Pros of TTS

• More comprehensive and actively maintained project with regular updates
• Supports multiple TTS architectures and models
• Provides pre-trained models and easy-to-use inference scripts

Cons of TTS

• Higher complexity and steeper learning curve
• Requires more computational resources for training and inference

Code Comparison

TTS:

from TTS.utils.synthesizer import Synthesizer

synthesizer = Synthesizer(
    tts_checkpoint="path/to/model.pth",
    tts_config_path="path/to/config.json",
    vocoder_checkpoint="path/to/vocoder.pth",
    vocoder_config="path/to/vocoder_config.json"
)
wav = synthesizer.tts("Hello world!")

dc_tts:

from synthesizer import Synthesizer

synthesizer = Synthesizer()
wav = synthesizer.synthesize("Hello world!")

Key Differences

• TTS offers more flexibility and options for model architectures
• dc_tts focuses on a single architecture (DC-TTS) with simpler implementation
• TTS provides more extensive documentation and community support
• dc_tts is lighter and easier to get started with for beginners

Use Cases

• TTS: Suitable for production environments and research projects requiring various TTS models
• dc_tts: Ideal for learning TTS basics and quick prototyping with DC-TTS architecture

Pros of Tacotron2

• Higher quality speech synthesis with more natural-sounding output
• Better handling of long sentences and complex pronunciations
• Includes a pre-trained model for quick start and experimentation

Cons of Tacotron2

• More computationally intensive, requiring more powerful hardware
• Longer training time compared to DC-TTS
• More complex architecture, potentially harder to modify or fine-tune

Code Comparison

DC-TTS:

def conv1d(inputs, filters, size, rate, padding, activation, training, scope):
    with tf.variable_scope(scope):
        outputs = tf.layers.conv1d(
            inputs,
            filters=filters,
            kernel_size=size,
            dilation_rate=rate,
            padding=padding,
            activation=None)
        outputs = tf.layers.batch_normalization(outputs, training=training)
        outputs = activation(outputs)
    return outputs

Tacotron2:

class ConvNorm(torch.nn.Module):
    def __init__(self, in_channels, out_channels, kernel_size=1, stride=1,
                 padding=None, dilation=1, bias=True, w_init_gain='linear'):
        super(ConvNorm, self).__init__()
        if padding is None:
            assert(kernel_size % 2 == 1)
            padding = int(dilation * (kernel_size - 1) / 2)

        self.conv = torch.nn.Conv1d(in_channels, out_channels,
                                    kernel_size=kernel_size, stride=stride,
                                    padding=padding, dilation=dilation,
                                    bias=bias)

Pros of Real-Time-Voice-Cloning

• Supports real-time voice cloning with as little as 5 seconds of audio
• Utilizes a more advanced architecture (SV2TTS) for improved voice quality
• Offers a user-friendly interface for easy experimentation

Cons of Real-Time-Voice-Cloning

• Requires more computational resources due to its complex architecture
• May have a steeper learning curve for beginners
• Potentially slower inference time compared to dc_tts

Code Comparison

Real-Time-Voice-Cloning:

encoder = SpeakerEncoder("encoder/saved_models/pretrained.pt")
synthesizer = Synthesizer("synthesizer/saved_models/pretrained.pt")
vocoder = WaveRNN("vocoder/saved_models/pretrained.pt")

dc_tts:

def synthesize(text, models, configs, dir_name, base_path):
    # Load models
    model = Text2Mel(configs.model)
    ssrn = SSRN(configs.model)

The Real-Time-Voice-Cloning code shows the initialization of three separate models (encoder, synthesizer, and vocoder), while dc_tts uses two models (Text2Mel and SSRN). This reflects the more complex architecture of Real-Time-Voice-Cloning, which contributes to its advanced capabilities but also increases resource requirements.

Pros of WaveRNN

• Faster synthesis speed and improved audio quality
• More flexible architecture, allowing for various vocoder implementations
• Active development and community support

Cons of WaveRNN

• More complex setup and training process
• Requires more computational resources for training
• May have longer inference times for high-quality output

Code Comparison

WaveRNN:

def forward(self, x, mels):
    bsize = x.size(0)
    h1, h2 = self.wavernn(x, mels)
    return self.fc(h1), self.fc(h2)

dc_tts:

def forward(self, inputs):
    enc_output = self.encoder(inputs)
    mel_output = self.decoder(enc_output)
    return mel_output

WaveRNN focuses on waveform generation using recurrent neural networks, while dc_tts uses a more traditional encoder-decoder architecture for mel-spectrogram synthesis. WaveRNN's code snippet shows the forward pass of the neural network, handling both waveform and mel-spectrogram inputs. In contrast, dc_tts processes text inputs through an encoder and generates mel-spectrograms using a decoder.

Pros of deepvoice3_pytorch

• Supports multi-speaker TTS with speaker embeddings
• Implements multiple vocoder options (WaveNet, Griffin-Lim)
• More extensive documentation and examples

Cons of deepvoice3_pytorch

• More complex architecture, potentially harder to understand and modify
• Longer training time due to additional components
• May require more computational resources

Code Comparison

dc_tts:

def text2mel(text):
    # Text-to-mel-spectrogram conversion
    return mel_spectrogram

def ssrn(mel):
    # Mel-spectrogram-to-waveform conversion
    return waveform

deepvoice3_pytorch:

def text2mel(text, speaker_id):
    # Text-to-mel-spectrogram conversion with speaker embedding
    return mel_spectrogram

def wavenet(mel):
    # Mel-spectrogram-to-waveform conversion using WaveNet
    return waveform

def griffin_lim(mel):
    # Alternative mel-spectrogram-to-waveform conversion
    return waveform

Both repositories implement text-to-speech systems, but deepvoice3_pytorch offers more flexibility with multi-speaker support and multiple vocoder options. However, this comes at the cost of increased complexity and computational requirements. dc_tts provides a simpler architecture that may be easier to understand and modify for specific use cases.

Pros of Tacotron

• More established and widely used in the research community
• Supports both character and phoneme inputs
• Includes pre-trained models for quick experimentation

Cons of Tacotron

• Generally slower training and inference times
• May require more computational resources
• Less focus on real-time applications

Code Comparison

Tacotron

def create_hparams(hparams_string=None, verbose=False):
    hparams = tf.contrib.training.HParams(
        # Comma-separated list of cleaners to run on text prior to training and eval.
        cleaners='english_cleaners',
        # Audio
        num_mels=80,
        num_freq=1025,
        sample_rate=20000,
        frame_length_ms=50,
        frame_shift_ms=12.5,
        # ...
    )

DC_TTS

def get_spectrograms(fpath):
    # Loading sound file
    y, sr = librosa.load(fpath, sr=hp.sr)

    # Trimming
    y, _ = librosa.effects.trim(y)

    # Preemphasis
    y = np.append(y[0], y[1:] - hp.preemphasis * y[:-1])

    # stft
    linear = librosa.stft(y=y,
                          n_fft=hp.n_fft,
                          hop_length=hp.hop_length,
                          win_length=hp.win_length)

The code snippets show different approaches to audio processing and hyperparameter management in the two projects.