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serving.py
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serving.py
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import json
import logging
import shlex
import subprocess
import tempfile
from pathlib import Path
from typing import Literal, Optional
import fastapi
import fastapi.middleware.cors
import torch
import tyro
import uvicorn
from attr import dataclass
from fastapi import Request
from fastapi.responses import Response
from huggingface_hub import snapshot_download
from fam.llm.sample import (
InferenceConfig,
Model,
build_models,
get_first_stage_path,
get_second_stage_path,
sample_utterance,
)
logger = logging.getLogger(__name__)
## Setup FastAPI server.
app = fastapi.FastAPI()
@dataclass
class ServingConfig:
huggingface_repo_id: str
"""Absolute path to the model directory."""
max_new_tokens: int = 864 * 2
"""Maximum number of new tokens to generate from the first stage model."""
temperature: float = 1.0
"""Temperature for sampling applied to both models."""
top_k: int = 200
"""Top k for sampling applied to both models."""
seed: int = 1337
"""Random seed for sampling."""
dtype: Literal["bfloat16", "float16", "float32", "tfloat32"] = "bfloat16"
"""Data type to use for sampling."""
enhancer: Optional[Literal["df"]] = "df"
"""Enhancer to use for post-processing."""
port: int = 58003
# Singleton
class _GlobalState:
spkemb_model: torch.nn.Module
first_stage_model: Model
second_stage_model: Model
config: ServingConfig
enhancer: object
GlobalState = _GlobalState()
@dataclass(frozen=True)
class TTSRequest:
text: str
guidance: Optional[float] = 3.0
top_p: Optional[float] = 0.95
speaker_ref_path: Optional[str] = None
top_k: Optional[int] = None
@app.post("/tts", response_class=Response)
async def text_to_speech(req: Request):
audiodata = await req.body()
payload = None
wav_out_path = None
try:
headers = req.headers
payload = headers["X-Payload"]
payload = json.loads(payload)
tts_req = TTSRequest(**payload)
with tempfile.NamedTemporaryFile(suffix=".wav") as wav_tmp:
if tts_req.speaker_ref_path is None:
wav_path = _convert_audiodata_to_wav_path(audiodata, wav_tmp)
else:
wav_path = tts_req.speaker_ref_path
wav_out_path = sample_utterance(
tts_req.text,
wav_path,
GlobalState.spkemb_model,
GlobalState.first_stage_model,
GlobalState.second_stage_model,
enhancer=GlobalState.enhancer,
first_stage_ckpt_path=None,
second_stage_ckpt_path=None,
guidance_scale=tts_req.guidance,
max_new_tokens=GlobalState.config.max_new_tokens,
temperature=GlobalState.config.temperature,
top_k=tts_req.top_k,
top_p=tts_req.top_p,
)
with open(wav_out_path, "rb") as f:
return Response(content=f.read(), media_type="audio/wav")
except Exception as e:
# traceback_str = "".join(traceback.format_tb(e.__traceback__))
logger.exception(f"Error processing request {payload}")
return Response(
content="Something went wrong. Please try again in a few mins or contact us on Discord",
status_code=500,
)
finally:
if wav_out_path is not None:
Path(wav_out_path).unlink(missing_ok=True)
def _convert_audiodata_to_wav_path(audiodata, wav_tmp):
with tempfile.NamedTemporaryFile() as unknown_format_tmp:
assert unknown_format_tmp.write(audiodata) > 0
unknown_format_tmp.flush()
subprocess.check_output(
# arbitrary 2 minute cutoff
shlex.split(f"ffmpeg -t 120 -y -i {unknown_format_tmp.name} -f wav {wav_tmp.name}")
)
return wav_tmp.name
if __name__ == "__main__":
# This has to be here to avoid some weird audiocraft shenaningans messing up matplotlib
from fam.llm.enhancers import get_enhancer
for name in logging.root.manager.loggerDict:
logger = logging.getLogger(name)
logger.setLevel(logging.INFO)
logging.root.setLevel(logging.INFO)
GlobalState.config = tyro.cli(ServingConfig)
app.add_middleware(
fastapi.middleware.cors.CORSMiddleware,
allow_origins=["*", f"http://localhost:{GlobalState.config.port}", "http://localhost:3000"],
allow_credentials=True,
allow_methods=["*"],
allow_headers=["*"],
)
device = "cuda" if torch.cuda.is_available() else "cpu"
common_config = dict(
num_samples=1,
seed=1337,
device=device,
dtype=GlobalState.config.dtype,
compile=False,
init_from="resume",
output_dir=tempfile.mkdtemp(),
)
model_dir = snapshot_download(repo_id=GlobalState.config.huggingface_repo_id)
config1 = InferenceConfig(
ckpt_path=get_first_stage_path(model_dir),
**common_config,
)
config2 = InferenceConfig(
ckpt_path=get_second_stage_path(model_dir),
**common_config,
)
spkemb, llm_stg1, llm_stg2 = build_models(config1, config2, device=device, use_kv_cache="flash_decoding")
GlobalState.spkemb_model = spkemb
GlobalState.first_stage_model = llm_stg1
GlobalState.second_stage_model = llm_stg2
GlobalState.enhancer = get_enhancer(GlobalState.config.enhancer)
# start server
uvicorn.run(
app,
host="127.0.0.1",
port=GlobalState.config.port,
log_level="info",
)