Feature/kemurin (#99)
* deepspeed无法使用时回退到通常路径 * 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 * 增加批次推理：长句实现至少 2~10 倍以上的速度提升~ * fix上层目录为空时报错 --------- Co-authored-by: kemuriririn <10inspiral@gmail.com> Co-authored-by: sunnyboxs <sjt2000@qq.com>
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kemuriririn
GitHub
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commit
6783f22fe4
@ -3,11 +3,13 @@ import re
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import time
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from subprocess import CalledProcessError
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import numpy as np
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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 tqdm import tqdm
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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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@ -113,6 +115,8 @@ class IndexTTS:
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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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# 进度引用显示（可选）
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self.gr_progress = None
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def preprocess_text(self, text):
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# chinese_punctuation = "，。！？；：“”‘’（）【】《》"
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@ -180,9 +184,230 @@ class IndexTTS:
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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 pad_tokens_cat(self, tokens):
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if len(tokens) <= 1:return tokens[-1]
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max_len = max(t.size(1) for t in tokens)
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outputs = []
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for tensor in tokens:
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pad_len = max_len - tensor.size(1)
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if pad_len > 0:
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padded = torch.nn.functional.pad(tensor,
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(0, pad_len),
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value=self.cfg.gpt.stop_text_token
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)
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outputs.append(padded)
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else:
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outputs.append(tensor)
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tokens = torch.cat(outputs, dim=0)
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return tokens
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def torch_empty_cache(self):
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try:
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if "cuda" in str(self.device):
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torch.cuda.empty_cache()
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elif "mps" in str(self.device):
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torch.mps.empty_cache()
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except Exception as e:
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pass
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def _set_gr_progress(self, value, desc):
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if self.gr_progress is not None:self.gr_progress(value, desc=desc)
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# 快速推理：对于“多句长文本”，可实现至少 2~10 倍以上的速度提升~ （First modified by sunnyboxs 2025-04-16）
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def infer_fast(self, audio_prompt, text, output_path, verbose=False):
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print(">> start fast inference...")
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self._set_gr_progress(0, "start fast 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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cond_mel_lengths = torch.tensor([cond_mel_frame],device=self.device)
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# text_tokens
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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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all_text_tokens = []
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self._set_gr_progress(0.1, "text processing...")
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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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all_text_tokens.append(text_tokens)
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batch_num = len(all_text_tokens)
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batch_text_tokens = self.pad_tokens_cat(all_text_tokens)
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batch_cond_mel_lengths = torch.cat([cond_mel_lengths] * batch_num, dim=0)
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batch_auto_conditioning = torch.cat([auto_conditioning] * batch_num, dim=0)
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# gpt speech
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self._set_gr_progress(0.2, "gpt inference speech...")
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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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batch_codes = self.gpt.inference_speech(batch_auto_conditioning, batch_text_tokens,
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cond_mel_lengths=batch_cond_mel_lengths,
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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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# clear cache
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batch_auto_conditioning = None
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batch_cond_mel_lengths = None
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batch_text_tokens = None
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self.torch_empty_cache()
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# gpt latent
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self._set_gr_progress(0.5, "gpt inference latents...")
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all_latents = []
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for i in range(batch_codes.shape[0]):
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codes = batch_codes[i] # [x]
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codes = codes[codes != self.cfg.gpt.stop_mel_token]
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codes, _ = torch.unique_consecutive(codes, return_inverse=True)
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codes = codes.unsqueeze(0) # [x] -> [1, x]
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code_lens = torch.tensor([codes.shape[-1]], device=codes.device, dtype=codes.dtype)
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codes, code_lens = self.remove_long_silence(codes, silent_token=52, max_consecutive=30)
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text_tokens = all_text_tokens[i]
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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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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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all_latents.append(latent)
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# bigvgan chunk
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chunk_size = 2
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chunk_latents = [all_latents[i:i + chunk_size] for i in range(0, len(all_latents), chunk_size)]
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chunk_length = len(chunk_latents)
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latent_length = len(all_latents)
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all_latents = None
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# bigvgan chunk decode
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self._set_gr_progress(0.7, "bigvgan decode...")
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tqdm_progress = tqdm(total=latent_length, desc="bigvgan")
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for items in chunk_latents:
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tqdm_progress.update(len(items))
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latent = torch.cat(items, dim=1)
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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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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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pass
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wav = torch.clamp(32767 * wav, -32767.0, 32767.0)
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wavs.append(wav)
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# clear cache
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tqdm_progress.close() # 确保进度条被关闭
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chunk_latents.clear()
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end_time = time.perf_counter()
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self.torch_empty_cache()
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# wav audio output
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self._set_gr_progress(0.9, "save audio...")
