GPT-SoVITS/api_v3.py

"""
# 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 asyncio
import os
import sys
import time
import traceback
import uuid
from dataclasses import dataclass
from pathlib import Path
from typing import Generator, List, 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
import torch
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.t2s_scheduler import (
    SchedulerRequestSpec,
    T2SFinishedItem,
    T2SRunningRequest,
    T2SRequestState,
    prepare_request_state,
    run_decode_step_for_running,
    run_prefill_step,
    run_scheduler_continuous,
)
from GPT_SoVITS.TTS_infer_pack.text_segmentation_method import get_method_names as get_cut_method_names
from pydantic import BaseModel
import threading

# 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)

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


@dataclass
class SchedulerPendingJob:
    request_id: str
    state: T2SRequestState
    done_event: threading.Event
    enqueue_time: float
    speed_factor: float
    sample_steps: int
    media_type: str
    prepare_ms: float = 0.0
    prepare_wall_ms: float = 0.0
    first_schedule_time: float | None = None
    prefill_ms: float = 0.0
    decode_ms: float = 0.0
    synth_ms: float = 0.0
    pack_ms: float = 0.0
    decode_steps: int = 0
    result: dict | None = None
    sample_rate: int | None = None
    audio_data: np.ndarray | None = None
    error: str | None = None


class SchedulerDebugWorker:
    def __init__(self, tts: TTS, max_steps: int = 1500, micro_batch_wait_ms: int = 5):
        self.tts = tts
        self.max_steps = max_steps
        self.micro_batch_wait_s = micro_batch_wait_ms / 1000.0
        self.prepare_lock = threading.Lock()
        self.condition = threading.Condition()
        self.pending_jobs: List[SchedulerPendingJob] = []
        self.running_requests: List[T2SRunningRequest] = []
        self.job_map: dict[str, SchedulerPendingJob] = {}
        self.total_finished = 0
        self.total_submitted = 0
        self.worker_thread = threading.Thread(target=self._run_loop, name="t2s-scheduler-debug-worker", daemon=True)
        self.worker_thread.start()

    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 prepare_state(self, spec: SchedulerRequestSpec) -> T2SRequestState:
        with self.prepare_lock:
            return prepare_request_state(self.tts, spec)

    def submit(
        self,
        state: T2SRequestState,
        speed_factor: float,
        sample_steps: int,
        media_type: str,
        prepare_ms: float,
        prepare_wall_ms: float,
    ) -> SchedulerPendingJob:
        job = SchedulerPendingJob(
            request_id=state.request_id,
            state=state,
            done_event=threading.Event(),
            enqueue_time=time.perf_counter(),
            speed_factor=float(speed_factor),
            sample_steps=int(sample_steps),
            media_type=media_type,
            prepare_ms=float(prepare_ms),
            prepare_wall_ms=float(prepare_wall_ms),
        )
        with self.condition:
            self.pending_jobs.append(job)
            self.job_map[job.request_id] = job
            self.total_submitted += 1
            self.condition.notify_all()
        return job

    def _mark_prefill_started(self, jobs: List[SchedulerPendingJob], started_at: float) -> None:
        with self.condition:
            for job in jobs:
                tracked_job = self.job_map.get(job.request_id)
                if tracked_job is not None and tracked_job.first_schedule_time is None:
                    tracked_job.first_schedule_time = started_at

    def _add_prefill_time(self, jobs: List[SchedulerPendingJob], elapsed_s: float) -> None:
        elapsed_ms = elapsed_s * 1000.0
        with self.condition:
            for job in jobs:
                tracked_job = self.job_map.get(job.request_id)
                if tracked_job is not None:
                    tracked_job.prefill_ms += elapsed_ms

    def _add_decode_time(self, request_ids: List[str], elapsed_s: float) -> None:
        elapsed_ms = elapsed_s * 1000.0
        with self.condition:
            for request_id in request_ids:
                job = self.job_map.get(request_id)
                if job is not None:
                    job.decode_ms += elapsed_ms
                    job.decode_steps += 1

