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melo/api.py: add a 'tts' iterator to greatly improve the response speed #88

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melo/api.py: add a 'tts' iterator to greatly improve the response speed #88

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24 changes: 19 additions & 5 deletions melo/api.py
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Original file line number Diff line number Diff line change
Expand Up @@ -80,10 +80,10 @@ def split_sentences_into_pieces(text, language, quiet=False):
print(" > ===========================")
return texts

def tts_to_file(self, text, speaker_id, output_path=None, sdp_ratio=0.2, noise_scale=0.6, noise_scale_w=0.8, speed=1.0, pbar=None, format=None, position=None, quiet=False,):
def tts_iter(self, text, speaker_id, sdp_ratio=0.2, noise_scale=0.6, noise_scale_w=0.8, speed=1.0, pbar=None, position=None, quiet=False,):
language = self.language
texts = self.split_sentences_into_pieces(text, language, quiet)
audio_list = []

if pbar:
tx = pbar(texts)
else:
Expand Down Expand Up @@ -121,10 +121,24 @@ def tts_to_file(self, text, speaker_id, output_path=None, sdp_ratio=0.2, noise_s
length_scale=1. / speed,
)[0][0, 0].data.cpu().float().numpy()
del x_tst, tones, lang_ids, bert, ja_bert, x_tst_lengths, speakers
#
audio_list.append(audio)



audio_segments = []
audio_segments += audio.reshape(-1).tolist()
audio_segments += [0] * int((self.hps.data.sampling_rate * 0.05) / speed)
audio_segments = np.array(audio_segments).astype(np.float32)

yield audio_segments

torch.cuda.empty_cache()
audio = self.audio_numpy_concat(audio_list, sr=self.hps.data.sampling_rate, speed=speed)

def tts_to_file(self, text, speaker_id, output_path=None, sdp_ratio=0.2, noise_scale=0.6, noise_scale_w=0.8, speed=1.0, pbar=None, format=None, position=None, quiet=False,):
audio_list = []
for audio in self.tts_iter(text, speaker_id, sdp_ratio, noise_scale, noise_scale_w, speed, pbar, position, quiet):
audio_list.append(audio)

audio = np.concatenate(audio_list)

if output_path is None:
return audio
Expand Down