Delete api_240604.py

api_240604.py

@@ -1,565 +0,0 @@
-#api for 240604 release version by Xiaokai
-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
-from multiprocessing import Queue, Process, cpu_count, freeze_support
-
-# 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.formant: float = 0.0
-        self.sr_type: str = "sr_model"
-        self.block_time: float = 0.25  # s
-        self.threhold: int = -60
-        self.crossfade_time: float = 0.05
-        self.extra_time: float = 2.5
-        self.I_noise_reduce: bool = False
-        self.O_noise_reduce: bool = False
-        self.use_pv: bool = False
-        self.rms_mix_rate: float = 0.0
-        self.index_rate: float = 0.0
-        self.n_cpu: int = 4
-        self.f0method: str = "fcpe"
-        self.sg_input_device: str = ""
-        self.sg_output_device: str = ""
-
-class ConfigData(BaseModel):
-    pth_path: str
-    index_path: str
-    sg_input_device: str
-    sg_output_device: str
-    threhold: int = -60
-    pitch: int = 0
-    formant: float = 0.0
-    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
-    use_pv: bool = False
-    f0method: str = "fcpe"
-
-class Harvest(Process):
-    def __init__(self, inp_q, opt_q):
-        super(Harvest, self).__init__()
-        self.inp_q = inp_q
-        self.opt_q = opt_q
-
-    def run(self):
-        import numpy as np
-        import pyworld
-        while True:
-            idx, x, res_f0, n_cpu, ts = self.inp_q.get()
-            f0, t = pyworld.harvest(
-                x.astype(np.double),
-                fs=16000,
-                f0_ceil=1100,
-                f0_floor=50,
-                frame_period=10,
-            )
-            res_f0[idx] = f0
-            if len(res_f0.keys()) >= n_cpu:
-                self.opt_q.put(ts)
-
-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
-        self.inp_q = None
-        self.opt_q = None
-        self.n_cpu = min(cpu_count(), 8)
-
-    def initialize_queues(self):
-        self.inp_q = Queue()
-        self.opt_q = Queue()
-        for _ in range(self.n_cpu):
-            p = Harvest(self.inp_q, self.opt_q)
-            p.daemon = True
-            p.start()
-
-    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)
-                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,
-                    "formant": 0.0,
-                    "index_rate": 0,
-                    "rms_mix_rate": 0,
-                    "block_time": 0.25,
-                    "crossfade_length": 0.05,
-                    "extra_time": 2.5,
-                    "n_cpu": 4,
-                    "f0method": "fcpe",
-                    "use_jit": False,
-                    "use_pv": False,
-                }
-                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.formant = values.formant
-        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.use_pv = values.use_pv
-        self.gui_config.f0method = values.f0method
-        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,
-            self.gui_config.n_cpu,
-            self.inp_q,
-            self.opt_q,
-            self.config,
-            self.rvc if self.rvc else None,
-        )
-        self.gui_config.samplerate = (
-            self.rvc.tgt_sr
-            if self.gui_config.sr_type == "sr_model"
-            else self.get_device_samplerate()
-        )
-        self.zc = self.gui_config.samplerate // 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_buffer_frame = min(self.crossfade_frame, 4 * 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_denoise = self.input_wav.clone()
-        self.input_wav_res = torch.zeros(
-            160 * self.input_wav.shape[0] // self.zc,
-            device=self.config.device,
-            dtype=torch.float32,
-        )
-        self.rms_buffer = np.zeros(4 * self.zc, dtype="float32")
-        self.sola_buffer = torch.zeros(
-            self.sola_buffer_frame, device=self.config.device, dtype=torch.float32
-        )
-        self.nr_buffer = self.sola_buffer.clone()
-        self.output_buffer = self.input_wav.clone()
-        self.skip_head = self.extra_frame // self.zc
-        self.return_length = (
-            self.block_frame + self.sola_buffer_frame + self.sola_search_frame
-        ) // self.zc
-        self.fade_in_window = (
-            torch.sin(
-                0.5
-                * np.pi
-                * torch.linspace(
-                    0.0,
-                    1.0,
-                    steps=self.sola_buffer_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)
-        if self.rvc.tgt_sr != self.gui_config.samplerate:
-            self.resampler2 = tat.Resample(
-                orig_freq=self.rvc.tgt_sr,
