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from fastapi import HTTPException, Body
from fastapi.responses import StreamingResponse
import io
from numpy import clip
import soundfile as sf
from pydantic import BaseModel, Field
from fastapi.responses import FileResponse
from modules.synthesize_audio import synthesize_audio
from modules.normalization import text_normalize
from modules import generate_audio as generate
from typing import Literal
import pyrubberband as pyrb
from modules.api import utils as api_utils
from modules.api.Api import APIManager
import numpy as np
class AudioSpeechRequest(BaseModel):
input: str # 需要合成的文本
model: str = "chattts-4w"
voice: str = "female2"
response_format: Literal["mp3", "wav"] = "mp3"
speed: int = Field(1, ge=1, le=10, description="Speed of the audio")
style: str = ""
# 是否开启batch合成，小于等于1表示不适用batch
# 开启batch合成会自动分割句子
batch_size: int = Field(1, ge=1, le=10, description="Batch size")
spliter_threshold: float = Field(
100, ge=10, le=1024, description="Threshold for sentence spliter"
)
async def openai_speech_api(
request: AudioSpeechRequest = Body(
..., description="JSON body with model, input text, and voice"
)
):
try:
model = request.model
input_text = request.input
voice = request.voice
style = request.style
response_format = request.response_format
batch_size = request.batch_size
spliter_threshold = request.spliter_threshold
speed = request.speed
speed = clip(speed, 0.1, 10)
if not input_text:
raise HTTPException(status_code=400, detail="Input text is required.")
# Normalize the text
text = text_normalize(input_text, is_end=True)
# Calculate speaker and style based on input voice
params = api_utils.calc_spk_style(spk=voice, style=style)
spk = params.get("spk", -1)
seed = params.get("seed", 42)
temperature = params.get("temperature", 0.3)
prompt1 = params.get("prompt1", "")
prompt2 = params.get("prompt2", "")
prefix = params.get("prefix", "")
# Generate audio
sample_rate, audio_data = synthesize_audio(
text,
temperature=temperature,
top_P=0.7,
top_K=20,
spk=spk,
infer_seed=seed,
batch_size=batch_size,
spliter_threshold=spliter_threshold,
prompt1=prompt1,
prompt2=prompt2,
prefix=prefix,
)
if speed != 1:
audio_data = pyrb.time_stretch(audio_data, sample_rate, speed)
# Convert audio data to wav format
buffer = io.BytesIO()
sf.write(buffer, audio_data, sample_rate, format="wav")
buffer.seek(0)
if response_format == "mp3":
# Convert wav to mp3
buffer = api_utils.wav_to_mp3(buffer)
return StreamingResponse(buffer, media_type="audio/mp3")
except Exception as e:
import logging
logging.exception(e)
raise HTTPException(status_code=500, detail=str(e))
def setup(api_manager: APIManager):
api_manager.post("/v1/openai/audio/speech", response_class=FileResponse)(
openai_speech_api
)
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