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Add a .gitattributes
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Fireblade
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commit
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5 changed files with 699 additions and 694 deletions
5
.gitattributes
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5
.gitattributes
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* text=auto
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*.py text eol=lf
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*.sh text eol=lf
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*.yml text eol=lf
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@ -1,413 +1,413 @@
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"""Async file and path operations."""
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import io
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import json
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import os
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from pathlib import Path
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from typing import Any, AsyncIterator, Callable, Dict, List, Optional, Set
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import aiofiles
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import aiofiles.os
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import torch
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from loguru import logger
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from .config import settings
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async def _find_file(
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filename: str,
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search_paths: List[str],
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filter_fn: Optional[Callable[[str], bool]] = None,
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) -> str:
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"""Find file in search paths.
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Args:
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filename: Name of file to find
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search_paths: List of paths to search in
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filter_fn: Optional function to filter files
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Returns:
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Absolute path to file
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Raises:
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RuntimeError: If file not found
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"""
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if os.path.isabs(filename) and await aiofiles.os.path.exists(filename):
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return filename
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for path in search_paths:
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full_path = os.path.join(path, filename)
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if await aiofiles.os.path.exists(full_path):
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if filter_fn is None or filter_fn(full_path):
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return full_path
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raise FileNotFoundError(f"File not found: {filename} in paths: {search_paths}")
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async def _scan_directories(
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search_paths: List[str], filter_fn: Optional[Callable[[str], bool]] = None
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) -> Set[str]:
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"""Scan directories for files.
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Args:
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search_paths: List of paths to scan
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filter_fn: Optional function to filter files
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Returns:
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Set of matching filenames
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"""
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results = set()
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for path in search_paths:
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if not await aiofiles.os.path.exists(path):
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continue
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try:
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# Get directory entries first
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entries = await aiofiles.os.scandir(path)
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# Then process entries after await completes
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for entry in entries:
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if filter_fn is None or filter_fn(entry.name):
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results.add(entry.name)
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except Exception as e:
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logger.warning(f"Error scanning {path}: {e}")
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return results
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async def get_model_path(model_name: str) -> str:
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"""Get path to model file.
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Args:
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model_name: Name of model file
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Returns:
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Absolute path to model file
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Raises:
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RuntimeError: If model not found
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"""
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# Get api directory path (two levels up from core)
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api_dir = os.path.dirname(os.path.dirname(os.path.dirname(__file__)))
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# Construct model directory path relative to api directory
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model_dir = os.path.join(api_dir, settings.model_dir)
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# Ensure model directory exists
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os.makedirs(model_dir, exist_ok=True)
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# Search in model directory
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search_paths = [model_dir]
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logger.debug(f"Searching for model in path: {model_dir}")
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return await _find_file(model_name, search_paths)
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async def get_voice_path(voice_name: str) -> str:
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"""Get path to voice file.
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Args:
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voice_name: Name of voice file (without .pt extension)
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Returns:
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Absolute path to voice file
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Raises:
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RuntimeError: If voice not found
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"""
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# Get api directory path
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api_dir = os.path.dirname(os.path.dirname(os.path.dirname(__file__)))
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# Construct voice directory path relative to api directory
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voice_dir = os.path.join(api_dir, settings.voices_dir)
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# Ensure voice directory exists
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os.makedirs(voice_dir, exist_ok=True)
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voice_file = f"{voice_name}.pt"
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# Search in voice directory/o
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search_paths = [voice_dir]
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logger.debug(f"Searching for voice in path: {voice_dir}")
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return await _find_file(voice_file, search_paths)
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async def list_voices() -> List[str]:
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"""List available voice files.
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Returns:
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List of voice names (without .pt extension)
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"""
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# Get api directory path
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api_dir = os.path.dirname(os.path.dirname(os.path.dirname(__file__)))
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# Construct voice directory path relative to api directory
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voice_dir = os.path.join(api_dir, settings.voices_dir)
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# Ensure voice directory exists
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os.makedirs(voice_dir, exist_ok=True)
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# Search in voice directory
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search_paths = [voice_dir]
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logger.debug(f"Scanning for voices in path: {voice_dir}")
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def filter_voice_files(name: str) -> bool:
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return name.endswith(".pt")
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voices = await _scan_directories(search_paths, filter_voice_files)
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return sorted([name[:-3] for name in voices]) # Remove .pt extension
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async def load_voice_tensor(
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voice_path: str, device: str = "cpu", weights_only=False
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) -> torch.Tensor:
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"""Load voice tensor from file.
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Args:
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voice_path: Path to voice file
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device: Device to load tensor to
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Returns:
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Voice tensor
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Raises:
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RuntimeError: If file cannot be read
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"""
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try:
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async with aiofiles.open(voice_path, "rb") as f:
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data = await f.read()
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return torch.load(
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io.BytesIO(data), map_location=device, weights_only=weights_only
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)
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except Exception as e:
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raise RuntimeError(f"Failed to load voice tensor from {voice_path}: {e}")
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async def save_voice_tensor(tensor: torch.Tensor, voice_path: str) -> None:
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"""Save voice tensor to file.
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Args:
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tensor: Voice tensor to save
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voice_path: Path to save voice file
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Raises:
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RuntimeError: If file cannot be written
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"""
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try:
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buffer = io.BytesIO()
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torch.save(tensor, buffer)
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async with aiofiles.open(voice_path, "wb") as f:
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await f.write(buffer.getvalue())
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except Exception as e:
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raise RuntimeError(f"Failed to save voice tensor to {voice_path}: {e}")
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async def load_json(path: str) -> dict:
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"""Load JSON file asynchronously.
