Pros of WaveRNN

• Lighter and faster than Tacotron2, making it more suitable for real-time applications
• More flexible architecture, allowing for easier customization and experimentation
• Better support for low-resource environments and edge devices

Cons of WaveRNN

• May produce slightly lower quality audio compared to Tacotron2 in some cases
• Less extensive documentation and community support
• Fewer pre-trained models available out-of-the-box

Code Comparison

WaveRNN:

def forward(self, x, mels):
    x = self.I(x)
    mels = self.mel_upsample(mels.transpose(1, 2))
    mels = self.mel_hidden(mels)
    x = self.rnn1(x, mels)
    return self.fc(x)

Tacotron2:

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

The code snippets show that WaveRNN has a simpler forward pass, focusing on upsampling and RNN processing, while Tacotron2 involves more complex encoder-decoder architecture with attention mechanisms.

Pros of TTS

• More comprehensive and flexible, supporting multiple TTS models and vocoders
• Active development with frequent updates and community contributions
• Extensive documentation and tutorials for easier implementation

Cons of TTS

• Potentially more complex setup due to wider range of options
• May require more computational resources for some models

Code Comparison

TTS:

from TTS.api import TTS

tts = TTS(model_name="tts_models/en/ljspeech/tacotron2-DDC")
tts.tts_to_file(text="Hello world!", file_path="output.wav")

Tacotron2:

from tacotron2_model import Tacotron2
from text import text_to_sequence

model = Tacotron2()
text = "Hello world!"
sequence = text_to_sequence(text, ['english_cleaners'])
mel_outputs, mel_outputs_postnet, _, alignments = model.inference(sequence)

Key Differences

• TTS offers a higher-level API for easier integration
• Tacotron2 provides more direct access to model internals
• TTS supports multiple languages and models out-of-the-box
• Tacotron2 focuses specifically on the Tacotron 2 architecture

Both repositories are valuable for text-to-speech tasks, with TTS offering more versatility and Tacotron2 providing a specialized implementation of a specific model.

Pros of Real-Time-Voice-Cloning

• Offers real-time voice cloning capabilities
• Includes a user-friendly interface for easy interaction
• Combines multiple models (encoder, synthesizer, vocoder) for improved performance

Cons of Real-Time-Voice-Cloning

• May require more computational resources due to its real-time nature
• Potentially less optimized for production use compared to Tacotron 2
• Could have a steeper learning curve for beginners due to its complexity

Code Comparison

Tacotron 2:

# Load model
model = load_model('tacotron2_model.pth')
# Generate mel-spectrogram
mel_outputs, mel_outputs_postnet, _, alignments = model.inference(text)

Real-Time-Voice-Cloning:

# Load models
encoder = SpeakerEncoder("encoder.pt")
synthesizer = Synthesizer("synthesizer.pt")
vocoder = WaveRNN("vocoder.pt")
# Generate audio
embed = encoder.embed_utterance(preprocessed_wav)
specs = synthesizer.synthesize_spectrograms([text], [embed])
generated_wav = vocoder.infer_waveform(specs[0])

The code snippets illustrate the different approaches:

• Tacotron 2 focuses on generating mel-spectrograms
• Real-Time-Voice-Cloning involves multiple steps: encoding, synthesis, and vocoding

Both repositories offer powerful text-to-speech capabilities, but Real-Time-Voice-Cloning provides more flexibility for voice cloning at the cost of increased complexity.

Pros of wavenet_vocoder

• More flexible and adaptable to different languages and datasets
• Potentially higher quality audio synthesis in some cases
• Easier to integrate with custom front-end models

Cons of wavenet_vocoder

• Slower inference time compared to Tacotron 2
• May require more computational resources for training and inference
• Less optimized for real-time applications

Code Comparison

wavenet_vocoder:

from wavenet_vocoder import WaveNet

model = WaveNet(
    layers=24,
    stacks=4,
    residual_channels=512,
    gate_channels=512,
    skip_out_channels=256,
    cin_channels=80,
    gin_channels=-1,
    weight_normalization=True,
    n_speakers=None,
    dropout=0.05,
    kernel_size=3
)

Tacotron2:

from tacotron2.model import Tacotron2

model = Tacotron2(
    n_mel_channels=80,
    n_symbols=len(symbols),
    symbols_embedding_dim=512,
    encoder_kernel_size=5,
    encoder_n_convolutions=3,
    encoder_embedding_dim=512,
    attention_rnn_dim=1024,
    attention_dim=128,
    attention_location_n_filters=32,
    attention_location_kernel_size=31
)

The code snippets show the initialization of the main models in each repository. wavenet_vocoder focuses on the WaveNet architecture for audio synthesis, while Tacotron2 implements the full end-to-end text-to-speech model, including the attention mechanism and mel-spectrogram generation.

Pros of Tacotron

• Simpler implementation, easier to understand and modify
• Supports both character and phoneme inputs
• More extensive documentation and examples

Cons of Tacotron

• Generally lower audio quality compared to Tacotron 2
• Slower inference speed
• Less robust to out-of-domain text

Code Comparison

Tacotron (Python):

def decoder_prenet(inputs, is_training, layer_sizes, scope=None):
    x = inputs
    drop_rate = 0.5 if is_training else 0.0
    with tf.variable_scope(scope or 'decoder_prenet'):
        for i, size in enumerate(layer_sizes):
            dense = tf.layers.dense(x, units=size, activation=tf.nn.relu, name='dense_%d' % (i+1))
            x = tf.layers.dropout(dense, rate=drop_rate, training=is_training, name='dropout_%d' % (i+1))
    return x

Tacotron 2 (PyTorch):

class Prenet(nn.Module):
    def __init__(self, in_dim, sizes):
        super(Prenet, self).__init__()
        in_sizes = [in_dim] + sizes[:-1]
        self.layers = nn.ModuleList(
            [LinearNorm(in_size, out_size, bias=False)
             for (in_size, out_size) in zip(in_sizes, sizes)])

    def forward(self, x):
        for linear in self.layers:
            x = F.dropout(F.relu(linear(x)), p=0.5, training=True)
        return x

Pros of Tacotron-2

• Offers more flexibility in model architecture and hyperparameters
• Includes additional features like multi-speaker support and global style tokens
• Provides more detailed documentation and explanations of the implementation

Cons of Tacotron-2

• Less optimized for performance compared to the NVIDIA implementation
• May require more effort to set up and configure due to additional features
• Potentially less stable or reliable as it's a community-maintained project

Code Comparison

Tacotron2:

mel_outputs, mel_outputs_postnet, _, alignments = model(inputs)

Tacotron-2:

mel_outputs, mel_outputs_postnet, alignments, stop_tokens = model(inputs)
global_step = sess.run(global_step)

The Tacotron-2 implementation includes additional outputs like stop tokens and explicitly manages the global step, which can be useful for more advanced training scenarios.

Both repositories implement the Tacotron 2 text-to-speech model, but they differ in their focus and features. The NVIDIA version prioritizes performance and simplicity, while the Rayhane-mamah version offers more flexibility and additional features at the cost of potentially increased complexity and setup time.