Abstract

Denoising diffusion probabilistic models (DDPMs) have recently achieved leading performances in many generative tasks. However, the inherited iterative sampling process costs hindered their applications to text-to-speech synthesis deployment. Through the preliminary study on diffusion model parameterization, we find that previous gradient-based TTS models require hundreds or thousands of iterations to guarantee high sample quality, which poses a challenge for accelerating sampling. In this work, we propose ProDiff, on progressive fast diffusion model for high-quality text-to-speech. Unlike estimating the gradient for data density, ProDiff parameterizes the denoising model by directly predicting clean data to avoid distinct quality degradation when reducing sampling iterations. To tackle the model convergence challenge with reduced iterations, ProDiff reduces the data variance in the target site via knowledge distillation. Specifically, the denoising model uses the generated mel-spectrogram from an N-step DDIM teacher as the training target and distills the behavior into a new model with N/2 steps. As such, it allows the TTS model to make sharp predictions and further reduces the sampling time by orders of magnitude. Our evaluation demonstrates that ProDiff needs only 2 iterations to synthesize high-fidelity mel-spectrograms, while it maintains sample quality and diversity competitive with state-of-the-art models using hundreds of steps. ProDiff enables a sampling speed of 24x faster than real-time on a single NVIDIA 2080Ti GPU, making diffusion models practically applicable to text-to-speech synthesis deployment for the first time. Our extensive ablation studies demonstrate that each design in ProDiff is effective, and we further show that ProDiff can be easily extended to the multi-speaker setting. Audio samples are available at https://prodiff.github.io/.

Progressive Fast Diffusion Model for High-Quality

Preliminary Analyses

Reference Text: This type was introduced into England by Wynkyn de Worde, Caxton’s successor

Method	Recording	128 iter	64 iter	32 iter	16 iter	8 iter	4 iter	2 iter
Gradient-Based
Generator-Based

Reference Text: the ends of many of the letters such as the t and e are hooked up in a vulgar and meaningless way

Method	Recording	128 iter	64 iter	32 iter	16 iter	8 iter	4 iter	2 iter
Gradient-Based
Generator-Based

Reference Text: The essential point to be remembered is that the ornament, whatever it is, whether picture or pattern-work, should form part of the page

Method	Recording	128 iter	64 iter	32 iter	16 iter	8 iter	4 iter	2 iter
Gradient-Based
Generator-Based

Reference Text: The prison population fluctuated a great deal

Method	Recording	128 iter	64 iter	32 iter	16 iter	8 iter	4 iter	2 iter
Gradient-Based
Generator-Based

Performance

LJSpeech

Reference/Target Text: This type was introduced into England by Wynkyn de Worde, Caxton’s successor

Recording	Tacotron 2	FastSpeech 2	GANSpeech	Glow-TTS	Grad-TTS	DiffSpeech	ProDiff Teacher	ProDiff

Reference/Target Text: the ends of many of the letters such as the t and e are hooked up in a vulgar and meaningless way

Recording	Tacotron 2	FastSpeech 2	GANSpeech	Glow-TTS	Grad-TTS	DiffSpeech	ProDiff Teacher	ProDiff

Reference/Target Text: The essential point to be remembered is that the ornament, whatever it is, whether picture or pattern-work, should form part of the page

Recording	Tacotron 2	FastSpeech 2	GANSpeech	Glow-TTS	Grad-TTS	DiffSpeech	ProDiff Teacher	ProDiff

Reference/Target Text: The prison population fluctuated a great deal

Recording	Tacotron 2	FastSpeech 2	GANSpeech	Glow-TTS	Grad-TTS	DiffSpeech	ProDiff Teacher	ProDiff

LibriTTS

Reference/Target Text: villeforts conduct , therefore , upon reflection , appeared to the baroness as if shaped for their mutual advantage .

Recording	Tacotron 2	FastSpeech 2	GANSpeech	Glow-TTS	Grad-TTS	DiffSpeech	ProDiff

Reference/Target Text: she would invoke the past , recall old recollections ; she would supplicate him by the remembrance of guilty , yet happy days .

Recording	Tacotron 2	FastSpeech 2	GANSpeech	Glow-TTS	Grad-TTS	DiffSpeech	ProDiff

Reference/Target Text: do you intend opening the door ? said the baroness .

Recording	Tacotron 2	FastSpeech 2	GANSpeech	Glow-TTS	Grad-TTS	DiffSpeech	ProDiff

Reference/Target Text: come , forget him for a moment , and instead of pursuing him let him go .

Recording	Tacotron 2	FastSpeech 2	GANSpeech	Glow-TTS	Grad-TTS	DiffSpeech	ProDiff

Ablation

Reference/Target Text: This type was introduced into England by Wynkyn de Worde, Caxton’s successor

Recording	w/o GP	w/o KD	Teacher(T=16)	Teacher(T=8)	ProDiff

Reference/Target Text: the ends of many of the letters such as the t and e are hooked up in a vulgar and meaningless way

Recording	w/o GP	w/o KD	Teacher(T=16)	Teacher(T=8)	ProDiff

Reference/Target Text: The essential point to be remembered is that the ornament, whatever it is, whether picture or pattern-work, should form part of the page

Recording	w/o GP	w/o KD	Teacher(T=16)	Teacher(T=8)	ProDiff

Reference/Target Text: The prison population fluctuated a great deal

Recording	w/o GP	w/o KD	Teacher(T=16)	Teacher(T=8)	ProDiff