diff --git a/GPT_SoVITS/AR/models/t2s_model.py b/GPT_SoVITS/AR/models/t2s_model.py index ac905f4b..f55b7508 100644 --- a/GPT_SoVITS/AR/models/t2s_model.py +++ b/GPT_SoVITS/AR/models/t2s_model.py @@ -351,6 +351,13 @@ class Text2SemanticDecoder(nn.Module): blocks.append(block) self.t2s_transformer = T2STransformer(self.num_layers, blocks) + self.last_infer_stats = {} + + def _set_last_infer_stats(self, stats): + self.last_infer_stats = stats + + def get_last_infer_stats(self): + return dict(self.last_infer_stats) def make_input_data(self, x, x_lens, y, y_lens, bert_feature): x = self.ar_text_embedding(x) @@ -593,7 +600,19 @@ class Text2SemanticDecoder(nn.Module): repetition_penalty: float = 1.35, **kwargs, ): + requested_enable_mask_free_fastpath = bool(kwargs.get("enable_mask_free_fastpath", True)) if prompts is None: + self._set_last_infer_stats( + { + "infer_mode": "batch_infer_prompt_free_fallback", + "requested_enable_mask_free_fastpath": requested_enable_mask_free_fastpath, + "batch_size": int(len(x)), + "prefill_after_mask_all_visible": None, + "fastpath_hit": False, + "generated_token_count": 0, + "generated_token_count_list": [], + } + ) print("Warning: Prompt free is not supported batch_infer! switch to naive_infer") return self.infer_panel_naive_batched( x, @@ -608,6 +627,7 @@ class Text2SemanticDecoder(nn.Module): ) max_len = kwargs.get("max_len", x_lens.max()) + enable_mask_free_fastpath = requested_enable_mask_free_fastpath x_list = [] for x_item, bert_item in zip(x, bert_feature): # max_len = max(max_len, x_item.shape[0], bert_item.shape[1]) @@ -698,17 +718,30 @@ class Text2SemanticDecoder(nn.Module): y_list = [None] * y.shape[0] batch_idx_map = list(range(y.shape[0])) idx_list = [None] * y.shape[0] + decode_attn_mask = attn_mask + prefill_after_mask_all_visible = None + fastpath_hit = False for idx in tqdm(range(1500)): if idx == 0: xy_dec, k_cache, v_cache = self.t2s_transformer.process_prompt(xy_pos, attn_mask, None) else: - xy_dec, k_cache, v_cache = self.t2s_transformer.decode_next_token(xy_pos, k_cache, v_cache, attn_mask) + xy_dec, k_cache, v_cache = self.t2s_transformer.decode_next_token( + xy_pos, k_cache, v_cache, decode_attn_mask + ) logits = self.ar_predict_layer(xy_dec[:, -1]) if idx == 0: attn_mask = F.pad(attn_mask[:, :, -1].unsqueeze(-2), (0, 1), value=False) + prefill_after_mask_all_visible = not attn_mask.any().item() + if enable_mask_free_fastpath and y.shape[0] == 1 and prefill_after_mask_all_visible: + decode_attn_mask = None + fastpath_hit = True + else: + decode_attn_mask = attn_mask else: - attn_mask = F.pad(attn_mask, (0, 1), value=False) + if decode_attn_mask is not None: + attn_mask = F.pad(attn_mask, (0, 1), value=False) + decode_attn_mask = attn_mask if idx < 11: ###至少预测出10个token不然不给停止（0.4s） logits = logits[:, :-1] @@ -740,7 +773,9 @@ class Text2SemanticDecoder(nn.Module): if reserved_idx_of_batch_for_y is not None: # index = torch.LongTensor(batch_idx_map).to(y.device) y = torch.index_select(y, dim=0, index=reserved_idx_of_batch_for_y) - attn_mask = torch.index_select(attn_mask, dim=0, index=reserved_idx_of_batch_for_y) + if decode_attn_mask is not None: + attn_mask = torch.index_select(attn_mask, dim=0, index=reserved_idx_of_batch_for_y) + decode_attn_mask = attn_mask if k_cache is not None: for i in range(len(k_cache)): k_cache[i] = torch.index_select(k_cache[i], dim=0, index=reserved_idx_of_batch_for_y) @@ -775,6 +810,18 @@ class Text2SemanticDecoder(nn.Module): if idx_list[i] is None: idx_list[i] = 1500 - 1 ###如果没有生成到EOS，就用最大长度代替 + self._set_last_infer_stats( + { + "infer_mode": "batch_infer", + "requested_enable_mask_free_fastpath": enable_mask_free_fastpath, + "batch_size": int(len(x)), + "prefill_after_mask_all_visible": prefill_after_mask_all_visible, + "fastpath_hit": fastpath_hit, + "generated_token_count": int(sum(idx_list)), + "generated_token_count_list": [int(item) for item in idx_list], + "max_len": int(max_len), + } + ) if ref_free: return y_list, [0] * x.shape[0] # print(idx_list) @@ -811,6 +858,17 @@ class Text2SemanticDecoder(nn.Module): y_list.append(y[0]) idx_list.append(idx) + self._set_last_infer_stats( + { + "infer_mode": "naive_batched", + "requested_enable_mask_free_fastpath": bool(kwargs.get("enable_mask_free_fastpath", True)), + "batch_size": int(len(x)), + "prefill_after_mask_all_visible": None, + "fastpath_hit": False, + "generated_token_count": int(sum(idx_list)), + "generated_token_count_list": [int(item) for item in idx_list], + } + ) return y_list, idx_list def infer_panel_naive( @@ -957,6 +1015,18 @@ class Text2SemanticDecoder(nn.Module): if not streaming_mode: + generated_token_count = max(int(y.shape[1] - prefix_len), 0) + self._set_last_infer_stats( + { + "infer_mode": "naive", + "requested_enable_mask_free_fastpath": bool(kwargs.get("enable_mask_free_fastpath", True)), + "batch_size": int(x.shape[0]), + "prefill_after_mask_all_visible": True if prompts is not None else None, + "fastpath_hit": True if prompts is not None else False, + "generated_token_count": generated_token_count, + "generated_token_count_list": [generated_token_count], + } + ) if ref_free: yield y, 0 yield y, idx diff --git a/GPT_SoVITS/AR/models/utils.py b/GPT_SoVITS/AR/models/utils.py index cc4f24d8..4b564ed8 100644 --- a/GPT_SoVITS/AR/models/utils.py +++ b/GPT_SoVITS/AR/models/utils.py @@ -147,6 +147,7 @@ def multinomial_sample_one_no_sync( def logits_to_probs( logits, previous_tokens: Optional[torch.Tensor] = None, + previous_token_mask: Optional[torch.Tensor] = None, temperature: float = 1.0, top_k: Optional[int] = None, top_p: Optional[int] = None, @@ -158,13 +159,27 @@ def logits_to_probs( # pdb.set_trace() if previous_tokens is not None and repetition_penalty != 1.0: previous_tokens = previous_tokens.long() - score = torch.gather(logits, dim=1, index=previous_tokens) - score = torch.where( - score < 0, - score * repetition_penalty, - score / repetition_penalty, - ) - logits.scatter_(dim=1, index=previous_tokens, src=score) + if previous_token_mask is None: + score = torch.gather(logits, dim=1, index=previous_tokens) + score = torch.where( + score < 0, + score * repetition_penalty, + score / repetition_penalty, + ) + logits.scatter_(dim=1, index=previous_tokens, src=score) + else: + previous_token_mask = previous_token_mask.to(dtype=torch.bool, device=logits.device) + if previous_token_mask.any(): + batch_index = torch.arange(logits.size(0), device=logits.device).unsqueeze(1).expand_as(previous_tokens) + valid_batch_index = batch_index[previous_token_mask] + valid_token_index = previous_tokens[previous_token_mask] + score = logits[valid_batch_index, valid_token_index] + score = torch.where( + score < 0, + score * repetition_penalty, + score / repetition_penalty, + ) + logits[valid_batch_index, valid_token_index] = score if top_p is not None and top_p < 1.0: sorted_logits, sorted_indices = torch.sort(logits, descending=True) @@ -192,9 +207,15 @@ def logits_to_probs( def sample( logits, previous_tokens: Optional[torch.Tensor] = None, + previous_token_mask: Optional[torch.Tensor] = None, **sampling_kwargs, ) -> Tuple[torch.Tensor, torch.Tensor]: - probs = logits_to_probs(logits=logits, previous_tokens=previous_tokens, **sampling_kwargs) + probs = logits_to_probs( + logits=logits, + previous_tokens=previous_tokens, + previous_token_mask=previous_token_mask, + **sampling_kwargs, + ) idx_next = multinomial_sample_one_no_sync(probs) return idx_next, probs diff --git a/GPT_SoVITS/TTS_infer_pack/TTS.py b/GPT_SoVITS/TTS_infer_pack/TTS.py index 9c8344b0..c7ae465c 100644 --- a/GPT_SoVITS/TTS_infer_pack/TTS.py +++ b/GPT_SoVITS/TTS_infer_pack/TTS.py @@ -1,4 +1,5 @@ import gc +import concurrent.futures import math import os import random @@ -7,19 +8,20 @@ import time import traceback from copy import deepcopy -import torchaudio -from tqdm import tqdm - now_dir = os.getcwd() sys.path.append(now_dir) -import os from typing import List, Tuple, Union +from runtime_preload import preload_text_runtime_deps + +preload_text_runtime_deps() + import ffmpeg import librosa import numpy as np import torch import torch.nn.functional as F +import torchaudio import yaml from AR.models.t2s_lightning_module import Text2SemanticLightningModule from BigVGAN.bigvgan import BigVGAN @@ -29,11 +31,17 @@ from module.models import SynthesizerTrn, SynthesizerTrnV3, Generator from peft import LoraConfig, get_peft_model from process_ckpt import get_sovits_version_from_path_fast, load_sovits_new from transformers import AutoModelForMaskedLM, AutoTokenizer +from tqdm import tqdm from tools.audio_sr import AP_BWE from tools.i18n.i18n import I18nAuto, scan_language_list from TTS_infer_pack.text_segmentation_method import splits -from TTS_infer_pack.TextPreprocessor import TextPreprocessor +from TTS_infer_pack.TextPreprocessor import TextPreprocessor, StageLimiter +from TTS_infer_pack.prepare_bert_batch_worker import PrepareBertBatchWorker +from TTS_infer_pack.prepare_ref_semantic_batch_worker import ( + PrepareRefSemanticBatchWorker, + prepare_prompt_semantic_wav16k, +) from sv import SV resample_transform_dict = {} @@ -442,12 +450,25 @@ class TTS: "upsample_rate": None, "overlapped_len": None, } + self.prepare_bert_stage_limiter = StageLimiter(int(os.environ.get("GPTSOVITS_PREPARE_BERT_SLOTS", "1"))) + self.prepare_ref_audio_stage_limiter = StageLimiter(int(os.environ.get("GPTSOVITS_PREPARE_REF_SLOTS", "4"))) + self.prepare_bert_batch_worker = None + self.prepare_ref_semantic_batch_worker = None + self.prepare_text_cpu_workers = max( + 0, + int(os.environ.get("GPTSOVITS_PREPARE_TEXT_CPU_WORKERS", "0")), + ) + self.prepare_text_cpu_executor = ( + concurrent.futures.ThreadPoolExecutor( + max_workers=self.prepare_text_cpu_workers, + thread_name_prefix="prepare-text-cpu", + ) + if self.prepare_text_cpu_workers > 0 + else None + ) self._init_models() - - self.text_preprocessor: TextPreprocessor = TextPreprocessor( - self.bert_model, self.bert_tokenizer, self.configs.device - ) + self.refresh_runtime_components() self.prompt_cache: dict = { "ref_audio_path": None, @@ -464,6 +485,57 @@ class TTS: self.stop_flag: bool = False self.precision: torch.dtype = torch.float16 if self.configs.is_half else torch.float32 + def refresh_runtime_components(self): + self.prepare_bert_batch_worker = None + self.prepare_ref_semantic_batch_worker = None + if os.environ.get("GPTSOVITS_PREPARE_BERT_BATCHING", "1") != "0": + self.prepare_bert_batch_worker = PrepareBertBatchWorker( + bert_model=self.bert_model, + tokenizer=self.bert_tokenizer, + device=self.configs.device, + stage_limiter=self.prepare_bert_stage_limiter, + batch_window_ms=int(os.environ.get("GPTSOVITS_PREPARE_BERT_BATCH_WINDOW_MS", "5")), + max_batch_items=int(os.environ.get("GPTSOVITS_PREPARE_BERT_BATCH_MAX_ITEMS", "16")), + max_batch_tokens=int(os.environ.get("GPTSOVITS_PREPARE_BERT_BATCH_MAX_TOKENS", "4096")), + max_pending_tasks=int(os.environ.get("GPTSOVITS_PREPARE_BERT_MAX_PENDING_TASKS", "0")), + admission_poll_ms=int(os.environ.get("GPTSOVITS_PREPARE_BERT_ADMISSION_POLL_MS", "1")), + high_pressure_pending_threshold=int( + os.environ.get("GPTSOVITS_PREPARE_BERT_HIGH_PRESSURE_PENDING_THRESHOLD", "0") + ), + high_pressure_batch_window_ms=int( + os.environ.get("GPTSOVITS_PREPARE_BERT_HIGH_PRESSURE_BATCH_WINDOW_MS", "1") + ), + high_pressure_max_batch_items=int( + os.environ.get("GPTSOVITS_PREPARE_BERT_HIGH_PRESSURE_MAX_ITEMS", "32") + ), + high_pressure_max_batch_tokens=int( + os.environ.get("GPTSOVITS_PREPARE_BERT_HIGH_PRESSURE_MAX_TOKENS", "8192") + ), + ) + if os.environ.get("GPTSOVITS_PREPARE_REF_BATCHING", "0") != "0": + ref_max_batch_samples = os.environ.get("GPTSOVITS_PREPARE_REF_BATCH_MAX_SAMPLES") + if ref_max_batch_samples is None: + ref_max_batch_samples = os.environ.get("GPTSOVITS_PREPARE_REF_BATCH_MAX_FRAMES", "960000") + self.prepare_ref_semantic_batch_worker = PrepareRefSemanticBatchWorker( + ssl_model=self.cnhuhbert_model, + vits_model=self.vits_model, + device=self.configs.device, + is_half=self.configs.is_half, + zero_wav_samples=int(self.configs.sampling_rate * 0.3), + stage_limiter=self.prepare_ref_audio_stage_limiter, + batch_window_ms=int(os.environ.get("GPTSOVITS_PREPARE_REF_BATCH_WINDOW_MS", "5")), + max_batch_items=int(os.environ.get("GPTSOVITS_PREPARE_REF_BATCH_MAX_ITEMS", "8")), + max_batch_samples=int(ref_max_batch_samples), + ) + + self.text_preprocessor = TextPreprocessor( + self.bert_model, + self.bert_tokenizer, + self.configs.device, + bert_stage_limiter=self.prepare_bert_stage_limiter, + bert_batch_worker=self.prepare_bert_batch_worker, + ) + def _init_models( self, ): @@ -755,33 +827,62 @@ class TTS: Args: ref_audio_path: str, the path of the reference audio. """ - self._set_prompt_semantic(ref_audio_path) - self._set_ref_spec(ref_audio_path) + bundle = self.extract_ref_audio_bundle(ref_audio_path) + if self.prompt_cache["refer_spec"] in [[], None]: + self.prompt_cache["refer_spec"] = [bundle["refer_spec"]] + else: + self.prompt_cache["refer_spec"][0] = bundle["refer_spec"] + self.prompt_cache["prompt_semantic"] = bundle["prompt_semantic"] + self.prompt_cache["raw_audio"] = bundle["raw_audio"] + self.prompt_cache["raw_sr"] = bundle["raw_sr"] self._set_ref_audio_path(ref_audio_path) - def _set_ref_audio_path(self, ref_audio_path): - self.prompt_cache["ref_audio_path"] = ref_audio_path - - def _set_ref_spec(self, ref_audio_path): - spec_audio = self._get_ref_spec(ref_audio_path) - if self.prompt_cache["refer_spec"] in [[], None]: - self.prompt_cache["refer_spec"] = [spec_audio] - else: - self.prompt_cache["refer_spec"][0] = spec_audio - - def _get_ref_spec(self, ref_audio_path): + def _load_ref_audio_raw(self, ref_audio_path: str): raw_audio, raw_sr = torchaudio.load(ref_audio_path) - raw_audio = raw_audio.to(self.configs.device).float() - self.prompt_cache["raw_audio"] = raw_audio - self.prompt_cache["raw_sr"] = raw_sr + return raw_audio.float(), int(raw_sr) + + @torch.inference_mode() + def _extract_prompt_semantic_from_prepared_wav16k(self, wav16k: torch.Tensor): + wav16k = wav16k.to(self.configs.device) + if self.configs.is_half: + wav16k = wav16k.half() + hubert_feature = self.cnhuhbert_model.model(wav16k.unsqueeze(0))["last_hidden_state"].transpose(1, 2) + codes = self.vits_model.extract_latent(hubert_feature) + return codes[0, 0].to(self.configs.device) + + @torch.inference_mode() + def _extract_prompt_semantic_profile_from_raw(self, raw_audio: torch.Tensor, raw_sr: int): + cpu_prepare_start = time.perf_counter() + wav16k = prepare_prompt_semantic_wav16k( + raw_audio=raw_audio, + raw_sr=raw_sr, + zero_wav_samples=int(self.configs.sampling_rate * 0.3), + ) + cpu_prepare_ms = (time.perf_counter() - cpu_prepare_start) * 1000.0 + forward_start = time.perf_counter() + prompt_semantic = self._extract_prompt_semantic_from_prepared_wav16k(wav16k) + forward_ms = (time.perf_counter() - forward_start) * 1000.0 + return prompt_semantic, cpu_prepare_ms, forward_ms + + @torch.inference_mode() + def _extract_prompt_semantic_from_raw(self, raw_audio: torch.Tensor, raw_sr: int): + prompt_semantic, _, _ = self._extract_prompt_semantic_profile_from_raw(raw_audio, raw_sr) + return prompt_semantic + + def extract_prompt_semantic(self, ref_wav_path: str): + raw_audio, raw_sr = self._load_ref_audio_raw(ref_wav_path) + return self._extract_prompt_semantic_from_raw(raw_audio, raw_sr) + + def _extract_ref_spec_from_raw(self, raw_audio: torch.Tensor, raw_sr: int): + raw_audio_device = raw_audio.to(self.configs.device).float() if raw_sr != self.configs.sampling_rate: - audio = raw_audio.to(self.configs.device) + audio = raw_audio_device if audio.shape[0] == 2: audio = audio.mean(0).unsqueeze(0) audio = resample(audio, raw_sr, self.configs.sampling_rate, self.configs.device) else: - audio = raw_audio.to(self.configs.device) + audio = raw_audio_device if audio.shape[0] == 2: audio = audio.mean(0).unsqueeze(0) @@ -804,33 +905,191 @@ class TTS: audio = audio.half() else: audio = None + return spec, audio, raw_audio, raw_sr + + def extract_ref_spec(self, ref_audio_path: str): + raw_audio, raw_sr = self._load_ref_audio_raw(ref_audio_path) + return self._extract_ref_spec_from_raw(raw_audio, raw_sr) + + def extract_ref_audio_bundle(self, ref_audio_path: str): + load_start = time.perf_counter() + raw_audio, raw_sr = self._load_ref_audio_raw(ref_audio_path) + load_ms = (time.perf_counter() - load_start) * 1000.0 + if self.prepare_ref_semantic_batch_worker is None: + with self.prepare_ref_audio_stage_limiter.enter() as limiter_stats: + prompt_semantic_start = time.perf_counter() + prompt_semantic, prompt_semantic_cpu_prepare_ms, prompt_semantic_forward_ms = ( + self._extract_prompt_semantic_profile_from_raw(raw_audio, raw_sr) + ) + prompt_semantic_ms = (time.perf_counter() - prompt_semantic_start) * 1000.0 + ref_spec_start = time.perf_counter() + refer_spec = self._extract_ref_spec_from_raw(raw_audio, raw_sr)[:2] + ref_spec_ms = (time.perf_counter() - ref_spec_start) * 1000.0 + audio_stage_wait_ms = float(limiter_stats["wait_ms"]) + audio_stage_slots = float(limiter_stats["slots"]) + audio_stage_inflight_peak = float(limiter_stats["peak_inflight"]) + prompt_semantic_profile = { + "prompt_semantic_wait_ms": float(limiter_stats["wait_ms"]), + "prompt_semantic_cpu_prepare_ms": float(prompt_semantic_cpu_prepare_ms), + "prompt_semantic_forward_ms": float(prompt_semantic_forward_ms), + "prompt_semantic_scatter_ms": 0.0, + "prompt_semantic_stage_slots": float(limiter_stats["slots"]), + "prompt_semantic_stage_inflight_peak": float(limiter_stats["peak_inflight"]), + "prompt_semantic_batch_size": 1.0, + "prompt_semantic_batch_samples": 0.0, + } + ref_spec_wait_ms = 0.0 + return { + "prompt_semantic": prompt_semantic, + "refer_spec": refer_spec, + "raw_audio": raw_audio, + "raw_sr": raw_sr, + "profile": { + "audio_load_ms": load_ms, + "audio_stage_wait_ms": audio_stage_wait_ms, + "audio_stage_slots": audio_stage_slots, + "audio_stage_inflight_peak": audio_stage_inflight_peak, + "prompt_semantic_ms": prompt_semantic_ms, + "prompt_semantic_wait_ms": float(prompt_semantic_profile.get("prompt_semantic_wait_ms", 0.0)), + "prompt_semantic_cpu_prepare_ms": float( + prompt_semantic_profile.get("prompt_semantic_cpu_prepare_ms", 0.0) + ), + "prompt_semantic_forward_ms": float( + prompt_semantic_profile.get("prompt_semantic_forward_ms", 0.0) + ), + "prompt_semantic_scatter_ms": float( + prompt_semantic_profile.get("prompt_semantic_scatter_ms", 0.0) + ), + "prompt_semantic_stage_slots": float( + prompt_semantic_profile.get("prompt_semantic_stage_slots", 0.0) + ), + "prompt_semantic_stage_inflight_peak": float( + prompt_semantic_profile.get("prompt_semantic_stage_inflight_peak", 0.0) + ), + "prompt_semantic_batch_size": float(prompt_semantic_profile.get("prompt_semantic_batch_size", 1.0)), + "prompt_semantic_batch_samples": float( + prompt_semantic_profile.get("prompt_semantic_batch_samples", 0.0) + ), + "ref_spec_wait_ms": ref_spec_wait_ms, + "ref_spec_ms": ref_spec_ms, + "bundle_total_ms": load_ms + audio_stage_wait_ms + prompt_semantic_ms + ref_spec_ms, + }, + } + + prompt_semantic_profile = { + "prompt_semantic_wait_ms": 0.0, + "prompt_semantic_cpu_prepare_ms": 0.0, + "prompt_semantic_forward_ms": 0.0, + "prompt_semantic_scatter_ms": 0.0, + "prompt_semantic_stage_slots": 0.0, + "prompt_semantic_stage_inflight_peak": 0.0, + "prompt_semantic_batch_size": 1.0, + "prompt_semantic_batch_samples": 0.0, + } + if self.prepare_ref_semantic_batch_worker is not None: + prompt_semantic, worker_profile = self.prepare_ref_semantic_batch_worker.submit(raw_audio, raw_sr) + prompt_semantic_profile.update(worker_profile) + prompt_semantic_ms = ( + float(prompt_semantic_profile.get("prompt_semantic_cpu_prepare_ms", 0.0)) + + float(prompt_semantic_profile.get("prompt_semantic_forward_ms", 0.0)) + + float(prompt_semantic_profile.get("prompt_semantic_scatter_ms", 0.0)) + ) + with self.prepare_ref_audio_stage_limiter.enter() as ref_spec_limiter_stats: + ref_spec_start = time.perf_counter() + refer_spec = self._extract_ref_spec_from_raw(raw_audio, raw_sr)[:2] + ref_spec_ms = (time.perf_counter() - ref_spec_start) * 1000.0 + audio_stage_wait_ms = float(prompt_semantic_profile.get("prompt_semantic_wait_ms", 0.0)) + float( + ref_spec_limiter_stats["wait_ms"] + ) + audio_stage_slots = max( + float(prompt_semantic_profile.get("prompt_semantic_stage_slots", 0.0)), + float(ref_spec_limiter_stats["slots"]), + ) + audio_stage_inflight_peak = max( + float(prompt_semantic_profile.get("prompt_semantic_stage_inflight_peak", 0.0)), + float(ref_spec_limiter_stats["peak_inflight"]), + ) + return { + "prompt_semantic": prompt_semantic, + "refer_spec": refer_spec, + "raw_audio": raw_audio, + "raw_sr": raw_sr, + "profile": { + "audio_load_ms": load_ms, + "audio_stage_wait_ms": audio_stage_wait_ms, + "audio_stage_slots": audio_stage_slots, + "audio_stage_inflight_peak": audio_stage_inflight_peak, + "prompt_semantic_ms": prompt_semantic_ms, + "prompt_semantic_wait_ms": float(prompt_semantic_profile.get("prompt_semantic_wait_ms", 0.0)), + "prompt_semantic_cpu_prepare_ms": float( + prompt_semantic_profile.get("prompt_semantic_cpu_prepare_ms", 0.0) + ), + "prompt_semantic_forward_ms": float(prompt_semantic_profile.get("prompt_semantic_forward_ms", 0.0)), + "prompt_semantic_scatter_ms": float(prompt_semantic_profile.get("prompt_semantic_scatter_ms", 0.0)), + "prompt_semantic_stage_slots": float(prompt_semantic_profile.get("prompt_semantic_stage_slots", 0.0)), + "prompt_semantic_stage_inflight_peak": float( + prompt_semantic_profile.get("prompt_semantic_stage_inflight_peak", 0.0) + ), + "prompt_semantic_batch_size": float(prompt_semantic_profile.get("prompt_semantic_batch_size", 1.0)), + "prompt_semantic_batch_samples": float( + prompt_semantic_profile.get("prompt_semantic_batch_samples", 0.0) + ), + "ref_spec_wait_ms": float(ref_spec_limiter_stats["wait_ms"]), + "ref_spec_ms": ref_spec_ms, + "bundle_total_ms": load_ms + audio_stage_wait_ms + prompt_semantic_ms + ref_spec_ms, + }, + } + + def extract_text_features(self, text: str, language: str, profile: dict | None = None): + return self.text_preprocessor.segment_and_extract_feature_for_text( + text, language, self.configs.version, profile=profile + ) + + def prepare_text_segments(self, text: str, language: str): + return self.text_preprocessor.preprocess_text_segments(text, language, self.configs.version) + + def build_text_features_from_segments(self, prepared_segments, profile: dict | None = None): + return self.text_preprocessor.build_phones_and_bert_from_segments(prepared_segments, profile=profile) + + async def build_text_features_from_segments_async(self, prepared_segments, profile: dict | None = None): + return await self.text_preprocessor.build_phones_and_bert_from_segments_async( + prepared_segments, + profile=profile, + ) + + async def build_text_feature_pair_from_segments_async( + self, + prompt_segments, + target_segments, + prompt_profile: dict | None = None, + target_profile: dict | None = None, + ): + return await self.text_preprocessor.build_phones_and_bert_pair_from_segments_async( + prompt_segments, + target_segments, + prompt_profile=prompt_profile, + target_profile=target_profile, + ) + + def _set_ref_audio_path(self, ref_audio_path): + self.prompt_cache["ref_audio_path"] = ref_audio_path + + def _set_ref_spec(self, ref_audio_path): + spec_audio = self._get_ref_spec(ref_audio_path) + if self.prompt_cache["refer_spec"] in [[], None]: + self.prompt_cache["refer_spec"] = [spec_audio] + else: + self.prompt_cache["refer_spec"][0] = spec_audio + + def _get_ref_spec(self, ref_audio_path): + spec, audio, raw_audio, raw_sr = self.extract_ref_spec(ref_audio_path) + self.prompt_cache["raw_audio"] = raw_audio + self.prompt_cache["raw_sr"] = raw_sr return spec, audio def _set_prompt_semantic(self, ref_wav_path: str): - zero_wav = np.zeros( - int(self.configs.sampling_rate * 0.3), - dtype=np.float16 if self.configs.is_half else np.float32, - ) - with torch.no_grad(): - wav16k, sr = librosa.load(ref_wav_path, sr=16000) - if wav16k.shape[0] > 160000 or wav16k.shape[0] < 48000: - raise OSError(i18n("参考音频在3~10秒范围外，请更换！")) - wav16k = torch.from_numpy(wav16k) - zero_wav_torch = torch.from_numpy(zero_wav) - wav16k = wav16k.to(self.configs.device) - zero_wav_torch = zero_wav_torch.to(self.configs.device) - if self.configs.is_half: - wav16k = wav16k.half() - zero_wav_torch = zero_wav_torch.half() - - wav16k = torch.cat([wav16k, zero_wav_torch]) - hubert_feature = self.cnhuhbert_model.model(wav16k.unsqueeze(0))["last_hidden_state"].transpose( - 1, 2 - ) # .float() - codes = self.vits_model.extract_latent(hubert_feature) - - prompt_semantic = codes[0, 0].to(self.configs.device) - self.prompt_cache["prompt_semantic"] = prompt_semantic + prompt_semantic = self.extract_prompt_semantic(ref_wav_path) + self.prompt_cache["prompt_semantic"] = prompt_semantic def batch_sequences(self, sequences: List[torch.Tensor], axis: int = 0, pad_value: int = 0, max_length: int = None): seq = sequences[0] @@ -1227,6 +1486,9 @@ class TTS: ###### inference ###### t_34 = 0.0 t_45 = 0.0 + t2s_observe_batch_count = 0 + t2s_observe_fastpath_hits = 0 + t2s_observe_generated_tokens = 0 audio = [] is_first_package = True output_sr = self.configs.sampling_rate if not self.configs.use_vocoder else self.vocoder_configs["sr"] @@ -1280,6 +1542,29 @@ class TTS: ) t4 = time.perf_counter() t_34 += t4 - t3 + if hasattr(self.t2s_model.model, "get_last_infer_stats"): + t2s_stats = self.t2s_model.model.get_last_infer_stats() + if t2s_stats: + generated_token_count = int(t2s_stats.get("generated_token_count", 0)) + t2s_total_ms = (t4 - t3) * 1000.0 + avg_decode_ms_per_token = ( + t2s_total_ms / generated_token_count if generated_token_count > 0 else 0.0 + ) + t2s_observe_batch_count += 1 + t2s_observe_generated_tokens += generated_token_count + if bool(t2s_stats.get("fastpath_hit", False)): + t2s_observe_fastpath_hits += 1 + print( + "[t2s_observe] " + f"mode={t2s_stats.get('infer_mode')} " + f"batch_size={t2s_stats.get('batch_size')} " + f"tokens={generated_token_count} " + f"t2s_ms={t2s_total_ms:.3f} " + f"avg_decode_ms_per_token={avg_decode_ms_per_token:.3f} " + f"requested_fastpath={t2s_stats.get('requested_enable_mask_free_fastpath')} " + f"prefill_all_visible={t2s_stats.get('prefill_after_mask_all_visible')} " + f"fastpath_hit={t2s_stats.get('fastpath_hit')}" + ) batch_audio_fragment = [] @@ -1500,6 +1785,18 @@ class TTS: if not (return_fragment or streaming_mode): print("%.3f\t%.3f\t%.3f\t%.3f" % (t1 - t0, t2 - t1, t_34, t_45)) + if t2s_observe_batch_count > 0: + request_avg_decode_ms_per_token = ( + (t_34 * 1000.0) / t2s_observe_generated_tokens if t2s_observe_generated_tokens > 0 else 0.0 + ) + print( + "[t2s_request_observe] " + f"batches={t2s_observe_batch_count} " + f"fastpath_hits={t2s_observe_fastpath_hits} " + f"generated_tokens={t2s_observe_generated_tokens} " + f"t2s_total_ms={t_34 * 1000.0:.3f} " + f"avg_decode_ms_per_token={request_avg_decode_ms_per_token:.3f}" + ) if len(audio) == 0: yield output_sr, np.zeros(int(output_sr), dtype=np.int16) return @@ -1663,6 +1960,189 @@ class TTS: return audio + def using_vocoder_synthesis_request_local( + self, + semantic_tokens: torch.Tensor, + phones: torch.Tensor, + prompt_semantic: torch.Tensor, + prompt_phones: torch.Tensor, + refer_audio_spec: torch.Tensor, + raw_audio: torch.Tensor, + raw_sr: int, + speed: float = 1.0, + sample_steps: int = 32, + ): + prompt_semantic_tokens = prompt_semantic.unsqueeze(0).unsqueeze(0).to(self.configs.device) + prompt_phones = prompt_phones.unsqueeze(0).to(self.configs.device) + refer_audio_spec = refer_audio_spec.to(dtype=self.precision, device=self.configs.device) + + fea_ref, ge = self.vits_model.decode_encp(prompt_semantic_tokens, prompt_phones, refer_audio_spec) + ref_audio = raw_audio.to(self.configs.device).float() + if ref_audio.shape[0] == 2: + ref_audio = ref_audio.mean(0).unsqueeze(0) + + tgt_sr = 24000 if self.configs.version == "v3" else 32000 + if raw_sr != tgt_sr: + ref_audio = resample(ref_audio, raw_sr, tgt_sr, self.configs.device) + + mel_spec_fn = mel_fn if self.configs.version == "v3" else mel_fn_v4 + mel2 = mel_spec_fn(ref_audio) + mel2 = norm_spec(mel2) + T_min = min(mel2.shape[2], fea_ref.shape[2]) + mel2 = mel2[:, :, :T_min] + fea_ref = fea_ref[:, :, :T_min] + T_ref = self.vocoder_configs["T_ref"] + T_chunk = self.vocoder_configs["T_chunk"] + if T_min > T_ref: + mel2 = mel2[:, :, -T_ref:] + fea_ref = fea_ref[:, :, -T_ref:] + T_min = T_ref + chunk_len = T_chunk - T_min + + mel2 = mel2.to(self.precision) + fea_todo, ge = self.vits_model.decode_encp(semantic_tokens, phones, refer_audio_spec, ge, speed) + + cfm_resss = [] + idx = 0 + while 1: + fea_todo_chunk = fea_todo[:, :, idx : idx + chunk_len] + if fea_todo_chunk.shape[-1] == 0: + break + idx += chunk_len + fea = torch.cat([fea_ref, fea_todo_chunk], 2).transpose(2, 1) + + cfm_res = self.vits_model.cfm.inference( + fea, torch.LongTensor([fea.size(1)]).to(fea.device), mel2, sample_steps, inference_cfg_rate=0 + ) + cfm_res = cfm_res[:, :, mel2.shape[2] :] + + mel2 = cfm_res[:, :, -T_min:] + fea_ref = fea_todo_chunk[:, :, -T_min:] + + cfm_resss.append(cfm_res) + cfm_res = torch.cat(cfm_resss, 2) + cfm_res = denorm_spec(cfm_res) + + with torch.inference_mode(): + wav_gen = self.vocoder(cfm_res) + audio = wav_gen[0][0] + + return audio + + @torch.inference_mode() + def synthesize_audio_request_local( + self, + semantic_tokens: torch.Tensor, + phones: torch.Tensor, + prompt_semantic: torch.Tensor, + prompt_phones: torch.Tensor, + refer_spec: tuple, + raw_audio: torch.Tensor, + raw_sr: int, + speed: float = 1.0, + sample_steps: int = 32, + ): + refer_audio_spec, audio_tensor = refer_spec + if not self.configs.use_vocoder: + refer_audio_spec_list = [refer_audio_spec.to(dtype=self.precision, device=self.configs.device)] + sv_emb = None + if self.is_v2pro: + if audio_tensor is None: + raise ValueError(i18n("v2Pro request-local synthesis 缺少 16k 参考音频")) + sv_emb = self.sv_model.compute_embedding3(audio_tensor).to(self.configs.device) + return self.vits_model.decode( + semantic_tokens, + phones, + refer_audio_spec_list, + speed=speed, + sv_emb=sv_emb, + ).detach()[0, 0, :] + + return self.using_vocoder_synthesis_request_local( + semantic_tokens=semantic_tokens, + phones=phones, + prompt_semantic=prompt_semantic, + prompt_phones=prompt_phones, + refer_audio_spec=refer_audio_spec, + raw_audio=raw_audio, + raw_sr=raw_sr, + speed=speed, + sample_steps=sample_steps, + ) + + @torch.inference_mode() + def synthesize_audio_requests_local_batched( + self, + semantic_tokens_list: List[torch.Tensor], + phones_list: List[torch.Tensor], + refer_specs: List[tuple], + speeds: List[float] | None = None, + sample_steps_list: List[int] | None = None, + ) -> List[torch.Tensor]: + batch_size = len(semantic_tokens_list) + if batch_size == 0: + return [] + if len(phones_list) != batch_size or len(refer_specs) != batch_size: + raise ValueError("batched request-local synthesis 输入长度不一致") + if speeds is None: + speeds = [1.0] * batch_size + if sample_steps_list is None: + sample_steps_list = [32] * batch_size + if len(speeds) != batch_size or len(sample_steps_list) != batch_size: + raise ValueError("batched request-local synthesis 参数长度不一致") + first_speed = float(speeds[0]) + first_sample_steps = int(sample_steps_list[0]) + if any(abs(float(item) - first_speed) > 1e-6 for item in speeds): + raise ValueError("batched request-local synthesis 目前要求 speed 一致") + if any(int(item) != first_sample_steps for item in sample_steps_list): + raise ValueError("batched request-local synthesis 目前要求 sample_steps 一致") + if self.configs.use_vocoder: + raise NotImplementedError("request-local batched VITS synthesis 暂不支持 vocoder 模型") + + device = self.configs.device + max_semantic_len = max(int(item.shape[-1]) for item in semantic_tokens_list) + max_phone_len = max(int(item.shape[-1]) for item in phones_list) + semantic_batch = torch.zeros((1, batch_size, max_semantic_len), dtype=torch.long, device=device) + phone_batch = torch.zeros((batch_size, max_phone_len), dtype=torch.long, device=device) + semantic_lengths = [] + phone_lengths = [] + refer_audio_specs: List[torch.Tensor] = [] + sv_emb_batch = None + sv_emb_list: List[torch.Tensor] = [] + + for batch_index, semantic_tokens in enumerate(semantic_tokens_list): + semantic_len = int(semantic_tokens.shape[-1]) + phone_len = int(phones_list[batch_index].shape[-1]) + semantic_batch[0, batch_index, :semantic_len] = semantic_tokens.to(device=device, dtype=torch.long) + phone_batch[batch_index, :phone_len] = phones_list[batch_index].to(device=device, dtype=torch.long) + semantic_lengths.append(semantic_len) + phone_lengths.append(phone_len) + + refer_audio_spec, audio_tensor = refer_specs[batch_index] + refer_audio_specs.append(refer_audio_spec.to(dtype=self.precision, device=device)) + if self.is_v2pro: + if audio_tensor is None: + raise ValueError(i18n("v2Pro request-local batched synthesis 缺少 16k 参考音频")) + sv_emb_list.append(self.sv_model.compute_embedding3(audio_tensor).to(device)) + + if self.is_v2pro: + sv_emb_batch = torch.cat(sv_emb_list, dim=0) + + audio_batch, audio_lengths = self.vits_model.decode_batched_request_local( + codes=semantic_batch, + code_lengths=torch.LongTensor(semantic_lengths).to(device), + text=phone_batch, + text_lengths=torch.LongTensor(phone_lengths).to(device), + refer_list=refer_audio_specs, + speed=first_speed, + sv_emb=sv_emb_batch, + ) + audios: List[torch.Tensor] = [] + for batch_index in range(batch_size): + audio_len = int(audio_lengths[batch_index].item()) + audios.append(audio_batch[batch_index, 0, :audio_len].detach()) + return audios + def using_vocoder_synthesis_batched_infer( self, idx_list: List[int], diff --git a/GPT_SoVITS/TTS_infer_pack/TextPreprocessor.py b/GPT_SoVITS/TTS_infer_pack/TextPreprocessor.py index 283e91c3..6bee49be 100644 --- a/GPT_SoVITS/TTS_infer_pack/TextPreprocessor.py +++ b/GPT_SoVITS/TTS_infer_pack/TextPreprocessor.py @@ -1,6 +1,10 @@ +import asyncio import os import sys import threading +import time +from contextlib import contextmanager +from dataclasses import dataclass from tqdm import tqdm @@ -11,11 +15,13 @@ import re import torch from text.LangSegmenter import LangSegmenter from text import chinese -from typing import Dict, List, Tuple +from typing import Dict, List, Optional, Tuple from text.cleaner import clean_text from text import cleaned_text_to_sequence from transformers import AutoModelForMaskedLM, AutoTokenizer from TTS_infer_pack.text_segmentation_method import split_big_text, splits, get_method as get_seg_method +from TTS_infer_pack.prepare_bert_batch_worker import PrepareBertBatchWorker +from TTS_infer_pack.text_cpu_preprocess import preprocess_text_segments_payload from tools.i18n.i18n import I18nAuto, scan_language_list @@ -49,12 +55,68 @@ def merge_short_text_in_array(texts: str, threshold: int) -> list: return result +class StageLimiter: + def __init__(self, slots: int): + self.slots = max(1, int(slots)) + self.semaphore = threading.BoundedSemaphore(self.slots) + self.lock = threading.Lock() + self.inflight = 0 + self.peak_inflight = 0 + + @contextmanager + def enter(self): + wait_start = time.perf_counter() + self.semaphore.acquire() + wait_ms = (time.perf_counter() - wait_start) * 1000.0 + with self.lock: + self.inflight += 1 + current_inflight = self.inflight + if current_inflight > self.peak_inflight: + self.peak_inflight = current_inflight + peak_inflight = self.peak_inflight + try: + yield { + "wait_ms": wait_ms, + "inflight": current_inflight, + "peak_inflight": peak_inflight, + "slots": self.slots, + } + finally: + with self.lock: + self.inflight = max(0, self.inflight - 1) + self.semaphore.release() + + def snapshot(self) -> Dict[str, int]: + with self.lock: + return { + "slots": self.slots, + "inflight": self.inflight, + "peak_inflight": self.peak_inflight, + } + + +@dataclass +class PreparedTextSegment: + language: str + phones: List[int] + word2ph: Optional[List[int]] + norm_text: str + + class TextPreprocessor: - def __init__(self, bert_model: AutoModelForMaskedLM, tokenizer: AutoTokenizer, device: torch.device): + def __init__( + self, + bert_model: AutoModelForMaskedLM, + tokenizer: AutoTokenizer, + device: torch.device, + bert_stage_limiter: StageLimiter | None = None, + bert_batch_worker: PrepareBertBatchWorker | None = None, + ): self.bert_model = bert_model self.tokenizer = tokenizer self.device = device - self.bert_lock = threading.RLock() + self.bert_stage_limiter = bert_stage_limiter + self.bert_batch_worker = bert_batch_worker def preprocess(self, text: str, lang: str, text_split_method: str, version: str = "v2") -> List[Dict]: print(f"############ {i18n('切分文本')} ############") @@ -98,7 +160,7 @@ class TextPreprocessor: # 解决输入目标文本的空行导致报错的问题 if len(text.strip()) == 0: continue - if not re.sub("\W+", "", text): + if not re.sub(r"\W+", "", text): # 检测一下，如果是纯符号，就跳过。 