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Args:
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path: Path to JSON file
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Returns:
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Parsed JSON data
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Raises:
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RuntimeError: If file cannot be read or parsed
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"""
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try:
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async with aiofiles.open(path, "r", encoding="utf-8") as f:
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content = await f.read()
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return json.loads(content)
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except Exception as e:
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raise RuntimeError(f"Failed to load JSON file {path}: {e}")
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async def load_model_weights(path: str, device: str = "cpu") -> dict:
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"""Load model weights asynchronously.
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Args:
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path: Path to model file (.pth or .onnx)
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device: Device to load model to
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Returns:
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Model weights
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Raises:
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RuntimeError: If file cannot be read
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"""
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try:
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async with aiofiles.open(path, "rb") as f:
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data = await f.read()
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return torch.load(io.BytesIO(data), map_location=device, weights_only=True)
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except Exception as e:
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raise RuntimeError(f"Failed to load model weights from {path}: {e}")
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async def read_file(path: str) -> str:
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"""Read text file asynchronously.
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Args:
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path: Path to file
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Returns:
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File contents as string
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Raises:
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RuntimeError: If file cannot be read
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"""
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try:
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async with aiofiles.open(path, "r", encoding="utf-8") as f:
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return await f.read()
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except Exception as e:
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raise RuntimeError(f"Failed to read file {path}: {e}")
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async def read_bytes(path: str) -> bytes:
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"""Read file as bytes asynchronously.
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Args:
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path: Path to file
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Returns:
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File contents as bytes
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Raises:
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RuntimeError: If file cannot be read
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"""
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try:
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async with aiofiles.open(path, "rb") as f:
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return await f.read()
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except Exception as e:
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raise RuntimeError(f"Failed to read file {path}: {e}")
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async def get_web_file_path(filename: str) -> str:
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"""Get path to web static file.
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Args:
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filename: Name of file in web directory
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Returns:
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Absolute path to file
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Raises:
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RuntimeError: If file not found
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"""
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# Get project root directory (four levels up from core to get to project root)
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root_dir = os.path.dirname(
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os.path.dirname(os.path.dirname(os.path.dirname(__file__)))
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)
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# Construct web directory path relative to project root
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web_dir = os.path.join("/app", settings.web_player_path)
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# Search in web directory
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search_paths = [web_dir]
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logger.debug(f"Searching for web file in path: {web_dir}")
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return await _find_file(filename, search_paths)
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async def get_content_type(path: str) -> str:
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"""Get content type for file.
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Args:
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path: Path to file
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Returns:
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Content type string
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"""
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ext = os.path.splitext(path)[1].lower()
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return {
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".html": "text/html",
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".js": "application/javascript",
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".css": "text/css",
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".png": "image/png",
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".jpg": "image/jpeg",
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".jpeg": "image/jpeg",
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".gif": "image/gif",
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".svg": "image/svg+xml",
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".ico": "image/x-icon",
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}.get(ext, "application/octet-stream")
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async def verify_model_path(model_path: str) -> bool:
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"""Verify model file exists at path."""
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return await aiofiles.os.path.exists(model_path)
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async def cleanup_temp_files() -> None:
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"""Clean up old temp files on startup"""
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try:
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if not await aiofiles.os.path.exists(settings.temp_file_dir):
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await aiofiles.os.makedirs(settings.temp_file_dir, exist_ok=True)
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return
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entries = await aiofiles.os.scandir(settings.temp_file_dir)
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for entry in entries:
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if entry.is_file():
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stat = await aiofiles.os.stat(entry.path)
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max_age = stat.st_mtime + (settings.max_temp_dir_age_hours * 3600)
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if max_age < stat.st_mtime:
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try:
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await aiofiles.os.remove(entry.path)
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logger.info(f"Cleaned up old temp file: {entry.name}")
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except Exception as e:
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logger.warning(
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f"Failed to delete old temp file {entry.name}: {e}"
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)
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except Exception as e:
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logger.warning(f"Error cleaning temp files: {e}")
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async def get_temp_file_path(filename: str) -> str:
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"""Get path to temporary audio file.
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Args:
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filename: Name of temp file
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Returns:
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Absolute path to temp file
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Raises:
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RuntimeError: If temp directory does not exist
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"""
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temp_path = os.path.join(settings.temp_file_dir, filename)
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# Ensure temp directory exists
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if not await aiofiles.os.path.exists(settings.temp_file_dir):
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await aiofiles.os.makedirs(settings.temp_file_dir, exist_ok=True)
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return temp_path
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async def list_temp_files() -> List[str]:
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"""List temporary audio files.
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Returns:
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List of temp file names
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"""
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if not await aiofiles.os.path.exists(settings.temp_file_dir):
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return []
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entries = await aiofiles.os.scandir(settings.temp_file_dir)
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return [entry.name for entry in entries if entry.is_file()]
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async def get_temp_dir_size() -> int:
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"""Get total size of temp directory in bytes.
