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Merge pull request #105 from zwa73/4.0 

flask_api 的音频推理部分添加参数传递  根据@ChrisPreston的代码添加了噪音过滤f0_filter参数
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YuriHead 2023-04-04 15:15:42 +08:00 committed by GitHub
parent 54f52177b7 41dd39790f
commit ea6f77096b
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2 changed files with 40 additions and 10 deletions
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10
flask_api.py
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@ -30,10 +30,13 @@ def voice_change_model():
# 模型推理
if raw_infer:
out_audio, out_sr = svc_model.infer(speaker_id, f_pitch_change, input_wav_path)
# out_audio, out_sr = svc_model.infer(speaker_id, f_pitch_change, input_wav_path)
out_audio, out_sr = svc_model.infer(speaker_id, f_pitch_change, input_wav_path, cluster_infer_ratio=0,
auto_predict_f0=False, noice_scale=0.4, f0_filter=False)
tar_audio = torchaudio.functional.resample(out_audio, svc_model.target_sample, daw_sample)
else:
out_audio = svc.process(svc_model, speaker_id, f_pitch_change, input_wav_path)
out_audio = svc.process(svc_model, speaker_id, f_pitch_change, input_wav_path, cluster_infer_ratio=0,
auto_predict_f0=False, noice_scale=0.4, f0_filter=False)
tar_audio = torchaudio.functional.resample(torch.from_numpy(out_audio), svc_model.target_sample, daw_sample)
# 返回音频
out_wav_path = io.BytesIO()
@ -50,7 +53,8 @@ if __name__ == '__main__':
# 每个模型和config是唯一对应的
model_name = "logs/32k/G_174000-Copy1.pth"
config_name = "configs/config.json"
svc_model = Svc(model_name, config_name)
cluster_model_path = "logs/44k/kmeans_10000.pt"
svc_model = Svc(model_name, config_name, cluster_model_path=cluster_model_path)
svc = RealTimeVC()
# 此处与vst插件对应，不建议更改
app.run(port=6842, host="0.0.0.0", debug=False, threaded=False)

38
inference/infer_tool.py
View File

@ -108,6 +108,9 @@ def split_list_by_n(list_collection, n, pre=0):
yield list_collection[i-pre if i-pre>=0 else i: i + n]
class F0FilterException(Exception):
pass
class Svc(object):
def __init__(self, net_g_path, config_path,
device=None,
@ -142,11 +145,15 @@ class Svc(object):
def get_unit_f0(self, in_path, tran, cluster_infer_ratio, speaker):
def get_unit_f0(self, in_path, tran, cluster_infer_ratio, speaker, f0_filter):
wav, sr = librosa.load(in_path, sr=self.target_sample)
f0 = utils.compute_f0_parselmouth(wav, sampling_rate=self.target_sample, hop_length=self.hop_size)
if f0_filter and sum(f0) == 0:
raise F0FilterException("未检测到人声")
f0, uv = utils.interpolate_f0(f0)
f0 = torch.FloatTensor(f0)
uv = torch.FloatTensor(uv)
@ -170,13 +177,15 @@ class Svc(object):
def infer(self, speaker, tran, raw_path,
cluster_infer_ratio=0,
auto_predict_f0=False,
noice_scale=0.4):
noice_scale=0.4,
f0_filter=False):
speaker_id = self.spk2id.__dict__.get(speaker)
if not speaker_id and type(speaker) is int:
if len(self.spk2id.__dict__) >= speaker:
speaker_id = speaker
sid = torch.LongTensor([int(speaker_id)]).to(self.dev).unsqueeze(0)
c, f0, uv = self.get_unit_f0(raw_path, tran, cluster_infer_ratio, speaker)
c, f0, uv = self.get_unit_f0(raw_path, tran, cluster_infer_ratio, speaker, f0_filter)
if "half" in self.net_g_path and torch.cuda.is_available():
c = c.half()
with torch.no_grad():
@ -252,14 +261,25 @@ class RealTimeVC:
"""输入输出都是1维numpy 音频波形数组"""
def process(self, svc_model, speaker_id, f_pitch_change, input_wav_path):
def process(self, svc_model, speaker_id, f_pitch_change, input_wav_path,
cluster_infer_ratio=0,
auto_predict_f0=False,
noice_scale=0.4,
f0_filter=False):
import maad
audio, sr = torchaudio.load(input_wav_path)
audio = audio.cpu().numpy()[0]
temp_wav = io.BytesIO()
if self.last_chunk is None:
input_wav_path.seek(0)
audio, sr = svc_model.infer(speaker_id, f_pitch_change, input_wav_path)
audio, sr = svc_model.infer(speaker_id, f_pitch_change, input_wav_path,
cluster_infer_ratio=cluster_infer_ratio,
auto_predict_f0=auto_predict_f0,
noice_scale=noice_scale,
f0_filter=f0_filter)
audio = audio.cpu().numpy()
self.last_chunk = audio[-self.pre_len:]
self.last_o = audio
@ -268,7 +288,13 @@ class RealTimeVC:
audio = np.concatenate([self.last_chunk, audio])
soundfile.write(temp_wav, audio, sr, format="wav")
temp_wav.seek(0)
audio, sr = svc_model.infer(speaker_id, f_pitch_change, temp_wav)
audio, sr = svc_model.infer(speaker_id, f_pitch_change, temp_wav,
cluster_infer_ratio=cluster_infer_ratio,
auto_predict_f0=auto_predict_f0,
noice_scale=noice_scale,
f0_filter=f0_filter)
audio = audio.cpu().numpy()
ret = maad.util.crossfade(self.last_o, audio, self.pre_len)
self.last_chunk = audio[-self.pre_len:]