import os import sys import json import re import time import librosa import torch import numpy as np import torch.nn.functional as F import torchaudio.transforms as tat import sounddevice as sd from dotenv import load_dotenv from fastapi import FastAPI, HTTPException from pydantic import BaseModel import threading import uvicorn import logging # Initialize the logger logging.basicConfig(level=logging.INFO) logger = logging.getLogger(__name__) # Define FastAPI app app = FastAPI() class GUIConfig: def __init__(self) -> None: self.pth_path: str = "" self.index_path: str = "" self.pitch: int = 0 self.samplerate: int = 40000 self.block_time: float = 1.0 # s self.buffer_num: int = 1 self.threhold: int = -60 self.crossfade_time: float = 0.05 self.extra_time: float = 2.5 self.I_noise_reduce = False self.O_noise_reduce = False self.rms_mix_rate = 0.0 self.index_rate = 0.3 self.f0method = "rmvpe" self.sg_input_device = "" self.sg_output_device = "" class ConfigData(BaseModel): pth_path: str index_path: str sg_input_device: str sg_output_device: str threhold: int = -60 pitch: int = 0 index_rate: float = 0.3 rms_mix_rate: float = 0.0 block_time: float = 0.25 crossfade_length: float = 0.05 extra_time: float = 2.5 n_cpu: int = 4 I_noise_reduce: bool = False O_noise_reduce: bool = False class AudioAPI: def __init__(self) -> None: self.gui_config = GUIConfig() self.config = None # Initialize Config object as None self.flag_vc = False self.function = "vc" self.delay_time = 0 self.rvc = None # Initialize RVC object as None def load(self): input_devices, output_devices, _, _ = self.get_devices() try: with open("configs/config.json", "r", encoding='utf-8') as j: data = json.load(j) data["rmvpe"] = True # Ensure rmvpe is the only f0method if data["sg_input_device"] not in input_devices: data["sg_input_device"] = input_devices[sd.default.device[0]] if data["sg_output_device"] not in output_devices: data["sg_output_device"] = output_devices[sd.default.device[1]] except Exception as e: logger.error(f"Failed to load configuration: {e}") with open("configs/config.json", "w", encoding='utf-8') as j: data = { "pth_path": " ", "index_path": " ", "sg_input_device": input_devices[sd.default.device[0]], "sg_output_device": output_devices[sd.default.device[1]], "threhold": "-60", "pitch": "0", "index_rate": "0", "rms_mix_rate": "0", "block_time": "0.25", "crossfade_length": "0.05", "extra_time": "2.5", "f0method": "rmvpe", "use_jit": False, } data["rmvpe"] = True # Ensure rmvpe is the only f0method json.dump(data, j, ensure_ascii=False) return data def set_values(self, values): logger.info(f"Setting values: {values}") if not values.pth_path.strip(): raise HTTPException(status_code=400, detail="Please select a .pth file") if not values.index_path.strip(): raise HTTPException(status_code=400, detail="Please select an index file") self.set_devices(values.sg_input_device, values.sg_output_device) self.config.use_jit = False self.gui_config.pth_path = values.pth_path self.gui_config.index_path = values.index_path self.gui_config.threhold = values.threhold self.gui_config.pitch = values.pitch self.gui_config.block_time = values.block_time self.gui_config.crossfade_time = values.crossfade_length self.gui_config.extra_time = values.extra_time self.gui_config.I_noise_reduce = values.I_noise_reduce self.gui_config.O_noise_reduce = values.O_noise_reduce self.gui_config.rms_mix_rate = values.rms_mix_rate self.gui_config.index_rate = values.index_rate self.gui_config.n_cpu = values.n_cpu self.gui_config.f0method = "rmvpe" return True def start_vc(self): torch.cuda.empty_cache() self.flag_vc = True self.rvc = rvc_for_realtime.RVC( self.gui_config.pitch, self.gui_config.pth_path, self.gui_config.index_path, self.gui_config.index_rate, 0, 0, 