mirror of
https://github.com/w-okada/voice-changer.git
synced 2025-01-23 21:45:00 +03:00
WIP: refactor, separate io analyzer, io recorder from main class
This commit is contained in:
wataru
parent
18a87d9d24
commit
f76fff5959
40
server/voice_changer/IOAnalyzer.py
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40
server/voice_changer/IOAnalyzer.py
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@ -0,0 +1,40 @@
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import os
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import numpy as np
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import pylab
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import librosa
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import librosa.display
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import pyworld as pw
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class IOAnalyzer:
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def _get_f0_dio(self, y, sr):
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_f0, time = pw.dio(y, sr, frame_period=5)
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f0 = pw.stonemask(y, _f0, time, sr)
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time = np.linspace(0, y.shape[0] / sr, len(time))
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return f0, time
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def _get_f0_harvest(self, y, sr):
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_f0, time = pw.harvest(y, sr, frame_period=5)
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f0 = pw.stonemask(y, _f0, time, sr)
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time = np.linspace(0, y.shape[0] / sr, len(time))
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return f0, time
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def analyze(self, inputDataFile: str, dioImageFile: str, harvestImageFile: str, samplingRate: int):
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y, sr = librosa.load(inputDataFile, samplingRate)
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y = y.astype(np.float64)
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spec = librosa.amplitude_to_db(np.abs(librosa.stft(y, n_fft=2048, win_length=2048, hop_length=128)), ref=np.max)
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f0_dio, times = self._get_f0_dio(y, sr=samplingRate)
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f0_harvest, times = self._get_f0_harvest(y, sr=samplingRate)
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pylab.close()
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HOP_LENGTH = 128
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img = librosa.display.specshow(spec, sr=samplingRate, hop_length=HOP_LENGTH, x_axis='time', y_axis='log', )
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pylab.plot(times, f0_dio, label='f0', color=(0, 1, 1, 0.6), linewidth=3)
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pylab.savefig(dioImageFile)
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pylab.close()
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HOP_LENGTH = 128
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img = librosa.display.specshow(spec, sr=samplingRate, hop_length=HOP_LENGTH, x_axis='time', y_axis='log', )
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pylab.plot(times, f0_harvest, label='f0', color=(0, 1, 1, 0.6), linewidth=3)
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pylab.savefig(harvestImageFile)
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37
server/voice_changer/IORecorder.py
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37
server/voice_changer/IORecorder.py
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@ -0,0 +1,37 @@
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import wave
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import os
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class IORecorder:
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def __init__(self, inputFilename: str, outputFilename: str, samplingRate: int):
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self._clearFile(inputFilename)
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self._clearFile(outputFilename)
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self.fi = wave.open(inputFilename, 'wb')
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self.fi.setnchannels(1)
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self.fi.setsampwidth(2)
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self.fi.setframerate(samplingRate)
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self.fo = wave.open(outputFilename, 'wb')
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self.fo.setnchannels(1)
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self.fo.setsampwidth(2)
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self.fo.setframerate(samplingRate)
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def _clearFile(self, filename: str):
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if os.path.exists(filename):
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print("[IORecorder] delete old analyze file.", filename)
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os.remove(filename)
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else:
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print("[IORecorder] old analyze file not exist.", filename)
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def writeInput(self, wav):
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self.fi.writeframes(wav)
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def writeOutput(self, wav):
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self.fo.writeframes(wav)
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def close(self):
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self.fi.close()
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self.fo.close()
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@ -18,6 +18,9 @@ import pyworld as pw
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from voice_changer.client_modules import convert_continuos_f0, spectrogram_torch, TextAudioSpeakerCollate, get_hparams_from_file, load_checkpoint
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from voice_changer.MMVCv15 import MMVCv15
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from voice_changer.IORecorder import IORecorder
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from voice_changer.IOAnalyzer import IOAnalyzer
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import time
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@ -34,53 +37,10 @@ import librosa.display
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SAMPLING_RATE = 24000
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class MockStream:
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"""gi
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オーディオストリーミング入出力をファイル入出力にそのまま置き換えるためのモック
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"""
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def __init__(self, sampling_rate):
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self.sampling_rate = sampling_rate
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self.start_count = 2
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self.end_count = 2
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self.fr = None
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self.fw = None
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def open_inputfile(self, input_filename):
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self.fr = wave.open(input_filename, 'rb')
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def open_outputfile(self, output_filename):
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self.fw = wave.open(output_filename, 'wb')
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self.fw.setnchannels(1)
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self.fw.setsampwidth(2)
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self.fw.setframerate(self.sampling_rate)
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def read(self, length, exception_on_overflow=False):
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if self.start_count > 0:
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wav = bytes(length * 2)
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self.start_count -= 1 # 最初の2回はダミーの空データ送る
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else:
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wav = self.fr.readframes(length)
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if len(wav) <= 0: # データなくなってから最後の2回はダミーの空データを送る
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wav = bytes(length * 2)
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self.end_count -= 1
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if self.end_count < 0:
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Hyperparameters.VC_END_FLAG = True
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return wav
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def write(self, wav):
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self.fw.writeframes(wav)
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def stop_stream(self):
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pass
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def close(self):
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if self.fr != None:
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self.fr.close()
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self.fr = None
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if self.fw != None:
