442 lines
18 KiB
Python
442 lines
18 KiB
Python
import torchaudio
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import os
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import copy
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import json
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import torch
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import numpy as np
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from transformers import AutoModelForCausalLM, AutoTokenizer
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from transformers.generation.logits_process import LogitsProcessor
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from transformers.generation.utils import LogitsProcessorList
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from tokenizer import StepAudioTokenizer
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from cosyvoice.cli.cosyvoice import CosyVoice
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node_dir = os.path.dirname(os.path.abspath(__file__))
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comfy_path = os.path.dirname(os.path.dirname(node_dir))
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model_path = os.path.join(comfy_path, "models/TTS")
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encoder_model_path = os.path.join(model_path, "Step-Audio-Tokenizer")
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tts_model_path = os.path.join(model_path, "Step-Audio-TTS-3B")
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speaker_path = os.path.join(model_path, "Step-Audio-speakers")
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class RepetitionAwareLogitsProcessor(LogitsProcessor):
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def __call__(
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self, input_ids: torch.LongTensor, scores: torch.FloatTensor
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) -> torch.FloatTensor:
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window_size = 10
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threshold = 0.1
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window = input_ids[:, -window_size:]
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if window.shape[1] < window_size:
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return scores
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last_tokens = window[:, -1].unsqueeze(-1)
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repeat_counts = (window == last_tokens).sum(dim=1)
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repeat_ratios = repeat_counts.float() / window_size
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mask = repeat_ratios > threshold
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scores[mask, last_tokens[mask].squeeze(-1)] = float("-inf")
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return scores
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class StepAudioTTS:
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def __init__(
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self,
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model_path,
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encoder,
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):
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self.model_path = model_path
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self._llm = None # 初始化为 None，表示尚未加载
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self._autotokenizer = None
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self._common_cosy_model = None
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self._music_cosy_model = None
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self.encoder = encoder
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@property
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def llm(self):
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if self._llm is None:
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self._llm = AutoModelForCausalLM.from_pretrained(
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self.model_path,
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torch_dtype=torch.bfloat16,
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device_map="cuda",
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trust_remote_code=True,
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) # 初始化模型
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return self._llm
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@property
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def autotokenizer(self):
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if self._autotokenizer is None:
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self._autotokenizer = AutoTokenizer.from_pretrained(
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self.model_path,
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trust_remote_code=True
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) # 初始化模型
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return self._autotokenizer
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@property
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def common_cosy_model(self):
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if self._common_cosy_model is None:
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self._common_cosy_model = CosyVoice(os.path.join(tts_model_path, "CosyVoice-300M-25Hz")) # 初始化模型
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return self._common_cosy_model
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@property
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def music_cosy_model(self):
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if self._music_cosy_model is None:
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self._music_cosy_model = CosyVoice(os.path.join(tts_model_path, "CosyVoice-300M-25Hz-Music"))
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return self._music_cosy_model
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def __call__(self, text: str, cosy_model, prompt_speaker_info, history):
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_prefix_tokens = self.autotokenizer.encode("\n")
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target_token_encode = self.autotokenizer.encode("\n" + text)
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target_tokens = target_token_encode[len(_prefix_tokens) :]
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# qrole_toks = self.autotokenizer.encode("human\n")
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arole_toks = self.autotokenizer.encode("assistant\n")
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history.extend(
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# [4]
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# + qrole_toks
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# + prompt_tokens
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# + [3]
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# + [4]
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# + arole_toks
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# + prompt_code
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# + [3]
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# + [4]
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# + qrole_toks
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target_tokens
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+ [3]
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+ [4]
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+ arole_toks
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)
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# token_ids = self.tokenize_history(
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# text,
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# prompt_speaker_info["prompt_text"],
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# prompt_speaker,
