341 lines
13 KiB
Python
Executable file
341 lines
13 KiB
Python
Executable file
#!/usr/bin/env python3
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import argparse
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import os
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import gc
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import re
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import subprocess
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import sys
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import threading
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import time
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import yaml
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import contextlib
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from fastapi.responses import StreamingResponse
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from loguru import logger
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from pydantic import BaseModel
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import uvicorn
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from openedai import OpenAIStub, BadRequestError, ServiceUnavailableError
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@contextlib.asynccontextmanager
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async def lifespan(app):
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yield
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gc.collect()
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try:
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import torch
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if torch.cuda.is_available():
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torch.cuda.empty_cache()
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torch.cuda.ipc_collect()
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except:
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pass
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app = OpenAIStub(lifespan=lifespan)
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xtts = None
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args = None
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def unload_model():
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import torch, gc
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global xtts
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if xtts:
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logger.info("Unloading model")
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xtts.xtts.to('cpu') # this was required to free up GPU memory...
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del xtts
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xtts = None
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gc.collect()
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torch.cuda.empty_cache()
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torch.cuda.ipc_collect()
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class xtts_wrapper():
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check_interval: int = 1
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def __init__(self, model_name, device, model_path=None, unload_timer=None):
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self.model_name = model_name
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self.unload_timer = unload_timer
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self.last_used = time.time()
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self.timer = None
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self.lock = threading.Lock()
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logger.info(f"Loading model {self.model_name} to {device}")
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if model_path is None:
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model_path = ModelManager().download_model(model_name)[0]
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config_path = os.path.join(model_path, 'config.json')
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config = XttsConfig()
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config.load_json(config_path)
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self.xtts = Xtts.init_from_config(config)
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self.xtts.load_checkpoint(config, checkpoint_dir=model_path, use_deepspeed=False) # XXX there are no prebuilt deepspeed wheels??
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self.xtts = self.xtts.to(device=device)
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self.xtts.eval()
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if self.unload_timer:
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logger.info(f"Setting unload timer to {self.unload_timer} seconds")
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self.not_idle()
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self.check_idle()
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def not_idle(self):
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with self.lock:
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self.last_used = time.time()
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def check_idle(self):
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with self.lock:
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if time.time() - self.last_used >= self.unload_timer:
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print("Unloading TTS model due to inactivity")
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unload_model()
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else:
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# Reschedule the check
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self.timer = threading.Timer(self.check_interval, self.check_idle)
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self.timer.daemon = True
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self.timer.start()
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def tts(self, text, language, speaker_wav, **hf_generate_kwargs):
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self.not_idle()
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try:
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with torch.no_grad():
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gpt_cond_latent, speaker_embedding = self.xtts.get_conditioning_latents(audio_path=[speaker_wav]) # XXX TODO: allow multiple wav
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for wav in self.xtts.inference_stream(text, language, gpt_cond_latent, speaker_embedding, **hf_generate_kwargs):
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yield wav.cpu().numpy().tobytes() # assumes wav data is f32le
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self.not_idle()
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finally:
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self.not_idle()
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def default_exists(filename: str):
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if not os.path.exists(filename):
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fpath, ext = os.path.splitext(filename)
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basename = os.path.basename(fpath)
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default = f"{basename}.default{ext}"
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logger.info(f"{filename} does not exist, setting defaults from {default}")
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with open(default, 'r', encoding='utf8') as from_file:
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with open(filename, 'w', encoding='utf8') as to_file:
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to_file.write(from_file.read())
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# Read pre process map on demand so it can be changed without restarting the server
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def preprocess(raw_input):
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logger.debug(f"preprocess: before: {[raw_input]}")
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default_exists('config/pre_process_map.yaml')
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with open('config/pre_process_map.yaml', 'r', encoding='utf8') as file:
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pre_process_map = yaml.safe_load(file)
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for a, b in pre_process_map:
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raw_input = re.sub(a, b, raw_input)
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raw_input = raw_input.strip()
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logger.debug(f"preprocess: after: {[raw_input]}")
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return raw_input
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# Read voice map on demand so it can be changed without restarting the server
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def map_voice_to_speaker(voice: str, model: str):
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default_exists('config/voice_to_speaker.yaml')
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with open('config/voice_to_speaker.yaml', 'r', encoding='utf8') as file:
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voice_map = yaml.safe_load(file)
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try:
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return voice_map[model][voice]
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except KeyError as e:
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raise BadRequestError(f"Error loading voice: {voice}, KeyError: {e}", param='voice')