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self.fw.close()
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self.fw = None
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STREAM_INPUT_FILE = os.path.join(TMP_DIR, "in.wav")
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STREAM_OUTPUT_FILE = os.path.join(TMP_DIR, "out.wav")
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STREAM_ANALYZE_FILE_DIO = os.path.join(TMP_DIR, "analyze-dio.png")
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STREAM_ANALYZE_FILE_HARVEST = os.path.join(TMP_DIR, "analyze-harvest.png")
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@dataclass
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@ -136,33 +96,6 @@ class VoiceChanger():
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print(f"VoiceChanger Initialized (GPU_NUM:{self.gpu_num}, mps_enabled:{self.mps_enabled})")
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def _setupRecordIO(self):
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# IO Recorder Setup
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if hasattr(self, "stream_out"):
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self.stream_out.close()
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mock_stream_out = MockStream(24000)
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stream_output_file = os.path.join(TMP_DIR, "out.wav")
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if os.path.exists(stream_output_file):
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print("delete old analyze file.", stream_output_file)
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os.remove(stream_output_file)
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else:
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print("old analyze file not exist.", stream_output_file)
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mock_stream_out.open_outputfile(stream_output_file)
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self.stream_out = mock_stream_out
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if hasattr(self, "stream_in"):
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self.stream_in.close()
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mock_stream_in = MockStream(24000)
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stream_input_file = os.path.join(TMP_DIR, "in.wav")
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if os.path.exists(stream_input_file):
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print("delete old analyze file.", stream_input_file)
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os.remove(stream_input_file)
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else:
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print("old analyze file not exist.", stream_output_file)
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mock_stream_in.open_outputfile(stream_input_file)
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self.stream_in = mock_stream_in
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def loadModel(self, config: str, pyTorch_model_file: str = None, onnx_model_file: str = None):
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self.settings.configFile = config
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self.hps = get_hparams_from_file(config)
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@ -222,18 +155,6 @@ class VoiceChanger():
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return data
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def _get_f0_dio(self, y, sr=SAMPLING_RATE):
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_f0, time = pw.dio(y, sr, frame_period=5)
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f0 = pw.stonemask(y, _f0, time, sr)
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time = np.linspace(0, y.shape[0] / sr, len(time))
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return f0, time
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def _get_f0_harvest(self, y, sr=SAMPLING_RATE):
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_f0, time = pw.harvest(y, sr, frame_period=5)
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f0 = pw.stonemask(y, _f0, time, sr)
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time = np.linspace(0, y.shape[0] / sr, len(time))
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return f0, time
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def update_setteings(self, key: str, val: any):
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if key == "onnxExecutionProvider" and self.onnx_session != None:
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if val == "CUDAExecutionProvider":
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@ -254,31 +175,22 @@ class VoiceChanger():
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if key == "crossFadeOffsetRate" or key == "crossFadeEndRate":
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self.unpackedData_length = 0
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if key == "recordIO" and val == 1:
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self._setupRecordIO()
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if hasattr(self, "ioRecorder"):
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self.ioRecorder.close()
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self.ioRecorder = IORecorder(STREAM_INPUT_FILE, STREAM_OUTPUT_FILE, self.settings.inputSampleRate)
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if key == "recordIO" and val == 0:
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if hasattr(self, "ioRecorder"):
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self.ioRecorder.close()
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pass
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if key == "recordIO" and val == 2:
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if hasattr(self, "ioRecorder"):
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self.ioRecorder.close()
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if hasattr(self, "ioAnalyzer") == False:
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self.ioAnalyzer = IOAnalyzer()
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try:
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stream_input_file = os.path.join(TMP_DIR, "in.wav")
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analyze_file_dio = os.path.join(TMP_DIR, "analyze-dio.png")
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analyze_file_harvest = os.path.join(TMP_DIR, "analyze-harvest.png")
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y, sr = librosa.load(stream_input_file, SAMPLING_RATE)
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y = y.astype(np.float64)
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spec = librosa.amplitude_to_db(np.abs(librosa.stft(y, n_fft=2048, win_length=2048, hop_length=128)), ref=np.max)
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f0_dio, times = self._get_f0_dio(y)
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f0_harvest, times = self._get_f0_harvest(y)
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pylab.close()
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HOP_LENGTH = 128
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img = librosa.display.specshow(spec, sr=SAMPLING_RATE, hop_length=HOP_LENGTH, x_axis='time', y_axis='log', )
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pylab.plot(times, f0_dio, label='f0', color=(0, 1, 1, 0.6), linewidth=3)
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pylab.savefig(analyze_file_dio)
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pylab.close()
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HOP_LENGTH = 128
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img = librosa.display.specshow(spec, sr=SAMPLING_RATE, hop_length=HOP_LENGTH, x_axis='time', y_axis='log', )
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pylab.plot(times, f0_harvest, label='f0', color=(0, 1, 1, 0.6), linewidth=3)
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pylab.savefig(analyze_file_harvest)
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self.ioAnalyzer.analyze(STREAM_INPUT_FILE, STREAM_ANALYZE_FILE_DIO, STREAM_ANALYZE_FILE_HARVEST, self.settings.inputSampleRate)
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except Exception as e:
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print("recordIO exception", e)
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@ -462,8 +374,10 @@ class VoiceChanger():
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result = result.astype(np.int16)
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# print("on_request result size:",result.shape)
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if self.settings.recordIO == 1:
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self.stream_in.write(unpackedData.astype(np.int16).tobytes())
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self.stream_out.write(result.tobytes())
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# self.stream_in.write(unpackedData.astype(np.int16).tobytes())
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# self.stream_out.write(result.tobytes())
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self.ioRecorder.writeInput(unpackedData.astype(np.int16).tobytes())
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self.ioRecorder.writeOutput(result.tobytes())
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if self.settings.inputSampleRate != 24000:
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result = resampy.resample(result, 24000, 48000).astype(np.int16)
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