Update API code for release version
API code for 240604 version and 231006 version
This commit is contained in:
Freidrichin
parent
c44a81b39e
commit
ce66d5b40e
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#api for 231006 release version by Xiaokai
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import os
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import sys
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import json
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565
api_240604.py
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565
api_240604.py
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#api for 240604 release version by Xiaokai
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import os
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import sys
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import json
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import re
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import time
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import librosa
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import torch
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import numpy as np
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import torch.nn.functional as F
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import torchaudio.transforms as tat
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import sounddevice as sd
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from dotenv import load_dotenv
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from fastapi import FastAPI, HTTPException
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from pydantic import BaseModel
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import threading
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import uvicorn
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import logging
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from multiprocessing import Queue, Process, cpu_count, freeze_support
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# Initialize the logger
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logging.basicConfig(level=logging.INFO)
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logger = logging.getLogger(__name__)
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# Define FastAPI app
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app = FastAPI()
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class GUIConfig:
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def __init__(self) -> None:
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self.pth_path: str = ""
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self.index_path: str = ""
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self.pitch: int = 0
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self.formant: float = 0.0
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self.sr_type: str = "sr_model"
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self.block_time: float = 0.25 # s
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self.threhold: int = -60
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self.crossfade_time: float = 0.05
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self.extra_time: float = 2.5
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self.I_noise_reduce: bool = False
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self.O_noise_reduce: bool = False
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self.use_pv: bool = False
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self.rms_mix_rate: float = 0.0
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self.index_rate: float = 0.0
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self.n_cpu: int = 4
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self.f0method: str = "fcpe"
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self.sg_input_device: str = ""
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self.sg_output_device: str = ""
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class ConfigData(BaseModel):
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pth_path: str
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index_path: str
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sg_input_device: str
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sg_output_device: str
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threhold: int = -60
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pitch: int = 0
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formant: float = 0.0
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index_rate: float = 0.3
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rms_mix_rate: float = 0.0
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block_time: float = 0.25
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crossfade_length: float = 0.05
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extra_time: float = 2.5
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n_cpu: int = 4
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I_noise_reduce: bool = False
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O_noise_reduce: bool = False
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use_pv: bool = False
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f0method: str = "fcpe"
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class Harvest(Process):
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def __init__(self, inp_q, opt_q):
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super(Harvest, self).__init__()
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self.inp_q = inp_q
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self.opt_q = opt_q
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def run(self):
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import numpy as np
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import pyworld
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while True:
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idx, x, res_f0, n_cpu, ts = self.inp_q.get()
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f0, t = pyworld.harvest(
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x.astype(np.double),
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fs=16000,
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f0_ceil=1100,
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f0_floor=50,
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frame_period=10,
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)
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res_f0[idx] = f0
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if len(res_f0.keys()) >= n_cpu:
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self.opt_q.put(ts)
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class AudioAPI:
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def __init__(self) -> None:
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self.gui_config = GUIConfig()
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self.config = None # Initialize Config object as None
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self.flag_vc = False
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self.function = "vc"
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self.delay_time = 0
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self.rvc = None # Initialize RVC object as None
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self.inp_q = None
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self.opt_q = None
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self.n_cpu = min(cpu_count(), 8)
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def initialize_queues(self):
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self.inp_q = Queue()
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self.opt_q = Queue()
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for _ in range(self.n_cpu):
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p = Harvest(self.inp_q, self.opt_q)
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p.daemon = True
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p.start()
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def load(self):
