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27
GPT_SoVITS/inference_webui.py
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@ -324,26 +324,28 @@ def get_first(text):
return text
def get_cleaned_text_fianl(text,language):
def get_cleaned_text_final(text,language):
if language in {"en","all_zh","all_ja"}:
phones, word2ph, norm_text = clean_text_inf(text, language)
elif language in {"zh", "ja","auto"}:
phones, word2ph, norm_text = nonen_clean_text_inf(text, language)
return phones, word2ph, norm_text
def get_bert_final(phones, word2ph, norm_text,language,device):
if text_language == "en":
bert = get_bert_inf(phones, word2ph, norm_text, text_language)
elif text_language in {"zh", "ja","auto"}:
bert = nonen_get_bert_inf(text, text_language)
elif text_language == "all_zh":
bert = get_bert_feature(norm_text, word2ph).to(device)
def get_bert_final(phones, word2ph, text,language,device):
if language == "en":
bert = get_bert_inf(phones, word2ph, text, language)
elif language in {"zh", "ja","auto"}:
bert = nonen_get_bert_inf(text, language)
elif language == "all_zh":
bert = get_bert_feature(text, word2ph).to(device)
else:
bert = torch.zeros((1024, len(phones))).to(device)
return bert
def get_tts_wav(ref_wav_path, prompt_text, prompt_language, text, text_language, how_to_cut=i18n("不切")):
t0 = ttime()
prompt_language = dict_language[prompt_language]
text_language = dict_language[text_language]
prompt_text = prompt_text.strip("\n")
if (prompt_text[-1] not in splits): prompt_text += "。" if prompt_language != "en" else "."
text = text.strip("\n")
@ -375,10 +377,8 @@ def get_tts_wav(ref_wav_path, prompt_text, prompt_language, text, text_language,
codes = vq_model.extract_latent(ssl_content)
prompt_semantic = codes[0, 0]
t1 = ttime()
prompt_language = dict_language[prompt_language]
text_language = dict_language[text_language]
phones1, word2ph1, norm_text1=get_cleaned_text_fianl(prompt_text, prompt_language)
phones1, word2ph1, norm_text1=get_cleaned_text_final(prompt_text, prompt_language)
if (how_to_cut == i18n("凑四句一切")):
text = cut1(text)
@ -402,9 +402,8 @@ def get_tts_wav(ref_wav_path, prompt_text, prompt_language, text, text_language,
continue
if (text[-1] not in splits): text += "。" if text_language != "en" else "."
print(i18n("实际输入的目标文本(每句):"), text)
phones2, word2ph2, norm_text2 = get_cleaned_text_fianl(text, text_language)
phones2, word2ph2, norm_text2 = get_cleaned_text_final(text, text_language)
bert2 = get_bert_final(phones2, word2ph2, norm_text2, text_language, device).to(dtype)
bert = torch.cat([bert1, bert2], 1)
all_phoneme_ids = torch.LongTensor(phones1 + phones2).to(device).unsqueeze(0)
@ -583,7 +582,7 @@ with gr.Blocks(title="GPT-SoVITS WebUI") as app:
inp_ref = gr.Audio(label=i18n("请上传3~10秒内参考音频，超过会报错！"), type="filepath")
prompt_text = gr.Textbox(label=i18n("参考音频的文本"), value="")
prompt_language = gr.Dropdown(
label=i18n("参考音频的语种"), choices=[i18n("中文"), i18n("英文"), i18n("日文")], value=i18n("中文")
label=i18n("参考音频的语种"), choices=[i18n("中文"), i18n("英文"), i18n("日文"), i18n("中英混合"), i18n("日英混合"), i18n("多语种混合")], value=i18n("中文")
)
gr.Markdown(value=i18n("*请填写需要合成的目标文本。中英混合选中文，日英混合选日文，中日混合暂不支持，非目标语言文本自动遗弃。"))
with gr.Row():