continue if text[-1] not in splits: @@ -115,86 +177,182 @@ class TextPreprocessor: return texts def segment_and_extract_feature_for_text( - self, text: str, language: str, version: str = "v1" + self, text: str, language: str, version: str = "v1", profile: Dict | None = None ) -> Tuple[list, torch.Tensor, str]: - return self.get_phones_and_bert(text, language, version) + prepared_segments = self.preprocess_text_segments(text, language, version) + return self.build_phones_and_bert_from_segments(prepared_segments, profile=profile) - def get_phones_and_bert(self, text: str, language: str, version: str, final: bool = False): - with self.bert_lock: - text = re.sub(r' {2,}', ' ', text) - textlist = [] - langlist = [] - if language == "all_zh": - for tmp in LangSegmenter.getTexts(text,"zh"): + def _split_text_by_language(self, text: str, language: str) -> Tuple[List[str], List[str]]: + textlist = [] + langlist = [] + if language == "all_zh": + for tmp in LangSegmenter.getTexts(text, "zh"): + langlist.append(tmp["lang"]) + textlist.append(tmp["text"]) + elif language == "all_yue": + for tmp in LangSegmenter.getTexts(text, "zh"): + if tmp["lang"] == "zh": + tmp["lang"] = "yue" + langlist.append(tmp["lang"]) + textlist.append(tmp["text"]) + elif language == "all_ja": + for tmp in LangSegmenter.getTexts(text, "ja"): + langlist.append(tmp["lang"]) + textlist.append(tmp["text"]) + elif language == "all_ko": + for tmp in LangSegmenter.getTexts(text, "ko"): + langlist.append(tmp["lang"]) + textlist.append(tmp["text"]) + elif language == "en": + langlist.append("en") + textlist.append(text) + elif language == "auto": + for tmp in LangSegmenter.getTexts(text): + langlist.append(tmp["lang"]) + textlist.append(tmp["text"]) + elif language == "auto_yue": + for tmp in LangSegmenter.getTexts(text): + if tmp["lang"] == "zh": + tmp["lang"] = "yue" + langlist.append(tmp["lang"]) + textlist.append(tmp["text"]) + else: + for tmp in LangSegmenter.getTexts(text): + if langlist: + same_group = (tmp["lang"] == "en" and langlist[-1] == "en") or ( + tmp["lang"] != "en" and langlist[-1] != "en" + ) + if same_group: + textlist[-1] += tmp["text"] + continue + if tmp["lang"] == "en": langlist.append(tmp["lang"]) - textlist.append(tmp["text"]) - elif language == "all_yue": - for tmp in LangSegmenter.getTexts(text,"zh"): - if tmp["lang"] == "zh": - tmp["lang"] = "yue" - langlist.append(tmp["lang"]) - textlist.append(tmp["text"]) - elif language == "all_ja": - for tmp in LangSegmenter.getTexts(text,"ja"): - langlist.append(tmp["lang"]) - textlist.append(tmp["text"]) - elif language == "all_ko": - for tmp in LangSegmenter.getTexts(text,"ko"): - langlist.append(tmp["lang"]) - textlist.append(tmp["text"]) - elif language == "en": - langlist.append("en") - textlist.append(text) - elif language == "auto": - for tmp in LangSegmenter.getTexts(text): - langlist.append(tmp["lang"]) - textlist.append(tmp["text"]) - elif language == "auto_yue": - for tmp in LangSegmenter.getTexts(text): - if tmp["lang"] == "zh": - tmp["lang"] = "yue" - langlist.append(tmp["lang"]) - textlist.append(tmp["text"]) - else: - for tmp in LangSegmenter.getTexts(text): - if langlist: - if (tmp["lang"] == "en" and langlist[-1] == "en") or (tmp["lang"] != "en" and langlist[-1] != "en"): - textlist[-1] += tmp["text"] - continue - if tmp["lang"] == "en": - langlist.append(tmp["lang"]) - else: - # 因无法区别中日韩文汉字,以用户输入为准 - langlist.append(language) - textlist.append(tmp["text"]) - # print(textlist) - # print(langlist) - phones_list = [] - bert_list = [] - norm_text_list = [] - for i in range(len(textlist)): - lang = langlist[i] - phones, word2ph, norm_text = self.clean_text_inf(textlist[i], lang, version) - bert = self.get_bert_inf(phones, word2ph, norm_text, lang) - phones_list.append(phones) - norm_text_list.append(norm_text) - bert_list.append(bert) - bert = torch.cat(bert_list, dim=1) - phones = sum(phones_list, []) - norm_text = "".join(norm_text_list) + else: + langlist.append(language) + textlist.append(tmp["text"]) + return textlist, langlist - if not final and len(phones) < 6: - return self.get_phones_and_bert("." + text, language, version, final=True) + def get_phones_and_bert( + self, text: str, language: str, version: str, final: bool = False, profile: Dict | None = None + ): + prepared_segments = self.preprocess_text_segments(text, language, version, final=final) + return self.build_phones_and_bert_from_segments(prepared_segments, profile=profile) - return phones, bert, norm_text + def preprocess_text_segments( + self, + text: str, + language: str, + version: str, + final: bool = False, + ) -> List[PreparedTextSegment]: + payloads = preprocess_text_segments_payload(text, language, version, final=final) + return [ + PreparedTextSegment( + language=str(payload["language"]), + phones=list(payload["phones"]), + word2ph=None if payload["word2ph"] is None else list(payload["word2ph"]), + norm_text=str(payload["norm_text"]), + ) + for payload in payloads + ] - def get_bert_feature(self, text: str, word2ph: list) -> torch.Tensor: - with torch.no_grad(): - inputs = self.tokenizer(text, return_tensors="pt") - for i in inputs: - inputs[i] = inputs[i].to(self.device) - res = self.bert_model(**inputs, output_hidden_states=True) - res = torch.cat(res["hidden_states"][-3:-2], -1)[0].cpu()[1:-1] + def build_phones_and_bert_from_segments( + self, + prepared_segments: List[PreparedTextSegment], + profile: Dict | None = None, + ) -> Tuple[list, torch.Tensor, str]: + phones_list: List[List[int]] = [] + bert_list: List[torch.Tensor] = [] + norm_text_list: List[str] = [] + for segment in prepared_segments: + bert = self.get_bert_inf( + segment.phones, + segment.word2ph, + segment.norm_text, + segment.language, + profile=profile, + ) + phones_list.append(segment.phones) + norm_text_list.append(segment.norm_text) + bert_list.append(bert) + bert = torch.cat(bert_list, dim=1) + phones = sum(phones_list, []) + norm_text = "".join(norm_text_list) + return phones, bert, norm_text + + def _accumulate_profile(self, profile: Dict | None, key: str, value: float) -> None: + if profile is None: + return + profile[key] = float(profile.get(key, 0.0)) + float(value) + + def _update_profile_peak(self, profile: Dict | None, key: str, value: float) -> None: + if profile is None: + return + profile[key] = float(max(float(profile.get(key, 0.0)), float(value))) + + def _merge_bert_worker_profile(self, profile: Dict | None, worker_profile: Dict[str, float]) -> None: + self._accumulate_profile(profile, "bert_wait_ms", worker_profile.get("bert_wait_ms", 0.0)) + self._accumulate_profile(profile, "bert_admission_wait_ms", worker_profile.get("bert_admission_wait_ms", 0.0)) + self._accumulate_profile(profile, "bert_queue_wait_ms", worker_profile.get("bert_queue_wait_ms", 0.0)) + self._accumulate_profile( + profile, + "bert_batch_collect_wait_ms", + worker_profile.get("bert_batch_collect_wait_ms", 0.0), + ) + self._accumulate_profile(profile, "bert_forward_ms", worker_profile.get("bert_forward_ms", 0.0)) + self._accumulate_profile(profile, "bert_tokenize_ms", worker_profile.get("bert_tokenize_ms", 0.0)) + self._accumulate_profile(profile, "bert_scatter_ms", worker_profile.get("bert_scatter_ms", 0.0)) + self._accumulate_profile(profile, "bert_calls", worker_profile.get("bert_calls", 1.0)) + self._update_profile_peak(profile, "bert_stage_inflight_peak", worker_profile.get("bert_stage_inflight_peak", 0.0)) + self._update_profile_peak(profile, "bert_batch_size_peak", worker_profile.get("bert_batch_size", 0.0)) + self._update_profile_peak(profile, "bert_batch_tokens_peak", worker_profile.get("bert_batch_tokens", 0.0)) + self._update_profile_peak( + profile, + "bert_pending_depth_on_enqueue_peak", + worker_profile.get("bert_pending_depth_on_enqueue", 0.0), + ) + self._update_profile_peak( + profile, + "bert_pending_depth_on_collect_peak", + worker_profile.get("bert_pending_depth_on_collect", 0.0), + ) + self._update_profile_peak(profile, "bert_high_pressure_mode_peak", worker_profile.get("bert_high_pressure_mode", 0.0)) + if profile is not None: + profile["bert_stage_slots"] = float(worker_profile.get("bert_stage_slots", 0.0)) + profile["bert_batch_window_ms"] = float(worker_profile.get("bert_batch_window_ms", 0.0)) + + def get_bert_feature(self, text: str, word2ph: list, profile: Dict | None = None) -> torch.Tensor: + if self.bert_batch_worker is not None: + feature, worker_profile = self.bert_batch_worker.submit(text, word2ph) + self._merge_bert_worker_profile(profile, worker_profile) + return feature + + limiter_stats = {"wait_ms": 0.0, "inflight": 1, "peak_inflight": 1, "slots": 0} + if self.bert_stage_limiter is None: + forward_start = time.perf_counter() + with torch.no_grad(): + inputs = self.tokenizer(text, return_tensors="pt") + for i in inputs: + inputs[i] = inputs[i].to(self.device) + res = self.bert_model(**inputs, output_hidden_states=True) + res = torch.cat(res["hidden_states"][-3:-2], -1)[0].cpu()[1:-1] + forward_ms = (time.perf_counter() - forward_start) * 1000.0 + else: + with self.bert_stage_limiter.enter() as limiter_stats: + forward_start = time.perf_counter() + with torch.no_grad(): + inputs = self.tokenizer(text, return_tensors="pt") + for i in inputs: + inputs[i] = inputs[i].to(self.device) + res = self.bert_model(**inputs, output_hidden_states=True) + res = torch.cat(res["hidden_states"][-3:-2], -1)[0].cpu()[1:-1] + forward_ms = (time.perf_counter() - forward_start) * 1000.0 + self._accumulate_profile(profile, "bert_wait_ms", limiter_stats["wait_ms"]) + self._accumulate_profile(profile, "bert_forward_ms", forward_ms) + self._accumulate_profile(profile, "bert_calls", 1.0) + self._update_profile_peak(profile, "bert_stage_inflight_peak", limiter_stats["peak_inflight"]) + if profile is not None: + profile["bert_stage_slots"] = float(limiter_stats["slots"]) assert len(word2ph) == len(text) phone_level_feature = [] for i in range(len(word2ph)): @@ -209,10 +367,19 @@ class TextPreprocessor: phones = cleaned_text_to_sequence(phones, version) return phones, word2ph, norm_text - def get_bert_inf(self, phones: list, word2ph: list, norm_text: str, language: str): + def get_bert_inf( + self, + phones: list, + word2ph: Optional[list], + norm_text: str, + language: str, + profile: Dict | None = None, + ): language = language.replace("all_", "") if language == "zh": - feature = self.get_bert_feature(norm_text, word2ph).to(self.device) + if word2ph is None: + raise ValueError("中文文本缺少 word2ph，无法提取 BERT 特征") + feature = self.get_bert_feature(norm_text, word2ph, profile=profile).to(self.device) else: feature = torch.zeros( (1024, len(phones)), @@ -221,6 +388,112 @@ class TextPreprocessor: return feature + async def build_phones_and_bert_from_segments_async( + self, + prepared_segments: List[PreparedTextSegment], + profile: Dict | None = None, + ) -> Tuple[list, torch.Tensor, str]: + segment_jobs = self._build_async_segment_jobs(prepared_segments, profile) + pending_items: List[Tuple[List[torch.Tensor | None], int, Dict | None, asyncio.Future]] = [] + for segment_index, segment in enumerate(prepared_segments): + if segment.language.replace("all_", "") != "zh" or self.bert_batch_worker is None: + continue + if segment.word2ph is None: + raise ValueError("中文文本缺少 word2ph，无法提取 BERT 特征") + pending_items.append( + ( + segment_jobs["bert_list"], + segment_index, + profile, + self.bert_batch_worker.submit_async(segment.norm_text, segment.word2ph), + ) + ) + + if pending_items: + pending_results = await asyncio.gather(*[future for _, _, _, future in pending_items]) + for (bert_list, bert_index, item_profile, _), (feature, worker_profile) in zip(pending_items, pending_results): + self._merge_bert_worker_profile(item_profile, worker_profile) + bert_list[bert_index] = feature.to(self.device) + + return self._finalize_async_segment_jobs(segment_jobs) + + def _build_async_segment_jobs( + self, + prepared_segments: List[PreparedTextSegment], + profile: Dict | None, + ) -> Dict[str, List]: + phones_list: List[List[int]] = [] + bert_list: List[torch.Tensor | None] = [] + norm_text_list: List[str] = [] + + for segment in prepared_segments: + phones_list.append(segment.phones) + norm_text_list.append(segment.norm_text) + segment_language = segment.language.replace("all_", "") + if segment_language == "zh" and self.bert_batch_worker is not None: + if segment.word2ph is None: + raise ValueError("中文文本缺少 word2ph，无法提取 BERT 特征") + bert_list.append(None) + continue + bert_list.append( + self.get_bert_inf( + segment.phones, + segment.word2ph, + segment.norm_text, + segment.language, + profile=profile, + ) + ) + return { + "phones_list": phones_list, + "bert_list": bert_list, + "norm_text_list": norm_text_list, + } + + @staticmethod + def _finalize_async_segment_jobs(segment_jobs: Dict[str, List]) -> Tuple[list, torch.Tensor, str]: + bert = torch.cat([feature for feature in segment_jobs["bert_list"] if feature is not None], dim=1) + phones = sum(segment_jobs["phones_list"], []) + norm_text = "".join(segment_jobs["norm_text_list"]) + return phones, bert, norm_text + + async def build_phones_and_bert_pair_from_segments_async( + self, + prompt_segments: List[PreparedTextSegment], + target_segments: List[PreparedTextSegment], + prompt_profile: Dict | None = None, + target_profile: Dict | None = None, + ) -> Tuple[Tuple[list, torch.Tensor, str], Tuple[list, torch.Tensor, str]]: + prompt_jobs = self._build_async_segment_jobs(prompt_segments, prompt_profile) + target_jobs = self._build_async_segment_jobs(target_segments, target_profile) + pending_items: List[Tuple[List[torch.Tensor | None], int, Dict | None, asyncio.Future]] = [] + + for segment_jobs, prepared_segments, profile in ( + (prompt_jobs, prompt_segments, prompt_profile), + (target_jobs, target_segments, target_profile), + ): + for segment_index, segment in enumerate(prepared_segments): + if segment.language.replace("all_", "") != "zh" or self.bert_batch_worker is None: + continue + if segment.word2ph is None: + raise ValueError("中文文本缺少 word2ph，无法提取 BERT 特征") + pending_items.append( + ( + segment_jobs["bert_list"], + segment_index, + profile, + self.bert_batch_worker.submit_async(segment.norm_text, segment.word2ph), + ) + ) + + if pending_items: + pending_results = await asyncio.gather(*[future for _, _, _, future in pending_items]) + for (bert_list, bert_index, profile, _), (feature, worker_profile) in zip(pending_items, pending_results): + self._merge_bert_worker_profile(profile, worker_profile) + bert_list[bert_index] = feature.to(self.device) + + return self._finalize_async_segment_jobs(prompt_jobs), self._finalize_async_segment_jobs(target_jobs) + def filter_text(self, texts): _text = [] if all(text in [None, " ", "\n", ""] for text in texts): @@ -236,4 +509,4 @@ class TextPreprocessor: punctuations = "".join(re.escape(p) for p in punctuation) pattern = f"([{punctuations}])([{punctuations}])+" result = re.sub(pattern, r"\1", text) - return result \ No newline at end of file + return result diff --git a/GPT_SoVITS/TTS_infer_pack/__init__.py b/GPT_SoVITS/TTS_infer_pack/__init__.py index 8579a632..09a257b2 100644 --- a/GPT_SoVITS/TTS_infer_pack/__init__.py +++ b/GPT_SoVITS/TTS_infer_pack/__init__.py @@ -1 +1,11 @@ -from . import TTS, text_segmentation_method +from __future__ import annotations + +import importlib + +__all__ = ["TTS", "TextPreprocessor", "text_segmentation_method", "t2s_scheduler"] + + +def __getattr__(name: str): + if name in __all__: + return importlib.import_module(f"{__name__}.{name}") + raise AttributeError(f"module {__name__!r} has no attribute {name!r}") diff --git a/GPT_SoVITS/TTS_infer_pack/prepare_bert_batch_worker.py b/GPT_SoVITS/TTS_infer_pack/prepare_bert_batch_worker.py new file mode 100644 index 00000000..1ac77faa --- /dev/null +++ b/GPT_SoVITS/TTS_infer_pack/prepare_bert_batch_worker.py @@ -0,0 +1,346 @@ +import asyncio +import threading +import time +import uuid +from collections import deque +from dataclasses import dataclass, field +from typing import Deque, Dict, List, Tuple + +import torch + + +@dataclass +class BertFeatureTask: + norm_text: str + word2ph: List[int] + task_id: str = field(default_factory=lambda: uuid.uuid4().hex) + created_at: float = field(default_factory=time.perf_counter) + enqueued_at: float = 0.0 + admission_wait_ms: float = 0.0 + pending_depth_on_enqueue: int = 0 + done_event: threading.Event = field(default_factory=threading.Event) + done_loop: asyncio.AbstractEventLoop | None = None + done_future: asyncio.Future | None = None + result_feature: torch.Tensor | None = None + error: Exception | None = None + profile: Dict[str, float] = field(default_factory=dict) + + +class PrepareBertBatchWorker: + def __init__( + self, + bert_model, + tokenizer, + device, + stage_limiter=None, + batch_window_ms: int = 5, + max_batch_items: int = 16, + max_batch_tokens: int = 4096, + max_pending_tasks: int = 0, + admission_poll_ms: int = 1, + high_pressure_pending_threshold: int = 0, + high_pressure_batch_window_ms: int | None = None, + high_pressure_max_batch_items: int | None = None, + high_pressure_max_batch_tokens: int | None = None, + ): + self.bert_model = bert_model + self.tokenizer = tokenizer + self.device = device + self.stage_limiter = stage_limiter + self.batch_window_ms = max(0, int(batch_window_ms)) + self.batch_window_s = float(self.batch_window_ms) / 1000.0 + self.max_batch_items = max(1, int(max_batch_items)) + self.max_batch_tokens = max(16, int(max_batch_tokens)) + self.max_pending_tasks = max(0, int(max_pending_tasks)) + self.admission_poll_s = max(0.0005, float(max(1, int(admission_poll_ms))) / 1000.0) + + self.high_pressure_pending_threshold = max( + 0, + int(high_pressure_pending_threshold) + if int(high_pressure_pending_threshold) > 0 + else max(self.max_batch_items * 2, 32), + ) + hp_window_ms = self.batch_window_ms if high_pressure_batch_window_ms is None else int(high_pressure_batch_window_ms) + hp_items = self.max_batch_items if high_pressure_max_batch_items is None else int(high_pressure_max_batch_items) + hp_tokens = self.max_batch_tokens if high_pressure_max_batch_tokens is None else int(high_pressure_max_batch_tokens) + self.high_pressure_batch_window_ms = max(0, hp_window_ms) + self.high_pressure_batch_window_s = float(self.high_pressure_batch_window_ms) / 1000.0 + self.high_pressure_max_batch_items = max(self.max_batch_items, hp_items) + self.high_pressure_max_batch_tokens = max(self.max_batch_tokens, hp_tokens) + + self.condition = threading.Condition() + self.pending_tasks: Deque[BertFeatureTask] = deque() + self.pending_peak = 0 + self.total_submitted = 0 + self.total_finished = 0 + self.total_batches = 0 + self.active_batch_size = 0 + self.active_batch_peak = 0 + self.active_batch_tokens = 0 + self.active_batch_tokens_peak = 0 + self.high_pressure_batches = 0 + self.admission_wait_total_ms = 0.0 + self.admission_wait_peak_ms = 0.0 + self.worker_thread = threading.Thread(target=self._run_loop, name="prepare-bert-batch-worker", daemon=True) + self.worker_thread.start() + + def _estimate_task_tokens(self, task: BertFeatureTask) -> int: + return max(1, len(task.norm_text) + 2) + + def _can_enqueue_locked(self) -> bool: + if self.max_pending_tasks <= 0: + return True + return (len(self.pending_tasks) + self.active_batch_size) < self.max_pending_tasks + + def _record_enqueue_locked(self, task: BertFeatureTask, admission_wait_ms: float) -> None: + task.admission_wait_ms = float(max(0.0, admission_wait_ms)) + task.enqueued_at = time.perf_counter() + task.pending_depth_on_enqueue = int(len(self.pending_tasks)) + self.pending_tasks.append(task) + self.total_submitted += 1 + self.admission_wait_total_ms += task.admission_wait_ms + self.admission_wait_peak_ms = max(self.admission_wait_peak_ms, task.admission_wait_ms) + if len(self.pending_tasks) > self.pending_peak: + self.pending_peak = len(self.pending_tasks) + self.condition.notify_all() + + def _enqueue_task(self, task: BertFeatureTask) -> None: + admission_started = time.perf_counter() + with self.condition: + while not self._can_enqueue_locked(): + self.condition.wait(timeout=self.admission_poll_s) + self._record_enqueue_locked(task, (time.perf_counter() - admission_started) * 1000.0) + + async def _enqueue_task_async(self, task: BertFeatureTask) -> None: + admission_started = time.perf_counter() + while True: + with self.condition: + if self._can_enqueue_locked(): + self._record_enqueue_locked(task, (time.perf_counter() - admission_started) * 1000.0) + return + await asyncio.sleep(self.admission_poll_s) + + def submit(self, norm_text: str, word2ph: List[int]) -> Tuple[torch.Tensor, Dict[str, float]]: + task = BertFeatureTask(norm_text=str(norm_text), word2ph=list(word2ph)) + self._enqueue_task(task) + task.done_event.wait() + if task.error is not None: + raise task.error + assert task.result_feature is not None + return task.result_feature, dict(task.profile) + + async def submit_async(self, norm_text: str, word2ph: List[int]) -> Tuple[torch.Tensor, Dict[str, float]]: + loop = asyncio.get_running_loop() + task = BertFeatureTask( + norm_text=str(norm_text), + word2ph=list(word2ph), + done_loop=loop, + done_future=loop.create_future(), + ) + await self._enqueue_task_async(task) + return await task.done_future + + def snapshot(self) -> Dict[str, int]: + with self.condition: + return { + "pending": len(self.pending_tasks), + "pending_peak": self.pending_peak, + "total_submitted": self.total_submitted, + "total_finished": self.total_finished, + "total_batches": self.total_batches, + "active_batch_size": self.active_batch_size, + "active_batch_peak": self.active_batch_peak, + "active_batch_tokens": self.active_batch_tokens, + "active_batch_tokens_peak": self.active_batch_tokens_peak, + "batch_window_ms": int(self.batch_window_s * 1000.0), + "max_batch_items": self.max_batch_items, + "max_batch_tokens": self.max_batch_tokens, + "max_pending_tasks": self.max_pending_tasks, + "high_pressure_pending_threshold": self.high_pressure_pending_threshold, + "high_pressure_batch_window_ms": self.high_pressure_batch_window_ms, + "high_pressure_max_batch_items": self.high_pressure_max_batch_items, + "high_pressure_max_batch_tokens": self.high_pressure_max_batch_tokens, + "high_pressure_batches": self.high_pressure_batches, + "admission_wait_total_ms": self.admission_wait_total_ms, + "admission_wait_peak_ms": self.admission_wait_peak_ms, + } + + def _select_batch_policy_locked(self) -> Tuple[float, int, int, bool, int]: + pending_depth = len(self.pending_tasks) + use_high_pressure = ( + self.high_pressure_pending_threshold > 0 + and pending_depth >= self.high_pressure_pending_threshold + ) + if use_high_pressure: + return ( + self.high_pressure_batch_window_s, + self.high_pressure_max_batch_items, + self.high_pressure_max_batch_tokens, + True, + pending_depth, + ) + return ( + self.batch_window_s, + self.max_batch_items, + self.max_batch_tokens, + False, + pending_depth, + ) + + def _collect_batch(self) -> Tuple[List[BertFeatureTask], Dict[str, float]]: + with self.condition: + while not self.pending_tasks: + self.condition.wait() + + collect_started = time.perf_counter() + batch_window_s, max_batch_items, max_batch_tokens, use_high_pressure, pending_depth_on_collect = ( + self._select_batch_policy_locked() + ) + batch: List[BertFeatureTask] = [self.pending_tasks.popleft()] + batch_tokens = self._estimate_task_tokens(batch[0]) + deadline = time.perf_counter() + batch_window_s + + while len(batch) < max_batch_items: + remaining = deadline - time.perf_counter() + if remaining <= 0: + break + if not self.pending_tasks: + self.condition.wait(timeout=remaining) + continue + next_task = self.pending_tasks[0] + next_tokens = self._estimate_task_tokens(next_task) + if len(batch) >= max_batch_items or (batch_tokens + next_tokens) > max_batch_tokens: + break + batch.append(self.pending_tasks.popleft()) + batch_tokens += next_tokens + + self.active_batch_size = len(batch) + self.active_batch_tokens = batch_tokens + if self.active_batch_size > self.active_batch_peak: + self.active_batch_peak = self.active_batch_size + if self.active_batch_tokens > self.active_batch_tokens_peak: + self.active_batch_tokens_peak = self.active_batch_tokens + if use_high_pressure: + self.high_pressure_batches += 1 + return batch, { + "collect_wait_ms": (time.perf_counter() - collect_started) * 1000.0, + "batch_tokens": float(batch_tokens), + "pending_depth_on_collect": float(pending_depth_on_collect), + "high_pressure_mode": 1.0 if use_high_pressure else 0.0, + "batch_window_ms": float(self.high_pressure_batch_window_ms if use_high_pressure else self.batch_window_ms), + } + + def _finalize_batch(self, batch: List[BertFeatureTask]) -> None: + with self.condition: + self.active_batch_size = 0 + self.active_batch_tokens = 0 + self.total_batches += 1 + self.total_finished += len(batch) + self.condition.notify_all() + + def _run_batch(self, batch: List[BertFeatureTask], batch_meta: Dict[str, float]) -> None: + batch_started = time.perf_counter() + texts = [task.norm_text for task in batch] + batch_tokens = int(batch_meta["batch_tokens"]) + + limiter_stats = {"wait_ms": 0.0, "peak_inflight": 1, "slots": 0} + if self.stage_limiter is None: + tokenize_start = time.perf_counter() + inputs = self.tokenizer(texts, return_tensors="pt", padding=True) + tokenize_ms = (time.perf_counter() - tokenize_start) * 1000.0 + attention_mask_cpu = inputs["attention_mask"].cpu() + for key in inputs: + inputs[key] = inputs[key].to(self.device) + forward_start = time.perf_counter() + with torch.no_grad(): + outputs = self.bert_model(**inputs, output_hidden_states=True) + forward_ms = (time.perf_counter() - forward_start) * 1000.0 + else: + with self.stage_limiter.enter() as limiter_stats: + tokenize_start = time.perf_counter() + inputs = self.tokenizer(texts, return_tensors="pt", padding=True) + tokenize_ms = (time.perf_counter() - tokenize_start) * 1000.0 + attention_mask_cpu = inputs["attention_mask"].cpu() + for key in inputs: + inputs[key] = inputs[key].to(self.device) + forward_start = time.perf_counter() + with torch.no_grad(): + outputs = self.bert_model(**inputs, output_hidden_states=True) + forward_ms = (time.perf_counter() - forward_start) * 1000.0 + + hidden = outputs["hidden_states"][-3].detach().cpu() + scatter_start = time.perf_counter() + for batch_index, task in enumerate(batch): + try: + text_len = len(task.word2ph) + if text_len != len(task.norm_text): + raise AssertionError( + f"word2ph/text length mismatch: task={task.task_id} word2ph={text_len} text={len(task.norm_text)}" + ) + seq_len = int(attention_mask_cpu[batch_index].sum().item()) + char_features = hidden[batch_index, 1 : seq_len - 1] + if char_features.shape[0] != text_len: + raise AssertionError( + f"bert token length mismatch: task={task.task_id} token_len={char_features.shape[0]} text_len={text_len}" + ) + phone_level_feature = [] + for char_index, repeat_count in enumerate(task.word2ph): + phone_level_feature.append(char_features[char_index].repeat(repeat_count, 1)) + task.result_feature = torch.cat(phone_level_feature, dim=0).T + task.profile = { + "bert_wait_ms": (batch_started - task.created_at) * 1000.0 + float(limiter_stats["wait_ms"]), + "bert_admission_wait_ms": float(task.admission_wait_ms), + "bert_queue_wait_ms": max(0.0, (batch_started - task.enqueued_at) * 1000.0), + "bert_batch_collect_wait_ms": float(batch_meta["collect_wait_ms"]), + "bert_forward_ms": float(forward_ms), + "bert_tokenize_ms": float(tokenize_ms), + "bert_scatter_ms": 0.0, + "bert_calls": 1.0, + "bert_stage_slots": float(limiter_stats["slots"]), + "bert_stage_inflight_peak": float(limiter_stats["peak_inflight"]), + "bert_batch_size": float(len(batch)), + "bert_batch_tokens": float(batch_tokens), + "bert_pending_depth_on_enqueue": float(task.pending_depth_on_enqueue), + "bert_pending_depth_on_collect": float(batch_meta["pending_depth_on_collect"]), + "bert_high_pressure_mode": float(batch_meta["high_pressure_mode"]), + "bert_batch_window_ms": float(batch_meta["batch_window_ms"]), + } + except Exception as exc: # noqa: PERF203 + task.error = exc + scatter_ms = (time.perf_counter() - scatter_start) * 1000.0 + for task in batch: + if task.result_feature is not None: + task.profile["bert_scatter_ms"] = float(scatter_ms) + task.done_event.set() + self._notify_done_future(task) + + @staticmethod + def _resolve_done_future(task: BertFeatureTask) -> None: + if task.done_future is None or task.done_future.done(): + return + if task.error is not None: + task.done_future.set_exception(task.error) + return + assert task.result_feature is not None + task.done_future.set_result((task.result_feature, dict(task.profile))) + + def _notify_done_future(self, task: BertFeatureTask) -> None: + if task.done_loop is None or task.done_future is None: + return + try: + task.done_loop.call_soon_threadsafe(self._resolve_done_future, task) + except RuntimeError: + pass + + def _run_loop(self) -> None: + while True: + batch, batch_meta = self._collect_batch() + try: + self._run_batch(batch, batch_meta) + except Exception as exc: # noqa: PERF203 + for task in batch: + task.error = exc + task.done_event.set() + self._notify_done_future(task) + finally: + self._finalize_batch(batch) diff --git a/GPT_SoVITS/TTS_infer_pack/prepare_coordinator.py b/GPT_SoVITS/TTS_infer_pack/prepare_coordinator.py new file mode 100644 index 00000000..1fdf95c5 --- /dev/null +++ b/GPT_SoVITS/TTS_infer_pack/prepare_coordinator.py @@ -0,0 +1,294 @@ +from __future__ import annotations + +import asyncio +import concurrent.futures +import os +import threading +import time +from dataclasses import dataclass +from typing import Any, Dict, Optional, Tuple + +from GPT_SoVITS.TTS_infer_pack.t2s_scheduler import ( + PreparedTextFeatures, + SchedulerRequestSpec, + T2SRequestState, + build_request_state_from_parts, + normalize_sentence, +) + + +@dataclass +class ProfiledResult: + result: Any + submit_at: float + started_at: float + finished_at: float + + @property + def queue_ms(self) -> float: + return max(0.0, (self.started_at - self.submit_at) * 1000.0) + + @property + def run_ms(self) -> float: + return max(0.0, (self.finished_at - self.started_at) * 1000.0) + + +class PrepareCoordinator: + def __init__(self, tts: Any): + self.tts = tts + self.lock = threading.Lock() + self.inflight = 0 + self.peak_inflight = 0 + self.use_async_text_feature_path = bool( + getattr(tts, "prepare_bert_batch_worker", None) is not None + and os.environ.get("GPTSOVITS_PREPARE_TEXT_FEATURE_DIRECT", "0") != "0" + ) + self.max_inflight = max(0, int(os.environ.get("GPTSOVITS_PREPARE_MAX_INFLIGHT", "0"))) + self._inflight_semaphore = asyncio.Semaphore(self.max_inflight) if self.max_inflight > 0 else None + self.text_feature_workers = 0 + self.text_feature_executor = None + if not self.use_async_text_feature_path: + text_feature_default_workers = max(1, int(getattr(tts, "prepare_text_cpu_workers", 16) or 16)) + self.text_feature_workers = max( + 1, + int(os.environ.get("GPTSOVITS_PREPARE_TEXT_FEATURE_WORKERS", str(text_feature_default_workers))), + ) + self.text_feature_executor = concurrent.futures.ThreadPoolExecutor( + max_workers=self.text_feature_workers, + thread_name_prefix="prepare-text-feature", + ) + ref_audio_default_workers = max(1, int(os.environ.get("GPTSOVITS_PREPARE_REF_SLOTS", "4"))) + self.ref_audio_workers = max( + 1, + int(os.environ.get("GPTSOVITS_PREPARE_REF_ASYNC_WORKERS", str(ref_audio_default_workers))), + ) + self.ref_audio_executor = concurrent.futures.ThreadPoolExecutor( + max_workers=self.ref_audio_workers, + thread_name_prefix="prepare-ref-audio", + ) + + def _mark_enter(self) -> Tuple[int, int]: + with self.lock: + self.inflight += 1 + current_inflight = self.inflight + if current_inflight > self.peak_inflight: + self.peak_inflight = current_inflight + return current_inflight, self.peak_inflight + + def _mark_leave(self) -> None: + with self.lock: + self.inflight = max(0, self.inflight - 1) + + def snapshot(self) -> Dict[str, int]: + with self.lock: + return { + "inflight": int(self.inflight), + "peak_inflight": int(self.peak_inflight), + "max_inflight": int(self.max_inflight), + "text_feature_workers": int(self.text_feature_workers), + "ref_audio_workers": int(self.ref_audio_workers), + } + + @staticmethod + def _run_profiled(fn, submit_at: float, *args) -> ProfiledResult: + started_at = time.perf_counter() + result = fn(*args) + finished_at = time.perf_counter() + return ProfiledResult( + result=result, + submit_at=float(submit_at), + started_at=float(started_at), + finished_at=float(finished_at), + ) + + def _prepare_text_cpu(self, text: str, language: str): + return self.tts.prepare_text_segments(text, language) + + def _build_text_features(self, prepared_segments, language: str, cpu_run_ms: float) -> PreparedTextFeatures: + profile: Dict[str, float] = {"cpu_preprocess_ms": float(cpu_run_ms)} + branch_start = time.perf_counter() + phones, bert_features, norm_text = self.tts.build_text_features_from_segments(prepared_segments, profile=profile) + total_ms = float(cpu_run_ms + (time.perf_counter() - branch_start) * 1000.0) + profile["bert_total_ms"] = max(0.0, total_ms - float(cpu_run_ms)) + return PreparedTextFeatures( + phones=phones, + bert_features=bert_features, + norm_text=norm_text, + profile=profile, + total_ms=total_ms, + cpu_preprocess_ms=float(cpu_run_ms), + ) + + async def _run_on_executor(self, executor, fn, *args) -> ProfiledResult: + loop = asyncio.get_running_loop() + submit_at = time.perf_counter() + return await loop.run_in_executor(executor, self._run_profiled, fn, float(submit_at), *args) + + async def _run_text_cpu_stage(self, text: str, language: str) -> ProfiledResult: + executor = getattr(self.tts, "prepare_text_cpu_executor", None) + if executor is None: + submit_at = time.perf_counter() + return self._run_profiled(self._prepare_text_cpu, submit_at, text, language) + return await self._run_on_executor(executor, self._prepare_text_cpu, text, language) + + async def _run_text_feature_stage(self, prepared_segments, language: str, cpu_run_ms: float) -> ProfiledResult: + return await self._run_on_executor(self.text_feature_executor, self._build_text_features, prepared_segments, language, cpu_run_ms) + + @staticmethod + def _estimate_text_feature_run_ms(profile: Dict[str, float]) -> float: + return float( + profile.get("bert_wait_ms", 0.0) + + profile.get("bert_tokenize_ms", 0.0) + + profile.get("bert_forward_ms", 0.0) + + profile.get("bert_scatter_ms", 0.0) + ) + + async def _run_text_feature_pair_stage( + self, + prompt_segments, + target_segments, + prompt_cpu_run_ms: float, + target_cpu_run_ms: float, + ) -> tuple[ProfiledResult, ProfiledResult]: + if self.text_feature_executor is not None: + prompt_feature_task = asyncio.create_task( + self._run_text_feature_stage(prompt_segments, None, prompt_cpu_run_ms) + ) + target_feature_task = asyncio.create_task( + self._run_text_feature_stage(target_segments, None, target_cpu_run_ms) + ) + return await asyncio.gather(prompt_feature_task, target_feature_task) + + prompt_profile: Dict[str, float] = {"cpu_preprocess_ms": float(prompt_cpu_run_ms)} + target_profile: Dict[str, float] = {"cpu_preprocess_ms": float(target_cpu_run_ms)} + submit_at = time.perf_counter() + started_at = float(submit_at) + prompt_result_raw, target_result_raw = await self.tts.build_text_feature_pair_from_segments_async( + prompt_segments, + target_segments, + prompt_profile=prompt_profile, + target_profile=target_profile, + ) + finished_at = time.perf_counter() + + prompt_result = PreparedTextFeatures( + phones=prompt_result_raw[0], + bert_features=prompt_result_raw[1], + norm_text=prompt_result_raw[2], + profile=prompt_profile, + total_ms=float(prompt_cpu_run_ms + self._estimate_text_feature_run_ms(prompt_profile)), + cpu_preprocess_ms=float(prompt_cpu_run_ms), + ) + target_result = PreparedTextFeatures( + phones=target_result_raw[0], + bert_features=target_result_raw[1], + norm_text=target_result_raw[2], + profile=target_profile, + total_ms=float(target_cpu_run_ms + self._estimate_text_feature_run_ms(target_profile)), + cpu_preprocess_ms=float(target_cpu_run_ms), + ) + prompt_profiled = ProfiledResult( + result=prompt_result, + submit_at=float(submit_at), + started_at=started_at, + finished_at=float(submit_at + self._estimate_text_feature_run_ms(prompt_profile) / 1000.0), + ) + target_profiled = ProfiledResult( + result=target_result, + submit_at=float(submit_at), + started_at=started_at, + finished_at=float(submit_at + self._estimate_text_feature_run_ms(target_profile) / 1000.0), + ) + if finished_at > prompt_profiled.finished_at: + prompt_result.profile["bert_total_ms"] = max( + self._estimate_text_feature_run_ms(prompt_profile), + (finished_at - submit_at) * 1000.0, + ) + target_result.profile["bert_total_ms"] = max( + self._estimate_text_feature_run_ms(target_profile), + (finished_at - submit_at) * 1000.0, + ) + else: + prompt_result.profile["bert_total_ms"] = self._estimate_text_feature_run_ms(prompt_profile) + target_result.profile["bert_total_ms"] = self._estimate_text_feature_run_ms(target_profile) + return prompt_profiled, target_profiled + + async def _run_ref_audio_stage(self, ref_audio_path: str) -> ProfiledResult: + return await self._run_on_executor(self.ref_audio_executor, self.tts.extract_ref_audio_bundle, ref_audio_path) + + async def prepare_state_profiled_async( + self, + spec: SchedulerRequestSpec, + prepare_submit_at: float, + ) -> tuple[T2SRequestState, float, float]: + admission_start = time.perf_counter() + if self._inflight_semaphore is not None: + await self._inflight_semaphore.acquire() + prepare_admission_wait_ms = max(0.0, (time.perf_counter() - admission_start) * 1000.0) + current_inflight, peak_inflight = self._mark_enter() + prepare_start = time.perf_counter() + prompt_text = normalize_sentence(spec.prompt_text, spec.prompt_lang) + text = spec.text.strip("\n") + try: + text_pair_start = time.perf_counter() + prompt_cpu_task = asyncio.create_task(self._run_text_cpu_stage(prompt_text, spec.prompt_lang)) + target_cpu_task = asyncio.create_task(self._run_text_cpu_stage(text, spec.text_lang)) + ref_audio_task = asyncio.create_task(self._run_ref_audio_stage(str(spec.ref_audio_path))) + prompt_cpu_profiled, target_cpu_profiled = await asyncio.gather(prompt_cpu_task, target_cpu_task) + text_feature_pair_task = asyncio.create_task( + self._run_text_feature_pair_stage( + prompt_cpu_profiled.result, + target_cpu_profiled.result, + prompt_cpu_profiled.run_ms, + target_cpu_profiled.run_ms, + ) + ) + (prompt_feature_profiled, target_feature_profiled), ref_audio_profiled = await asyncio.gather( + text_feature_pair_task, + ref_audio_task, + ) + text_pair_end = time.perf_counter() + state = build_request_state_from_parts( + tts=self.tts, + spec=spec, + prompt_text=prompt_text, + text=text, + prompt_result=prompt_feature_profiled.result, + target_result=target_feature_profiled.result, + ref_audio_bundle=ref_audio_profiled.result, + prepare_start=prepare_start, + prepare_sync_start=prepare_start, + profile_overrides={ + "executor_queue_ms": max(0.0, (prepare_start - prepare_submit_at) * 1000.0), + "prepare_admission_wait_ms": prepare_admission_wait_ms, + "executor_run_wall_ms": max(0.0, (time.perf_counter() - prepare_start) * 1000.0), + "text_feature_pair_ms": max(0.0, (text_pair_end - text_pair_start) * 1000.0), + "prompt_text_parallel_future_wait_ms": 0.0, + "prompt_text_parallel_future_executor_queue_ms": 0.0, + "prompt_text_parallel_future_run_ms": 0.0, + "prompt_text_parallel_future_finish_after_submit_ms": 0.0, + "prompt_text_parallel_future_queue_tail_after_target_ms": 0.0, + "prompt_text_parallel_future_run_tail_after_target_ms": 0.0, + "prompt_text_cpu_queue_ms": prompt_cpu_profiled.queue_ms, + "prompt_text_cpu_run_ms": prompt_cpu_profiled.run_ms, + "prompt_text_feature_queue_ms": prompt_feature_profiled.queue_ms, + "prompt_text_feature_run_ms": prompt_feature_profiled.run_ms, + "text_cpu_queue_ms": target_cpu_profiled.queue_ms, + "text_cpu_run_ms": target_cpu_profiled.run_ms, + "text_feature_queue_ms": target_feature_profiled.queue_ms, + "text_feature_run_ms": target_feature_profiled.run_ms, + "ref_audio_task_queue_ms": ref_audio_profiled.queue_ms, + "ref_audio_task_run_ms": ref_audio_profiled.run_ms, + "worker_prepare_inflight_on_enter": float(current_inflight), + "worker_prepare_peak_inflight": float(peak_inflight), + }, + ) + prepare_exec_finished_at = time.perf_counter() + state.prepare_profile["executor_run_wall_ms"] = max( + 0.0, (prepare_exec_finished_at - prepare_start) * 1000.0 + ) + return state, prepare_start, prepare_exec_finished_at + finally: + self._mark_leave() + if self._inflight_semaphore is not None: + self._inflight_semaphore.release() diff --git a/GPT_SoVITS/TTS_infer_pack/prepare_ref_semantic_batch_worker.py b/GPT_SoVITS/TTS_infer_pack/prepare_ref_semantic_batch_worker.py new file mode 100644 index 00000000..7a1f9a53 --- /dev/null +++ b/GPT_SoVITS/TTS_infer_pack/prepare_ref_semantic_batch_worker.py @@ -0,0 +1,262 @@ +import threading +import time +import uuid +from collections import deque +from dataclasses import dataclass, field +from typing import Deque, Dict, List, Tuple + +import librosa +import numpy as np +import torch + + +REF_AUDIO_MIN_SAMPLES_16K = 48000 +REF_AUDIO_MAX_SAMPLES_16K = 160000 + + +def prepare_prompt_semantic_wav16k(raw_audio: torch.Tensor, raw_sr: int, zero_wav_samples: int) -> torch.Tensor: + wav_mono = raw_audio + if wav_mono.dim() == 2 and wav_mono.shape[0] != 1: + wav_mono = wav_mono.mean(0, keepdim=True) + wav16k = wav_mono.squeeze(0).cpu().numpy() + if raw_sr != 16000: + wav16k = librosa.resample(wav16k, orig_sr=raw_sr, target_sr=16000) + if wav16k.shape[0] > REF_AUDIO_MAX_SAMPLES_16K or wav16k.shape[0] < REF_AUDIO_MIN_SAMPLES_16K: + raise OSError("参考音频在3~10秒范围外，请更换！") + wav16k = np.ascontiguousarray(wav16k, dtype=np.float32) + if zero_wav_samples > 0: + wav16k = np.concatenate([wav16k, np.zeros(int(zero_wav_samples), dtype=np.float32)], axis=0) + return torch.from_numpy(wav16k) + + +def conv1d_output_lengths(input_lengths: torch.Tensor, conv1d: torch.nn.Conv1d | None) -> torch.Tensor: + if conv1d is None: + return input_lengths.to(dtype=torch.long) + kernel_size = int(conv1d.kernel_size[0]) + stride = int(conv1d.stride[0]) + padding = int(conv1d.padding[0]) + dilation = int(conv1d.dilation[0]) + output_lengths = torch.div( + input_lengths + 2 * padding - dilation * (kernel_size - 1) - 1, + stride, + rounding_mode="floor", + ) + 1 + return torch.clamp(output_lengths, min=0).to(dtype=torch.long) + + +@dataclass +class RefSemanticTask: + raw_audio: torch.Tensor + raw_sr: int + task_id: str = field(default_factory=lambda: uuid.uuid4().hex) + created_at: float = field(default_factory=time.perf_counter) + done_event: threading.Event = field(default_factory=threading.Event) + result_prompt_semantic: torch.Tensor | None = None + error: Exception | None = None + profile: Dict[str, float] = field(default_factory=dict) + + +class PrepareRefSemanticBatchWorker: + def __init__( + self, + ssl_model, + vits_model, + device, + is_half: bool, + zero_wav_samples: int, + stage_limiter=None, + batch_window_ms: int = 5, + max_batch_items: int = 8, + max_batch_samples: int = 960000, + ): + self.ssl_model = ssl_model + self.vits_model = vits_model + self.device = device + self.is_half = bool(is_half) + self.zero_wav_samples = max(0, int(zero_wav_samples)) + self.stage_limiter = stage_limiter + self.batch_window_s = max(0.0, float(batch_window_ms) / 1000.0) + self.max_batch_items = max(1, int(max_batch_items)) + self.max_batch_samples = max(REF_AUDIO_MIN_SAMPLES_16K + self.zero_wav_samples, int(max_batch_samples)) + + self.condition = threading.Condition() + self.pending_tasks: Deque[RefSemanticTask] = deque() + self.pending_peak = 0 + self.total_submitted = 0 + self.total_finished = 0 + self.total_batches = 0 + self.active_batch_size = 0 + self.active_batch_peak = 0 + self.active_batch_samples = 0 + self.active_batch_samples_peak = 0 + self.worker_thread = threading.Thread( + target=self._run_loop, + name="prepare-ref-semantic-batch-worker", + daemon=True, + ) + self.worker_thread.start() + + def _estimate_task_samples(self, task: RefSemanticTask) -> int: + raw_len = int(task.raw_audio.shape[-1]) if task.raw_audio.dim() > 0 else 0 + base = int(round(raw_len * 16000.0 / max(1, int(task.raw_sr)))) + return max(REF_AUDIO_MIN_SAMPLES_16K, base) + self.zero_wav_samples + + def submit(self, raw_audio: torch.Tensor, raw_sr: int) -> Tuple[torch.Tensor, Dict[str, float]]: + task = RefSemanticTask(raw_audio=raw_audio, raw_sr=int(raw_sr)) + with self.condition: + self.pending_tasks.append(task) + self.total_submitted += 1 + if len(self.pending_tasks) > self.pending_peak: + self.pending_peak = len(self.pending_tasks) + self.condition.notify_all() + task.done_event.wait() + if task.error is not None: + raise task.error + assert task.result_prompt_semantic is not None + return task.result_prompt_semantic, dict(task.profile) + + def snapshot(self) -> Dict[str, int]: + with self.condition: + return { + "pending": len(self.pending_tasks), + "pending_peak": self.pending_peak, + "total_submitted": self.total_submitted, + "total_finished": self.total_finished, + "total_batches": self.total_batches, + "active_batch_size": self.active_batch_size, + "active_batch_peak": self.active_batch_peak, + "active_batch_samples": self.active_batch_samples, + "active_batch_samples_peak": self.active_batch_samples_peak, + "batch_window_ms": int(self.batch_window_s * 1000.0), + "max_batch_items": self.max_batch_items, + "max_batch_samples": self.max_batch_samples, + } + + def _collect_batch(self) -> List[RefSemanticTask]: + with self.condition: + while not self.pending_tasks: + self.condition.wait() + + batch: List[RefSemanticTask] = [self.pending_tasks.popleft()] + batch_samples = self._estimate_task_samples(batch[0]) + deadline = time.perf_counter() + self.batch_window_s + + while len(batch) < self.max_batch_items: + remaining = deadline - time.perf_counter() + if remaining <= 0: + break + if not self.pending_tasks: + self.condition.wait(timeout=remaining) + continue + next_task = self.pending_tasks[0] + next_samples = self._estimate_task_samples(next_task) + if len(batch) >= self.max_batch_items or (batch_samples + next_samples) > self.max_batch_samples: + break + batch.append(self.pending_tasks.popleft()) + batch_samples += next_samples + + self.active_batch_size = len(batch) + self.active_batch_samples = batch_samples + if self.active_batch_size > self.active_batch_peak: + self.active_batch_peak = self.active_batch_size + if self.active_batch_samples > self.active_batch_samples_peak: + self.active_batch_samples_peak = self.active_batch_samples + return batch + + def _finalize_batch(self, batch: List[RefSemanticTask]) -> None: + with self.condition: + self.active_batch_size = 0 + self.active_batch_samples = 0 + self.total_batches += 1 + self.total_finished += len(batch) + + def _get_hidden_lengths(self, attention_mask: torch.Tensor, hidden_length: int) -> torch.Tensor: + model = self.ssl_model.model + if hasattr(model, "_get_feature_vector_attention_mask"): + feature_mask = model._get_feature_vector_attention_mask(hidden_length, attention_mask) + return feature_mask.to(dtype=torch.long).sum(dim=1) + raw_lengths = attention_mask.to(dtype=torch.long).sum(dim=1) + if hasattr(model, "_get_feat_extract_output_lengths"): + return model._get_feat_extract_output_lengths(raw_lengths).to(dtype=torch.long) + return torch.full((attention_mask.shape[0],), int(hidden_length), dtype=torch.long, device=attention_mask.device) + + @torch.inference_mode() + def _run_batch(self, batch: List[RefSemanticTask]) -> None: + batch_started = time.perf_counter() + prepared_start = time.perf_counter() + prepared_wavs = [ + prepare_prompt_semantic_wav16k(task.raw_audio, int(task.raw_sr), self.zero_wav_samples) for task in batch + ] + cpu_prepare_ms = (time.perf_counter() - prepared_start) * 1000.0 + wav_lengths = torch.tensor([int(wav.shape[0]) for wav in prepared_wavs], dtype=torch.long) + batch_samples = int(wav_lengths.sum().item()) + max_wav_len = int(wav_lengths.max().item()) + + input_values_cpu = torch.zeros((len(batch), max_wav_len), dtype=torch.float32) + attention_mask_cpu = torch.zeros((len(batch), max_wav_len), dtype=torch.long) + for batch_index, wav in enumerate(prepared_wavs): + wav_len = int(wav.shape[0]) + input_values_cpu[batch_index, :wav_len] = wav + attention_mask_cpu[batch_index, :wav_len] = 1 + + limiter_stats = {"wait_ms": 0.0, "peak_inflight": 1, "slots": 0} + if self.stage_limiter is None: + input_values = input_values_cpu.to(self.device) + attention_mask = attention_mask_cpu.to(self.device) + if self.is_half: + input_values = input_values.half() + forward_start = time.perf_counter() + outputs = self.ssl_model.model(input_values, attention_mask=attention_mask) + hubert_feature = outputs["last_hidden_state"].transpose(1, 2) + hidden_lengths = self._get_hidden_lengths(attention_mask, int(hubert_feature.shape[-1])) + codes = self.vits_model.extract_latent(hubert_feature) + forward_ms = (time.perf_counter() - forward_start) * 1000.0 + else: + with self.stage_limiter.enter() as limiter_stats: + input_values = input_values_cpu.to(self.device) + attention_mask = attention_mask_cpu.to(self.device) + if self.is_half: + input_values = input_values.half() + forward_start = time.perf_counter() + outputs = self.ssl_model.model(input_values, attention_mask=attention_mask) + hubert_feature = outputs["last_hidden_state"].transpose(1, 2) + hidden_lengths = self._get_hidden_lengths(attention_mask, int(hubert_feature.shape[-1])) + codes = self.vits_model.extract_latent(hubert_feature) + forward_ms = (time.perf_counter() - forward_start) * 1000.0 + + code_lengths = conv1d_output_lengths(hidden_lengths.detach().cpu(), getattr(self.vits_model, "ssl_proj", None)) + scatter_start = time.perf_counter() + for batch_index, task in enumerate(batch): + try: + code_len = int(code_lengths[batch_index].item()) + task.result_prompt_semantic = codes[batch_index, 0, :code_len].detach().clone() + task.profile = { + "prompt_semantic_wait_ms": (batch_started - task.created_at) * 1000.0 + float(limiter_stats["wait_ms"]), + "prompt_semantic_cpu_prepare_ms": float(cpu_prepare_ms), + "prompt_semantic_forward_ms": float(forward_ms), + "prompt_semantic_scatter_ms": 0.0, + "prompt_semantic_calls": 1.0, + "prompt_semantic_stage_slots": float(limiter_stats["slots"]), + "prompt_semantic_stage_inflight_peak": float(limiter_stats["peak_inflight"]), + "prompt_semantic_batch_size": float(len(batch)), + "prompt_semantic_batch_samples": float(batch_samples), + } + except Exception as exc: # noqa: PERF203 + task.error = exc + scatter_ms = (time.perf_counter() - scatter_start) * 1000.0 + for task in batch: + if task.result_prompt_semantic is not None: + task.profile["prompt_semantic_scatter_ms"] = float(scatter_ms) + task.done_event.set() + + def _run_loop(self) -> None: + while True: + batch = self._collect_batch() + try: + self._run_batch(batch) + except Exception as exc: # noqa: PERF203 + for task in batch: + task.error = exc + task.done_event.set() + finally: + self._finalize_batch(batch) diff --git a/GPT_SoVITS/TTS_infer_pack/t2s_scheduler.py b/GPT_SoVITS/TTS_infer_pack/t2s_scheduler.py new file mode 100644 index 00000000..8aabd286 --- /dev/null +++ b/GPT_SoVITS/TTS_infer_pack/t2s_scheduler.py @@ -0,0 +1,1103 @@ +from __future__ import annotations + +from dataclasses import dataclass +from pathlib import Path +import time +from typing import Any, Dict, List, Optional, Sequence, Tuple + +import torch +import torch.nn.functional as F + +from AR.models.utils import logits_to_probs, make_pad_mask_left, multinomial_sample_one_no_sync, sample + + +def _sync_device(device: Any) -> None: + try: + device_str = str(device) + if device_str.startswith("cuda") and torch.cuda.is_available(): + torch.cuda.synchronize(device) + elif device_str == "mps" and hasattr(torch, "mps") and hasattr(torch.mps, "synchronize"): + torch.mps.synchronize() + except Exception: + pass + + +@dataclass +class SchedulerRequestSpec: + request_id: str + ref_audio_path: Path + prompt_text: str + prompt_lang: str + text: str + text_lang: str + top_k: int + top_p: float + temperature: float + repetition_penalty: float + early_stop_num: int + ready_step: int = 0 + + +@dataclass +class T2SRequestState: + request_id: str + ref_audio_path: Path + prompt_text: str + prompt_lang: str + text: str + text_lang: str + norm_prompt_text: str + norm_text: str + phones: torch.LongTensor + prompt_phones: torch.LongTensor + all_phones: torch.LongTensor + all_bert_features: torch.Tensor + prompt_semantic: torch.LongTensor + refer_spec: Tuple[torch.Tensor, Optional[torch.Tensor]] + raw_audio: torch.Tensor + raw_sr: int + top_k: int + top_p: float + temperature: float + repetition_penalty: float + early_stop_num: int + ready_step: int + prepare_profile: Dict[str, float] + + +@dataclass +class T2SRunningRequest: + state: T2SRequestState + y_sequence: torch.LongTensor + prefix_len: int + decode_attn_mask: Optional[torch.Tensor] + k_cache: List[torch.Tensor] + v_cache: List[torch.Tensor] + step_idx: int + + +@dataclass +class T2SFinishedItem: + request_id: str + semantic_tokens: torch.LongTensor + finish_idx: int + finish_reason: str + + +@dataclass +class T2SActiveBatch: + request_ids: List[str] + states: List[T2SRequestState] + x: Optional[torch.Tensor] + x_lens: Optional[torch.LongTensor] + y_sequences: List[torch.LongTensor] + prefix_lens: torch.LongTensor + xy_pos: torch.Tensor + key_padding_mask: Optional[torch.Tensor] + prefill_attn_mask: Optional[torch.Tensor] + decode_attn_mask: Optional[torch.Tensor] + k_cache: Optional[List[torch.Tensor]] + v_cache: Optional[List[torch.Tensor]] + kv_lens: Optional[torch.LongTensor] + step_indices: torch.LongTensor + prefill_done: bool + + +@dataclass +class PreparedTextFeatures: + phones: List[int] + bert_features: torch.Tensor + norm_text: str + profile: Dict[str, float] + total_ms: float + cpu_preprocess_ms: float + + +def normalize_sentence(text: str, language: str) -> str: + text = text.strip("\n").strip() + if not text: + return text + if text[-1] not in {",", ".", "?", "!", "，", "。", "？", "！", "…", "；", ";", ":"}: + text += "。" if language != "en" else "." + return text + + +@torch.inference_mode() +def prepare_text_features( + tts: Any, + text: str, + language: str, +) -> PreparedTextFeatures: + device = tts.configs.device + profile: Dict[str, float] = {} + branch_start = time.perf_counter() + _sync_device(device) + cpu_start = time.perf_counter() + prepared_segments = tts.prepare_text_segments(text, language) + _sync_device(device) + cpu_preprocess_ms = (time.perf_counter() - cpu_start) * 1000.0 + profile["cpu_preprocess_ms"] = float(cpu_preprocess_ms) + bert_start = time.perf_counter() + phones, bert_features, norm_text = tts.build_text_features_from_segments(prepared_segments, profile=profile) + _sync_device(device) + profile["bert_total_ms"] = (time.perf_counter() - bert_start) * 1000.0 + total_ms = (time.perf_counter() - branch_start) * 1000.0 + return PreparedTextFeatures( + phones=phones, + bert_features=bert_features, + norm_text=norm_text, + profile=profile, + total_ms=float(total_ms), + cpu_preprocess_ms=float(cpu_preprocess_ms), + ) + + +@torch.inference_mode() +def build_request_state_from_parts( + tts: Any, + spec: SchedulerRequestSpec, + prompt_text: str, + text: str, + prompt_result: PreparedTextFeatures, + target_result: PreparedTextFeatures, + ref_audio_bundle: Dict[str, Any], + prepare_start: float, + prepare_sync_start: float, + profile_overrides: Optional[Dict[str, float]] = None, +) -> T2SRequestState: + device = tts.configs.device + _sync_device(device) + ref_audio_bundle_ms = float(ref_audio_bundle.get("profile", {}).get("bundle_total_ms", 0.0)) + bundle_profile = ref_audio_bundle.get("profile", {}) + prompt_semantic = ref_audio_bundle["prompt_semantic"].long() + spec_audio, audio_16k = ref_audio_bundle["refer_spec"] + raw_audio = ref_audio_bundle["raw_audio"] + raw_sr = int(ref_audio_bundle["raw_sr"]) + prompt_semantic_ms = float(bundle_profile.get("prompt_semantic_ms", ref_audio_bundle_ms)) + ref_spec_ms = float(bundle_profile.get("ref_spec_ms", 0.0)) + audio_load_ms = float(bundle_profile.get("audio_load_ms", 0.0)) + + _sync_device(device) + tensorize_start = time.perf_counter() + phones_tensor = torch.LongTensor(target_result.phones).to(tts.configs.device) + prompt_phones_tensor = torch.LongTensor(prompt_result.phones).to(tts.configs.device) + all_phones = torch.LongTensor(prompt_result.phones + target_result.phones).to(tts.configs.device) + all_bert_features = torch.cat([prompt_result.bert_features, target_result.bert_features], dim=1).to( + dtype=tts.precision, device=tts.configs.device + ) + _sync_device(device) + tensorize_ms = (time.perf_counter() - tensorize_start) * 1000.0 + + prepare_profile = { + "prompt_text_features_ms": float(prompt_result.total_ms), + "text_features_ms": float(target_result.total_ms), + "prompt_text_cpu_preprocess_ms": float(prompt_result.cpu_preprocess_ms), + "text_cpu_preprocess_ms": float(target_result.cpu_preprocess_ms), + "prompt_text_bert_wait_ms": float(prompt_result.profile.get("bert_wait_ms", 0.0)), + "prompt_text_bert_admission_wait_ms": float(prompt_result.profile.get("bert_admission_wait_ms", 0.0)), + "prompt_text_bert_queue_wait_ms": float(prompt_result.profile.get("bert_queue_wait_ms", 0.0)), + "prompt_text_bert_batch_collect_wait_ms": float(prompt_result.profile.get("bert_batch_collect_wait_ms", 0.0)), + "prompt_text_bert_forward_ms": float(prompt_result.profile.get("bert_forward_ms", 0.0)), + "prompt_text_bert_tokenize_ms": float(prompt_result.profile.get("bert_tokenize_ms", 0.0)), + "prompt_text_bert_scatter_ms": float(prompt_result.profile.get("bert_scatter_ms", 0.0)), + "prompt_text_bert_calls": float(prompt_result.profile.get("bert_calls", 0.0)), + "prompt_text_bert_stage_slots": float(prompt_result.profile.get("bert_stage_slots", 0.0)), + "prompt_text_bert_stage_inflight_peak": float(prompt_result.profile.get("bert_stage_inflight_peak", 0.0)), + "prompt_text_bert_batch_size_peak": float(prompt_result.profile.get("bert_batch_size_peak", 0.0)), + "prompt_text_bert_batch_tokens_peak": float(prompt_result.profile.get("bert_batch_tokens_peak", 0.0)), + "prompt_text_bert_pending_depth_on_enqueue_peak": float( + prompt_result.profile.get("bert_pending_depth_on_enqueue_peak", 0.0) + ), + "prompt_text_bert_pending_depth_on_collect_peak": float( + prompt_result.profile.get("bert_pending_depth_on_collect_peak", 0.0) + ), + "prompt_text_bert_high_pressure_mode_peak": float( + prompt_result.profile.get("bert_high_pressure_mode_peak", 0.0) + ), + "prompt_text_bert_batch_window_ms": float(prompt_result.profile.get("bert_batch_window_ms", 0.0)), + "prompt_text_parallel_future_wait_ms": 0.0, + "prompt_text_parallel_future_executor_queue_ms": 0.0, + "prompt_text_parallel_future_run_ms": float(prompt_result.total_ms), + "prompt_text_parallel_future_finish_after_submit_ms": float(prompt_result.total_ms), + "prompt_text_parallel_future_queue_tail_after_target_ms": 0.0, + "prompt_text_parallel_future_run_tail_after_target_ms": 0.0, + "text_bert_wait_ms": float(target_result.profile.get("bert_wait_ms", 0.0)), + "text_bert_admission_wait_ms": float(target_result.profile.get("bert_admission_wait_ms", 0.0)), + "text_bert_queue_wait_ms": float(target_result.profile.get("bert_queue_wait_ms", 0.0)), + "text_bert_batch_collect_wait_ms": float(target_result.profile.get("bert_batch_collect_wait_ms", 0.0)), + "text_bert_forward_ms": float(target_result.profile.get("bert_forward_ms", 0.0)), + "text_bert_tokenize_ms": float(target_result.profile.get("bert_tokenize_ms", 0.0)), + "text_bert_scatter_ms": float(target_result.profile.get("bert_scatter_ms", 0.0)), + "text_bert_calls": float(target_result.profile.get("bert_calls", 0.0)), + "text_bert_stage_slots": float(target_result.profile.get("bert_stage_slots", 0.0)), + "text_bert_stage_inflight_peak": float(target_result.profile.get("bert_stage_inflight_peak", 0.0)), + "text_bert_batch_size_peak": float(target_result.profile.get("bert_batch_size_peak", 0.0)), + "text_bert_batch_tokens_peak": float(target_result.profile.get("bert_batch_tokens_peak", 0.0)), + "text_bert_pending_depth_on_enqueue_peak": float( + target_result.profile.get("bert_pending_depth_on_enqueue_peak", 0.0) + ), + "text_bert_pending_depth_on_collect_peak": float( + target_result.profile.get("bert_pending_depth_on_collect_peak", 0.0) + ), + "text_bert_high_pressure_mode_peak": float(target_result.profile.get("bert_high_pressure_mode_peak", 0.0)), + "text_bert_batch_window_ms": float(target_result.profile.get("bert_batch_window_ms", 0.0)), + "text_feature_pair_ms": float(max(prompt_result.total_ms, target_result.total_ms)), + "text_cpu_parallel_workers": float(getattr(tts, "prepare_text_cpu_workers", 0)), + "audio_load_ms": audio_load_ms, + "audio_stage_wait_ms": float(bundle_profile.get("audio_stage_wait_ms", 0.0)), + "audio_stage_slots": float(bundle_profile.get("audio_stage_slots", 0.0)), + "audio_stage_inflight_peak": float(bundle_profile.get("audio_stage_inflight_peak", 0.0)), + "prompt_semantic_ms": prompt_semantic_ms, + "prompt_semantic_wait_ms": float(bundle_profile.get("prompt_semantic_wait_ms", 0.0)), + "prompt_semantic_cpu_prepare_ms": float(bundle_profile.get("prompt_semantic_cpu_prepare_ms", 0.0)), + "prompt_semantic_forward_ms": float(bundle_profile.get("prompt_semantic_forward_ms", 0.0)), + "prompt_semantic_scatter_ms": float(bundle_profile.get("prompt_semantic_scatter_ms", 0.0)), + "prompt_semantic_stage_slots": float(bundle_profile.get("prompt_semantic_stage_slots", 0.0)), + "prompt_semantic_stage_inflight_peak": float(bundle_profile.get("prompt_semantic_stage_inflight_peak", 0.0)), + "prompt_semantic_batch_size": float(bundle_profile.get("prompt_semantic_batch_size", 0.0)), + "prompt_semantic_batch_samples": float(bundle_profile.get("prompt_semantic_batch_samples", 0.0)), + "ref_spec_wait_ms": float(bundle_profile.get("ref_spec_wait_ms", 0.0)), + "ref_spec_ms": ref_spec_ms, + "ref_audio_bundle_ms": ref_audio_bundle_ms, + "tensorize_ms": tensorize_ms, + "total_ms": (time.perf_counter() - prepare_sync_start) * 1000.0, + "wall_total_ms": (time.perf_counter() - prepare_start) * 1000.0, + } + if profile_overrides: + prepare_profile.update({key: float(value) for key, value in profile_overrides.items()}) + return T2SRequestState( + request_id=spec.request_id, + ref_audio_path=spec.ref_audio_path, + prompt_text=prompt_text, + prompt_lang=spec.prompt_lang, + text=text, + text_lang=spec.text_lang, + norm_prompt_text=prompt_result.norm_text, + norm_text=target_result.norm_text, + phones=phones_tensor, + prompt_phones=prompt_phones_tensor, + all_phones=all_phones, + all_bert_features=all_bert_features, + prompt_semantic=prompt_semantic, + refer_spec=(spec_audio, audio_16k), + raw_audio=raw_audio, + raw_sr=raw_sr, + top_k=spec.top_k, + top_p=spec.top_p, + temperature=spec.temperature, + repetition_penalty=spec.repetition_penalty, + early_stop_num=spec.early_stop_num, + ready_step=spec.ready_step, + prepare_profile=prepare_profile, + ) + + +@torch.inference_mode() +def prepare_request_state( + tts: Any, + spec: SchedulerRequestSpec, +) -> T2SRequestState: + prepare_start = time.perf_counter() + prepare_sync_start = time.perf_counter() + prompt_text = normalize_sentence(spec.prompt_text, spec.prompt_lang) + text = spec.text.strip("\n") + prompt_result = prepare_text_features(tts, prompt_text, spec.prompt_lang) + target_result = prepare_text_features(tts, text, spec.text_lang) + if target_result.phones is None: + raise ValueError(f"{spec.request_id} text preprocessing returned no phones") + ref_audio_bundle = tts.extract_ref_audio_bundle(str(spec.ref_audio_path)) + return build_request_state_from_parts( + tts=tts, + spec=spec, + prompt_text=prompt_text, + text=text, + prompt_result=prompt_result, + target_result=target_result, + ref_audio_bundle=ref_audio_bundle, + prepare_start=prepare_start, + prepare_sync_start=prepare_sync_start, + ) + + +def _left_pad_hidden(hidden: torch.Tensor, target_len: int) -> torch.Tensor: + if hidden.shape[0] >= target_len: + return hidden + return F.pad(hidden, (0, 0, target_len - hidden.shape[0], 0), value=0) + + +def _ensure_audio_pe(model: Any, max_position: int, dtype: torch.dtype, device: torch.device) -> None: + required_len = max_position + 1 + if model.ar_audio_position.pe is not None and model.ar_audio_position.pe.size(1) >= required_len: + if model.ar_audio_position.pe.dtype != dtype or model.ar_audio_position.pe.device != device: + model.ar_audio_position.pe = model.ar_audio_position.pe.to(dtype=dtype, device=device) + return + model.ar_audio_position.extend_pe( + torch.zeros(1, required_len, model.ar_audio_position.embedding_dim, device=device, dtype=dtype) + ) + + +def _pad_token_sequences( + token_sequences: Sequence[torch.LongTensor], +) -> Tuple[torch.LongTensor, torch.BoolTensor]: + if not token_sequences: + raise ValueError("token_sequences 不能为空") + device = token_sequences[0].device + max_len = max(int(sequence.shape[0]) for sequence in token_sequences) + padded = torch.zeros((len(token_sequences), max_len), dtype=token_sequences[0].dtype, device=device) + mask = torch.zeros((len(token_sequences), max_len), dtype=torch.bool, device=device) + for row_index, sequence in enumerate(token_sequences): + seq_len = int(sequence.shape[0]) + padded[row_index, :seq_len] = sequence + mask[row_index, :seq_len] = True + return padded, mask + + +def _sampling_group_key( + top_k: int, + top_p: float, + temperature: float, + repetition_penalty: float, + trim_eos: bool, +) -> Tuple[int, float, float, float, bool]: + return ( + int(top_k), + float(top_p), + float(temperature), + float(repetition_penalty), + bool(trim_eos), + ) + + +def _iter_contiguous_sampling_groups( + sampling_keys: Sequence[Tuple[int, float, float, float, bool]], +) -> List[Tuple[Tuple[int, float, float, float, bool], List[int]]]: + groups: List[Tuple[Tuple[int, float, float, float, bool], List[int]]] = [] + if not sampling_keys: + return groups + current_key = sampling_keys[0] + current_indices: List[int] = [0] + for index in range(1, len(sampling_keys)): + key = sampling_keys[index] + if key == current_key: + current_indices.append(index) + continue + groups.append((current_key, current_indices)) + current_key = key + current_indices = [index] + groups.append((current_key, current_indices)) + return groups + + +def _batched_sample_by_group( + logits: torch.Tensor, + histories: Sequence[torch.LongTensor], + sampling_keys: Sequence[Tuple[int, float, float, float, bool]], +) -> Tuple[List[torch.Tensor], List[int]]: + sampled_list: List[Optional[torch.Tensor]] = [None] * len(histories) + argmax_list: List[Optional[int]] = [None] * len(histories) + for group_key, group_indices in _iter_contiguous_sampling_groups(sampling_keys): + top_k, top_p, temperature, repetition_penalty, trim_eos = group_key + index_tensor = torch.tensor(group_indices, dtype=torch.long, device=logits.device) + group_logits = torch.index_select(logits, dim=0, index=index_tensor) + if trim_eos: + group_logits = group_logits[:, :-1] + group_histories = [histories[index] for index in group_indices] + padded_histories, history_mask = _pad_token_sequences(group_histories) + probs = logits_to_probs( + logits=group_logits, + previous_tokens=padded_histories, + previous_token_mask=history_mask, + top_k=top_k, + top_p=top_p, + repetition_penalty=repetition_penalty, + temperature=temperature, + ) + argmax_tokens = torch.argmax(group_logits, dim=-1) + for local_index, global_index in enumerate(group_indices): + sampled_list[global_index] = multinomial_sample_one_no_sync(probs[local_index : local_index + 1]) + argmax_list[global_index] = int(argmax_tokens[local_index].item()) + + return [item for item in sampled_list if item is not None], [int(item) for item in argmax_list if item is not None] + + +@torch.inference_mode() +def build_prefill_batch(model: Any, states: Sequence[T2SRequestState]) -> T2SActiveBatch: + x_items: List[torch.Tensor] = [] + y_pos_items: List[torch.Tensor] = [] + x_lens: List[int] = [] + prefix_lens: List[int] = [] + y_sequences: List[torch.LongTensor] = [] + + for state in states: + text_emb = model.ar_text_embedding(state.all_phones.unsqueeze(0)) + bert_proj = model.bert_proj(state.all_bert_features.transpose(0, 1).unsqueeze(0)) + x_pos = model.ar_text_position(text_emb + bert_proj).squeeze(0) + y_emb = model.ar_audio_embedding(state.prompt_semantic.unsqueeze(0)) + y_pos = model.ar_audio_position(y_emb).squeeze(0) + x_items.append(x_pos) + y_pos_items.append(y_pos) + x_lens.append(x_pos.shape[0]) + prefix_lens.append(y_pos.shape[0]) + y_sequences.append(state.prompt_semantic.clone()) + + max_x_len = max(x_lens) + max_prefix_len = max(prefix_lens) + x_batch = torch.stack([_left_pad_hidden(item, max_x_len) for item in x_items], dim=0) + y_pos_batch = torch.stack([_left_pad_hidden(item, max_prefix_len) for item in y_pos_items], dim=0) + xy_pos = torch.cat([x_batch, y_pos_batch], dim=1) + + device = x_batch.device + x_lens_tensor = torch.LongTensor(x_lens).to(device) + prefix_lens_tensor = torch.LongTensor(prefix_lens).to(device) + src_len = max_x_len + max_prefix_len + + x_padding_mask = make_pad_mask_left(x_lens_tensor, max_x_len) + y_padding_mask = make_pad_mask_left(prefix_lens_tensor, max_prefix_len) + key_padding_mask = torch.cat([x_padding_mask, y_padding_mask], dim=1).bool() + x_mask = F.pad(torch.zeros(max_x_len, max_x_len, dtype=torch.bool, device=device), (0, max_prefix_len), value=True) + y_mask = F.pad( + torch.triu(torch.ones(max_prefix_len, max_prefix_len, dtype=torch.bool, device=device), diagonal=1), + (max_x_len, 0), + value=False, + ) + causal_mask = torch.cat([x_mask, y_mask], dim=0).unsqueeze(0) + attn_mask = causal_mask.logical_or(key_padding_mask.unsqueeze(1)).unsqueeze(1) + + return T2SActiveBatch( + request_ids=[state.request_id for state in states], + states=list(states), + x=x_batch, + x_lens=x_lens_tensor, + y_sequences=y_sequences, + prefix_lens=prefix_lens_tensor, + xy_pos=xy_pos, + key_padding_mask=key_padding_mask, + prefill_attn_mask=attn_mask, + decode_attn_mask=None, + k_cache=None, + v_cache=None, + kv_lens=None, + step_indices=torch.zeros((len(states),), dtype=torch.long, device=device), + prefill_done=False, + ) + + +def build_next_xy_pos(model: Any, y_sequences: Sequence[torch.LongTensor]) -> torch.Tensor: + last_tokens = torch.stack([seq[-1:] for seq in y_sequences], dim=0) + y_emb = model.ar_audio_embedding(last_tokens) + position_ids = torch.LongTensor([int(seq.shape[0] - 1) for seq in y_sequences]).to(y_emb.device) + _ensure_audio_pe(model, int(position_ids.max().item()), y_emb.dtype, y_emb.device) + pos_emb = model.ar_audio_position.pe[0].index_select(0, position_ids).unsqueeze(1) + return y_emb * model.ar_audio_position.x_scale + model.ar_audio_position.alpha * pos_emb.to( + dtype=y_emb.dtype, device=y_emb.device + ) + + +def _compact_cache_to_kv_lens( + cache: torch.Tensor, + kv_lens: torch.LongTensor, +) -> torch.Tensor: + target_len = int(kv_lens.max().item()) + if cache.shape[1] == target_len and torch.all(kv_lens == target_len).item(): + return cache + compacted = cache.new_zeros((cache.shape[0], target_len, cache.shape[2])) + for batch_index, kv_len in enumerate(kv_lens.tolist()): + if kv_len <= 0: + continue + compacted[batch_index, -kv_len:, :] = cache[batch_index, -kv_len:, :] + return compacted + + +def _compact_decode_mask_to_kv_lens( + decode_attn_mask: Optional[torch.Tensor], + kv_lens: torch.LongTensor, +) -> Optional[torch.Tensor]: + target_len = int(kv_lens.max().item()) + 1 + if decode_attn_mask is None: + return None + if decode_attn_mask.shape[-1] == target_len and torch.all(kv_lens + 1 == target_len).item(): + return decode_attn_mask + compacted = torch.ones( + (decode_attn_mask.shape[0], 1, 1, target_len), + dtype=decode_attn_mask.dtype, + device=decode_attn_mask.device, + ) + for batch_index, kv_len in enumerate(kv_lens.tolist()): + current_len = kv_len + 1 + compacted[batch_index, :, :, -current_len:] = decode_attn_mask[batch_index, :, :, -current_len:] + if not compacted.any().item(): + return None + return compacted + + +def _advance_decode_mask( + decode_attn_mask: Optional[torch.Tensor], + kv_lens: torch.LongTensor, +) -> Optional[torch.Tensor]: + if decode_attn_mask is None: + return None + target_len = int(kv_lens.max().item()) + 2 + advanced = torch.zeros( + (decode_attn_mask.shape[0], 1, 1, target_len), + dtype=decode_attn_mask.dtype, + device=decode_attn_mask.device, + ) + for batch_index, kv_len in enumerate(kv_lens.tolist()): + current_len = kv_len + 1 + next_mask = F.pad(decode_attn_mask[batch_index : batch_index + 1, :, :, -current_len:], (0, 1), value=False) + advanced[batch_index : batch_index + 1, :, :, -next_mask.shape[-1] :] = next_mask + if not advanced.any().item(): + return None + return advanced + + +def _sample_per_request( + model: Any, + active_batch: T2SActiveBatch, + logits: torch.Tensor, + max_steps: int, +) -> Tuple[List[T2SFinishedItem], List[int], List[torch.LongTensor]]: + finished_items: List[T2SFinishedItem] = [] + keep_indices: List[int] = [] + updated_sequences: List[torch.LongTensor] = [] + + sampling_keys = [ + _sampling_group_key( + top_k=state.top_k, + top_p=state.top_p, + temperature=state.temperature, + repetition_penalty=state.repetition_penalty, + trim_eos=int(active_batch.step_indices[batch_index].item()) < 11, + ) + for batch_index, state in enumerate(active_batch.states) + ] + sampled_items, argmax_tokens = _batched_sample_by_group( + logits=logits, + histories=active_batch.y_sequences, + sampling_keys=sampling_keys, + ) + for batch_index, state in enumerate(active_batch.states): + step_index = int(active_batch.step_indices[batch_index].item()) + current_history = active_batch.y_sequences[batch_index] + sampled = sampled_items[batch_index] + sampled_token = int(sampled[0, 0].item()) + argmax_token = argmax_tokens[batch_index] + new_history = torch.cat([current_history, sampled.view(-1)], dim=0) + + finish_reason: Optional[str] = None + if state.early_stop_num != -1 and (new_history.shape[0] - int(active_batch.prefix_lens[batch_index].item())) > state.early_stop_num: + finish_reason = "early_stop" + elif step_index + 1 >= max_steps: + finish_reason = "max_step" + elif sampled_token == model.EOS: + finish_reason = "eos_sample" + elif argmax_token == model.EOS: + finish_reason = "eos_argmax" + + if finish_reason is not None: + prefix_len = int(active_batch.prefix_lens[batch_index].item()) + finished_items.append( + T2SFinishedItem( + request_id=state.request_id, + semantic_tokens=new_history[prefix_len:-1].clone(), + finish_idx=step_index, + finish_reason=finish_reason, + ) + ) + else: + keep_indices.append(batch_index) + updated_sequences.append(new_history) + + return finished_items, keep_indices, updated_sequences + + +@torch.inference_mode() +def decode_one_step( + model: Any, + active_batch: T2SActiveBatch, + max_steps: int, +) -> Tuple[Optional[T2SActiveBatch], List[T2SFinishedItem]]: + was_prefill = not active_batch.prefill_done + if was_prefill: + if active_batch.prefill_attn_mask is None or active_batch.key_padding_mask is None: + raise ValueError("prefill 阶段缺少必要 mask") + xy_dec, active_batch.k_cache, active_batch.v_cache = model.t2s_transformer.process_prompt( + active_batch.xy_pos, active_batch.prefill_attn_mask, None + ) + active_batch.kv_lens = active_batch.x_lens + active_batch.prefix_lens + active_batch.decode_attn_mask = F.pad(active_batch.key_padding_mask.unsqueeze(1).unsqueeze(1), (0, 1), value=False) + if active_batch.k_cache is None or active_batch.v_cache is None or active_batch.kv_lens is None: + raise ValueError("prefill 阶段未生成完整 KV cache") + active_batch.k_cache = [_compact_cache_to_kv_lens(layer, active_batch.kv_lens) for layer in active_batch.k_cache] + active_batch.v_cache = [_compact_cache_to_kv_lens(layer, active_batch.kv_lens) for layer in active_batch.v_cache] + active_batch.decode_attn_mask = _compact_decode_mask_to_kv_lens(active_batch.decode_attn_mask, active_batch.kv_lens) + active_batch.x = None + active_batch.x_lens = None + active_batch.key_padding_mask = None + active_batch.prefill_attn_mask = None + active_batch.prefill_done = True + else: + if active_batch.k_cache is None or active_batch.v_cache is None or active_batch.kv_lens is None: + raise ValueError("decode 阶段缺少 KV cache") + batched_decode_attn_mask = None + if active_batch.decode_attn_mask is not None: + batched_decode_attn_mask = _materialize_decode_mask_for_active_batch(active_batch) + if not batched_decode_attn_mask.any().item(): + batched_decode_attn_mask = None + xy_dec, active_batch.k_cache, active_batch.v_cache = model.t2s_transformer.decode_next_token( + active_batch.xy_pos, + active_batch.k_cache, + active_batch.v_cache, + batched_decode_attn_mask, + ) + active_batch.decode_attn_mask = _advance_decode_mask(active_batch.decode_attn_mask, active_batch.kv_lens) + + logits = model.ar_predict_layer(xy_dec[:, -1]) + + finished_items, keep_indices, updated_sequences = _sample_per_request(model, active_batch, logits, max_steps=max_steps) + if len(keep_indices) == 0: + return None, finished_items + + device = logits.device + keep_tensor = torch.LongTensor(keep_indices).to(device) + active_batch.request_ids = [active_batch.request_ids[i] for i in keep_indices] + active_batch.states = [active_batch.states[i] for i in keep_indices] + active_batch.y_sequences = updated_sequences + active_batch.prefix_lens = torch.index_select(active_batch.prefix_lens, dim=0, index=keep_tensor) + next_step_indices = torch.index_select(active_batch.step_indices, dim=0, index=keep_tensor) + next_kv_lens = None if active_batch.kv_lens is None else torch.index_select(active_batch.kv_lens, dim=0, index=keep_tensor) + active_batch.step_indices = next_step_indices + 1 + if not was_prefill: + if next_kv_lens is not None: + active_batch.kv_lens = next_kv_lens + 1 + else: + active_batch.kv_lens = next_kv_lens + + if active_batch.decode_attn_mask is not None: + active_batch.decode_attn_mask = torch.index_select(active_batch.decode_attn_mask, dim=0, index=keep_tensor) + if not active_batch.decode_attn_mask.any().item(): + active_batch.decode_attn_mask = None + if active_batch.k_cache is not None and active_batch.v_cache is not None: + for cache_index in range(len(active_batch.k_cache)): + active_batch.k_cache[cache_index] = torch.index_select(active_batch.k_cache[cache_index], dim=0, index=keep_tensor) + active_batch.v_cache[cache_index] = torch.index_select(active_batch.v_cache[cache_index], dim=0, index=keep_tensor) + if active_batch.kv_lens is not None: + active_batch.k_cache = [_compact_cache_to_kv_lens(layer, active_batch.kv_lens) for layer in active_batch.k_cache] + active_batch.v_cache = [_compact_cache_to_kv_lens(layer, active_batch.kv_lens) for layer in active_batch.v_cache] + active_batch.decode_attn_mask = _compact_decode_mask_to_kv_lens( + active_batch.decode_attn_mask, + active_batch.kv_lens, + ) + + active_batch.xy_pos = build_next_xy_pos(model, active_batch.y_sequences) + return active_batch, finished_items + + +def run_scheduler_batch( + model: Any, + states: Sequence[T2SRequestState], + max_steps: int, +) -> List[T2SFinishedItem]: + return run_scheduler_continuous(model, states, max_steps=max_steps) + + +def _pad_cache_left(cache: torch.Tensor, target_len: int) -> torch.Tensor: + pad_len = target_len - cache.shape[1] + if pad_len <= 0: + return cache + return F.pad(cache, (0, 0, pad_len, 0), value=0) + + +def _pad_decode_mask_left(mask: torch.Tensor, target_len: int) -> torch.Tensor: + pad_len = target_len - mask.shape[-1] + if pad_len <= 0: + return mask + return F.pad(mask, (pad_len, 0), value=True) + + +def _fit_decode_mask_length(mask: torch.Tensor, target_len: int) -> torch.Tensor: + if mask.shape[-1] > target_len: + return mask[:, :, :, -target_len:] + if mask.shape[-1] < target_len: + return _pad_decode_mask_left(mask, target_len) + return mask + + +def _materialize_decode_mask_for_request(running_request: T2SRunningRequest) -> torch.Tensor: + expected_mask_len = running_request.k_cache[0].shape[1] + 1 + if running_request.decode_attn_mask is not None: + return _fit_decode_mask_length(running_request.decode_attn_mask, expected_mask_len) + current_mask_len = running_request.k_cache[0].shape[1] + 1 + return torch.zeros( + (1, 1, 1, current_mask_len), + dtype=torch.bool, + device=running_request.k_cache[0].device, + ) + + +def _materialize_decode_mask_for_active_batch( + active_batch: T2SActiveBatch, + target_mask_len: Optional[int] = None, +) -> torch.Tensor: + if active_batch.k_cache is None or active_batch.kv_lens is None: + raise ValueError("active batch 缺少 KV cache 或 kv_lens") + current_mask_len = active_batch.k_cache[0].shape[1] + 1 + if target_mask_len is None: + target_mask_len = current_mask_len + if active_batch.decode_attn_mask is None: + mask = torch.zeros( + (len(active_batch.request_ids), 1, 1, current_mask_len), + dtype=torch.bool, + device=active_batch.k_cache[0].device, + ) + else: + rows: List[torch.Tensor] = [] + for batch_index, kv_len in enumerate(active_batch.kv_lens.tolist()): + row_len = kv_len + 1 + row_mask = _fit_decode_mask_length( + active_batch.decode_attn_mask[batch_index : batch_index + 1], + row_len, + ) + rows.append(_pad_decode_mask_left(row_mask, target_mask_len)) + mask = torch.cat(rows, dim=0) + if target_mask_len != current_mask_len and active_batch.decode_attn_mask is None: + mask = _pad_decode_mask_left(mask, target_mask_len) + return mask + + +@torch.inference_mode() +def run_prefill_active_batch( + model: Any, + states: Sequence[T2SRequestState], + max_steps: int, +) -> Tuple[Optional[T2SActiveBatch], List[T2SFinishedItem]]: + if not states: + return None, [] + active_batch = build_prefill_batch(model, states) + return decode_one_step(model, active_batch, max_steps=max_steps) + + +@torch.inference_mode() +def merge_active_batches( + model: Any, + left_batch: Optional[T2SActiveBatch], + right_batch: Optional[T2SActiveBatch], +) -> Optional[T2SActiveBatch]: + if left_batch is None: + return right_batch + if right_batch is None: + return left_batch + if not left_batch.prefill_done or not right_batch.prefill_done: + raise ValueError("只有 prefill 完成后的 active batch 才能 merge") + if left_batch.k_cache is None or left_batch.v_cache is None or right_batch.k_cache is None or right_batch.v_cache is None: + raise ValueError("merge active batch 时缺少 KV cache") + + left_kv_len = int(left_batch.k_cache[0].shape[1]) + right_kv_len = int(right_batch.k_cache[0].shape[1]) + merged_kv_len = max(left_kv_len, right_kv_len) + merged_mask_len = merged_kv_len + 1 + + merged_k_cache: List[torch.Tensor] = [] + merged_v_cache: List[torch.Tensor] = [] + for layer_index in range(len(left_batch.k_cache)): + merged_k_cache.append( + torch.cat( + [ + _pad_cache_left(left_batch.k_cache[layer_index], merged_kv_len), + _pad_cache_left(right_batch.k_cache[layer_index], merged_kv_len), + ], + dim=0, + ) + ) + merged_v_cache.append( + torch.cat( + [ + _pad_cache_left(left_batch.v_cache[layer_index], merged_kv_len), + _pad_cache_left(right_batch.v_cache[layer_index], merged_kv_len), + ], + dim=0, + ) + ) + + merged_decode_attn_mask = torch.cat( + [ + _materialize_decode_mask_for_active_batch(left_batch, merged_mask_len), + _materialize_decode_mask_for_active_batch(right_batch, merged_mask_len), + ], + dim=0, + ) + merged_request_ids = list(left_batch.request_ids) + list(right_batch.request_ids) + merged_states = list(left_batch.states) + list(right_batch.states) + merged_y_sequences = list(left_batch.y_sequences) + list(right_batch.y_sequences) + merged_prefix_lens = torch.cat([left_batch.prefix_lens, right_batch.prefix_lens], dim=0) + if left_batch.kv_lens is None or right_batch.kv_lens is None: + raise ValueError("merge active batch 时缺少 kv_lens") + merged_kv_lens = torch.cat([left_batch.kv_lens, right_batch.kv_lens], dim=0) + merged_decode_attn_mask = _compact_decode_mask_to_kv_lens(merged_decode_attn_mask, merged_kv_lens) + merged_step_indices = torch.cat([left_batch.step_indices, right_batch.step_indices], dim=0) + + return