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class GenerateSpeechRequest(BaseModel):
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model: str = "tts-1" # or "tts-1-hd"
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input: str
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voice: str = "alloy" # alloy, echo, fable, onyx, nova, and shimmer
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response_format: str = "mp3" # mp3, opus, aac, flac
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speed: float = 1.0 # 0.25 - 4.0
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def build_ffmpeg_args(response_format, input_format, sample_rate):
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# Convert the output to the desired format using ffmpeg
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if input_format == 'WAV':
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ffmpeg_args = ["ffmpeg", "-loglevel", "error", "-f", "WAV", "-i", "-"]
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else:
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ffmpeg_args = ["ffmpeg", "-loglevel", "error", "-f", input_format, "-ar", sample_rate, "-ac", "1", "-i", "-"]
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if response_format == "mp3":
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ffmpeg_args.extend(["-f", "mp3", "-c:a", "libmp3lame", "-ab", "64k"])
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elif response_format == "opus":
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ffmpeg_args.extend(["-f", "ogg", "-c:a", "libopus"])
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elif response_format == "aac":
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ffmpeg_args.extend(["-f", "adts", "-c:a", "aac", "-ab", "64k"])
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elif response_format == "flac":
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ffmpeg_args.extend(["-f", "flac", "-c:a", "flac"])
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elif response_format == "wav":
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ffmpeg_args.extend(["-f", "wav", "-c:a", "pcm_s16le"])
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elif response_format == "pcm": # even though pcm is technically 'raw', we still use ffmpeg to adjust the speed
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ffmpeg_args.extend(["-f", "s16le", "-c:a", "pcm_s16le"])
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return ffmpeg_args
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@app.post("/v1/audio/speech", response_class=StreamingResponse)
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async def generate_speech(request: GenerateSpeechRequest):
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global xtts, args
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if len(request.input) < 1:
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raise BadRequestError("Empty Input", param='input')
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input_text = preprocess(request.input)
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if len(input_text) < 1:
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raise BadRequestError("Input text empty after preprocess.", param='input')
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model = request.model
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voice = request.voice
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response_format = request.response_format.lower()
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speed = request.speed
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# Set the Content-Type header based on the requested format
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if response_format == "mp3":
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media_type = "audio/mpeg"
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elif response_format == "opus":
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media_type = "audio/ogg;codec=opus" # codecs?
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elif response_format == "aac":
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media_type = "audio/aac"
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elif response_format == "flac":
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media_type = "audio/x-flac"
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elif response_format == "wav":
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media_type = "audio/wav"
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elif response_format == "pcm":
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if model == 'tts-1': # piper
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media_type = "audio/pcm;rate=22050"
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elif model == 'tts-1-hd':
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media_type = "audio/pcm;rate=24000"
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else:
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BadRequestError(f"Invalid response_format: '{response_format}'", param='response_format')
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ffmpeg_args = None
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tts_io_out = None
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# Use piper for tts-1, and if xtts_device == none use for all models.
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if model == 'tts-1' or args.xtts_device == 'none':
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voice_map = map_voice_to_speaker(voice, 'tts-1')
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try:
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piper_model = voice_map['model']
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except KeyError as e:
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raise ServiceUnavailableError(f"Configuration error: tts-1 voice '{voice}' is missing 'model:' setting. KeyError: {e}")
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speaker = voice_map.get('speaker', None)
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tts_args = ["piper", "--model", str(piper_model), "--data-dir", "voices", "--download-dir", "voices", "--output-raw"]
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if speaker:
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tts_args.extend(["--speaker", str(speaker)])
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if speed != 1.0:
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tts_args.extend(["--length-scale", f"{1.0/speed}"])
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tts_proc = subprocess.Popen(tts_args, stdin=subprocess.PIPE, stdout=subprocess.PIPE)
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tts_proc.stdin.write(bytearray(input_text.encode('utf-8')))
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tts_proc.stdin.close()
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tts_io_out = tts_proc.stdout
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ffmpeg_args = build_ffmpeg_args(response_format, input_format="s16le", sample_rate="22050")
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# Pipe the output from piper/xtts to the input of ffmpeg
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ffmpeg_args.extend(["-"])
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ffmpeg_proc = subprocess.Popen(ffmpeg_args, stdin=tts_io_out, stdout=subprocess.PIPE)
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return StreamingResponse(content=ffmpeg_proc.stdout, media_type=media_type)
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# Use xtts for tts-1-hd
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elif model == 'tts-1-hd':
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voice_map = map_voice_to_speaker(voice, 'tts-1-hd')
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try:
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tts_model = voice_map.pop('model')
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speaker = voice_map.pop('speaker')
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except KeyError as e:
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raise ServiceUnavailableError(f"Configuration error: tts-1-hd voice '{voice}' is missing setting. KeyError: {e}")
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if xtts and xtts.model_name != tts_model:
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unload_model()
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tts_model_path = voice_map.pop('model_path', None) # XXX changing this on the fly is ignored if you keep the same name
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if xtts is None:
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xtts = xtts_wrapper(tts_model, device=args.xtts_device, model_path=tts_model_path, unload_timer=args.unload_timer)
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ffmpeg_args = build_ffmpeg_args(response_format, input_format="f32le", sample_rate="24000")
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# tts speed doesn't seem to work well
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speed = voice_map.pop('speed', speed)
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if speed < 0.5:
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speed = speed / 0.5
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ffmpeg_args.extend(["-af", "atempo=0.5"])
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if speed > 1.0:
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ffmpeg_args.extend(["-af", f"atempo={speed}"])
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speed = 1.0
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language = voice_map.pop('language', 'en')
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comment = voice_map.pop('comment', None) # ignored.
