diff --git a/indextts/infer.py b/indextts/infer.py
index d6a6cd2..84bd56b 100644
--- a/indextts/infer.py
+++ b/indextts/infer.py
@@ -3,11 +3,13 @@ import re
import time
from subprocess import CalledProcessError
+import numpy as np
import sentencepiece as spm
import torch
import torchaudio
from torch.nn.utils.rnn import pad_sequence
from omegaconf import OmegaConf
+from tqdm import tqdm
from indextts.BigVGAN.models import BigVGAN as Generator
from indextts.gpt.model import UnifiedVoice
@@ -113,6 +115,8 @@ class IndexTTS:
# 缓存参考音频mel：
self.cache_audio_prompt = None
self.cache_cond_mel = None
+ # 进度引用显示（可选）
+ self.gr_progress = None
def preprocess_text(self, text):
# chinese_punctuation = "，。！？；：“”‘’（）【】《》"
@@ -180,9 +184,230 @@ class IndexTTS:
return [
sentence.strip() for sentence in sentences if sentence.strip() and sentence.strip() not in {"'", ".", ","}
]
+
+ def pad_tokens_cat(self, tokens):
+ if len(tokens) <= 1:return tokens[-1]
+ max_len = max(t.size(1) for t in tokens)
+ outputs = []
+ for tensor in tokens:
+ pad_len = max_len - tensor.size(1)
+ if pad_len > 0:
+ padded = torch.nn.functional.pad(tensor,
+ (0, pad_len),
+ value=self.cfg.gpt.stop_text_token
+ )
+ outputs.append(padded)
+ else:
+ outputs.append(tensor)
+ tokens = torch.cat(outputs, dim=0)
+ return tokens
+
+ def torch_empty_cache(self):
+ try:
+ if "cuda" in str(self.device):
+ torch.cuda.empty_cache()
+ elif "mps" in str(self.device):
+ torch.mps.empty_cache()
+ except Exception as e:
+ pass
+
+ def _set_gr_progress(self, value, desc):
+ if self.gr_progress is not None:self.gr_progress(value, desc=desc)
+
+
+
+ # 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）
+ def infer_fast(self, audio_prompt, text, output_path, verbose=False):
+ print(">> start fast inference...")
+ self._set_gr_progress(0, "start fast inference...")
+ if verbose:
+ print(f"origin text:{text}")
+ start_time = time.perf_counter()
+ normalized_text = self.preprocess_text(text)
+ print(f"normalized text:{normalized_text}")
+
+ # 如果参考音频改变了，才需要重新生成 cond_mel, 提升速度
+ if self.cache_cond_mel is None or self.cache_audio_prompt != audio_prompt:
+ audio, sr = torchaudio.load(audio_prompt)
+ audio = torch.mean(audio, dim=0, keepdim=True)
+ if audio.shape[0] > 1:
+ audio = audio[0].unsqueeze(0)
+ audio = torchaudio.transforms.Resample(sr, 24000)(audio)
+ cond_mel = MelSpectrogramFeatures()(audio).to(self.device)
+ cond_mel_frame = cond_mel.shape[-1]
+ if verbose:
+ print(f"cond_mel shape: {cond_mel.shape}", "dtype:", cond_mel.dtype)
+
+ self.cache_audio_prompt = audio_prompt
+ self.cache_cond_mel = cond_mel
+ else:
+ cond_mel = self.cache_cond_mel
+ cond_mel_frame = cond_mel.shape[-1]
+ pass
+
+ auto_conditioning = cond_mel
+ cond_mel_lengths = torch.tensor([cond_mel_frame],device=self.device)
+
+ # text_tokens
+ sentences = self.split_sentences(normalized_text)
+ if verbose:
+ print("sentences:", sentences)
+
+ top_p = .8
+ top_k = 30
+ temperature = 1.0
+ autoregressive_batch_size = 1
+ length_penalty = 0.0
+ num_beams = 3
+ repetition_penalty = 10.0
+ max_mel_tokens = 600
+ sampling_rate = 24000
+ # lang = "EN"
+ # lang = "ZH"
+ wavs = []
+ gpt_gen_time = 0
+ gpt_forward_time = 0
+ bigvgan_time = 0
+
+ all_text_tokens = []
+ self._set_gr_progress(0.1, "text processing...")
+ for sent in sentences:
+ # sent = " ".join([char for char in sent.upper()]) if lang == "ZH" else sent.upper()
+ cleand_text = tokenize_by_CJK_char(sent)
+ # 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 ."
+ if verbose:
+ print("cleand_text:", cleand_text)
+
+ text_tokens = torch.tensor(self.tokenizer.EncodeAsIds(cleand_text),dtype=torch.int32, device=self.device).unsqueeze(0)
+ # text_tokens = F.pad(text_tokens, (0, 1)) # This may not be necessary.