10
docs/cn/Changelog_CN.md
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@ -87,8 +87,18 @@
2-引入paddlespeech的Normalizer https://github.com/RVC-Boss/GPT-SoVITS/pull/377 修复一些问题，例如：xx.xx%(带百分号类)，元/吨 会读成 元吨 而不是元每吨,下划线不再会报错
### 20240207更新
1-修正语种传参混乱导致中文推理效果下降 https://github.com/RVC-Boss/GPT-SoVITS/issues/391
2-uvr5适配高版本librosa https://github.com/RVC-Boss/GPT-SoVITS/pull/403
3-修复uvr5 inf everywhere报错的问题(is_half传参未转换bool导致恒定半精度推理，16系显卡会inf) https://github.com/RVC-Boss/GPT-SoVITS/commit/14a285109a521679f8846589c22da8f656a46ad8
todolist：
1-中文多音字推理优化
2-测试集成faster whisper ASR日文英文

3
requirements.txt
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@ -5,10 +5,11 @@ librosa==0.9.2
numba==0.56.4
pytorch-lightning
gradio==3.38.0
gradio_client==0.8.1
ffmpeg-python
onnxruntime
tqdm
funasr>=1.0.0
funasr==1.0.0
cn2an
pypinyin
pyopenjtalk

60
tools/uvr5/lib/lib_v5/spec_utils.py
View File

@ -43,8 +43,8 @@ def wave_to_spectrogram(
wave_left = np.asfortranarray(wave[0])
wave_right = np.asfortranarray(wave[1])
spec_left = librosa.stft(wave_left, n_fft, hop_length=hop_length)
spec_right = librosa.stft(wave_right, n_fft, hop_length=hop_length)
spec_left = librosa.stft(wave_left, n_fft=n_fft, hop_length=hop_length)
spec_right = librosa.stft(wave_right, n_fft=n_fft, hop_length=hop_length)
spec = np.asfortranarray([spec_left, spec_right])
@ -78,7 +78,7 @@ def wave_to_spectrogram_mt(
kwargs={"y": wave_left, "n_fft": n_fft, "hop_length": hop_length},
)
thread.start()
spec_right = librosa.stft(wave_right, n_fft, hop_length=hop_length)
spec_right = librosa.stft(wave_right, n_fft=n_fft, hop_length=hop_length)
thread.join()
spec = np.asfortranarray([spec_left, spec_right])
@ -230,27 +230,31 @@ def cache_or_load(mix_path, inst_path, mp):
if d == len(mp.param["band"]): # high-end band
X_wave[d], _ = librosa.load(
mix_path, bp["sr"], False, dtype=np.float32, res_type=bp["res_type"]
mix_path,
sr = bp["sr"],
mono = False,
dtype = np.float32,
res_type = bp["res_type"]
)
y_wave[d], _ = librosa.load(
inst_path,
bp["sr"],
False,
dtype=np.float32,
res_type=bp["res_type"],
sr = bp["sr"],
mono = False,
dtype = np.float32,
res_type = bp["res_type"],
)
else: # lower bands
X_wave[d] = librosa.resample(
X_wave[d + 1],
mp.param["band"][d + 1]["sr"],
bp["sr"],
res_type=bp["res_type"],
orig_sr = mp.param["band"][d + 1]["sr"],
target_sr = bp["sr"],
res_type = bp["res_type"],
)
y_wave[d] = librosa.resample(
y_wave[d + 1],
mp.param["band"][d + 1]["sr"],
bp["sr"],
res_type=bp["res_type"],
orig_sr = mp.param["band"][d + 1]["sr"],
target_sr = bp["sr"],
res_type = bp["res_type"],
)
X_wave[d], y_wave[d] = align_wave_head_and_tail(X_wave[d], y_wave[d])
@ -401,9 +405,9 @@ def cmb_spectrogram_to_wave(spec_m, mp, extra_bins_h=None, extra_bins=None):
mp.param["mid_side_b2"],
mp.param["reverse"],
),
bp["sr"],
sr,
res_type="sinc_fastest",
orig_sr = bp["sr"],
target_sr = sr,
res_type = "sinc_fastest",
)
else: # mid
spec_s = fft_hp_filter(spec_s, bp["hpf_start"], bp["hpf_stop"] - 1)
@ -418,8 +422,8 @@ def cmb_spectrogram_to_wave(spec_m, mp, extra_bins_h=None, extra_bins=None):
mp.param["reverse"],
),
)
# wave = librosa.core.resample(wave2, bp['sr'], sr, res_type="sinc_fastest")
wave = librosa.core.resample(wave2, bp["sr"], sr, res_type="scipy")
# wave = librosa.core.resample(wave2, orig_sr=bp['sr'], target_sr=sr, res_type="sinc_fastest")
wave = librosa.core.resample(wave2, orig_sr=bp["sr"], target_sr=sr, res_type="scipy")
return wave.T
@ -506,8 +510,8 @@ def ensembling(a, specs):
def stft(wave, nfft, hl):
wave_left = np.asfortranarray(wave[0])
wave_right = np.asfortranarray(wave[1])
spec_left = librosa.stft(wave_left, nfft, hop_length=hl)
spec_right = librosa.stft(wave_right, nfft, hop_length=hl)
spec_left = librosa.stft(wave_left, n_fft=nfft, hop_length=hl)
spec_right = librosa.stft(wave_right, n_fft=nfft, hop_length=hl)
spec = np.asfortranarray([spec_left, spec_right])
return spec
@ -569,10 +573,10 @@ if __name__ == "__main__":
if d == len(mp.param["band"]): # high-end band
wave[d], _ = librosa.load(
args.input[i],
bp["sr"],
False,
dtype=np.float32,
res_type=bp["res_type"],
sr = bp["sr"],
mono = False,
dtype = np.float32,
res_type = bp["res_type"],
)
if len(wave[d].shape) == 1: # mono to stereo
@ -580,9 +584,9 @@ if __name__ == "__main__":
else: # lower bands
wave[d] = librosa.resample(
wave[d + 1],
mp.param["band"][d + 1]["sr"],
bp["sr"],
res_type=bp["res_type"],
orig_sr = mp.param["band"][d + 1]["sr"],
target_sr = bp["sr"],
res_type = bp["res_type"],
)
spec[d] = wave_to_spectrogram(