    def _synthesize_finished_audio(self, job: SchedulerPendingJob, item: T2SFinishedItem) -> tuple[int, np.ndarray]:
        semantic_tokens = item.semantic_tokens.unsqueeze(0).unsqueeze(0).to(self.tts.configs.device)
        phones = job.state.phones.unsqueeze(0).to(self.tts.configs.device)
        audio_fragment = self.tts.synthesize_audio_request_local(
            semantic_tokens=semantic_tokens,
            phones=phones,
            prompt_semantic=job.state.prompt_semantic,
            prompt_phones=job.state.prompt_phones,
            refer_spec=job.state.refer_spec,
            raw_audio=job.state.raw_audio,
            raw_sr=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 get_state(self) -> dict:
        with self.condition:
            return {
                "pending_jobs": len(self.pending_jobs),
                "running_requests": len(self.running_requests),
                "tracked_jobs": len(self.job_map),
                "total_submitted": self.total_submitted,
                "total_finished": self.total_finished,
                "max_steps": self.max_steps,
                "micro_batch_wait_ms": int(self.micro_batch_wait_s * 1000),
            }

    def _finalize_finished(self, items: List[T2SFinishedItem]) -> None:
        if not items:
            return
        jobs_to_finalize: List[tuple[SchedulerPendingJob, T2SFinishedItem]] = []
        with self.condition:
            for item in items:
                job = self.job_map.get(item.request_id)
                if job is not None:
                    jobs_to_finalize.append((job, item))

        for job, item in jobs_to_finalize:
            try:
                self._sync_device()
                synth_start = time.perf_counter()
                sample_rate, audio_data = self._synthesize_finished_audio(job, item)
                self._sync_device()
                synth_ms = (time.perf_counter() - synth_start) * 1000.0
            except Exception as exc:
                self._finalize_error([item.request_id], str(exc))
                continue

            finished_at = time.perf_counter()
            with self.condition:
                if self.job_map.get(item.request_id) is not job:
                    continue
                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)
                job.synth_ms += synth_ms
                job.sample_rate = int(sample_rate)
                job.audio_data = audio_data
                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,
                    "prepare_ms": job.prepare_ms,
                    "prepare_wall_ms": job.prepare_wall_ms,
                    "prepare_profile": prepare_profile,
                    "queue_wait_ms": queue_wait_ms,
                    "prefill_ms": job.prefill_ms,
                    "decode_ms": job.decode_ms,
                    "synth_ms": job.synth_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.job_map.pop(item.request_id, None)
                self.total_finished += 1

    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.job_map.pop(request_id, None)
                self.total_finished += 1

    def _take_pending_snapshot(self, wait_for_batch: bool) -> List[SchedulerPendingJob]:
        with self.condition:
            if not self.pending_jobs and not self.running_requests:
                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_loop(self) -> None:
        while True:
            wait_for_batch = len(self.running_requests) == 0
            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_running, admitted_finished = run_prefill_step(
                        self.tts.t2s_model.model,
                        [job.state for job in pending_jobs],
                        max_steps=self.max_steps,
                    )
                    self._sync_device()
                    self._add_prefill_time(pending_jobs, time.perf_counter() - prefill_start)
                    self._finalize_finished(admitted_finished)
                    self.running_requests.extend(admitted_running)
                except Exception as exc:
                    self._finalize_error([job.request_id for job in pending_jobs], str(exc))

            if self.running_requests:
                try:
                    active_request_ids = [item.state.request_id for item in self.running_requests]
                    self._sync_device()
                    decode_start = time.perf_counter()
                    self.running_requests, step_finished = run_decode_step_for_running(
                        self.tts.t2s_model.model,
                        self.running_requests,
                        max_steps=self.max_steps,
                    )
                    self._sync_device()
                    self._add_decode_time(active_request_ids, time.perf_counter() - decode_start)
                    self._finalize_finished(step_finished)
                except Exception as exc:
                    self._finalize_error(active_request_ids, str(exc))
                    self.running_requests = []
                continue

            if not pending_jobs:
                time.sleep(self.micro_batch_wait_s)


scheduler_debug_worker = SchedulerDebugWorker(tts_pipeline)


