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| import os | |
| import librosa | |
| import commons | |
| import sys | |
| import re | |
| import numpy as np | |
| import torch | |
| import xml.etree.ElementTree as ET | |
| import config | |
| import logging | |
| import soundfile as sf | |
| from torch import no_grad, LongTensor, inference_mode, FloatTensor | |
| from io import BytesIO | |
| from graiax import silkcoder | |
| from utils.nlp import sentence_split | |
| from mel_processing import spectrogram_torch | |
| from text import text_to_sequence | |
| from models import SynthesizerTrn | |
| from utils import utils | |
| # torch.set_num_threads(1) # 设置torch线程为1 | |
| device = torch.device("cuda" if torch.cuda.is_available() else "cpu") | |
| class vits: | |
| def __init__(self, model, config, model_=None, model_type=None): | |
| self.model_type = model_type | |
| self.hps_ms = utils.get_hparams_from_file(config) | |
| self.n_speakers = self.hps_ms.data.n_speakers if 'n_speakers' in self.hps_ms.data.keys() else 0 | |
| self.n_symbols = len(self.hps_ms.symbols) if 'symbols' in self.hps_ms.keys() else 0 | |
| self.speakers = self.hps_ms.speakers if 'speakers' in self.hps_ms.keys() else ['0'] | |
| self.use_f0 = self.hps_ms.data.use_f0 if 'use_f0' in self.hps_ms.data.keys() else False | |
| self.emotion_embedding = self.hps_ms.data.emotion_embedding if 'emotion_embedding' in self.hps_ms.data.keys() else False | |
| self.net_g_ms = SynthesizerTrn( | |
| self.n_symbols, | |
| self.hps_ms.data.filter_length // 2 + 1, | |
| self.hps_ms.train.segment_size // self.hps_ms.data.hop_length, | |
| n_speakers=self.n_speakers, | |
| emotion_embedding=self.emotion_embedding, | |
| **self.hps_ms.model) | |
| _ = self.net_g_ms.eval() | |
| # load model | |
| self.load_model(model, model_) | |
| def load_model(self, model, model_=None): | |
| utils.load_checkpoint(model, self.net_g_ms) | |
| self.net_g_ms.to(device) | |
| if self.model_type == "hubert": | |
| self.hubert = model_ | |
| elif self.model_type == "w2v2": | |
| self.emotion_reference = model_ | |
| def get_cleaned_text(self, text, hps, cleaned=False): | |
| if cleaned: | |
| text_norm = text_to_sequence(text, hps.symbols, []) | |
| else: | |
| text_norm = text_to_sequence(text, hps.symbols, hps.data.text_cleaners) | |
| if hps.data.add_blank: | |
| text_norm = commons.intersperse(text_norm, 0) | |
| text_norm = LongTensor(text_norm) | |
| return text_norm | |
| def get_cleaner(self): | |
| return getattr(self.hps_ms.data, 'text_cleaners', [None])[0] | |
| def get_speakers(self, escape=False): | |
| return self.speakers | |
| def infer(self, params): | |
| emotion = params.get("emotion", None) | |
| emotion = emotion.to(device) if emotion != None else None | |
| with no_grad(): | |
| x_tst = params.get("stn_tst").unsqueeze(0) | |
| x_tst_lengths = LongTensor([params.get("stn_tst").size(0)]) | |
| audio = self.net_g_ms.infer(x_tst.to(device), x_tst_lengths.to(device), sid=params.get("sid").to(device), | |
| noise_scale=params.get("noise_scale"), | |
| noise_scale_w=params.get("noise_scale_w"), | |
| length_scale=params.get("length_scale"), | |
| emotion_embedding=emotion)[0][0, 0].data.float().cpu().numpy() | |
| torch.cuda.empty_cache() | |
| return audio | |
| def get_infer_param(self, length_scale, noise_scale, noise_scale_w, text=None, speaker_id=None, audio_path=None, | |
| emotion=None, cleaned=False, f0_scale=1): | |
| emo = None | |