0, self.config, self.rvc if self.rvc else None, ) self.gui_config.samplerate = self.rvc.tgt_sr self.zc = self.rvc.tgt_sr // 100 self.block_frame = ( int( np.round( self.gui_config.block_time * self.gui_config.samplerate / self.zc ) ) * self.zc ) self.block_frame_16k = 160 * self.block_frame // self.zc self.crossfade_frame = ( int( np.round( self.gui_config.crossfade_time * self.gui_config.samplerate / self.zc ) ) * self.zc ) self.sola_search_frame = self.zc self.extra_frame = ( int( np.round( self.gui_config.extra_time * self.gui_config.samplerate / self.zc ) ) * self.zc ) self.input_wav = torch.zeros( self.extra_frame + self.crossfade_frame + self.sola_search_frame + self.block_frame, device=self.config.device, dtype=torch.float32, ) self.input_wav_res = torch.zeros( 160 * self.input_wav.shape[0] // self.zc, device=self.config.device, dtype=torch.float32, ) self.pitch = np.zeros(self.input_wav.shape[0] // self.zc, dtype="int32") self.pitchf = np.zeros(self.input_wav.shape[0] // self.zc, dtype="float64") self.sola_buffer = torch.zeros(self.crossfade_frame, device=self.config.device, dtype=torch.float32) self.nr_buffer = self.sola_buffer.clone() self.output_buffer = self.input_wav.clone() self.res_buffer = torch.zeros(2 * self.zc, device=self.config.device, dtype=torch.float32) self.valid_rate = 1 - (self.extra_frame - 1) / self.input_wav.shape[0] self.fade_in_window = ( torch.sin(0.5 * np.pi * torch.linspace(0.0, 1.0, steps=self.crossfade_frame, device=self.config.device, dtype=torch.float32)) ** 2 ) self.fade_out_window = 1 - self.fade_in_window self.resampler = tat.Resample( orig_freq=self.gui_config.samplerate, new_freq=16000, dtype=torch.float32, ).to(self.config.device) self.tg = TorchGate( sr=self.gui_config.samplerate, n_fft=4 * self.zc, prop_decrease=0.9 ).to(self.config.device) thread_vc = threading.Thread(target=self.soundinput) thread_vc.start() def soundinput(self): channels = 1 if sys.platform == "darwin" else 2 with sd.Stream( channels=channels, callback=self.audio_callback, blocksize=self.block_frame, samplerate=self.gui_config.samplerate, dtype="float32", ) as stream: global stream_latency stream_latency = stream.latency[-1] while self.flag_vc: time.sleep(self.gui_config.block_time) logger.info("Audio block passed.") logger.info("Ending VC") def audio_callback(self, indata: np.ndarray, outdata: np.ndarray, frames, times, status): start_time = time.perf_counter() indata = librosa.to_mono(indata.T) if self.gui_config.threhold > -60: rms = librosa.feature.rms(y=indata, frame_length=4 * self.zc, hop_length=self.zc) db_threhold = (librosa.amplitude_to_db(rms, ref=1.0)[0] < self.gui_config.threhold) for i in range(db_threhold.shape[0]): if db_threhold[i]: indata[i * self.zc : (i + 1) * self.zc] = 0 self.input_wav[: -self.block_frame] = self.input_wav[self.block_frame :].clone() self.input_wav[-self.block_frame :] = torch.from_numpy(indata).to(self.config.device) self.input_wav_res[: -self.block_frame_16k] = self.input_wav_res[self.block_frame_16k :].clone() if self.gui_config.I_noise_reduce and self.function == "vc": input_wav = self.input_wav[-self.crossfade_frame - self.block_frame - 2 * self.zc :] input_wav = self.tg(input_wav.unsqueeze(0), self.input_wav.unsqueeze(0))[0, 2 * self.zc :] input_wav[: self.crossfade_frame] *= self.fade_in_window input_wav[: self.crossfade_frame] += self.nr_buffer * self.fade_out_window self.nr_buffer[:] = input_wav[-self.crossfade_frame :] input_wav = torch.cat((self.res_buffer[:], input_wav[: self.block_frame])) self.res_buffer[:] = input_wav[-2 * self.zc :] self.input_wav_res[-self.block_frame_16k - 160 :] = self.resampler(input_wav)[160:] else: self.input_wav_res[-self.block_frame_16k - 160 :] = self.resampler(self.input_wav[-self.block_frame - 2 * self.zc :])[160:] if