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 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 build_scheduler_request_specs(request_items: List[Scheduler_Debug_Request_Item]) -> List[SchedulerRequestSpec]:
    specs: List[SchedulerRequestSpec] = []
    for index, item in enumerate(request_items):
        payload = item.dict()
        req = {
            "text": payload["text"],
            "text_lang": payload["text_lang"].lower(),
            "ref_audio_path": payload["ref_audio_path"],
            "aux_ref_audio_paths": None,
            "prompt_text": payload["prompt_text"],
            "prompt_lang": payload["prompt_lang"].lower(),
            "top_k": payload["top_k"],
            "top_p": payload["top_p"],
            "temperature": payload["temperature"],
            "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,
            "parallel_infer": False,
            "repetition_penalty": payload["repetition_penalty"],
            "sample_steps": 32,
            "super_sampling": False,
            "overlap_length": 2,
            "min_chunk_length": 16,
        }
        check_res = check_params(req)
        if check_res is not None:
            detail = check_res.body.decode("utf-8") if hasattr(check_res, "body") else str(check_res)
            raise ValueError(f"request[{index}] 参数非法: {detail}")
        specs.append(
            SchedulerRequestSpec(
                request_id=payload["request_id"] or f"req_{index:03d}",
                ref_audio_path=Path(payload["ref_audio_path"]),
                prompt_text=payload["prompt_text"],
                prompt_lang=payload["prompt_lang"].lower(),
                text=payload["text"],
                text_lang=payload["text_lang"].lower(),
                top_k=int(payload["top_k"]),
                top_p=float(payload["top_p"]),
                temperature=float(payload["temperature"]),
                repetition_penalty=float(payload["repetition_penalty"]),
                early_stop_num=int(payload["early_stop_num"]),
                ready_step=int(payload["ready_step"]),
            )
        )
    return specs


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
    ]


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
    ]


def prepare_scheduler_states_batch(specs: List[SchedulerRequestSpec]) -> List[T2SRequestState]:
    return [scheduler_debug_worker.prepare_state(spec) for spec in specs]


def build_scheduler_submit_spec(request: Scheduler_Submit_Request) -> SchedulerRequestSpec:
    payload = request.dict()
    request_id = payload["request_id"] or f"job_{uuid.uuid4().hex[:12]}"
    req = {
        "text": payload["text"],
        "text_lang": payload["text_lang"].lower(),
        "ref_audio_path": payload["ref_audio_path"],
        "aux_ref_audio_paths": None,
        "prompt_text": payload["prompt_text"],
        "prompt_lang": payload["prompt_lang"].lower(),
        "top_k": payload["top_k"],
        "top_p": payload["top_p"],
        "temperature": payload["temperature"],
        "text_split_method": "cut5",
        "batch_size": 1,
        "batch_threshold": 0.75,
        "speed_factor": float(payload["speed_factor"]),
        "split_bucket": False,
        "fragment_interval": 0.3,
        "seed": -1,
        "media_type": payload["media_type"],
        "streaming_mode": False,
        "parallel_infer": False,
        "repetition_penalty": payload["repetition_penalty"],
        "sample_steps": int(payload["sample_steps"]),
        "super_sampling": False,
        "overlap_length": 2,
        "min_chunk_length": 16,
    }
    check_res = check_params(req)
    if check_res is not None:
        detail = check_res.body.decode("utf-8") if hasattr(check_res, "body") else str(check_res)
        raise ValueError(f"request 参数非法: {detail}")
    return SchedulerRequestSpec(
        request_id=request_id,
        ref_audio_path=Path(payload["ref_audio_path"]),
        prompt_text=payload["prompt_text"],
        prompt_lang=payload["prompt_lang"].lower(),
        text=payload["text"],
        text_lang=payload["text_lang"].lower(),
        top_k=int(payload["top_k"]),
        top_p=float(payload["top_p"]),
        temperature=float(payload["temperature"]),
        repetition_penalty=float(payload["repetition_penalty"]),
        early_stop_num=int(payload["early_stop_num"]),
        ready_step=0,
    )