-                new_freq=self.gui_config.samplerate,
-                dtype=torch.float32,
-            ).to(self.config.device)
-        else:
-            self.resampler2 = None
-        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:
-            indata = np.append(self.rms_buffer, indata)
-            rms = librosa.feature.rms(y=indata, frame_length=4 * self.zc, hop_length=self.zc)[:, 2:]
-            self.rms_buffer[:] = indata[-4 * self.zc :]
-            indata = indata[2 * self.zc - self.zc // 2 :]
-            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
-            indata = indata[self.zc // 2 :]
-        self.input_wav[: -self.block_frame] = self.input_wav[self.block_frame :].clone()
-        self.input_wav[-indata.shape[0] :] = 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()
-        # input noise reduction and resampling
-        if self.gui_config.I_noise_reduce:
-            self.input_wav_denoise[: -self.block_frame] = self.input_wav_denoise[self.block_frame :].clone()
-            input_wav = self.input_wav[-self.sola_buffer_frame - self.block_frame :]
-            input_wav = self.tg(input_wav.unsqueeze(0), self.input_wav.unsqueeze(0)).squeeze(0)
-            input_wav[: self.sola_buffer_frame] *= self.fade_in_window
-            input_wav[: self.sola_buffer_frame] += self.nr_buffer * self.fade_out_window
-            self.input_wav_denoise[-self.block_frame :] = input_wav[: self.block_frame]
-            self.nr_buffer[:] = input_wav[self.block_frame :]
-            self.input_wav_res[-self.block_frame_16k - 160 :] = self.resampler(
-                self.input_wav_denoise[-self.block_frame - 2 * self.zc :]
-            )[160:]
-        else:
-            self.input_wav_res[-160 * (indata.shape[0] // self.zc + 1) :] = (
-                self.resampler(self.input_wav[-indata.shape[0] - 2 * self.zc :])[160:]
-            )
-        # infer
-        if self.function == "vc":
-            infer_wav = self.rvc.infer(
-                self.input_wav_res,
-                self.block_frame_16k,
-                self.skip_head,
-                self.return_length,
-                self.gui_config.f0method,
-            )
-            if self.resampler2 is not None:
-                infer_wav = self.resampler2(infer_wav)
-        elif self.gui_config.I_noise_reduce:
-            infer_wav = self.input_wav_denoise[self.extra_frame :].clone()
-        else:
-            infer_wav = self.input_wav[self.extra_frame :].clone()
-        # output noise reduction
-        if self.gui_config.O_noise_reduce and self.function == "vc":
-            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)
-        # volume envelop mixing
-        if self.gui_config.rms_mix_rate < 1 and self.function == "vc":
-            if self.gui_config.I_noise_reduce:
-                input_wav = self.input_wav_denoise[self.extra_frame :]
-            else:
-                input_wav = self.input_wav[self.extra_frame :]
-            rms1 = librosa.feature.rms(
-                y=input_wav[: infer_wav.shape[0]].cpu().numpy(),
-                frame_length=4 * self.zc,
-                hop_length=self.zc,
-            )
-            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)
-            )
-        # SOLA algorithm from https://github.com/yxlllc/DDSP-SVC
-        conv_input = infer_wav[None, None, : self.sola_buffer_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.sola_buffer_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:]
-        if "privateuseone" in str(self.config.device) or not self.gui_config.use_pv:
-            infer_wav[: self.sola_buffer_frame] *= self.fade_in_window
-            infer_wav[: self.sola_buffer_frame] += self.sola_buffer * self.fade_out_window
-        else:
-            infer_wav[: self.sola_buffer_frame] = phase_vocoder(
-                self.sola_buffer,
-                infer_wav[: self.sola_buffer_frame],
-                self.fade_out_window,
-                self.fade_in_window,
-            )
-        self.sola_buffer[:] = infer_wav[
-            self.block_frame : self.block_frame + self.sola_buffer_frame
-        ]
-        if sys.platform == "darwin":
-            outdata[:] = infer_wav[: self.block_frame].cpu().numpy()[:, np.newaxis]
-        else:
-            outdata[:] = infer_wav[: self.block_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
-            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="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="Failed to stop conversion: {e}")
-
-if __name__ == "__main__":
-    if sys.platform == "win32":
-        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()
-    audio_api.initialize_queues()
-    uvicorn.run(app, host="0.0.0.0", port=6242)