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input_devices, output_devices, _, _ = self.get_devices()
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try:
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with open("configs/config.json", "r", encoding='utf-8') as j:
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data = json.load(j)
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if data["sg_input_device"] not in input_devices:
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data["sg_input_device"] = input_devices[sd.default.device[0]]
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if data["sg_output_device"] not in output_devices:
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data["sg_output_device"] = output_devices[sd.default.device[1]]
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except Exception as e:
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logger.error(f"Failed to load configuration: {e}")
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with open("configs/config.json", "w", encoding='utf-8') as j:
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data = {
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"pth_path": "",
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"index_path": "",
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"sg_input_device": input_devices[sd.default.device[0]],
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"sg_output_device": output_devices[sd.default.device[1]],
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"threhold": -60,
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"pitch": 0,
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"formant": 0.0,
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"index_rate": 0,
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"rms_mix_rate": 0,
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"block_time": 0.25,
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"crossfade_length": 0.05,
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"extra_time": 2.5,
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"n_cpu": 4,
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"f0method": "fcpe",
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"use_jit": False,
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"use_pv": False,
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}
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json.dump(data, j, ensure_ascii=False)
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return data
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def set_values(self, values):
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logger.info(f"Setting values: {values}")
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if not values.pth_path.strip():
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raise HTTPException(status_code=400, detail="Please select a .pth file")
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if not values.index_path.strip():
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raise HTTPException(status_code=400, detail="Please select an index file")
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self.set_devices(values.sg_input_device, values.sg_output_device)
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self.config.use_jit = False
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self.gui_config.pth_path = values.pth_path
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self.gui_config.index_path = values.index_path
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self.gui_config.threhold = values.threhold
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self.gui_config.pitch = values.pitch
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self.gui_config.formant = values.formant
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self.gui_config.block_time = values.block_time
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self.gui_config.crossfade_time = values.crossfade_length
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self.gui_config.extra_time = values.extra_time
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self.gui_config.I_noise_reduce = values.I_noise_reduce
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self.gui_config.O_noise_reduce = values.O_noise_reduce
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self.gui_config.rms_mix_rate = values.rms_mix_rate
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self.gui_config.index_rate = values.index_rate
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self.gui_config.n_cpu = values.n_cpu
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self.gui_config.use_pv = values.use_pv
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self.gui_config.f0method = values.f0method
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return True
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def start_vc(self):
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torch.cuda.empty_cache()
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self.flag_vc = True
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self.rvc = rvc_for_realtime.RVC(
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self.gui_config.pitch,
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self.gui_config.pth_path,
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self.gui_config.index_path,
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self.gui_config.index_rate,
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self.gui_config.n_cpu,
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self.inp_q,
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self.opt_q,
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self.config,
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self.rvc if self.rvc else None,
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)
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self.gui_config.samplerate = (
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self.rvc.tgt_sr
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if self.gui_config.sr_type == "sr_model"
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else self.get_device_samplerate()
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)
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self.zc = self.gui_config.samplerate // 100
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self.block_frame = (
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int(
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np.round(
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self.gui_config.block_time
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* self.gui_config.samplerate
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/ self.zc
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)
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)
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* self.zc
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)
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self.block_frame_16k = 160 * self.block_frame // self.zc
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self.crossfade_frame = (
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int(
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np.round(
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self.gui_config.crossfade_time
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* self.gui_config.samplerate
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/ self.zc
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)
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)
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* self.zc
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)
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self.sola_buffer_frame = min(self.crossfade_frame, 4 * self.zc)
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self.sola_search_frame = self.zc
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self.extra_frame = (
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int(
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np.round(
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self.gui_config.extra_time
