Pros of TTS

• More comprehensive and actively maintained project
• Supports multiple TTS models and voice conversion techniques
• Offers pre-trained models and easy-to-use inference scripts

Cons of TTS

• More complex setup and configuration process
• Steeper learning curve for beginners
• Requires more computational resources due to its extensive features

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!")

Tacotron:

from synthesizer import Synthesizer

synthesizer = Synthesizer()
synthesizer.load('path/to/model.ckpt')
wav = synthesizer.synthesize('Hello world!')

TTS offers a more modular approach with separate configurations for TTS and vocoder models, while Tacotron provides a simpler interface for quick synthesis. TTS's flexibility comes at the cost of increased complexity, whereas Tacotron's simplicity may limit advanced customization options.

Pros of Tacotron2

• Improved audio quality with WaveNet vocoder integration
• Better handling of long sentences and complex pronunciations
• Faster inference time due to optimizations

Cons of Tacotron2

• More complex architecture, requiring more computational resources
• Potentially harder to train and fine-tune for specific use cases
• May require larger datasets for optimal performance

Code Comparison

Tacotron:

def griffin_lim(spectrogram):
    X_best = copy.deepcopy(spectrogram)
    for i in range(n_iter):
        X_t = invert_spectrogram(X_best)
        est = librosa.stft(X_t, n_fft, hop_length, win_length=win_length)
        phase = est / np.maximum(1e-8, np.abs(est))
        X_best = spectrogram * phase
    X_t = invert_spectrogram(X_best)
    y = np.real(X_t)
    return y

Tacotron2:

def griffin_lim(magnitudes, n_iters=30, n_fft=1024, hop_length=256):
    phase = np.exp(2j * np.pi * np.random.rand(*magnitudes.shape))
    for i in range(n_iters):
        stft = magnitudes * phase
        y = librosa.istft(stft, hop_length=hop_length)
        stft = librosa.stft(y, n_fft=n_fft, hop_length=hop_length)
        phase = np.exp(1j * np.angle(stft))
    return y

Both implementations use the Griffin-Lim algorithm for phase reconstruction, but Tacotron2's version is more concise and potentially more efficient.

Pros of Real-Time-Voice-Cloning

• Offers real-time voice cloning capabilities
• Includes a user-friendly interface for easy interaction
• Supports multi-speaker voice synthesis

Cons of Real-Time-Voice-Cloning

• More complex setup and dependencies
• Requires more computational resources
• May have longer processing times for voice synthesis

Code Comparison

Tacotron (Python):

def griffin_lim(spectrogram):
    X_best = copy.deepcopy(spectrogram)
    for i in range(n_iter):
        X_t = invert_spectrogram(X_best)
        est = librosa.stft(X_t, n_fft, hop_length, win_length=win_length)
        phase = est / np.maximum(1e-8, np.abs(est))
        X_best = spectrogram * phase
    X_t = invert_spectrogram(X_best)
    y = np.real(X_t)
    return y

Real-Time-Voice-Cloning (Python):

def load_model(model_dir: Path):
    json_config = model_dir.joinpath("config.json")
    with json_config.open() as f:
        config = json.load(f)
    model = SpeakerEncoder(config["model"]["hidden_size"], config["model"]["num_layers"])
    checkpoint = torch.load(model_dir.joinpath("model.pt"))
    model.load_state_dict(checkpoint["model_state"])
    return model

Pros of WaveRNN

• Faster inference time due to efficient neural network architecture
• Higher quality audio output, especially for longer sequences
• More flexible and adaptable to different languages and accents

Cons of WaveRNN

• Requires more computational resources for training
• Less documentation and community support compared to Tacotron
• May be more challenging to fine-tune for specific use cases

Code Comparison

WaveRNN:

def forward(self, x, h):
    h = self.I(x) + self.H(h)
    h = F.relu(h)
    return self.O(h)

Tacotron:

def forward(self, inputs, input_lengths, mel_targets=None):
    embedded_inputs = self.embedding(inputs).transpose(1, 2)
    encoder_outputs = self.encoder(embedded_inputs, input_lengths)
    mel_outputs, alignments = self.decoder(encoder_outputs, mel_targets)
    return mel_outputs, alignments

The WaveRNN code snippet shows a simpler forward pass, focusing on efficient waveform generation. In contrast, the Tacotron code demonstrates a more complex encoder-decoder architecture for text-to-speech synthesis. WaveRNN's approach allows for faster inference, while Tacotron's structure provides more control over the generated speech characteristics.

Pros of wavenet_vocoder

• Produces higher quality audio output with more natural-sounding speech
• Offers more flexibility in voice characteristics and speaking styles
• Supports multi-speaker synthesis out of the box

Cons of wavenet_vocoder

• Slower inference time compared to Tacotron
• Requires more computational resources for training and generation
• More complex architecture, potentially harder to fine-tune or modify

Code Comparison

Tacotron (Python):

def griffin_lim(spectrogram):
    X_best = copy.deepcopy(spectrogram)
    for i in range(n_iter):
        X_t = invert_spectrogram(X_best)
        est = librosa.stft(X_t, n_fft, hop_length, win_length=win_length)
        phase = est / np.maximum(1e-8, np.abs(est))
        X_best = spectrogram * phase
    X_t = invert_spectrogram(X_best)
    y = np.real(X_t)
    return y

wavenet_vocoder (Python):

def wavenet(x, h):
    """WaveNet layer"""
    tanh_out = tf.tanh(conv1d(x, self.filter_width, self.residual_channels))
    sigm_out = tf.sigmoid(conv1d(x, self.filter_width, self.residual_channels))
    return tf.multiply(tanh_out, sigm_out)

The code snippets showcase different approaches: Tacotron uses the Griffin-Lim algorithm for spectrogram inversion, while wavenet_vocoder implements the core WaveNet layer for direct waveform generation.

Pros of Tacotron-2

• Implements the full Tacotron 2 architecture, including the WaveNet vocoder
• Supports both phoneme and grapheme inputs
• Includes pre-trained models and detailed documentation

Cons of Tacotron-2

• More complex setup and dependencies
• Requires more computational resources for training and inference
• May have a steeper learning curve for beginners

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',
        # ...
    )

Tacotron-2:

def create_hparams(hparams_string=None, verbose=False):
    hparams = tf.contrib.training.HParams(
        ################################
        # Experiment Parameters        #
        ################################
        epochs=500,
        iters_per_checkpoint=1000,
        # ...
    )

Both repositories provide implementations of Tacotron for text-to-speech synthesis. Tacotron offers a simpler, more accessible implementation, while Tacotron-2 provides a more advanced and complete architecture. The choice between them depends on the user's specific requirements, available resources, and level of expertise in the field.