91b7fa6148
* ninja支持中文路径编译补丁：BigVGAN fused cuda kernel * 缓存参考音频的Mel * ninja支持中文路径编译方案2：BigVGAN fused cuda kernel
333 lines
15 KiB
Python
333 lines
15 KiB
Python
import os
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import re
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import time
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from subprocess import CalledProcessError
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import sentencepiece as spm
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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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from omegaconf import OmegaConf
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from indextts.BigVGAN.models import BigVGAN as Generator
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from indextts.gpt.model import UnifiedVoice
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from indextts.utils.checkpoint import load_checkpoint
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from indextts.utils.feature_extractors import MelSpectrogramFeatures
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from indextts.utils.common import tokenize_by_CJK_char
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from indextts.utils.front import TextNormalizer
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class IndexTTS:
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def __init__(
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self, cfg_path="checkpoints/config.yaml", model_dir="checkpoints", is_fp16=True, device=None, use_cuda_kernel=None,
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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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is_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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"""
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if device is not None:
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self.device = device
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self.is_fp16 = False if device == "cpu" else is_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.is_fp16 = is_fp16
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self.use_cuda_kernel = use_cuda_kernel is None or use_cuda_kernel
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elif torch.mps.is_available():
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self.device = "mps"
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self.is_fp16 = is_fp16
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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.is_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.is_fp16 else None
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self.stop_mel_token = self.cfg.gpt.stop_mel_token
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# Comment-off to load the VQ-VAE model for debugging tokenizer
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# https://github.com/index-tts/index-tts/issues/34
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#
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# from indextts.vqvae.xtts_dvae import DiscreteVAE
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# self.dvae = DiscreteVAE(**self.cfg.vqvae)
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# self.dvae_path = os.path.join(self.model_dir, self.cfg.dvae_checkpoint)
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# load_checkpoint(self.dvae, self.dvae_path)
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# self.dvae = self.dvae.to(self.device)
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# if self.is_fp16:
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# self.dvae.eval().half()
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# else:
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# self.dvae.eval()
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# print(">> vqvae weights restored from:", self.dvae_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.is_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 self.is_fp16:
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try:
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import deepspeed
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use_deepspeed = True
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except (ImportError, OSError,CalledProcessError) as e:
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use_deepspeed = False
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print(f">> DeepSpeed加载失败，回退到标准推理: {e}")
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self.gpt.post_init_gpt2_config(use_deepspeed=use_deepspeed, kv_cache=True, half=True)
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else:
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self.gpt.post_init_gpt2_config(use_deepspeed=False, kv_cache=False, half=False)
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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.bigvgan = Generator(self.cfg.bigvgan, use_cuda_kernel=self.use_cuda_kernel)
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self.bigvgan_path = os.path.join(self.model_dir, self.cfg.bigvgan_checkpoint)
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vocoder_dict = torch.load(self.bigvgan_path, map_location="cpu")
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self.bigvgan.load_state_dict(vocoder_dict["generator"])
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self.bigvgan = self.bigvgan.to(self.device)
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# remove weight norm on eval mode
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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:", self.bigvgan_path)
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self.bpe_path = os.path.join(self.model_dir, self.cfg.dataset['bpe_model'])
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self.tokenizer = spm.SentencePieceProcessor(model_file=self.bpe_path)
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print(">> bpe model loaded from:", self.bpe_path)
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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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# 缓存参考音频mel：
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self.cache_audio_prompt = None
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self.cache_cond_mel = None
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def preprocess_text(self, text):
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# chinese_punctuation = "，。！？；：“”‘’（）【】《》"
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# english_punctuation = ",.!?;:\"\"''()[]<>"
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#
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# # 创建一个映射字典
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# punctuation_map = str.maketrans(chinese_punctuation, english_punctuation)
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# 使用translate方法替换标点符号
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# return text.translate(punctuation_map)
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return self.normalizer.infer(text)
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def remove_long_silence(self, codes: torch.Tensor, silent_token=52, max_consecutive=30):
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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 self.cfg.gpt.stop_mel_token not in code:
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code_lens.append(len(code))
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len_ = len(code)
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else:
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# len_ = code.cpu().tolist().index(8193)+1
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len_ = (code == self.stop_mel_token).nonzero(as_tuple=False)[0] + 1
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len_ = len_ - 2
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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 = []
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n = 0
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for k in range(0, len_):
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if code[k] != silent_token:
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ncode.append(code[k])
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n = 0
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elif code[k] == silent_token and n < 10:
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ncode.append(code[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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len_ = len(ncode)
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ncode = torch.LongTensor(ncode)
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codes_list.append(ncode.to(device, dtype=dtype))
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isfix = True
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#codes[i] = self.stop_mel_token
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#codes[i, 0:len_] = ncode
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else:
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codes_list.append(codes[i])
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code_lens.append(len_)
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codes = pad_sequence(codes_list, batch_first=True) if isfix else codes[:, :-2]
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code_lens = torch.LongTensor(code_lens).to(device, dtype=dtype)
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return codes, code_lens
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def split_sentences(self, text):
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"""
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Split the text into sentences based on punctuation marks.
