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"""Dia TTS provider implementation."""
import logging
import numpy as np
import soundfile as sf
import io
from typing import Iterator, TYPE_CHECKING
if TYPE_CHECKING:
from ...domain.models.speech_synthesis_request import SpeechSynthesisRequest
from ..base.tts_provider_base import TTSProviderBase
from ...domain.exceptions import SpeechSynthesisException
logger = logging.getLogger(__name__)
# Flag to track Dia availability
DIA_AVAILABLE = False
DEFAULT_SAMPLE_RATE = 24000
# Try to import Dia dependencies
try:
import torch
from dia.model import Dia
DIA_AVAILABLE = True
logger.info("Dia TTS engine is available")
except ImportError:
logger.warning("Dia TTS engine is not available")
except ModuleNotFoundError as e:
if "dac" in str(e):
logger.warning("Dia TTS engine is not available due to missing 'dac' module")
else:
logger.warning(f"Dia TTS engine is not available: {str(e)}")
DIA_AVAILABLE = False
class DiaTTSProvider(TTSProviderBase):
"""Dia TTS provider implementation."""
def __init__(self, lang_code: str = 'z'):
"""Initialize the Dia TTS provider."""
super().__init__(
provider_name="Dia",
supported_languages=['en', 'z'] # Dia supports English and multilingual
)
self.lang_code = lang_code
self.model = None
def _ensure_model(self):
"""Ensure the model is loaded."""
if self.model is None and DIA_AVAILABLE:
try:
import torch
from dia.model import Dia
self.model = Dia.from_pretrained()
logger.info("Dia model successfully loaded")
except ImportError as e:
logger.error(f"Failed to import Dia dependencies: {str(e)}")
self.model = None
except FileNotFoundError as e:
logger.error(f"Failed to load Dia model files: {str(e)}")
self.model = None
except Exception as e:
logger.error(f"Failed to initialize Dia model: {str(e)}")
self.model = None
return self.model is not None
def is_available(self) -> bool:
"""Check if Dia TTS is available."""
return DIA_AVAILABLE and self._ensure_model()
def get_available_voices(self) -> list[str]:
"""Get available voices for Dia."""
# Dia typically uses a default voice
return ['default']
def _generate_audio(self, request: 'SpeechSynthesisRequest') -> tuple[bytes, int]:
"""Generate audio using Dia TTS."""
if not self.is_available():
raise SpeechSynthesisException("Dia TTS engine is not available")
try:
import torch
# Extract parameters from request
text = request.text_content.text
# Generate audio using Dia
with torch.inference_mode():
output_audio_np = self.model.generate(
text,
max_tokens=None,
cfg_scale=3.0,
temperature=1.3,
top_p=0.95,
cfg_filter_top_k=35,
use_torch_compile=False,
verbose=False
)
if output_audio_np is None:
raise SpeechSynthesisException("Dia model returned None for audio output")
# Convert numpy array to bytes
audio_bytes = self._numpy_to_bytes(output_audio_np, sample_rate=DEFAULT_SAMPLE_RATE)
return audio_bytes, DEFAULT_SAMPLE_RATE
except ModuleNotFoundError as e:
if "dac" in str(e):
raise SpeechSynthesisException("Dia TTS engine failed due to missing 'dac' module") from e
else:
self._handle_provider_error(e, "audio generation")
except Exception as e:
self._handle_provider_error(e, "audio generation")
def _generate_audio_stream(self, request: 'SpeechSynthesisRequest') -> Iterator[tuple[bytes, int, bool]]:
"""Generate audio stream using Dia TTS."""
if not self.is_available():
raise SpeechSynthesisException("Dia TTS engine is not available")
try:
import torch
# Extract parameters from request
text = request.text_content.text
# Generate audio using Dia
with torch.inference_mode():
output_audio_np = self.model.generate(
text,
max_tokens=None,
cfg_scale=3.0,
temperature=1.3,
top_p=0.95,
cfg_filter_top_k=35,
use_torch_compile=False,
verbose=False
)
if output_audio_np is None:
raise SpeechSynthesisException("Dia model returned None for audio output")
# Convert numpy array to bytes
audio_bytes = self._numpy_to_bytes(output_audio_np, sample_rate=DEFAULT_SAMPLE_RATE)
# Dia generates complete audio in one go
yield audio_bytes, DEFAULT_SAMPLE_RATE, True
except ModuleNotFoundError as e:
if "dac" in str(e):
raise SpeechSynthesisException("Dia TTS engine failed due to missing 'dac' module") from e
else:
self._handle_provider_error(e, "streaming audio generation")
except Exception as e:
self._handle_provider_error(e, "streaming audio generation")
def _numpy_to_bytes(self, audio_array: np.ndarray, sample_rate: int) -> bytes:
"""Convert numpy audio array to bytes."""
try:
# Create an in-memory buffer
buffer = io.BytesIO()
# Write audio data to buffer as WAV
sf.write(buffer, audio_array, sample_rate, format='WAV')
# Get bytes from buffer
buffer.seek(0)
return buffer.read()
except Exception as e:
raise SpeechSynthesisException(f"Failed to convert audio to bytes: {str(e)}") from e |