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https://github.com/jamiepine/voicebox.git
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jamiepineandcapy-ai-staging[bot]
b1323ed0a8
Review follow-ups: clear_voice_prompt_memory_cache() now runs before backend.unload_model() at all three call sites so the device-resident prompt tensors are already released when empty_device_cache() / empty_mlx_cache() runs, instead of going back into the caching allocator afterwards. empty_mlx_cache's docstring states why it may run off the MLX worker thread.
37 lines
863 B
Python
37 lines
863 B
Python
"""
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TTS inference module - delegates to backend abstraction layer.
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"""
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from typing import Optional
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import numpy as np
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import io
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import soundfile as sf
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from ..backends import get_tts_backend, TTSBackend
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from ..utils.cache import clear_voice_prompt_memory_cache
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def get_tts_model() -> TTSBackend:
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"""
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Get TTS backend instance (MLX or PyTorch based on platform).
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Returns:
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TTS backend instance
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"""
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return get_tts_backend()
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def unload_tts_model():
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"""Unload TTS model to free memory."""
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backend = get_tts_backend()
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clear_voice_prompt_memory_cache()
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backend.unload_model()
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def audio_to_wav_bytes(audio: np.ndarray, sample_rate: int) -> bytes:
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"""Convert audio array to WAV bytes."""
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buffer = io.BytesIO()
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sf.write(buffer, audio, sample_rate, format="WAV")
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buffer.seek(0)
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return buffer.read()
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