import os
import sys
import io,time
from fastapi import FastAPI, Response, File, UploadFile, Form
from fastapi.responses import HTMLResponse
from contextlib import asynccontextmanager
ROOT_DIR = os.path.dirname(os.path.abspath(__file__))
sys.path.append('{}/../..'.format(ROOT_DIR))
sys.path.append('{}/../../third_party/Matcha-TTS'.format(ROOT_DIR))
from cosyvoice.cli.cosyvoice import CosyVoice
from cosyvoice.utils.file_utils import load_wav
import numpy as np
import torch
import torchaudio
import logging
logging.getLogger('matplotlib').setLevel(logging.WARNING)
class LaunchFailed(Exception):
pass
@asynccontextmanager
async def lifespan(app: FastAPI):
model_dir = os.getenv("MODEL_DIR", "pretrained_models/CosyVoice-300M-SFT")
if model_dir:
logging.info("MODEL_DIR is {}", model_dir)
app.cosyvoice = CosyVoice('../../'+model_dir)
# sft usage
logging.info("Avaliable speakers {}", app.cosyvoice.list_avaliable_spks())
else:
raise LaunchFailed("MODEL_DIR environment must set")
yield
app = FastAPI(lifespan=lifespan)
def buildResponse(output):
buffer = io.BytesIO()
torchaudio.save(buffer, output, 22050, format="wav")
buffer.seek(0)
return Response(content=buffer.read(-1), media_type="audio/wav")
@app.post("/api/inference/sft")
@app.get("/api/inference/sft")
async def sft(tts: str = Form(), role: str = Form()):
start = time.process_time()
output = app.cosyvoice.inference_sft(tts, role)
end = time.process_time()
logging.info("infer time is {} seconds", end-start)
return buildResponse(output['tts_speech'])
@app.post("/api/inference/zero-shot")
async def zeroShot(tts: str = Form(), prompt: str = Form(), audio: UploadFile = File()):
start = time.process_time()
prompt_speech = load_wav(audio.file, 16000)
prompt_audio = (prompt_speech.numpy() * (2**15)).astype(np.int16).tobytes()
prompt_speech_16k = torch.from_numpy(np.array(np.frombuffer(prompt_audio, dtype=np.int16))).unsqueeze(dim=0)
prompt_speech_16k = prompt_speech_16k.float() / (2**15)
output = app.cosyvoice.inference_zero_shot(tts, prompt, prompt_speech_16k)
end = time.process_time()
logging.info("infer time is {} seconds", end-start)
return buildResponse(output['tts_speech'])
@app.post("/api/inference/cross-lingual")
async def crossLingual(tts: str = Form(), audio: UploadFile = File()):
start = time.process_time()
prompt_speech = load_wav(audio.file, 16000)
prompt_audio = (prompt_speech.numpy() * (2**15)).astype(np.int16).tobytes()
prompt_speech_16k = torch.from_numpy(np.array(np.frombuffer(prompt_audio, dtype=np.int16))).unsqueeze(dim=0)
prompt_speech_16k = prompt_speech_16k.float() / (2**15)
output = app.cosyvoice.inference_cross_lingual(tts, prompt_speech_16k)
end = time.process_time()
logging.info("infer time is {} seconds", end-start)
return buildResponse(output['tts_speech'])
@app.post("/api/inference/instruct")
@app.get("/api/inference/instruct")
async def instruct(tts: str = Form(), role: str = Form(), instruct: str = Form()):
start = time.process_time()
output = app.cosyvoice.inference_instruct(tts, role, instruct)
end = time.process_time()
logging.info("infer time is {} seconds", end-start)
return buildResponse(output['tts_speech'])
@app.get("/api/roles")
async def roles():
return {"roles": app.cosyvoice.list_avaliable_spks()}
@app.get("/", response_class=HTMLResponse)
async def root():
return """
Api information
Get the supported tones from the Roles API first, then enter the tones and textual content in the TTS API for synthesis. Documents of API
"""