Pros of voice-changer

• Real-time voice conversion capabilities
• Supports multiple voice conversion models (RVC, MMVCv13, MMVCv15, So-VITS-SVC)
• Cross-platform compatibility (Windows, Mac, Linux)

Cons of voice-changer

• Less focus on training custom voice models
• May have higher system requirements for real-time processing
• Potentially more complex setup for beginners

Code Comparison

voice-changer:

def _onnx_inference(self, wave):
    inputs = {self.input_name: wave}
    out = self.onnx_session.run(None, inputs)[0]
    return out

Retrieval-based-Voice-Conversion-WebUI:

def vc_single(
    sid,
    input_audio,
    f0_up_key,
    f0_file,
    f0_method,
    file_index,
    file_index2,
    # ... (additional parameters)
):
    # Function implementation

The code snippets show different approaches:

• voice-changer focuses on ONNX inference for real-time processing
• Retrieval-based-Voice-Conversion-WebUI has a more comprehensive function for voice conversion with various parameters

Both projects aim to provide voice conversion capabilities, but voice-changer emphasizes real-time performance and multiple model support, while Retrieval-based-Voice-Conversion-WebUI offers more customization options and focuses on training custom voice models.

Pros of Real-Time-Voice-Cloning

• Focuses on real-time voice cloning, allowing for immediate results
• Utilizes a pre-trained model, reducing the need for extensive training
• Provides a more straightforward approach for quick voice cloning tasks

Cons of Real-Time-Voice-Cloning

• Less customizable compared to Retrieval-based-Voice-Conversion-WebUI
• May have lower audio quality in some cases due to real-time processing
• Limited to English language support

Code Comparison

Real-Time-Voice-Cloning:

def load_model(weights_fpath):
    model = SpeakerEncoder()
    checkpoint = torch.load(weights_fpath)
    model.load_state_dict(checkpoint["model_state"])
    return model

Retrieval-based-Voice-Conversion-WebUI:

def get_vc(sid, to_return_protect0):
    global n_spk, tgt_sr, net_g, vc, cpt, version
    if sid == "" or sid == []:
        global hubert_model
        if hubert_model is not None:
            print("clean_empty_cache")
            del net_g, n_spk, vc, hubert_model, tgt_sr
            hubert_model = net_g = n_spk = vc = hubert_model = tgt_sr = None
            if torch.cuda.is_available():
                torch.cuda.empty_cache()
            if_f0 = cpt.get("f0", 1)
            version = cpt.get("version", "v1")
            return (
                {"visible": False, "__type__": "update"},
                {"visible": False, "__type__": "update"},
                {"visible": False, "__type__": "update"},
                "clean_empty_cache",
            )

Pros of TTS

• More comprehensive text-to-speech solution with multiple models and languages
• Better documentation and easier integration into existing projects
• Active development with frequent updates and community support

Cons of TTS

• Requires more computational resources for training and inference
• Less focused on voice conversion, primarily a text-to-speech system
• Steeper learning curve for customization and fine-tuning

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")

Retrieval-based-Voice-Conversion-WebUI:

from infer_web import get_vc
from tools.infer_tools import infer_tool

vc = get_vc()
audio = infer_tool.infer(vc, "input.wav", "output.wav")

The code snippets demonstrate that TTS is more straightforward for text-to-speech tasks, while Retrieval-based-Voice-Conversion-WebUI is specifically designed for voice conversion. TTS offers a simpler API for generating speech from text, whereas Retrieval-based-Voice-Conversion-WebUI requires more setup and is tailored for converting one voice to another.

Pros of fairseq

• Broader scope: fairseq is a comprehensive sequence modeling toolkit, supporting various tasks beyond voice conversion
• Extensive documentation and examples: Provides detailed guides and tutorials for different use cases
• Active development and community support: Regular updates and contributions from Facebook AI Research and the open-source community

Cons of fairseq

• Steeper learning curve: Requires more technical expertise to set up and use effectively
• Less specialized for voice conversion: May require additional configuration or fine-tuning for specific voice conversion tasks

Code Comparison

Retrieval-based-Voice-Conversion-WebUI:

import torch
from infer_pack.models import SynthesizerTrnMs256NSFsid, SynthesizerTrnMs256NSFsid_nono
from vc_infer_pipeline import VC

model = SynthesizerTrnMs256NSFsid(*args)
vc = VC(model)
audio = vc.pipeline(input_audio)

fairseq:

from fairseq.models.text_to_speech import TTSHubInterface

model = TTSHubInterface.from_pretrained("tts_transformer_lj")
wav, rate = model.predict("Hello world", voice="ljspeech")

Pros of StyleTTS2

• More advanced text-to-speech capabilities, including style transfer and prosody control
• Potentially higher quality voice synthesis with more natural-sounding results
• Supports multi-speaker and multi-lingual voice conversion

Cons of StyleTTS2

• May require more computational resources due to its advanced features
• Potentially more complex to set up and use compared to Retrieval-based-Voice-Conversion-WebUI
• Less focus on real-time voice conversion, which might be important for some use cases

Code Comparison

StyleTTS2:

style_vector = model.get_style_vector(ref_wav, ref_wav_lengths)
audio = model.infer(text, text_lengths, speakers, style_vector=style_vector)

Retrieval-based-Voice-Conversion-WebUI:

f0_up_key = int(tgt_sr / 16000 * 12)
audio = vc.pipeline(hubert_model, net_g, sid, audio, tgt_sr, f0_up_key)

Both projects offer voice conversion capabilities, but StyleTTS2 focuses more on text-to-speech with style transfer, while Retrieval-based-Voice-Conversion-WebUI emphasizes real-time voice conversion. StyleTTS2 provides more advanced features for controlling voice characteristics, while Retrieval-based-Voice-Conversion-WebUI may be simpler to use for basic voice conversion tasks.