Comments (3)
Hi, there are actually two parts in the TTS pipeline. The front-end part generates high quality mel-spectrogram from text, and the back-end part generates real audio given high quality mel-spectrogram. Basically, to generate voice from text using a whole TTS pipeline, you will need both the front-end and the back-end.
For the front-end part, some models like tacotron2 and fastspeech do very good job. And SqueezeWave is designed to be an efficient model for the back-end part. There are also some other models designed for the back-end part, such as waveRNN, waveGlow, and LPCNet.
So here, to generate voice, we assume you already have high quality mel-spectrogram as input for SqueezeWave.
from squeezewave.
@tianrengao hello,may i ask how to use the denoiser.py .thank you
from squeezewave.
i cannot run that.
from squeezewave.
Related Issues (20)
- How long does a training from scratch take? HOT 2
- Slower than Waveglow on GPU
- Loss calculation HOT 1
- want to see some speed tests
- hop_length
- [Question] published latency/delay of SW for real-time streaming inference
- License
- [Question] MOS results HOT 1
- why is "segment_length" set to 16384? HOT 1
- Docker
- Parameter configs for different sample rate, hop length etc.
- bias from the model
- 'Upsample1d' object has no attribute 'weight'
- denoiser of the squeezewave HOT 1
- train.py
- wav have no voice
- convert squeezewave model to pytorch script for C++ inference. HOT 4
- preprocess HOT 2
- Question if the code line is a typo or not.
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from squeezewave.