+ # text_tokens = F.pad(text_tokens, (1, 0), value=0)
+ # text_tokens = F.pad(text_tokens, (0, 1), value=1)
+ 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.IdToPiece(text_tokens[0].tolist())
+ print(text_token_syms)
+
+ all_text_tokens.append(text_tokens)
+
+ batch_num = len(all_text_tokens)
+ batch_text_tokens = self.pad_tokens_cat(all_text_tokens)
+ batch_cond_mel_lengths = torch.cat([cond_mel_lengths] * batch_num, dim=0)
+ batch_auto_conditioning = torch.cat([auto_conditioning] * batch_num, dim=0)
+
+ # gpt speech
+ self._set_gr_progress(0.2, "gpt inference speech...")
+ m_start_time = time.perf_counter()
+ with torch.no_grad():
+ with torch.amp.autocast(self.device, enabled=self.dtype is not None, dtype=self.dtype):
+ batch_codes = self.gpt.inference_speech(batch_auto_conditioning, batch_text_tokens,
+ cond_mel_lengths=batch_cond_mel_lengths,
+ # text_lengths=text_len,
+ 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)
+ gpt_gen_time += time.perf_counter() - m_start_time
+
+ # clear cache
+ batch_auto_conditioning = None
+ batch_cond_mel_lengths = None
+ batch_text_tokens = None
+ self.torch_empty_cache()
+
+ # gpt latent
+ self._set_gr_progress(0.5, "gpt inference latents...")
+ all_latents = []
+ for i in range(batch_codes.shape[0]):
+ codes = batch_codes[i] # [x]
+ codes = codes[codes != self.cfg.gpt.stop_mel_token]
+ codes, _ = torch.unique_consecutive(codes, return_inverse=True)
+ codes = codes.unsqueeze(0) # [x] -> [1, x]
+ code_lens = torch.tensor([codes.shape[-1]], device=codes.device, dtype=codes.dtype)
+ codes, code_lens = self.remove_long_silence(codes, silent_token=52, max_consecutive=30)
+ text_tokens = all_text_tokens[i]
+ m_start_time = time.perf_counter()
+ with torch.no_grad():
+ with torch.amp.autocast(self.device, enabled=self.dtype is not None, dtype=self.dtype):
+ latent = \
+ self.gpt(auto_conditioning, text_tokens,
+ torch.tensor([text_tokens.shape[-1]], device=text_tokens.device), codes,
+ code_lens*self.gpt.mel_length_compression,
+ cond_mel_lengths=torch.tensor([auto_conditioning.shape[-1]], device=text_tokens.device),
+ return_latent=True, clip_inputs=False)
+ gpt_forward_time += time.perf_counter() - m_start_time
+ all_latents.append(latent)
+
+ # bigvgan chunk
+ chunk_size = 2
+ chunk_latents = [all_latents[i:i + chunk_size] for i in range(0, len(all_latents), chunk_size)]
+ chunk_length = len(chunk_latents)
+ latent_length = len(all_latents)
+ all_latents = None
+
+ # bigvgan chunk decode
+ self._set_gr_progress(0.7, "bigvgan decode...")
+ tqdm_progress = tqdm(total=latent_length, desc="bigvgan")
+ for items in chunk_latents:
+ tqdm_progress.update(len(items))
+ latent = torch.cat(items, dim=1)
+ with torch.no_grad():
+ with torch.amp.autocast(self.device, enabled=self.dtype is not None, dtype=self.dtype):
+ m_start_time = time.perf_counter()
+ wav, _ = self.bigvgan(latent, auto_conditioning.transpose(1, 2))
+ bigvgan_time += time.perf_counter() - m_start_time
+ wav = wav.squeeze(1)
+ pass
+ wav = torch.clamp(32767 * wav, -32767.0, 32767.0)
+ wavs.append(wav)
+
+ # clear cache
+ tqdm_progress.close() # 确保进度条被关闭
+ chunk_latents.clear()
+ end_time = time.perf_counter()
+ self.torch_empty_cache()
+
+ # wav audio output
+ self._set_gr_progress(0.9, "save audio...")
+ wav = torch.cat(wavs, dim=1)
+ wav_length = wav.shape[-1] / sampling_rate
+ print(f">> Reference audio length: {cond_mel_frame*256 / sampling_rate:.2f} seconds")
+ print(f">> gpt_gen_time: {gpt_gen_time:.2f} seconds")
+ print(f">> gpt_forward_time: {gpt_forward_time:.2f} seconds")
+ print(f">> bigvgan_time: {bigvgan_time:.2f} seconds")
+ print(f">> Total fast inference time: {end_time - start_time:.2f} seconds")
+ print(f">> Generated audio length: {wav_length:.2f} seconds")
+ print(f">> [fast] bigvgan chunk_length: {chunk_length}")
+ print(f">> [fast] batch_num: {batch_num}")
+ print(f">> [fast] 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
+ 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)
+
+
+
+ # 原始推理模式
def infer(self, audio_prompt, text, output_path, verbose=False):
print(">> start inference...")