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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 fast 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">> [fast] bigvgan chunk_length: {chunk_length}")
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print(f">> [fast] batch_num: {batch_num}")
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print(f">> [fast] RTF: {(end_time - start_time) / wav_length:.4f}")
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# save audio
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wav = wav.cpu() # to cpu
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if output_path:
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# 直接保存音频到指定路径中
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os.makedirs(os.path.dirname(output_path),exist_ok=True)
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torchaudio.save(output_path, wav.type(torch.int16), sampling_rate)
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print(">> wav file saved to:", output_path)
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return output_path
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else:
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# 返回以符合Gradio的格式要求
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wav_data = wav.type(torch.int16)
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wav_data = wav_data.numpy().T
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return (sampling_rate, wav_data)
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# 原始推理模式
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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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self._set_gr_progress(0, "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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@ -317,16 +542,33 @@ class IndexTTS:
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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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# 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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# save audio
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wav = wav.cpu() # to cpu
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if output_path:
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# 直接保存音频到指定路径中
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if os.path.isfile(output_path):
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os.remove(output_path)
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print(">> remove old wav file:", output_path)
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if os.path.dirname(output_path) != "":
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os.makedirs(os.path.dirname(output_path),exist_ok=True)
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torchaudio.save(output_path, wav.type(torch.int16), sampling_rate)
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print(">> wav file saved to:", output_path)
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return output_path
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else:
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# 返回以符合Gradio的格式要求
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wav_data = wav.type(torch.int16)
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wav_data = wav_data.numpy().T
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return (sampling_rate, wav_data)
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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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prompt_wav="test_data/input.wav"
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#text="晕 XUAN4 是 一 种 GAN3 觉"
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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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text="There is a vehicle arriving in dock number 7?"
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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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34
webui.py
34
webui.py
@ -4,12 +4,16 @@ import sys
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import threading
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import time
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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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current_dir = os.path.dirname(os.path.abspath(__file__))
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sys.path.append(current_dir)
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sys.path.append(os.path.join(current_dir, "indextts"))
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import gradio as gr
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from utils.webui_utils import next_page, prev_page
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from indextts.utils.webui_utils import next_page, prev_page
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from indextts.infer import IndexTTS
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from tools.i18n.i18n import I18nAuto
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@ -21,15 +25,18 @@ tts = IndexTTS(model_dir="checkpoints",cfg_path="checkpoints/config.yaml")
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os.makedirs("outputs/tasks",exist_ok=True)
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os.makedirs("prompts",exist_ok=True)
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def infer(voice, text,output_path=None):
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def gen_single(prompt, text, infer_mode, progress=gr.Progress()):
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output_path = None
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if not output_path:
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output_path = os.path.join("outputs", f"spk_{int(time.time())}.wav")
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tts.infer(voice, text, output_path)
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return output_path
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def gen_single(prompt, text):
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output_path = infer(prompt, text)
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return gr.update(value=output_path,visible=True)
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# set gradio progress
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tts.gr_progress = progress
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if infer_mode == "普通推理":
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output = tts.infer(prompt, text, output_path) # 普通推理
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else:
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output = tts.infer_fast(prompt, text, output_path) # 批次推理
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return gr.update(value=output,visible=True)
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def update_prompt_audio():
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update_button = gr.update(interactive=True)
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@ -40,6 +47,7 @@ with gr.Blocks() as demo:
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mutex = threading.Lock()
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gr.HTML('''
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<h2><center>IndexTTS: An Industrial-Level Controllable and Efficient Zero-Shot Text-To-Speech System</h2>
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<h2><center>(一款工业级可控且高效的零样本文本转语音系统)</h2>
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<p align="center">
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<a href='https://arxiv.org/abs/2502.05512'><img src='https://img.shields.io/badge/ArXiv-2502.05512-red'></a>
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@ -53,16 +61,18 @@ with gr.Blocks() as demo:
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default = ''
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if prompt_list:
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default = prompt_list[0]
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input_text_single = gr.Textbox(label="请输入目标文本",key="input_text_single")
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gen_button = gr.Button("生成语音",key="gen_button",interactive=True)
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output_audio = gr.Audio(label="生成结果", visible=False,key="output_audio")
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with gr.Column():
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input_text_single = gr.TextArea(label="请输入目标文本",key="input_text_single")
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infer_mode = gr.Radio(choices=["普通推理", "批次推理"], label="选择推理模式（批次推理：更适合长句，性能翻倍）",value="普通推理")
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gen_button = gr.Button("生成语音",key="gen_button",interactive=True)
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output_audio = gr.Audio(label="生成结果", visible=True,key="output_audio")
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prompt_audio.upload(update_prompt_audio,
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inputs=[],
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outputs=[gen_button])
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gen_button.click(gen_single,
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inputs=[prompt_audio, input_text_single],
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inputs=[prompt_audio, input_text_single, infer_mode],
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outputs=[output_audio])
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