Mirrors/OpenVoice
1
0
Fork 0
mirror of https://github.com/myshell-ai/OpenVoice.git synced 2025-12-16 16:37:56 +01:00
Code Issues Packages Projects Releases Wiki Activity

Add split_sentence func to BaseTTS

Browse Source
This commit is contained in:
yuxumin
2023-12-16 00:28:18 +08:00
parent 47942d50bf
commit 2acbfe1fe2
3 changed files with 111 additions and 44 deletions
Download Patch File Download Diff File Expand all files Collapse all files

67
api.py
View File

@@ -1,16 +1,15 @@
import torch
import torch.nn as nn
import utils
from models import SynthesizerTrn
import torchaudio
import commons
import os
from mel_processing import spectrogram_torch, spectrogram_torch_conv
import librosa
import numpy as np
from text import text_to_sequence
import re
import soundfile
import utils
import commons
import os
import librosa
from text import text_to_sequence
from mel_processing import spectrogram_torch
from models import SynthesizerTrn
class OpenVoiceBaseClass(object):
def __init__(self,
@@ -53,23 +52,47 @@ class BaseSpeakerTTS(OpenVoiceBaseClass):
text_norm = torch.LongTensor(text_norm)
return text_norm
@staticmethod
def audio_numpy_concat(segment_data_list, sr, speed=1.):
audio_segments = []
for segment_data in segment_data_list:
audio_segments += segment_data.reshape(-1).tolist()
audio_segments += [0] * int((sr * 0.05)/speed)
audio_segments = np.array(audio_segments).astype(np.float32)
return audio_segments
@staticmethod
def split_sentences_into_pieces(text):
texts = utils.split_sentences_latin(text)
print(" > Text splitted to sentences.")
print('\n'.join(texts))
print(" > ===========================")
return texts
def tts(self, text, output_path, speaker, language='English', speed=1.0):
mark = self.language_marks.get(language.lower(), None)
assert mark is not None, f"language {language} is not supported"
text = re.sub(r'([a-z])([A-Z])', r'\1 \2', text)
text = mark + text + mark
stn_tst = self.get_text(text, self.hps, False)
device = self.device
speaker_id = self.hps.speakers[speaker]
with torch.no_grad():
x_tst = stn_tst.unsqueeze(0).to(device)
x_tst_lengths = torch.LongTensor([stn_tst.size(0)]).to(device)
sid = torch.LongTensor([speaker_id]).to(device)
audio = self.model.infer(x_tst, x_tst_lengths, sid=sid, noise_scale=0.667, noise_scale_w=0.6,
length_scale=1.0 / speed)[0][0, 0].data.cpu().float().numpy()
texts = self.split_sentences_into_pieces(text)
audio_list = []
for t in texts:
t = re.sub(r'([a-z])([A-Z])', r'\1 \2', t)
t = mark + t + mark
stn_tst = self.get_text(t, self.hps, False)
device = self.device
speaker_id = self.hps.speakers[speaker]
with torch.no_grad():
x_tst = stn_tst.unsqueeze(0).to(device)
x_tst_lengths = torch.LongTensor([stn_tst.size(0)]).to(device)
sid = torch.LongTensor([speaker_id]).to(device)
audio = self.model.infer(x_tst, x_tst_lengths, sid=sid, noise_scale=0.667, noise_scale_w=0.6,
length_scale=1.0 / speed)[0][0, 0].data.cpu().float().numpy()
audio_list.append(audio)
audio = self.audio_numpy_concat(audio_list, sr=self.hps.data.sampling_rate, speed=speed)
if output_path is None:
return audio.numpy()
return audio
else:
soundfile.write(output_path, audio, self.hps.data.sampling_rate)