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-rw-r--r--scripts/process.py21
1 files changed, 13 insertions, 8 deletions
diff --git a/scripts/process.py b/scripts/process.py
index 60c8e16..46c4ca8 100644
--- a/scripts/process.py
+++ b/scripts/process.py
@@ -34,6 +34,14 @@ gpu = args.gpu
conv_config = create_conv_config(conv_model / 'config.json')
sr_config = create_sr_config(sr_model / 'config.json')
+param = sr_config.dataset.param
+acoustic_feature_process = AcousticFeatureProcess(
+ frame_period=param.acoustic_feature_param.frame_period,
+ order=param.acoustic_feature_param.order,
+ alpha=param.acoustic_feature_param.alpha,
+ f0_estimating_method=param.acoustic_feature_param.f0_estimating_method,
+)
+
def extract_number(f):
s = re.findall("\d+", str(f))
return int(s[-1]) if s else -1
@@ -57,15 +65,12 @@ def process(p: Path, acoustic_converter: AcousticConverter, super_resolution: Su
print(str(p))
input = acoustic_converter(p)
- param = sr_config.dataset.param
- acoustic_feature_process = AcousticFeatureProcess(
- frame_period=param.acoustic_feature_param.frame_period,
- order=param.acoustic_feature_param.order,
- alpha=param.acoustic_feature_param.alpha,
- f0_estimating_method=param.acoustic_feature_param.f0_estimating_method,
- )
+ input_wave = acoustic_converter._wave_process(str(p), test=True)
+ input_feature = acoustic_converter._feature_process(input_wave, test=True)
+ converted_feature = acoustic_converter.convert_to_feature(input=input_wav, out_sampling_rate=param.voice_param.sample_rate)
+ # converted_feature.f0 = input_feature.f0
- wave = super_resolution(acoustic_feature_process(input).spectrogram, acoustic_feature=input, sampling_rate=param.voice_param.sample_rate)
+ wave = super_resolution(converted_feature.spectrogram, acoustic_feature=converted_feature, sampling_rate=param.voice_param.sample_rate)
librosa.output.write_wav(str(output / p.stem) + '.wav', wave.wave, wave.sampling_rate, norm=True)
except:
import traceback