Google AI proposes end-to-end broadcast-based text-to-speech E3-TTS: a simple and efficient broadcast-based end-to-end text-to-speech model

In machine learning, a diffusion model is a generative model commonly used for image and audio generation tasks. The diffusion model uses a diffusion process, transforming a complex data distribution into simpler distributions. The key advantage lies in its ability to generate high-quality results, particularly in tasks such as image and audio synthesis.

In the context of text-to-speech (TTS) systems, the application of diffusion models has revealed notable improvements compared to traditional TTS systems. This progress is due to its power to address problems faced by existing systems, such as the heavy dependency on the quality of intermediate functions and the complexity associated with deployment, training, and configuration procedures.

A team of Google researchers has formulated E3 TTS: Easy text-to-speech based on end-to-end broadcast. This text-to-speech model relies on the diffusion process to maintain the temporal structure. This approach allows the model to take plain text as input and directly produce audio waveforms.

The E3 TTS model efficiently processes input text in a non-autoregressive manner, allowing it to generate a waveform directly without requiring sequential processing. Furthermore, the determination of speaker identity and alignment occurs dynamically during diffusion. This model consists of two main modules: a pre-trained BERT model is used to extract relevant information from the input text, and a broadcast UNet model processes the BERT output. It iteratively refines the initial noisy waveform and ultimately predicts the final raw waveform.

The E3 TTS employs an iterative refinement process to generate an audio waveform. It models the temporal structure of the waveform through the diffusion process, allowing for flexible latent structures within the given audio without the need for additional conditioning information.

It is built on a pre-trained BERT model. Furthermore, the system works without relying on speech representations such as phonemes or graphemes. The BERT model takes subword inputs and its output is processed using a 1D U-Net structure. It includes downsampling and upsampling blocks connected by residual connections.

E3 TTS uses text representations from the pre-trained BERT model, taking advantage of current developments in large language models. The E3 TTS is based on a pre-trained text language model, which speeds up the generation process.

The adaptability of the system increases as this model can be trained in many languages ​​using text input.

The U-Net structure employed in E3 TTS comprises a series of downsampling and upsampling blocks connected by residual connections. To improve the extraction of information from the BERT output, cross-attention is incorporated into the upper upsampling and downsampling blocks. An adaptive softmax convolutional neural network (CNN) kernel is used in the lower blocks, the kernel size of which is determined by the time step and the speaker. Speaker embeddings and time steps are combined using feature linear modulation (FiLM), which includes a composite layer for channel scaling and bias prediction.

The downsampler in E3 TTS plays a critical role in refining noisy information, converting it from 24 kHz to a sequence of similar length to the encoded BERT output, significantly improving overall quality. In contrast, the upsampler predicts noise with the same length as the input waveform.

In summary, E3 TTS demonstrates the ability to generate high-fidelity audio, approaching a notable level of quality in this field.

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