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"""Clean Kokoro implementation with controlled resource management."""
import os
from typing import AsyncGenerator, Dict, Optional, Tuple, Union
import numpy as np
import torch
from kokoro import KModel, KPipeline
from loguru import logger
from ..core import paths
from ..core.config import settings
from ..core.model_config import model_config
from ..structures.schemas import WordTimestamp
from .base import AudioChunk, BaseModelBackend
class KokoroV1(BaseModelBackend):
"""Kokoro backend with controlled resource management."""
def __init__(self):
"""Initialize backend with environment-based configuration."""
super().__init__()
# Strictly respect settings.use_gpu
self._device = settings.get_device()
self._model: Optional[KModel] = None
self._pipelines: Dict[str, KPipeline] = {} # Store pipelines by lang_code
async def load_model(self, path: str) -> None:
"""Load pre-baked model.
Args:
path: Path to model file
Raises:
RuntimeError: If model loading fails
"""
try:
# Get verified model path
model_path = await paths.get_model_path(path)
config_path = os.path.join(os.path.dirname(model_path), "config.json")
if not os.path.exists(config_path):
raise RuntimeError(f"Config file not found: {config_path}")
logger.info(f"Loading Kokoro model on {self._device}")
logger.info(f"Config path: {config_path}")
logger.info(f"Model path: {model_path}")
# Load model and let KModel handle device mapping
self._model = KModel(config=config_path, model=model_path).eval()
# For MPS, manually move ISTFT layers to CPU while keeping rest on MPS
if self._device == "mps":
logger.info(
"Moving model to MPS device with CPU fallback for unsupported operations"
)
self._model = self._model.to(torch.device("mps"))
elif self._device == "cuda":
self._model = self._model.cuda()
else:
self._model = self._model.cpu()
except FileNotFoundError as e:
raise e
except Exception as e:
raise RuntimeError(f"Failed to load Kokoro model: {e}")
def _get_pipeline(self, lang_code: str) -> KPipeline:
"""Get or create pipeline for language code.
Args:
lang_code: Language code to use
Returns:
KPipeline instance for the language
"""
if not self._model:
raise RuntimeError("Model not loaded")
if lang_code not in self._pipelines:
logger.info(f"Creating new pipeline for language code: {lang_code}")
self._pipelines[lang_code] = KPipeline(
lang_code=lang_code, model=self._model, device=self._device
)
return self._pipelines[lang_code]
async def generate_from_tokens(
self,
tokens: str,
voice: Union[str, Tuple[str, Union[torch.Tensor, str]]],
speed: float = 1.0,
lang_code: Optional[str] = None,
) -> AsyncGenerator[np.ndarray, None]:
"""Generate audio from phoneme tokens.
Args:
tokens: Input phoneme tokens to synthesize
voice: Either a voice path string or a tuple of (voice_name, voice_tensor/path)
speed: Speed multiplier
lang_code: Optional language code override
Yields:
Generated audio chunks
Raises:
RuntimeError: If generation fails
"""
if not self.is_loaded:
raise RuntimeError("Model not loaded")
try:
# Memory management for GPU
if self._device == "cuda":
if self._check_memory():
self._clear_memory()
# Handle voice input
voice_path: str
voice_name: str
if isinstance(voice, tuple):
voice_name, voice_data = voice
if isinstance(voice_data, str):
voice_path = voice_data
else:
# Save tensor to temporary file
import tempfile
temp_dir = tempfile.gettempdir()
voice_path = os.path.join(temp_dir, f"{voice_name}.pt")
# Save tensor with CPU mapping for portability
torch.save(voice_data.cpu(), voice_path)
else:
voice_path = voice
voice_name = os.path.splitext(os.path.basename(voice_path))[0]
# Load voice tensor with proper device mapping
voice_tensor = await paths.load_voice_tensor(
voice_path, device=self._device
)
# Save back to a temporary file with proper device mapping
import tempfile
temp_dir = tempfile.gettempdir()
temp_path = os.path.join(
temp_dir, f"temp_voice_{os.path.basename(voice_path)}"
)
await paths.save_voice_tensor(voice_tensor, temp_path)
voice_path = temp_path
# Use provided lang_code, settings voice code override, or first letter of voice name
if lang_code: # api is given priority
pipeline_lang_code = lang_code
elif settings.default_voice_code: # settings is next priority
pipeline_lang_code = settings.default_voice_code
else: # voice name is default/fallback
pipeline_lang_code = voice_name[0].lower()
pipeline = self._get_pipeline(pipeline_lang_code)
logger.debug(
f"Generating audio from tokens with lang_code '{pipeline_lang_code}': '{tokens[:100]}{'...' if len(tokens) > 100 else ''}'"
)
for result in pipeline.generate_from_tokens(
tokens=tokens, voice=voice_path, speed=speed, model=self._model
):
if result.audio is not None:
logger.debug(f"Got audio chunk with shape: {result.audio.shape}")
yield result.audio.numpy()
else:
logger.warning("No audio in chunk")
except Exception as e:
logger.error(f"Generation failed: {e}")
if (
self._device == "cuda"
and model_config.pytorch_gpu.retry_on_oom
and "out of memory" in str(e).lower()
):
self._clear_memory()
async for chunk in self.generate_from_tokens(
tokens, voice, speed, lang_code
):
yield chunk
raise
async def generate(
self,
text: str,
voice: Union[str, Tuple[str, Union[torch.Tensor, str]]],
speed: float = 1.0,
lang_code: Optional[str] = None,
return_timestamps: Optional[bool] = False,
) -> AsyncGenerator[AudioChunk, None]:
"""Generate audio using model.