T2SActiveBatch( + request_ids=merged_request_ids, + states=merged_states, + x=None, + x_lens=None, + y_sequences=merged_y_sequences, + prefix_lens=merged_prefix_lens, + xy_pos=build_next_xy_pos(model, merged_y_sequences), + key_padding_mask=None, + prefill_attn_mask=None, + decode_attn_mask=merged_decode_attn_mask, + k_cache=merged_k_cache, + v_cache=merged_v_cache, + kv_lens=merged_kv_lens, + step_indices=merged_step_indices, + prefill_done=True, + ) + + +@torch.inference_mode() +def run_prefill_step( + model: Any, + states: Sequence[T2SRequestState], + max_steps: int, +) -> Tuple[List[T2SRunningRequest], List[T2SFinishedItem]]: + if not states: + return [], [] + + active_batch = build_prefill_batch(model, states) + xy_dec, k_cache, v_cache = model.t2s_transformer.process_prompt(active_batch.xy_pos, active_batch.prefill_attn_mask, None) + decode_attn_mask = F.pad(active_batch.key_padding_mask.unsqueeze(1).unsqueeze(1), (0, 1), value=False) + if len(states) == 1 and not decode_attn_mask.any().item(): + decode_attn_mask = None + logits = model.ar_predict_layer(xy_dec[:, -1]) + sampling_keys = [ + _sampling_group_key( + top_k=state.top_k, + top_p=state.top_p, + temperature=state.temperature, + repetition_penalty=state.repetition_penalty, + trim_eos=True, + ) + for state in states + ] + sampled_items, argmax_tokens = _batched_sample_by_group( + logits=logits, + histories=active_batch.y_sequences, + sampling_keys=sampling_keys, + ) + + running_requests: List[T2SRunningRequest] = [] + finished_items: List[T2SFinishedItem] = [] + + for batch_index, state in enumerate(states): + current_history = active_batch.y_sequences[batch_index] + sampled = sampled_items[batch_index] + sampled_token = int(sampled[0, 0].item()) + argmax_token = argmax_tokens[batch_index] + new_history = torch.cat([current_history, sampled.view(-1)], dim=0) + prefix_len = int(active_batch.prefix_lens[batch_index].item()) + + finish_reason: Optional[str] = None + if state.early_stop_num != -1 and (new_history.shape[0] - prefix_len) > state.early_stop_num: + finish_reason = "early_stop" + elif 1 >= max_steps: + finish_reason = "max_step" + elif sampled_token == model.EOS: + finish_reason = "eos_sample" + elif argmax_token == model.EOS: + finish_reason = "eos_argmax" + + if finish_reason is not None: + finished_items.append( + T2SFinishedItem( + request_id=state.request_id, + semantic_tokens=new_history[prefix_len:-1].clone(), + finish_idx=0, + finish_reason=finish_reason, + ) + ) + continue + + real_kv_len = int(active_batch.x_lens[batch_index].item()) + prefix_len + request_k_cache = [layer[batch_index : batch_index + 1, -real_kv_len:, :].clone() for layer in k_cache] + request_v_cache = [layer[batch_index : batch_index + 1, -real_kv_len:, :].clone() for layer in v_cache] + request_decode_attn_mask = None + if decode_attn_mask is not None: + request_decode_attn_mask = decode_attn_mask[batch_index : batch_index + 1].clone() + request_decode_attn_mask = _fit_decode_mask_length(request_decode_attn_mask, real_kv_len + 1) + if not request_decode_attn_mask.any().item(): + request_decode_attn_mask = None + + running_requests.append( + T2SRunningRequest( + state=state, + y_sequence=new_history, + prefix_len=prefix_len, + decode_attn_mask=request_decode_attn_mask, + k_cache=request_k_cache, + v_cache=request_v_cache, + step_idx=1, + ) + ) + + return running_requests, finished_items + + +def _build_decode_batch_from_running( + model: Any, + running_requests: Sequence[T2SRunningRequest], +) -> Tuple[torch.Tensor, List[torch.Tensor], List[torch.Tensor], Optional[torch.Tensor]]: + xy_pos = build_next_xy_pos(model, [item.y_sequence for item in running_requests]) + max_kv_len = max(item.k_cache[0].shape[1] for item in running_requests) + num_layers = len(running_requests[0].k_cache) + + batched_k_cache: List[torch.Tensor] = [] + batched_v_cache: List[torch.Tensor] = [] + for layer_index in range(num_layers): + batched_k_cache.append( + torch.cat([_pad_cache_left(item.k_cache[layer_index], max_kv_len) for item in running_requests], dim=0) + ) + batched_v_cache.append( + torch.cat([_pad_cache_left(item.v_cache[layer_index], max_kv_len) for item in running_requests], dim=0) + ) + + if all(item.decode_attn_mask is None for item in running_requests): + batched_decode_attn_mask = None + else: + materialized_masks = [_materialize_decode_mask_for_request(item) for item in running_requests] + max_mask_len = max(mask.shape[-1] for mask in materialized_masks) + batched_decode_attn_mask = torch.cat( + [_pad_decode_mask_left(mask, max_mask_len) for mask in materialized_masks], + dim=0, + ) + return xy_pos, batched_k_cache, batched_v_cache, batched_decode_attn_mask + + +@torch.inference_mode() +def run_decode_step_for_running( + model: Any, + running_requests: Sequence[T2SRunningRequest], + max_steps: int, +) -> Tuple[List[T2SRunningRequest], List[T2SFinishedItem]]: + if not running_requests: + return [], [] + + xy_pos, batched_k_cache, batched_v_cache, batched_decode_attn_mask = _build_decode_batch_from_running( + model, running_requests + ) + xy_dec, next_k_cache, next_v_cache = model.t2s_transformer.decode_next_token( + xy_pos, + batched_k_cache, + batched_v_cache, + batched_decode_attn_mask, + ) + logits = model.ar_predict_layer(xy_dec[:, -1]) + sampling_keys = [ + _sampling_group_key( + top_k=running_request.state.top_k, + top_p=running_request.state.top_p, + temperature=running_request.state.temperature, + repetition_penalty=running_request.state.repetition_penalty, + trim_eos=running_request.step_idx < 11, + ) + for running_request in running_requests + ] + histories = [running_request.y_sequence for running_request in running_requests] + sampled_items, argmax_tokens = _batched_sample_by_group( + logits=logits, + histories=histories, + sampling_keys=sampling_keys, + ) + + next_running: List[T2SRunningRequest] = [] + finished_items: List[T2SFinishedItem] = [] + + for batch_index, running_request in enumerate(running_requests): + current_idx = running_request.step_idx + sampled = sampled_items[batch_index] + sampled_token = int(sampled[0, 0].item()) + argmax_token = argmax_tokens[batch_index] + new_history = torch.cat([running_request.y_sequence, sampled.view(-1)], dim=0) + + finish_reason: Optional[str] = None + if running_request.state.early_stop_num != -1 and (new_history.shape[0] - running_request.prefix_len) > running_request.state.early_stop_num: + finish_reason = "early_stop" + elif current_idx + 1 >= max_steps: + finish_reason = "max_step" + elif sampled_token == model.EOS: + finish_reason = "eos_sample" + elif argmax_token == model.EOS: + finish_reason = "eos_argmax" + + if finish_reason is not None: + finished_items.append( + T2SFinishedItem( + request_id=running_request.state.request_id, + semantic_tokens=new_history[running_request.prefix_len:-1].clone(), + finish_idx=current_idx, + finish_reason=finish_reason, + ) + ) + continue + + real_next_kv_len = running_request.k_cache[0].shape[1] + 1 + request_k_cache = [layer[batch_index : batch_index + 1, -real_next_kv_len:, :].clone() for layer in next_k_cache] + request_v_cache = [layer[batch_index : batch_index + 1, -real_next_kv_len:, :].clone() for layer in next_v_cache] + if batched_decode_attn_mask is None: + next_decode_attn_mask = None + else: + current_decode_mask_len = running_request.k_cache[0].shape[1] + 1 + current_decode_attn_mask = batched_decode_attn_mask[ + batch_index : batch_index + 1, :, :, -current_decode_mask_len: + ] + next_decode_attn_mask = F.pad(current_decode_attn_mask, (0, 1), value=False) + next_decode_attn_mask = _fit_decode_mask_length(next_decode_attn_mask, real_next_kv_len + 1) + if not next_decode_attn_mask.any().item(): + next_decode_attn_mask = None + next_running.append( + T2SRunningRequest( + state=running_request.state, + y_sequence=new_history, + prefix_len=running_request.prefix_len, + decode_attn_mask=next_decode_attn_mask, + k_cache=request_k_cache, + v_cache=request_v_cache, + step_idx=current_idx + 1, + ) + ) + + return next_running, finished_items + + +@torch.inference_mode() +def run_scheduler_continuous( + model: Any, + states: Sequence[T2SRequestState], + max_steps: int, +) -> List[T2SFinishedItem]: + pending = sorted(states, key=lambda item: (item.ready_step, item.request_id)) + active_batch: Optional[T2SActiveBatch] = None + finished: List[T2SFinishedItem] = [] + current_tick = 0 + + while pending or active_batch is not None: + admitted: List[T2SRequestState] = [] + while pending and pending[0].ready_step <= current_tick: + admitted.append(pending.pop(0)) + + admitted_active_batch, admitted_finished = run_prefill_active_batch(model, admitted, max_steps=max_steps) + finished.extend(admitted_finished) + active_batch = merge_active_batches(model, active_batch, admitted_active_batch) + + if active_batch is not None: + active_batch, step_finished = decode_one_step(model, active_batch, max_steps=max_steps) + finished.extend(step_finished) + + if active_batch is None and pending: + current_tick = max(current_tick + 1, pending[0].ready_step) + continue + + current_tick += 1 + + finished.sort(key=lambda item: item.request_id) + return finished diff --git a/GPT_SoVITS/TTS_infer_pack/text_cpu_preprocess.py b/GPT_SoVITS/TTS_infer_pack/text_cpu_preprocess.py new file mode 100644 index 00000000..e2398251 --- /dev/null +++ b/GPT_SoVITS/TTS_infer_pack/text_cpu_preprocess.py @@ -0,0 +1,100 @@ +import os +import re +import sys +from typing import Dict, List, Optional, Tuple + +now_dir = os.getcwd() +sys.path.append(now_dir) + +from text.LangSegmenter import LangSegmenter +from text import cleaned_text_to_sequence +from text.cleaner import clean_text + + +PreparedTextSegmentPayload = Dict[str, object] + + +def split_text_by_language(text: str, language: str) -> Tuple[List[str], List[str]]: + textlist: List[str] = [] + langlist: List[str] = [] + if language == "all_zh": + for tmp in LangSegmenter.getTexts(text, "zh"): + langlist.append(tmp["lang"]) + textlist.append(tmp["text"]) + elif language == "all_yue": + for tmp in LangSegmenter.getTexts(text, "zh"): + if tmp["lang"] == "zh": + tmp["lang"] = "yue" + langlist.append(tmp["lang"]) + textlist.append(tmp["text"]) + elif language == "all_ja": + for tmp in LangSegmenter.getTexts(text, "ja"): + langlist.append(tmp["lang"]) + textlist.append(tmp["text"]) + elif language == "all_ko": + for tmp in LangSegmenter.getTexts(text, "ko"): + langlist.append(tmp["lang"]) + textlist.append(tmp["text"]) + elif language == "en": + langlist.append("en") + textlist.append(text) + elif language == "auto": + for tmp in LangSegmenter.getTexts(text): + langlist.append(tmp["lang"]) + textlist.append(tmp["text"]) + elif language == "auto_yue": + for tmp in LangSegmenter.getTexts(text): + if tmp["lang"] == "zh": + tmp["lang"] = "yue" + langlist.append(tmp["lang"]) + textlist.append(tmp["text"]) + else: + for tmp in LangSegmenter.getTexts(text): + if langlist: + same_group = (tmp["lang"] == "en" and langlist[-1] == "en") or ( + tmp["lang"] != "en" and langlist[-1] != "en" + ) + if same_group: + textlist[-1] += tmp["text"] + continue + if tmp["lang"] == "en": + langlist.append(tmp["lang"]) + else: + langlist.append(language) + textlist.append(tmp["text"]) + return textlist, langlist + + +def clean_text_segment(text: str, language: str, version: str) -> Tuple[List[int], Optional[List[int]], str]: + normalized_language = language.replace("all_", "") + phones, word2ph, norm_text = clean_text(text, normalized_language, version) + phones = cleaned_text_to_sequence(phones, version) + return list(phones), None if word2ph is None else list(word2ph), str(norm_text) + + +def preprocess_text_segments_payload( + text: str, + language: str, + version: str, + final: bool = False, +) -> List[PreparedTextSegmentPayload]: + text = re.sub(r" {2,}", " ", text) + textlist, langlist = split_text_by_language(text, language) + payloads: List[PreparedTextSegmentPayload] = [] + total_phones_len = 0 + for segment_text, segment_lang in zip(textlist, langlist): + phones, word2ph, norm_text = clean_text_segment(segment_text, segment_lang, version) + payloads.append( + { + "language": segment_lang.replace("all_", ""), + "phones": phones, + "word2ph": word2ph, + "norm_text": norm_text, + } + ) + total_phones_len += len(phones) + + if not final and total_phones_len < 6: + return preprocess_text_segments_payload("." + text, language, version, final=True) + + return payloads diff --git a/GPT_SoVITS/TTS_infer_pack/unified_engine.py b/GPT_SoVITS/TTS_infer_pack/unified_engine.py new file mode 100644 index 00000000..aed7b146 --- /dev/null +++ b/GPT_SoVITS/TTS_infer_pack/unified_engine.py @@ -0,0 +1,2640 @@ +from __future__ import annotations + +import asyncio +import os +import signal +import subprocess +import sys +import threading +import time +import uuid +import wave +from collections import deque +from dataclasses import dataclass, field +from io import BytesIO +from pathlib import Path +from typing import Any, Callable, Deque, Dict, Generator, List, Optional, Sequence, Tuple, Union + +import numpy as np +import soundfile as sf +import torch + +from GPT_SoVITS.TTS_infer_pack.TTS import TTS +from GPT_SoVITS.TTS_infer_pack.prepare_coordinator import PrepareCoordinator +from GPT_SoVITS.TTS_infer_pack.t2s_scheduler import ( + SchedulerRequestSpec, + T2SActiveBatch, + T2SFinishedItem, + T2SRequestState, + decode_one_step, + merge_active_batches, + run_prefill_active_batch, + run_scheduler_continuous, +) + + +@dataclass +class RuntimeControlCallbacks: + restart: Callable[[], None] | None = None + exit: Callable[[], None] | None = None + + +@dataclass +class DefaultReferenceState: + ref_audio_path: str | None = None + updated_at: float = 0.0 + + +class ReferenceRegistry: + def __init__(self) -> None: + self._lock = threading.Lock() + self._state = DefaultReferenceState() + + def set_default(self, ref_audio_path: str) -> DefaultReferenceState: + with self._lock: + self._state = DefaultReferenceState(ref_audio_path=str(ref_audio_path), updated_at=time.time()) + return self._state + + def clear(self) -> DefaultReferenceState: + with self._lock: + self._state = DefaultReferenceState() + return self._state + + def get_default(self) -> DefaultReferenceState: + with self._lock: + return DefaultReferenceState( + ref_audio_path=self._state.ref_audio_path, + updated_at=self._state.updated_at, + ) + + +@dataclass +class ModelRegistryState: + t2s_weights_path: str + vits_weights_path: str + generation: int = 0 + t2s_generation: int = 0 + vits_generation: int = 0 + updated_at: float = field(default_factory=time.time) + + +class ModelRegistry: + def __init__(self, t2s_weights_path: str, vits_weights_path: str) -> None: + self._lock = threading.Lock() + self._state = ModelRegistryState( + t2s_weights_path=str(t2s_weights_path), + vits_weights_path=str(vits_weights_path), + ) + + def snapshot(self) -> ModelRegistryState: + with self._lock: + return ModelRegistryState( + t2s_weights_path=self._state.t2s_weights_path, + vits_weights_path=self._state.vits_weights_path, + generation=self._state.generation, + t2s_generation=self._state.t2s_generation, + vits_generation=self._state.vits_generation, + updated_at=self._state.updated_at, + ) + + def mark_t2s_reload(self, weights_path: str) -> ModelRegistryState: + with self._lock: + self._state.t2s_weights_path = str(weights_path) + self._state.generation += 1 + self._state.t2s_generation += 1 + self._state.updated_at = time.time() + return ModelRegistryState( + t2s_weights_path=self._state.t2s_weights_path, + vits_weights_path=self._state.vits_weights_path, + generation=self._state.generation, + t2s_generation=self._state.t2s_generation, + vits_generation=self._state.vits_generation, + updated_at=self._state.updated_at, + ) + + def mark_vits_reload(self, weights_path: str) -> ModelRegistryState: + with self._lock: + self._state.vits_weights_path = str(weights_path) + self._state.generation += 1 + self._state.vits_generation += 1 + self._state.updated_at = time.time() + return ModelRegistryState( + t2s_weights_path=self._state.t2s_weights_path, + vits_weights_path=self._state.vits_weights_path, + generation=self._state.generation, + t2s_generation=self._state.t2s_generation, + vits_generation=self._state.vits_generation, + updated_at=self._state.updated_at, + ) + + +@dataclass +class DirectTTSExecution: + media_type: str + streaming: bool + audio_generator: Optional[Generator[bytes, None, None]] = None + audio_bytes: Optional[bytes] = None + request_id: Optional[str] = None + + +@dataclass +class NormalizedEngineRequest: + request_id: str + text: str + text_lang: str + ref_audio_path: str + prompt_lang: str + prompt_text: str = "" + aux_ref_audio_paths: List[str] | None = None + top_k: int = 15 + top_p: float = 1.0 + temperature: float = 1.0 + repetition_penalty: float = 1.35 + early_stop_num: int = -1 + ready_step: int = 0 + text_split_method: str = "cut5" + batch_size: int = 1 + batch_threshold: float = 0.75 + split_bucket: bool = False + speed_factor: float = 1.0 + fragment_interval: float = 0.3 + seed: int = -1 + media_type: str = "wav" + streaming_mode: bool | int = False + return_fragment: bool = False + fixed_length_chunk: bool = False + response_streaming: bool = False + parallel_infer: bool = False + sample_steps: int = 32 + super_sampling: bool = False + overlap_length: int = 2 + min_chunk_length: int = 16 + timeout_sec: float | None = None + + def to_payload(self) -> Dict[str, Any]: + return { + "request_id": self.request_id, + "text": self.text, + "text_lang": self.text_lang, + "ref_audio_path": self.ref_audio_path, + "aux_ref_audio_paths": list(self.aux_ref_audio_paths) if self.aux_ref_audio_paths else None, + "prompt_text": self.prompt_text, + "prompt_lang": self.prompt_lang, + "top_k": self.top_k, + "top_p": self.top_p, + "temperature": self.temperature, + "text_split_method": self.text_split_method, + "batch_size": self.batch_size, + "batch_threshold": self.batch_threshold, + "speed_factor": self.speed_factor, + "split_bucket": self.split_bucket, + "fragment_interval": self.fragment_interval, + "seed": self.seed, + "media_type": self.media_type, + "streaming_mode": self.streaming_mode, + "return_fragment": self.return_fragment, + "fixed_length_chunk": self.fixed_length_chunk, + "response_streaming": self.response_streaming, + "parallel_infer": self.parallel_infer, + "repetition_penalty": self.repetition_penalty, + "sample_steps": self.sample_steps, + "super_sampling": self.super_sampling, + "overlap_length": self.overlap_length, + "min_chunk_length": self.min_chunk_length, + "early_stop_num": self.early_stop_num, + "ready_step": self.ready_step, + "timeout_sec": self.timeout_sec, + } + + def to_scheduler_spec(self) -> SchedulerRequestSpec: + return SchedulerRequestSpec( + request_id=self.request_id, + ref_audio_path=Path(self.ref_audio_path), + prompt_text=self.prompt_text, + prompt_lang=self.prompt_lang, + text=self.text, + text_lang=self.text_lang, + top_k=self.top_k, + top_p=self.top_p, + temperature=self.temperature, + repetition_penalty=self.repetition_penalty, + early_stop_num=self.early_stop_num, + ready_step=self.ready_step, + ) + + +@dataclass +class SchedulerDebugExecution: + payload: Dict[str, Any] + + +@dataclass +class SchedulerSubmitExecution: + audio_bytes: bytes + media_type: str + headers: Dict[str, str] + + +class EngineStatus: + NEW = "NEW" + QUEUED = "QUEUED" + VALIDATED = "VALIDATED" + CPU_PREPARING = "CPU_PREPARING" + GPU_PREPARING = "GPU_PREPARING" + READY_FOR_PREFILL = "READY_FOR_PREFILL" + ACTIVE_DECODE = "ACTIVE_DECODE" + READY_FOR_FINALIZE = "READY_FOR_FINALIZE" + FINALIZING = "FINALIZING" + STREAMING = "STREAMING" + COMPLETED = "COMPLETED" + FAILED = "FAILED" + + +@dataclass +class EngineRequestState: + request_id: str + api_mode: str + backend: str + media_type: str + response_streaming: bool + submit_ts: float + deadline_ts: float | None = None + status: str = EngineStatus.NEW + updated_ts: float = 0.0 + error: str | None = None + finish_reason: str | None = None + meta: Dict[str, Any] = field(default_factory=dict) + profile: Dict[str, Any] = field(default_factory=dict) + lifecycle_timestamps: Dict[str, float] = field(default_factory=dict) + + def to_summary(self) -> Dict[str, Any]: + return { + "request_id": self.request_id, + "api_mode": self.api_mode, + "backend": self.backend, + "media_type": self.media_type, + "response_streaming": self.response_streaming, + "status": self.status, + "submit_ts": self.submit_ts, + "updated_ts": self.updated_ts, + "deadline_ts": self.deadline_ts, + "error": self.error, + "finish_reason": self.finish_reason, + "meta": dict(self.meta), + "profile": dict(self.profile), + "lifecycle_timestamps": dict(self.lifecycle_timestamps), + } + + +@dataclass +class SchedulerPendingJob: + request_id: str + state: T2SRequestState + done_event: threading.Event + done_loop: asyncio.AbstractEventLoop | None + done_future: asyncio.Future | None + enqueue_time: float + speed_factor: float + sample_steps: int + media_type: str + admission_wait_ms: float = 0.0 + prepare_wall_ms: float = 0.0 + prepare_profile_total_ms: float = 0.0 + first_schedule_time: float | None = None + prefill_ms: float = 0.0 + merge_ms: float = 0.0 + decode_ms: float = 0.0 + finalize_wait_ms: float = 0.0 + synth_ms: float = 0.0 + pack_ms: float = 0.0 + decode_steps: int = 0 + result_ready_time: float | None = None + result: dict | None = None + sample_rate: int | None = None + audio_data: np.ndarray | None = None + error: str | None = None + engine_request_id: str | None = None + + +@dataclass +class SchedulerFinalizeTask: + request_id: str + item: T2SFinishedItem + enqueued_time: float + + +@dataclass +class RuntimeStateCallbacks: + update: Callable[[str, str, Optional[Dict[str, Any]]], None] | None = None + complete: Callable[[str, Optional[Dict[str, Any]]], None] | None = None + fail: Callable[[str, str], None] | None = None + + +class UnifiedSchedulerWorker: + def __init__( + self, + tts: TTS, + max_steps: int = 1500, + micro_batch_wait_ms: int = 5, + runtime_callbacks: RuntimeStateCallbacks | None = None, + ): + self.tts = tts + self.max_steps = int(max_steps) + self.micro_batch_wait_s = float(micro_batch_wait_ms) / 1000.0 + self.runtime_callbacks = runtime_callbacks or RuntimeStateCallbacks() + self.prepare_coordinator = PrepareCoordinator(tts) + self.condition = threading.Condition() + self.prepare_inflight = 0 + self.prepare_peak_inflight = 0 + self.finalize_condition = threading.Condition() + self.finalize_pending_tasks: Deque[SchedulerFinalizeTask] = deque() + self.finalize_pending_peak = 0 + self.finalize_inflight = 0 + self.finalize_inflight_peak = 0 + self.finalize_workers = max(1, int(os.environ.get("GPTSOVITS_FINALIZE_WORKERS", 1))) + self.finalize_mode = os.environ.get("GPTSOVITS_FINALIZE_MODE", "async").strip().lower() + self.finalize_batch_max_items = max(1, int(os.environ.get("GPTSOVITS_FINALIZE_BATCH_MAX_ITEMS", 16))) + self.finalize_batch_wait_s = max(0.0, float(os.environ.get("GPTSOVITS_FINALIZE_BATCH_WAIT_MS", "2")) / 1000.0) + self.decode_backlog_max = max(0, int(os.environ.get("GPTSOVITS_ENGINE_DECODE_BACKLOG_MAX", "0"))) + self.finalize_pending_max = max(0, int(os.environ.get("GPTSOVITS_ENGINE_FINALIZE_PENDING_MAX", "0"))) + self.pending_jobs: List[SchedulerPendingJob] = [] + self.active_batch: T2SActiveBatch | None = None + self.job_map: Dict[str, SchedulerPendingJob] = {} + self.total_finished = 0 + self.total_submitted = 0 + self.worker_thread = threading.Thread(target=self._run_loop, name="unified-t2s-scheduler-worker", daemon=True) + self.worker_thread.start() + self.finalize_threads = [ + threading.Thread( + target=self._run_finalize_loop, + name=f"unified-t2s-finalize-{worker_index}", + daemon=True, + ) + for worker_index in range(self.finalize_workers) + ] + for finalize_thread in self.finalize_threads: + finalize_thread.start() + + def _current_decode_backlog_locked(self) -> int: + running_requests = 0 if self.active_batch is None else len(self.active_batch.request_ids) + return int(len(self.pending_jobs) + running_requests) + + def _can_accept_submit_locked(self) -> tuple[bool, Dict[str, int]]: + decode_backlog = self._current_decode_backlog_locked() + finalize_pending = int(len(self.finalize_pending_tasks)) + prepare_inflight = int(self.prepare_coordinator.snapshot()["inflight"]) + blocked_decode = self.decode_backlog_max > 0 and decode_backlog >= self.decode_backlog_max + blocked_finalize = self.finalize_pending_max > 0 and finalize_pending >= self.finalize_pending_max + return ( + not blocked_decode and not blocked_finalize, + { + "decode_backlog": decode_backlog, + "finalize_pending": finalize_pending, + "prepare_inflight": prepare_inflight, + "decode_backlog_max": int(self.decode_backlog_max), + "finalize_pending_max": int(self.finalize_pending_max), + }, + ) + + def wait_for_submit_capacity_blocking(self, timeout_sec: float | None = None) -> tuple[float, Dict[str, int]]: + start = time.perf_counter() + deadline = None if timeout_sec in [None, ""] else (start + max(0.0, float(timeout_sec))) + last_snapshot: Dict[str, int] = {} + while True: + with self.condition: + allowed, snapshot = self._can_accept_submit_locked() + last_snapshot = snapshot + if allowed: + return max(0.0, (time.perf_counter() - start) * 1000.0), snapshot + if deadline is not None and time.perf_counter() >= deadline: + raise TimeoutError( + "scheduler submit admission timeout " + f"(decode_backlog={snapshot['decode_backlog']}, finalize_pending={snapshot['finalize_pending']})" + ) + self.condition.wait(timeout=self.micro_batch_wait_s) + + async def submit_async( + self, + state: T2SRequestState, + speed_factor: float, + sample_steps: int, + media_type: str, + prepare_wall_ms: float, + prepare_profile_total_ms: float, + done_loop: asyncio.AbstractEventLoop | None = None, + done_future: asyncio.Future | None = None, + engine_request_id: str | None = None, + timeout_sec: float | None = None, + ) -> SchedulerPendingJob: + return await asyncio.to_thread( + self.submit, + state, + speed_factor, + sample_steps, + media_type, + prepare_wall_ms, + prepare_profile_total_ms, + done_loop, + done_future, + engine_request_id, + timeout_sec, + ) + + def snapshot(self) -> dict: + with self.condition: + finalize_pending = len(self.finalize_pending_tasks) + prepare_state = self.prepare_coordinator.snapshot() + active_batch = self.active_batch + active_batch_summary = None + if active_batch is not None: + active_batch_summary = { + "request_count": int(len(active_batch.request_ids)), + "request_ids": list(active_batch.request_ids), + "prefill_done": bool(active_batch.prefill_done), + "decode_step_index_max": ( + int(active_batch.step_indices.max().item()) + if active_batch.step_indices is not None and active_batch.step_indices.numel() > 0 + else 0 + ), + } + return { + "pending_jobs": len(self.pending_jobs), + "running_requests": 0 if active_batch is None else len(active_batch.request_ids), + "prepare_inflight": prepare_state["inflight"], + "prepare_peak_inflight": prepare_state["peak_inflight"], + "prepare_max_inflight": prepare_state.get("max_inflight", 0), + "prepare_state": dict(prepare_state), + "finalize_pending": finalize_pending, + "finalize_pending_peak": self.finalize_pending_peak, + "finalize_inflight": self.finalize_inflight, + "finalize_inflight_peak": self.finalize_inflight_peak, + "finalize_workers": self.finalize_workers, + "finalize_mode": self.finalize_mode, + "finalize_batch_max_items": self.finalize_batch_max_items, + "finalize_batch_wait_ms": self.finalize_batch_wait_s * 1000.0, + "decode_backlog_max": self.decode_backlog_max, + "finalize_pending_max": self.finalize_pending_max, + "active_batch": active_batch_summary, + "total_submitted": self.total_submitted, + "total_finished": self.total_finished, + "drained": self.is_drained(), + } + + def is_drained(self) -> bool: + with self.condition: + with self.finalize_condition: + return ( + self.active_batch is None + and not self.pending_jobs + and not self.job_map + and self.prepare_coordinator.snapshot()["inflight"] <= 0 + and self.finalize_inflight <= 0 + and not self.finalize_pending_tasks + ) + + def wait_until_idle(self, timeout_sec: float = 60.0, poll_interval_sec: float = 0.01) -> bool: + deadline = time.perf_counter() + max(0.0, timeout_sec) + while time.perf_counter() < deadline: + if self.is_drained(): + return True + time.sleep(poll_interval_sec) + return self.is_drained() + + def _sync_device(self) -> None: + try: + device_str = str(self.tts.configs.device) + if device_str.startswith("cuda") and torch.cuda.is_available(): + torch.cuda.synchronize(self.tts.configs.device) + elif device_str == "mps" and hasattr(torch, "mps") and hasattr(torch.mps, "synchronize"): + torch.mps.synchronize() + except Exception: + pass + + def submit( + self, + state: T2SRequestState, + speed_factor: float, + sample_steps: int, + media_type: str, + prepare_wall_ms: float, + prepare_profile_total_ms: float, + done_loop: asyncio.AbstractEventLoop | None = None, + done_future: asyncio.Future | None = None, + engine_request_id: str | None = None, + timeout_sec: float | None = None, + ) -> SchedulerPendingJob: + admission_wait_ms, admission_snapshot = self.wait_for_submit_capacity_blocking(timeout_sec=timeout_sec) + job = SchedulerPendingJob( + request_id=state.request_id, + state=state, + done_event=threading.Event(), + done_loop=done_loop, + done_future=done_future, + enqueue_time=time.perf_counter(), + speed_factor=float(speed_factor), + sample_steps=int(sample_steps), + media_type=media_type, + admission_wait_ms=float(admission_wait_ms), + prepare_wall_ms=float(prepare_wall_ms), + prepare_profile_total_ms=float(prepare_profile_total_ms), + engine_request_id=engine_request_id or state.request_id, + ) + with self.condition: + self.pending_jobs.append(job) + self.job_map[job.request_id] = job + self.total_submitted += 1 + self.condition.notify_all() + self._runtime_update( + job.engine_request_id, + EngineStatus.QUEUED, + { + "scheduler_request_id": job.request_id, + "decode_admission_wait_ms": float(admission_wait_ms), + "admission_snapshot": dict(admission_snapshot), + }, + ) + with self.finalize_condition: + self.finalize_condition.notify_all() + return job + + async def prepare_state_profiled_async( + self, + spec: SchedulerRequestSpec, + prepare_submit_at: float, + ) -> tuple[T2SRequestState, float, float]: + with self.condition: + self.prepare_inflight += 1 + self.prepare_peak_inflight = max(self.prepare_peak_inflight, self.prepare_inflight) + try: + return await self.prepare_coordinator.prepare_state_profiled_async(spec, prepare_submit_at) + finally: + with self.condition: + self.prepare_inflight = max(0, self.prepare_inflight - 1) + self.condition.notify_all() + with self.finalize_condition: + self.finalize_condition.notify_all() + + async def prepare_states_batch_async(self, specs: List[SchedulerRequestSpec]) -> List[T2SRequestState]: + results = await asyncio.gather( + *[self.prepare_state_profiled_async(spec, time.perf_counter()) for spec in specs] + ) + return [state for state, _, _ in results] + + def _mark_prefill_started(self, pending_jobs: List[SchedulerPendingJob], started_at: float) -> None: + with self.condition: + for job in pending_jobs: + tracked_job = self.job_map.get(job.request_id) + if tracked_job is None: + continue + tracked_job.first_schedule_time = float(started_at) + self._runtime_update( + tracked_job.engine_request_id, + EngineStatus.GPU_PREPARING, + {"scheduler_request_id": tracked_job.request_id, "prefill_started_at": float(started_at)}, + ) + + def _add_prefill_time(self, request_ids: List[str], elapsed_s: float) -> None: + delta_ms = float(elapsed_s) * 1000.0 + if not request_ids: + return + with self.condition: + for request_id in request_ids: + job = self.job_map.get(request_id) + if job is not None: + job.prefill_ms += delta_ms + + def _add_merge_time(self, request_ids: List[str], elapsed_s: float) -> None: + delta_ms = float(elapsed_s) * 1000.0 + if not request_ids: + return + with self.condition: + for request_id in request_ids: + job = self.job_map.get(request_id) + if job is not None: + job.merge_ms += delta_ms + + def _add_decode_time(self, request_ids: List[str], elapsed_s: float) -> None: + delta_ms = float(elapsed_s) * 1000.0 + if not request_ids: + return + activate_request_ids: List[str] = [] + with self.condition: + for request_id in request_ids: + job = self.job_map.get(request_id) + if job is not None: + if job.decode_steps == 0: + activate_request_ids.append(job.engine_request_id) + job.decode_ms += delta_ms + job.decode_steps += 1 + for engine_request_id in activate_request_ids: + self._runtime_update(engine_request_id, EngineStatus.ACTIVE_DECODE, None) + + def _add_finalize_wait_ms(self, request_ids: List[str], delta_ms: float) -> None: + if not request_ids: + return + with self.condition: + for request_id in request_ids: + job = self.job_map.get(request_id) + if job is not None: + job.finalize_wait_ms += float(delta_ms) + + def _enqueue_finalize_finished(self, items: List[T2SFinishedItem]) -> None: + if not items: + return + enqueued_at = time.perf_counter() + with self.finalize_condition: + for item in items: + job = self.job_map.get(item.request_id) + if job is not None: + self._runtime_update( + job.engine_request_id, + EngineStatus.READY_FOR_FINALIZE, + { + "finish_reason": item.finish_reason, + "semantic_len": int(item.semantic_tokens.shape[0]), + "finish_idx": int(item.finish_idx), + }, + ) + self.finalize_pending_tasks.append( + SchedulerFinalizeTask(request_id=item.request_id, item=item, enqueued_time=enqueued_at) + ) + self.finalize_pending_peak = max(self.finalize_pending_peak, len(self.finalize_pending_tasks)) + self.finalize_condition.notify_all() + + def _take_finalize_task_batch(self) -> List[SchedulerFinalizeTask]: + with self.finalize_condition: + while not self.finalize_pending_tasks: + self.finalize_condition.wait() + selected_tasks = [self.finalize_pending_tasks.popleft()] + if self.finalize_mode == "sync" or self.tts.configs.use_vocoder: + self.finalize_inflight += len(selected_tasks) + self.finalize_inflight_peak = max(self.finalize_inflight_peak, self.finalize_inflight) + return selected_tasks + batch_deadline = time.perf_counter() + self.finalize_batch_wait_s + while len(selected_tasks) < self.finalize_batch_max_items: + if not self.finalize_pending_tasks: + remaining = batch_deadline - time.perf_counter() + if remaining <= 0: + break + self.finalize_condition.wait(timeout=remaining) + continue + first_task = selected_tasks[0] + matched_index = None + for index, task in enumerate(self.finalize_pending_tasks): + if abs(task.enqueued_time - first_task.enqueued_time) < 1.0: + matched_index = index + break + if matched_index is not None: + selected_tasks.append(self.finalize_pending_tasks[matched_index]) + del self.finalize_pending_tasks[matched_index] + continue + remaining = batch_deadline - time.perf_counter() + if remaining <= 0: + break + self.finalize_condition.wait(timeout=remaining) + self.finalize_inflight += len(selected_tasks) + self.finalize_inflight_peak = max(self.finalize_inflight_peak, self.finalize_inflight) + with self.condition: + self.condition.notify_all() + return selected_tasks + + def _finalize_task_done(self, count: int) -> None: + with self.finalize_condition: + self.finalize_inflight = max(0, self.finalize_inflight - count) + self.finalize_condition.notify_all() + with self.condition: + self.condition.notify_all() + + def _synthesize_finished_audio(self, job: SchedulerPendingJob, item: T2SFinishedItem) -> tuple[int, np.ndarray]: + audio_fragment = self.tts.synthesize_audio_request_local( + semantic_tokens=item.semantic_tokens.detach().clone().unsqueeze(0).unsqueeze(0), + phones=job.state.phones.detach().clone().unsqueeze(0), + prompt_semantic=job.state.prompt_semantic.detach().clone(), + prompt_phones=job.state.prompt_phones.detach().clone(), + refer_spec=( + job.state.refer_spec[0].detach().clone(), + None if job.state.refer_spec[1] is None else job.state.refer_spec[1].detach().clone(), + ), + raw_audio=job.state.raw_audio.detach().clone(), + raw_sr=int(job.state.raw_sr), + speed=float(job.speed_factor), + sample_steps=int(job.sample_steps), + ) + output_sr = self.tts.configs.sampling_rate if not self.tts.configs.use_vocoder else self.tts.vocoder_configs["sr"] + return self.tts.audio_postprocess( + audio=[[audio_fragment]], + sr=int(output_sr), + batch_index_list=None, + speed_factor=float(job.speed_factor), + split_bucket=False, + fragment_interval=0.0, + super_sampling=False, + ) + + def _synthesize_finished_audio_batch( + self, + jobs_and_items: List[tuple[SchedulerPendingJob, T2SFinishedItem]], + ) -> List[tuple[int, np.ndarray]]: + semantic_tokens_list = [item.semantic_tokens.detach().clone() for _, item in jobs_and_items] + phones_list = [job.state.phones.detach().clone() for job, _ in jobs_and_items] + refer_specs = [] + speeds = [] + sample_steps_list = [] + for job, _ in jobs_and_items: + refer_specs.append( + ( + job.state.refer_spec[0].detach().clone(), + None if job.state.refer_spec[1] is None else job.state.refer_spec[1].detach().clone(), + ) + ) + speeds.append(float(job.speed_factor)) + sample_steps_list.append(int(job.sample_steps)) + audio_fragments = self.tts.synthesize_audio_requests_local_batched( + semantic_tokens_list=semantic_tokens_list, + phones_list=phones_list, + refer_specs=refer_specs, + speeds=speeds, + sample_steps_list=sample_steps_list, + ) + output_sr = self.tts.configs.sampling_rate if not self.tts.configs.use_vocoder else self.tts.vocoder_configs["sr"] + results: List[tuple[int, np.ndarray]] = [] + for (job, _), audio_fragment in zip(jobs_and_items, audio_fragments): + results.append( + self.tts.audio_postprocess( + audio=[[audio_fragment]], + sr=int(output_sr), + batch_index_list=None, + speed_factor=float(job.speed_factor), + split_bucket=False, + fragment_interval=0.0, + super_sampling=False, + ) + ) + return results + + def _complete_finalize_task(self, job: SchedulerPendingJob, item: T2SFinishedItem, sample_rate: int, audio_data: np.ndarray) -> None: + finished_at = time.perf_counter() + with self.condition: + if self.job_map.get(item.request_id) is not job: + return + queue_wait_ms = 0.0 + if job.first_schedule_time is not None: + queue_wait_ms = max(0.0, (job.first_schedule_time - job.enqueue_time) * 1000.0) + worker_total_ms = max(0.0, (finished_at - job.enqueue_time) * 1000.0) + worker_residual_ms = max( + 0.0, + worker_total_ms + - queue_wait_ms + - job.prefill_ms + - job.merge_ms + - job.decode_ms + - job.finalize_wait_ms + - job.synth_ms, + ) + worker_other_ms = max(0.0, job.merge_ms + job.finalize_wait_ms + worker_residual_ms) + job.sample_rate = int(sample_rate) + job.audio_data = audio_data + job.result_ready_time = finished_at + prepare_profile = dict(job.state.prepare_profile) + job.result = { + "request_id": item.request_id, + "semantic_len": int(item.semantic_tokens.shape[0]), + "finish_idx": int(item.finish_idx), + "finish_reason": item.finish_reason, + "decode_admission_wait_ms": float(job.admission_wait_ms), + "prepare_ms": job.prepare_wall_ms, + "prepare_wall_ms": job.prepare_wall_ms, + "prepare_profile_total_ms": job.prepare_profile_total_ms, + "prepare_profile": prepare_profile, + "queue_wait_ms": queue_wait_ms, + "prefill_ms": job.prefill_ms, + "merge_ms": job.merge_ms, + "decode_ms": job.decode_ms, + "finalize_wait_ms": job.finalize_wait_ms, + "synth_ms": job.synth_ms, + "worker_residual_ms": worker_residual_ms, + "worker_other_ms": worker_other_ms, + "worker_total_ms": worker_total_ms, + "decode_steps": int(job.decode_steps), + "sample_rate": int(sample_rate), + "media_type": job.media_type, + } + job.done_event.set() + self._notify_done_future(job) + self.job_map.pop(item.request_id, None) + self.total_finished += 1 + self.condition.notify_all() + self._runtime_complete( + job.engine_request_id, + { + "finish_reason": item.finish_reason, + "semantic_len": int(item.semantic_tokens.shape[0]), + "finish_idx": int(item.finish_idx), + "sample_rate": int(sample_rate), + "worker_profile": dict(job.result or {}), + }, + ) + + def _finalize_error(self, request_ids: List[str], error: str) -> None: + if not request_ids: + return + with self.condition: + for request_id in request_ids: + job = self.job_map.get(request_id) + if job is None: + continue + job.error = error + job.done_event.set() + self._notify_done_future(job) + self.job_map.pop(request_id, None) + self.total_finished += 1 + self._runtime_fail(job.engine_request_id, error) + self.condition.notify_all() + + @staticmethod + def _resolve_done_future(job: SchedulerPendingJob) -> None: + future = job.done_future + if future is None or future.done(): + return + future.set_result(True) + + def _notify_done_future(self, job: SchedulerPendingJob) -> None: + if job.done_loop is None or job.done_future is None: + return + try: + job.done_loop.call_soon_threadsafe(self._resolve_done_future, job) + except RuntimeError: + pass + + def _runtime_update(self, request_id: str | None, status: str, extra: Optional[Dict[str, Any]] = None) -> None: + if request_id is None or self.runtime_callbacks.update is None: + return + self.runtime_callbacks.update(request_id, status, extra) + + def _runtime_complete(self, request_id: str | None, extra: Optional[Dict[str, Any]] = None) -> None: + if request_id is None or self.runtime_callbacks.complete is None: + return + self.runtime_callbacks.complete(request_id, extra) + + def _runtime_fail(self, request_id: str | None, error: str) -> None: + if request_id is None or self.runtime_callbacks.fail is None: + return + self.runtime_callbacks.fail(request_id, error) + + def _take_pending_snapshot(self, wait_for_batch: bool) -> List[SchedulerPendingJob]: + with self.condition: + if not self.pending_jobs and self.active_batch is None: + self.condition.wait(timeout=self.micro_batch_wait_s) + elif wait_for_batch and self.pending_jobs: + self.condition.wait(timeout=self.micro_batch_wait_s) + if not self.pending_jobs: + return [] + pending = list(self.pending_jobs) + self.pending_jobs.clear() + return pending + + def _run_finalize_loop(self) -> None: + while True: + tasks = self._take_finalize_task_batch() + try: + jobs_and_items: List[tuple[SchedulerPendingJob, T2SFinishedItem]] = [] + with self.condition: + for task in tasks: + job = self.job_map.get(task.request_id) + if job is None: + continue + jobs_and_items.append((job, task.item)) + if not jobs_and_items: + continue + now = time.perf_counter() + for task in tasks: + self._add_finalize_wait_ms([task.request_id], max(0.0, (now - task.enqueued_time) * 1000.0)) + for job, item in jobs_and_items: + self._runtime_update( + job.engine_request_id, + EngineStatus.FINALIZING, + { + "finish_reason": item.finish_reason, + "semantic_len": int(item.semantic_tokens.shape[0]), + }, + ) + self._sync_device() + synth_start = time.perf_counter() + if len(jobs_and_items) == 1 or self.tts.configs.use_vocoder: + job, item = jobs_and_items[0] + batch_results = [self._synthesize_finished_audio(job, item)] + else: + batch_results = self._synthesize_finished_audio_batch(jobs_and_items) + self._sync_device() + synth_ms = (time.perf_counter() - synth_start) * 1000.0 + with self.condition: + for job, _ in jobs_and_items: + tracked_job = self.job_map.get(job.request_id) + if tracked_job is not None: + tracked_job.synth_ms += synth_ms + for (job, item), (sample_rate, audio_data) in zip(jobs_and_items, batch_results): + self._complete_finalize_task(job, item, sample_rate=sample_rate, audio_data=audio_data) + except Exception as exc: + self._finalize_error([task.request_id for task in tasks], str(exc)) + finally: + self._finalize_task_done(len(tasks)) + + def _run_loop(self) -> None: + while True: + wait_for_batch = self.active_batch is None + pending_jobs = self._take_pending_snapshot(wait_for_batch=wait_for_batch) + + if pending_jobs: + try: + self._sync_device() + prefill_start = time.perf_counter() + self._mark_prefill_started(pending_jobs, prefill_start) + admitted_active_batch, admitted_finished = run_prefill_active_batch( + self.tts.t2s_model.model, + [job.state for job in pending_jobs], + max_steps=self.max_steps, + ) + self._sync_device() + self._add_prefill_time([job.request_id for job in pending_jobs], time.perf_counter() - prefill_start) + self._enqueue_finalize_finished(admitted_finished) + merge_start = time.perf_counter() + self.active_batch = merge_active_batches( + self.tts.t2s_model.model, + self.active_batch, + admitted_active_batch, + ) + self._add_merge_time( + [] if self.active_batch is None else list(self.active_batch.request_ids), + time.perf_counter() - merge_start, + ) + except Exception as exc: + self._finalize_error([job.request_id for job in pending_jobs], str(exc)) + + if self.active_batch is not None: + active_request_ids: List[str] = [] + try: + active_request_ids = [state.request_id for state in self.active_batch.states] + self._sync_device() + decode_start = time.perf_counter() + self.active_batch, step_finished = decode_one_step( + self.tts.t2s_model.model, + self.active_batch, + max_steps=self.max_steps, + ) + self._sync_device() + self._add_decode_time(active_request_ids, time.perf_counter() - decode_start) + self._enqueue_finalize_finished(step_finished) + except Exception as exc: + self._finalize_error(active_request_ids, str(exc)) + self.active_batch = None + continue + + if not pending_jobs: + time.sleep(self.micro_batch_wait_s) + + +def set_scheduler_seed(seed: int): + if seed in ["", None]: + return + seed = int(seed) + if seed < 0: + return + np.random.seed(seed) + torch.manual_seed(seed) + if torch.cuda.is_available(): + torch.cuda.manual_seed(seed) + torch.cuda.manual_seed_all(seed) + + +def pack_ogg(io_buffer: BytesIO, data: np.ndarray, rate: int): + def handle_pack_ogg(): + with sf.SoundFile(io_buffer, mode="w", samplerate=rate, channels=1, format="ogg") as audio_file: + audio_file.write(data) + + stack_size = 4096 * 4096 + try: + threading.stack_size(stack_size) + pack_ogg_thread = threading.Thread(target=handle_pack_ogg) + pack_ogg_thread.start() + pack_ogg_thread.join() + except (RuntimeError, ValueError): + handle_pack_ogg() + return io_buffer + + +def pack_raw(io_buffer: BytesIO, data: np.ndarray, rate: int): + io_buffer.write(data.tobytes()) + return io_buffer + + +def pack_wav(io_buffer: BytesIO, data: np.ndarray, rate: int): + io_buffer = BytesIO() + sf.write(io_buffer, data, rate, format="wav") + return io_buffer + + +def pack_aac(io_buffer: BytesIO, data: np.ndarray, rate: int): + process = subprocess.Popen( + [ + "ffmpeg", + "-f", + "s16le", + "-ar", + str(rate), + "-ac", + "1", + "-i", + "pipe:0", + "-c:a", + "aac", + "-b:a", + "192k", + "-vn", + "-f", + "adts", + "pipe:1", + ], + stdin=subprocess.PIPE, + stdout=subprocess.PIPE, + stderr=subprocess.PIPE, + ) + out, _ = process.communicate(input=data.tobytes()) + io_buffer.write(out) + return io_buffer + + +def pack_audio(io_buffer: BytesIO, data: np.ndarray, rate: int, media_type: str): + if media_type == "ogg": + io_buffer = pack_ogg(io_buffer, data, rate) + elif media_type == "aac": + io_buffer = pack_aac(io_buffer, data, rate) + elif media_type == "wav": + io_buffer = pack_wav(io_buffer, data, rate) + else: + io_buffer = pack_raw(io_buffer, data, rate) + io_buffer.seek(0) + return io_buffer + + +def wave_header_chunk(frame_input=b"", channels=1, sample_width=2, sample_rate=32000): + wav_buf = BytesIO() + with wave.open(wav_buf, "wb") as vfout: + vfout.setnchannels(channels) + vfout.setsampwidth(sample_width) + vfout.setframerate(sample_rate) + vfout.writeframes(frame_input) + wav_buf.seek(0) + return wav_buf.read() + + +class UnifiedTTSEngine: + def __init__( + self, + tts: TTS, + cut_method_names: Sequence[str], + control_callbacks: RuntimeControlCallbacks | None = None, + max_steps: int = 1500, + micro_batch_wait_ms: int = 5, + ) -> None: + self.tts = tts + self.cut_method_names = set(cut_method_names) + self.control_callbacks = control_callbacks or RuntimeControlCallbacks() + self.reference_registry = ReferenceRegistry() + self.model_registry = ModelRegistry( + t2s_weights_path=str(self.tts.configs.t2s_weights_path), + vits_weights_path=str(self.tts.configs.vits_weights_path), + ) + self.request_registry_lock = threading.Lock() + self.active_requests: Dict[str, EngineRequestState] = {} + self.recent_requests: Deque[EngineRequestState] = deque() + self.recent_request_limit = max(1, int(os.environ.get("GPTSOVITS_ENGINE_RECENT_REQUEST_LIMIT", "64"))) + self.scheduler_worker = UnifiedSchedulerWorker( + tts, + max_steps=max_steps, + micro_batch_wait_ms=micro_batch_wait_ms, + runtime_callbacks=RuntimeStateCallbacks( + update=self._update_request_state, + complete=self._complete_request_state, + fail=self._fail_request_state, + ), + ) + self.direct_tts_lock = threading.RLock() + self.management_lock = threading.RLock() + + def _register_request_state( + self, + request_id: str, + api_mode: str, + backend: str, + media_type: str, + response_streaming: bool, + deadline_ts: float | None = None, + meta: Optional[Dict[str, Any]] = None, + ) -> EngineRequestState: + now = time.perf_counter() + state = EngineRequestState( + request_id=request_id, + api_mode=api_mode, + backend=backend, + media_type=media_type, + response_streaming=bool(response_streaming), + submit_ts=now, + deadline_ts=deadline_ts, + updated_ts=now, + meta=dict(meta or {}), + lifecycle_timestamps={EngineStatus.NEW: now}, + ) + with self.request_registry_lock: + self.active_requests[request_id] = state + return state + + def _move_to_recent_locked(self, state: EngineRequestState) -> None: + self.recent_requests.appendleft(state) + while len(self.recent_requests) > self.recent_request_limit: + self.recent_requests.pop() + + def _update_request_state( + self, + request_id: str, + status: str, + extra: Optional[Dict[str, Any]] = None, + ) -> None: + now = time.perf_counter() + with self.request_registry_lock: + state = self.active_requests.get(request_id) + if state is None: + return + state.status = status + state.updated_ts = now + state.lifecycle_timestamps[status] = now + if extra: + backend = extra.pop("backend", None) + if backend is not None: + state.backend = str(backend) + finish_reason = extra.pop("finish_reason", None) + if finish_reason is not None: + state.finish_reason = str(finish_reason) + error = extra.pop("error", None) + if error is not None: + state.error = str(error) + state.profile.update(extra) + + def _merge_request_state_profile(self, request_id: str, extra: Optional[Dict[str, Any]] = None) -> None: + if not extra: + return + now = time.perf_counter() + with self.request_registry_lock: + state = self.active_requests.get(request_id) + if state is None: + for recent_state in self.recent_requests: + if recent_state.request_id == request_id: + state = recent_state + break + if state is None: + return + state.updated_ts = now + backend = extra.get("backend") + if backend is not None: + state.backend = str(backend) + finish_reason = extra.get("finish_reason") + if finish_reason is not None: + state.finish_reason = str(finish_reason) + error = extra.get("error") + if error is not None: + state.error = str(error) + merged = dict(extra) + merged.pop("backend", None) + merged.pop("finish_reason", None) + merged.pop("error", None) + state.profile.update(merged) + + def _complete_request_state(self, request_id: str, extra: Optional[Dict[str, Any]] = None) -> None: + now = time.perf_counter() + with self.request_registry_lock: + state = self.active_requests.pop(request_id, None) + if state is None: + return + state.status = EngineStatus.COMPLETED + state.updated_ts = now + state.lifecycle_timestamps[EngineStatus.COMPLETED] = now + if extra: + finish_reason = extra.pop("finish_reason", None) + if finish_reason is not None: + state.finish_reason = str(finish_reason) + state.profile.update(extra) + self._move_to_recent_locked(state) + + def _fail_request_state(self, request_id: str, error: str) -> None: + now = time.perf_counter() + with self.request_registry_lock: + state = self.active_requests.pop(request_id, None) + if state is None: + return + state.status = EngineStatus.FAILED + state.updated_ts = now + state.error = str(error) + state.lifecycle_timestamps[EngineStatus.FAILED] = now + self._move_to_recent_locked(state) + + def _snapshot_request_registry(self) -> Dict[str, Any]: + with self.request_registry_lock: + active = [state.to_summary() for state in self.active_requests.values()] + recent = [state.to_summary() for state in list(self.recent_requests)] + active.sort(key=lambda item: item["submit_ts"]) + return { + "active_count": len(active), + "recent_count": len(recent), + "recent_limit": self.recent_request_limit, + "active_requests": active, + "recent_requests": recent, + } + + @staticmethod + def _safe_component_snapshot(component: Any) -> Dict[str, Any] | None: + if component is None or not hasattr(component, "snapshot"): + return None + try: + return dict(component.snapshot()) + except Exception: + return None + + def _build_stage_summary( + self, + request_registry: Dict[str, Any], + worker_state: Dict[str, Any], + ) -> Dict[str, Any]: + active_requests = list(request_registry.get("active_requests", [])) + status_counts: Dict[str, int] = {} + for item in active_requests: + status = str(item.get("status", "UNKNOWN")) + status_counts[status] = status_counts.get(status, 0) + 1 + + bert_worker_state = self._safe_component_snapshot(getattr(self.tts, "prepare_bert_batch_worker", None)) + ref_semantic_worker_state = self._safe_component_snapshot(getattr(self.tts, "prepare_ref_semantic_batch_worker", None)) + text_preprocessor_state = self._safe_component_snapshot(getattr(self.tts, "text_preprocessor", None)) + + return { + "active_request_count": int(len(active_requests)), + "status_counts": status_counts, + "queued_request_count": int(status_counts.get(EngineStatus.QUEUED, 0)), + "cpu_prepare_request_count": int(status_counts.get(EngineStatus.CPU_PREPARING, 0)), + "gpu_prepare_request_count": int(status_counts.get(EngineStatus.GPU_PREPARING, 0)), + "ready_for_prefill_request_count": int(status_counts.get(EngineStatus.READY_FOR_PREFILL, 0)), + "active_decode_request_count": int(status_counts.get(EngineStatus.ACTIVE_DECODE, 0)), + "ready_for_finalize_request_count": int(status_counts.get(EngineStatus.READY_FOR_FINALIZE, 0)), + "finalizing_request_count": int(status_counts.get(EngineStatus.FINALIZING, 0)), + "streaming_request_count": int(status_counts.get(EngineStatus.STREAMING, 0)), + "worker_pending_jobs": int(worker_state.get("pending_jobs", 0)), + "worker_decode_active_size": int(worker_state.get("running_requests", 0)), + "worker_prepare_inflight": int(worker_state.get("prepare_inflight", 0)), + "worker_finalize_pending": int(worker_state.get("finalize_pending", 0)), + "worker_finalize_inflight": int(worker_state.get("finalize_inflight", 0)), + "admission_config": { + "decode_backlog_max": int(worker_state.get("decode_backlog_max", 0)), + "finalize_pending_max": int(worker_state.get("finalize_pending_max", 0)), + }, + "active_batch": dict(worker_state.get("active_batch") or {}), + "prepare_state": dict(worker_state.get("prepare_state") or {}), + "bert_batch_worker_state": bert_worker_state, + "ref_semantic_worker_state": ref_semantic_worker_state, + "text_preprocessor_state": text_preprocessor_state, + } + + def _collect_request_summaries(self, request_ids: Sequence[str]) -> List[Dict[str, Any]]: + requested = set(request_ids) + results: List[Dict[str, Any]] = [] + with self.request_registry_lock: + for state in self.active_requests.values(): + if state.request_id in requested: + results.append(state.to_summary()) + for state in self.recent_requests: + if state.request_id in requested and all(item["request_id"] != state.request_id for item in results): + results.append(state.to_summary()) + results.sort(key=lambda item: item["request_id"]) + return results + + def _has_active_request(self, request_id: str) -> bool: + with self.request_registry_lock: + return request_id in self.active_requests + + @staticmethod + def _build_request_meta(payload: Dict[str, Any]) -> Dict[str, Any]: + text = payload.get("text") + prompt_text = payload.get("prompt_text") + return { + "text_len": 0 if text is None else len(str(text)), + "prompt_text_len": 0 if prompt_text is None else len(str(prompt_text)), + "text_lang": payload.get("text_lang"), + "prompt_lang": payload.get("prompt_lang"), + "ref_audio_path": payload.get("ref_audio_path"), + } + + @staticmethod + def _sum_profile_field(items: Sequence[Dict[str, Any]], key: str) -> float: + total = 0.0 + for item in items: + value = item.get(key, 0.0) + if isinstance(value, (int, float)): + total += float(value) + return total + + def _build_direct_segment_trace( + self, + segment_texts: Sequence[str], + prepare_profiles: Sequence[Dict[str, Any]], + worker_profiles: Sequence[Dict[str, Any]], + ) -> List[Dict[str, Any]]: + results: List[Dict[str, Any]] = [] + for index, segment_text in enumerate(segment_texts): + prepare_item = prepare_profiles[index] if index < len(prepare_profiles) else {} + worker_item = worker_profiles[index] if index < len(worker_profiles) else {} + prepare_profile = dict(prepare_item.get("prepare_profile", {})) + results.append( + { + "segment_index": index, + "request_id": prepare_item.get("request_id") or worker_item.get("request_id"), + "text_len": len(str(segment_text)), + "prepare_wall_ms": float(prepare_item.get("prepare_wall_ms", 0.0)), + "prepare_profile_total_ms": float(prepare_item.get("prepare_profile_total_ms", 0.0)), + "decode_admission_wait_ms": float(worker_item.get("decode_admission_wait_ms", 0.0)), + "queue_wait_ms": float(worker_item.get("queue_wait_ms", 0.0)), + "prefill_ms": float(worker_item.get("prefill_ms", 0.0)), + "merge_ms": float(worker_item.get("merge_ms", 0.0)), + "decode_ms": float(worker_item.get("decode_ms", 0.0)), + "finalize_wait_ms": float(worker_item.get("finalize_wait_ms", 0.0)), + "synth_ms": float(worker_item.get("synth_ms", 0.0)), + "worker_total_ms": float(worker_item.get("worker_total_ms", 0.0)), + "decode_steps": int(worker_item.get("decode_steps", 0)), + "semantic_len": int(worker_item.get("semantic_len", 0)), + "finish_reason": worker_item.get("finish_reason"), + "norm_text": prepare_profile.get("norm_text"), + } + ) + return results + + def _build_direct_scheduler_profile( + self, + *, + backend: str, + request_start: float, + response_ready_at: float, + audio_bytes: int, + sample_rate: int, + segment_texts: Sequence[str], + prepare_profiles: Sequence[Dict[str, Any]], + worker_profiles: Sequence[Dict[str, Any]], + pack_ms: float, + response_overhead_ms: float, + ) -> Dict[str, Any]: + segment_trace = self._build_direct_segment_trace(segment_texts, prepare_profiles, worker_profiles) + prepare_profile_dicts = [dict(item.get("prepare_profile", {})) for item in prepare_profiles] + request_total_ms = max(0.0, (response_ready_at - request_start) * 1000.0) + prepare_wall_ms = self._sum_profile_field(prepare_profiles, "prepare_wall_ms") + prepare_profile_total_ms = self._sum_profile_field(prepare_profiles, "prepare_profile_total_ms") + decode_admission_wait_ms = self._sum_profile_field(worker_profiles, "decode_admission_wait_ms") + queue_wait_ms = self._sum_profile_field(worker_profiles, "queue_wait_ms") + prefill_ms = self._sum_profile_field(worker_profiles, "prefill_ms") + merge_ms = self._sum_profile_field(worker_profiles, "merge_ms") + decode_ms = self._sum_profile_field(worker_profiles, "decode_ms") + finalize_wait_ms = self._sum_profile_field(worker_profiles, "finalize_wait_ms") + synth_ms = self._sum_profile_field(worker_profiles, "synth_ms") + worker_total_ms = self._sum_profile_field(worker_profiles, "worker_total_ms") + decode_steps = sum(int(item.get("decode_steps", 0)) for item in worker_profiles) + semantic_len = sum(int(item.get("semantic_len", 0)) for item in worker_profiles) + request_other_ms = max( + 0.0, + request_total_ms - prepare_wall_ms - worker_total_ms - pack_ms - response_overhead_ms, + ) + return { + "backend": backend, + "backend_mode": backend, + "segment_count": len(segment_texts), + "sample_rate": int(sample_rate), + "audio_bytes": int(audio_bytes), + "request_total_ms": request_total_ms, + "prepare_ms": prepare_wall_ms, + "prepare_wall_ms": prepare_wall_ms, + "prepare_profile_total_ms": prepare_profile_total_ms, + "decode_admission_wait_ms": decode_admission_wait_ms, + "queue_wait_ms": queue_wait_ms, + "prefill_ms": prefill_ms, + "merge_ms": merge_ms, + "decode_ms": decode_ms, + "finalize_wait_ms": finalize_wait_ms, + "synth_ms": synth_ms, + "pack_ms": pack_ms, + "response_overhead_ms": response_overhead_ms, + "worker_total_ms": worker_total_ms, + "request_other_ms": request_other_ms, + "decode_steps": decode_steps, + "semantic_len": semantic_len, + "prepare_segments": list(prepare_profiles), + "worker_segments": list(worker_profiles), + "segment_trace": segment_trace, + "prepare_aggregate": self._aggregate_numeric_dicts(prepare_profile_dicts), + } + + def _build_legacy_direct_profile( + self, + *, + backend: str, + fallback_reason: str | None, + request_start: float, + finished_at: float, + sample_rate: int | None = None, + audio_bytes: int = 0, + pack_ms: float = 0.0, + chunk_count: int = 0, + stream_total_bytes: int = 0, + first_chunk_ms: float | None = None, + ) -> Dict[str, Any]: + request_total_ms = max(0.0, (finished_at - request_start) * 1000.0) + legacy_infer_ms = max(0.0, request_total_ms - pack_ms) + return { + "backend": backend, + "backend_mode": backend, + "fallback_reason": fallback_reason, + "request_total_ms": request_total_ms, + "prepare_ms": 0.0, + "queue_wait_ms": 0.0, + "prefill_ms": 0.0, + "merge_ms": 0.0, + "decode_ms": 0.0, + "finalize_wait_ms": 0.0, + "synth_ms": 0.0, + "pack_ms": pack_ms, + "worker_total_ms": legacy_infer_ms, + "request_other_ms": 0.0, + "legacy_infer_ms": legacy_infer_ms, + "sample_rate": int(sample_rate) if sample_rate is not None else None, + "audio_bytes": int(audio_bytes), + "chunk_count": int(chunk_count), + "stream_total_bytes": int(stream_total_bytes), + "first_chunk_ms": None if first_chunk_ms is None else float(first_chunk_ms), + } + + def _build_scheduler_submit_profile( + self, + *, + backend: str, + request_start: float, + response_ready_at: float, + audio_bytes: int, + sample_rate: int, + prepare_spec_build_ms: float, + prepare_wall_ms: float, + prepare_executor_queue_ms: float, + prepare_executor_run_ms: float, + prepare_profile_total_ms: float, + prepare_profile_wall_ms: float, + prepare_other_ms: float, + api_after_prepare_ms: float, + api_wait_result_ms: float, + pack_ms: float, + response_overhead_ms: float, + worker_profile: Dict[str, Any], + ) -> Dict[str, Any]: + worker_total_ms = float(worker_profile.get("worker_total_ms", 0.0)) + request_total_ms = max(0.0, (response_ready_at - request_start) * 1000.0) + request_other_ms = max( + 0.0, + request_total_ms - prepare_wall_ms - api_after_prepare_ms - worker_total_ms - api_wait_result_ms - pack_ms, + ) + result = { + "backend": backend, + "backend_mode": backend, + "audio_bytes": int(audio_bytes), + "sample_rate": int(sample_rate), + "prepare_spec_build_ms": prepare_spec_build_ms, + "prepare_ms": prepare_wall_ms, + "prepare_wall_ms": prepare_wall_ms, + "prepare_executor_queue_ms": prepare_executor_queue_ms, + "prepare_executor_run_ms": prepare_executor_run_ms, + "prepare_profile_total_ms": prepare_profile_total_ms, + "prepare_profile_wall_ms": prepare_profile_wall_ms, + "prepare_other_ms": prepare_other_ms, + "api_after_prepare_ms": api_after_prepare_ms, + "api_wait_result_ms": api_wait_result_ms, + "pack_ms": pack_ms, + "response_overhead_ms": response_overhead_ms, + "request_total_ms": request_total_ms, + "request_other_ms": request_other_ms, + } + result.update({key: value for key, value in worker_profile.items()}) + return result + + @staticmethod + def _format_ms_header(value: Any) -> str: + return f"{float(value):.3f}" + + def _build_scheduler_submit_headers( + self, + *, + request_id: str, + media_type: str, + sample_rate: int, + profile: Dict[str, Any], + ) -> Dict[str, str]: + prepare_profile = dict(profile.get("prepare_profile", {})) + headers = { + "X-Request-Id": request_id, + "X-Semantic-Len": str(int(profile.get("semantic_len", 0))), + "X-Finish-Reason": str(profile.get("finish_reason", "unknown")), + "X-Queue-Wait-Ms": self._format_ms_header(profile.get("queue_wait_ms", 0.0)), + "X-Decode-Admission-Wait-Ms": self._format_ms_header(profile.get("decode_admission_wait_ms", 0.0)), + "X-Prepare-Ms": self._format_ms_header(profile.get("prepare_wall_ms", 0.0)), + "X-Prepare-Wall-Ms": self._format_ms_header(profile.get("prepare_wall_ms", 0.0)), + "X-Prepare-Spec-Build-Ms": self._format_ms_header(profile.get("prepare_spec_build_ms", 0.0)), + "X-Prepare-Executor-Queue-Ms": self._format_ms_header(profile.get("prepare_executor_queue_ms", 0.0)), + "X-Prepare-Admission-Wait-Ms": self._format_ms_header(prepare_profile.get("prepare_admission_wait_ms", 0.0)), + "X-Prepare-Executor-Run-Ms": self._format_ms_header(profile.get("prepare_executor_run_ms", 0.0)), + "X-Prepare-Profile-Total-Ms": self._format_ms_header(profile.get("prepare_profile_total_ms", 0.0)), + "X-Prepare-Profile-Wall-Ms": self._format_ms_header(profile.get("prepare_profile_wall_ms", 0.0)), + "X-Prepare-Other-Ms": self._format_ms_header(profile.get("prepare_other_ms", 0.0)), + "X-Api-After-Prepare-Ms": self._format_ms_header(profile.get("api_after_prepare_ms", 0.0)), + "X-Prefill-Ms": self._format_ms_header(profile.get("prefill_ms", 0.0)), + "X-Merge-Ms": self._format_ms_header(profile.get("merge_ms", 0.0)), + "X-Decode-Ms": self._format_ms_header(profile.get("decode_ms", 0.0)), + "X-Finalize-Wait-Ms": self._format_ms_header(profile.get("finalize_wait_ms", 0.0)), + "X-Synth-Ms": self._format_ms_header(profile.get("synth_ms", 0.0)), + "X-Worker-Residual-Ms": self._format_ms_header(profile.get("worker_residual_ms", 0.0)), + "X-Worker-Other-Ms": self._format_ms_header(profile.get("worker_other_ms", 0.0)), + "X-Pack-Ms": self._format_ms_header(profile.get("pack_ms", 0.0)), + "X-Worker-Total-Ms": self._format_ms_header(profile.get("worker_total_ms", 0.0)), + "X-Api-Wait-Result-Ms": self._format_ms_header(profile.get("api_wait_result_ms", 0.0)), + "X-Decode-Steps": str(int(profile.get("decode_steps", 0))), + "X-Sample-Rate": str(int(sample_rate)), + "X-Response-Overhead-Ms": self._format_ms_header(profile.get("response_overhead_ms", 0.0)), + "X-Request-Other-Ms": self._format_ms_header(profile.get("request_other_ms", 0.0)), + "X-Request-Total-Ms": self._format_ms_header(profile.get("request_total_ms", 0.0)), + } + headers.update( + { + "X-Prepare-Prompt-Text-Ms": self._format_ms_header(prepare_profile.get("prompt_text_features_ms", 0.0)), + "X-Prepare-Target-Text-Ms": self._format_ms_header(prepare_profile.get("text_features_ms", 0.0)), + "X-Prepare-Prompt-Text-CPU-Preprocess-Ms": self._format_ms_header(prepare_profile.get("prompt_text_cpu_preprocess_ms", 0.0)), + "X-Prepare-Target-Text-CPU-Preprocess-Ms": self._format_ms_header(prepare_profile.get("text_cpu_preprocess_ms", 0.0)), + "X-Prepare-Prompt-Text-CPU-Queue-Ms": self._format_ms_header(prepare_profile.get("prompt_text_cpu_queue_ms", 0.0)), + "X-Prepare-Target-Text-CPU-Queue-Ms": self._format_ms_header(prepare_profile.get("text_cpu_queue_ms", 0.0)), + "X-Prepare-Prompt-Text-Feature-Queue-Ms": self._format_ms_header(prepare_profile.get("prompt_text_feature_queue_ms", 0.0)), + "X-Prepare-Target-Text-Feature-Queue-Ms": self._format_ms_header(prepare_profile.get("text_feature_queue_ms", 0.0)), + "X-Prepare-Prompt-Bert-Wait-Ms": self._format_ms_header(prepare_profile.get("prompt_text_bert_wait_ms", 0.0)), + "X-Prepare-Target-Bert-Wait-Ms": self._format_ms_header(prepare_profile.get("text_bert_wait_ms", 0.0)), + "X-Prepare-Prompt-Bert-Admission-Wait-Ms": self._format_ms_header(prepare_profile.get("prompt_text_bert_admission_wait_ms", 0.0)), + "X-Prepare-Target-Bert-Admission-Wait-Ms": self._format_ms_header(prepare_profile.get("text_bert_admission_wait_ms", 0.0)), + "X-Prepare-Prompt-Bert-Queue-Wait-Ms": self._format_ms_header(prepare_profile.get("prompt_text_bert_queue_wait_ms", 0.0)), + "X-Prepare-Target-Bert-Queue-Wait-Ms": self._format_ms_header(prepare_profile.get("text_bert_queue_wait_ms", 0.0)), + "X-Prepare-Prompt-Bert-Batch-Collect-Wait-Ms": self._format_ms_header(prepare_profile.get("prompt_text_bert_batch_collect_wait_ms", 0.0)), + "X-Prepare-Target-Bert-Batch-Collect-Wait-Ms": self._format_ms_header(prepare_profile.get("text_bert_batch_collect_wait_ms", 0.0)), + "X-Prepare-Prompt-Bert-Forward-Ms": self._format_ms_header(prepare_profile.get("prompt_text_bert_forward_ms", 0.0)), + "X-Prepare-Target-Bert-Forward-Ms": self._format_ms_header(prepare_profile.get("text_bert_forward_ms", 0.0)), + "X-Prepare-Prompt-Bert-Pending-On-Enqueue-Peak": str(int(prepare_profile.get("prompt_text_bert_pending_depth_on_enqueue_peak", 0.0))), + "X-Prepare-Target-Bert-Pending-On-Enqueue-Peak": str(int(prepare_profile.get("text_bert_pending_depth_on_enqueue_peak", 0.0))), + "X-Prepare-Prompt-Bert-Pending-On-Collect-Peak": str(int(prepare_profile.get("prompt_text_bert_pending_depth_on_collect_peak", 0.0))), + "X-Prepare-Target-Bert-Pending-On-Collect-Peak": str(int(prepare_profile.get("text_bert_pending_depth_on_collect_peak", 0.0))), + "X-Prepare-Prompt-Bert-High-Pressure-Peak": str(int(prepare_profile.get("prompt_text_bert_high_pressure_mode_peak", 0.0))), + "X-Prepare-Target-Bert-High-Pressure-Peak": str(int(prepare_profile.get("text_bert_high_pressure_mode_peak", 0.0))), + "X-Prepare-Prompt-Bert-Batch-Window-Ms": self._format_ms_header(prepare_profile.get("prompt_text_bert_batch_window_ms", 0.0)), + "X-Prepare-Target-Bert-Batch-Window-Ms": self._format_ms_header(prepare_profile.get("text_bert_batch_window_ms", 0.0)), + "X-Prepare-Text-Pair-Wall-Ms": self._format_ms_header(prepare_profile.get("text_feature_pair_ms", 0.0)), + "X-Prepare-Text-CPU-Workers": str(int(prepare_profile.get("text_cpu_parallel_workers", 0.0))), + "X-Prepare-Audio-Load-Ms": self._format_ms_header(prepare_profile.get("audio_load_ms", 0.0)), + "X-Prepare-Audio-Stage-Wait-Ms": self._format_ms_header(prepare_profile.get("audio_stage_wait_ms", 0.0)), + "X-Prepare-Prompt-Semantic-Ms": self._format_ms_header(prepare_profile.get("prompt_semantic_ms", 0.0)), + "X-Prepare-Prompt-Semantic-Wait-Ms": self._format_ms_header(prepare_profile.get("prompt_semantic_wait_ms", 0.0)), + "X-Prepare-Prompt-Semantic-CPU-Ms": self._format_ms_header(prepare_profile.get("prompt_semantic_cpu_prepare_ms", 0.0)), + "X-Prepare-Prompt-Semantic-Forward-Ms": self._format_ms_header(prepare_profile.get("prompt_semantic_forward_ms", 0.0)), + "X-Prepare-Ref-Spec-Ms": self._format_ms_header(prepare_profile.get("ref_spec_ms", 0.0)), + "X-Prepare-Ref-Spec-Wait-Ms": self._format_ms_header(prepare_profile.get("ref_spec_wait_ms", 0.0)), + "X-Prepare-Ref-Bundle-Ms": self._format_ms_header(prepare_profile.get("ref_audio_bundle_ms", 0.0)), + "X-Prepare-Tensorize-Ms": self._format_ms_header(prepare_profile.get("tensorize_ms", 0.0)), + "X-Prepare-Inflight-On-Enter": str(int(prepare_profile.get("worker_prepare_inflight_on_enter", 0.0))), + "X-Prepare-Inflight-Peak": str(int(prepare_profile.get("worker_prepare_peak_inflight", 0.0))), + } + ) + return headers + + def _build_scheduler_debug_request_profile( + self, + *, + state: T2SRequestState, + item: T2SFinishedItem, + batch_request_count: int, + prepare_batch_wall_ms: float, + decode_batch_wall_ms: float, + batch_request_total_ms: float, + ) -> Dict[str, Any]: + prepare_profile = dict(state.prepare_profile) + prepare_wall_ms = float(prepare_profile.get("wall_total_ms", 0.0)) + return { + "backend": "scheduler_debug", + "backend_mode": "scheduler_debug", + "batch_request_count": int(batch_request_count), + "batch_prepare_wall_ms": float(prepare_batch_wall_ms), + "batch_decode_wall_ms": float(decode_batch_wall_ms), + "batch_request_total_ms": float(batch_request_total_ms), + "prepare_ms": prepare_wall_ms, + "prepare_wall_ms": prepare_wall_ms, + "prepare_profile_total_ms": float(prepare_profile.get("wall_total_ms", prepare_wall_ms)), + "prepare_profile": prepare_profile, + "decode_steps": int(item.finish_idx), + "finish_idx": int(item.finish_idx), + "semantic_len": int(item.semantic_tokens.shape[0]), + "finish_reason": item.finish_reason, + "norm_text": state.norm_text, + "norm_prompt_text": state.norm_prompt_text, + } + + @staticmethod + def _build_scheduler_debug_batch_profile( + *, + request_count: int, + max_steps: int, + prepare_batch_wall_ms: float, + decode_batch_wall_ms: float, + request_total_ms: float, + finished_items: Sequence[T2SFinishedItem], + ) -> Dict[str, Any]: + finish_reason_counts: Dict[str, int] = {} + total_semantic_len = 0 + for item in finished_items: + finish_reason_counts[item.finish_reason] = finish_reason_counts.get(item.finish_reason, 0) + 1 + total_semantic_len += int(item.semantic_tokens.shape[0]) + return { + "request_count": int(request_count), + "max_steps": int(max_steps), + "prepare_batch_wall_ms": float(prepare_batch_wall_ms), + "decode_batch_wall_ms": float(decode_batch_wall_ms), + "request_total_ms": float(request_total_ms), + "total_semantic_len": int(total_semantic_len), + "finish_reason_counts": finish_reason_counts, + } + + def _normalize_lang(self, value: str | None) -> str | None: + if value in [None, ""]: + return value + return str(value).lower() + + @staticmethod + def _aggregate_numeric_dicts(items: Sequence[Dict[str, Any]]) -> Dict[str, float]: + totals: Dict[str, float] = {} + for item in items: + for key, value in item.items(): + if isinstance(value, (int, float)): + totals[key] = totals.get(key, 0.0) + float(value) + return totals + + def _apply_default_reference(self, req: dict) -> dict: + normalized = dict(req) + default_ref = self.reference_registry.get_default() + if normalized.get("ref_audio_path") in [None, ""] and default_ref.ref_audio_path not in [None, ""]: + normalized["ref_audio_path"] = default_ref.ref_audio_path + if "text_lang" in normalized: + normalized["text_lang"] = self._normalize_lang(normalized.get("text_lang")) + if "prompt_lang" in normalized: + normalized["prompt_lang"] = self._normalize_lang(normalized.get("prompt_lang")) + return normalized + + def check_params(self, req: dict) -> Optional[str]: + text = req.get("text", "") + text_lang = req.get("text_lang", "") + ref_audio_path = req.get("ref_audio_path", "") + media_type = req.get("media_type", "wav") + prompt_lang = req.get("prompt_lang", "") + text_split_method = req.get("text_split_method", "cut5") + + if ref_audio_path in [None, ""]: + return "ref_audio_path is required" + if text in [None, ""]: + return "text is required" + if text_lang in [None, ""]: + return "text_lang is required" + if text_lang.lower() not in self.tts.configs.languages: + return f"text_lang: {text_lang} is not supported in version {self.tts.configs.version}" + if prompt_lang in [None, ""]: + return "prompt_lang is required" + if prompt_lang.lower() not in self.tts.configs.languages: + return f"prompt_lang: {prompt_lang} is not supported in version {self.tts.configs.version}" + if media_type not in ["wav", "raw", "ogg", "aac"]: + return f"media_type: {media_type} is not supported" + if text_split_method not in self.cut_method_names: + return f"text_split_method:{text_split_method} is not supported" + return None + + @staticmethod + def _base_request_defaults() -> Dict[str, Any]: + return { + "request_id": None, + "text": None, + "text_lang": None, + "ref_audio_path": None, + "aux_ref_audio_paths": None, + "prompt_text": "", + "prompt_lang": None, + "top_k": 15, + "top_p": 1.0, + "temperature": 1.0, + "text_split_method": "cut5", + "batch_size": 1, + "batch_threshold": 0.75, + "speed_factor": 1.0, + "split_bucket": False, + "fragment_interval": 0.3, + "seed": -1, + "media_type": "wav", + "streaming_mode": False, + "return_fragment": False, + "fixed_length_chunk": False, + "response_streaming": False, + "parallel_infer": False, + "repetition_penalty": 1.35, + "sample_steps": 32, + "super_sampling": False, + "overlap_length": 2, + "min_chunk_length": 16, + "early_stop_num": -1, + "ready_step": 0, + "timeout_sec": None, + } + + def _normalize_engine_request( + self, + payload: dict | NormalizedEngineRequest, + *, + request_id: str | None = None, + normalize_streaming: bool = False, + error_prefix: str = "request 参数非法: ", + ) -> NormalizedEngineRequest: + if isinstance(payload, NormalizedEngineRequest): + normalized_payload = payload.to_payload() + else: + normalized_payload = self._base_request_defaults() + normalized_payload.update(dict(payload)) + if request_id not in [None, ""]: + normalized_payload["request_id"] = str(request_id) + elif normalized_payload.get("request_id") in [None, ""]: + raise ValueError("request_id is required after normalization") + normalized_payload = self._apply_default_reference(normalized_payload) + if normalize_streaming: + normalized_payload = self._normalize_streaming_mode(normalized_payload) + error = self.check_params(normalized_payload) + if error is not None: + raise ValueError(f"{error_prefix}{error}") + timeout_sec = normalized_payload.get("timeout_sec") + if timeout_sec in [None, ""]: + parsed_timeout = None + else: + parsed_timeout = float(timeout_sec) + aux_ref_audio_paths = normalized_payload.get("aux_ref_audio_paths") + if aux_ref_audio_paths in [None, "", []]: + normalized_aux_ref_audio_paths = None + else: + normalized_aux_ref_audio_paths = [str(item) for item in aux_ref_audio_paths] + return NormalizedEngineRequest( + request_id=str(normalized_payload["request_id"]), + text=str(normalized_payload["text"]), + text_lang=str(normalized_payload["text_lang"]), + ref_audio_path=str(normalized_payload["ref_audio_path"]), + prompt_lang=str(normalized_payload["prompt_lang"]), + prompt_text="" if normalized_payload.get("prompt_text") is None else str(normalized_payload.get("prompt_text")), + aux_ref_audio_paths=normalized_aux_ref_audio_paths, + top_k=int(normalized_payload["top_k"]), + top_p=float(normalized_payload["top_p"]), + temperature=float(normalized_payload["temperature"]), + repetition_penalty=float(normalized_payload["repetition_penalty"]), + early_stop_num=int(normalized_payload.get("early_stop_num", -1)), + ready_step=int(normalized_payload.get("ready_step", 0)), + text_split_method=str(normalized_payload["text_split_method"]), + batch_size=int(normalized_payload["batch_size"]), + batch_threshold=float(normalized_payload["batch_threshold"]), + split_bucket=bool(normalized_payload["split_bucket"]), + speed_factor=float(normalized_payload["speed_factor"]), + fragment_interval=float(normalized_payload["fragment_interval"]), + seed=int(normalized_payload["seed"]), + media_type=str(normalized_payload["media_type"]), + streaming_mode=normalized_payload["streaming_mode"], + return_fragment=bool(normalized_payload.get("return_fragment", False)), + fixed_length_chunk=bool(normalized_payload.get("fixed_length_chunk", False)), + response_streaming=bool(normalized_payload.get("response_streaming", False)), + parallel_infer=bool(normalized_payload["parallel_infer"]), + sample_steps=int(normalized_payload["sample_steps"]), + super_sampling=bool(normalized_payload["super_sampling"]), + overlap_length=int(normalized_payload["overlap_length"]), + min_chunk_length=int(normalized_payload["min_chunk_length"]), + timeout_sec=parsed_timeout, + ) + + @staticmethod + def _normalize_streaming_mode(req: dict) -> dict: + normalized = dict(req) + streaming_mode = normalized.get("streaming_mode", False) + return_fragment = normalized.get("return_fragment", False) + if streaming_mode is False: + normalized["streaming_mode"] = False + normalized["return_fragment"] = False + normalized["fixed_length_chunk"] = False + elif streaming_mode == 0: + normalized["streaming_mode"] = False + normalized["return_fragment"] = False + normalized["fixed_length_chunk"] = False + elif streaming_mode == 1 or streaming_mode is True: + normalized["streaming_mode"] = False + normalized["return_fragment"] = True + normalized["fixed_length_chunk"] = False + elif streaming_mode == 2: + normalized["streaming_mode"] = True + normalized["return_fragment"] = False + normalized["fixed_length_chunk"] = False + elif streaming_mode == 3: + normalized["streaming_mode"] = True + normalized["return_fragment"] = False + normalized["fixed_length_chunk"] = True + else: + raise ValueError("the value of streaming_mode must be 0, 1, 2, 3(int) or true/false(bool)") + normalized["response_streaming"] = bool(normalized["streaming_mode"] or normalized["return_fragment"] or return_fragment) + return normalized + + @staticmethod + def _is_aux_ref_enabled(aux_ref_audio_paths: List[str] | None) -> bool: + return aux_ref_audio_paths not in [None, [], ()] + + def _select_direct_backend(self, normalized: NormalizedEngineRequest) -> Tuple[str, str | None]: + if normalized.response_streaming: + if normalized.return_fragment or normalized.fixed_length_chunk: + return "legacy_direct_fragment", "fragment_streaming_mode" + return "legacy_direct_streaming", "streaming_mode" + if self._is_aux_ref_enabled(normalized.aux_ref_audio_paths): + return "legacy_direct_aux_ref", "aux_ref_audio_paths" + if normalized.super_sampling: + return "legacy_direct_super_sampling", "super_sampling" + if normalized.prompt_text in [None, ""]: + return "legacy_direct_missing_prompt", "missing_prompt_text" + return "scheduler_v1_direct", None + + def _iter_legacy_direct_tts_bytes( + self, + normalized: NormalizedEngineRequest, + *, + backend: str, + fallback_reason: str | None, + ) -> Generator[bytes, None, None]: + payload = normalized.to_payload() + media_type = normalized.media_type + request_id = normalized.request_id + request_start = time.perf_counter() + chunk_count = 0 + stream_total_bytes = 0 + first_chunk_ms: float | None = None + self._update_request_state( + request_id, + EngineStatus.ACTIVE_DECODE, + {"backend": backend, "backend_mode": backend, "fallback_reason": fallback_reason}, + ) + try: + with self.direct_tts_lock: + tts_generator = self.tts.run(payload) + first_chunk = True + current_media_type = media_type + for sr, chunk in tts_generator: + if first_chunk: + first_chunk_ms = max(0.0, (time.perf_counter() - request_start) * 1000.0) + self._update_request_state( + request_id, + EngineStatus.STREAMING, + { + "backend": backend, + "backend_mode": backend, + "fallback_reason": fallback_reason, + "sample_rate": int(sr), + }, + ) + if first_chunk and media_type == "wav": + header = wave_header_chunk(sample_rate=sr) + chunk_count += 1 + stream_total_bytes += len(header) + yield header + current_media_type = "raw" + first_chunk = False + elif first_chunk: + first_chunk = False + packed_chunk = pack_audio(BytesIO(), chunk, sr, current_media_type).getvalue() + chunk_count += 1 + stream_total_bytes += len(packed_chunk) + yield packed_chunk + except Exception as exc: + self._fail_request_state(request_id, str(exc)) + raise + self._complete_request_state( + request_id, + dict( + self._build_legacy_direct_profile( + backend=backend, + fallback_reason=fallback_reason, + request_start=request_start, + finished_at=time.perf_counter(), + audio_bytes=stream_total_bytes, + chunk_count=chunk_count, + stream_total_bytes=stream_total_bytes, + first_chunk_ms=first_chunk_ms, + ), + streaming_completed=True, + ), + ) + + def _should_use_scheduler_backend_for_direct(self, req: dict | NormalizedEngineRequest) -> bool: + if isinstance(req, NormalizedEngineRequest): + normalized = req + else: + normalized = self._normalize_engine_request( + req, + request_id=str(req.get("request_id") or f"direct_{uuid.uuid4().hex[:12]}"), + normalize_streaming=True, + ) + backend, _ = self._select_direct_backend(normalized) + return backend == "scheduler_v1_direct" + + def _segment_direct_text(self, normalized: dict | NormalizedEngineRequest) -> List[str]: + payload = normalized.to_payload() if isinstance(normalized, NormalizedEngineRequest) else normalized + return self.tts.text_preprocessor.pre_seg_text( + str(payload["text"]), + str(payload["text_lang"]), + str(payload.get("text_split_method", "cut5")), + ) + + def _build_segment_request( + self, + normalized: NormalizedEngineRequest, + *, + request_id: str, + text: str, + ) -> NormalizedEngineRequest: + payload = normalized.to_payload() + payload["request_id"] = request_id + payload["text"] = text + payload["streaming_mode"] = False + payload["return_fragment"] = False + payload["fixed_length_chunk"] = False + payload["response_streaming"] = False + return self._normalize_engine_request(payload, error_prefix="segment request 参数非法: ") + + async def _run_direct_tts_via_scheduler(self, normalized: NormalizedEngineRequest) -> DirectTTSExecution: + request_start = time.perf_counter() + request_id = normalized.request_id + media_type = normalized.media_type + segment_texts = self._segment_direct_text(normalized) + if not segment_texts: + raise ValueError("text preprocessing returned no valid segments") + self._update_request_state( + request_id, + EngineStatus.CPU_PREPARING, + {"backend": "scheduler_v1_direct", "backend_mode": "scheduler_v1_direct", "segment_count": len(segment_texts)}, + ) + segment_specs: List[SchedulerRequestSpec] = [] + for segment_index, segment_text in enumerate(segment_texts): + segment_request = self._build_segment_request( + normalized, + request_id=f"{request_id}_seg_{segment_index:03d}", + text=segment_text, + ) + segment_specs.append(self.build_scheduler_submit_spec(segment_request)) + + prepared_items = await asyncio.gather( + *[ + self.scheduler_worker.prepare_state_profiled_async(spec, time.perf_counter()) + for spec in segment_specs + ] + ) + prepare_profiles: List[Dict[str, Any]] = [] + jobs: List[SchedulerPendingJob] = [] + loop = asyncio.get_running_loop() + done_futures: List[asyncio.Future] = [] + for spec, (state, prepare_exec_started_at, prepare_exec_finished_at) in zip(segment_specs, prepared_items): + prepare_wall_ms = max(0.0, (prepare_exec_finished_at - prepare_exec_started_at) * 1000.0) + prepare_profile_total_ms = float(state.prepare_profile.get("wall_total_ms", prepare_wall_ms)) + prepare_profiles.append( + { + "request_id": spec.request_id, + "prepare_wall_ms": prepare_wall_ms, + "prepare_profile_total_ms": prepare_profile_total_ms, + "prepare_profile": dict(state.prepare_profile), + } + ) + done_future = loop.create_future() + done_futures.append(done_future) + jobs.append( + await self.scheduler_worker.submit_async( + state=state, + speed_factor=float(normalized.speed_factor), + sample_steps=int(normalized.sample_steps), + media_type=media_type, + prepare_wall_ms=prepare_wall_ms, + prepare_profile_total_ms=prepare_profile_total_ms, + done_loop=loop, + done_future=done_future, + engine_request_id=None, + timeout_sec=normalized.timeout_sec, + ) + ) + self._update_request_state( + request_id, + EngineStatus.READY_FOR_PREFILL, + { + "backend": "scheduler_v1_direct", + "backend_mode": "scheduler_v1_direct", + "segment_count": len(segment_specs), + "prepare_aggregate": self._aggregate_numeric_dicts( + [item["prepare_profile"] for item in prepare_profiles] + ), + }, + ) + self._update_request_state( + request_id, + EngineStatus.ACTIVE_DECODE, + {"backend": "scheduler_v1_direct", "backend_mode": "scheduler_v1_direct"}, + ) + timeout_sec = float(normalized.timeout_sec if normalized.timeout_sec is not None else 30.0) + await asyncio.wait_for(asyncio.gather(*done_futures), timeout=timeout_sec) + + sample_rate: int | None = None + audio_parts: List[np.ndarray] = [] + worker_profiles: List[Dict[str, Any]] = [] + fragment_interval = float(normalized.fragment_interval) + silence_chunk: Optional[np.ndarray] = None + for job in jobs: + if job.error is not None: + raise RuntimeError(job.error) + if job.audio_data is None or job.sample_rate is None or job.result is None: + raise RuntimeError(f"{job.request_id} finished without audio result") + if sample_rate is None: + sample_rate = int(job.sample_rate) + silence_samples = int(fragment_interval * float(sample_rate)) + if silence_samples > 0: + silence_chunk = np.zeros(silence_samples, dtype=np.int16) + elif int(job.sample_rate) != sample_rate: + raise RuntimeError("segment sample rate mismatch") + audio_parts.append(job.audio_data) + if silence_chunk is not None: + audio_parts.append(silence_chunk.copy()) + worker_profiles.append(dict(job.result)) + if sample_rate is None or not audio_parts: + raise RuntimeError("direct scheduler backend produced no audio") + self._update_request_state( + request_id, + EngineStatus.FINALIZING, + {"backend": "scheduler_v1_direct", "backend_mode": "scheduler_v1_direct"}, + ) + merged_audio = np.concatenate(audio_parts, axis=0) + pack_start = time.perf_counter() + audio_bytes = pack_audio(BytesIO(), merged_audio, sample_rate, media_type).getvalue() + pack_ms = max(0.0, (time.perf_counter() - pack_start) * 1000.0) + direct_profile = self._build_direct_scheduler_profile( + backend="scheduler_v1_direct", + request_start=request_start, + response_ready_at=time.perf_counter(), + audio_bytes=len(audio_bytes), + sample_rate=int(sample_rate), + segment_texts=segment_texts, + prepare_profiles=prepare_profiles, + worker_profiles=worker_profiles, + pack_ms=pack_ms, + response_overhead_ms=0.0, + ) + self._complete_request_state( + request_id, + dict(direct_profile, streaming_completed=False), + ) + return DirectTTSExecution( + media_type=media_type, + streaming=False, + audio_bytes=audio_bytes, + request_id=request_id, + ) + + def _run_legacy_direct_tts_blocking( + self, + normalized: NormalizedEngineRequest, + *, + backend: str, + fallback_reason: str | None, + ) -> DirectTTSExecution: + normalized_payload = normalized.to_payload() + request_id = normalized.request_id + media_type = normalized.media_type + request_start = time.perf_counter() + self._update_request_state( + request_id, + EngineStatus.ACTIVE_DECODE, + {"backend": backend, "backend_mode": backend, "fallback_reason": fallback_reason}, + ) + with self.direct_tts_lock: + tts_generator = self.tts.run(normalized_payload) + try: + sr, audio_data = next(tts_generator) + except Exception as exc: + self._fail_request_state(request_id, str(exc)) + raise + self._update_request_state( + request_id, + EngineStatus.FINALIZING, + {"backend": backend, "backend_mode": backend, "fallback_reason": fallback_reason}, + ) + pack_start = time.perf_counter() + packed_audio = pack_audio(BytesIO(), audio_data, sr, media_type).getvalue() + pack_ms = max(0.0, (time.perf_counter() - pack_start) * 1000.0) + self._complete_request_state( + request_id, + dict( + self._build_legacy_direct_profile( + backend=backend, + fallback_reason=fallback_reason, + request_start=request_start, + finished_at=time.perf_counter(), + sample_rate=int(sr), + audio_bytes=len(packed_audio), + pack_ms=pack_ms, + ), + streaming_completed=False, + ), + ) + return DirectTTSExecution( + media_type=media_type, + streaming=False, + audio_bytes=packed_audio, + request_id=request_id, + ) + + async def _run_direct_tts_via_legacy_backend( + self, + normalized: NormalizedEngineRequest, + *, + backend: str, + fallback_reason: str | None, + ) -> DirectTTSExecution: + if normalized.response_streaming: + return DirectTTSExecution( + media_type=normalized.media_type, + streaming=True, + audio_generator=self._iter_legacy_direct_tts_bytes( + normalized, + backend=backend, + fallback_reason=fallback_reason, + ), + request_id=normalized.request_id, + ) + return await asyncio.to_thread( + self._run_legacy_direct_tts_blocking, + normalized, + backend=backend, + fallback_reason=fallback_reason, + ) + + async def run_direct_tts_async(self, req: dict) -> DirectTTSExecution: + normalized = self._normalize_engine_request( + req, + request_id=str(req.get("request_id") or f"direct_{uuid.uuid4().hex[:12]}"), + normalize_streaming=True, + error_prefix="", + ) + request_id = normalized.request_id + media_type = normalized.media_type + backend, fallback_reason = self._select_direct_backend(normalized) + self._register_request_state( + request_id=request_id, + api_mode="tts", + backend=backend, + media_type=media_type, + response_streaming=bool(normalized.response_streaming), + deadline_ts=( + time.perf_counter() + float(normalized.timeout_sec) + if normalized.timeout_sec is not None + else None + ), + meta=self._build_request_meta(normalized.to_payload()), + ) + self._update_request_state( + request_id, + EngineStatus.VALIDATED, + { + "request_source": "direct_tts", + "selected_backend": backend, + "fallback_reason": fallback_reason, + }, + ) + if backend == "scheduler_v1_direct": + try: + return await self._run_direct_tts_via_scheduler(normalized) + except Exception as exc: + self._fail_request_state(request_id, str(exc)) + raise + return await self._run_direct_tts_via_legacy_backend( + normalized, + backend=backend, + fallback_reason=fallback_reason, + ) + + def run_direct_tts(self, req: dict) -> DirectTTSExecution: + normalized = self._normalize_engine_request( + req, + request_id=str(req.get("request_id") or f"direct_{uuid.uuid4().hex[:12]}"), + normalize_streaming=True, + error_prefix="", + ) + request_id = normalized.request_id + media_type = normalized.media_type + backend, fallback_reason = self._select_direct_backend(normalized) + if not self._has_active_request(request_id): + self._register_request_state( + request_id=request_id, + api_mode="tts", + backend=backend, + media_type=media_type, + response_streaming=bool(normalized.response_streaming), + meta=self._build_request_meta(normalized.to_payload()), + ) + self._update_request_state( + request_id, + EngineStatus.VALIDATED, + { + "request_source": "direct_tts", + "selected_backend": backend, + "fallback_reason": fallback_reason, + }, + ) + if backend != "scheduler_v1_direct": + if normalized.response_streaming: + return DirectTTSExecution( + media_type=media_type, + streaming=True, + audio_generator=self._iter_legacy_direct_tts_bytes( + normalized, + backend=backend, + fallback_reason=fallback_reason, + ), + request_id=request_id, + ) + return self._run_legacy_direct_tts_blocking( + normalized, + backend=backend, + fallback_reason=fallback_reason, + ) + normalized_payload = normalized.to_payload() + if normalized.response_streaming: + return DirectTTSExecution( + media_type=media_type, + streaming=True, + audio_generator=self._iter_legacy_direct_tts_bytes( + normalized, + backend="legacy_direct_sync_compat", + fallback_reason="sync_direct_compat", + ), + request_id=request_id, + ) + return self._run_legacy_direct_tts_blocking( + normalized, + backend="legacy_direct_sync_compat", + fallback_reason="sync_direct_compat", + ) + + def build_scheduler_request_specs(self, request_items: List[dict]) -> List[SchedulerRequestSpec]: + specs: List[SchedulerRequestSpec] = [] + for index, payload in enumerate(request_items): + normalized = self._normalize_engine_request( + payload, + request_id=str(payload.get("request_id") or f"req_{index:03d}"), + error_prefix=f"request[{index}] 参数非法: ", + ) + specs.append(normalized.to_scheduler_spec()) + return specs + + def build_scheduler_submit_spec(self, payload: dict | NormalizedEngineRequest) -> SchedulerRequestSpec: + normalized = self._normalize_engine_request( + payload, + request_id=( + payload.request_id + if isinstance(payload, NormalizedEngineRequest) + else str(payload.get("request_id") or f"job_{uuid.uuid4().hex[:12]}") + ), + ) + return normalized.to_scheduler_spec() + + @staticmethod + def summarize_scheduler_states(states: List[T2SRequestState]) -> List[dict]: + return [ + { + "request_id": state.request_id, + "ready_step": int(state.ready_step), + "ref_audio_path": str(state.ref_audio_path), + "prompt_semantic_len": int(state.prompt_semantic.shape[0]), + "all_phone_len": int(state.all_phones.shape[0]), + "bert_len": int(state.all_bert_features.shape[-1]), + "norm_text": state.norm_text, + } + for state in states + ] + + @staticmethod + def summarize_scheduler_finished(items: List[T2SFinishedItem]) -> List[dict]: + return [ + { + "request_id": item.request_id, + "semantic_len": int(item.semantic_tokens.shape[0]), + "finish_idx": int(item.finish_idx), + "finish_reason": item.finish_reason, + } + for item in items + ] + + async def run_scheduler_debug(self, request_items: List[dict], max_steps: int, seed: int) -> SchedulerDebugExecution: + request_start = time.perf_counter() + set_scheduler_seed(seed) + specs = self.build_scheduler_request_specs(request_items) + request_ids = [spec.request_id for spec in specs] + for spec in specs: + self._register_request_state( + request_id=spec.request_id, + api_mode="scheduler_debug", + backend="scheduler_debug", + media_type="wav", + response_streaming=False, + meta={ + "text_len": len(spec.text), + "prompt_text_len": len(spec.prompt_text), + "text_lang": spec.text_lang, + "prompt_lang": spec.prompt_lang, + "ref_audio_path": str(spec.ref_audio_path), + "ready_step": int(spec.ready_step), + }, + ) + self._update_request_state(spec.request_id, EngineStatus.VALIDATED, {"request_source": "scheduler_debug"}) + self._update_request_state(spec.request_id, EngineStatus.CPU_PREPARING, None) + prepare_started_at = time.perf_counter() + try: + states = await self.scheduler_worker.prepare_states_batch_async(specs) + except Exception as exc: + for request_id in request_ids: + self._fail_request_state(request_id, str(exc)) + raise + prepare_finished_at = time.perf_counter() + prepare_batch_wall_ms = max(0.0, (prepare_finished_at - prepare_started_at) * 1000.0) + for state in states: + self._update_request_state( + state.request_id, + EngineStatus.ACTIVE_DECODE, + { + "prepare_profile": dict(state.prepare_profile), + "norm_text": state.norm_text, + "norm_prompt_text": state.norm_prompt_text, + }, + ) + decode_started_at = time.perf_counter() + try: + finished = run_scheduler_continuous(self.tts.t2s_model.model, states, max_steps=int(max_steps)) + except Exception as exc: + for request_id in request_ids: + self._fail_request_state(request_id, str(exc)) + raise + decode_finished_at = time.perf_counter() + decode_batch_wall_ms = max(0.0, (decode_finished_at - decode_started_at) * 1000.0) + request_total_ms = max(0.0, (decode_finished_at - request_start) * 1000.0) + finished_map = {item.request_id: item for item in finished} + request_profiles: List[Dict[str, Any]] = [] + for state in states: + item = finished_map.get(state.request_id) + if item is None: + self._fail_request_state(state.request_id, "scheduler_debug finished without result") + continue + request_profile = self._build_scheduler_debug_request_profile( + state=state, + item=item, + batch_request_count=len(states), + prepare_batch_wall_ms=prepare_batch_wall_ms, + decode_batch_wall_ms=decode_batch_wall_ms, + batch_request_total_ms=request_total_ms, + ) + request_profiles.append( + { + "request_id": state.request_id, + "profile": dict(request_profile), + } + ) + self._complete_request_state( + state.request_id, + dict(request_profile), + ) + return SchedulerDebugExecution( + payload={ + "message": "success", + "request_count": len(states), + "max_steps": int(max_steps), + "batch_profile": self._build_scheduler_debug_batch_profile( + request_count=len(states), + max_steps=int(max_steps), + prepare_batch_wall_ms=prepare_batch_wall_ms, + decode_batch_wall_ms=decode_batch_wall_ms, + request_total_ms=request_total_ms, + finished_items=finished, + ), + "requests": self.summarize_scheduler_states(states), + "finished": self.summarize_scheduler_finished(finished), + "request_profiles": request_profiles, + "request_traces": self._collect_request_summaries(request_ids), + } + ) + + async def run_scheduler_submit(self, payload: dict) -> SchedulerSubmitExecution: + request_start = time.perf_counter() + prepare_start = request_start + normalized = self._normalize_engine_request( + payload, + request_id=str(payload.get("request_id") or f"job_{uuid.uuid4().hex[:12]}"), + ) + spec = self.build_scheduler_submit_spec(normalized) + deadline_ts = None + timeout_sec = normalized.timeout_sec + if timeout_sec is not None: + try: + deadline_ts = request_start + float(timeout_sec) + except Exception: + deadline_ts = None + self._register_request_state( + request_id=spec.request_id, + api_mode="scheduler_submit", + backend="scheduler_v1", + media_type=normalized.media_type, + response_streaming=False, + deadline_ts=deadline_ts, + meta=self._build_request_meta(normalized.to_payload()), + ) + self._update_request_state(spec.request_id, EngineStatus.VALIDATED, {"request_source": "scheduler_submit"}) + spec_ready_at = time.perf_counter() + prepare_spec_build_ms = max(0.0, (spec_ready_at - prepare_start) * 1000.0) + self._update_request_state(spec.request_id, EngineStatus.CPU_PREPARING, {"prepare_spec_build_ms": prepare_spec_build_ms}) + try: + state, prepare_exec_started_at, prepare_exec_finished_at = await self.scheduler_worker.prepare_state_profiled_async( + spec, + spec_ready_at, + ) + except Exception as exc: + self._fail_request_state(spec.request_id, str(exc)) + raise + prepare_wall_ms = max(0.0, (prepare_exec_finished_at - spec_ready_at) * 1000.0) + prepare_executor_queue_ms = max(0.0, (prepare_exec_started_at - spec_ready_at) * 1000.0) + prepare_executor_run_ms = max(0.0, (prepare_exec_finished_at - prepare_exec_started_at) * 1000.0) + prepare_profile = dict(state.prepare_profile) + prepare_profile_total_ms = float(prepare_profile.get("wall_total_ms", prepare_wall_ms)) + prepare_profile_wall_ms = float(prepare_profile.get("wall_total_ms", prepare_wall_ms)) + prepare_other_ms = max(0.0, prepare_wall_ms - prepare_spec_build_ms - prepare_executor_queue_ms - prepare_executor_run_ms) + self._update_request_state( + spec.request_id, + EngineStatus.READY_FOR_PREFILL, + { + "prepare_wall_ms": prepare_wall_ms, + "prepare_profile_total_ms": prepare_profile_total_ms, + "prepare_profile": prepare_profile, + }, + ) + api_after_prepare_start = time.perf_counter() + loop = asyncio.get_running_loop() + done_future = loop.create_future() + job = await self.scheduler_worker.submit_async( + state=state, + speed_factor=float(normalized.speed_factor), + sample_steps=int(normalized.sample_steps), + media_type=normalized.media_type, + prepare_wall_ms=prepare_wall_ms, + prepare_profile_total_ms=prepare_profile_total_ms, + done_loop=loop, + done_future=done_future, + engine_request_id=spec.request_id, + timeout_sec=normalized.timeout_sec, + ) + api_after_prepare_ms = max(0.0, (time.perf_counter() - api_after_prepare_start) * 1000.0) + try: + await asyncio.wait_for(done_future, timeout=float(normalized.timeout_sec if normalized.timeout_sec is not None else 30.0)) + except Exception as exc: + self._fail_request_state(spec.request_id, str(exc)) + raise + wait_return_at = time.perf_counter() + if job.error is not None: + raise RuntimeError(job.error) + if job.audio_data is None or job.sample_rate is None or job.result is None: + self._fail_request_state(spec.request_id, f"{job.request_id} finished without audio result") + raise RuntimeError(f"{job.request_id} finished without audio result") + pack_start = time.perf_counter() + audio_data = pack_audio(BytesIO(), job.audio_data, int(job.sample_rate), job.media_type).getvalue() + pack_end = time.perf_counter() + pack_ms = (pack_end - pack_start) * 1000.0 + api_wait_result_ms = 0.0 + if job.result_ready_time is not None: + api_wait_result_ms = max(0.0, (wait_return_at - job.result_ready_time) * 1000.0) + response_ready_at = time.perf_counter() + response_overhead_ms = max(0.0, (response_ready_at - pack_end) * 1000.0) + submit_profile = self._build_scheduler_submit_profile( + backend="scheduler_v1", + request_start=request_start, + response_ready_at=response_ready_at, + audio_bytes=len(audio_data), + sample_rate=int(job.sample_rate), + prepare_spec_build_ms=prepare_spec_build_ms, + prepare_wall_ms=prepare_wall_ms, + prepare_executor_queue_ms=prepare_executor_queue_ms, + prepare_executor_run_ms=prepare_executor_run_ms, + prepare_profile_total_ms=prepare_profile_total_ms, + prepare_profile_wall_ms=prepare_profile_wall_ms, + prepare_other_ms=prepare_other_ms, + api_after_prepare_ms=api_after_prepare_ms, + api_wait_result_ms=api_wait_result_ms, + pack_ms=pack_ms, + response_overhead_ms=response_overhead_ms, + worker_profile=dict(job.result or {}), + ) + headers = self._build_scheduler_submit_headers( + request_id=job.request_id, + media_type=job.media_type, + sample_rate=int(job.sample_rate), + profile=submit_profile, + ) + self._merge_request_state_profile( + spec.request_id, + dict(submit_profile, response_headers_emitted=True), + ) + return SchedulerSubmitExecution(audio_bytes=audio_data, media_type=f"audio/{job.media_type}", headers=headers) + + def get_scheduler_state(self) -> dict: + return self.scheduler_worker.snapshot() + + def get_runtime_state(self) -> dict: + model_state = self.model_registry.snapshot() + default_ref = self.reference_registry.get_default() + scheduler_state = self.get_scheduler_state() + request_registry = self._snapshot_request_registry() + return { + "message": "success", + "default_reference": { + "ref_audio_path": default_ref.ref_audio_path, + "updated_at": default_ref.updated_at, + }, + "model_registry": { + "generation": model_state.generation, + "t2s_generation": model_state.t2s_generation, + "vits_generation": model_state.vits_generation, + "t2s_weights_path": model_state.t2s_weights_path, + "vits_weights_path": model_state.vits_weights_path, + "updated_at": model_state.updated_at, + }, + "worker_state": scheduler_state, + "request_registry": request_registry, + "stage_summary": self._build_stage_summary(request_registry, scheduler_state), + } + + def _wait_for_safe_reload(self, timeout_sec: float = 300.0) -> None: + if not self.scheduler_worker.wait_until_idle(timeout_sec=timeout_sec): + raise TimeoutError("scheduler worker did not drain before model reload") + + def set_refer_audio(self, refer_audio_path: str | None) -> dict: + if refer_audio_path in [None, ""]: + state = self.reference_registry.clear() + return {"message": "success", "default_ref_audio_path": state.ref_audio_path} + if not os.path.exists(str(refer_audio_path)): + raise FileNotFoundError(f"{refer_audio_path} not exists") + with self.management_lock: + with self.direct_tts_lock: + self.tts.set_ref_audio(str(refer_audio_path)) + state = self.reference_registry.set_default(str(refer_audio_path)) + return {"message": "success", "default_ref_audio_path": state.ref_audio_path} + + def set_gpt_weights(self, weights_path: str) -> dict: + if weights_path in ["", None]: + raise ValueError("gpt weight path is required") + with self.management_lock: + self._wait_for_safe_reload() + with self.direct_tts_lock: + self.tts.init_t2s_weights(weights_path) + self.tts.refresh_runtime_components() + state = self.model_registry.mark_t2s_reload(str(weights_path)) + return {"message": "success", "t2s_generation": state.t2s_generation, "generation": state.generation} + + def set_sovits_weights(self, weights_path: str) -> dict: + if weights_path in ["", None]: + raise ValueError("sovits weight path is required") + with self.management_lock: + self._wait_for_safe_reload() + with self.direct_tts_lock: + self.tts.init_vits_weights(weights_path) + self.tts.refresh_runtime_components() + state = self.model_registry.mark_vits_reload(str(weights_path)) + return {"message": "success", "vits_generation": state.vits_generation, "generation": state.generation} + + def handle_control(self, command: str) -> None: + if command == "restart": + if self.control_callbacks.restart is None: + os.execl(sys.executable, sys.executable, *sys.argv) + self.control_callbacks.restart() + return + if command == "exit": + if self.control_callbacks.exit is None: + os.kill(os.getpid(), signal.SIGTERM) + return + self.control_callbacks.exit() + return + raise ValueError(f"unsupported command: {command}") diff --git a/GPT_SoVITS/module/models.py b/GPT_SoVITS/module/models.py index 348ddb3f..c6d147cf 100644 --- a/GPT_SoVITS/module/models.py +++ b/GPT_SoVITS/module/models.py @@ -2,6 +2,7 @@ import warnings warnings.filterwarnings("ignore") import math +from typing import List import torch from torch import nn @@ -1038,6 +1039,67 @@ class SynthesizerTrn(nn.Module): o = self.dec((z * y_mask)[:, :, :], g=ge) return o + @torch.no_grad() + def decode_batched_request_local( + self, + codes: torch.Tensor, + code_lengths: torch.Tensor, + text: torch.Tensor, + text_lengths: torch.Tensor, + refer_list: List[torch.Tensor], + noise_scale: float = 0.5, + speed: float = 1, + sv_emb: torch.Tensor | None = None, + ): + batch_size = int(codes.size(1)) + if batch_size <= 0: + raise ValueError("decode_batched_request_local 收到空 batch") + if len(refer_list) != batch_size: + raise ValueError("refer_list 数量与 batch size 不一致") + + refer_lengths = torch.LongTensor([int(item.size(2)) for item in refer_list]).to(codes.device) + max_refer_len = int(refer_lengths.max().item()) + refer_batch = torch.zeros( + (batch_size, int(refer_list[0].size(1)), max_refer_len), + dtype=refer_list[0].dtype, + device=codes.device, + ) + for batch_index, refer in enumerate(refer_list): + refer_batch[batch_index, :, : int(refer.size(2))] = refer.squeeze(0) + refer_mask = torch.unsqueeze(commons.sequence_mask(refer_lengths, max_refer_len), 1).to(refer_batch.dtype) + if self.version == "v1": + ge = self.ref_enc(refer_batch * refer_mask, refer_mask) + else: + ge = self.ref_enc(refer_batch[:, :704] * refer_mask, refer_mask) + if self.is_v2pro: + if sv_emb is None: + raise ValueError("v2Pro batched request-local synthesis 缺少 sv_emb") + ge = ge + self.sv_emb(sv_emb).unsqueeze(-1) + ge = self.prelu(ge) + + quantized = self.quantizer.decode(codes) + if self.semantic_frame_rate == "25hz": + quantized = F.interpolate(quantized, scale_factor=2, mode="nearest") + y_lengths = code_lengths.to(device=codes.device, dtype=torch.long) * 2 + text_lengths = text_lengths.to(device=text.device, dtype=torch.long) + x, m_p, logs_p, y_mask, _, _ = self.enc_p( + quantized, + y_lengths, + text, + text_lengths, + self.ge_to512(ge.transpose(2, 1)).transpose(2, 1) if self.is_v2pro else ge, + speed, + ) + z_p = m_p + torch.randn_like(m_p) * torch.exp(logs_p) * noise_scale + z = self.flow(z_p, y_mask, g=ge, reverse=True) + audio = self.dec((z * y_mask)[:, :, :], g=ge) + upsample_factor = 1 + for up_layer in self.dec.ups: + stride = up_layer.stride[0] if isinstance(up_layer.stride, tuple) else int(up_layer.stride) + upsample_factor *= int(stride) + audio_lengths = y_mask.squeeze(1).sum(dim=1).to(dtype=torch.long) * int(upsample_factor) + return audio, audio_lengths + @torch.no_grad() def decode_streaming(self, codes, text, refer, noise_scale=0.5, speed=1, sv_emb=None, result_length:int=None, overlap_frames:torch.Tensor=None, padding_length:int=None): diff --git a/GPT_SoVITS/text/chinese2.py b/GPT_SoVITS/text/chinese2.py index dcce0d96..acfebfe2 100644 --- a/GPT_SoVITS/text/chinese2.py +++ b/GPT_SoVITS/text/chinese2.py @@ -180,10 +180,15 @@ def _merge_erhua(initials: list[str], finals: list[str], word: str, pos: str) -> def _g2p(segments): phones_list = [] word2ph = [] - for seg in segments: + g2pw_batch_results = [] + g2pw_batch_cursor = 0 + processed_segments = [re.sub("[a-zA-Z]+", "", seg) for seg in segments] + if is_g2pw: + batch_inputs = [seg for seg in processed_segments if seg] + g2pw_batch_results = g2pw._g2pw(batch_inputs) if batch_inputs else [] + + for seg in processed_segments: pinyins = [] - # Replace all English words in the sentence - seg = re.sub("[a-zA-Z]+", "", seg) seg_cut = psg.lcut(seg) seg_cut = tone_modifier.pre_merge_for_modify(seg_cut) initials = [] @@ -204,8 +209,10 @@ def _g2p(segments): finals = sum(finals, []) print("pypinyin结果", initials, finals) else: - # g2pw采用整句推理 - pinyins = g2pw.lazy_pinyin(seg, neutral_tone_with_five=True, style=Style.TONE3) + # g2pw采用整句推理（批量推理，逐句取结果） + if seg: + pinyins = g2pw_batch_results[g2pw_batch_cursor] + g2pw_batch_cursor += 1 pre_word_length = 0 for word, pos in seg_cut: diff --git a/GPT_SoVITS/text/g2pw/dataset.py b/GPT_SoVITS/text/g2pw/dataset.py index ff09cbc2..e464c29a 100644 --- a/GPT_SoVITS/text/g2pw/dataset.py +++ b/GPT_SoVITS/text/g2pw/dataset.py @@ -18,6 +18,7 @@ Credits from typing import Dict from typing import List +from typing import Optional from typing import Tuple import numpy as np @@ -37,6 +38,8 @@ def prepare_onnx_input( use_mask: bool = False, window_size: int = None, max_len: int = 512, + char2id: Optional[Dict[str, int]] = None, + char_phoneme_masks: Optional[Dict[str, List[int]]] = None, ) -> Dict[str, np.array]: if window_size is not None: truncated_texts, truncated_query_ids = _truncate_texts( @@ -48,33 +51,88 @@ def prepare_onnx_input( phoneme_masks = [] char_ids = [] position_ids = [] + tokenized_cache = {} + + if char2id is None: + char2id = {char: idx for idx, char in enumerate(chars)} + if use_mask: + if char_phoneme_masks is None: + char_phoneme_masks = { + char: [1 if i in char2phonemes[char] else 0 for i in range(len(labels))] + for char in char2phonemes + } + else: + full_phoneme_mask = [1] * len(labels) for idx in range(len(texts)): text = (truncated_texts if window_size else texts)[idx].lower() query_id = (truncated_query_ids if window_size else query_ids)[idx] - try: - tokens, text2token, token2text = tokenize_and_map(tokenizer=tokenizer, text=text) - except Exception: - print(f'warning: text "{text}" is invalid') - return {} + cached = tokenized_cache.get(text) + if cached is None: + try: + tokens, text2token, token2text = tokenize_and_map(tokenizer=tokenizer, text=text) + except Exception: + print(f'warning: text "{text}" is invalid') + return {} - text, query_id, tokens, text2token, token2text = _truncate( - max_len=max_len, text=text, query_id=query_id, tokens=tokens, text2token=text2token, token2text=token2text - ) + if len(tokens) <= max_len - 2: + processed_tokens = ["[CLS]"] + tokens + ["[SEP]"] + shared_input_id = list(np.array(tokenizer.convert_tokens_to_ids(processed_tokens))) + shared_token_type_id = list(np.zeros((len(processed_tokens),), dtype=int)) + shared_attention_mask = list(np.ones((len(processed_tokens),), dtype=int)) + cached = { + "is_short": True, + "tokens": tokens, + "text2token": text2token, + "token2text": token2text, + "input_id": shared_input_id, + "token_type_id": shared_token_type_id, + "attention_mask": shared_attention_mask, + } + else: + cached = { + "is_short": False, + "tokens": tokens, + "text2token": text2token, + "token2text": token2text, + } + tokenized_cache[text] = cached - processed_tokens = ["[CLS]"] + tokens + ["[SEP]"] + if cached["is_short"]: + text_for_query = text + query_id_for_query = query_id + text2token_for_query = cached["text2token"] + input_id = cached["input_id"] + token_type_id = cached["token_type_id"] + attention_mask = cached["attention_mask"] + else: + ( + text_for_query, + query_id_for_query, + tokens_for_query, + text2token_for_query, + _token2text_for_query, + ) = _truncate( + max_len=max_len, + text=text, + query_id=query_id, + tokens=cached["tokens"], + text2token=cached["text2token"], + token2text=cached["token2text"], + ) + processed_tokens = ["[CLS]"] + tokens_for_query + ["[SEP]"] + input_id = list(np.array(tokenizer.convert_tokens_to_ids(processed_tokens))) + token_type_id = list(np.zeros((len(processed_tokens),), dtype=int)) + attention_mask = list(np.ones((len(processed_tokens),), dtype=int)) - input_id = list(np.array(tokenizer.convert_tokens_to_ids(processed_tokens))) - token_type_id = list(np.zeros((len(processed_tokens),), dtype=int)) - attention_mask = list(np.ones((len(processed_tokens),), dtype=int)) - - query_char = text[query_id] - phoneme_mask = ( - [1 if i in char2phonemes[query_char] else 0 for i in range(len(labels))] if use_mask else [1] * len(labels) - ) - char_id = chars.index(query_char) - position_id = text2token[query_id] + 1 # [CLS] token locate at first place + query_char = text_for_query[query_id_for_query] + if use_mask: + phoneme_mask = char_phoneme_masks[query_char] + else: + phoneme_mask = full_phoneme_mask + char_id = char2id[query_char] + position_id = text2token_for_query[query_id_for_query] + 1 # [CLS] token locate at first place input_ids.append(input_id) token_type_ids.append(token_type_id) @@ -83,10 +141,15 @@ def prepare_onnx_input( char_ids.append(char_id) position_ids.append(position_id) + max_token_length = max(len(seq) for seq in input_ids) + + def _pad_sequences(sequences, pad_value=0): + return [seq + [pad_value] * (max_token_length - len(seq)) for seq in sequences] + outputs = { - "input_ids": np.array(input_ids).astype(np.int64), - "token_type_ids": np.array(token_type_ids).astype(np.int64), - "attention_masks": np.array(attention_masks).astype(np.int64), + "input_ids": np.array(_pad_sequences(input_ids, pad_value=0)).astype(np.int64), + "token_type_ids": np.array(_pad_sequences(token_type_ids, pad_value=0)).astype(np.int64), + "attention_masks": np.array(_pad_sequences(attention_masks, pad_value=0)).astype(np.int64), "phoneme_masks": np.array(phoneme_masks).astype(np.float32), "char_ids": np.array(char_ids).astype(np.int64), "position_ids": np.array(position_ids).astype(np.int64), diff --git a/GPT_SoVITS/text/g2pw/onnx_api.py b/GPT_SoVITS/text/g2pw/onnx_api.py index 1d5e4231..3c2b0169 100644 --- a/GPT_SoVITS/text/g2pw/onnx_api.py +++ b/GPT_SoVITS/text/g2pw/onnx_api.py @@ -10,7 +10,6 @@ from typing import Any, Dict, List, Tuple import numpy as np import onnxruntime import requests -import torch from opencc import OpenCC from pypinyin import Style, pinyin from transformers.models.auto.tokenization_auto import AutoTokenizer @@ -22,9 +21,8 @@ from .utils import load_config onnxruntime.set_default_logger_severity(3) try: onnxruntime.preload_dlls() -except: +except Exception: pass - # traceback.print_exc() warnings.filterwarnings("ignore") model_version = "1.1" @@ -55,6 +53,24 @@ def predict(session, onnx_input: Dict[str, Any], labels: List[str]) -> Tuple[Lis return all_preds, all_confidences +def _load_json_from_candidates(filename: str, candidate_dirs: List[str]) -> Dict[str, Any]: + for candidate_dir in candidate_dirs: + if not candidate_dir: + continue + json_path = os.path.join(candidate_dir, filename) + if os.path.exists(json_path): + with open(json_path, "r", encoding="utf-8") as fr: + return json.load(fr) + raise FileNotFoundError(f"Cannot locate {filename} in candidate dirs: {candidate_dirs}") + + +def _find_first_existing_file(*paths: str) -> str: + for path in paths: + if path and os.path.exists(path): + return path + raise FileNotFoundError(f"Files not found: {paths}") + + def download_and_decompress(model_dir: str = "G2PWModel/"): if not os.path.exists(model_dir): parent_directory = os.path.dirname(model_dir) @@ -62,7 +78,7 @@ def download_and_decompress(model_dir: str = "G2PWModel/"): extract_dir = os.path.join(parent_directory, "G2PWModel_1.1") extract_dir_new = os.path.join(parent_directory, "G2PWModel") print("Downloading g2pw model...") - modelscope_url = "https://www.modelscope.cn/models/kamiorinn/g2pw/resolve/master/G2PWModel_1.1.zip" # "https://paddlespeech.cdn.bcebos.com/Parakeet/released_models/g2p/G2PWModel_1.1.zip" + modelscope_url = "https://www.modelscope.cn/models/kamiorinn/g2pw/resolve/master/G2PWModel_1.1.zip" with requests.get(modelscope_url, stream=True) as r: r.raise_for_status() with open(zip_dir, "wb") as f: @@ -79,7 +95,7 @@ def download_and_decompress(model_dir: str = "G2PWModel/"): return model_dir -class G2PWOnnxConverter: +class _G2PWBaseOnnxConverter: def __init__( self, model_dir: str = "G2PWModel/", @@ -87,33 +103,16 @@ class G2PWOnnxConverter: model_source: str = None, enable_non_tradional_chinese: bool = False, ): - uncompress_path = download_and_decompress(model_dir) - - sess_options = onnxruntime.SessionOptions() - sess_options.graph_optimization_level = onnxruntime.GraphOptimizationLevel.ORT_ENABLE_ALL - sess_options.execution_mode = onnxruntime.ExecutionMode.ORT_SEQUENTIAL - sess_options.intra_op_num_threads = 2 if torch.cuda.is_available() else 0 - if "CUDAExecutionProvider" in onnxruntime.get_available_providers(): - self.session_g2pW = onnxruntime.InferenceSession( - os.path.join(uncompress_path, "g2pW.onnx"), - sess_options=sess_options, - providers=["CUDAExecutionProvider", "CPUExecutionProvider"], - ) - else: - self.session_g2pW = onnxruntime.InferenceSession( - os.path.join(uncompress_path, "g2pW.onnx"), - sess_options=sess_options, - providers=["CPUExecutionProvider"], - ) - self.config = load_config(config_path=os.path.join(uncompress_path, "config.py"), use_default=True) + self.model_dir = download_and_decompress(model_dir) + self.config = load_config(config_path=os.path.join(self.model_dir, "config.py"), use_default=True) self.model_source = model_source if model_source else self.config.model_source self.enable_opencc = enable_non_tradional_chinese - self.tokenizer = AutoTokenizer.from_pretrained(self.model_source) - polyphonic_chars_path = os.path.join(uncompress_path, "POLYPHONIC_CHARS.txt") - monophonic_chars_path = os.path.join(uncompress_path, "MONOPHONIC_CHARS.txt") + polyphonic_chars_path = os.path.join(self.model_dir, "POLYPHONIC_CHARS.txt") + monophonic_chars_path = os.path.join(self.model_dir, "MONOPHONIC_CHARS.txt") + self.polyphonic_chars = [ line.split("\t") for line in open(polyphonic_chars_path, encoding="utf-8").read().strip().split("\n") ] @@ -149,31 +148,47 @@ class G2PWOnnxConverter: ) self.chars = sorted(list(self.char2phonemes.keys())) + self.char2id = {char: idx for idx, char in enumerate(self.chars)} + self.char_phoneme_masks = ( + { + char: [1 if i in self.char2phonemes[char] else 0 for i in range(len(self.labels))] + for char in self.char2phonemes + } + if self.config.use_mask + else None + ) self.polyphonic_chars_new = set(self.chars) for char in self.non_polyphonic: - if char in self.polyphonic_chars_new: - self.polyphonic_chars_new.remove(char) + self.polyphonic_chars_new.discard(char) self.monophonic_chars_dict = {char: phoneme for char, phoneme in self.monophonic_chars} for char in self.non_monophonic: - if char in self.monophonic_chars_dict: - self.monophonic_chars_dict.pop(char) + self.monophonic_chars_dict.pop(char, None) - self.pos_tags = ["UNK", "A", "C", "D", "I", "N", "P", "T", "V", "DE", "SHI"] + default_asset_dir = os.path.normpath(os.path.join(os.path.dirname(__file__), "..", "G2PWModel")) + candidate_asset_dirs = [self.model_dir, default_asset_dir] + self.bopomofo_convert_dict = _load_json_from_candidates( + "bopomofo_to_pinyin_wo_tune_dict.json", candidate_asset_dirs + ) + self.char_bopomofo_dict = _load_json_from_candidates("char_bopomofo_dict.json", candidate_asset_dirs) - with open(os.path.join(uncompress_path, "bopomofo_to_pinyin_wo_tune_dict.json"), "r", encoding="utf-8") as fr: - self.bopomofo_convert_dict = json.load(fr) self.style_convert_func = { "bopomofo": lambda x: x, "pinyin": self._convert_bopomofo_to_pinyin, }[style] - with open(os.path.join(uncompress_path, "char_bopomofo_dict.json"), "r", encoding="utf-8") as fr: - self.char_bopomofo_dict = json.load(fr) - if self.enable_opencc: self.cc = OpenCC("s2tw") + self.enable_sentence_dedup = os.getenv("g2pw_sentence_dedup", "true").strip().lower() in { + "1", + "true", + "yes", + "y", + "on", + } + # 聚焦到多音字附近上下文，默认左右各16字；设为0表示关闭裁剪（整句）。 + self.polyphonic_context_chars = max(0, int(os.getenv("g2pw_polyphonic_context_chars", "16"))) def _convert_bopomofo_to_pinyin(self, bopomofo: str) -> str: tone = bopomofo[-1] @@ -181,9 +196,8 @@ class G2PWOnnxConverter: component = self.bopomofo_convert_dict.get(bopomofo[:-1]) if component: return component + tone - else: - print(f'Warning: "{bopomofo}" cannot convert to pinyin') - return None + print(f'Warning: "{bopomofo}" cannot convert to pinyin') + return None def __call__(self, sentences: List[str]) -> List[List[str]]: if isinstance(sentences, str): @@ -197,51 +211,147 @@ class G2PWOnnxConverter: translated_sentences.append(translated_sent) sentences = translated_sentences - texts, query_ids, sent_ids, partial_results = self._prepare_data(sentences=sentences) + texts, model_query_ids, result_query_ids, sent_ids, partial_results = self._prepare_data(sentences=sentences) if len(texts) == 0: - # sentences no polyphonic words return partial_results - onnx_input = prepare_onnx_input( + model_input = prepare_onnx_input( tokenizer=self.tokenizer, labels=self.labels, char2phonemes=self.char2phonemes, chars=self.chars, texts=texts, - query_ids=query_ids, + query_ids=model_query_ids, use_mask=self.config.use_mask, window_size=None, + char2id=self.char2id, + char_phoneme_masks=self.char_phoneme_masks, ) - preds, confidences = predict(session=self.session_g2pW, onnx_input=onnx_input, labels=self.labels) + if not model_input: + return partial_results + + if self.enable_sentence_dedup: + preds, _confidences = self._predict_with_sentence_dedup(model_input=model_input, texts=texts) + else: + preds, _confidences = self._predict(model_input=model_input) + if self.config.use_char_phoneme: preds = [pred.split(" ")[1] for pred in preds] results = partial_results - for sent_id, query_id, pred in zip(sent_ids, query_ids, preds): + for sent_id, query_id, pred in zip(sent_ids, result_query_ids, preds): results[sent_id][query_id] = self.style_convert_func(pred) return results - def _prepare_data(self, sentences: List[str]) -> Tuple[List[str], List[int], List[int], List[List[str]]]: - texts, query_ids, sent_ids, partial_results = [], [], [], [] + def _prepare_data( + self, sentences: List[str] + ) -> Tuple[List[str], List[int], List[int], List[int], List[List[str]]]: + texts, model_query_ids, result_query_ids, sent_ids, partial_results = [], [], [], [], [] for sent_id, sent in enumerate(sentences): - # pypinyin works well for Simplified Chinese than Traditional Chinese sent_s = tranditional_to_simplified(sent) pypinyin_result = pinyin(sent_s, neutral_tone_with_five=True, style=Style.TONE3) partial_result = [None] * len(sent) + polyphonic_indices: List[int] = [] for i, char in enumerate(sent): if char in self.polyphonic_chars_new: - texts.append(sent) - query_ids.append(i) - sent_ids.append(sent_id) + polyphonic_indices.append(i) elif char in self.monophonic_chars_dict: partial_result[i] = self.style_convert_func(self.monophonic_chars_dict[char]) elif char in self.char_bopomofo_dict: partial_result[i] = pypinyin_result[i][0] - # partial_result[i] = self.style_convert_func(self.char_bopomofo_dict[char][0]) else: partial_result[i] = pypinyin_result[i][0] + if polyphonic_indices: + if self.polyphonic_context_chars > 0: + left = max(0, polyphonic_indices[0] - self.polyphonic_context_chars) + right = min(len(sent), polyphonic_indices[-1] + self.polyphonic_context_chars + 1) + sent_for_predict = sent[left:right] + query_offset = left + else: + sent_for_predict = sent + query_offset = 0 + + for index in polyphonic_indices: + texts.append(sent_for_predict) + model_query_ids.append(index - query_offset) + result_query_ids.append(index) + sent_ids.append(sent_id) + partial_results.append(partial_result) - return texts, query_ids, sent_ids, partial_results + return texts, model_query_ids, result_query_ids, sent_ids, partial_results + + def _predict(self, model_input: Dict[str, Any]) -> Tuple[List[str], List[float]]: + raise NotImplementedError + + def _predict_with_sentence_dedup( + self, model_input: Dict[str, Any], texts: List[str] + ) -> Tuple[List[str], List[float]]: + if len(texts) <= 1: + return self._predict(model_input=model_input) + + grouped_indices: Dict[str, List[int]] = {} + for idx, text in enumerate(texts): + grouped_indices.setdefault(text, []).append(idx) + + if all(len(indices) == 1 for indices in grouped_indices.values()): + return self._predict(model_input=model_input) + + preds: List[str] = [""] * len(texts) + confidences: List[float] = [0.0] * len(texts) + for indices in grouped_indices.values(): + group_input = {name: value[indices] for name, value in model_input.items()} + if len(indices) > 1: + for name in ("input_ids", "token_type_ids", "attention_masks"): + group_input[name] = group_input[name][:1] + + group_preds, group_confidences = self._predict(model_input=group_input) + for output_idx, pred, confidence in zip(indices, group_preds, group_confidences): + preds[output_idx] = pred + confidences[output_idx] = confidence + + return preds, confidences + + +class G2PWOnnxConverter(_G2PWBaseOnnxConverter): + def __init__( + self, + model_dir: str = "G2PWModel/", + style: str = "bopomofo", + model_source: str = None, + enable_non_tradional_chinese: bool = False, + ): + super().__init__( + model_dir=model_dir, + style=style, + model_source=model_source, + enable_non_tradional_chinese=enable_non_tradional_chinese, + ) + + sess_options = onnxruntime.SessionOptions() + sess_options.graph_optimization_level = onnxruntime.GraphOptimizationLevel.ORT_ENABLE_ALL + sess_options.execution_mode = onnxruntime.ExecutionMode.ORT_SEQUENTIAL + sess_options.intra_op_num_threads = 2 + + onnx_path = _find_first_existing_file( + os.path.join(self.model_dir, "g2pW.onnx"), + os.path.join(self.model_dir, "g2pw.onnx"), + ) + + if "CUDAExecutionProvider" in onnxruntime.get_available_providers(): + self.session_g2pw = onnxruntime.InferenceSession( + onnx_path, + sess_options=sess_options, + providers=["CUDAExecutionProvider", "CPUExecutionProvider"], + ) + else: + self.session_g2pw = onnxruntime.InferenceSession( + onnx_path, + sess_options=sess_options, + providers=["CPUExecutionProvider"], + ) + + def _predict(self, model_input: Dict[str, Any]) -> Tuple[List[str], List[float]]: + return predict(session=self.session_g2pw, onnx_input=model_input, labels=self.labels) diff --git a/api_v2.py b/api_v2.py index 21511db3..35b70c8e 100644 --- a/api_v2.py +++ b/api_v2.py @@ -104,27 +104,22 @@ RESP: import os import sys import traceback -from typing import Generator, Union +from typing import Union now_dir = os.getcwd() sys.path.append(now_dir) sys.path.append("%s/GPT_SoVITS" % (now_dir)) import argparse -import subprocess -import wave import signal -import numpy as np -import soundfile as sf from fastapi import FastAPI, Response from fastapi.responses import StreamingResponse, JSONResponse import uvicorn -from io import BytesIO from tools.i18n.i18n import I18nAuto from GPT_SoVITS.TTS_infer_pack.TTS import TTS, TTS_Config from GPT_SoVITS.TTS_infer_pack.text_segmentation_method import get_method_names as get_cut_method_names +from GPT_SoVITS.TTS_infer_pack.unified_engine import RuntimeControlCallbacks, UnifiedTTSEngine from pydantic import BaseModel -import threading # print(sys.path) i18n = I18nAuto() @@ -147,6 +142,14 @@ if config_path in [None, ""]: tts_config = TTS_Config(config_path) print(tts_config) tts_pipeline = TTS(tts_config) +tts_engine = UnifiedTTSEngine( + tts_pipeline, + cut_method_names=cut_method_names, + control_callbacks=RuntimeControlCallbacks( + restart=lambda: os.execl(sys.executable, sys.executable, *argv), + exit=lambda: os.kill(os.getpid(), signal.SIGTERM), + ), +) APP = FastAPI() @@ -178,168 +181,8 @@ class TTS_Request(BaseModel): min_chunk_length: int = 16 -def pack_ogg(io_buffer: BytesIO, data: np.ndarray, rate: int): - # Author: AkagawaTsurunaki - # Issue: - # Stack overflow probabilistically occurs - # when the function `sf_writef_short` of `libsndfile_64bit.dll` is called - # using the Python library `soundfile` - # Note: - # This is an issue related to `libsndfile`, not this project itself. - # It happens when you generate a large audio tensor (about 499804 frames in my PC) - # and try to convert it to an ogg file. - # Related: - # https://github.com/RVC-Boss/GPT-SoVITS/issues/1199 - # https://github.com/libsndfile/libsndfile/issues/1023 - # https://github.com/bastibe/python-soundfile/issues/396 - # Suggestion: - # Or split the whole audio data into smaller audio segment to avoid stack overflow? - - def handle_pack_ogg(): - with sf.SoundFile(io_buffer, mode="w", samplerate=rate, channels=1, format="ogg") as audio_file: - audio_file.write(data) - - - - # See: https://docs.python.org/3/library/threading.html - # The stack size of this thread is at least 32768 - # If stack overflow error still occurs, just modify the `stack_size`. - # stack_size = n * 4096, where n should be a positive integer. - # Here we chose n = 4096. - stack_size = 4096 * 4096 - try: - threading.stack_size(stack_size) - pack_ogg_thread = threading.Thread(target=handle_pack_ogg) - pack_ogg_thread.start() - pack_ogg_thread.join() - except RuntimeError as e: - # If changing the thread stack size is unsupported, a RuntimeError is raised. - print("RuntimeError: {}".format(e)) - print("Changing the thread stack size is unsupported.") - except ValueError as e: - # If the specified stack size is invalid, a ValueError is raised and the stack size is unmodified. - print("ValueError: {}".format(e)) - print("The specified stack size is invalid.") - - return io_buffer - - -def pack_raw(io_buffer: BytesIO, data: np.ndarray, rate: int): - io_buffer.write(data.tobytes()) - return io_buffer - - -def pack_wav(io_buffer: BytesIO, data: np.ndarray, rate: int): - io_buffer = BytesIO() - sf.write(io_buffer, data, rate, format="wav") - return io_buffer - - -def pack_aac(io_buffer: BytesIO, data: np.ndarray, rate: int): - process = subprocess.Popen( - [ - "ffmpeg", - "-f", - "s16le", # 输入16位有符号小端整数PCM - "-ar", - str(rate), # 设置采样率 - "-ac", - "1", # 单声道 - "-i", - "pipe:0", # 从管道读取输入 - "-c:a", - "aac", # 音频编码器为AAC - "-b:a", - "192k", # 比特率 - "-vn", # 不包含视频 - "-f", - "adts", # 输出AAC数据流格式 - "pipe:1", # 将输出写入管道 - ], - stdin=subprocess.PIPE, - stdout=subprocess.PIPE, - stderr=subprocess.PIPE, - ) - out, _ = process.communicate(input=data.tobytes()) - io_buffer.write(out) - return io_buffer - - -def pack_audio(io_buffer: BytesIO, data: np.ndarray, rate: int, media_type: str): - if media_type == "ogg": - io_buffer = pack_ogg(io_buffer, data, rate) - elif media_type == "aac": - io_buffer = pack_aac(io_buffer, data, rate) - elif media_type == "wav": - io_buffer = pack_wav(io_buffer, data, rate) - else: - io_buffer = pack_raw(io_buffer, data, rate) - io_buffer.seek(0) - return io_buffer - - -# from https://huggingface.co/spaces/coqui/voice-chat-with-mistral/blob/main/app.py -def wave_header_chunk(frame_input=b"", channels=1, sample_width=2, sample_rate=32000): - # This will create a wave header then append the frame input - # It should be first on a streaming wav file - # Other frames better should not have it (else you will hear some artifacts each chunk start) - wav_buf = BytesIO() - with wave.open(wav_buf, "wb") as vfout: - vfout.setnchannels(channels) - vfout.setsampwidth(sample_width) - vfout.setframerate(sample_rate) - vfout.writeframes(frame_input) - - wav_buf.seek(0) - return wav_buf.read() - - -def handle_control(command: str): - if command == "restart": - os.execl(sys.executable, sys.executable, *argv) - elif command == "exit": - os.kill(os.getpid(), signal.SIGTERM) - exit(0) - - -def check_params(req: dict): - text: str = req.get("text", "") - text_lang: str = req.get("text_lang", "") - ref_audio_path: str = req.get("ref_audio_path", "") - streaming_mode: bool = req.get("streaming_mode", False) - media_type: str = req.get("media_type", "wav") - prompt_lang: str = req.get("prompt_lang", "") - text_split_method: str = req.get("text_split_method", "cut5") - - if ref_audio_path in [None, ""]: - return JSONResponse(status_code=400, content={"message": "ref_audio_path is required"}) - if text in [None, ""]: - return JSONResponse(status_code=400, content={"message": "text is required"}) - if text_lang in [None, ""]: - return JSONResponse(status_code=400, content={"message": "text_lang is required"}) - elif text_lang.lower() not in tts_config.languages: - return JSONResponse( - status_code=400, - content={"message": f"text_lang: {text_lang} is not supported in version {tts_config.version}"}, - ) - if prompt_lang in [None, ""]: - return JSONResponse(status_code=400, content={"message": "prompt_lang is required"}) - elif prompt_lang.lower() not in tts_config.languages: - return JSONResponse( - status_code=400, - content={"message": f"prompt_lang: {prompt_lang} is not supported in version {tts_config.version}"}, - ) - if media_type not in ["wav", "raw", "ogg", "aac"]: - return JSONResponse(status_code=400, content={"message": f"media_type: {media_type} is not supported"}) - # elif media_type == "ogg" and not streaming_mode: - # return JSONResponse(status_code=400, content={"message": "ogg format is not supported in non-streaming mode"}) - - if text_split_method not in cut_method_names: - return JSONResponse( - status_code=400, content={"message": f"text_split_method:{text_split_method} is not supported"} - ) - - return None +def _lower_or_none(value: str | None) -> str | None: + return value.lower() if isinstance(value, str) else value async def tts_handle(req: dict): @@ -377,70 +220,11 @@ async def tts_handle(req: dict): StreamingResponse: audio stream response. """ - streaming_mode = req.get("streaming_mode", False) - return_fragment = req.get("return_fragment", False) - media_type = req.get("media_type", "wav") - - check_res = check_params(req) - if check_res is not None: - return check_res - - if streaming_mode == 0: - streaming_mode = False - return_fragment = False - fixed_length_chunk = False - elif streaming_mode == 1: - streaming_mode = False - return_fragment = True - fixed_length_chunk = False - elif streaming_mode == 2: - streaming_mode = True - return_fragment = False - fixed_length_chunk = False - elif streaming_mode == 3: - streaming_mode = True - return_fragment = False - fixed_length_chunk = True - - else: - return JSONResponse(status_code=400, content={"message": f"the value of streaming_mode must be 0, 1, 2, 3(int) or true/false(bool)"}) - - req["streaming_mode"] = streaming_mode - req["return_fragment"] = return_fragment - req["fixed_length_chunk"] = fixed_length_chunk - - print(f"{streaming_mode} {return_fragment} {fixed_length_chunk}") - - streaming_mode = streaming_mode or return_fragment - - try: - tts_generator = tts_pipeline.run(req) - - if streaming_mode: - - def streaming_generator(tts_generator: Generator, media_type: str): - if_frist_chunk = True - for sr, chunk in tts_generator: - if if_frist_chunk and media_type == "wav": - yield wave_header_chunk(sample_rate=sr) - media_type = "raw" - if_frist_chunk = False - yield pack_audio(BytesIO(), chunk, sr, media_type).getvalue() - - # _media_type = f"audio/{media_type}" if not (streaming_mode and media_type in ["wav", "raw"]) else f"audio/x-{media_type}" - return StreamingResponse( - streaming_generator( - tts_generator, - media_type, - ), - media_type=f"audio/{media_type}", - ) - - else: - sr, audio_data = next(tts_generator) - audio_data = pack_audio(BytesIO(), audio_data, sr, media_type).getvalue() - return Response(audio_data, media_type=f"audio/{media_type}") + result = await tts_engine.run_direct_tts_async(req) + if result.streaming: + return StreamingResponse(result.audio_generator, media_type=f"audio/{result.media_type}") + return Response(result.audio_bytes, media_type=f"audio/{result.media_type}") except Exception as e: return JSONResponse(status_code=400, content={"message": "tts failed", "Exception": str(e)}) @@ -449,7 +233,11 @@ async def tts_handle(req: dict): async def control(command: str = None): if command is None: return JSONResponse(status_code=400, content={"message": "command is required"}) - handle_control(command) + try: + tts_engine.handle_control(command) + return JSONResponse(status_code=200, content={"message": "success"}) + except Exception as e: + return JSONResponse(status_code=400, content={"message": "control failed", "Exception": str(e)}) @APP.get("/tts") @@ -481,11 +269,11 @@ async def tts_get_endpoint( ): req = { "text": text, - "text_lang": text_lang.lower(), + "text_lang": _lower_or_none(text_lang), "ref_audio_path": ref_audio_path, "aux_ref_audio_paths": aux_ref_audio_paths, "prompt_text": prompt_text, - "prompt_lang": prompt_lang.lower(), + "prompt_lang": _lower_or_none(prompt_lang), "top_k": top_k, "top_p": top_p, "temperature": temperature, @@ -517,10 +305,10 @@ async def tts_post_endpoint(request: TTS_Request): @APP.get("/set_refer_audio") async def set_refer_aduio(refer_audio_path: str = None): try: - tts_pipeline.set_ref_audio(refer_audio_path) + payload = tts_engine.set_refer_audio(refer_audio_path) except Exception as e: return JSONResponse(status_code=400, content={"message": "set refer audio failed", "Exception": str(e)}) - return JSONResponse(status_code=200, content={"message": "success"}) + return JSONResponse(status_code=200, content=payload) # @APP.post("/set_refer_audio") @@ -545,24 +333,19 @@ async def set_refer_aduio(refer_audio_path: str = None): @APP.get("/set_gpt_weights") async def set_gpt_weights(weights_path: str = None): try: - if weights_path in ["", None]: - return JSONResponse(status_code=400, content={"message": "gpt weight path is required"}) - tts_pipeline.init_t2s_weights(weights_path) + payload = tts_engine.set_gpt_weights(weights_path) except Exception as e: return JSONResponse(status_code=400, content={"message": "change gpt weight failed", "Exception": str(e)}) - - return JSONResponse(status_code=200, content={"message": "success"}) + return JSONResponse(status_code=200, content=payload) @APP.get("/set_sovits_weights") async def set_sovits_weights(weights_path: str = None): try: - if weights_path in ["", None]: - return JSONResponse(status_code=400, content={"message": "sovits weight path is required"}) - tts_pipeline.init_vits_weights(weights_path) + payload = tts_engine.set_sovits_weights(weights_path) except Exception as e: return JSONResponse(status_code=400, content={"message": "change sovits weight failed", "Exception": str(e)}) - return JSONResponse(status_code=200, content={"message": "success"}) + return JSONResponse(status_code=200, content=payload) if __name__ == "__main__": diff --git a/api_v3.py b/api_v3.py new file mode 100644 index 00000000..1a6457ec --- /dev/null +++ b/api_v3.py @@ -0,0 +1,443 @@ +""" +# WebAPI文档 + +` python api_v2.py -a 127.0.0.1 -p 9880 -c GPT_SoVITS/configs/tts_infer.yaml ` + +## 执行参数: + `-a` - `绑定地址, 默认"127.0.0.1"` + `-p` - `绑定端口, 默认9880` + `-c` - `TTS配置文件路径, 默认"GPT_SoVITS/configs/tts_infer.yaml"` + +## 调用: + +### 推理 + +endpoint: `/tts` +GET: +``` +http://127.0.0.1:9880/tts?text=先帝创业未半而中道崩殂，今天下三分，益州疲弊，此诚危急存亡之秋也。&text_lang=zh&ref_audio_path=archive_jingyuan_1.wav&prompt_lang=zh&prompt_text=我是「罗浮」云骑将军景元。不必拘谨，「将军」只是一时的身份，你称呼我景元便可&text_split_method=cut5&batch_size=1&media_type=wav&streaming_mode=true +``` + +POST: +```json +{ + "text": "", # str.(required) text to be synthesized + "text_lang: "", # str.(required) language of the text to be synthesized + "ref_audio_path": "", # str.(required) reference audio path + "aux_ref_audio_paths": [], # list.(optional) auxiliary reference audio paths for multi-speaker tone fusion + "prompt_text": "", # str.(optional) prompt text for the reference audio + "prompt_lang": "", # str.(required) language of the prompt text for the reference audio + "top_k": 15, # int. top k sampling + "top_p": 1, # float. top p sampling + "temperature": 1, # float. temperature for sampling + "text_split_method": "cut5", # str. text split method, see text_segmentation_method.py for details. + "batch_size": 1, # int. batch size for inference + "batch_threshold": 0.75, # float. threshold for batch splitting. + "split_bucket": True, # bool. whether to split the batch into multiple buckets. + "speed_factor":1.0, # float. control the speed of the synthesized audio. + "fragment_interval":0.3, # float. to control the interval of the audio fragment. + "seed": -1, # int. random seed for reproducibility. + "parallel_infer": True, # bool. whether to use parallel inference. + "repetition_penalty": 1.35, # float. repetition penalty for T2S model. + "sample_steps": 32, # int. number of sampling steps for VITS model V3. + "super_sampling": False, # bool. whether to use super-sampling for audio when using VITS model V3. + "streaming_mode": False, # bool or int. return audio chunk by chunk.T he available options are: 0,1,2,3 or True/False (0/False: Disabled | 1/True: Best Quality, Slowest response speed (old version streaming_mode) | 2: Medium Quality, Slow response speed | 3: Lower Quality, Faster response speed ) + "overlap_length": 2, # int. overlap length of semantic tokens for streaming mode. + "min_chunk_length": 16, # int. The minimum chunk length of semantic tokens for streaming mode. (affects audio chunk size) +} +``` + +RESP: +成功: 直接返回 wav 音频流， http code 200 +失败: 返回包含错误信息的 json, http code 400 + +### 命令控制 + +endpoint: `/control` + +command: +"restart": 重新运行 +"exit": 结束运行 + +GET: +``` +http://127.0.0.1:9880/control?command=restart +``` +POST: +```json +{ + "command": "restart" +} +``` + +RESP: 无 + + +### 切换GPT模型 + +endpoint: `/set_gpt_weights` + +GET: +``` +http://127.0.0.1:9880/set_gpt_weights?weights_path=GPT_SoVITS/pretrained_models/s1bert25hz-2kh-longer-epoch=68e-step=50232.ckpt +``` +RESP: +成功: 返回"success", http code 200 +失败: 返回包含错误信息的 json, http code 400 + + +### 切换Sovits模型 + +endpoint: `/set_sovits_weights` + +GET: +``` +http://127.0.0.1:9880/set_sovits_weights?weights_path=GPT_SoVITS/pretrained_models/s2G488k.pth +``` + +RESP: +成功: 返回"success", http code 200 +失败: 返回包含错误信息的 json, http code 400 + +""" + +import os +import sys +import traceback +from typing import List, Union + +now_dir = os.getcwd() +sys.path.append(now_dir) +sys.path.append("%s/GPT_SoVITS" % (now_dir)) + +from runtime_preload import preload_text_runtime_deps + +preload_text_runtime_deps() + +import argparse +import signal +from fastapi import FastAPI, Response +from fastapi.responses import StreamingResponse, JSONResponse +import uvicorn +from tools.i18n.i18n import I18nAuto +from GPT_SoVITS.TTS_infer_pack.TTS import TTS, TTS_Config +from GPT_SoVITS.TTS_infer_pack.unified_engine import RuntimeControlCallbacks, UnifiedTTSEngine +from GPT_SoVITS.TTS_infer_pack.text_segmentation_method import get_method_names as get_cut_method_names +from pydantic import BaseModel + +# print(sys.path) +i18n = I18nAuto() +cut_method_names = get_cut_method_names() + +parser = argparse.ArgumentParser(description="GPT-SoVITS api") +parser.add_argument("-c", "--tts_config", type=str, default="GPT_SoVITS/configs/tts_infer.yaml", help="tts_infer路径") +parser.add_argument("-a", "--bind_addr", type=str, default="127.0.0.1", help="default: 127.0.0.1") +parser.add_argument("-p", "--port", type=int, default="9880", help="default: 9880") +args = parser.parse_args() +config_path = args.tts_config +# device = args.device +port = args.port +host = args.bind_addr +argv = sys.argv + +if config_path in [None, ""]: + config_path = "GPT-SoVITS/configs/tts_infer.yaml" + +tts_config = TTS_Config(config_path) +print(tts_config) +tts_pipeline = TTS(tts_config) +tts_engine = UnifiedTTSEngine( + tts_pipeline, + cut_method_names=cut_method_names, + control_callbacks=RuntimeControlCallbacks( + restart=lambda: os.execl(sys.executable, sys.executable, *argv), + exit=lambda: os.kill(os.getpid(), signal.SIGTERM), + ), +) + +APP = FastAPI() + + +class TTS_Request(BaseModel): + text: str = None + text_lang: str = None + ref_audio_path: str = None + aux_ref_audio_paths: list = None + prompt_lang: str = None + prompt_text: str = "" + top_k: int = 15 + top_p: float = 1 + temperature: float = 1 + text_split_method: str = "cut5" + batch_size: int = 1 + batch_threshold: float = 0.75 + split_bucket: bool = True + speed_factor: float = 1.0 + fragment_interval: float = 0.3 + seed: int = -1 + media_type: str = "wav" + streaming_mode: Union[bool, int] = False + parallel_infer: bool = True + repetition_penalty: float = 1.35 + sample_steps: int = 32 + super_sampling: bool = False + overlap_length: int = 2 + min_chunk_length: int = 16 + + +class Scheduler_Debug_Request_Item(BaseModel): + request_id: str | None = None + text: str + text_lang: str + ref_audio_path: str + prompt_lang: str + prompt_text: str = "" + top_k: int = 15 + top_p: float = 1 + temperature: float = 1 + repetition_penalty: float = 1.35 + early_stop_num: int = -1 + ready_step: int = 0 + + +class Scheduler_Debug_Request(BaseModel): + requests: List[Scheduler_Debug_Request_Item] + max_steps: int = 1500 + seed: int = -1 + + +class Scheduler_Submit_Request(BaseModel): + request_id: str | None = None + text: str + text_lang: str + ref_audio_path: str + prompt_lang: str + prompt_text: str = "" + top_k: int = 15 + top_p: float = 1 + temperature: float = 1 + repetition_penalty: float = 1.35 + early_stop_num: int = -1 + speed_factor: float = 1.0 + sample_steps: int = 32 + media_type: str = "wav" + timeout_sec: float = 30.0 + + +def _lower_or_none(value: str | None) -> str | None: + return value.lower() if isinstance(value, str) else value + + +async def tts_scheduler_debug_handle(request: Scheduler_Debug_Request): + try: + result = await tts_engine.run_scheduler_debug( + request_items=[item.dict() for item in request.requests], + max_steps=int(request.max_steps), + seed=int(request.seed), + ) + return JSONResponse(status_code=200, content=result.payload) + except Exception as e: + return JSONResponse( + status_code=400, + content={"message": "scheduler debug failed", "Exception": str(e)}, + ) + + +async def tts_scheduler_submit_handle(request: Scheduler_Submit_Request): + try: + result = await tts_engine.run_scheduler_submit(request.dict()) + return Response(result.audio_bytes, media_type=result.media_type, headers=result.headers) + except Exception as e: + return JSONResponse( + status_code=400, + content={"message": "scheduler submit failed", "Exception": str(e)}, + ) + + +async def tts_handle(req: dict): + """ + Text to speech handler. + + Args: + req (dict): + { + "text": "", # str.(required) text to be synthesized + "text_lang: "", # str.(required) language of the text to be synthesized + "ref_audio_path": "", # str.(required) reference audio path + "aux_ref_audio_paths": [], # list.(optional) auxiliary reference audio paths for multi-speaker tone fusion + "prompt_text": "", # str.(optional) prompt text for the reference audio + "prompt_lang": "", # str.(required) language of the prompt text for the reference audio + "top_k": 15, # int. top k sampling + "top_p": 1, # float. top p sampling + "temperature": 1, # float. temperature for sampling + "text_split_method": "cut5", # str. text split method, see text_segmentation_method.py for details. + "batch_size": 1, # int. batch size for inference + "batch_threshold": 0.75, # float. threshold for batch splitting. + "split_bucket": True, # bool. whether to split the batch into multiple buckets. + "speed_factor":1.0, # float. control the speed of the synthesized audio. + "fragment_interval":0.3, # float. to control the interval of the audio fragment. + "seed": -1, # int. random seed for reproducibility. + "parallel_infer": True, # bool. whether to use parallel inference. + "repetition_penalty": 1.35, # float. repetition penalty for T2S model. + "sample_steps": 32, # int. number of sampling steps for VITS model V3. + "super_sampling": False, # bool. whether to use super-sampling for audio when using VITS model V3. + "streaming_mode": False, # bool or int. return audio chunk by chunk.T he available options are: 0,1,2,3 or True/False (0/False: Disabled | 1/True: Best Quality, Slowest response speed (old version streaming_mode) | 2: Medium Quality, Slow response speed | 3: Lower Quality, Faster response speed ) + "overlap_length": 2, # int. overlap length of semantic tokens for streaming mode. + "min_chunk_length": 16, # int. The minimum chunk length of semantic tokens for streaming mode. (affects audio chunk size) + } + returns: + StreamingResponse: audio stream response. + """ + + try: + result = await tts_engine.run_direct_tts_async(req) + if result.streaming: + return StreamingResponse(result.audio_generator, media_type=f"audio/{result.media_type}") + return Response(result.audio_bytes, media_type=f"audio/{result.media_type}") + except Exception as e: + return JSONResponse(status_code=400, content={"message": "tts failed", "Exception": str(e)}) + + +@APP.get("/control") +async def control(command: str = None): + if command is None: + return JSONResponse(status_code=400, content={"message": "command is required"}) + try: + tts_engine.handle_control(command) + return JSONResponse(status_code=200, content={"message": "success"}) + except Exception as e: + return JSONResponse(status_code=400, content={"message": "control failed", "Exception": str(e)}) + + +@APP.get("/tts") +async def tts_get_endpoint( + text: str = None, + text_lang: str = None, + ref_audio_path: str = None, + aux_ref_audio_paths: list = None, + prompt_lang: str = None, + prompt_text: str = "", + top_k: int = 15, + top_p: float = 1, + temperature: float = 1, + text_split_method: str = "cut5", + batch_size: int = 1, + batch_threshold: float = 0.75, + split_bucket: bool = True, + speed_factor: float = 1.0, + fragment_interval: float = 0.3, + seed: int = -1, + media_type: str = "wav", + parallel_infer: bool = True, + repetition_penalty: float = 1.35, + sample_steps: int = 32, + super_sampling: bool = False, + streaming_mode: Union[bool, int] = False, + overlap_length: int = 2, + min_chunk_length: int = 16, +): + req = { + "text": text, + "text_lang": _lower_or_none(text_lang), + "ref_audio_path": ref_audio_path, + "aux_ref_audio_paths": aux_ref_audio_paths, + "prompt_text": prompt_text, + "prompt_lang": _lower_or_none(prompt_lang), + "top_k": top_k, + "top_p": top_p, + "temperature": temperature, + "text_split_method": text_split_method, + "batch_size": int(batch_size), + "batch_threshold": float(batch_threshold), + "speed_factor": float(speed_factor), + "split_bucket": split_bucket, + "fragment_interval": fragment_interval, + "seed": seed, + "media_type": media_type, + "streaming_mode": streaming_mode, + "parallel_infer": parallel_infer, + "repetition_penalty": float(repetition_penalty), + "sample_steps": int(sample_steps), + "super_sampling": super_sampling, + "overlap_length": int(overlap_length), + "min_chunk_length": int(min_chunk_length), + } + return await tts_handle(req) + + +@APP.post("/tts") +async def tts_post_endpoint(request: TTS_Request): + req = request.dict() + return await tts_handle(req) + + +@APP.post("/tts_scheduler_debug") +async def tts_scheduler_debug_endpoint(request: Scheduler_Debug_Request): + return await tts_scheduler_debug_handle(request) + + +@APP.post("/tts_scheduler_submit") +async def tts_scheduler_submit_endpoint(request: Scheduler_Submit_Request): + return await tts_scheduler_submit_handle(request) + + +@APP.get("/tts_scheduler_state") +async def tts_scheduler_state_endpoint(): + return JSONResponse(status_code=200, content=tts_engine.get_runtime_state()) + + +@APP.get("/set_refer_audio") +async def set_refer_aduio(refer_audio_path: str = None): + try: + payload = tts_engine.set_refer_audio(refer_audio_path) + except Exception as e: + return JSONResponse(status_code=400, content={"message": "set refer audio failed", "Exception": str(e)}) + return JSONResponse(status_code=200, content=payload) + + +# @APP.post("/set_refer_audio") +# async def set_refer_aduio_post(audio_file: UploadFile = File(...)): +# try: +# # 检查文件类型，确保是音频文件 +# if not audio_file.content_type.startswith("audio/"): +# return JSONResponse(status_code=400, content={"message": "file type is not supported"}) + +# os.makedirs("uploaded_audio", exist_ok=True) +# save_path = os.path.join("uploaded_audio", audio_file.filename) +# # 保存音频文件到服务器上的一个目录 +# with open(save_path , "wb") as buffer: +# buffer.write(await audio_file.read()) + +# tts_pipeline.set_ref_audio(save_path) +# except Exception as e: +# return JSONResponse(status_code=400, content={"message": f"set refer audio failed", "Exception": str(e)}) +# return JSONResponse(status_code=200, content={"message": "success"}) + + +@APP.get("/set_gpt_weights") +async def set_gpt_weights(weights_path: str = None): + try: + payload = tts_engine.set_gpt_weights(weights_path) + except Exception as e: + return JSONResponse(status_code=400, content={"message": "change gpt weight failed", "Exception": str(e)}) + return JSONResponse(status_code=200, content=payload) + + +@APP.get("/set_sovits_weights") +async def set_sovits_weights(weights_path: str = None): + try: + payload = tts_engine.set_sovits_weights(weights_path) + except Exception as e: + return JSONResponse(status_code=400, content={"message": "change sovits weight failed", "Exception": str(e)}) + return JSONResponse(status_code=200, content=payload) + + +if __name__ == "__main__": + try: + if host == "None": # 在调用时使用 -a None 参数，可以让api监听双栈 + host = None + uvicorn.run(app=APP, host=host, port=port, workers=1) + except Exception: + traceback.print_exc() + os.kill(os.getpid(), signal.SIGTERM) + exit(0) diff --git a/tools/bench_api_v3_scheduler_submit.py b/tools/bench_api_v3_scheduler_submit.py new file mode 100644 index 00000000..c16468e1 --- /dev/null +++ b/tools/bench_api_v3_scheduler_submit.py @@ -0,0 +1,250 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- + +from __future__ import annotations + +import argparse +import asyncio +import json +import subprocess +import threading +import time +import wave +from pathlib import Path +from typing import Any, Dict, List, Optional + +import httpx + +ROOT_DIR = Path(__file__).resolve().parents[1] + + +def parse_args() -> argparse.Namespace: + parser = argparse.ArgumentParser(description="Benchmark api_v3 /tts_scheduler_submit concurrency and GPU memory.") + parser.add_argument("--base-url", type=str, default="http://127.0.0.1:9880") + parser.add_argument("--endpoint", type=str, default="/tts_scheduler_submit") + parser.add_argument("--concurrency", type=int, required=True) + parser.add_argument("--timeout-sec", type=float, default=120.0) + parser.add_argument("--server-pid", type=int, default=None) + parser.add_argument("--poll-interval-sec", type=float, default=0.1) + parser.add_argument("--text-lang", type=str, default="zh") + parser.add_argument("--prompt-lang", type=str, default="zh") + parser.add_argument("--media-type", type=str, default="wav") + parser.add_argument("--top-k", type=int, default=15) + parser.add_argument("--top-p", type=float, default=1.0) + parser.add_argument("--temperature", type=float, default=1.0) + parser.add_argument("--repetition-penalty", type=float, default=1.35) + parser.add_argument("--sample-steps", type=int, default=32) + parser.add_argument("--text-file", type=Path, default=ROOT_DIR / "test_cn.txt") + parser.add_argument("--wav-dir", type=Path, default=ROOT_DIR / "testwav") + parser.add_argument("--output-dir", type=Path, default=ROOT_DIR / "TEMP/api_v3_bench") + return parser.parse_args() + + +def load_requests(args: argparse.Namespace) -> List[Dict[str, Any]]: + wav_paths_all = sorted(args.wav_dir.glob("*.wav")) + wav_paths: List[Path] = [] + for wav_path in wav_paths_all: + with wave.open(str(wav_path), "rb") as handle: + duration = handle.getnframes() / float(handle.getframerate()) + if 3.0 <= duration <= 10.0: + wav_paths.append(wav_path) + if not wav_paths: + raise FileNotFoundError(f"没有找到 3-10 秒合法 wav: {args.wav_dir}") + text_lines = [line.strip() for line in args.text_file.read_text(encoding="utf-8").splitlines() if line.strip()] + if not text_lines: + raise ValueError(f"没有找到有效文本行: {args.text_file}") + + requests: List[Dict[str, Any]] = [] + for index in range(args.concurrency): + wav_path = wav_paths[index % len(wav_paths)] + lab_path = wav_path.with_suffix(".lab") + if not lab_path.exists(): + raise FileNotFoundError(f"缺少参考文本: {lab_path}") + requests.append( + { + "request_id": f"bench_{args.concurrency:03d}_{index:03d}", + "text": text_lines[index % len(text_lines)], + "text_lang": args.text_lang, + "ref_audio_path": str(wav_path), + "prompt_lang": args.prompt_lang, + "prompt_text": lab_path.read_text(encoding="utf-8").strip(), + "top_k": int(args.top_k), + "top_p": float(args.top_p), + "temperature": float(args.temperature), + "repetition_penalty": float(args.repetition_penalty), + "sample_steps": int(args.sample_steps), + "media_type": args.media_type, + "timeout_sec": float(args.timeout_sec), + } + ) + return requests + + +class GpuMemoryPoller: + def __init__(self, server_pid: Optional[int], interval_sec: float): + self.server_pid = server_pid + self.interval_sec = interval_sec + self._stop = threading.Event() + self.samples: List[Dict[str, Any]] = [] + self.thread: Optional[threading.Thread] = None + + def _query_memory_mb(self) -> Optional[int]: + try: + result = subprocess.run( + [ + "nvidia-smi", + "--query-compute-apps=pid,used_gpu_memory", + "--format=csv,noheader,nounits", + ], + check=True, + capture_output=True, + text=True, + ) + except Exception: + return None + total = 0 + found = False + for line in result.stdout.splitlines(): + line = line.strip() + if not line: + continue + parts = [item.strip() for item in line.split(",")] + if len(parts) != 2: + continue + try: + pid = int(parts[0]) + used_mb = int(parts[1]) + except ValueError: + continue + if self.server_pid is None or pid == self.server_pid: + total += used_mb + found = True + if self.server_pid is None: + return total + return total if found else 0 + + def _run(self) -> None: + while not self._stop.is_set(): + used_mb = self._query_memory_mb() + self.samples.append({"ts": time.time(), "used_mb": used_mb}) + self._stop.wait(self.interval_sec) + + def start(self) -> None: + self.thread = threading.Thread(target=self._run, daemon=True) + self.thread.start() + + def stop(self) -> None: + self._stop.set() + if self.thread is not None: + self.thread.join(timeout=2.0) + + def summary(self) -> Dict[str, Any]: + valid = [item for item in self.samples if item["used_mb"] is not None] + peak = max(valid, key=lambda item: item["used_mb"]) if valid else None + first = valid[0] if valid else None + last = valid[-1] if