Pros of TTS

• More comprehensive and feature-rich, offering a wider range of TTS models and voice synthesis techniques
• Better documentation and community support, making it easier for developers to integrate and customize
• Actively maintained by Mozilla, ensuring regular updates and improvements

Cons of TTS

• Requires more computational resources and setup time compared to Real-Time-Voice-Cloning
• Less focused on real-time voice cloning, which may be a drawback for specific use cases
• Steeper learning curve due to its broader scope and more complex architecture

Code Comparison

Real-Time-Voice-Cloning:

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

TTS:

from TTS.utils.synthesizer import Synthesizer
synthesizer = Synthesizer(
    tts_checkpoint="path/to/tts_model.pth",
    tts_config_path="path/to/tts_config.json",
    vocoder_checkpoint="path/to/vocoder_model.pth",
    vocoder_config="path/to/vocoder_config.json"
)

Pros of fairseq

• Broader scope: Supports a wide range of sequence-to-sequence tasks, including machine translation, text summarization, and speech recognition
• Extensive documentation and examples: Provides comprehensive guides and tutorials for various use cases
• Active development and community support: Regularly updated with new features and improvements

Cons of fairseq

• Steeper learning curve: Requires more in-depth knowledge of NLP and deep learning concepts
• Higher computational requirements: May need more powerful hardware for training and inference
• Less focused on voice cloning: Not specifically designed for real-time voice cloning tasks

Code Comparison

Real-Time-Voice-Cloning:

from encoder.params_model import model_embedding_size as speaker_embedding_size
from utils.argutils import print_args
from synthesizer.inference import Synthesizer
from encoder import inference as encoder
from vocoder import inference as vocoder

fairseq:

from fairseq import checkpoint_utils, options, tasks, utils
from fairseq.data import encoders
from fairseq.token_generation_constraints import pack_constraints, unpack_constraints
from fairseq_cli.generate import get_symbols_to_strip_from_output

Both repositories provide powerful tools for speech and language processing, but they have different focuses. Real-Time-Voice-Cloning is specifically designed for voice cloning tasks, while fairseq offers a more versatile platform for various sequence-to-sequence tasks. The code snippets demonstrate the different import structures and functionalities of each project.

Pros of tacotron2

• Developed by NVIDIA, leveraging their expertise in deep learning and GPU optimization
• Focuses on high-quality speech synthesis with attention to prosody and naturalness
• Provides pre-trained models for immediate use and experimentation

Cons of tacotron2

• Limited to text-to-speech synthesis, not designed for voice cloning
• Requires more computational resources and training time
• Less user-friendly for non-technical users or quick prototyping

Code Comparison

Real-Time-Voice-Cloning:

encoder = VoiceEncoder()
embed = encoder.embed_utterance(wav)
specs = synthesizer.synthesize_spectrograms([text], [embed])
generated_wav = vocoder.infer_waveform(specs[0])

tacotron2:

text = torch.LongTensor(text_to_sequence(text, ['english_cleaners']))[None, :]
mel_outputs, mel_outputs_postnet, _, alignments = model.inference(text)
audio = waveglow.infer(mel_outputs_postnet, sigma=0.666)

Both repositories offer powerful speech synthesis capabilities, but Real-Time-Voice-Cloning is more focused on voice cloning and real-time applications, while tacotron2 emphasizes high-quality text-to-speech synthesis. Real-Time-Voice-Cloning provides a more user-friendly interface for quick voice cloning tasks, while tacotron2 offers a robust foundation for advanced speech synthesis research and development.

Pros of wavenet_vocoder

• Focuses specifically on WaveNet-based vocoder implementation
• Provides a lightweight and modular codebase
• Supports multiple datasets and languages

Cons of wavenet_vocoder

• Limited to vocoder functionality, not a complete voice cloning solution
• Requires more technical expertise to use and integrate
• Less active development and community support

Code Comparison

wavenet_vocoder:

def _assert_tensor_shape(x, shape):
    assert x.shape == shape, (
        "Shape of tensor {} does not match expected shape {}".format(
            x.shape, shape))

Real-Time-Voice-Cloning:

def load_preprocess_wav(fpath):
    wav = librosa.load(fpath, sr=sampling_rate)[0]
    if len(wav.shape) == 2:
        wav = wav.mean(-1)
    return wav

The wavenet_vocoder code focuses on tensor shape validation, while Real-Time-Voice-Cloning includes audio preprocessing functionality. This reflects the different scopes of the projects, with wavenet_vocoder being more focused on the vocoder implementation and Real-Time-Voice-Cloning providing a more comprehensive voice cloning solution.

Pros of dc_tts

• Simpler architecture, potentially easier to understand and implement
• Faster inference time due to its lightweight design
• Focuses specifically on text-to-speech, which may be beneficial for certain use cases

Cons of dc_tts

• Limited to text-to-speech functionality, lacking voice cloning capabilities
• May produce less natural-sounding speech compared to more advanced models
• Requires pre-trained model weights, which might not be as flexible for custom voices

Code Comparison

dc_tts:

def text2mel(text):
    cleaner_names = [x.strip() for x in hparams.cleaners.split(',')]
    seq = text_to_sequence(text, cleaner_names)
    mel = np.zeros((len(seq), hparams.n_mels), dtype=np.float32)
    return mel

Real-Time-Voice-Cloning:

def synthesize_spectrograms(texts, embeddings, return_alignments=False):
    specs = []
    for text, embed in zip(texts, embeddings):
        spec = synthesizer.synthesize_spectrograms([text], [embed])[0]
        specs.append(spec)
    return specs

The code snippets demonstrate the different approaches:

• dc_tts focuses on converting text to mel spectrograms
• Real-Time-Voice-Cloning uses embeddings for voice cloning and synthesis