) model that generates audio from mel spectrograms.

Model Architecture

SqueezeWave improves upon WaveGlow by changing the Wavenet portions to use depthwsie separable convolutions.

Training

This model is trained on LJSpeech sampled at 22050Hz, and has been tested on generating female English voices with an American accent.

Performance

No performance information available at this time.

How to Use this Model

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 Melgan
from nemo.collections.tts.models import SqueezeWaveModel
model = SqueezeWaveModel.from_pretrained(model_name="tts_squeezewave")

# 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 = model.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)

Input

This model accepts batches of mel spectrograms.

Output

This model outputs audio at 22050Hz.

Limitations

There are no known limitations at this time.

Versions

1.0.0 (current): The original version that was released with NeMo 1.0.0

References

SqueezeWave paper: https://arxiv.org/abs/2001.05685

Licence

License to use this model is covered by the NGC TERMS OF USE unless another License/Terms Of Use/EULA is clearly specified. By downloading the public and release version of the model, you accept the terms and conditions of the NGC TERMS OF USE.