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Returns:
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Size in bytes
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"""
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if not await aiofiles.os.path.exists(settings.temp_file_dir):
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return 0
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total = 0
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entries = await aiofiles.os.scandir(settings.temp_file_dir)
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for entry in entries:
|
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if entry.is_file():
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stat = await aiofiles.os.stat(entry.path)
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total += stat.st_size
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return total
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"""Async file and path operations."""
|
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|
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import io
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import json
|
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import os
|
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from pathlib import Path
|
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from typing import Any, AsyncIterator, Callable, Dict, List, Optional, Set
|
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|
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import aiofiles
|
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import aiofiles.os
|
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import torch
|
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from loguru import logger
|
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|
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from .config import settings
|
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|
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|
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async def _find_file(
|
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filename: str,
|
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search_paths: List[str],
|
||||
filter_fn: Optional[Callable[[str], bool]] = None,
|
||||
) -> str:
|
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"""Find file in search paths.
|
||||
|
||||
Args:
|
||||
filename: Name of file to find
|
||||
search_paths: List of paths to search in
|
||||
filter_fn: Optional function to filter files
|
||||
|
||||
Returns:
|
||||
Absolute path to file
|
||||
|
||||
Raises:
|
||||
RuntimeError: If file not found
|
||||
"""
|
||||
if os.path.isabs(filename) and await aiofiles.os.path.exists(filename):
|
||||
return filename
|
||||
|
||||
for path in search_paths:
|
||||
full_path = os.path.join(path, filename)
|
||||
if await aiofiles.os.path.exists(full_path):
|
||||
if filter_fn is None or filter_fn(full_path):
|
||||
return full_path
|
||||
|
||||
raise FileNotFoundError(f"File not found: {filename} in paths: {search_paths}")
|
||||
|
||||
|
||||
async def _scan_directories(
|
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search_paths: List[str], filter_fn: Optional[Callable[[str], bool]] = None
|
||||
) -> Set[str]:
|
||||
"""Scan directories for files.
|
||||
|
||||
Args:
|
||||
search_paths: List of paths to scan
|
||||
filter_fn: Optional function to filter files
|
||||
|
||||
Returns:
|
||||
Set of matching filenames
|
||||
"""
|
||||
results = set()
|
||||
|
||||
for path in search_paths:
|
||||
if not await aiofiles.os.path.exists(path):
|
||||
continue
|
||||
|
||||
try:
|
||||
# Get directory entries first
|
||||
entries = await aiofiles.os.scandir(path)
|
||||
# Then process entries after await completes
|
||||
for entry in entries:
|
||||
if filter_fn is None or filter_fn(entry.name):
|
||||
results.add(entry.name)
|
||||
except Exception as e:
|
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logger.warning(f"Error scanning {path}: {e}")
|
||||
|
||||
return results
|
||||
|
||||
|
||||
async def get_model_path(model_name: str) -> str:
|
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"""Get path to model file.
|
||||
|
||||
Args:
|
||||
model_name: Name of model file
|
||||
|
||||
Returns:
|
||||
Absolute path to model file
|
||||
|
||||
Raises:
|
||||
RuntimeError: If model not found
|
||||
"""
|
||||
# Get api directory path (two levels up from core)
|
||||
api_dir = os.path.dirname(os.path.dirname(os.path.dirname(__file__)))
|
||||
|
||||
# Construct model directory path relative to api directory
|
||||
model_dir = os.path.join(api_dir, settings.model_dir)
|
||||
|
||||
# Ensure model directory exists
|
||||
os.makedirs(model_dir, exist_ok=True)
|
||||
|
||||
# Search in model directory
|
||||
search_paths = [model_dir]
|
||||
logger.debug(f"Searching for model in path: {model_dir}")
|
||||
|
||||
return await _find_file(model_name, search_paths)
|
||||
|
||||
|
||||
async def get_voice_path(voice_name: str) -> str:
|
||||
"""Get path to voice file.
|
||||
|
||||
Args:
|
||||
voice_name: Name of voice file (without .pt extension)
|
||||
|
||||
Returns:
|
||||
Absolute path to voice file
|
||||
|
||||
Raises:
|
||||
RuntimeError: If voice not found
|
||||
"""
|
||||
# Get api directory path
|
||||
api_dir = os.path.dirname(os.path.dirname(os.path.dirname(__file__)))
|
||||
|
||||
# Construct voice directory path relative to api directory
|
||||
voice_dir = os.path.join(api_dir, settings.voices_dir)
|
||||
|
||||
# Ensure voice directory exists
|
||||
os.makedirs(voice_dir, exist_ok=True)
|
||||
|
||||
voice_file = f"{voice_name}.pt"
|
||||
|
||||
# Search in voice directory/o
|
||||
search_paths = [voice_dir]
|
||||
logger.debug(f"Searching for voice in path: {voice_dir}")
|
||||
|
||||
return await _find_file(voice_file, search_paths)
|
||||
|
||||
|
||||
async def list_voices() -> List[str]:
|
||||
"""List available voice files.
|
||||
|
||||
Returns:
|
||||
List of voice names (without .pt extension)
|
||||
"""
|
||||
# Get api directory path
|
||||
api_dir = os.path.dirname(os.path.dirname(os.path.dirname(__file__)))
|
||||
|
||||
# Construct voice directory path relative to api directory
|
||||
voice_dir = os.path.join(api_dir, settings.voices_dir)
|
||||
|
||||
# Ensure voice directory exists
|
||||
os.makedirs(voice_dir, exist_ok=True)
|
||||
|
||||
# Search in voice directory
|
||||
search_paths = [voice_dir]
|
||||
logger.debug(f"Scanning for voices in path: {voice_dir}")
|
||||
|
||||
def filter_voice_files(name: str) -> bool:
|
||||
return name.endswith(".pt")
|
||||
|
||||
voices = await _scan_directories(search_paths, filter_voice_files)
|
||||
return sorted([name[:-3] for name in voices]) # Remove .pt extension
|
||||
|
||||
|
||||
async def load_voice_tensor(
|
||||
voice_path: str, device: str = "cpu", weights_only=False
|
||||
) -> torch.Tensor:
|
||||
"""Load voice tensor from file.
