mirror of
https://github.com/w-okada/voice-changer.git
synced 2025-01-24 22:15:02 +03:00
87 lines
3.7 KiB
Python
Executable File
87 lines
3.7 KiB
Python
Executable File
import torch
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from scipy.io.wavfile import write, read
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import numpy as np
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import traceback
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import utils
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import commons
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from models import SynthesizerTrn
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from text.symbols import symbols
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from data_utils import TextAudioSpeakerLoader, TextAudioSpeakerCollate
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from mel_processing import spectrogram_torch
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from text import text_to_sequence, cleaned_text_to_sequence
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class VoiceChanger():
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def __init__(self, config, model):
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self.hps = utils.get_hparams_from_file(config)
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self.net_g = SynthesizerTrn(
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len(symbols),
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self.hps.data.filter_length // 2 + 1,
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self.hps.train.segment_size // self.hps.data.hop_length,
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n_speakers=self.hps.data.n_speakers,
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**self.hps.model)
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self.net_g.eval()
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self.gpu_num = torch.cuda.device_count()
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utils.load_checkpoint( model, self.net_g, None)
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text_norm = text_to_sequence("a", self.hps.data.text_cleaners)
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text_norm = commons.intersperse(text_norm, 0)
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self.text_norm = torch.LongTensor(text_norm)
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self.audio_buffer = torch.zeros(1, 0)
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self.prev_audio = np.zeros(1)
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print(f"VoiceChanger Initialized (GPU_NUM:{self.gpu_num})")
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def destroy(self):
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del self.net_g
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def on_request(self, gpu, srcId, dstId, timestamp, prefixChunkSize, wav):
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unpackedData = wav
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convertSize = unpackedData.shape[0] + (prefixChunkSize * 512)
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try:
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audio = torch.FloatTensor(unpackedData.astype(np.float32))
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audio_norm = audio /self.hps.data.max_wav_value
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audio_norm = audio_norm.unsqueeze(0)
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self.audio_buffer = torch.cat([self.audio_buffer, audio_norm], axis=1)
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audio_norm = self.audio_buffer[:,-convertSize:]
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self.audio_buffer = audio_norm
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spec = spectrogram_torch(audio_norm, self.hps.data.filter_length,
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self.hps.data.sampling_rate, self.hps.data.hop_length, self.hps.data.win_length,
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center=False)
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spec = torch.squeeze(spec, 0)
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sid = torch.LongTensor([int(srcId)])
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data = (self.text_norm , spec, audio_norm, sid)
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data = TextAudioSpeakerCollate()([data])
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if gpu<0 or self.gpu_num==0 :
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with torch.no_grad():
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x, x_lengths, spec, spec_lengths, y, y_lengths, sid_src = [x.cpu() for x in data]
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sid_tgt1 = torch.LongTensor([dstId]).cpu()
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audio1 = (self.net_g.cpu().voice_conversion(spec, spec_lengths, sid_src=sid_src, sid_tgt=sid_tgt1)[0][0,0].data * self.hps.data.max_wav_value).cpu().float().numpy()
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else:
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with torch.no_grad():
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x, x_lengths, spec, spec_lengths, y, y_lengths, sid_src = [x.cuda(gpu) for x in data]
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sid_tgt1 = torch.LongTensor([dstId]).cuda(gpu)
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audio1 = (self.net_g.cuda(gpu).voice_conversion(spec, spec_lengths, sid_src=sid_src, sid_tgt=sid_tgt1)[0][0,0].data * self.hps.data.max_wav_value).cpu().float().numpy()
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# if len(self.prev_audio) > unpackedData.shape[0]:
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# prevLastFragment = self.prev_audio[-unpackedData.shape[0]:]
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# curSecondLastFragment = audio1[-unpackedData.shape[0]*2:-unpackedData.shape[0]]
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# print("prev, cur", prevLastFragment.shape, curSecondLastFragment.shape)
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# self.prev_audio = audio1
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# print("self.prev_audio", self.prev_audio.shape)
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audio1 = audio1[-unpackedData.shape[0]*2:]
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except Exception as e:
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print("VC PROCESSING!!!! EXCEPTION!!!", e)
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print(traceback.format_exc())
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audio1 = audio1.astype(np.int16)
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return audio1 |