| if self.model_type != "hubert": | |
| stn_tst = self.get_cleaned_text(text, self.hps_ms, cleaned=cleaned) | |
| sid = LongTensor([speaker_id]) | |
| if self.model_type == "w2v2": | |
| # if emotion_reference.endswith('.npy'): | |
| # emotion = np.load(emotion_reference) | |
| # emotion = FloatTensor(emotion).unsqueeze(0) | |
| # else: | |
| # audio16000, sampling_rate = librosa.load( | |
| # emotion_reference, sr=16000, mono=True) | |
| # emotion = self.w2v2(audio16000, sampling_rate)[ | |
| # 'hidden_states'] | |
| # emotion_reference = re.sub( | |
| # r'\..*$', '', emotion_reference) | |
| # np.save(emotion_reference, emotion.squeeze(0)) | |
| # emotion = FloatTensor(emotion) | |
| emo = torch.FloatTensor(self.emotion_reference[emotion]).unsqueeze(0) | |
| elif self.model_type == "hubert": | |
| if self.use_f0: | |
| audio, sampling_rate = librosa.load(audio_path, sr=self.hps_ms.data.sampling_rate, mono=True) | |
| audio16000 = librosa.resample(audio, orig_sr=sampling_rate, target_sr=16000) | |
| else: | |
| audio16000, sampling_rate = librosa.load(audio_path, sr=16000, mono=True) | |
| with inference_mode(): | |
| units = self.hubert.units(FloatTensor(audio16000).unsqueeze(0).unsqueeze(0)).squeeze(0).numpy() | |
| if self.use_f0: | |
| f0 = librosa.pyin(audio, | |
| sr=sampling_rate, | |
| fmin=librosa.note_to_hz('C0'), | |
| fmax=librosa.note_to_hz('C7'), | |
| frame_length=1780)[0] | |
| target_length = len(units[:, 0]) | |
| f0 = np.nan_to_num(np.interp(np.arange(0, len(f0) * target_length, len(f0)) / target_length, | |
| np.arange(0, len(f0)), f0)) * f0_scale | |
| units[:, 0] = f0 / 10 | |
| stn_tst = FloatTensor(units) | |
| sid = LongTensor([speaker_id]) | |
| params = {"length_scale": length_scale, "noise_scale": noise_scale, | |
| "noise_scale_w": noise_scale_w, "stn_tst": stn_tst, | |
| "sid": sid, "emotion": emo} | |
| return params | |
| def get_audio(self, voice, auto_break=False): | |
| text = voice.get("text", None) | |
| speaker_id = voice.get("id", 0) | |
| length = voice.get("length", 1) | |
| noise = voice.get("noise", 0.667) | |
| noisew = voice.get("noisew", 0.8) | |
| max = voice.get("max", 50) | |
| lang = voice.get("lang", "auto") | |
| speaker_lang = voice.get("speaker_lang", None) | |
| audio_path = voice.get("audio_path", None) | |
| emotion = voice.get("emotion", 0) | |
| # 去除所有多余的空白字符 | |
| if text is not None: text = re.sub(r'\s+', ' ', text).strip() | |
| # 停顿0.75s，避免语音分段合成再拼接后的连接突兀 | |
| brk = np.zeros(int(0.75 * 22050), dtype=np.int16) | |
| tasks = [] | |
| if self.model_type == "vits": | |
| sentence_list = sentence_split(text, max, lang, speaker_lang) | |
| for sentence in sentence_list: | |
| tasks.append( | |
| self.get_infer_param(text=sentence, speaker_id=speaker_id, length_scale=length, noise_scale=noise, | |
| noise_scale_w=noisew)) | |
| audios = [] | |
| for task in tasks: | |
| audios.append(self.infer(task)) | |
| if auto_break: | |
| audios.append(brk) | |
| audio = np.concatenate(audios, axis=0) | |
| elif self.model_type == "hubert": | |
| params = self.get_infer_param(speaker_id=speaker_id, length_scale=length, noise_scale=noise, | |
| noise_scale_w=noisew, audio_path=audio_path) | |
| audio = self.infer(params) | |
| elif self.model_type == "w2v2": | |
| sentence_list = sentence_split(text, max, lang, speaker_lang) | |
| for sentence in sentence_list: | |
| tasks.append( | |
| self.get_infer_param(text=sentence, speaker_id=speaker_id, length_scale=length, noise_scale=noise, | |