|
||||
|
||||
Args:
|
||||
voice_path: Path to voice file
|
||||
device: Device to load tensor to
|
||||
|
||||
Returns:
|
||||
Voice tensor
|
||||
|
||||
Raises:
|
||||
RuntimeError: If file cannot be read
|
||||
"""
|
||||
try:
|
||||
async with aiofiles.open(voice_path, "rb") as f:
|
||||
data = await f.read()
|
||||
return torch.load(
|
||||
io.BytesIO(data), map_location=device, weights_only=weights_only
|
||||
)
|
||||
except Exception as e:
|
||||
raise RuntimeError(f"Failed to load voice tensor from {voice_path}: {e}")
|
||||
|
||||
|
||||
async def save_voice_tensor(tensor: torch.Tensor, voice_path: str) -> None:
|
||||
"""Save voice tensor to file.
|
||||
|
||||
Args:
|
||||
tensor: Voice tensor to save
|
||||
voice_path: Path to save voice file
|
||||
|
||||
Raises:
|
||||
RuntimeError: If file cannot be written
|
||||
"""
|
||||
try:
|
||||
buffer = io.BytesIO()
|
||||
torch.save(tensor, buffer)
|
||||
async with aiofiles.open(voice_path, "wb") as f:
|
||||
await f.write(buffer.getvalue())
|
||||
except Exception as e:
|
||||
raise RuntimeError(f"Failed to save voice tensor to {voice_path}: {e}")
|
||||
|
||||
|
||||
async def load_json(path: str) -> dict:
|
||||
"""Load JSON file asynchronously.
|
||||
|
||||
Args:
|
||||
path: Path to JSON file
|
||||
|
||||
Returns:
|
||||
Parsed JSON data
|
||||
|
||||
Raises:
|
||||
RuntimeError: If file cannot be read or parsed
|
||||
"""
|
||||
try:
|
||||
async with aiofiles.open(path, "r", encoding="utf-8") as f:
|
||||
content = await f.read()
|
||||
return json.loads(content)
|
||||
except Exception as e:
|
||||
raise RuntimeError(f"Failed to load JSON file {path}: {e}")
|
||||
|
||||
|
||||
async def load_model_weights(path: str, device: str = "cpu") -> dict:
|
||||
"""Load model weights asynchronously.
|
||||
|
||||
Args:
|
||||
path: Path to model file (.pth or .onnx)
|
||||
device: Device to load model to
|
||||
|
||||
Returns:
|
||||
Model weights
|
||||
|
||||
Raises:
|
||||
RuntimeError: If file cannot be read
|
||||
"""
|
||||
try:
|
||||
async with aiofiles.open(path, "rb") as f:
|
||||
data = await f.read()
|
||||
return torch.load(io.BytesIO(data), map_location=device, weights_only=True)
|
||||
except Exception as e:
|
||||
raise RuntimeError(f"Failed to load model weights from {path}: {e}")
|
||||
|
||||
|
||||
async def read_file(path: str) -> str:
|
||||
"""Read text file asynchronously.
|
||||
|
||||
Args:
|
||||
path: Path to file
|
||||
|
||||
Returns:
|
||||
File contents as string
|
||||
|
||||
Raises:
|
||||
RuntimeError: If file cannot be read
|
||||
"""
|
||||
try:
|
||||
async with aiofiles.open(path, "r", encoding="utf-8") as f:
|
||||
return await f.read()
|
||||
except Exception as e:
|
||||
raise RuntimeError(f"Failed to read file {path}: {e}")
|
||||
|
||||
|
||||
async def read_bytes(path: str) -> bytes:
|
||||
"""Read file as bytes asynchronously.
|
||||
|
||||
Args:
|
||||
path: Path to file
|
||||
|
||||
Returns:
|
||||
File contents as bytes
|
||||
|
||||
Raises:
|
||||
RuntimeError: If file cannot be read
|
||||
"""
|
||||
try:
|
||||
async with aiofiles.open(path, "rb") as f:
|
||||
return await f.read()
|
||||
except Exception as e:
|
||||
raise RuntimeError(f"Failed to read file {path}: {e}")
|
||||
|
||||
|
||||
async def get_web_file_path(filename: str) -> str:
|
||||
"""Get path to web static file.
|
||||
|
||||
Args:
|
||||
filename: Name of file in web directory
|
||||
|
||||
Returns:
|
||||
Absolute path to file
|
||||
|
||||
Raises:
|
||||
RuntimeError: If file not found
|
||||
"""
|
||||
# Get project root directory (four levels up from core to get to project root)
|
||||
root_dir = os.path.dirname(
|
||||
os.path.dirname(os.path.dirname(os.path.dirname(__file__)))
|
||||
)
|
||||
|
||||
# Construct web directory path relative to project root
|
||||
web_dir = os.path.join("/app", settings.web_player_path)
|
||||
|
||||
# Search in web directory
|
||||
search_paths = [web_dir]
|
||||
logger.debug(f"Searching for web file in path: {web_dir}")
|
||||
|
||||
return await _find_file(filename, search_paths)
|
||||
|
||||
|
||||
async def get_content_type(path: str) -> str:
|
||||
"""Get content type for file.
|
||||
|
||||
Args:
|
||||
path: Path to file
|
||||
|
||||
Returns:
|
||||
Content type string
|
||||
"""
|
||||
ext = os.path.splitext(path)[1].lower()
|
||||
return {
|
||||
".html": "text/html",
|
||||
".js": "application/javascript",
|
||||
".css": "text/css",
|
||||
".png": "image/png",
|
||||
".jpg": "image/jpeg",
|
||||
".jpeg": "image/jpeg",
|
||||
".gif": "image/gif",
|
||||
".svg": "image/svg+xml",
|
||||
".ico": "image/x-icon",
|
||||
}.get(ext, "application/octet-stream")
|
||||
|
||||
|
||||
async def verify_model_path(model_path: str) -> bool:
|
||||
"""Verify model file exists at path."""