valid else None + return { + "server_pid": self.server_pid, + "sample_count": int(len(self.samples)), + "start_used_mb": None if first is None else int(first["used_mb"]), + "peak_used_mb": None if peak is None else int(peak["used_mb"]), + "peak_delta_mb": None if peak is None or first is None else int(peak["used_mb"] - first["used_mb"]), + "end_used_mb": None if last is None else int(last["used_mb"]), + "peak_ts": None if peak is None else float(peak["ts"]), + "samples": self.samples, + } + + +async def submit_one(client: httpx.AsyncClient, url: str, payload: Dict[str, Any]) -> Dict[str, Any]: + started = time.perf_counter() + try: + response = await client.post(url, json=payload) + elapsed_ms = (time.perf_counter() - started) * 1000.0 + item = { + "request_id": payload["request_id"], + "status_code": int(response.status_code), + "elapsed_ms": float(elapsed_ms), + "content_type": response.headers.get("content-type"), + "audio_bytes": int(len(response.content)), + "headers": {key: value for key, value in response.headers.items() if key.lower().startswith("x-")}, + } + if response.status_code != 200: + try: + item["error_body"] = response.json() + except Exception: + item["error_body"] = response.text + return item + except Exception as exc: + return { + "request_id": payload["request_id"], + "status_code": -1, + "elapsed_ms": float((time.perf_counter() - started) * 1000.0), + "exception": repr(exc), + } + + +async def run_benchmark(args: argparse.Namespace) -> Dict[str, Any]: + payloads = load_requests(args) + url = args.base_url.rstrip("/") + args.endpoint + poller = GpuMemoryPoller(server_pid=args.server_pid, interval_sec=args.poll_interval_sec) + + limits = httpx.Limits(max_connections=args.concurrency, max_keepalive_connections=args.concurrency) + timeout = httpx.Timeout(connect=10.0, read=args.timeout_sec + 10.0, write=10.0, pool=10.0) + + started = time.perf_counter() + poller.start() + try: + async with httpx.AsyncClient(limits=limits, timeout=timeout) as client: + results = await asyncio.gather(*[submit_one(client, url, payload) for payload in payloads]) + finally: + poller.stop() + wall_ms = (time.perf_counter() - started) * 1000.0 + + ok_results = [item for item in results if item["status_code"] == 200] + failed_results = [item for item in results if item["status_code"] != 200] + request_total_ms = [] + worker_total_ms = [] + for item in ok_results: + headers = item.get("headers", {}) + if "x-request-total-ms" in headers: + request_total_ms.append(float(headers["x-request-total-ms"])) + if "x-worker-total-ms" in headers: + worker_total_ms.append(float(headers["x-worker-total-ms"])) + + return { + "concurrency": int(args.concurrency), + "server_pid": args.server_pid, + "request_count": int(len(payloads)), + "wall_ms": float(wall_ms), + "success_count": int(len(ok_results)), + "failure_count": int(len(failed_results)), + "request_total_ms_avg": float(sum(request_total_ms) / len(request_total_ms)) if request_total_ms else None, + "request_total_ms_max": float(max(request_total_ms)) if request_total_ms else None, + "worker_total_ms_avg": float(sum(worker_total_ms) / len(worker_total_ms)) if worker_total_ms else None, + "worker_total_ms_max": float(max(worker_total_ms)) if worker_total_ms else None, + "gpu_memory": poller.summary(), + "results": results, + } + + +def main() -> None: + args = parse_args() + output_dir = args.output_dir / f"concurrency_{args.concurrency:02d}" + output_dir.mkdir(parents=True, exist_ok=True) + summary = asyncio.run(run_benchmark(args)) + summary_path = output_dir / "summary.json" + summary_path.write_text(json.dumps(summary, ensure_ascii=False, indent=2), encoding="utf-8") + print(json.dumps({ + "concurrency": summary["concurrency"], + "success_count": summary["success_count"], + "failure_count": summary["failure_count"], + "wall_ms": summary["wall_ms"], + "gpu_peak_used_mb": summary["gpu_memory"]["peak_used_mb"], + "request_total_ms_avg": summary["request_total_ms_avg"], + "request_total_ms_max": summary["request_total_ms_max"], + "summary_path": str(summary_path), + }, ensure_ascii=False, indent=2)) + + +if __name__ == "__main__": + main() diff --git a/tools/t2s_memory_breakdown.py b/tools/t2s_memory_breakdown.py new file mode 100644 index 00000000..18127953 --- /dev/null +++ b/tools/t2s_memory_breakdown.py @@ -0,0 +1,887 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- + +from __future__ import annotations + +import argparse +import gc +import contextlib +import json +import random +import sys +import time +from pathlib import Path +from typing import Any, Dict, List, Optional, Sequence, Tuple + +import numpy as np +import torch + +ROOT_DIR = Path(__file__).resolve().parents[1] +if str(ROOT_DIR) not in sys.path: + sys.path.append(str(ROOT_DIR)) +gpt_sovits_dir = ROOT_DIR / "GPT_SoVITS" +if str(gpt_sovits_dir) not in sys.path: + sys.path.append(str(gpt_sovits_dir)) + +from GPT_SoVITS.TTS_infer_pack.TTS import TTS, TTS_Config # noqa: E402 +from GPT_SoVITS.TTS_infer_pack.t2s_scheduler import ( # noqa: E402 + SchedulerRequestSpec, + T2SRequestState, + T2SRunningRequest, + _build_decode_batch_from_running, + build_prefill_batch, + prepare_request_state, + run_decode_step_for_running, + run_prefill_step, +) + + +def parse_args() -> argparse.Namespace: + parser = argparse.ArgumentParser(description="Break down T2S CUDA memory by stage and tensor groups.") + parser.add_argument("--config", type=Path, default=ROOT_DIR / "GPT_SoVITS/configs/tts_infer.yaml") + parser.add_argument("--request-manifest", type=Path, default=None) + parser.add_argument("--scenario", type=str, default="auto4", choices=["auto4", "single"]) + parser.add_argument("--auto-count", type=int, default=4) + parser.add_argument("--auto-wav-dir", type=Path, default=ROOT_DIR / "testwav") + parser.add_argument("--auto-text-file", type=Path, default=ROOT_DIR / "test_cn.txt") + parser.add_argument("--ref-audio", type=Path, default=ROOT_DIR / "test.wav") + parser.add_argument("--prompt-text", type=str, default="是啊，主要是因为有调研需求的学者少了。") + parser.add_argument("--prompt-lang", type=str, default="zh") + parser.add_argument("--text", type=str, default=None) + parser.add_argument("--text-file", type=Path, default=ROOT_DIR / "test_en.txt") + parser.add_argument("--text-lang", type=str, default="zh") + parser.add_argument("--top-k", type=int, default=15) + parser.add_argument("--top-p", type=float, default=1.0) + parser.add_argument("--temperature", type=float, default=1.0) + parser.add_argument("--repetition-penalty", type=float, default=1.35) + parser.add_argument("--early-stop-num", type=int, default=-1) + parser.add_argument("--max-steps", type=int, default=1500) + parser.add_argument("--seed", type=int, default=1234) + parser.add_argument("--warmup", action="store_true", default=False) + parser.add_argument("--worker-rounds", type=int, default=1) + parser.add_argument("--worker-grad-mode", type=str, default="default", choices=["default", "inference_mode"]) + parser.add_argument("--compare-worker-grad-modes", action="store_true", default=False) + parser.add_argument( + "--output-dir", + type=Path, + default=ROOT_DIR / "TEMP/t2s_memory_breakdown/run1", + ) + return parser.parse_args() + + +def set_seed(seed: int, use_cuda: bool) -> None: + random.seed(seed) + np.random.seed(seed) + torch.manual_seed(seed) + if use_cuda and torch.cuda.is_available(): + torch.cuda.manual_seed(seed) + torch.cuda.manual_seed_all(seed) + + +def _sync_device(device: Any) -> None: + try: + device_str = str(device) + if device_str.startswith("cuda") and torch.cuda.is_available(): + torch.cuda.synchronize(device) + elif device_str == "mps" and hasattr(torch, "mps") and hasattr(torch.mps, "synchronize"): + torch.mps.synchronize() + except Exception: + pass + + +def bytes_to_mb(num_bytes: int) -> float: + return float(num_bytes) / (1024.0 * 1024.0) + + +def tensor_nbytes(tensor: Optional[torch.Tensor]) -> int: + if tensor is None: + return 0 + return int(tensor.numel() * tensor.element_size()) + + +def tensor_list_nbytes(items: Sequence[torch.Tensor]) -> int: + return int(sum(tensor_nbytes(item) for item in items)) + + +def model_nbytes(module: torch.nn.Module) -> int: + total = 0 + for parameter in module.parameters(): + total += tensor_nbytes(parameter) + for buffer in module.buffers(): + total += tensor_nbytes(buffer) + return int(total) + + +def build_module_weight_summary(tts: TTS) -> Dict[str, Any]: + modules = { + "t2s_model": tts.t2s_model, + "t2s_core": tts.t2s_model.model if tts.t2s_model is not None else None, + "vits_model": tts.vits_model, + "bert_model": tts.bert_model, + "cnhuhbert_model": tts.cnhuhbert_model, + "vocoder": tts.vocoder, + "sv_model": tts.sv_model, + } + by_module = {} + total_bytes = 0 + for name, module in modules.items(): + module_bytes = model_nbytes(module) if module is not None else 0 + by_module[name] = { + "bytes": int(module_bytes), + "mb": bytes_to_mb(module_bytes), + } + total_bytes += module_bytes + return { + "by_module": by_module, + "total_bytes": int(total_bytes), + "total_mb": bytes_to_mb(total_bytes), + } + + +def snapshot_live_cuda_tensors(top_k: int = 40) -> Dict[str, Any]: + storages: Dict[int, Dict[str, Any]] = {} + tensor_views: List[Dict[str, Any]] = [] + for obj in gc.get_objects(): + try: + tensor = None + if torch.is_tensor(obj): + tensor = obj + elif hasattr(obj, "data") and torch.is_tensor(obj.data): + tensor = obj.data + if tensor is None or not tensor.is_cuda: + continue + storage = tensor.untyped_storage() + storage_ptr = int(storage.data_ptr()) + if storage_ptr not in storages: + storages[storage_ptr] = { + "storage_ptr": storage_ptr, + "storage_bytes": int(storage.nbytes()), + "dtype": str(tensor.dtype), + "shape": list(tensor.shape), + "device": str(tensor.device), + } + tensor_views.append( + { + "shape": list(tensor.shape), + "dtype": str(tensor.dtype), + "bytes": tensor_nbytes(tensor), + "device": str(tensor.device), + } + ) + except Exception: + continue + storage_list = sorted(storages.values(), key=lambda item: item["storage_bytes"], reverse=True) + tensor_views.sort(key=lambda item: item["bytes"], reverse=True) + return { + "unique_storage_count": int(len(storage_list)), + "unique_storage_total_bytes": int(sum(item["storage_bytes"] for item in storage_list)), + "unique_storage_total_mb": bytes_to_mb(sum(item["storage_bytes"] for item in storage_list)), + "top_storages": storage_list[:top_k], + "top_tensor_views": tensor_views[:top_k], + } + + +def build_single_spec(args: argparse.Namespace) -> List[SchedulerRequestSpec]: + text = args.text if args.text is not None else args.text_file.read_text(encoding="utf-8").strip() + return [ + SchedulerRequestSpec( + request_id="req_000", + ref_audio_path=args.ref_audio, + prompt_text=args.prompt_text, + prompt_lang=args.prompt_lang, + text=text, + text_lang=args.text_lang, + top_k=args.top_k, + top_p=args.top_p, + temperature=args.temperature, + repetition_penalty=args.repetition_penalty, + early_stop_num=args.early_stop_num, + ready_step=0, + ) + ] + + +def build_auto_specs(args: argparse.Namespace) -> List[SchedulerRequestSpec]: + wav_paths = sorted(args.auto_wav_dir.glob("*.wav"))[: args.auto_count] + if len(wav_paths) < args.auto_count: + raise ValueError(f"auto wav count不足，目录 {args.auto_wav_dir} 只有 {len(wav_paths)} 条 wav") + text_lines = [line.strip() for line in args.auto_text_file.read_text(encoding="utf-8").splitlines() if line.strip()] + if len(text_lines) < args.auto_count: + raise ValueError(f"auto text lines不足，文件 {args.auto_text_file} 只有 {len(text_lines)} 行有效文本") + specs: List[SchedulerRequestSpec] = [] + for index, wav_path in enumerate(wav_paths): + lab_path = wav_path.with_suffix(".lab") + if not lab_path.exists(): + raise FileNotFoundError(f"找不到参考文本 {lab_path}") + specs.append( + SchedulerRequestSpec( + request_id=f"req_{index:03d}", + ref_audio_path=wav_path, + prompt_text=lab_path.read_text(encoding="utf-8").strip(), + prompt_lang="zh", + text=text_lines[index], + text_lang=args.text_lang, + top_k=args.top_k, + top_p=args.top_p, + temperature=args.temperature, + repetition_penalty=args.repetition_penalty, + early_stop_num=args.early_stop_num, + ready_step=0, + ) + ) + return specs + + +def load_request_specs(args: argparse.Namespace) -> List[SchedulerRequestSpec]: + if args.request_manifest is not None: + payload = json.loads(args.request_manifest.read_text(encoding="utf-8")) + raw_requests = payload["requests"] if isinstance(payload, dict) else payload + specs: List[SchedulerRequestSpec] = [] + for index, item in enumerate(raw_requests): + text = item.get("text") + text_file = item.get("text_file") + if text is None and text_file is None: + raise ValueError(f"request[{index}] must provide text or text_file") + if text is None: + text = Path(text_file).read_text(encoding="utf-8").strip() + specs.append( + SchedulerRequestSpec( + request_id=item.get("request_id", f"req_{index:03d}"), + ref_audio_path=Path(item["ref_audio_path"]), + prompt_text=item["prompt_text"], + prompt_lang=item.get("prompt_lang", "zh"), + text=text, + text_lang=item.get("text_lang", "zh"), + top_k=int(item.get("top_k", args.top_k)), + top_p=float(item.get("top_p", args.top_p)), + temperature=float(item.get("temperature", args.temperature)), + repetition_penalty=float(item.get("repetition_penalty", args.repetition_penalty)), + early_stop_num=int(item.get("early_stop_num", args.early_stop_num)), + ready_step=int(item.get("ready_step", 0)), + ) + ) + return specs + if args.scenario == "single": + return build_single_spec(args) + return build_auto_specs(args) + + +def load_pipeline(config_path: Path) -> TTS: + tts_config = TTS_Config(str(config_path)) + print(tts_config) + return TTS(tts_config) + + +def cuda_mem_snapshot(device: Any) -> Dict[str, float]: + if not (str(device).startswith("cuda") and torch.cuda.is_available()): + return { + "allocated_mb": 0.0, + "reserved_mb": 0.0, + "max_allocated_mb": 0.0, + "max_reserved_mb": 0.0, + } + _sync_device(device) + return { + "allocated_mb": bytes_to_mb(torch.cuda.memory_allocated(device)), + "reserved_mb": bytes_to_mb(torch.cuda.memory_reserved(device)), + "max_allocated_mb": bytes_to_mb(torch.cuda.max_memory_allocated(device)), + "max_reserved_mb": bytes_to_mb(torch.cuda.max_memory_reserved(device)), + } + + +def stage_run(device: Any, fn) -> Tuple[Any, Dict[str, float]]: + if str(device).startswith("cuda") and torch.cuda.is_available(): + gc.collect() + _sync_device(device) + torch.cuda.reset_peak_memory_stats(device) + before = cuda_mem_snapshot(device) + started = time.perf_counter() + result = fn() + _sync_device(device) + elapsed_ms = (time.perf_counter() - started) * 1000.0 + after = cuda_mem_snapshot(device) + after["elapsed_ms"] = float(elapsed_ms) + after["delta_allocated_mb"] = float(after["allocated_mb"] - before["allocated_mb"]) + after["delta_reserved_mb"] = float(after["reserved_mb"] - before["reserved_mb"]) + after["stage_peak_over_before_mb"] = float(max(after["max_allocated_mb"] - before["allocated_mb"], 0.0)) + return result, after + + +class GlobalPeakRecorder: + def __init__(self, device: Any): + self.device = device + self.checkpoints: List[Dict[str, Any]] = [] + if str(device).startswith("cuda") and torch.cuda.is_available(): + gc.collect() + _sync_device(device) + torch.cuda.empty_cache() + torch.cuda.reset_peak_memory_stats(device) + + def record(self, label: str, **extra: Any) -> None: + snapshot = cuda_mem_snapshot(self.device) + snapshot["label"] = label + snapshot.update(extra) + self.checkpoints.append(snapshot) + + def summary(self) -> Dict[str, Any]: + peak = max(self.checkpoints, key=lambda item: item["max_allocated_mb"]) if self.checkpoints else None + return { + "peak_allocated_mb": 0.0 if peak is None else float(peak["max_allocated_mb"]), + "peak_reserved_mb": 0.0 if peak is None else float(peak["max_reserved_mb"]), + "peak_label": None if peak is None else peak["label"], + "checkpoints": self.checkpoints, + } + + +def summarise_state_tensors(states: Sequence[T2SRequestState]) -> Dict[str, Any]: + per_request = [] + total = { + "phones_bytes": 0, + "prompt_phones_bytes": 0, + "all_phones_bytes": 0, + "all_bert_features_bytes": 0, + "prompt_semantic_bytes": 0, + "refer_spec_bytes": 0, + "raw_audio_bytes": 0, + "audio_16k_bytes": 0, + } + for state in states: + spec_audio, audio_16k = state.refer_spec + item = { + "request_id": state.request_id, + "prompt_semantic_len": int(state.prompt_semantic.shape[0]), + "phones_len": int(state.phones.shape[0]), + "all_phones_len": int(state.all_phones.shape[0]), + "bert_frames": int(state.all_bert_features.shape[-1]), + "phones_bytes": tensor_nbytes(state.phones), + "prompt_phones_bytes": tensor_nbytes(state.prompt_phones), + "all_phones_bytes": tensor_nbytes(state.all_phones), + "all_bert_features_bytes": tensor_nbytes(state.all_bert_features), + "prompt_semantic_bytes": tensor_nbytes(state.prompt_semantic), + "refer_spec_bytes": tensor_nbytes(spec_audio), + "audio_16k_bytes": tensor_nbytes(audio_16k), + "raw_audio_bytes": tensor_nbytes(state.raw_audio), + } + for key in total: + total[key] += int(item[key]) + per_request.append(item) + total["total_bytes"] = int(sum(total.values())) + total["total_mb"] = bytes_to_mb(total["total_bytes"]) + return {"per_request": per_request, "total": total} + + +def summarise_prefill_batch(active_batch: Any) -> Dict[str, Any]: + y_sequence_bytes = int(sum(tensor_nbytes(item) for item in active_batch.y_sequences)) + fields = { + "x_bytes": tensor_nbytes(active_batch.x), + "x_lens_bytes": tensor_nbytes(active_batch.x_lens), + "prefix_lens_bytes": tensor_nbytes(active_batch.prefix_lens), + "xy_pos_bytes": tensor_nbytes(active_batch.xy_pos), + "key_padding_mask_bytes": tensor_nbytes(active_batch.key_padding_mask), + "prefill_attn_mask_bytes": tensor_nbytes(active_batch.prefill_attn_mask), + "y_sequence_bytes": y_sequence_bytes, + } + fields["total_bytes"] = int(sum(fields.values())) + fields["total_mb"] = bytes_to_mb(fields["total_bytes"]) + fields["batch_size"] = int(len(active_batch.states)) + fields["max_x_len"] = int(active_batch.x.shape[1]) + fields["src_len"] = int(active_batch.xy_pos.shape[1]) + fields["prefill_attn_mask_shape"] = list(active_batch.prefill_attn_mask.shape) + return fields + + +def summarise_running_requests(running_requests: Sequence[T2SRunningRequest]) -> Dict[str, Any]: + per_request = [] + total_private_k_bytes = 0 + total_private_v_bytes = 0 + total_decode_mask_bytes = 0 + total_y_sequence_bytes = 0 + for item in running_requests: + k_bytes = tensor_list_nbytes(item.k_cache) + v_bytes = tensor_list_nbytes(item.v_cache) + mask_bytes = tensor_nbytes(item.decode_attn_mask) + y_bytes = tensor_nbytes(item.y_sequence) + total_private_k_bytes += k_bytes + total_private_v_bytes += v_bytes + total_decode_mask_bytes += mask_bytes + total_y_sequence_bytes += y_bytes + per_request.append( + { + "request_id": item.state.request_id, + "step_idx": int(item.step_idx), + "prefix_len": int(item.prefix_len), + "history_len": int(item.y_sequence.shape[0]), + "kv_len": int(item.k_cache[0].shape[1]), + "k_cache_bytes": k_bytes, + "v_cache_bytes": v_bytes, + "decode_mask_bytes": mask_bytes, + "y_sequence_bytes": y_bytes, + } + ) + total_bytes = total_private_k_bytes + total_private_v_bytes + total_decode_mask_bytes + total_y_sequence_bytes + return { + "per_request": per_request, + "totals": { + "private_k_cache_bytes": int(total_private_k_bytes), + "private_v_cache_bytes": int(total_private_v_bytes), + "private_kv_cache_bytes": int(total_private_k_bytes + total_private_v_bytes), + "decode_mask_bytes": int(total_decode_mask_bytes), + "y_sequence_bytes": int(total_y_sequence_bytes), + "total_bytes": int(total_bytes), + "total_mb": bytes_to_mb(total_bytes), + }, + } + + +def summarise_decode_batch( + xy_pos: torch.Tensor, + batched_k_cache: Sequence[torch.Tensor], + batched_v_cache: Sequence[torch.Tensor], + batched_decode_attn_mask: Optional[torch.Tensor], + running_requests: Sequence[T2SRunningRequest], +) -> Dict[str, Any]: + private_k_bytes = int(sum(tensor_list_nbytes(item.k_cache) for item in running_requests)) + private_v_bytes = int(sum(tensor_list_nbytes(item.v_cache) for item in running_requests)) + batched_k_bytes = tensor_list_nbytes(batched_k_cache) + batched_v_bytes = tensor_list_nbytes(batched_v_cache) + batched_mask_bytes = tensor_nbytes(batched_decode_attn_mask) + xy_pos_bytes = tensor_nbytes(xy_pos) + total_bytes = batched_k_bytes + batched_v_bytes + batched_mask_bytes + xy_pos_bytes + return { + "batch_size": int(len(running_requests)), + "xy_pos_bytes": int(xy_pos_bytes), + "batched_k_cache_bytes": int(batched_k_bytes), + "batched_v_cache_bytes": int(batched_v_bytes), + "batched_kv_cache_bytes": int(batched_k_bytes + batched_v_bytes), + "batched_decode_mask_bytes": int(batched_mask_bytes), + "private_kv_cache_bytes_reference": int(private_k_bytes + private_v_bytes), + "kv_padding_overhead_bytes": int((batched_k_bytes + batched_v_bytes) - (private_k_bytes + private_v_bytes)), + "total_bytes": int(total_bytes), + "total_mb": bytes_to_mb(total_bytes), + "xy_pos_shape": list(xy_pos.shape), + "batched_decode_mask_shape": None if batched_decode_attn_mask is None else list(batched_decode_attn_mask.shape), + "layer_k_cache_shape": list(batched_k_cache[0].shape), + } + + +def summarise_decode_outputs( + xy_dec: torch.Tensor, + next_k_cache: Sequence[torch.Tensor], + next_v_cache: Sequence[torch.Tensor], +) -> Dict[str, Any]: + xy_dec_bytes = tensor_nbytes(xy_dec) + next_k_bytes = tensor_list_nbytes(next_k_cache) + next_v_bytes = tensor_list_nbytes(next_v_cache) + total_bytes = xy_dec_bytes + next_k_bytes + next_v_bytes + return { + "xy_dec_bytes": int(xy_dec_bytes), + "next_k_cache_bytes": int(next_k_bytes), + "next_v_cache_bytes": int(next_v_bytes), + "next_kv_cache_bytes": int(next_k_bytes + next_v_bytes), + "total_bytes": int(total_bytes), + "total_mb": bytes_to_mb(total_bytes), + "xy_dec_shape": list(xy_dec.shape), + "layer_next_k_cache_shape": list(next_k_cache[0].shape), + } + + +def top_rankings(summary: Dict[str, Any]) -> List[Dict[str, Any]]: + ranking = [ + ("request_state_total", summary["prepare_stage"]["request_state"]["total"]["total_bytes"]), + ("prefill_batch_total", summary["prefill_batch"]["tensor_bytes"]["total_bytes"]), + ("running_private_kv", summary["prefill_step"]["running_requests"]["totals"]["private_kv_cache_bytes"]), + ("decode_batched_kv", summary["decode_batch"]["tensor_bytes"]["batched_kv_cache_bytes"]), + ("decode_kv_padding_overhead", summary["decode_batch"]["tensor_bytes"]["kv_padding_overhead_bytes"]), + ("decode_outputs_next_kv", summary["decode_outputs"]["tensor_bytes"]["next_kv_cache_bytes"]), + ("prefill_attn_mask", summary["prefill_batch"]["tensor_bytes"]["prefill_attn_mask_bytes"]), + ] + ranking.sort(key=lambda item: item[1], reverse=True) + return [{"name": name, "bytes": int(value), "mb": bytes_to_mb(int(value))} for name, value in ranking] + + +def synthesize_finished_item(tts: TTS, state: T2SRequestState, semantic_tokens: torch.Tensor) -> Tuple[int, np.ndarray]: + semantic_tokens = semantic_tokens.unsqueeze(0).unsqueeze(0).to(tts.configs.device) + phones = state.phones.unsqueeze(0).to(tts.configs.device) + audio_fragment = tts.synthesize_audio_request_local( + semantic_tokens=semantic_tokens, + phones=phones, + prompt_semantic=state.prompt_semantic, + prompt_phones=state.prompt_phones, + refer_spec=state.refer_spec, + raw_audio=state.raw_audio, + raw_sr=state.raw_sr, + speed=1.0, + sample_steps=32, + ) + output_sr = tts.configs.sampling_rate if not tts.configs.use_vocoder else tts.vocoder_configs["sr"] + return tts.audio_postprocess( + audio=[[audio_fragment]], + sr=int(output_sr), + batch_index_list=None, + speed_factor=1.0, + split_bucket=False, + fragment_interval=0.0, + super_sampling=False, + ) + + +def simulate_worker_end_to_end( + tts: TTS, + specs: Sequence[SchedulerRequestSpec], + max_steps: int, + rounds: int, + grad_mode: str = "default", +) -> Dict[str, Any]: + device = tts.configs.device + recorder = GlobalPeakRecorder(device) + recorder.record("after_model_load") + + state_map: Dict[str, T2SRequestState] = {} + per_round: List[Dict[str, Any]] = [] + + for round_index in range(rounds): + grad_context = torch.inference_mode if grad_mode == "inference_mode" else contextlib.nullcontext + with grad_context(): + states = [prepare_request_state(tts, spec) for spec in specs] + state_map = {state.request_id: state for state in states} + recorder.record( + "after_prepare_states", + round_index=int(round_index), + request_count=int(len(states)), + grad_mode=grad_mode, + ) + + pending = list(states) + running_requests: List[T2SRunningRequest] = [] + round_events: List[Dict[str, Any]] = [] + current_tick = 0 + + while pending or running_requests: + admitted = pending + pending = [] + + if admitted: + recorder.record( + "before_prefill", + round_index=int(round_index), + tick=int(current_tick), + admitted_count=int(len(admitted)), + running_count=int(len(running_requests)), + grad_mode=grad_mode, + ) + with grad_context(): + admitted_running, admitted_finished = run_prefill_step(tts.t2s_model.model, admitted, max_steps=max_steps) + recorder.record( + "after_prefill", + round_index=int(round_index), + tick=int(current_tick), + admitted_running_count=int(len(admitted_running)), + admitted_finished_count=int(len(admitted_finished)), + running_count=int(len(running_requests)), + grad_mode=grad_mode, + ) + round_events.append( + { + "tick": int(current_tick), + "event": "prefill", + "admitted_count": int(len(admitted)), + "admitted_running_count": int(len(admitted_running)), + "admitted_finished_count": int(len(admitted_finished)), + } + ) + for item in admitted_finished: + recorder.record( + "before_synth_prefill_finished", + round_index=int(round_index), + tick=int(current_tick), + running_count=int(len(running_requests)), + finished_request_id=item.request_id, + semantic_len=int(item.semantic_tokens.shape[0]), + grad_mode=grad_mode, + ) + with grad_context(): + sample_rate, audio_data = synthesize_finished_item(tts, state_map[item.request_id], item.semantic_tokens) + recorder.record( + "after_synth_prefill_finished", + round_index=int(round_index), + tick=int(current_tick), + running_count=int(len(running_requests)), + finished_request_id=item.request_id, + sample_rate=int(sample_rate), + audio_samples=int(audio_data.shape[0]), + grad_mode=grad_mode, + ) + running_requests.extend(admitted_running) + recorder.record( + "after_extend_running", + round_index=int(round_index), + tick=int(current_tick), + running_count=int(len(running_requests)), + grad_mode=grad_mode, + ) + + if running_requests: + recorder.record( + "before_decode", + round_index=int(round_index), + tick=int(current_tick), + running_count=int(len(running_requests)), + grad_mode=grad_mode, + ) + with grad_context(): + running_requests, step_finished = run_decode_step_for_running( + tts.t2s_model.model, + running_requests, + max_steps=max_steps, + ) + recorder.record( + "after_decode", + round_index=int(round_index), + tick=int(current_tick), + running_count=int(len(running_requests)), + finished_count=int(len(step_finished)), + grad_mode=grad_mode, + ) + round_events.append( + { + "tick": int(current_tick), + "event": "decode", + "running_count_after_decode": int(len(running_requests)), + "finished_count": int(len(step_finished)), + } + ) + for item in step_finished: + recorder.record( + "before_synth_decode_finished", + round_index=int(round_index), + tick=int(current_tick), + running_count=int(len(running_requests)), + finished_request_id=item.request_id, + semantic_len=int(item.semantic_tokens.shape[0]), + grad_mode=grad_mode, + ) + with grad_context(): + sample_rate, audio_data = synthesize_finished_item(tts, state_map[item.request_id], item.semantic_tokens) + recorder.record( + "after_synth_decode_finished", + round_index=int(round_index), + tick=int(current_tick), + running_count=int(len(running_requests)), + finished_request_id=item.request_id, + sample_rate=int(sample_rate), + audio_samples=int(audio_data.shape[0]), + grad_mode=grad_mode, + ) + current_tick += 1 + + recorder.record( + "after_round_complete", + round_index=int(round_index), + running_count=0, + grad_mode=grad_mode, + ) + per_round.append( + { + "round_index": int(round_index), + "events": round_events, + } + ) + + return { + "grad_mode": grad_mode, + "rounds": per_round, + "timeline": recorder.summary(), + } + + +def main() -> None: + args = parse_args() + args.output_dir.mkdir(parents=True, exist_ok=True) + + tts = load_pipeline(args.config) + model = tts.t2s_model.model + device = tts.configs.device + use_cuda = str(device).startswith("cuda") and torch.cuda.is_available() + set_seed(args.seed, use_cuda) + + specs = load_request_specs(args) + if args.early_stop_num == -1: + for spec in specs: + spec.early_stop_num = int(tts.configs.hz * tts.configs.max_sec) + + if args.warmup and specs: + warmup_spec = specs[:1] + _ = [prepare_request_state(tts, spec) for spec in warmup_spec] + gc.collect() + if use_cuda: + torch.cuda.empty_cache() + _sync_device(device) + + states, prepare_mem = stage_run(device, lambda: [prepare_request_state(tts, spec) for spec in specs]) + request_state_summary = summarise_state_tensors(states) + + active_batch, prefill_batch_mem = stage_run(device, lambda: build_prefill_batch(model, states)) + prefill_batch_tensor_summary = summarise_prefill_batch(active_batch) + + prefill_result, prefill_step_mem = stage_run(device, lambda: run_prefill_step(model, states, max_steps=args.max_steps)) + running_requests, finished_items = prefill_result + running_requests_summary = summarise_running_requests(running_requests) + finished_after_prefill_summary = [ + { + "request_id": item.request_id, + "finish_idx": int(item.finish_idx), + "finish_reason": item.finish_reason, + "semantic_len": int(item.semantic_tokens.shape[0]), + } + for item in finished_items + ] + + if not running_requests: + raise RuntimeError(f"prefill 后没有 running requests，全部在首步结束: {[item.request_id for item in finished_items]}") + + decode_batch_result, decode_batch_mem = stage_run( + device, + lambda: _build_decode_batch_from_running(model, running_requests), + ) + xy_pos, batched_k_cache, batched_v_cache, batched_decode_attn_mask = decode_batch_result + decode_batch_tensor_summary = summarise_decode_batch( + xy_pos, + batched_k_cache, + batched_v_cache, + batched_decode_attn_mask, + running_requests, + ) + + decode_out_result, decode_step_mem = stage_run( + device, + lambda: model.t2s_transformer.decode_next_token( + xy_pos, + batched_k_cache, + batched_v_cache, + batched_decode_attn_mask, + ), + ) + xy_dec, next_k_cache, next_v_cache = decode_out_result + decode_output_tensor_summary = summarise_decode_outputs(xy_dec, next_k_cache, next_v_cache) + del active_batch + del running_requests + del finished_items + del xy_pos + del batched_k_cache + del batched_v_cache + del batched_decode_attn_mask + del xy_dec + del next_k_cache + del next_v_cache + gc.collect() + if use_cuda: + _sync_device(device) + torch.cuda.empty_cache() + end_to_end_worker = simulate_worker_end_to_end( + tts=tts, + specs=specs, + max_steps=args.max_steps, + rounds=args.worker_rounds, + grad_mode=args.worker_grad_mode, + ) + live_cuda_tensors_after_worker = snapshot_live_cuda_tensors() + worker_inference_mode = None + if args.compare_worker_grad_modes: + gc.collect() + if use_cuda: + _sync_device(device) + torch.cuda.empty_cache() + worker_inference_mode = simulate_worker_end_to_end( + tts=tts, + specs=specs, + max_steps=args.max_steps, + rounds=args.worker_rounds, + grad_mode="inference_mode", + ) + + summary = { + "meta": { + "scenario": args.scenario if args.request_manifest is None else "manifest", + "seed": int(args.seed), + "device": str(device), + "dtype": str(next(model.parameters()).dtype), + "request_count": int(len(specs)), + "num_layers": int(model.num_layers), + "num_heads": int(model.num_head), + "model_dim": int(model.model_dim), + "model_weights_mb": bytes_to_mb(model_nbytes(model)), + }, + "loaded_module_weights": build_module_weight_summary(tts), + "requests": [ + { + "request_id": spec.request_id, + "ref_audio_path": str(spec.ref_audio_path), + "prompt_text": spec.prompt_text, + "text": spec.text, + } + for spec in specs + ], + "prepare_stage": { + "memory": prepare_mem, + "request_state": request_state_summary, + }, + "prefill_batch": { + "memory": prefill_batch_mem, + "tensor_bytes": prefill_batch_tensor_summary, + }, + "prefill_step": { + "memory": prefill_step_mem, + "running_requests": running_requests_summary, + "finished_after_prefill": finished_after_prefill_summary, + }, + "decode_batch": { + "memory": decode_batch_mem, + "tensor_bytes": decode_batch_tensor_summary, + }, + "decode_outputs": { + "memory": decode_step_mem, + "tensor_bytes": decode_output_tensor_summary, + }, + "end_to_end_worker": end_to_end_worker, + "live_cuda_tensors_after_worker": live_cuda_tensors_after_worker, + "end_to_end_worker_inference_mode": worker_inference_mode, + } + summary["top_rankings"] = top_rankings(summary) + + summary_path = args.output_dir / "t2s_memory_breakdown_summary.json" + summary_path.write_text(json.dumps(summary, ensure_ascii=False, indent=2), encoding="utf-8") + + print(json.dumps(summary["meta"], ensure_ascii=False, indent=2)) + print("[top_rankings]") + for item in summary["top_rankings"]: + print(f"- {item['name']}: {item['mb']:.3f} MB") + print("[worker_peak]") + print( + json.dumps( + { + "peak_label": summary["end_to_end_worker"]["timeline"]["peak_label"], + "peak_allocated_mb": summary["end_to_end_worker"]["timeline"]["peak_allocated_mb"], + "peak_reserved_mb": summary["end_to_end_worker"]["timeline"]["peak_reserved_mb"], + }, + ensure_ascii=False, + indent=2, + ) + ) + if worker_inference_mode is not None: + print("[worker_peak_inference_mode]") + print( + json.dumps( + { + "peak_label": worker_inference_mode["timeline"]["peak_label"], + "peak_allocated_mb": worker_inference_mode["timeline"]["peak_allocated_mb"], + "peak_reserved_mb": worker_inference_mode["timeline"]["peak_reserved_mb"], + }, + ensure_ascii=False, + indent=2, + ) + ) + print(f"[summary] {summary_path}") + + +if __name__ == "__main__": + main() diff --git a/tools/t2s_scheduler_prototype.py b/tools/t2s_scheduler_prototype.py new file mode 100644 index 00000000..cd4b9c6d --- /dev/null +++ b/tools/t2s_scheduler_prototype.py @@ -0,0 +1,180 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- + +from __future__ import annotations + +import argparse +import json +import random +import sys +from pathlib import Path +from typing import Any, Dict, List + +import numpy as np +import torch + +ROOT_DIR = Path(__file__).resolve().parents[1] +if str(ROOT_DIR) not in sys.path: + sys.path.append(str(ROOT_DIR)) +gpt_sovits_dir = ROOT_DIR / "GPT_SoVITS" +if str(gpt_sovits_dir) not in sys.path: + sys.path.append(str(gpt_sovits_dir)) + +from GPT_SoVITS.TTS_infer_pack.t2s_scheduler import ( # noqa: E402 + SchedulerRequestSpec, + T2SFinishedItem, + T2SRequestState, + prepare_request_state, + run_scheduler_continuous, +) + + +def parse_args() -> argparse.Namespace: + parser = argparse.ArgumentParser(description="T2S request-local scheduler prototype.") + parser.add_argument("--config", type=Path, default=ROOT_DIR / "GPT_SoVITS/configs/tts_infer.yaml") + parser.add_argument("--request-manifest", type=Path, default=None) + parser.add_argument("--ref-audio", type=Path, default=ROOT_DIR / "test.wav") + parser.add_argument("--prompt-text", type=str, default="是啊，主要是因为有调研需求的学者少了。") + parser.add_argument("--prompt-lang", type=str, default="zh") + parser.add_argument("--text-file", type=Path, default=ROOT_DIR / "test_en.txt") + parser.add_argument("--text", type=str, default=None) + parser.add_argument("--text-lang", type=str, default="en") + parser.add_argument("--top-k", type=int, default=15) + parser.add_argument("--top-p", type=float, default=1.0) + parser.add_argument("--temperature", type=float, default=1.0) + parser.add_argument("--repetition-penalty", type=float, default=1.35) + parser.add_argument("--early-stop-num", type=int, default=-1) + parser.add_argument("--max-steps", type=int, default=1500) + parser.add_argument("--seed", type=int, default=1234) + parser.add_argument("--output-dir", type=Path, default=ROOT_DIR / "TEMP/t2s_scheduler/output_run") + return parser.parse_args() + + +def set_seed(seed: int, use_cuda: bool) -> None: + random.seed(seed) + np.random.seed(seed) + torch.manual_seed(seed) + if use_cuda and torch.cuda.is_available(): + torch.cuda.manual_seed(seed) + torch.cuda.manual_seed_all(seed) + + +def load_pipeline(config_path: Path): + try: + from GPT_SoVITS.TTS_infer_pack.TTS import TTS, TTS_Config + except ModuleNotFoundError as exc: + raise ModuleNotFoundError( + "缺少运行依赖，请先在 GPT-SoVITS 推理环境中安装 requirements 后再运行该脚本。" + ) from exc + tts_config = TTS_Config(str(config_path)) + print(tts_config) + return TTS(tts_config) + + +def load_request_specs(args: argparse.Namespace) -> List[SchedulerRequestSpec]: + if args.request_manifest is not None: + payload = json.loads(args.request_manifest.read_text(encoding="utf-8")) + raw_requests = payload["requests"] if isinstance(payload, dict) else payload + specs: List[SchedulerRequestSpec] = [] + for index, item in enumerate(raw_requests): + text = item.get("text") + text_file = item.get("text_file") + if text is None and text_file is None: + raise ValueError(f"request[{index}] must provide text or text_file") + if text is None: + text = Path(text_file).read_text(encoding="utf-8") + specs.append( + SchedulerRequestSpec( + request_id=item.get("request_id", f"req_{index:03d}"), + ref_audio_path=Path(item["ref_audio_path"]), + prompt_text=item["prompt_text"], + prompt_lang=item.get("prompt_lang", "zh"), + text=text, + text_lang=item.get("text_lang", "zh"), + top_k=int(item.get("top_k", args.top_k)), + top_p=float(item.get("top_p", args.top_p)), + temperature=float(item.get("temperature", args.temperature)), + repetition_penalty=float(item.get("repetition_penalty", args.repetition_penalty)), + early_stop_num=int(item.get("early_stop_num", args.early_stop_num)), + ready_step=int(item.get("ready_step", 0)), + ) + ) + return specs + + text = args.text if args.text is not None else args.text_file.read_text(encoding="utf-8") + return [ + SchedulerRequestSpec( + request_id="req_000", + ref_audio_path=args.ref_audio, + prompt_text=args.prompt_text, + prompt_lang=args.prompt_lang, + text=text, + text_lang=args.text_lang, + top_k=args.top_k, + top_p=args.top_p, + temperature=args.temperature, + repetition_penalty=args.repetition_penalty, + early_stop_num=args.early_stop_num, + ready_step=0, + ) + ] + + +def summarise_requests(states: List[T2SRequestState]) -> List[Dict[str, Any]]: + return [ + { + "request_id": state.request_id, + "ready_step": int(state.ready_step), + "ref_audio_path": str(state.ref_audio_path), + "prompt_semantic_len": int(state.prompt_semantic.shape[0]), + "all_phone_len": int(state.all_phones.shape[0]), + "bert_len": int(state.all_bert_features.shape[-1]), + "norm_text": state.norm_text, + } + for state in states + ] + + +def summarise_finished(items: List[T2SFinishedItem]) -> List[Dict[str, Any]]: + return [ + { + "request_id": item.request_id, + "semantic_len": int(item.semantic_tokens.shape[0]), + "finish_idx": int(item.finish_idx), + "finish_reason": item.finish_reason, + } + for item in items + ] + + +def main() -> None: + args = parse_args() + args.output_dir.mkdir(parents=True, exist_ok=True) + + tts = load_pipeline(args.config) + model = tts.t2s_model.model + use_cuda = str(tts.configs.device).startswith("cuda") + set_seed(args.seed, use_cuda) + + request_specs = load_request_specs(args) + states = [prepare_request_state(tts, spec) for spec in request_specs] + finished = run_scheduler_continuous(model, states, max_steps=args.max_steps) + + summary = { + "request_count": len(states), + "max_steps": args.max_steps, + "requests": summarise_requests(states), + "finished": summarise_finished(finished), + } + output_path = args.output_dir / "scheduler_prototype_summary.json" + output_path.write_text(json.dumps(summary, ensure_ascii=False, indent=2), encoding="utf-8") + print(json.dumps(summary, ensure_ascii=False, indent=2)) + print(f"[saved] {output_path}") + + +if __name__ == "__main__": + try: + main() + except ModuleNotFoundError as exc: + print(f"[error] {exc}") + raise SystemExit(1) from None