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* self.gui_config.samplerate
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/ self.zc
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)
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)
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* self.zc
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)
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self.input_wav = torch.zeros(
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self.extra_frame
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+ self.crossfade_frame
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+ self.sola_search_frame
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+ self.block_frame,
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device=self.config.device,
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dtype=torch.float32,
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)
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self.input_wav_denoise = self.input_wav.clone()
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self.input_wav_res = torch.zeros(
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160 * self.input_wav.shape[0] // self.zc,
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device=self.config.device,
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dtype=torch.float32,
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)
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self.rms_buffer = np.zeros(4 * self.zc, dtype="float32")
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self.sola_buffer = torch.zeros(
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self.sola_buffer_frame, device=self.config.device, dtype=torch.float32
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)
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self.nr_buffer = self.sola_buffer.clone()
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self.output_buffer = self.input_wav.clone()
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self.skip_head = self.extra_frame // self.zc
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self.return_length = (
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self.block_frame + self.sola_buffer_frame + self.sola_search_frame
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) // self.zc
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self.fade_in_window = (
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torch.sin(
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0.5
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* np.pi
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* torch.linspace(
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0.0,
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1.0,
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steps=self.sola_buffer_frame,
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device=self.config.device,
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dtype=torch.float32,
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)
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)
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** 2
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)
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self.fade_out_window = 1 - self.fade_in_window
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self.resampler = tat.Resample(
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orig_freq=self.gui_config.samplerate,
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new_freq=16000,
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dtype=torch.float32,
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).to(self.config.device)
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if self.rvc.tgt_sr != self.gui_config.samplerate:
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self.resampler2 = tat.Resample(
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orig_freq=self.rvc.tgt_sr,
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new_freq=self.gui_config.samplerate,
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dtype=torch.float32,
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).to(self.config.device)
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else:
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self.resampler2 = None
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self.tg = TorchGate(
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sr=self.gui_config.samplerate, n_fft=4 * self.zc, prop_decrease=0.9
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).to(self.config.device)
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thread_vc = threading.Thread(target=self.soundinput)
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thread_vc.start()
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def soundinput(self):
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channels = 1 if sys.platform == "darwin" else 2
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with sd.Stream(
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channels=channels,
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callback=self.audio_callback,
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blocksize=self.block_frame,
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samplerate=self.gui_config.samplerate,
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dtype="float32",
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) as stream:
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global stream_latency
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stream_latency = stream.latency[-1]
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while self.flag_vc:
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time.sleep(self.gui_config.block_time)
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logger.info("Audio block passed.")
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logger.info("Ending VC")
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def audio_callback(self, indata: np.ndarray, outdata: np.ndarray, frames, times, status):
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start_time = time.perf_counter()
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indata = librosa.to_mono(indata.T)
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if self.gui_config.threhold > -60:
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indata = np.append(self.rms_buffer, indata)
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rms = librosa.feature.rms(y=indata, frame_length=4 * self.zc, hop_length=self.zc)[:, 2:]
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self.rms_buffer[:] = indata[-4 * self.zc :]
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indata = indata[2 * self.zc - self.zc // 2 :]
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db_threhold = (
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librosa.amplitude_to_db(rms, ref=1.0)[0] < self.gui_config.threhold
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)
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for i in range(db_threhold.shape[0]):
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if db_threhold[i]:
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indata[i * self.zc : (i + 1) * self.zc] = 0
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indata = indata[self.zc // 2 :]
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self.input_wav[: -self.block_frame] = self.input_wav[self.block_frame :].clone()
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self.input_wav[-indata.shape[0] :] = torch.from_numpy(indata).to(self.config.device)
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self.input_wav_res[: -self.block_frame_16k] = self.input_wav_res[self.block_frame_16k :].clone()
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# input noise reduction and resampling
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if self.gui_config.I_noise_reduce:
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self.input_wav_denoise[: -self.block_frame] = self.input_wav_denoise[self.block_frame :].clone()
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input_wav = self.input_wav[-self.sola_buffer_frame - self.block_frame :]