+ self._set_gr_progress(0, "start inference...")
if verbose:
print(f"origin text:{text}")
start_time = time.perf_counter()
@@ -317,16 +542,33 @@ class IndexTTS:
print(f">> Generated audio length: {wav_length:.2f} seconds")
print(f">> RTF: {(end_time - start_time) / wav_length:.4f}")
- torchaudio.save(output_path, wav.cpu().type(torch.int16), sampling_rate)
- print(">> wav file saved to:", output_path)
+ # torchaudio.save(output_path, wav.cpu().type(torch.int16), sampling_rate)
+ # print(">> wav file saved to:", output_path)
+
+ # 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)
if __name__ == "__main__":
- prompt_wav = "testwav/input.wav"
- prompt_wav = "testwav/spk_1744181067_1.wav"
- text="晕 XUAN4 是 一 种 GAN3 觉"
- text = "There is a vehicle arriving in dock number 7?"
- 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！'
+ prompt_wav="test_data/input.wav"
+ #text="晕 XUAN4 是 一 种 GAN3 觉"
+ #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！'
+ text="There is a vehicle arriving in dock number 7?"
tts = IndexTTS(cfg_path="checkpoints/config.yaml", model_dir="checkpoints", is_fp16=True, use_cuda_kernel=False)
tts.infer(audio_prompt=prompt_wav, text=text, output_path="gen.wav", verbose=True)
diff --git a/webui.py b/webui.py
index 5a4527f..29d44e8 100644
--- a/webui.py
+++ b/webui.py
@@ -4,12 +4,16 @@ import sys
import threading
import time
+import warnings
+warnings.filterwarnings("ignore", category=FutureWarning)
+warnings.filterwarnings("ignore", category=UserWarning)
+
current_dir = os.path.dirname(os.path.abspath(__file__))
sys.path.append(current_dir)
sys.path.append(os.path.join(current_dir, "indextts"))
import gradio as gr
-from utils.webui_utils import next_page, prev_page
+from indextts.utils.webui_utils import next_page, prev_page
from indextts.infer import IndexTTS
from tools.i18n.i18n import I18nAuto
@@ -21,15 +25,18 @@ tts = IndexTTS(model_dir="checkpoints",cfg_path="checkpoints/config.yaml")
os.makedirs("outputs/tasks",exist_ok=True)
os.makedirs("prompts",exist_ok=True)
-def infer(voice, text,output_path=None):
+
+def gen_single(prompt, text, infer_mode, progress=gr.Progress()):
+ output_path = None
if not output_path:
output_path = os.path.join("outputs", f"spk_{int(time.time())}.wav")
- tts.infer(voice, text, output_path)
- return output_path
-
-def gen_single(prompt, text):
- output_path = infer(prompt, text)
- return gr.update(value=output_path,visible=True)
+ # set gradio progress
+ tts.gr_progress = progress
+ if infer_mode == "普通推理":
+ output = tts.infer(prompt, text, output_path) # 普通推理
+ else:
+ output = tts.infer_fast(prompt, text, output_path) # 批次推理
+ return gr.update(value=output,visible=True)
def update_prompt_audio():
update_button = gr.update(interactive=True)
@@ -40,6 +47,7 @@ with gr.Blocks() as demo:
mutex = threading.Lock()
gr.HTML('''
IndexTTS: An Industrial-Level Controllable and Efficient Zero-Shot Text-To-Speech System
+ (一款工业级可控且高效的零样本文本转语音系统)
@@ -53,16 +61,18 @@ with gr.Blocks() as demo:
default = ''
if prompt_list:
default = prompt_list[0]
- input_text_single = gr.Textbox(label="请输入目标文本",key="input_text_single")
- gen_button = gr.Button("生成语音",key="gen_button",interactive=True)
- output_audio = gr.Audio(label="生成结果", visible=False,key="output_audio")
+ with gr.Column():
+ input_text_single = gr.TextArea(label="请输入目标文本",key="input_text_single")
+ 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")
+ gen_button = gr.Button("生成语音",key="gen_button",interactive=True)
+ output_audio = gr.Audio(label="生成结果", visible=True,key="output_audio")
prompt_audio.upload(update_prompt_audio,
inputs=[],
outputs=[gen_button])
gen_button.click(gen_single,
- inputs=[prompt_audio, input_text_single],
+ inputs=[prompt_audio, input_text_single, infer_mode],
outputs=[output_audio])