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# prompt_speaker_info["prompt_code"],
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# )
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token_ids = history
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output_ids = self.llm.generate(
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torch.tensor([token_ids]).to(torch.long).to("cuda"),
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max_length=8192,
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temperature=0.7,
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do_sample=True,
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logits_processor=LogitsProcessorList([RepetitionAwareLogitsProcessor()]),
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)
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output_ids = output_ids[:, len(token_ids) : -1] # skip eos token
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return (
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cosy_model.token_to_wav_offline(
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output_ids - 65536,
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prompt_speaker_info["cosy_speech_feat"].to(torch.bfloat16),
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prompt_speaker_info["cosy_speech_feat_len"],
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prompt_speaker_info["cosy_prompt_token"],
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prompt_speaker_info["cosy_prompt_token_len"],
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prompt_speaker_info["cosy_speech_embedding"].to(torch.bfloat16),
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),
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22050,
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)
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def data_preprocess(self, marks, prompt_speaker: str, clone_dict: dict | None = None):
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# instruction_name = self.detect_instruction_name(text)
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if "(RAP)" in marks or "(哼唱)" in marks:
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cosy_model = self.music_cosy_model
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else:
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cosy_model = self.common_cosy_model
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prompt_speaker_info = {}
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if clone_dict:
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clone_prompt_code, clone_prompt_token, clone_prompt_token_len, clone_speech_feat, clone_speech_feat_len, clone_speech_embedding = (
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self.preprocess_prompt_wav(clone_dict['audio'], cosy_model)
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)
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prompt_speaker_info = {
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"prompt_text": clone_dict['prompt_text'],
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"prompt_code": clone_prompt_code,
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"cosy_speech_feat": clone_speech_feat.to(torch.bfloat16),
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"cosy_speech_feat_len": clone_speech_feat_len,
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"cosy_speech_embedding": clone_speech_embedding.to(torch.bfloat16),
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"cosy_prompt_token": clone_prompt_token,
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"cosy_prompt_token_len": clone_prompt_token_len,
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}
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prompt_speaker = clone_dict['speaker']
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# print(prompt_speaker, " 内置文本: ", prompt_speaker_info["prompt_text"], end="\n\n")
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else:
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encodings = ["utf-8", "gbk", "utf-8-sig"] # utf-8-sig 处理带 BOM 的 UTF-8
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for encoding in encodings:
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try:
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with open(f"{speaker_path}/speakers_info.json", "r", encoding=encoding) as f:
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speakers_info = json.load(f)
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break
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except UnicodeDecodeError:
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continue
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else:
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raise UnicodeDecodeError(f"Failed to decode {speaker_path}/speakers_info.json with encodings {encodings}")
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for speaker_id, prompt_text in speakers_info.items():
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if speaker_id == prompt_speaker:
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prompt_wav_path = f"{speaker_path}/{speaker_id}_prompt.wav"
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waveform, sample_rate = torchaudio.load(prompt_wav_path)
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audio = {"waveform": waveform.unsqueeze(0), "sample_rate": sample_rate}
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prompt_code, prompt_token, prompt_token_len, speech_feat, speech_feat_len, speech_embedding = (
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self.preprocess_prompt_wav(audio, cosy_model)
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)
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prompt_speaker_info = {
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"prompt_text": prompt_text,
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"prompt_code": prompt_code,
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"cosy_speech_feat": speech_feat.to(torch.bfloat16),
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"cosy_speech_feat_len": speech_feat_len,
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"cosy_speech_embedding": speech_embedding.to(torch.bfloat16),
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"cosy_prompt_token": prompt_token,
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"cosy_prompt_token_len": prompt_token_len,
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}
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# print(prompt_speaker, " 内置文本: ", prompt_speaker_info["prompt_text"], end="\n\n")
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break
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elif prompt_speaker not in speakers_info.keys():
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raise ValueError("There is no such speaker")
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history = self.tokenize_history(
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marks,
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prompt_speaker_info["prompt_text"],
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prompt_speaker,
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prompt_speaker_info["prompt_code"],
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)
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return cosy_model, prompt_speaker_info, history
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# def detect_instruction_name(self, text):
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# instruction_names = []
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# pattern = r"\(.*?\)|（.*?）"
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# matches = re.findall(pattern, text)
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# if matches:
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# instruction_names = [i.strip("() （）") for i in matches if i.strip("() （）")]
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# return instruction_names
