util.py

import sys
import asyncio
from io import BytesIO

from fairseq import checkpoint_utils

import torch

import edge_tts
import librosa


# https://github.com/fumiama/Retrieval-based-Voice-Conversion-WebUI/blob/main/config.py#L43-L55  # noqa
def has_mps() -> bool:
    if sys.platform != "darwin":
        return False
    else:
        if not getattr(torch, 'has_mps', False):
            return False

        try:
            torch.zeros(1).to(torch.device("mps"))
            return True
        except Exception:
            return False


# https://github.com/fumiama/Retrieval-based-Voice-Conversion-WebUI/blob/main/config.py#L58-L71  # noqa
def is_half(device: str) -> bool:
    if device == 'cpu':
        return False
    else:
        if has_mps():
            return True

        gpu_name = torch.cuda.get_device_name(int(device.split(':')[-1]))
        if '16' in gpu_name or 'MX' in gpu_name:
            return False

    return True


def load_hubert_model(device: str, model_path: str = 'hubert_base.pt'):
    model = checkpoint_utils.load_model_ensemble_and_task(
        [model_path]
    )[0][0].to(device)

    if is_half(device):
        return model.half()
    else:
        return model.float()


async def call_edge_tts(speaker_name: str, text: str):
    tts_com = edge_tts.Communicate(text, speaker_name)
    tts_raw = b''

    # Stream TTS audio to bytes
    async for chunk in tts_com.stream():
        if chunk['type'] == 'audio':
            tts_raw += chunk['data']

    # Convert mp3 stream to wav
    ffmpeg_proc = await asyncio.create_subprocess_exec(
        'ffmpeg',
        '-f', 'mp3',
        '-i', '-',
        '-f', 'wav',
        '-',
        stdin=asyncio.subprocess.PIPE,
        stdout=asyncio.subprocess.PIPE
    )
    (tts_wav, _) = await ffmpeg_proc.communicate(tts_raw)

    return librosa.load(BytesIO(tts_wav))