self.function == "vc": f0_extractor_frame = self.block_frame_16k + 800 if self.gui_config.f0method == "rmvpe": f0_extractor_frame = (5120 * ((f0_extractor_frame - 1) // 5120 + 1) - 160) infer_wav = self.rvc.infer( self.input_wav_res, self.input_wav_res[-f0_extractor_frame:].cpu().numpy(), self.block_frame_16k, self.valid_rate, self.pitch, self.pitchf, self.gui_config.f0method, ) infer_wav = infer_wav[-self.crossfade_frame - self.sola_search_frame - self.block_frame :] else: infer_wav = self.input_wav[-self.crossfade_frame - self.sola_search_frame - self.block_frame :].clone() if (self.gui_config.O_noise_reduce and self.function == "vc") or (self.gui_config.I_noise_reduce and self.function == "im"): self.output_buffer[: -self.block_frame] = self.output_buffer[self.block_frame :].clone() self.output_buffer[-self.block_frame :] = infer_wav[-self.block_frame :] infer_wav = self.tg(infer_wav.unsqueeze(0), self.output_buffer.unsqueeze(0)).squeeze(0) if self.gui_config.rms_mix_rate < 1 and self.function == "vc": rms1 = librosa.feature.rms(y=self.input_wav_res[-160 * infer_wav.shape[0] // self.zc :].cpu().numpy(), frame_length=640, hop_length=160) rms1 = torch.from_numpy(rms1).to(self.config.device) rms1 = F.interpolate(rms1.unsqueeze(0), size=infer_wav.shape[0] + 1, mode="linear", align_corners=True)[0, 0, :-1] rms2 = librosa.feature.rms(y=infer_wav[:].cpu().numpy(), frame_length=4 * self.zc, hop_length=self.zc) rms2 = torch.from_numpy(rms2).to(self.config.device) rms2 = F.interpolate(rms2.unsqueeze(0), size=infer_wav.shape[0] + 1, mode="linear", align_corners=True)[0, 0, :-1] rms2 = torch.max(rms2, torch.zeros_like(rms2) + 1e-3) infer_wav *= torch.pow(rms1 / rms2, torch.tensor(1 - self.gui_config.rms_mix_rate)) conv_input = infer_wav[None, None, : self.crossfade_frame + self.sola_search_frame] cor_nom = F.conv1d(conv_input, self.sola_buffer[None, None, :]) cor_den = torch.sqrt(F.conv1d(conv_input**2, torch.ones(1, 1, self.crossfade_frame, device=self.config.device)) + 1e-8) if sys.platform == "darwin": _, sola_offset = torch.max(cor_nom[0, 0] / cor_den[0, 0]) sola_offset = sola_offset.item() else: sola_offset = torch.argmax(cor_nom[0, 0] / cor_den[0, 0]) logger.info(f"sola_offset = {sola_offset}") infer_wav = infer_wav[sola_offset : sola_offset + self.block_frame + self.crossfade_frame] infer_wav[: self.crossfade_frame] *= self.fade_in_window infer_wav[: self.crossfade_frame] += self.sola_buffer * self.fade_out_window self.sola_buffer[:] = infer_wav[-self.crossfade_frame :] if sys.platform == "darwin": outdata[:] = infer_wav[: -self.crossfade_frame].cpu().numpy()[:, np.newaxis] else: outdata[:] = infer_wav[: -self.crossfade_frame].repeat(2, 1).t().cpu().numpy() total_time = time.perf_counter() - start_time logger.info(f"Infer time: {total_time:.2f}") def get_devices(self, update: bool = True): if update: sd._terminate() sd._initialize() devices = sd.query_devices() hostapis = sd.query_hostapis() for hostapi in hostapis: for device_idx in hostapi["devices"]: devices[device_idx]["hostapi_name"] = hostapi["name"] input_devices = [ f"{d['name']} ({d['hostapi_name']})" for d in devices if d["max_input_channels"] > 0 ] output_devices = [ f"{d['name']} ({d['hostapi_name']})" for d in devices if d["max_output_channels"] > 0 ] input_devices_indices = [ d["index"] if "index" in d else d["name"] for d in devices if d["max_input_channels"] > 0 ] output_devices_indices = [ d["index"] if "index" in d else d["name"] for d in devices if d["max_output_channels"] > 0 ] return ( input_devices, output_devices, input_devices_indices, output_devices_indices, ) def set_devices(self, input_device, output_device): ( input_devices, output_devices, input_device_indices, output_device_indices, ) = self.get_devices() logger.debug(f"Available