|
||||
return await aiofiles.os.path.exists(model_path)
|
||||
|
||||
|
||||
async def cleanup_temp_files() -> None:
|
||||
"""Clean up old temp files on startup"""
|
||||
try:
|
||||
if not await aiofiles.os.path.exists(settings.temp_file_dir):
|
||||
await aiofiles.os.makedirs(settings.temp_file_dir, exist_ok=True)
|
||||
return
|
||||
|
||||
entries = await aiofiles.os.scandir(settings.temp_file_dir)
|
||||
for entry in entries:
|
||||
if entry.is_file():
|
||||
stat = await aiofiles.os.stat(entry.path)
|
||||
max_age = stat.st_mtime + (settings.max_temp_dir_age_hours * 3600)
|
||||
if max_age < stat.st_mtime:
|
||||
try:
|
||||
await aiofiles.os.remove(entry.path)
|
||||
logger.info(f"Cleaned up old temp file: {entry.name}")
|
||||
except Exception as e:
|
||||
logger.warning(
|
||||
f"Failed to delete old temp file {entry.name}: {e}"
|
||||
)
|
||||
except Exception as e:
|
||||
logger.warning(f"Error cleaning temp files: {e}")
|
||||
|
||||
|
||||
async def get_temp_file_path(filename: str) -> str:
|
||||
"""Get path to temporary audio file.
|
||||
|
||||
Args:
|
||||
filename: Name of temp file
|
||||
|
||||
Returns:
|
||||
Absolute path to temp file
|
||||
|
||||
Raises:
|
||||
RuntimeError: If temp directory does not exist
|
||||
"""
|
||||
temp_path = os.path.join(settings.temp_file_dir, filename)
|
||||
|
||||
# Ensure temp directory exists
|
||||
if not await aiofiles.os.path.exists(settings.temp_file_dir):
|
||||
await aiofiles.os.makedirs(settings.temp_file_dir, exist_ok=True)
|
||||
|
||||
return temp_path
|
||||
|
||||
|
||||
async def list_temp_files() -> List[str]:
|
||||
"""List temporary audio files.
|
||||
|
||||
Returns:
|
||||
List of temp file names
|
||||
"""
|
||||
if not await aiofiles.os.path.exists(settings.temp_file_dir):
|
||||
return []
|
||||
|
||||
entries = await aiofiles.os.scandir(settings.temp_file_dir)
|
||||
return [entry.name for entry in entries if entry.is_file()]
|
||||
|
||||
|
||||
async def get_temp_dir_size() -> int:
|
||||
"""Get total size of temp directory in bytes.
|
||||
|
||||
Returns:
|
||||
Size in bytes
|
||||
"""
|
||||
if not await aiofiles.os.path.exists(settings.temp_file_dir):
|
||||
return 0
|
||||
|
||||
total = 0
|
||||
entries = await aiofiles.os.scandir(settings.temp_file_dir)
|
||||
for entry in entries:
|
||||
if entry.is_file():
|
||||
stat = await aiofiles.os.stat(entry.path)
|
||||
total += stat.st_size
|
||||
return total
|
||||
|
|
|
|||
|
|
@ -1,12 +1,12 @@
|
|||
"""Model inference package."""
|
||||
|
||||
from .base import BaseModelBackend
|
||||
from .kokoro_v1 import KokoroV1
|
||||
from .model_manager import ModelManager, get_manager
|
||||
|
||||
__all__ = [
|
||||
"BaseModelBackend",
|
||||
"ModelManager",
|
||||
"get_manager",
|
||||
"KokoroV1",
|
||||
]
|
||||
"""Model inference package."""
|
||||
|
||||
from .base import BaseModelBackend
|
||||
from .kokoro_v1 import KokoroV1
|
||||
from .model_manager import ModelManager, get_manager
|
||||
|
||||
__all__ = [
|
||||
"BaseModelBackend",
|
||||
"ModelManager",
|
||||
"get_manager",
|
||||
"KokoroV1",
|
||||
]
|
||||
|
|
|
|||
|
|
@ -1,98 +1,98 @@
|
|||
"""Base interface for Kokoro inference."""
|
||||
|
||||
from abc import ABC, abstractmethod
|
||||
from typing import AsyncGenerator, Optional, Tuple, Union
|
||||
|
||||
import numpy as np
|
||||
import torch
|
||||
|
||||
|
||||
class ModelBackend(ABC):
|
||||
"""Abstract base class for model inference backend."""
|
||||
|
||||
@abstractmethod
|
||||
async def load_model(self, path: str) -> None:
|
||||
"""Load model from path.
|
||||
|
||||
Args:
|
||||
path: Path to model file
|
||||
|
||||
Raises:
|
||||
RuntimeError: If model loading fails
|
||||
"""
|
||||
pass
|
||||
|
||||
@abstractmethod
|
||||
async def generate(
|
||||
self,
|
||||
text: str,
|
||||
voice: Union[str, Tuple[str, Union[torch.Tensor, str]]],
|
||||
speed: float = 1.0,
|
||||
) -> AsyncGenerator[np.ndarray, None]:
|
||||
"""Generate audio from text.
|
||||
|
||||
Args:
|
||||
text: Input text to synthesize
|
||||
voice: Either a voice path or tuple of (name, tensor/path)
|
||||
speed: Speed multiplier
|
||||
|
||||
Yields:
|
||||
Generated audio chunks
|
||||
|
||||
Raises:
|
||||
RuntimeError: If generation fails
|
||||
"""
|
||||
pass
|
||||
|
||||
@abstractmethod
|
||||
def unload(self) -> None:
|
||||
"""Unload model and free resources."""
|
||||
pass
|
||||
|
||||
@property
|
||||
@abstractmethod
|
||||
def is_loaded(self) -> bool:
|
||||
"""Check if model is loaded.
