mirror of
https://github.com/index-tts/index-tts.git
synced 2025-11-25 19:37:47 +08:00
740 lines
34 KiB
Python
740 lines
34 KiB
Python
import os
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from subprocess import CalledProcessError
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os.environ['HF_HUB_CACHE'] = './checkpoints/hf_cache'
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import json
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import re
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import time
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import librosa
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import torch
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import torchaudio
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from torch.nn.utils.rnn import pad_sequence
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import warnings
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warnings.filterwarnings("ignore", category=FutureWarning)
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warnings.filterwarnings("ignore", category=UserWarning)
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from omegaconf import OmegaConf
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from indextts.gpt.model_v2 import UnifiedVoice
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from indextts.utils.maskgct_utils import build_semantic_model, build_semantic_codec
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from indextts.utils.checkpoint import load_checkpoint
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from indextts.utils.front import TextNormalizer, TextTokenizer
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from indextts.s2mel.modules.commons import load_checkpoint2, MyModel
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from indextts.s2mel.modules.bigvgan import bigvgan
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from indextts.s2mel.modules.campplus.DTDNN import CAMPPlus
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from indextts.s2mel.modules.audio import mel_spectrogram
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from transformers import AutoTokenizer
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from modelscope import AutoModelForCausalLM
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from huggingface_hub import hf_hub_download
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import safetensors
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from transformers import SeamlessM4TFeatureExtractor
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import random
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import torch.nn.functional as F
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class IndexTTS2:
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def __init__(
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self, cfg_path="checkpoints/config.yaml", model_dir="checkpoints", use_fp16=False, device=None,
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use_cuda_kernel=None,use_deepspeed=False
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):
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"""
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Args:
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cfg_path (str): path to the config file.
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model_dir (str): path to the model directory.
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use_fp16 (bool): whether to use fp16.
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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.
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use_cuda_kernel (None | bool): whether to use BigVGan custom fused activation CUDA kernel, only for CUDA device.
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use_deepspeed (bool): whether to use DeepSpeed or not.
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"""
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if device is not None:
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self.device = device
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self.use_fp16 = False if device == "cpu" else use_fp16
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self.use_cuda_kernel = use_cuda_kernel is not None and use_cuda_kernel and device.startswith("cuda")
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elif torch.cuda.is_available():
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self.device = "cuda:0"
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self.use_fp16 = use_fp16
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self.use_cuda_kernel = use_cuda_kernel is None or use_cuda_kernel
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elif hasattr(torch, "xpu") and torch.xpu.is_available():
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self.device = "xpu"
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self.use_fp16 = use_fp16
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self.use_cuda_kernel = False
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elif hasattr(torch, "mps") and torch.backends.mps.is_available():
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self.device = "mps"
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self.use_fp16 = False # Use float16 on MPS is overhead than float32
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self.use_cuda_kernel = False
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else:
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self.device = "cpu"
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self.use_fp16 = False
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self.use_cuda_kernel = False
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print(">> Be patient, it may take a while to run in CPU mode.")
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self.cfg = OmegaConf.load(cfg_path)
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self.model_dir = model_dir
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self.dtype = torch.float16 if self.use_fp16 else None
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self.stop_mel_token = self.cfg.gpt.stop_mel_token
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self.qwen_emo = QwenEmotion(os.path.join(self.model_dir, self.cfg.qwen_emo_path))
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self.gpt = UnifiedVoice(**self.cfg.gpt)
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self.gpt_path = os.path.join(self.model_dir, self.cfg.gpt_checkpoint)
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load_checkpoint(self.gpt, self.gpt_path)
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self.gpt = self.gpt.to(self.device)
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if self.use_fp16:
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self.gpt.eval().half()
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else:
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self.gpt.eval()
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print(">> GPT weights restored from:", self.gpt_path)
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if use_deepspeed:
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try:
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import deepspeed
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except (ImportError, OSError, CalledProcessError) as e:
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use_deepspeed = False
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print(f">> Failed to load DeepSpeed. Falling back to normal inference. Error: {e}")
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self.gpt.post_init_gpt2_config(use_deepspeed=use_deepspeed, kv_cache=True, half=self.use_fp16)
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if self.use_cuda_kernel:
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# preload the CUDA kernel for BigVGAN
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try:
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from indextts.BigVGAN.alias_free_activation.cuda import load
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anti_alias_activation_cuda = load.load()
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print(">> Preload custom CUDA kernel for BigVGAN", anti_alias_activation_cuda)
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except:
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print(">> Failed to load custom CUDA kernel for BigVGAN. Falling back to torch.")
