import os from subprocess import CalledProcessError import time import librosa import torch import torchaudio from torch.nn.utils.rnn import pad_sequence import warnings warnings.filterwarnings("ignore", category=FutureWarning) warnings.filterwarnings("ignore", category=UserWarning) from omegaconf import OmegaConf from indextts.gpt.model_v2 import UnifiedVoice from indextts.utils.maskgct_utils import build_semantic_model, build_semantic_codec from indextts.utils.checkpoint import load_checkpoint from indextts.utils.front import TextNormalizer, TextTokenizer from indextts.s2mel.modules.commons import load_checkpoint2, MyModel from indextts.s2mel.modules.bigvgan import bigvgan from indextts.s2mel.modules.campplus.DTDNN import CAMPPlus from indextts.s2mel.modules.audio import mel_spectrogram from transformers import AutoTokenizer from modelscope import AutoModelForCausalLM from huggingface_hub import hf_hub_download import safetensors from transformers import SeamlessM4TFeatureExtractor import random import torch.nn.functional as F class IndexTTS2: def __init__( self, cfg_path="checkpoints/config.yaml", model_dir="checkpoints", is_fp16=False, device=None, use_cuda_kernel=None,use_deepspeed=False ): """ Args: cfg_path (str): path to the config file. model_dir (str): path to the model directory. is_fp16 (bool): whether to use fp16. device (str): device to use (e.g., 'cuda:0', 'cpu'). If None, it will be set automatically based on the availability of CUDA or MPS. use_cuda_kernel (None | bool): whether to use BigVGan custom fused activation CUDA kernel, only for CUDA device. """ if device is not None: self.device = device self.is_fp16 = False if device == "cpu" else is_fp16 self.use_cuda_kernel = use_cuda_kernel is not None and use_cuda_kernel and device.startswith("cuda") elif torch.cuda.is_available(): self.device = "cuda:0" self.is_fp16 = is_fp16 self.use_cuda_kernel = use_cuda_kernel is None or use_cuda_kernel elif hasattr(torch, "mps") and torch.backends.mps.is_available(): self.device = "mps" self.is_fp16 = False # Use float16 on MPS is overhead than float32 self.use_cuda_kernel = False else: self.device = "cpu" self.is_fp16 = False self.use_cuda_kernel = False print(">> Be patient, it may take a while to run in CPU mode.") self.cfg = OmegaConf.load(cfg_path) self.model_dir = model_dir self.dtype = torch.float16 if self.is_fp16 else None self.stop_mel_token = self.cfg.gpt.stop_mel_token self.qwen_emo = QwenEmotion(os.path.join(self.model_dir, self.cfg.qwen_emo_path)) self.gpt = UnifiedVoice(**self.cfg.gpt) self.gpt_path = os.path.join(self.model_dir, self.cfg.gpt_checkpoint) load_checkpoint(self.gpt, self.gpt_path) self.gpt = self.gpt.to(self.device) if self.is_fp16: self.gpt.eval().half() else: self.gpt.eval() print(">> GPT weights restored from:", self.gpt_path) if self.is_fp16: try: import deepspeed except (ImportError, OSError, CalledProcessError) as e: use_deepspeed = False print(f">> DeepSpeed加载失败，回退到标准推理: {e}") self.gpt.post_init_gpt2_config(use_deepspeed=use_deepspeed, kv_cache=True, half=True) else: self.gpt.post_init_gpt2_config(use_deepspeed=use_deepspeed, kv_cache=True, half=False) if self.use_cuda_kernel: # preload the CUDA kernel for BigVGAN try: from indextts.BigVGAN.alias_free_activation.cuda import load anti_alias_activation_cuda = load.load() print(">> Preload custom CUDA kernel for BigVGAN", anti_alias_activation_cuda) except: print(">> Failed to load custom CUDA kernel for BigVGAN. Falling back to torch.") self.use_cuda_kernel = False self.extract_features = SeamlessM4TFeatureExtractor.from_pretrained("facebook/w2v-bert-2.0") self.semantic_model, self.semantic_mean, self.semantic_std = build_semantic_model( os.path.join(self.model_dir, self.cfg.w2v_stat)) self.semantic_model = self.semantic_model.to(self.device) self.semantic_model.eval() self.semantic_mean = self.semantic_mean.to(self.device) self.semantic_std = self.semantic_std.to(self.device) semantic_codec = build_semantic_codec(self.cfg.semantic_codec) semantic_code_ckpt = hf_hub_download("amphion/MaskGCT", filename="semantic_codec/model.safetensors") safetensors.torch.load_model(semantic_codec, semantic_code_ckpt) self.semantic_codec = semantic_codec.to(self.device) self.semantic_codec.eval() print('>> semantic_codec weights restored from: {}'.format(semantic_code_ckpt)) s2mel_path = os.path.join(self.model_dir, self.cfg.s2mel_checkpoint) s2mel = MyModel(self.cfg.s2mel, use_gpt_latent=True) s2mel, _, _, _ = load_checkpoint2( s2mel, None, s2mel_path, load_only_params=True, ignore_modules=[], is_distributed=False, ) self.s2mel = s2mel.to(self.device) self.s2mel.models['cfm'].estimator.setup_caches(max_batch_size=1, max_seq_length=8192) self.s2mel.eval() print(">> s2mel weights restored from:", s2mel_path) # load campplus_model campplus_ckpt_path = hf_hub_download( "funasr/campplus", filename="campplus_cn_common.bin" ) campplus_model = CAMPPlus(feat_dim=80, embedding_size=192) campplus_model.load_state_dict(torch.load(campplus_ckpt_path, map_location="cpu")) self.campplus_model = campplus_model.to(self.device) self.campplus_model.eval() print(">> campplus_model weights restored from:", campplus_ckpt_path) bigvgan_name = self.cfg.vocoder.name self.bigvgan = bigvgan.BigVGAN.from_pretrained(bigvgan_name, use_cuda_kernel=False) self.bigvgan = self.bigvgan.to(self.device) self.bigvgan.remove_weight_norm() self.bigvgan.eval() print(">> bigvgan weights restored from:", bigvgan_name) self.bpe_path = os.path.join(self.model_dir, self.cfg.dataset["bpe_model"]) self.normalizer = TextNormalizer() self.normalizer.load() print(">> TextNormalizer loaded") self.tokenizer = TextTokenizer(self.bpe_path, self.normalizer) print(">> bpe model loaded from:", self.bpe_path) emo_matrix = torch.load(os.path.join(self.model_dir, self.cfg.emo_matrix)) self.emo_matrix = emo_matrix.to(self.device) self.emo_num = list(self.cfg.emo_num) spk_matrix = torch.load(os.path.join(self.model_dir, self.cfg.spk_matrix)) self.spk_matrix = spk_matrix.to(self.device) self.emo_matrix = torch.split(self.emo_matrix, self.emo_num) self.spk_matrix = torch.split(self.spk_matrix, self.emo_num) mel_fn_args = { "n_fft": self.cfg.s2mel['preprocess_params']['spect_params']['n_fft'], "win_size": self.cfg.s2mel['preprocess_params']['spect_params']['win_length'], "hop_size": self.cfg.s2mel['preprocess_params']['spect_params']['hop_length'], "num_mels": self.cfg.s2mel['preprocess_params']['spect_params']['n_mels'], "sampling_rate": self.cfg.s2mel["preprocess_params"]["sr"], "fmin": self.cfg.s2mel['preprocess_params']['spect_params'].get('fmin', 0), "fmax": None if self.cfg.s2mel['preprocess_params']['spect_params'].get('fmax', "None") == "None" else 8000, "center": False } self.mel_fn = lambda x: mel_spectrogram(x, **mel_fn_args) # 缓存参考音频： self.cache_spk_cond = None self.cache_s2mel_style = None self.cache_s2mel_prompt = None self.cache_spk_audio_prompt = None self.cache_emo_cond = None self.cache_emo_audio_prompt = None self.cache_mel = None # 进度引用显示（可选） self.gr_progress = None self.model_version = self.cfg.version if hasattr(self.cfg, "version") else None @torch.no_grad() def get_emb(self, input_features, attention_mask): vq_emb = self.semantic_model( input_features=input_features, attention_mask=attention_mask, output_hidden_states=True, ) feat = vq_emb.hidden_states[17] # (B, T, C) feat = (feat - self.semantic_mean) / self.semantic_std return feat def remove_long_silence(self, codes: torch.Tensor, silent_token=52, max_consecutive=30): """ Shrink special tokens (silent_token and stop_mel_token) in codes codes: [B, T] """ code_lens = [] codes_list = [] device = codes.device dtype = codes.dtype isfix = False for i in range(0, codes.shape[0]): code = codes[i] if