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hf_generate_kwargs = dict(
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speed=speed,
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**voice_map,
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)
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hf_generate_kwargs['enable_text_splitting'] = hf_generate_kwargs.get('enable_text_splitting', True) # change the default to true
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# Pipe the output from piper/xtts to the input of ffmpeg
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ffmpeg_args.extend(["-"])
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ffmpeg_proc = subprocess.Popen(ffmpeg_args, stdin=subprocess.PIPE, stdout=subprocess.PIPE)
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def generator():
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try:
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for chunk in xtts.tts(text=input_text, language=language, speaker_wav=speaker, **hf_generate_kwargs):
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ffmpeg_proc.stdin.write(chunk)
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except Exception as e:
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logger.error(f"Exception: {repr(e)}")
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raise e
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finally:
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ffmpeg_proc.stdin.close()
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worker = threading.Thread(target=generator)
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worker.daemon = True
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worker.start()
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return StreamingResponse(content=ffmpeg_proc.stdout, media_type=media_type)
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else:
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raise BadRequestError("No such model, must be tts-1 or tts-1-hd.", param='model')
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# We return 'mps' but currently XTTS will not work with mps devices as the cuda support is incomplete
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def auto_torch_device():
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try:
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import torch
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return 'cuda' if torch.cuda.is_available() else 'mps' if ( torch.backends.mps.is_available() and torch.backends.mps.is_built() ) else 'cpu'
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except:
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return 'none'
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if __name__ == "__main__":
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parser = argparse.ArgumentParser(
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description='OpenedAI Speech API Server',
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formatter_class=argparse.ArgumentDefaultsHelpFormatter)
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parser.add_argument('--xtts_device', action='store', default=auto_torch_device(), help="Set the device for the xtts model. The special value of 'none' will use piper for all models.")
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parser.add_argument('--preload', action='store', default=None, help="Preload a model (Ex. 'xtts' or 'xtts_v2.0.2'). By default it's loaded on first use.")
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parser.add_argument('--unload-timer', action='store', default=None, type=int, help="Idle unload timer for the XTTS model in seconds")
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parser.add_argument('-P', '--port', action='store', default=8000, type=int, help="Server tcp port")
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parser.add_argument('-H', '--host', action='store', default='0.0.0.0', help="Host to listen on, Ex. 0.0.0.0")
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parser.add_argument('-L', '--log-level', default="INFO", choices=["DEBUG", "INFO", "WARNING", "ERROR", "CRITICAL"], help="Set the log level")
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args = parser.parse_args()
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default_exists('config/pre_process_map.yaml')
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default_exists('config/voice_to_speaker.yaml')
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logger.remove()
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logger.add(sink=sys.stderr, level=args.log_level)
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if args.xtts_device != "none":
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import torch
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from TTS.tts.configs.xtts_config import XttsConfig
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from TTS.tts.models.xtts import Xtts
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from TTS.utils.manage import ModelManager
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if args.preload:
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xtts = xtts_wrapper(args.preload, device=args.xtts_device, unload_timer=args.unload_timer)
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app.register_model('tts-1')
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app.register_model('tts-1-hd')
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uvicorn.run(app, host=args.host, port=args.port)
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