| noise_scale_w=noisew, emotion=emotion)) | |
| audios = [] | |
| for task in tasks: | |
| audios.append(self.infer(task)) | |
| if auto_break: | |
| audios.append(brk) | |
| audio = np.concatenate(audios, axis=0) | |
| return audio | |
| def voice_conversion(self, voice): | |
| audio_path = voice.get("audio_path") | |
| original_id = voice.get("original_id") | |
| target_id = voice.get("target_id") | |
| audio = utils.load_audio_to_torch( | |
| audio_path, self.hps_ms.data.sampling_rate) | |
| y = audio.unsqueeze(0) | |
| spec = spectrogram_torch(y, self.hps_ms.data.filter_length, | |
| self.hps_ms.data.sampling_rate, self.hps_ms.data.hop_length, | |
| self.hps_ms.data.win_length, | |
| center=False) | |
| spec_lengths = LongTensor([spec.size(-1)]) | |
| sid_src = LongTensor([original_id]) | |
| with no_grad(): | |
| sid_tgt = LongTensor([target_id]) | |
| audio = self.net_g_ms.voice_conversion(spec.to(device), | |
| spec_lengths.to(device), | |
| sid_src=sid_src.to(device), | |
| sid_tgt=sid_tgt.to(device))[0][0, 0].data.cpu().float().numpy() | |
| torch.cuda.empty_cache() | |
| return audio | |
| class TTS: | |
| def __init__(self, voice_obj, voice_speakers): | |
| self._voice_obj = voice_obj | |
| self._voice_speakers = voice_speakers | |
| self._strength_dict = {"x-weak": 0.25, "weak": 0.5, "Medium": 0.75, "Strong": 1, "x-strong": 1.25} | |
| self._speakers_count = sum([len(self._voice_speakers[i]) for i in self._voice_speakers]) | |
| self._vits_speakers_count = len(self._voice_speakers["VITS"]) | |
| self._hubert_speakers_count = len(self._voice_speakers["HUBERT-VITS"]) | |
| self._w2v2_speakers_count = len(self._voice_speakers["W2V2-VITS"]) | |
| self.dem = None | |
| # Initialization information | |
| self.logger = logging.getLogger("vits-simple-api") | |
| self.logger.info(f"torch:{torch.__version__} cuda_available:{torch.cuda.is_available()}") | |
| self.logger.info(f'device:{device} device.type:{device.type}') | |
| if getattr(config, "DIMENSIONAL_EMOTION_MODEL", None) != None: | |
| try: | |
| import audonnx | |
| root = os.path.dirname(config.DIMENSIONAL_EMOTION_MODEL) | |
| model_file = config.DIMENSIONAL_EMOTION_MODEL | |
| self.dem = audonnx.load(root=root, model_file=model_file) | |
| except Exception as e: | |
| self.logger.warning(f"Load DIMENSIONAL_EMOTION_MODEL failed {e}") | |
| if self._vits_speakers_count != 0: self.logger.info(f"[VITS] {self._vits_speakers_count} speakers") | |
| if self._hubert_speakers_count != 0: self.logger.info(f"[hubert] {self._hubert_speakers_count} speakers") | |
| if self._w2v2_speakers_count != 0: self.logger.info(f"[w2v2] {self._w2v2_speakers_count} speakers") | |
| self.logger.info(f"{self._speakers_count} speakers in total") | |
| if self._speakers_count == 0: | |
| self.logger.warning(f"No model was loaded") | |
| def voice_speakers(self): | |
| return self._voice_speakers | |
| def speakers_count(self): | |
| return self._speakers_count | |
| def vits_speakers_count(self): | |
| return self._vits_speakers_count | |
| def hubert_speakers_count(self): | |
| return self._hubert_speakers_count | |
| def w2v2_speakers_count(self): | |
| return self._w2v2_speakers_count | |
| def encode(self, sampling_rate, audio, format): | |
| with BytesIO() as f: | |
| if format.upper() == 'OGG': | |
| sf.write(f, audio, sampling_rate, format="ogg") | |
| return BytesIO(f.getvalue()) | |
| elif format.upper() == 'SILK': | |