|
||||
|
||||
Returns:
|
||||
True if model is loaded, False otherwise
|
||||
"""
|
||||
pass
|
||||
|
||||
@property
|
||||
@abstractmethod
|
||||
def device(self) -> str:
|
||||
"""Get device model is running on.
|
||||
|
||||
Returns:
|
||||
Device string ('cpu' or 'cuda')
|
||||
"""
|
||||
pass
|
||||
|
||||
|
||||
class BaseModelBackend(ModelBackend):
|
||||
"""Base implementation of model backend."""
|
||||
|
||||
def __init__(self):
|
||||
"""Initialize base backend."""
|
||||
self._model: Optional[torch.nn.Module] = None
|
||||
self._device: str = "cpu"
|
||||
|
||||
@property
|
||||
def is_loaded(self) -> bool:
|
||||
"""Check if model is loaded."""
|
||||
return self._model is not None
|
||||
|
||||
@property
|
||||
def device(self) -> str:
|
||||
"""Get device model is running on."""
|
||||
return self._device
|
||||
|
||||
def unload(self) -> None:
|
||||
"""Unload model and free resources."""
|
||||
if self._model is not None:
|
||||
del self._model
|
||||
self._model = None
|
||||
if torch.cuda.is_available():
|
||||
torch.cuda.empty_cache()
|
||||
torch.cuda.synchronize()
|
||||
"""Base interface for Kokoro inference."""
|
||||
|
||||
from abc import ABC, abstractmethod
|
||||
from typing import AsyncGenerator, Optional, Tuple, Union
|
||||
|
||||
import numpy as np
|
||||
import torch
|
||||
|
||||
|
||||
class ModelBackend(ABC):
|
||||
"""Abstract base class for model inference backend."""
|
||||
|
||||
@abstractmethod
|
||||
async def load_model(self, path: str) -> None:
|
||||
"""Load model from path.
|
||||
|
||||
Args:
|
||||
path: Path to model file
|
||||
|
||||
Raises:
|
||||
RuntimeError: If model loading fails
|
||||
"""
|
||||
pass
|
||||
|
||||
@abstractmethod
|
||||
async def generate(
|
||||
self,
|
||||
text: str,
|
||||
voice: Union[str, Tuple[str, Union[torch.Tensor, str]]],
|
||||
speed: float = 1.0,
|
||||
) -> AsyncGenerator[np.ndarray, None]:
|
||||
"""Generate audio from text.
|
||||
|
||||
Args:
|
||||
text: Input text to synthesize
|
||||
voice: Either a voice path or tuple of (name, tensor/path)
|
||||
speed: Speed multiplier
|
||||
|
||||
Yields:
|
||||
Generated audio chunks
|
||||
|
||||
Raises:
|
||||
RuntimeError: If generation fails
|
||||
"""
|
||||
pass
|
||||
|
||||
@abstractmethod
|
||||
def unload(self) -> None:
|
||||
"""Unload model and free resources."""
|
||||
pass
|
||||
|
||||
@property
|
||||
@abstractmethod
|
||||
def is_loaded(self) -> bool:
|
||||
"""Check if model is loaded.
|
||||
|
||||
Returns:
|
||||
True if model is loaded, False otherwise
|
||||
"""
|
||||
pass
|
||||
|
||||
@property
|
||||
@abstractmethod
|
||||
def device(self) -> str:
|
||||
"""Get device model is running on.
|
||||
|
||||
Returns:
|
||||
Device string ('cpu' or 'cuda')
|
||||
"""
|
||||
pass
|
||||
|
||||
|
||||
class BaseModelBackend(ModelBackend):
|
||||
"""Base implementation of model backend."""
|
||||
|
||||
def __init__(self):
|
||||
"""Initialize base backend."""
|
||||
self._model: Optional[torch.nn.Module] = None
|
||||
self._device: str = "cpu"
|
||||
|
||||
@property
|
||||
def is_loaded(self) -> bool:
|
||||
"""Check if model is loaded."""
|
||||
return self._model is not None
|
||||
|
||||
@property
|
||||
def device(self) -> str:
|
||||
"""Get device model is running on."""
|
||||
return self._device
|
||||
|
||||
def unload(self) -> None:
|
||||
"""Unload model and free resources."""
|
||||
if self._model is not None:
|
||||
del self._model
|
||||
self._model = None
|
||||
if torch.cuda.is_available():
|
||||
torch.cuda.empty_cache()
|
||||
torch.cuda.synchronize()
|
||||
|
|
|
|||
|
|
@ -1,171 +1,171 @@
|
|||
"""Kokoro V1 model management."""
|
||||
|
||||
from typing import Optional
|
||||
|
||||
from loguru import logger
|
||||
|
||||
from ..core import paths
|
||||
from ..core.config import settings
|
||||
from ..core.model_config import ModelConfig, model_config
|
||||
from .base import BaseModelBackend
|
||||
from .kokoro_v1 import KokoroV1
|
||||
|
||||
|
||||
class ModelManager:
|
||||
"""Manages Kokoro V1 model loading and inference."""
|
||||
|
||||
# Singleton instance
|
||||
_instance = None
|
||||
|
||||
def __init__(self, config: Optional[ModelConfig] = None):
|
||||
"""Initialize manager.
|
||||
|
||||
Args:
|
||||
config: Optional model configuration override
|
||||
"""
|
||||
self._config = config or model_config
|
||||
self._backend: Optional[KokoroV1] = None # Explicitly type as KokoroV1
|
||||
self._device: Optional[str] = None
|
||||
|
||||
def _determine_device(self) -> str:
|
||||
"""Determine device based on settings."""