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self.use_cuda_kernel = False
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self.extract_features = SeamlessM4TFeatureExtractor.from_pretrained("facebook/w2v-bert-2.0")
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self.semantic_model, self.semantic_mean, self.semantic_std = build_semantic_model(
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os.path.join(self.model_dir, self.cfg.w2v_stat))
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self.semantic_model = self.semantic_model.to(self.device)
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self.semantic_model.eval()
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self.semantic_mean = self.semantic_mean.to(self.device)
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self.semantic_std = self.semantic_std.to(self.device)
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semantic_codec = build_semantic_codec(self.cfg.semantic_codec)
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semantic_code_ckpt = hf_hub_download("amphion/MaskGCT", filename="semantic_codec/model.safetensors")
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safetensors.torch.load_model(semantic_codec, semantic_code_ckpt)
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self.semantic_codec = semantic_codec.to(self.device)
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self.semantic_codec.eval()
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print('>> semantic_codec weights restored from: {}'.format(semantic_code_ckpt))
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s2mel_path = os.path.join(self.model_dir, self.cfg.s2mel_checkpoint)
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s2mel = MyModel(self.cfg.s2mel, use_gpt_latent=True)
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s2mel, _, _, _ = load_checkpoint2(
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s2mel,
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None,
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s2mel_path,
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load_only_params=True,
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ignore_modules=[],
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is_distributed=False,
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)
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self.s2mel = s2mel.to(self.device)
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self.s2mel.models['cfm'].estimator.setup_caches(max_batch_size=1, max_seq_length=8192)
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self.s2mel.eval()
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print(">> s2mel weights restored from:", s2mel_path)
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# load campplus_model
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campplus_ckpt_path = hf_hub_download(
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"funasr/campplus", filename="campplus_cn_common.bin"
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)
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campplus_model = CAMPPlus(feat_dim=80, embedding_size=192)
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campplus_model.load_state_dict(torch.load(campplus_ckpt_path, map_location="cpu"))
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self.campplus_model = campplus_model.to(self.device)
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self.campplus_model.eval()
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print(">> campplus_model weights restored from:", campplus_ckpt_path)
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bigvgan_name = self.cfg.vocoder.name
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self.bigvgan = bigvgan.BigVGAN.from_pretrained(bigvgan_name, use_cuda_kernel=self.use_cuda_kernel)
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self.bigvgan = self.bigvgan.to(self.device)
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self.bigvgan.remove_weight_norm()
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self.bigvgan.eval()
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print(">> bigvgan weights restored from:", bigvgan_name)
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self.bpe_path = os.path.join(self.model_dir, self.cfg.dataset["bpe_model"])
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self.normalizer = TextNormalizer()
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self.normalizer.load()
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print(">> TextNormalizer loaded")
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self.tokenizer = TextTokenizer(self.bpe_path, self.normalizer)
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print(">> bpe model loaded from:", self.bpe_path)
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emo_matrix = torch.load(os.path.join(self.model_dir, self.cfg.emo_matrix))
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self.emo_matrix = emo_matrix.to(self.device)
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self.emo_num = list(self.cfg.emo_num)
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spk_matrix = torch.load(os.path.join(self.model_dir, self.cfg.spk_matrix))
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self.spk_matrix = spk_matrix.to(self.device)
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self.emo_matrix = torch.split(self.emo_matrix, self.emo_num)
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self.spk_matrix = torch.split(self.spk_matrix, self.emo_num)
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mel_fn_args = {
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"n_fft": self.cfg.s2mel['preprocess_params']['spect_params']['n_fft'],
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"win_size": self.cfg.s2mel['preprocess_params']['spect_params']['win_length'],
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"hop_size": self.cfg.s2mel['preprocess_params']['spect_params']['hop_length'],
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"num_mels": self.cfg.s2mel['preprocess_params']['spect_params']['n_mels'],
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"sampling_rate": self.cfg.s2mel["preprocess_params"]["sr"],
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"fmin": self.cfg.s2mel['preprocess_params']['spect_params'].get('fmin', 0),
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"fmax": None if self.cfg.s2mel['preprocess_params']['spect_params'].get('fmax', "None") == "None" else 8000,
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"center": False
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}
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self.mel_fn = lambda x: mel_spectrogram(x, **mel_fn_args)
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# 缓存参考音频：
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self.cache_spk_cond = None
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self.cache_s2mel_style = None
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self.cache_s2mel_prompt = None
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self.cache_spk_audio_prompt = None
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self.cache_emo_cond = None
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self.cache_emo_audio_prompt = None
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self.cache_mel = None
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# 进度引用显示（可选）
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self.gr_progress = None
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self.model_version = self.cfg.version if hasattr(self.cfg, "version") else None
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@torch.no_grad()
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def get_emb(self, input_features, attention_mask):
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vq_emb = self.semantic_model(
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input_features=input_features,
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attention_mask=attention_mask,
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output_hidden_states=True,
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)