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def tokenize_history(
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self,
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marks: list,
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prompt_text: str,
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prompt_speaker: str,
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prompt_code: list
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):
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sys_prompt_dict = {
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"sys_prompt_for_rap": "请用 RAP 方式将文本内容大声说唱出来。[] 括号内标注了说唱者的名字, 请使用 [{}] 的声音, 大声说唱出其后面的文本内容: ",
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"sys_prompt_for_vocal": "请用哼唱的方式将文本内容大声唱出来。[] 括号内标注了唱歌者的名字, 请使用 [{}] 的声音, 大声唱出其后面的文本内容: ",
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"sys_prompt_for_spk": '作为一名卓越的声优演员，你的任务是根据文本中 （） 或 () 括号内标注的情感、语种或方言、音乐哼唱、语音调整等标签，以丰富细腻的情感和自然顺畅的语调，来朗读文本。\n# 情感标签涵盖了多种情绪状态，包括但不限于：\n- "高兴1"\n- "高兴2"\n- "生气1"\n- "生气2"\n- "悲伤1"\n- "撒娇1"\n\n# 语种或方言标签包含多种语言或方言，包括但不限于：\n- "中文"\n- "英文"\n- "韩语"\n- "日语"\n- "四川话"\n- "粤语"\n\n# 音乐哼唱标签包含多种类型歌曲哼唱，包括但不限于：\n- "RAP"\n- "哼唱"\n\n# 语音调整标签，包括但不限于：\n- "慢速1"\n- "慢速2"\n- "快速1"\n- "快速2"\n\n请在朗读时，根据这些情感标签的指示，调整你的情感、语气、语调和哼唱节奏，以确保文本的情感和意义得到准确而生动的传达，如果没有 () 或 （） 括号，则根据文本语义内容恰到好处地演绎。[] 括号内标注了朗读者的名字, 请使用 [{}] 的声音, 大声朗读出其后面的文本内容: ',
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}
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# rap_or_vocal = self.detect_instruction_name(text)
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if marks[0] == "(哼唱)":
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prompt = sys_prompt_dict["sys_prompt_for_vocal"].format(prompt_speaker)
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# print("哼唱系统消息: ", prompt, end="\n\n")
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elif marks[0] == "(RAP)":
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prompt = sys_prompt_dict["sys_prompt_for_rap"].format(prompt_speaker)
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# print("RAP系统消息: ", prompt, end="\n\n")
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else:
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prompt = sys_prompt_dict["sys_prompt_for_spk"].format(prompt_speaker)
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# print("其他系统消息: ", prompt, end="\n\n")
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sys_tokens = self.autotokenizer.encode(f"system\n{prompt}")
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history = [1]
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history.extend([4] + sys_tokens + [3])
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_prefix_tokens = self.autotokenizer.encode("\n")
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prompt_token_encode = self.autotokenizer.encode("\n" + prompt_text)
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prompt_tokens = prompt_token_encode[len(_prefix_tokens) :]
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# target_token_encode = self.autotokenizer.encode("\n" + text)
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# target_tokens = target_token_encode[len(_prefix_tokens) :]
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qrole_toks = self.autotokenizer.encode("human\n")
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arole_toks = self.autotokenizer.encode("assistant\n")
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history.extend(
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[4]
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+ qrole_toks
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+ prompt_tokens
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+ [3]
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+ [4]
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+ arole_toks
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+ prompt_code
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+ [3]
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+ [4]
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+ qrole_toks
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# + target_tokens
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# + [3]
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# + [4]
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# + arole_toks
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)
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return history
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def preprocess_prompt_wav(self, audio, cosy_model):
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prompt_wav = audio["waveform"].squeeze(0)
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prompt_wav_sr = audio["sample_rate"]
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if prompt_wav.shape[0] > 1:
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prompt_wav = prompt_wav.mean(dim=0, keepdim=True) # 将多通道音频转换为单通道
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prompt_wav_16k = torchaudio.transforms.Resample(
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orig_freq=prompt_wav_sr, new_freq=16000
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)(prompt_wav)
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prompt_wav_22k = torchaudio.transforms.Resample(
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orig_freq=prompt_wav_sr, new_freq=22050
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)(prompt_wav)
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speech_feat, speech_feat_len = (
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cosy_model.frontend._extract_speech_feat(prompt_wav_22k)
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)
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speech_embedding = cosy_model.frontend._extract_spk_embedding(
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prompt_wav_16k
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)
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prompt_code, _, _ = self.encoder.wav2token(prompt_wav, prompt_wav_sr)
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prompt_token = torch.tensor([prompt_code], dtype=torch.long) - 65536
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prompt_token_len = torch.tensor([prompt_token.shape[1]], dtype=torch.long)
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return (
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prompt_code,
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prompt_token,
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prompt_token_len,
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speech_feat,
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speech_feat_len,
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speech_embedding,
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)
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encoder = StepAudioTokenizer(encoder_model_path)
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tts_engine = StepAudioTTS(tts_model_path, encoder)
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# 选项列表
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emotion_options = ["高兴1", "高兴2", "生气1", "生气2", "悲伤1", "撒娇1", "None"]
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language_options = ["中文", "英文", "韩语", "日语", "四川话", "粤语", "None"]
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speed_options = ["慢速1", "慢速2", "快速1", "快速2", "None"]
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speaker_options = ["婷婷", "婷婷RAP", "婷婷哼唱", "明文"]
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express_options = ["RAP", "哼唱", "None"]
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def gen_text(*args):
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formatted_args = []
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for arg in args:
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if arg != "None":
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formatted_args.append(f"({arg})")