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input_wav = self.tg(input_wav.unsqueeze(0), self.input_wav.unsqueeze(0)).squeeze(0)
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input_wav[: self.sola_buffer_frame] *= self.fade_in_window
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input_wav[: self.sola_buffer_frame] += self.nr_buffer * self.fade_out_window
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self.input_wav_denoise[-self.block_frame :] = input_wav[: self.block_frame]
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self.nr_buffer[:] = input_wav[self.block_frame :]
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self.input_wav_res[-self.block_frame_16k - 160 :] = self.resampler(
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self.input_wav_denoise[-self.block_frame - 2 * self.zc :]
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)[160:]
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else:
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self.input_wav_res[-160 * (indata.shape[0] // self.zc + 1) :] = (
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self.resampler(self.input_wav[-indata.shape[0] - 2 * self.zc :])[160:]
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)
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# infer
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if self.function == "vc":
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infer_wav = self.rvc.infer(
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self.input_wav_res,
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self.block_frame_16k,
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self.skip_head,
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self.return_length,
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self.gui_config.f0method,
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)
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if self.resampler2 is not None:
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infer_wav = self.resampler2(infer_wav)
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elif self.gui_config.I_noise_reduce:
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infer_wav = self.input_wav_denoise[self.extra_frame :].clone()
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else:
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infer_wav = self.input_wav[self.extra_frame :].clone()
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# output noise reduction
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if self.gui_config.O_noise_reduce and self.function == "vc":
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self.output_buffer[: -self.block_frame] = self.output_buffer[self.block_frame :].clone()
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self.output_buffer[-self.block_frame :] = infer_wav[-self.block_frame :]
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infer_wav = self.tg(infer_wav.unsqueeze(0), self.output_buffer.unsqueeze(0)).squeeze(0)
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# volume envelop mixing
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if self.gui_config.rms_mix_rate < 1 and self.function == "vc":
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if self.gui_config.I_noise_reduce:
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input_wav = self.input_wav_denoise[self.extra_frame :]
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else:
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input_wav = self.input_wav[self.extra_frame :]
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rms1 = librosa.feature.rms(
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y=input_wav[: infer_wav.shape[0]].cpu().numpy(),
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frame_length=4 * self.zc,
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hop_length=self.zc,
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)
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rms1 = torch.from_numpy(rms1).to(self.config.device)
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rms1 = F.interpolate(
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rms1.unsqueeze(0),
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size=infer_wav.shape[0] + 1,
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mode="linear",
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align_corners=True,
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)[0, 0, :-1]
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rms2 = librosa.feature.rms(
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y=infer_wav[:].cpu().numpy(),
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frame_length=4 * self.zc,
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hop_length=self.zc,
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)
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rms2 = torch.from_numpy(rms2).to(self.config.device)
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rms2 = F.interpolate(
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rms2.unsqueeze(0),
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size=infer_wav.shape[0] + 1,
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mode="linear",
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align_corners=True,
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)[0, 0, :-1]
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rms2 = torch.max(rms2, torch.zeros_like(rms2) + 1e-3)
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infer_wav *= torch.pow(
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rms1 / rms2, torch.tensor(1 - self.gui_config.rms_mix_rate)
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)
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# SOLA algorithm from https://github.com/yxlllc/DDSP-SVC
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conv_input = infer_wav[None, None, : self.sola_buffer_frame + self.sola_search_frame]
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cor_nom = F.conv1d(conv_input, self.sola_buffer[None, None, :])
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cor_den = torch.sqrt(
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F.conv1d(
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conv_input**2,
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torch.ones(1, 1, self.sola_buffer_frame, device=self.config.device),
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)
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+ 1e-8
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)
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if sys.platform == "darwin":
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_, sola_offset = torch.max(cor_nom[0, 0] / cor_den[0, 0])
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sola_offset = sola_offset.item()
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else:
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sola_offset = torch.argmax(cor_nom[0, 0] / cor_den[0, 0])
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logger.info(f"sola_offset = {sola_offset}")
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infer_wav = infer_wav[sola_offset:]
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if "privateuseone" in str(self.config.device) or not self.gui_config.use_pv:
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infer_wav[: self.sola_buffer_frame] *= self.fade_in_window
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infer_wav[: self.sola_buffer_frame] += self.sola_buffer * self.fade_out_window
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else:
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infer_wav[: self.sola_buffer_frame] = phase_vocoder(
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self.sola_buffer,
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infer_wav[: self.sola_buffer_frame],
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self.fade_out_window,
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self.fade_in_window,
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)
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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)
|
||||
Loading…
Reference in New Issue
Block a user