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feat = vq_emb.hidden_states[17] # (B, T, C)
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feat = (feat - self.semantic_mean) / self.semantic_std
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return feat
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def remove_long_silence(self, codes: torch.Tensor, silent_token=52, max_consecutive=30):
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"""
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Shrink special tokens (silent_token and stop_mel_token) in codes
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codes: [B, T]
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"""
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code_lens = []
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codes_list = []
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device = codes.device
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dtype = codes.dtype
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isfix = False
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for i in range(0, codes.shape[0]):
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code = codes[i]
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if not torch.any(code == self.stop_mel_token).item():
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len_ = code.size(0)
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else:
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stop_mel_idx = (code == self.stop_mel_token).nonzero(as_tuple=False)
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len_ = stop_mel_idx[0].item() if len(stop_mel_idx) > 0 else code.size(0)
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count = torch.sum(code == silent_token).item()
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if count > max_consecutive:
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# code = code.cpu().tolist()
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ncode_idx = []
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n = 0
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for k in range(len_):
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assert code[
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k] != self.stop_mel_token, f"stop_mel_token {self.stop_mel_token} should be shrinked here"
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if code[k] != silent_token:
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ncode_idx.append(k)
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n = 0
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elif code[k] == silent_token and n < 10:
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ncode_idx.append(k)
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n += 1
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# if (k == 0 and code[k] == 52) or (code[k] == 52 and code[k-1] == 52):
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# n += 1
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# new code
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len_ = len(ncode_idx)
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codes_list.append(code[ncode_idx])
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isfix = True
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else:
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# shrink to len_
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codes_list.append(code[:len_])
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code_lens.append(len_)
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if isfix:
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if len(codes_list) > 1:
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codes = pad_sequence(codes_list, batch_first=True, padding_value=self.stop_mel_token)
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else:
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codes = codes_list[0].unsqueeze(0)
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else:
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# unchanged
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pass
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# clip codes to max length
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max_len = max(code_lens)
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if max_len < codes.shape[1]:
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codes = codes[:, :max_len]
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code_lens = torch.tensor(code_lens, dtype=torch.long, device=device)
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return codes, code_lens
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def insert_interval_silence(self, wavs, sampling_rate=22050, interval_silence=200):
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"""
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Insert silences between generated segments.
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wavs: List[torch.tensor]
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"""
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if not wavs or interval_silence <= 0:
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return wavs
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# get channel_size
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channel_size = wavs[0].size(0)
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# get silence tensor
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sil_dur = int(sampling_rate * interval_silence / 1000.0)
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sil_tensor = torch.zeros(channel_size, sil_dur)
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wavs_list = []
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for i, wav in enumerate(wavs):
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wavs_list.append(wav)
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if i < len(wavs) - 1:
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wavs_list.append(sil_tensor)
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return wavs_list
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def _set_gr_progress(self, value, desc):
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if self.gr_progress is not None:
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self.gr_progress(value, desc=desc)
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def _load_and_cut_audio(self,audio_path,max_audio_length_seconds,verbose=False,sr=None):
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if not sr:
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audio, sr = librosa.load(audio_path)
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else:
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audio, _ = librosa.load(audio_path,sr=sr)
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audio = torch.tensor(audio).unsqueeze(0)
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max_audio_samples = int(max_audio_length_seconds * sr)
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if audio.shape[1] > max_audio_samples:
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if verbose:
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print(f"Audio too long ({audio.shape[1]} samples), truncating to {max_audio_samples} samples")
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audio = audio[:, :max_audio_samples]
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return audio, sr
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# 原始推理模式
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def infer(self, spk_audio_prompt, text, output_path,
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emo_audio_prompt=None, emo_alpha=1.0,
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emo_vector=None,
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use_emo_text=False, emo_text=None, use_random=False, interval_silence=200,
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verbose=False, max_text_tokens_per_segment=120, **generation_kwargs):
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print(">> starting inference...")
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self._set_gr_progress(0, "starting inference...")