| sf.write(f, audio, sampling_rate, format="wav") | |
| return BytesIO(silkcoder.encode(f)) | |
| elif format.upper() == 'MP3': | |
| sf.write(f, audio, sampling_rate, format="mp3") | |
| return BytesIO(f.getvalue()) | |
| elif format.upper() == 'WAV': | |
| sf.write(f, audio, sampling_rate, format="wav") | |
| return BytesIO(f.getvalue()) | |
| elif format.upper() == 'FLAC': | |
| sf.write(f, audio, sampling_rate, format="flac") | |
| return BytesIO(f.getvalue()) | |
| else: | |
| raise ValueError(f"Unsupported format:{format}") | |
| def convert_time_string(self, time_string): | |
| time_value = float(re.findall(r'\d+\.?\d*', time_string)[0]) | |
| time_unit = re.findall(r'[a-zA-Z]+', time_string)[0].lower() | |
| if time_unit.upper() == 'MS': | |
| return time_value / 1000 | |
| elif time_unit.upper() == 'S': | |
| return time_value | |
| elif time_unit.upper() == 'MIN': | |
| return time_value * 60 | |
| elif time_unit.upper() == 'H': | |
| return time_value * 3600 | |
| elif time_unit.upper() == 'D': | |
| return time_value * 24 * 3600 # 不会有人真写D吧？ | |
| else: | |
| raise ValueError("Unsupported time unit: {}".format(time_unit)) | |
| def parse_ssml(self, ssml): | |
| root = ET.fromstring(ssml) | |
| format = root.attrib.get("format", "wav") | |
| voice_tasks = [] | |
| brk_count = 0 | |
| strength_dict = {"x-weak": 0.25, "weak": 0.5, "Medium": 0.75, "Strong": 1, "x-strong": 1.25} | |
| for element in root.iter(): | |
| if element.tag == "voice": | |
| id = int(element.attrib.get("id", root.attrib.get("id", config.ID))) | |
| lang = element.attrib.get("lang", root.attrib.get("lang", config.LANG)) | |
| length = float(element.attrib.get("length", root.attrib.get("length", config.LENGTH))) | |
| noise = float(element.attrib.get("noise", root.attrib.get("noise", config.NOISE))) | |
| noisew = float(element.attrib.get("noisew", root.attrib.get("noisew", config.NOISEW))) | |
| max = int(element.attrib.get("max", root.attrib.get("max", "0"))) | |
| # 不填写默认就是vits | |
| model = element.attrib.get("model", root.attrib.get("model", "vits")) | |
| # w2v2-vits/emotion-vits才有emotion | |
| emotion = int(element.attrib.get("emotion", root.attrib.get("emotion", 0))) | |
| voice_element = ET.tostring(element, encoding='unicode') | |
| pattern_voice = r'<voice.*?>(.*?)</voice>' | |
| pattern_break = r'<break\s*?(.*?)\s*?/>' | |
| matches_voice = re.findall(pattern_voice, voice_element)[0] | |
| matches_break = re.split(pattern_break, matches_voice) | |
| for match in matches_break: | |
| strength = re.search(r'\s*strength\s*=\s*[\'\"](.*?)[\'\"]', match) | |
| time = re.search(r'\s*time\s*=\s*[\'\"](.*?)[\'\"]', match) | |
| # break标签 strength属性 | |
| if strength: | |
| brk = strength_dict[strength.group(1)] | |
| voice_tasks.append({"break": brk}) | |
| brk_count += 1 | |
| # break标签 time属性 | |
| elif time: | |
| brk = self.convert_time_string(time.group(1)) | |
| voice_tasks.append({"break": brk}) | |
| brk_count += 1 | |
| # break标签 为空说明只写了break，默认停顿0.75s | |
| elif match == "": | |
| voice_tasks.append({"break": 0.75}) | |
| brk_count += 1 | |
| # voice标签中除了break剩下的就是文本 | |
| else: | |
| voice_tasks.append({"id": id, | |
| "text": match, | |
| "lang": lang, | |
| "length": length, | |
| "noise": noise, | |
| "noisew": noisew, | |
| "max": max, | |
| "model": model, | |
| "emotion": emotion | |
| }) | |
| # 分段末尾停顿0.75s | |
| voice_tasks.append({"break": 0.75}) | |
| elif element.tag == "break": | |