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return formatted_args
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class StepAudioRun:
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@classmethod
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def INPUT_TYPES(s):
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return {
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"required": {
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"text": ("STRING", {"default": "", "multiline": True}),
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"speaker": (speaker_options, {"default": "婷婷"}),
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},
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"optional": {
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"custom_speaker": ("STRING", {"default": "", "multiline": False}),
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"emotion": (emotion_options, {"default": "None"}),
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"language": (language_options, {"default": "None"}),
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"express": (express_options, {"default": "None"}),
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"speed": (speed_options, {"default": "None"}),
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"custom_mark": ("STRING", {"default": "(温柔)", "multiline": False}),
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}
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}
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RETURN_TYPES = ("AUDIO",)
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RETURN_NAMES = ("audio",)
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FUNCTION = "speak"
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CATEGORY = "MW-Step-Audio"
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def speak(self, text, speaker, custom_speaker, emotion, language, express, speed, custom_mark):
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if custom_speaker.strip():
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speaker = custom_speaker
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if express == "哼唱":
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conditions = ["(哼唱)"]
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elif express == "RAP":
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conditions = ["(RAP)"]
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else:
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conditions = gen_text(emotion, language, speed, custom_mark)
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# print(conditions, end="\n\n")
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cosy_model, prompt_speaker_info, history = tts_engine.data_preprocess(conditions, speaker)
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texts = [i.strip() for i in text.split("\n\n") if i.strip()]
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audio_data = []
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for i in texts:
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text = "".join(conditions) + f"[{speaker}]: {i}"
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# print(text, end="\n\n")
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output_audio, sr = tts_engine(text, cosy_model, prompt_speaker_info, copy.deepcopy(history))
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audio_data.append(output_audio)
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audio_tensor = torch.cat(audio_data, dim=1).unsqueeze(0).float()
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return ({"waveform": audio_tensor, "sample_rate": sr},)
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class StepAudioClone:
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@classmethod
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def INPUT_TYPES(s):
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return {
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"required": {
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"text": ("STRING", {"default": "", "multiline": True}),
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"speaker_name": ("STRING", {"default": "", "multiline": False}),
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"clone_text": ("STRING", {"default": "", "multiline": True, "tooltip": "The clone audio's text."}),
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"clone_audio": ("AUDIO", ),
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},
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"optional": {
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"emotion": (emotion_options, {"default": "None"}),
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"language": (language_options, {"default": "None"}),
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"express": (express_options, {"default": "None"}),
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"speed": (speed_options, {"default": "None"}),
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"custom_mark": ("STRING", {"default": "(温柔)", "multiline": False}),
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}
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}
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RETURN_TYPES = ("AUDIO",)
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RETURN_NAMES = ("audio",)
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FUNCTION = "clone"
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CATEGORY = "MW-Step-Audio"
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def clone(self, text, clone_audio, clone_text, speaker_name, emotion, language, express, speed, custom_mark):
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if express == "哼唱":
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conditions = ["(哼唱)"]
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elif express == "RAP":
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conditions = ["(RAP)"]
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else:
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conditions = gen_text(emotion, language, speed, custom_mark)
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# print(conditions, end="\n\n")
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if not speaker_name.strip():
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speaker_name = "wuming"
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clone_text = "".join(conditions) + f"[{speaker_name}]: {clone_text}"
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# print(clone_text, end="\n\n")
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clone_speaker_info = {
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"audio": clone_audio,
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"speaker": speaker_name,
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"prompt_text": clone_text
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}
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cosy_model, prompt_speaker_info, history = tts_engine.data_preprocess(conditions, speaker_name, clone_speaker_info)
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texts = [i.strip() for i in text.split("\n\n") if i.strip()]
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audio_data = []
|
||
for i in texts:
|
||
text = "".join(conditions) + f"[{speaker_name}]: {i}"
|
||
# print(text, end="\n\n")
|
||
output_audio, sr = tts_engine(text, cosy_model, prompt_speaker_info, copy.deepcopy(history))
|
||
audio_data.append(output_audio)
|
||
|
||
audio_tensor = torch.cat(audio_data, dim=1).unsqueeze(0).float()
|
||
return ({"waveform": audio_tensor, "sample_rate": sr},)
|
||
|
||
from MWAudioRecorder import AudioRecorder
|
||
|
||
NODE_CLASS_MAPPINGS = {
|
||
"StepAudioRun": StepAudioRun,
|
||
"StepAudioClone": StepAudioClone,
|
||
"AudioRecorder": AudioRecorder
|
||
}
|
||
|
||
NODE_DISPLAY_NAME_MAPPINGS = {
|
||
"StepAudioRun": "Step Audio Run",
|
||
"StepAudioClone": "Step Audio Clone",
|
||
"AudioRecorder": "MW Audio Recorder"
|
||
} |