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if verbose:
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print(f"origin text:{text}, spk_audio_prompt:{spk_audio_prompt}, "
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f"emo_audio_prompt:{emo_audio_prompt}, emo_alpha:{emo_alpha}, "
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f"emo_vector:{emo_vector}, use_emo_text:{use_emo_text}, "
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f"emo_text:{emo_text}")
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start_time = time.perf_counter()
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if use_emo_text or emo_vector is not None:
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# we're using a text or emotion vector guidance; so we must remove
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# "emotion reference voice", to ensure we use correct emotion mixing!
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emo_audio_prompt = None
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if use_emo_text:
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# automatically generate emotion vectors from text prompt
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if emo_text is None:
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emo_text = text # use main text prompt
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emo_dict = self.qwen_emo.inference(emo_text)
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print(f"detected emotion vectors from text: {emo_dict}")
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# convert ordered dict to list of vectors; the order is VERY important!
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emo_vector = list(emo_dict.values())
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if emo_vector is not None:
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# we have emotion vectors; they can't be blended via alpha mixing
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# in the main inference process later, so we must pre-calculate
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# their new strengths here based on the alpha instead!
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emo_vector_scale = max(0.0, min(1.0, emo_alpha))
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if emo_vector_scale != 1.0:
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# scale each vector and truncate to 4 decimals (for nicer printing)
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emo_vector = [int(x * emo_vector_scale * 10000) / 10000 for x in emo_vector]
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print(f"scaled emotion vectors to {emo_vector_scale}x: {emo_vector}")
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if emo_audio_prompt is None:
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# we are not using any external "emotion reference voice"; use
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# speaker's voice as the main emotion reference audio.
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emo_audio_prompt = spk_audio_prompt
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# must always use alpha=1.0 when we don't have an external reference voice
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emo_alpha = 1.0
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# 如果参考音频改变了，才需要重新生成, 提升速度
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if self.cache_spk_cond is None or self.cache_spk_audio_prompt != spk_audio_prompt:
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audio,sr = self._load_and_cut_audio(spk_audio_prompt,15,verbose)
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audio_22k = torchaudio.transforms.Resample(sr, 22050)(audio)
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audio_16k = torchaudio.transforms.Resample(sr, 16000)(audio)
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inputs = self.extract_features(audio_16k, sampling_rate=16000, return_tensors="pt")
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input_features = inputs["input_features"]
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attention_mask = inputs["attention_mask"]
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input_features = input_features.to(self.device)
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attention_mask = attention_mask.to(self.device)
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spk_cond_emb = self.get_emb(input_features, attention_mask)
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_, S_ref = self.semantic_codec.quantize(spk_cond_emb)
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ref_mel = self.mel_fn(audio_22k.to(spk_cond_emb.device).float())
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ref_target_lengths = torch.LongTensor([ref_mel.size(2)]).to(ref_mel.device)
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feat = torchaudio.compliance.kaldi.fbank(audio_16k.to(ref_mel.device),
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num_mel_bins=80,
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dither=0,
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sample_frequency=16000)
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feat = feat - feat.mean(dim=0, keepdim=True) # feat2另外一个滤波器能量组特征[922, 80]
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style = self.campplus_model(feat.unsqueeze(0)) # 参考音频的全局style2[1,192]
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prompt_condition = self.s2mel.models['length_regulator'](S_ref,
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ylens=ref_target_lengths,
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n_quantizers=3,
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f0=None)[0]
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self.cache_spk_cond = spk_cond_emb
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self.cache_s2mel_style = style
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self.cache_s2mel_prompt = prompt_condition
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self.cache_spk_audio_prompt = spk_audio_prompt
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self.cache_mel = ref_mel
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else:
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style = self.cache_s2mel_style
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prompt_condition = self.cache_s2mel_prompt
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spk_cond_emb = self.cache_spk_cond
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ref_mel = self.cache_mel
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if emo_vector is not None:
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weight_vector = torch.tensor(emo_vector).to(self.device)
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if use_random:
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random_index = [random.randint(0, x - 1) for x in self.emo_num]
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else:
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random_index = [find_most_similar_cosine(style, tmp) for tmp in self.spk_matrix]
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emo_matrix = [tmp[index].unsqueeze(0) for index, tmp in zip(random_index, self.emo_matrix)]
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emo_matrix = torch.cat(emo_matrix, 0)
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emovec_mat = weight_vector.unsqueeze(1) * emo_matrix
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emovec_mat = torch.sum(emovec_mat, 0)
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emovec_mat = emovec_mat.unsqueeze(0)
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if self.cache_emo_cond is None or self.cache_emo_audio_prompt != emo_audio_prompt:
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emo_audio, _ = self._load_and_cut_audio(emo_audio_prompt,15,verbose,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)
|
||
segments = self.tokenizer.split_segments(text_tokens_list, max_text_tokens_per_segment)
|
||
segments_count = len(segments)
|
||
if verbose:
|
||
print("text_tokens_list:", text_tokens_list)
|
||
print("segments count:", segments_count)
|
||
print("max_text_tokens_per_segment:", max_text_tokens_per_segment)
|
||
print(*segments, 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
|
||
has_warned = False
|
||
for seg_idx, sent in enumerate(segments):
|
||
self._set_gr_progress(0.2 + 0.7 * seg_idx / segments_count,
|
||
f"speech synthesis {seg_idx + 1}/{segments_count}...")