|
||||
return "cuda" if settings.use_gpu else "cpu"
|
||||
|
||||
async def initialize(self) -> None:
|
||||
"""Initialize Kokoro V1 backend."""
|
||||
try:
|
||||
self._device = self._determine_device()
|
||||
logger.info(f"Initializing Kokoro V1 on {self._device}")
|
||||
self._backend = KokoroV1()
|
||||
|
||||
except Exception as e:
|
||||
raise RuntimeError(f"Failed to initialize Kokoro V1: {e}")
|
||||
|
||||
async def initialize_with_warmup(self, voice_manager) -> tuple[str, str, int]:
|
||||
"""Initialize and warm up model.
|
||||
|
||||
Args:
|
||||
voice_manager: Voice manager instance for warmup
|
||||
|
||||
Returns:
|
||||
Tuple of (device, backend type, voice count)
|
||||
|
||||
Raises:
|
||||
RuntimeError: If initialization fails
|
||||
"""
|
||||
import time
|
||||
|
||||
start = time.perf_counter()
|
||||
|
||||
try:
|
||||
# Initialize backend
|
||||
await self.initialize()
|
||||
|
||||
# Load model
|
||||
model_path = self._config.pytorch_kokoro_v1_file
|
||||
await self.load_model(model_path)
|
||||
|
||||
# Use paths module to get voice path
|
||||
try:
|
||||
voices = await paths.list_voices()
|
||||
voice_path = await paths.get_voice_path(settings.default_voice)
|
||||
|
||||
# Warm up with short text
|
||||
warmup_text = "Warmup text for initialization."
|
||||
# Use default voice name for warmup
|
||||
voice_name = settings.default_voice
|
||||
logger.debug(f"Using default voice '{voice_name}' for warmup")
|
||||
async for _ in self.generate(warmup_text, (voice_name, voice_path)):
|
||||
pass
|
||||
except Exception as e:
|
||||
raise RuntimeError(f"Failed to get default voice: {e}")
|
||||
|
||||
ms = int((time.perf_counter() - start) * 1000)
|
||||
logger.info(f"Warmup completed in {ms}ms")
|
||||
|
||||
return self._device, "kokoro_v1", len(voices)
|
||||
except FileNotFoundError as e:
|
||||
logger.error("""
|
||||
Model files not found! You need to download the Kokoro V1 model:
|
||||
|
||||
1. Download model using the script:
|
||||
python docker/scripts/download_model.py --output api/src/models/v1_0
|
||||
|
||||
2. Or set environment variable in docker-compose:
|
||||
DOWNLOAD_MODEL=true
|
||||
""")
|
||||
exit(0)
|
||||
except Exception as e:
|
||||
raise RuntimeError(f"Warmup failed: {e}")
|
||||
|
||||
def get_backend(self) -> BaseModelBackend:
|
||||
"""Get initialized backend.
|
||||
|
||||
Returns:
|
||||
Initialized backend instance
|
||||
|
||||
Raises:
|
||||
RuntimeError: If backend not initialized
|
||||
"""
|
||||
if not self._backend:
|
||||
raise RuntimeError("Backend not initialized")
|
||||
return self._backend
|
||||
|
||||
async def load_model(self, path: str) -> None:
|
||||
"""Load model using initialized backend.
|
||||
|
||||
Args:
|
||||
path: Path to model file
|
||||
|
||||
Raises:
|
||||
RuntimeError: If loading fails
|
||||
"""
|
||||
if not self._backend:
|
||||
raise RuntimeError("Backend not initialized")
|
||||
|
||||
try:
|
||||
await self._backend.load_model(path)
|
||||
except FileNotFoundError as e:
|
||||
raise e
|
||||
except Exception as e:
|
||||
raise RuntimeError(f"Failed to load model: {e}")
|
||||
|
||||
async def generate(self, *args, **kwargs):
|
||||
"""Generate audio using initialized backend.
|
||||
|
||||
Raises:
|
||||
RuntimeError: If generation fails
|
||||
"""
|
||||
if not self._backend:
|
||||
raise RuntimeError("Backend not initialized")
|
||||
|
||||
try:
|
||||
async for chunk in self._backend.generate(*args, **kwargs):
|
||||
yield chunk
|
||||
except Exception as e:
|
||||
raise RuntimeError(f"Generation failed: {e}")
|
||||
|
||||
def unload_all(self) -> None:
|
||||
"""Unload model and free resources."""
|
||||
if self._backend:
|
||||
self._backend.unload()
|
||||
self._backend = None
|
||||
|
||||
@property
|
||||
def current_backend(self) -> str:
|
||||
"""Get current backend type."""
|
||||
return "kokoro_v1"
|
||||
|
||||
|
||||
async def get_manager(config: Optional[ModelConfig] = None) -> ModelManager:
|
||||
"""Get model manager instance.
|
||||
|
||||
Args:
|
||||
config: Optional configuration override
|
||||
|
||||
Returns:
|
||||
ModelManager instance
|
||||
"""
|
||||
if ModelManager._instance is None:
|
||||
ModelManager._instance = ModelManager(config)
|
||||
return ModelManager._instance
|
||||
"""Kokoro V1 model management."""
|
||||
|
||||
from typing import Optional
|
||||
|
||||
from loguru import logger
|
||||
|
||||
from ..core import paths
|
||||
from ..core.config import settings
|
||||
from ..core.model_config import ModelConfig, model_config
|
||||
from .base import BaseModelBackend
|
||||
from .kokoro_v1 import KokoroV1
|
||||
|
||||
|
||||
class ModelManager:
|
||||
"""Manages Kokoro V1 model loading and inference."""