not torch.any(code == self.stop_mel_token).item(): len_ = code.size(0) else: stop_mel_idx = (code == self.stop_mel_token).nonzero(as_tuple=False) len_ = stop_mel_idx[0].item() if len(stop_mel_idx) > 0 else code.size(0) count = torch.sum(code == silent_token).item() if count > max_consecutive: # code = code.cpu().tolist() ncode_idx = [] n = 0 for k in range(len_): assert code[ k] != self.stop_mel_token, f"stop_mel_token {self.stop_mel_token} should be shrinked here" if code[k] != silent_token: ncode_idx.append(k) n = 0 elif code[k] == silent_token and n < 10: ncode_idx.append(k) n += 1 # if (k == 0 and code[k] == 52) or (code[k] == 52 and code[k-1] == 52): # n += 1 # new code len_ = len(ncode_idx) codes_list.append(code[ncode_idx]) isfix = True else: # shrink to len_ codes_list.append(code[:len_]) code_lens.append(len_) if isfix: if len(codes_list) > 1: codes = pad_sequence(codes_list, batch_first=True, padding_value=self.stop_mel_token) else: codes = codes_list[0].unsqueeze(0) else: # unchanged pass # clip codes to max length max_len = max(code_lens) if max_len < codes.shape[1]: codes = codes[:, :max_len] code_lens = torch.tensor(code_lens, dtype=torch.long, device=device) return codes, code_lens def insert_interval_silence(self, wavs, sampling_rate=22050, interval_silence=200): """ Insert silences between sentences. wavs: List[torch.tensor] """ if not wavs or interval_silence <= 0: return wavs # get channel_size channel_size = wavs[0].size(0) # get silence tensor sil_dur = int(sampling_rate * interval_silence / 1000.0) sil_tensor = torch.zeros(channel_size, sil_dur) wavs_list = [] for i, wav in enumerate(wavs): wavs_list.append(wav) if i < len(wavs) - 1: wavs_list.append(sil_tensor) return wavs_list def _set_gr_progress(self, value, desc): if self.gr_progress is not None: self.gr_progress(value, desc=desc) # 原始推理模式 def infer(self, spk_audio_prompt, text, output_path, emo_audio_prompt=None, emo_alpha=1.0, emo_vector=None, use_emo_text=False, emo_text=None, use_random=False, interval_silence=200, verbose=False, max_text_tokens_per_sentence=120, **generation_kwargs): print(">> start inference...") self._set_gr_progress(0, "start inference...") if verbose: print(f"origin text:{text}, spk_audio_prompt:{spk_audio_prompt}," f" emo_audio_prompt:{emo_audio_prompt}, emo_alpha:{emo_alpha}, " f"emo_vector:{emo_vector}, use_emo_text:{use_emo_text}, " f"emo_text:{emo_text}") start_time = time.perf_counter() if use_emo_text: emo_audio_prompt = None emo_alpha = 1.0 # assert emo_audio_prompt is None # assert emo_alpha == 1.0 if emo_text is None: emo_text = text emo_dict, content = self.qwen_emo.inference(emo_text) print(emo_dict) emo_vector = list(emo_dict.values()) if emo_vector is not None: emo_audio_prompt = None emo_alpha = 1.0 # assert emo_audio_prompt is None # assert emo_alpha == 1.0 if emo_audio_prompt is None: emo_audio_prompt = spk_audio_prompt emo_alpha = 1.0 # assert emo_alpha == 1.0 # 如果参考音频改变了，才需要重新生成, 提升速度 if self.cache_spk_cond is None or self.cache_spk_audio_prompt != spk_audio_prompt: audio, sr = librosa.load(spk_audio_prompt) audio = torch.tensor(audio).unsqueeze(0) audio_22k = torchaudio.transforms.Resample(sr, 22050)(audio) audio_16k = torchaudio.transforms.Resample(sr, 16000)(audio) inputs = self.extract_features(audio_16k, sampling_rate=16000, return_tensors="pt") input_features = inputs["input_features"] attention_mask = inputs["attention_mask"] input_features = input_features.to(self.device) attention_mask = attention_mask.to(self.device) spk_cond_emb = self.get_emb(input_features, attention_mask) _, S_ref = self.semantic_codec.quantize(spk_cond_emb) ref_mel = self.mel_fn(audio_22k.to(spk_cond_emb.device).float()) ref_target_lengths = torch.LongTensor([ref_mel.size(2)]).to(ref_mel.device) feat = torchaudio.compliance.kaldi.fbank(audio_16k.to(ref_mel.device), num_mel_bins=80, dither=0, sample_frequency=16000) feat = feat - feat.mean(dim=0, keepdim=True) # feat2另外一个滤波器能量组特征[922, 80] style = self.campplus_model(feat.unsqueeze(0)) # 参考音频的全局style2[1,192] prompt_condition = self.s2mel.models['length_regulator'](S_ref, ylens=ref_target_lengths, n_quantizers=3, f0=None)[0] self.cache_spk_cond = spk_cond_emb self.cache_s2mel_style = style self.cache_s2mel_prompt = prompt_condition self.cache_spk_audio_prompt = spk_audio_prompt self.cache_mel = ref_mel else: style = self.cache_s2mel_style prompt_condition = self.cache_s2mel_prompt spk_cond_emb = self.cache_spk_cond ref_mel = self.cache_mel if emo_vector is not None: weight_vector = torch.tensor(emo_vector).to(self.device) if use_random: random_index = [random.randint(0, x - 1) for x in self.emo_num] else: random_index = [find_most_similar_cosine(style, tmp) for tmp in self.spk_matrix] emo_matrix = [tmp[index].unsqueeze(0) for index, tmp in zip(random_index, self.emo_matrix)] emo_matrix = torch.cat(emo_matrix, 0) emovec_mat = weight_vector.unsqueeze(1) * emo_matrix emovec_mat = torch.sum(emovec_mat, 0) emovec_mat = emovec_mat.unsqueeze(0) if self.cache_emo_cond is None or self.cache_emo_audio_prompt != emo_audio_prompt: emo_audio, _ = librosa.load(emo_audio_prompt, sr=16000) emo_inputs = self.extract_features(emo_audio, sampling_rate=16000, return_tensors="pt") emo_input_features = emo_inputs["input_features"] emo_attention_mask = emo_inputs["attention_mask"] emo_input_features = emo_input_features.to(self.device) emo_attention_mask = emo_attention_mask.to(self.device) emo_cond_emb = self.get_emb(emo_input_features, emo_attention_mask) self.cache_emo_cond = emo_cond_emb self.cache_emo_audio_prompt = emo_audio_prompt else: emo_cond_emb = self.cache_emo_cond self._set_gr_progress(0.1, "text processing...") text_tokens_list = self.tokenizer.tokenize(text) sentences = self.tokenizer.split_sentences(text_tokens_list, max_text_tokens_per_sentence) if verbose: print("text_tokens_list:", text_tokens_list) print("sentences count:", len(sentences)) print("max_text_tokens_per_sentence:", max_text_tokens_per_sentence) print(*sentences, sep="\n") do_sample = generation_kwargs.pop("do_sample", True) top_p = generation_kwargs.pop("top_p", 0.8) top_k = generation_kwargs.pop("top_k", 30) temperature = generation_kwargs.pop("temperature", 0.8) autoregressive_batch_size = 1 length_penalty = generation_kwargs.pop("length_penalty", 0.0) num_beams = generation_kwargs.pop("num_beams", 3) repetition_penalty = generation_kwargs.pop("repetition_penalty", 10.0) max_mel_tokens = generation_kwargs.pop("max_mel_tokens", 1500) sampling_rate = 22050 wavs = [] gpt_gen_time = 0 gpt_forward_time = 0 s2mel_time = 0 bigvgan_time = 0 progress = 0 has_warned = False for sent in sentences: text_tokens = self.tokenizer.convert_tokens_to_ids(sent) text_tokens = torch.tensor(text_tokens, dtype=torch.int32, device=self.device).unsqueeze(0) if verbose: print(text_tokens) print(f"text_tokens shape: {text_tokens.shape}, text_tokens type: {text_tokens.dtype}") # debug tokenizer text_token_syms = self.tokenizer.convert_ids_to_tokens(text_tokens[0].tolist()) print("text_token_syms is same as sentence tokens", text_token_syms == sent) m_start_time = time.perf_counter() with torch.no_grad(): with torch.amp.autocast(text_tokens.device.type, enabled=self.dtype is not None, dtype=self.dtype): emovec = self.gpt.merge_emovec( spk_cond_emb, emo_cond_emb, torch.tensor([spk_cond_emb.shape[-1]], device=text_tokens.device), torch.tensor([emo_cond_emb.shape[-1]], device=text_tokens.device), alpha=emo_alpha ) if emo_vector is not None: emovec = emovec_mat + (1 - torch.sum(weight_vector)) * emovec # emovec = emovec_mat codes, speech_conditioning_latent = self.gpt.inference_speech( spk_cond_emb, text_tokens, emo_cond_emb, cond_lengths=torch.tensor([spk_cond_emb.shape[-1]], device=text_tokens.device), emo_cond_lengths=torch.tensor([emo_cond_emb.shape[-1]], device=text_tokens.device), emo_vec=emovec, do_sample=True, top_p=top_p, top_k=top_k, temperature=temperature, num_return_sequences=autoregressive_batch_size, length_penalty=length_penalty, num_beams=num_beams, repetition_penalty=repetition_penalty, max_generate_length=max_mel_tokens, **generation_kwargs ) gpt_gen_time += time.perf_counter() - m_start_time if not has_warned and (codes[:, -1] != self.stop_mel_token).any(): warnings.warn( f"WARN: generation stopped due to exceeding `max_mel_tokens` ({max_mel_tokens}). " f"Input text tokens: {text_tokens.shape[1]}. " f"Consider reducing `max_text_tokens_per_sentence`({max_text_tokens_per_sentence}) or increasing `max_mel_tokens`.", category=RuntimeWarning ) has_warned = True code_lens = torch.tensor([codes.shape[-1]], device=codes.device, dtype=codes.dtype) # if verbose: # print(codes, type(codes)) # print(f"codes shape: {codes.shape}, codes type: {codes.dtype}") # print(f"code len: {code_lens}") code_lens = [] for code in codes: if self.stop_mel_token not in code: code_lens.append(len(code)) code_len = len(code) else: len_ = (code == self.stop_mel_token).nonzero(as_tuple=False)[0] + 1 code_len = len_ - 1 code_lens.append(code_len) codes = codes[:, :code_len] code_lens = torch.LongTensor(code_lens) code_lens = code_lens.to(self.device) if verbose: print(codes, type(codes)) print(f"fix codes shape: {codes.shape}, codes type: {codes.dtype}") print(f"code len: {code_lens}") m_start_time = time.perf_counter() use_speed = torch.zeros(spk_cond_emb.size(0)).to(spk_cond_emb.device).long() with torch.amp.autocast(text_tokens.device.type, enabled=self.dtype is not None, dtype=self.dtype): latent = self.gpt( speech_conditioning_latent, text_tokens, torch.tensor([text_tokens.shape[-1]], device=text_tokens.device), codes, torch.tensor([codes.shape[-1]], device=text_tokens.device), emo_cond_emb, cond_mel_lengths=torch.tensor([spk_cond_emb.shape[-1]], device=text_tokens.device), emo_cond_mel_lengths=torch.tensor([emo_cond_emb.shape[-1]], device=text_tokens.device), emo_vec=emovec, use_speed=use_speed, ) gpt_forward_time += time.perf_counter() - m_start_time dtype = None with torch.amp.autocast(text_tokens.device.type, enabled=dtype is not None, dtype=dtype): m_start_time = time.perf_counter() diffusion_steps = 25 inference_cfg_rate = 0.7 latent = self.s2mel.models['gpt_layer'](latent) S_infer = self.semantic_codec.quantizer.vq2emb(codes.unsqueeze(1)) S_infer = S_infer.transpose(1, 2) S_infer = S_infer + latent target_lengths = (code_lens * 1.72).long() cond = self.s2mel.models['length_regulator'](S_infer, ylens=target_lengths, n_quantizers=3, f0=None)[0] cat_condition = torch.cat([prompt_condition, cond], dim=1) vc_target = self.s2mel.models['cfm'].inference(cat_condition, torch.LongTensor([cat_condition.size(1)]).to( cond.device), ref_mel, style, None, diffusion_steps, inference_cfg_rate=inference_cfg_rate) vc_target = vc_target[:, :, ref_mel.size(-1):] s2mel_time += time.perf_counter() - m_start_time m_start_time = time.perf_counter() wav = self.bigvgan(vc_target.float()).squeeze().unsqueeze(0) print(wav.shape) bigvgan_time += time.perf_counter() - m_start_time wav = wav.squeeze(1) wav = torch.clamp(32767 * wav, -32767.0, 32767.0) if verbose: print(f"wav shape: {wav.shape}", "min:", wav.min(), "max:", wav.max()) # wavs.append(wav[:, :-512]) wavs.append(wav.cpu()) # to cpu before saving end_time = time.perf_counter() self._set_gr_progress(0.9, "save audio...") wavs = self.insert_interval_silence(wavs, sampling_rate=sampling_rate, interval_silence=interval_silence) wav = torch.cat(wavs, dim=1) wav_length = wav.shape[-1] / sampling_rate print(f">> gpt_gen_time: {gpt_gen_time:.2f} seconds") print(f">> gpt_forward_time: {gpt_forward_time:.2f} seconds") print(f">> s2mel_time: {s2mel_time:.2f} seconds") print(f">> bigvgan_time: {bigvgan_time:.2f} seconds") print(f">> Total inference time: {end_time - start_time:.2f} seconds") print(f">> Generated audio length: {wav_length:.2f} seconds") print(f">> RTF: {(end_time - start_time) / wav_length:.4f}") # save audio wav = wav.cpu() # to cpu if output_path: # 直接保存音频到指定路径中 if os.path.isfile(output_path): os.remove(output_path) print(">> remove old wav file:", output_path) if os.path.dirname(output_path) != "": os.makedirs(os.path.dirname(output_path), exist_ok=True) torchaudio.save(output_path, wav.type(torch.int16), sampling_rate) print(">> wav file saved to:", output_path) return output_path else: # 返回以符合Gradio的格式要求 wav_data = wav.type(torch.int16) wav_data = wav_data.numpy().T return (sampling_rate, wav_data) def find_most_similar_cosine(query_vector, matrix): query_vector = query_vector.float() matrix = matrix.float() similarities = F.cosine_similarity(query_vector, matrix, dim=1) most_similar_index = torch.argmax(similarities) return most_similar_index class QwenEmotion: def __init__(self, model_dir): self.model_dir = model_dir self.tokenizer = AutoTokenizer.from_pretrained(self.model_dir) self.model = AutoModelForCausalLM.from_pretrained( self.model_dir, torch_dtype="float16", # "auto" device_map="auto" ) self.prompt = "文本情感分类" self.convert_dict = { "愤怒": "angry", "高兴": "happy", "恐惧": "fear", "反感": "hate", "悲伤": "sad", "低落": "low", "惊讶": "surprise", "自然": "neutral", } self.backup_dict = {"happy": 0, "angry": 0, "sad": 0, "fear": 0, "hate": 0, "low": 0, "surprise": 0, "neutral": 1.0} self.max_score = 1.2 self.min_score = 0.0 def convert(self, content): content = content.replace("\n", " ") content = content.replace(" ", "") content = content.replace("{", "") content = content.replace("}", "") content = content.replace('"', "") parts = content.strip().split(',') print(parts) parts_dict = {} desired_order = ["高兴", "愤怒", "悲伤", "恐惧", "反感", "低落", "惊讶", "自然"] for part in parts: key_value = part.strip().split(':') if len(key_value) == 2: parts_dict[key_value[0].strip()] = part # 按照期望顺序重新排列 ordered_parts = [parts_dict[key] for key in desired_order if key in parts_dict] parts = ordered_parts if len(parts) != len(self.convert_dict): return self.backup_dict emotion_dict = {} for part in parts: key_value = part.strip().split(':') if len(key_value) == 2: try: key = self.convert_dict[key_value[0].strip()] value = float(key_value[1].strip()) value = max(self.min_score, min(self.max_score, value)) emotion_dict[key] = value except Exception: continue for key in self.backup_dict: if key not in emotion_dict: emotion_dict[key] = 0.0 if sum(emotion_dict.values()) <= 0: return self.backup_dict return emotion_dict def inference(self, text_input): start = time.time() messages = [ {"role": "system", "content": f"{self.prompt}"}, {"role": "user", "content": f"{text_input}"} ] text = self.tokenizer.apply_chat_template( messages, tokenize=False, add_generation_prompt=True, enable_thinking=False, ) model_inputs = self.tokenizer([text], return_tensors="pt").to(self.model.device) # conduct text completion generated_ids = self.model.generate( **model_inputs, max_new_tokens=32768, pad_token_id=self.tokenizer.eos_token_id ) output_ids = generated_ids[0][len(model_inputs.input_ids[0]):].tolist() # parsing thinking content try: # rindex finding 151668 () index = len(output_ids) - output_ids[::-1].index(151668) except ValueError: index = 0 content = self.tokenizer.decode(output_ids[index:], skip_special_tokens=True).strip("\n") emotion_dict = self.convert(content) return emotion_dict, content if __name__ == "__main__": prompt_wav = "examples/voice_01.wav" text = '欢迎大家来体验indextts2，并给予我们意见与反馈，谢谢大家。' tts = IndexTTS2(cfg_path="checkpoints/config.yaml", model_dir="checkpoints", use_cuda_kernel=False) tts.infer(spk_audio_prompt=prompt_wav, text=text, output_path="gen.wav", verbose=True)