mirror of
https://github.com/remsky/Kokoro-FastAPI.git
synced 2025-08-31 21:59:28 +00:00
127 lines
3.1 KiB
Python
127 lines
3.1 KiB
Python
"""Base interface for Kokoro inference."""
|
|
|
|
from abc import ABC, abstractmethod
|
|
from typing import AsyncGenerator, List, Optional, Tuple, Union
|
|
|
|
import numpy as np
|
|
import torch
|
|
|
|
|
|
class AudioChunk:
|
|
"""Class for audio chunks returned by model backends"""
|
|
|
|
def __init__(
|
|
self,
|
|
audio: np.ndarray,
|
|
word_timestamps: Optional[List] = [],
|
|
output: Optional[Union[bytes, np.ndarray]] = b"",
|
|
):
|
|
self.audio = audio
|
|
self.word_timestamps = word_timestamps
|
|
self.output = output
|
|
|
|
@staticmethod
|
|
def combine(audio_chunk_list: List):
|
|
output = AudioChunk(
|
|
audio_chunk_list[0].audio, audio_chunk_list[0].word_timestamps
|
|
)
|
|
|
|
for audio_chunk in audio_chunk_list[1:]:
|
|
output.audio = np.concatenate(
|
|
(output.audio, audio_chunk.audio), dtype=np.int16
|
|
)
|
|
if output.word_timestamps is not None:
|
|
output.word_timestamps += audio_chunk.word_timestamps
|
|
|
|
return output
|
|
|
|
|
|
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[AudioChunk, 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()
|