input devices: {input_devices}") logger.debug(f"Available output devices: {output_devices}") logger.debug(f"Selected input device: {input_device}") logger.debug(f"Selected output device: {output_device}") if input_device not in input_devices: logger.error(f"Input device '{input_device}' is not in the list of available devices") raise HTTPException(status_code=400, detail=f"Input device '{input_device}' is not available") if output_device not in output_devices: logger.error(f"Output device '{output_device}' is not in the list of available devices") raise HTTPException(status_code=400, detail=f"Output device '{output_device}' is not available") sd.default.device[0] = input_device_indices[input_devices.index(input_device)] sd.default.device[1] = output_device_indices[output_devices.index(output_device)] logger.info(f"Input device set to {sd.default.device[0]}: {input_device}") logger.info(f"Output device set to {sd.default.device[1]}: {output_device}") audio_api = AudioAPI() @app.get("/inputDevices", response_model=list) def get_input_devices(): try: input_devices, _, _, _ = audio_api.get_devices() return input_devices except Exception as e: logger.error(f"Failed to get input devices: {e}") raise HTTPException(status_code=500, detail="Failed to get input devices") @app.get("/outputDevices", response_model=list) def get_output_devices(): try: _, output_devices, _, _ = audio_api.get_devices() return output_devices except Exception as e: logger.error(f"Failed to get output devices: {e}") raise HTTPException(status_code=500, detail="Failed to get output devices") @app.post("/config") def configure_audio(config_data: ConfigData): try: logger.info(f"Configuring audio with data: {config_data}") if audio_api.set_values(config_data): settings = config_data.dict() settings["use_jit"] = False settings["f0method"] = "rmvpe" with open("configs/config.json", "w", encoding='utf-8') as j: json.dump(settings, j, ensure_ascii=False) logger.info("Configuration set successfully") return {"message": "Configuration set successfully"} except HTTPException as e: logger.error(f"Configuration error: {e.detail}") raise except Exception as e: logger.error(f"Configuration failed: {e}") raise HTTPException(status_code=400, detail=f"Configuration failed: {e}") @app.post("/start") def start_conversion(): try: if not audio_api.flag_vc: audio_api.start_vc() return {"message": "Audio conversion started"} else: logger.warning("Audio conversion already running") raise HTTPException(status_code=400, detail="Audio conversion already running") except HTTPException as e: logger.error(f"Start conversion error: {e.detail}") raise except Exception as e: logger.error(f"Failed to start conversion: {e}") raise HTTPException(status_code=500, detail=f"Failed to start conversion: {e}") @app.post("/stop") def stop_conversion(): try: if audio_api.flag_vc: audio_api.flag_vc = False global stream_latency stream_latency = -1 return {"message": "Audio conversion stopped"} else: logger.warning("Audio conversion not running") raise HTTPException(status_code=400, detail="Audio conversion not running") except HTTPException as e: logger.error(f"Stop conversion error: {e.detail}") raise except Exception as e: logger.error(f"Failed to stop conversion: {e}") raise HTTPException(status_code=500, detail=f"Failed to stop conversion: {e}") if __name__ == "__main__": if sys.platform == "win32": from multiprocessing import freeze_support freeze_support() load_dotenv() os.environ["OMP_NUM_THREADS"] = "4" if sys.platform == "darwin": os.environ["PYTORCH_ENABLE_MPS_FALLBACK"] = "1" from tools.torchgate import TorchGate import tools.rvc_for_realtime as rvc_for_realtime from configs.config import Config audio_api.config = Config() uvicorn.run(app, host="0.0.0.0", port=6242)