| # brk_count大于0说明voice标签中有break | |
| if brk_count > 0: | |
| brk_count -= 1 | |
| continue | |
| brk = strength_dict.get(element.attrib.get("strength"), | |
| self.convert_time_string(element.attrib.get("time", "750ms"))) | |
| voice_tasks.append({"break": brk}) | |
| for i in voice_tasks: | |
| self.logger.debug(i) | |
| return voice_tasks, format | |
| def create_ssml_infer_task(self, ssml): | |
| voice_tasks, format = self.parse_ssml(ssml) | |
| audios = [] | |
| for voice in voice_tasks: | |
| if voice.get("break"): | |
| audios.append(np.zeros(int(voice.get("break") * 22050), dtype=np.int16)) | |
| else: | |
| model = voice.get("model").upper() | |
| if model != "VITS" and model != "W2V2-VITS" and model != "EMOTION-VITS": | |
| raise ValueError(f"Unsupported model: {voice.get('model')}") | |
| voice_obj = self._voice_obj[model][voice.get("id")][1] | |
| voice["id"] = self._voice_obj[model][voice.get("id")][0] | |
| audio = voice_obj.get_audio(voice) | |
| audios.append(audio) | |
| audio = np.concatenate(audios, axis=0) | |
| output = self.encode(voice_obj.hps_ms.data.sampling_rate, audio, format) | |
| return output, format | |
| def vits_infer(self, voice): | |
| format = voice.get("format", "wav") | |
| voice_obj = self._voice_obj["VITS"][voice.get("id")][1] | |
| voice["id"] = self._voice_obj["VITS"][voice.get("id")][0] | |
| audio = voice_obj.get_audio(voice, auto_break=True) | |
| output = self.encode(voice_obj.hps_ms.data.sampling_rate, audio, format) | |
| return output | |
| def hubert_vits_infer(self, voice): | |
| format = voice.get("format", "wav") | |
| voice_obj = self._voice_obj["HUBERT-VITS"][voice.get("id")][1] | |
| voice["id"] = self._voice_obj["HUBERT-VITS"][voice.get("id")][0] | |
| audio = voice_obj.get_audio(voice) | |
| output = self.encode(voice_obj.hps_ms.data.sampling_rate, audio, format) | |
| return output | |
| def w2v2_vits_infer(self, voice): | |
| format = voice.get("format", "wav") | |
| voice_obj = self._voice_obj["W2V2-VITS"][voice.get("id")][1] | |
| voice["id"] = self._voice_obj["W2V2-VITS"][voice.get("id")][0] | |
| audio = voice_obj.get_audio(voice, auto_break=True) | |
| output = self.encode(voice_obj.hps_ms.data.sampling_rate, audio, format) | |
| return output | |
| def vits_voice_conversion(self, voice): | |
| original_id = voice.get("original_id") | |
| target_id = voice.get("target_id") | |
| format = voice.get("format") | |
| original_id_obj = int(self._voice_obj["VITS"][original_id][2]) | |
| target_id_obj = int(self._voice_obj["VITS"][target_id][2]) | |
| if original_id_obj != target_id_obj: | |
| raise ValueError(f"speakers are in diffrent VITS Model") | |
| voice["original_id"] = int(self._voice_obj["VITS"][original_id][0]) | |
| voice["target_id"] = int(self._voice_obj["VITS"][target_id][0]) | |
| voice_obj = self._voice_obj["VITS"][original_id][1] | |
| audio = voice_obj.voice_conversion(voice) | |
| output = self.encode(voice_obj.hps_ms.data.sampling_rate, audio, format) | |
| return output | |
| def get_dimensional_emotion_npy(self, audio): | |
| if self.dem is None: | |
| raise ValueError(f"Please configure DIMENSIONAL_EMOTION_MODEL path in config.py") | |
| audio16000, sampling_rate = librosa.load(audio, sr=16000, mono=True) | |
| emotion = self.dem(audio16000, sampling_rate)['hidden_states'] | |
| emotion_npy = BytesIO() | |
| np.save(emotion_npy, emotion.squeeze(0)) | |
| emotion_npy.seek(0) | |
| return emotion_npy | |