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Finally, the **vocoder** takes the mel-spectrogram frames generated by the decoder and converts them into raw audio waveforms. This is the final step in the Deep Voice 3 pipeline, and it is crucial for producing speech that sounds natural and realistic. Deep Voice 3 uses a WaveNet-based vocoder, which is a type of autoregressive model that generates audio samples one at a time. The WaveNet vocoder is trained to predict the next audio sample based on the previous samples and the mel-spectrogram frames. It uses a stack of dilated convolutional layers to capture long-range dependencies in the audio signal. The dilated convolutions allow the model to process a large context window without increasing the number of parameters. The WaveNet vocoder is trained to minimize the difference between the predicted audio samples and the ground truth audio samples. This is typically done using a cross-entropy loss or a similar loss function. The WaveNet vocoder is a powerful and versatile component of Deep Voice 3. It can generate high-quality audio waveforms from mel-spectrogram frames, and it can be trained to produce a variety of different voices and speaking styles. The WaveNet vocoder is also relatively efficient, which makes it suitable for real-time speech synthesis. In addition to WaveNet, other vocoders can be used with Deep Voice 3, such as Griffin-Lim and WORLD. However, WaveNet typically produces the best results in terms of audio quality. The vocoder is a critical component of Deep Voice 3, as it is responsible for generating the final audio output. By using a WaveNet-based vocoder, Deep Voice 3 can produce speech that sounds natural, realistic, and expressive.
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