|
||
|
||
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 segment 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_segment`({max_text_tokens_per_segment}) 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, "saving 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.cn_key_to_en = {
|
||
"高兴": "happy",
|
||
"愤怒": "angry",
|
||
"悲伤": "sad",
|
||
"恐惧": "afraid",
|
||
"反感": "disgusted",
|
||
# TODO: the "低落" (melancholic) emotion will always be mapped to
|
||
# "悲伤" (sad) by QwenEmotion's text analysis. it doesn't know the
|
||
# difference between those emotions even if user writes exact words.
|
||
# SEE: `self.melancholic_words` for current workaround.
|
||
"低落": "melancholic",
|
||
"惊讶": "surprised",
|
||
"自然": "calm",
|
||
}
|
||
self.desired_vector_order = ["高兴", "愤怒", "悲伤", "恐惧", "反感", "低落", "惊讶", "自然"]
|
||
self.melancholic_words = {
|
||
# emotion text phrases that will force QwenEmotion's "悲伤" (sad) detection
|
||
# to become "低落" (melancholic) instead, to fix limitations mentioned above.
|
||
"低落",
|
||
"melancholy",
|
||
"melancholic",
|
||
"depression",
|
||
"depressed",
|
||
"gloomy",
|
||
}
|
||
self.max_score = 1.2
|
||
self.min_score = 0.0
|
||
|
||
def clamp_score(self, value):
|
||
return max(self.min_score, min(self.max_score, value))
|
||
|
||
def convert(self, content):
|
||
# generate emotion vector dictionary:
|
||
# - insert values in desired order (Python 3.7+ `dict` remembers insertion order)
|
||
# - convert Chinese keys to English
|
||
# - clamp all values to the allowed min/max range
|
||
# - use 0.0 for any values that were missing in `content`
|
||
emotion_dict = {
|
||
self.cn_key_to_en[cn_key]: self.clamp_score(content.get(cn_key, 0.0))
|
||
for cn_key in self.desired_vector_order
|
||
}
|
||
|
||
# default to a calm/neutral voice if all emotion vectors were empty
|
||
if all(val <= 0.0 for val in emotion_dict.values()):
|
||
print(">> no emotions detected; using default calm/neutral voice")
|
||
emotion_dict["calm"] = 1.0
|
||
|
||
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 (</think>)
|
||
index = len(output_ids) - output_ids[::-1].index(151668)
|
||
except ValueError:
|
||
index = 0
|
||
|
||
content = self.tokenizer.decode(output_ids[index:], skip_special_tokens=True)
|
||
|
||
# decode the JSON emotion detections as a dictionary
|
||
try:
|
||
content = json.loads(content)
|
||
except json.decoder.JSONDecodeError:
|
||
# invalid JSON; fallback to manual string parsing
|
||
# print(">> parsing QwenEmotion response", content)
|
||
content = {
|
||
m.group(1): float(m.group(2))
|
||
for m in re.finditer(r'([^\s":.,]+?)"?\s*:\s*([\d.]+)', content)
|
||
}
|
||
# print(">> dict result", content)
|
||
|
||
# workaround for QwenEmotion's inability to distinguish "悲伤" (sad) vs "低落" (melancholic).
|
||
# if we detect any of the IndexTTS "melancholic" words, we swap those vectors
|
||
# to encode the "sad" emotion as "melancholic" (instead of sadness).
|
||
text_input_lower = text_input.lower()
|
||
if any(word in text_input_lower for word in self.melancholic_words):
|
||
# print(">> before vec swap", content)
|
||
content["悲伤"], content["低落"] = content.get("低落", 0.0), content.get("悲伤", 0.0)
|
||
# print(">> after vec swap", content)
|
||
|
||
return self.convert(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)
|