def main(args, hp):
model = VoiceFilter(hp).cuda()
chkpt_model = torch.load(args.checkpoint_path)['model']
model.load_state_dict(chkpt_model)
model.eval()
embedder = SpeechEmbedder(hp).cuda()
chkpt_embed = torch.load(args.embedder_path)
embedder.load_state_dict(chkpt_embed)
embedder.eval()
audio = Audio(hp)
dvec_wav, _ = librosa.load(args.reference_file, sr=16000)
dvec_mel = audio.get_mel(dvec_wav)
dvec_mel = torch.from_numpy(dvec_mel).float().cuda()
dvec = embedder(dvec_mel)
dvec = dvec.unsqueeze(0)
mixed_wav, _ = librosa.load(args.mixed_file, sr=16000)
mag, phase = audio.wav2spec(mixed_wav)
mag = torch.from_numpy(mag).float().cuda()
mag = mag.unsqueeze(0)
mask = model(mag, dvec)
est_mag = mag * mask
est_mag = est_mag[0].cpu().detach().numpy()
est_wav = audio.spec2wav(est_mag, phase)
os.makedirs(args.out_dir, exist_ok=True)
out_path = os.path.join(args.out_dir, 'result.wav')
librosa.output.write_wav(out_path, est_wav, sr=16000)
def main(args, hp):
with torch.no_grad():
model = VoiceFilter(hp).cuda()
chkpt_model = torch.load(args.checkpoint_path)['model']
model.load_state_dict(chkpt_model)
model.eval()
embedder = SpeechEmbedder(hp).cuda()
chkpt_embed = torch.load(args.embedder_path)
embedder.load_state_dict(chkpt_embed)
embedder.eval()
audio = Audio(hp)
ref_wav, _ = librosa.load(args.reference_file, sr=16000)
ref_mel = audio.get_mel(ref_wav)
ref_mel = torch.from_numpy(ref_mel).float().cuda()
dvec = embedder(ref_mel)
dvec = dvec.unsqueeze(0)
mixed_wav, _ = librosa.load(args.mixed_file, sr=16000)
mixed_mag, mixed_phase = audio.wav2spec(mixed_wav)
mixed_mag = torch.from_numpy(mixed_mag).float().cuda()
mixed_mag = mixed_mag.unsqueeze(0)
shadow_mag = model(mixed_mag, dvec)
shadow_mag = shadow_mag[0].cpu().detach().numpy()
recorded_mag = tensor_normalize(mixed_mag + shadow_mag)
recorded_mag = recorded_mag[0].cpu().detach().numpy()
recorded_wav = audio.spec2wav(recorded_mag, mixed_mag)
os.makedirs(args.out_dir, exist_ok=True)
out_path = os.path.join(args.out_dir, 'result.wav')
librosa.output.write_wav(out_path, recorded_wav, sr=16000)
def main(args):
args = {
"config": 'config/config.yaml',
"embedder_path": 'model/embedder.pt',
"checkpoint_path": 'enhance_my_voice/chkpt_201000.pt',
"mixed_file": 'utils/speakerA.wav',
"reference_file": 'utils/speakerA.wav',
"out_dir": 'output',
}
hp = HParam(args['config'])
with torch.no_grad():
model = VoiceFilter(hp).cuda()
chkpt_model = torch.load(args['checkpoint_path'])['model']
model.load_state_dict(chkpt_model)
model.eval()
embedder = SpeechEmbedder(hp).cuda()
chkpt_embed = torch.load(args['embedder_path'])
embedder.load_state_dict(chkpt_embed)
embedder.eval()
audio = Audio(hp)
dvec_wav, _ = librosa.load(args['reference_file'], sr=16000)
dvec_mel = audio.get_mel(dvec_wav)
dvec_mel = torch.from_numpy(dvec_mel).float().cuda()
dvec = embedder(dvec_mel)
dvec = dvec.unsqueeze(0)
mixed_wav, _ = librosa.load(args['mixed_file'], sr=16000)
mag, phase = audio.wav2spec(mixed_wav)
mag = torch.from_numpy(mag).float().cuda()
mag = mag.unsqueeze(0)
mask = model(mag, dvec)
est_mag = mag * mask
est_mag = est_mag[0].cpu().detach().numpy()
# est_wav = audio.spec2wav(est_mag, phase)
# os.makedirs(args['out_dir'], exist_ok=True)
# out_path = os.path.join(args['out_dir'], 'result.wav')
# librosa.output.write_wav(out_path, est_wav, sr=16000)
return audio.spec2wav(est_mag, phase)
def main(args, hp):
with open('out1.txt') as f:
for line in f:
res = line.split('\t')
with torch.no_grad():
model = VoiceFilter(hp)
chkpt_model = torch.load(args.checkpoint_path, map_location='cpu')['model']
model.load_state_dict(chkpt_model)
model.eval()
embedder = SpeechEmbedder(hp)
chkpt_embed = torch.load(args.embedder_path, map_location='cpu')
embedder.load_state_dict(chkpt_embed)
embedder.eval()
audio = Audio(hp)
dvec_wav, _ = librosa.load(res[1], sr=16000)
dvec_mel = audio.get_mel(dvec_wav)
dvec_mel = torch.from_numpy(dvec_mel).float()
dvec = embedder(dvec_mel)
dvec = dvec.unsqueeze(0)
mixed_wav, _ = librosa.load(res[0], sr=16000)
mag, phase = audio.wav2spec(mixed_wav)
mag = torch.from_numpy(mag).float()
mag = mag.unsqueeze(0)
mask = model(mag, dvec)
est_mag = mag * mask
est_mag = est_mag[0].cpu().detach().numpy()
est_wav = audio.spec2wav(est_mag, phase)
os.makedirs('/root/voicefilter/res', exist_ok=True)
out_path = os.path.join('/root/voicefilter/res', f'{res[2]}')
librosa.output.write_wav(out_path, est_wav, sr=16000)
mixed_wav, _ = librosa.load(mixed_wav_path, sr=16000)
mixed_mag, mixed_phase = audio.wav2spec(mixed_wav)
mixed_mag = torch.from_numpy(mixed_mag).float().cuda()
mixed_mag = mixed_mag.unsqueeze(0)
shadow_mag = model(mixed_mag, dvec)
# shadow_mag.size() = [1, 301, 601]
recorded_mag = tensor_normalize(mixed_mag + shadow_mag)
recorded_mag = recorded_mag[0].cpu().detach().numpy()
mixed_mag = mixed_mag[0].cpu().detach().numpy()
shadow_mag = shadow_mag[0].cpu().detach().numpy()
shadow_wav = audio.spec2wav(shadow_mag, mixed_phase)
# scale is frequency pass to time domain, used on wav signal normalization
recorded_wav1 = audio.spec2wav(recorded_mag, mixed_phase) # path 1
# mixed_Wav_path = '/data/our_dataset/test/13/babble/000001-mixed.wav'
hide1 = mixed_wav_path[:-9] + 'hide1.wav'
hide2 = mixed_wav_path[:-9] + 'hide2.wav'
# purified3 = os.path.join(args.out_dir, 'result3.wav')
# original mixed wav and expected_focused wav are not PCM, cannot be read by google cloud
wavio.write(hide1, recorded_wav1, 16000,
sampwidth=2) # frequency +
wavio.write(hide2, shadow_wav, 16000, sampwidth=2) # est noise
# wavio.write(purified3, enhanced_wav, 16000, sampwidth=2) # mix + est noise