28
tools/uvr5/vr.py
View File

@ -61,19 +61,19 @@ class AudioPre:
_,
) = librosa.core.load( # 理论上librosa读取可能对某些音频有bug，应该上ffmpeg读取，但是太麻烦了弃坑
music_file,
bp["sr"],
False,
dtype=np.float32,
res_type=bp["res_type"],
sr = bp["sr"],
mono = False,
dtype = np.float32,
res_type = bp["res_type"],
)
if X_wave[d].ndim == 1:
X_wave[d] = np.asfortranarray([X_wave[d], X_wave[d]])
else: # lower bands
X_wave[d] = librosa.core.resample(
X_wave[d + 1],
self.mp.param["band"][d + 1]["sr"],
bp["sr"],
res_type=bp["res_type"],
orig_sr = self.mp.param["band"][d + 1]["sr"],
target_sr = bp["sr"],
res_type = bp["res_type"],
)
# Stft of wave source
X_spec_s[d] = spec_utils.wave_to_spectrogram_mt(
@ -245,19 +245,19 @@ class AudioPreDeEcho:
_,
) = librosa.core.load( # 理论上librosa读取可能对某些音频有bug，应该上ffmpeg读取，但是太麻烦了弃坑
music_file,
bp["sr"],
False,
dtype=np.float32,
res_type=bp["res_type"],
sr = bp["sr"],
mono = False,
dtype = np.float32,
res_type = bp["res_type"],
)
if X_wave[d].ndim == 1:
X_wave[d] = np.asfortranarray([X_wave[d], X_wave[d]])
else: # lower bands
X_wave[d] = librosa.core.resample(
X_wave[d + 1],
self.mp.param["band"][d + 1]["sr"],
bp["sr"],
res_type=bp["res_type"],
orig_sr = self.mp.param["band"][d + 1]["sr"],
target_sr = bp["sr"],
res_type = bp["res_type"],
)
# Stft of wave source
X_spec_s[d] = spec_utils.wave_to_spectrogram_mt(

2
tools/uvr5/webui.py
View File

@ -19,7 +19,7 @@ for name in os.listdir(weight_uvr5_root):
uvr5_names.append(name.replace(".pth", ""))
device=sys.argv[1]
is_half=sys.argv[2]
is_half=eval(sys.argv[2])
webui_port_uvr5=int(sys.argv[3])
is_share=eval(sys.argv[4])