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"""
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# 匹配标点符号（包括中英文标点）
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pattern = r'(?<=[.!?;。！？；])\s*'
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sentences = re.split(pattern, text)
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# 过滤掉空字符串和仅包含标点符号的字符串
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return [
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sentence.strip() for sentence in sentences if sentence.strip() and sentence.strip() not in {"'", ".", ","}
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]
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def infer(self, audio_prompt, text, output_path, verbose=False):
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print(">> start inference...")
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if verbose:
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print(f"origin text:{text}")
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start_time = time.perf_counter()
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normalized_text = self.preprocess_text(text)
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print(f"normalized text:{normalized_text}")
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# 如果参考音频改变了，才需要重新生成 cond_mel, 提升速度
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if self.cache_cond_mel is None or self.cache_audio_prompt != audio_prompt:
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audio, sr = torchaudio.load(audio_prompt)
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audio = torch.mean(audio, dim=0, keepdim=True)
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if audio.shape[0] > 1:
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audio = audio[0].unsqueeze(0)
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audio = torchaudio.transforms.Resample(sr, 24000)(audio)
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cond_mel = MelSpectrogramFeatures()(audio).to(self.device)
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cond_mel_frame = cond_mel.shape[-1]
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if verbose:
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print(f"cond_mel shape: {cond_mel.shape}", "dtype:", cond_mel.dtype)
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self.cache_audio_prompt = audio_prompt
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self.cache_cond_mel = cond_mel
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else:
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cond_mel = self.cache_cond_mel
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cond_mel_frame = cond_mel.shape[-1]
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pass
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auto_conditioning = cond_mel
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sentences = self.split_sentences(normalized_text)
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if verbose:
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print("sentences:", sentences)
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top_p = .8
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top_k = 30
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temperature = 1.0
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autoregressive_batch_size = 1
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length_penalty = 0.0
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num_beams = 3
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repetition_penalty = 10.0
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max_mel_tokens = 600
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sampling_rate = 24000
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# lang = "EN"
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# lang = "ZH"
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wavs = []
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gpt_gen_time = 0
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gpt_forward_time = 0
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bigvgan_time = 0
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for sent in sentences:
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# sent = " ".join([char for char in sent.upper()]) if lang == "ZH" else sent.upper()
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cleand_text = tokenize_by_CJK_char(sent)
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# cleand_text = "他 那 像 HONG3 小 孩 似 的 话 , 引 得 人 们 HONG1 堂 大 笑 , 大 家 听 了 一 HONG3 而 散 ."
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if verbose:
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print("cleand_text:", cleand_text)
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text_tokens = torch.tensor(self.tokenizer.EncodeAsIds(cleand_text),dtype=torch.int32, device=self.device).unsqueeze(0)
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# text_tokens = F.pad(text_tokens, (0, 1)) # This may not be necessary.
