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import os
import modal
import fastapi
import fastapi.staticfiles
from modal_const import CACHE_PATH
from pydantic import BaseModel
from fastapi.responses import Response
stub = modal.Stub("bert-vits2")
web_app = fastapi.FastAPI()
def download_model_weights():
import requests
from huggingface_hub import snapshot_download
from transformers.utils import move_cache
model_files = ["config.json", "D_88000.pth", "DUR_88000.pth", "G_88000.pth", "WD_88000.pth"]
os.makedirs(CACHE_PATH, exist_ok=True)
for model_file in model_files:
rsp = requests.get("HOSTURL" + model_file)
rsp.raise_for_status()
with open(os.path.join(CACHE_PATH, model_file), "wb") as f:
for chunk in rsp.iter_content(chunk_size=1024*1024):
if chunk:
f.write(chunk)
os.makedirs(CACHE_PATH + "/bert/deberta-v2-large-japanese-char-wwm", exist_ok=True)
snapshot_download(
"ku-nlp/deberta-v2-large-japanese-char-wwm",
local_dir=CACHE_PATH + "/bert/deberta-v2-large-japanese-char-wwm",
)
move_cache()
os.makedirs(CACHE_PATH + "/bert/chinese-roberta-wwm-ext-large", exist_ok=True)
snapshot_download(
"hfl/chinese-roberta-wwm-ext-large",
local_dir=CACHE_PATH + "/bert/chinese-roberta-wwm-ext-large",
)
move_cache()
os.makedirs(CACHE_PATH + "/bert/deberta-v3-large", exist_ok=True)
snapshot_download(
"microsoft/deberta-v3-large",
local_dir=CACHE_PATH + "/bert/deberta-v3-large",
)
move_cache()
os.makedirs(CACHE_PATH + "/bert/deberta-v2-large-japanese", exist_ok=True)
snapshot_download(
"ku-nlp/deberta-v2-large-japanese",
local_dir=CACHE_PATH + "/bert/deberta-v2-large-japanese",
)
move_cache()
os.makedirs(CACHE_PATH + "/bert/bert-base-japanese-v3", exist_ok=True)
snapshot_download(
"cl-tohoku/bert-base-japanese-v3",
local_dir=CACHE_PATH + "/bert/bert-base-japanese-v3",
)
move_cache()
import nltk
nltk.download('averaged_perceptron_tagger')
nltk.download('cmudict')
image = (
modal.Image.debian_slim(python_version="3.10")
.pip_install(
"librosa==0.9.2",
"matplotlib",
"numpy",
"numba",
"phonemizer",
"scipy",
"tensorboard",
"Unidecode",
"amfm_decompy",
"jieba",
"transformers",
"pypinyin",
"cn2an",
"gradio==3.50.2",
"av",
"mecab-python3",
"loguru",
"unidic-lite",
"cmudict",
"fugashi",
"num2words",
"PyYAML",
"requests",
"pyopenjtalk-prebuilt",
"jaconv",
"psutil",
"GPUtil",
"vector_quantize_pytorch",
"g2p_en",
"sentencepiece",
"pykakasi",
"langid",
"torch",
"torchvision",
"torchaudio",
)
.run_function(download_model_weights)
)
@stub.function(
gpu="l4",
image=image,
retries=3,
mounts=[
modal.Mount.from_local_python_packages("config"),
modal.Mount.from_local_python_packages("tools"),
modal.Mount.from_local_python_packages("utils"),
modal.Mount.from_local_python_packages("infer"),
modal.Mount.from_local_python_packages("re_matching"),
modal.Mount.from_local_python_packages("modal_const"),
modal.Mount.from_local_python_packages("commons"),
modal.Mount.from_local_python_packages("text"),
modal.Mount.from_local_python_packages("models"),
modal.Mount.from_local_python_packages("modules"),
modal.Mount.from_local_python_packages("transforms"),
modal.Mount.from_local_python_packages("attentions"),
modal.Mount.from_local_python_packages("monotonic_align"),
modal.Mount.from_local_python_packages("oldVersion"),
modal.Mount.from_local_file("config.yml", CACHE_PATH + "/config.yml"),
modal.Mount.from_local_file("bert/bert_models.json", CACHE_PATH + "/bert/bert_models.json"),
]
)
def speech(
text,
speaker,
sdp_ratio,
noise_scale,
noise_scale_w,
length_scale,
language,
style_text,
style_weight,
):
import torch
import utils
from infer import infer, latest_version, get_net_g
import gradio as gr
import numpy as np
from config import config
import wave
import tempfile
net_g = None
device = config.webui_config.device
hps = utils.get_hparams_from_file(config.webui_config.config_path)
version = hps.version if hasattr(hps, "version") else latest_version
net_g = get_net_g(
model_path=config.webui_config.model, version=version, device=device, hps=hps
)
speaker_ids = hps.data.spk2id
# speakers = list(speaker_ids.keys())
# languages = ["ZH", "JP", "EN", "mix", "auto"]
def generate_audio(
text,
sdp_ratio,
noise_scale,
noise_scale_w,
length_scale,
speaker,
language,
reference_audio,
emotion,
style_text,
style_weight,
skip_start=False,
skip_end=False,
):
slices = text.split("|")
audio_list = []
# silence = np.zeros(hps.data.sampling_rate // 2, dtype=np.int16)
with torch.no_grad():
for idx, piece in enumerate(slices):
skip_start = idx != 0
skip_end = idx != len(slices) - 1
audio = infer(
piece,
reference_audio=reference_audio,
emotion=emotion,
sdp_ratio=sdp_ratio,
noise_scale=noise_scale,
noise_scale_w=noise_scale_w,
length_scale=length_scale,
sid=speaker,
language=language,
hps=hps,
net_g=net_g,
device=device,
skip_start=skip_start,
skip_end=skip_end,
style_text=style_text,
style_weight=style_weight,
)
audio16bit = gr.processing_utils.convert_to_16_bit_wav(audio)
audio_list.append(audio16bit)
return np.concatenate(audio_list)
res = generate_audio(text, sdp_ratio, noise_scale, noise_scale_w, length_scale, speaker, language, None, "Happy", style_text, style_weight)
data = res.tobytes()
tempfd, temppath = tempfile.mkstemp()
with wave.open(temppath, "wb") as wav_file:
wav_file.setparams((1, 2, 44100, 0, 'NONE', 'NONE'))
wav_file.writeframes(data)
ret = b""
with open(temppath, "rb") as wav_file:
ret = wav_file.read()
os.close(tempfd)
return ret
class SpeechReq(BaseModel):
text: str
speaker: str
sdp_ratio: float = 0.5
noise_scale: float = 0.6
noise_scale_w: float = 0.9
length_scale: float = 1.0
language: str = "ZH"
style_text: str = ""
style_weight: float = 0.7
@web_app.post("/submit")
async def submit(req: SpeechReq):
speech = modal.Function.lookup("bert-vits2", "speech")
call = speech.spawn(
req.text,
req.speaker,
req.sdp_ratio,
req.noise_scale,
req.noise_scale_w,
req.length_scale,
req.language,
req.style_text,
req.style_weight,
)
return {"call_id": call.object_id}
@web_app.get("/result/{call_id}")
async def poll_results(call_id: str):
from modal.functions import FunctionCall
function_call = FunctionCall.from_id(call_id)
try:
result = function_call.get(timeout=0)
except TimeoutError:
return fastapi.responses.JSONResponse(content="Still running", status_code=202)
return Response(content=result, media_type="audio/x-wav")
@stub.function()
@modal.asgi_app()
def wrapper():
return web_app
|