|
||||
|
||||
# Singleton instance
|
||||
_instance = None
|
||||
|
||||
def __init__(self, config: Optional[ModelConfig] = None):
|
||||
"""Initialize manager.
|
||||
|
||||
Args:
|
||||
config: Optional model configuration override
|
||||
"""
|
||||
self._config = config or model_config
|
||||
self._backend: Optional[KokoroV1] = None # Explicitly type as KokoroV1
|
||||
self._device: Optional[str] = None
|
||||
|
||||
def _determine_device(self) -> str:
|
||||
"""Determine device based on settings."""
|
||||
return "cuda" if settings.use_gpu else "cpu"
|
||||
|
||||
async def initialize(self) -> None:
|
||||
"""Initialize Kokoro V1 backend."""
|
||||
try:
|
||||
self._device = self._determine_device()
|
||||
logger.info(f"Initializing Kokoro V1 on {self._device}")
|
||||
self._backend = KokoroV1()
|
||||
|
||||
except Exception as e:
|
||||
raise RuntimeError(f"Failed to initialize Kokoro V1: {e}")
|
||||
|
||||
async def initialize_with_warmup(self, voice_manager) -> tuple[str, str, int]:
|
||||
"""Initialize and warm up model.
|
||||
|
||||
Args:
|
||||
voice_manager: Voice manager instance for warmup
|
||||
|
||||
Returns:
|
||||
Tuple of (device, backend type, voice count)
|
||||
|
||||
Raises:
|
||||
RuntimeError: If initialization fails
|
||||
"""
|
||||
import time
|
||||
|
||||
start = time.perf_counter()
|
||||
|
||||
try:
|
||||
# Initialize backend
|
||||
await self.initialize()
|
||||
|
||||
# Load model
|
||||
model_path = self._config.pytorch_kokoro_v1_file
|
||||
await self.load_model(model_path)
|
||||
|
||||
# Use paths module to get voice path
|
||||
try:
|
||||
voices = await paths.list_voices()
|
||||
voice_path = await paths.get_voice_path(settings.default_voice)
|
||||
|
||||
# Warm up with short text
|
||||
warmup_text = "Warmup text for initialization."
|
||||
# Use default voice name for warmup
|
||||
voice_name = settings.default_voice
|
||||
logger.debug(f"Using default voice '{voice_name}' for warmup")
|
||||
async for _ in self.generate(warmup_text, (voice_name, voice_path)):
|
||||
pass
|
||||
except Exception as e:
|
||||
raise RuntimeError(f"Failed to get default voice: {e}")
|
||||
|
||||
ms = int((time.perf_counter() - start) * 1000)
|
||||
logger.info(f"Warmup completed in {ms}ms")
|
||||
|
||||
return self._device, "kokoro_v1", len(voices)
|
||||
except FileNotFoundError as e:
|
||||
logger.error("""
|
||||
Model files not found! You need to download the Kokoro V1 model:
|
||||
|
||||
1. Download model using the script:
|
||||
python docker/scripts/download_model.py --output api/src/models/v1_0
|
||||
|
||||
2. Or set environment variable in docker-compose:
|
||||
DOWNLOAD_MODEL=true
|
||||
""")
|
||||
exit(0)
|
||||
except Exception as e:
|
||||
raise RuntimeError(f"Warmup failed: {e}")
|
||||
|
||||
def get_backend(self) -> BaseModelBackend:
|
||||
"""Get initialized backend.
|
||||
|
||||
Returns:
|
||||
Initialized backend instance
|
||||
|
||||
Raises:
|
||||
RuntimeError: If backend not initialized
|
||||
"""
|
||||
if not self._backend:
|
||||
raise RuntimeError("Backend not initialized")
|
||||
return self._backend
|
||||
|
||||
async def load_model(self, path: str) -> None:
|
||||
"""Load model using initialized backend.
|
||||
|
||||
Args:
|
||||
path: Path to model file
|
||||
|
||||
Raises:
|
||||
RuntimeError: If loading fails
|
||||
"""
|
||||
if not self._backend:
|
||||
raise RuntimeError("Backend not initialized")
|
||||
|
||||
try:
|
||||
await self._backend.load_model(path)
|
||||
except FileNotFoundError as e:
|
||||
raise e
|
||||
except Exception as e:
|
||||
raise RuntimeError(f"Failed to load model: {e}")
|
||||
|
||||
async def generate(self, *args, **kwargs):
|
||||
"""Generate audio using initialized backend.
|
||||
|
||||
Raises:
|
||||
RuntimeError: If generation fails
|
||||
"""
|
||||
if not self._backend:
|
||||
raise RuntimeError("Backend not initialized")
|
||||
|
||||
try:
|
||||
async for chunk in self._backend.generate(*args, **kwargs):
|
||||
yield chunk
|
||||
except Exception as e:
|
||||
raise RuntimeError(f"Generation failed: {e}")
|
||||
|
||||
def unload_all(self) -> None:
|
||||
"""Unload model and free resources."""
|
||||
if self._backend:
|
||||
self._backend.unload()
|
||||
self._backend = None
|
||||
|
||||
@property
|
||||
def current_backend(self) -> str:
|
||||
"""Get current backend type."""
|
||||
return "kokoro_v1"
|
||||
|
||||
|
||||
async def get_manager(config: Optional[ModelConfig] = None) -> ModelManager:
|
||||
"""Get model manager instance.
|
||||
|
||||
Args:
|
||||
config: Optional configuration override
|
||||
|
||||
Returns:
|
||||
ModelManager instance
|
||||
"""
|
||||
if ModelManager._instance is None:
|
||||
ModelManager._instance = ModelManager(config)
|
||||
return ModelManager._instance
|
||||
|
|
|
|||
Loading…
Add table
Reference in a new issue