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# text_tokens = F.pad(text_tokens, (1, 0), value=0)
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# text_tokens = F.pad(text_tokens, (0, 1), value=1)
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if verbose:
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print(text_tokens)
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print(f"text_tokens shape: {text_tokens.shape}, text_tokens type: {text_tokens.dtype}")
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# debug tokenizer
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text_token_syms = self.tokenizer.IdToPiece(text_tokens[0].tolist())
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print(text_token_syms)
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# text_len = torch.IntTensor([text_tokens.size(1)], device=text_tokens.device)
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# print(text_len)
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m_start_time = time.perf_counter()
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with torch.no_grad():
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with torch.amp.autocast(self.device, enabled=self.dtype is not None, dtype=self.dtype):
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codes = self.gpt.inference_speech(auto_conditioning, text_tokens,
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cond_mel_lengths=torch.tensor([auto_conditioning.shape[-1]],
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device=text_tokens.device),
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# text_lengths=text_len,
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do_sample=True,
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top_p=top_p,
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top_k=top_k,
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temperature=temperature,
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num_return_sequences=autoregressive_batch_size,
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length_penalty=length_penalty,
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num_beams=num_beams,
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repetition_penalty=repetition_penalty,
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max_generate_length=max_mel_tokens)
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gpt_gen_time += time.perf_counter() - m_start_time
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#codes = codes[:, :-2]
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code_lens = torch.tensor([codes.shape[-1]], device=codes.device, dtype=codes.dtype)
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if verbose:
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print(codes, type(codes))
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print(f"codes shape: {codes.shape}, codes type: {codes.dtype}")
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print(f"code len: {code_lens}")
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# remove ultra-long silence if exits
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# temporarily fix the long silence bug.
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codes, code_lens = self.remove_long_silence(codes, silent_token=52, max_consecutive=30)
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if verbose:
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print(codes, type(codes))
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print(f"fix codes shape: {codes.shape}, codes type: {codes.dtype}")
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print(f"code len: {code_lens}")
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m_start_time = time.perf_counter()
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# latent, text_lens_out, code_lens_out = \
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with torch.amp.autocast(self.device, enabled=self.dtype is not None, dtype=self.dtype):
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latent = \
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self.gpt(auto_conditioning, text_tokens,
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torch.tensor([text_tokens.shape[-1]], device=text_tokens.device), codes,
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code_lens*self.gpt.mel_length_compression,
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cond_mel_lengths=torch.tensor([auto_conditioning.shape[-1]], device=text_tokens.device),
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return_latent=True, clip_inputs=False)
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gpt_forward_time += time.perf_counter() - m_start_time
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m_start_time = time.perf_counter()
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wav, _ = self.bigvgan(latent, auto_conditioning.transpose(1, 2))
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bigvgan_time += time.perf_counter() - m_start_time
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wav = wav.squeeze(1)
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wav = torch.clamp(32767 * wav, -32767.0, 32767.0)
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print(f"wav shape: {wav.shape}", "min:", wav.min(), "max:", wav.max())
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# wavs.append(wav[:, :-512])
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wavs.append(wav)
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end_time = time.perf_counter()
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wav = torch.cat(wavs, dim=1)
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wav_length = wav.shape[-1] / sampling_rate
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print(f">> Reference audio length: {cond_mel_frame*256 / sampling_rate:.2f} seconds")
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print(f">> gpt_gen_time: {gpt_gen_time:.2f} seconds")
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print(f">> gpt_forward_time: {gpt_forward_time:.2f} seconds")
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print(f">> bigvgan_time: {bigvgan_time:.2f} seconds")
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print(f">> Total inference time: {end_time - start_time:.2f} seconds")
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print(f">> Generated audio length: {wav_length:.2f} seconds")
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print(f">> RTF: {(end_time - start_time) / wav_length:.4f}")
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torchaudio.save(output_path, wav.cpu().type(torch.int16), sampling_rate)
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print(">> wav file saved to:", output_path)
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if __name__ == "__main__":
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prompt_wav = "testwav/input.wav"
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prompt_wav = "testwav/spk_1744181067_1.wav"
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text="晕 XUAN4 是 一 种 GAN3 觉"
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text = "There is a vehicle arriving in dock number 7?"
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text='大家好，我现在正在bilibili 体验 ai 科技，说实话，来之前我绝对想不到！AI技术已经发展到这样匪夷所思的地步了！'
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tts = IndexTTS(cfg_path="checkpoints/config.yaml", model_dir="checkpoints", is_fp16=True, use_cuda_kernel=False)
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tts.infer(audio_prompt=prompt_wav, text=text, output_path="gen.wav", verbose=True)
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