Args:
text: Input text to synthesize
voice: Either a voice path string or a tuple of (voice_name, voice_tensor/path)
speed: Speed multiplier
lang_code: Optional language code override
Yields:
Generated audio chunks
Raises:
RuntimeError: If generation fails
"""
if not self.is_loaded:
raise RuntimeError("Model not loaded")
try:
# Memory management for GPU
if self._device == "cuda":
if self._check_memory():
self._clear_memory()
# Handle voice input
voice_path: str
voice_name: str
if isinstance(voice, tuple):
voice_name, voice_data = voice
if isinstance(voice_data, str):
voice_path = voice_data
else:
# Save tensor to temporary file
import tempfile
temp_dir = tempfile.gettempdir()
voice_path = os.path.join(temp_dir, f"{voice_name}.pt")
# Save tensor with CPU mapping for portability
torch.save(voice_data.cpu(), voice_path)
else:
voice_path = voice
voice_name = os.path.splitext(os.path.basename(voice_path))[0]
# Load voice tensor with proper device mapping
voice_tensor = await paths.load_voice_tensor(
voice_path, device=self._device
)
# Save back to a temporary file with proper device mapping
import tempfile
temp_dir = tempfile.gettempdir()
temp_path = os.path.join(
temp_dir, f"temp_voice_{os.path.basename(voice_path)}"
)
await paths.save_voice_tensor(voice_tensor, temp_path)
voice_path = temp_path
# Use provided lang_code, settings voice code override, or first letter of voice name
pipeline_lang_code = (
lang_code
if lang_code
else (
settings.default_voice_code
if settings.default_voice_code
else voice_name[0].lower()
)
)
pipeline = self._get_pipeline(pipeline_lang_code)
logger.debug(
f"Generating audio for text with lang_code '{pipeline_lang_code}': '{text[:100]}{'...' if len(text) > 100 else ''}'"
)
for result in pipeline(
text, voice=voice_path, speed=speed, model=self._model
):
if result.audio is not None:
logger.debug(f"Got audio chunk with shape: {result.audio.shape}")
word_timestamps = None
if (
return_timestamps
and hasattr(result, "tokens")
and result.tokens
):
word_timestamps = []
current_offset = 0.0
logger.debug(
f"Processing chunk timestamps with {len(result.tokens)} tokens"
)
if result.pred_dur is not None:
try:
# Add timestamps with offset
for token in result.tokens:
if not all(
hasattr(token, attr)
for attr in [
"text",
"start_ts",
"end_ts",
]
):
continue
if not token.text or not token.text.strip():
continue
start_time = float(token.start_ts) + current_offset
end_time = float(token.end_ts) + current_offset
word_timestamps.append(
WordTimestamp(
word=str(token.text).strip(),
start_time=start_time,
end_time=end_time,
)
)
logger.debug(
f"Added timestamp for word '{token.text}': {start_time:.3f}s - {end_time:.3f}s"
)
except Exception as e:
logger.error(
f"Failed to process timestamps for chunk: {e}"
)
yield AudioChunk(
result.audio.numpy(), word_timestamps=word_timestamps
)
else:
logger.warning("No audio in chunk")
except Exception as e:
logger.error(f"Generation failed: {e}")
if (
self._device == "cuda"
and model_config.pytorch_gpu.retry_on_oom
and "out of memory" in str(e).lower()
):
self._clear_memory()
async for chunk in self.generate(text, voice, speed, lang_code):
yield chunk
raise
def _check_memory(self) -> bool:
"""Check if memory usage is above threshold."""
if self._device == "cuda":
memory_gb = torch.cuda.memory_allocated() / 1e9
return memory_gb > model_config.pytorch_gpu.memory_threshold
# MPS doesn't provide memory management APIs
return False
def _clear_memory(self) -> None:
"""Clear device memory."""
if self._device == "cuda":
torch.cuda.empty_cache()
torch.cuda.synchronize()
elif self._device == "mps":
# Empty cache if available (future-proofing)
if hasattr(torch.mps, "empty_cache"):
torch.mps.empty_cache()
def unload(self) -> None:
"""Unload model and free resources."""
if self._model is not None:
del self._model
self._model = None
for pipeline in self._pipelines.values():
del pipeline
self._pipelines.clear()
if torch.cuda.is_available():
torch.cuda.empty_cache()
torch.cuda.synchronize()
@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
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