async def tts_scheduler_debug_handle(request: Scheduler_Debug_Request):
    try:
        set_scheduler_seed(request.seed)
        specs = build_scheduler_request_specs(request.requests)
        states = await asyncio.to_thread(prepare_scheduler_states_batch, specs)
        finished = run_scheduler_continuous(tts_pipeline.t2s_model.model, states, max_steps=int(request.max_steps))
        return JSONResponse(
            status_code=200,
            content={
                "message": "success",
                "request_count": len(states),
                "max_steps": int(request.max_steps),
                "requests": summarize_scheduler_states(states),
                "finished": summarize_scheduler_finished(finished),
            },
        )
    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:
        request_start = time.perf_counter()
        spec = build_scheduler_submit_spec(request)
        prepare_start = time.perf_counter()
        state = await asyncio.to_thread(scheduler_debug_worker.prepare_state, spec)
        prepare_wall_ms = (time.perf_counter() - prepare_start) * 1000.0
        prepare_ms = float(state.prepare_profile.get("total_ms", prepare_wall_ms))
        job = scheduler_debug_worker.submit(
            state,
            speed_factor=float(request.speed_factor),
            sample_steps=int(request.sample_steps),
            media_type=request.media_type,
            prepare_ms=prepare_ms,
            prepare_wall_ms=prepare_wall_ms,
        )
        timeout_ok = await asyncio.to_thread(job.done_event.wait, float(request.timeout_sec))
        if not timeout_ok:
            return JSONResponse(
                status_code=202,
                content={
                    "message": "queued",
                    "request_id": job.request_id,
                    "timings": {
                        "prepare_ms": prepare_ms,
                        "prepare_wall_ms": prepare_wall_ms,
                        "request_elapsed_ms": max(0.0, (time.perf_counter() - request_start) * 1000.0),
                    },
                    "worker_state": scheduler_debug_worker.get_state(),
                },
            )
        if job.error is not None:
            return JSONResponse(
                status_code=400,
                content={"message": "scheduler submit failed", "request_id": job.request_id, "Exception": job.error},
            )
        if job.audio_data is None or job.sample_rate is None:
            return JSONResponse(
                status_code=500,
                content={
                    "message": "scheduler submit failed",
                    "request_id": job.request_id,
                    "Exception": "job finished without audio payload",
                },
            )
        pack_start = time.perf_counter()
        audio_data = pack_audio(BytesIO(), job.audio_data, int(job.sample_rate), job.media_type).getvalue()
        pack_ms = (time.perf_counter() - pack_start) * 1000.0
        job.pack_ms = pack_ms
        request_total_ms = max(0.0, (time.perf_counter() - request_start) * 1000.0)
        headers = {
            "X-Request-Id": job.request_id,
            "X-Semantic-Len": str(job.result["semantic_len"]) if job.result is not None else "0",
            "X-Finish-Reason": job.result["finish_reason"] if job.result is not None else "unknown",
            "X-Queue-Wait-Ms": (
                f"{float(job.result['queue_wait_ms']):.3f}" if job.result is not None else "0.000"
            ),
            "X-Prepare-Ms": f"{prepare_ms:.3f}",
            "X-Prepare-Wall-Ms": f"{prepare_wall_ms:.3f}",
            "X-Prefill-Ms": f"{float(job.result['prefill_ms']):.3f}" if job.result is not None else "0.000",
            "X-Decode-Ms": f"{float(job.result['decode_ms']):.3f}" if job.result is not None else "0.000",
            "X-Synth-Ms": f"{float(job.result['synth_ms']):.3f}" if job.result is not None else "0.000",
            "X-Pack-Ms": f"{pack_ms:.3f}",
            "X-Worker-Total-Ms": (
                f"{float(job.result['worker_total_ms']):.3f}" if job.result is not None else "0.000"
            ),
            "X-Request-Total-Ms": f"{request_total_ms:.3f}",
            "X-Decode-Steps": str(job.result["decode_steps"]) if job.result is not None else "0",
        }
        if job.result is not None:
            prepare_profile = job.result.get("prepare_profile", {})
            headers.update(
                {
                    "X-Prepare-Prompt-Text-Ms": f"{float(prepare_profile.get('prompt_text_features_ms', 0.0)):.3f}",
                    "X-Prepare-Target-Text-Ms": f"{float(prepare_profile.get('text_features_ms', 0.0)):.3f}",
                    "X-Prepare-Prompt-Semantic-Ms": f"{float(prepare_profile.get('prompt_semantic_ms', 0.0)):.3f}",
                    "X-Prepare-Ref-Spec-Ms": f"{float(prepare_profile.get('ref_spec_ms', 0.0)):.3f}",
                    "X-Prepare-Tensorize-Ms": f"{float(prepare_profile.get('tensorize_ms', 0.0)):.3f}",
                    "X-Prepare-Profile-Wall-Ms": f"{float(prepare_profile.get('wall_total_ms', 0.0)):.3f}",
                }
            )
        return Response(audio_data, media_type=f"audio/{job.media_type}", headers=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.
    """

    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}")
    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"})
    handle_control(command)


@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": text_lang.lower(),
        "ref_audio_path": ref_audio_path,
        "aux_ref_audio_paths": aux_ref_audio_paths,
        "prompt_text": prompt_text,
        "prompt_lang": prompt_lang.lower(),
        "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={"message": "success", "worker_state": scheduler_debug_worker.get_state()})


@APP.get("/set_refer_audio")
async def set_refer_aduio(refer_audio_path: str = None):
    try:
        tts_pipeline.set_ref_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"})


# @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:
        if weights_path in ["", None]:
            return JSONResponse(status_code=400, content={"message": "gpt weight path is required"})
        tts_pipeline.init_t2s_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"})


@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)
    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"})


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)