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feat: Implement character voice preview playback and regeneration, and add a turbo mode status indicator for audiobook projects.

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This commit is contained in:
bdim404
2026-03-11 15:36:43 +08:00
parent 5dded459fc
commit d3c6297a09
8 changed files with 401 additions and 76 deletions
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107
qwen3-tts-backend/core/tts_service.py
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@@ -32,6 +32,8 @@ class TTSBackend(ABC):
class LocalTTSBackend(TTSBackend):
def __init__(self):
self.model_manager = None
# Add a lock to prevent concurrent VRAM contention and CUDA errors on local GPU models
self._gpu_lock = asyncio.Lock()
async def initialize(self):
from core.model_manager import ModelManager
@@ -42,21 +44,22 @@ class LocalTTSBackend(TTSBackend):
_, tts = await self.model_manager.get_current_model()
loop = asyncio.get_event_loop()
result = await loop.run_in_executor(
None,
functools.partial(
tts.generate_custom_voice,
text=params['text'],
language=params['language'],
speaker=params['speaker'],
instruct=params.get('instruct', ''),
max_new_tokens=params['max_new_tokens'],
temperature=params['temperature'],
top_k=params['top_k'],
top_p=params['top_p'],
repetition_penalty=params['repetition_penalty'],
async with self._gpu_lock:
result = await loop.run_in_executor(
None,
functools.partial(
tts.generate_custom_voice,
text=params['text'],
language=params['language'],
speaker=params['speaker'],
instruct=params.get('instruct', ''),
max_new_tokens=params['max_new_tokens'],
temperature=params['temperature'],
top_k=params['top_k'],
top_p=params['top_p'],
repetition_penalty=params['repetition_penalty'],
)
)
)
import numpy as np
wavs, sample_rate = result if isinstance(result, tuple) else (result, 24000)
@@ -68,20 +71,21 @@ class LocalTTSBackend(TTSBackend):
_, tts = await self.model_manager.get_current_model()
loop = asyncio.get_event_loop()
result = await loop.run_in_executor(
None,
functools.partial(
tts.generate_voice_design,
text=params['text'],
language=params['language'],
instruct=params['instruct'],
max_new_tokens=params['max_new_tokens'],
temperature=params['temperature'],
top_k=params['top_k'],
top_p=params['top_p'],
repetition_penalty=params['repetition_penalty'],
async with self._gpu_lock:
result = await loop.run_in_executor(
None,
functools.partial(
tts.generate_voice_design,
text=params['text'],
language=params['language'],
instruct=params['instruct'],
max_new_tokens=params['max_new_tokens'],
temperature=params['temperature'],
top_k=params['top_k'],
top_p=params['top_p'],
repetition_penalty=params['repetition_penalty'],
)
)
)
import numpy as np
wavs, sample_rate = result if isinstance(result, tuple) else (result, 24000)
@@ -96,37 +100,38 @@ class LocalTTSBackend(TTSBackend):
loop = asyncio.get_event_loop()
if x_vector is None:
if ref_audio_bytes is None:
raise ValueError("Either ref_audio_bytes or x_vector must be provided")
async with self._gpu_lock:
if x_vector is None:
if ref_audio_bytes is None:
raise ValueError("Either ref_audio_bytes or x_vector must be provided")
ref_audio_array, ref_sr = process_ref_audio(ref_audio_bytes)
ref_audio_array, ref_sr = process_ref_audio(ref_audio_bytes)
x_vector = await loop.run_in_executor(
x_vector = await loop.run_in_executor(
None,
functools.partial(
tts.create_voice_clone_prompt,
ref_audio=(ref_audio_array, ref_sr),
ref_text=params.get('ref_text', ''),
x_vector_only_mode=False,
)
)
wavs, sample_rate = await loop.run_in_executor(
None,
functools.partial(
tts.create_voice_clone_prompt,
ref_audio=(ref_audio_array, ref_sr),
ref_text=params.get('ref_text', ''),
x_vector_only_mode=False,
tts.generate_voice_clone,
text=params['text'],
language=params['language'],
voice_clone_prompt=x_vector,
max_new_tokens=params['max_new_tokens'],
temperature=params['temperature'],
top_k=params['top_k'],
top_p=params['top_p'],
repetition_penalty=params['repetition_penalty'],
)
)
wavs, sample_rate = await loop.run_in_executor(
None,
functools.partial(
tts.generate_voice_clone,
text=params['text'],
language=params['language'],
voice_clone_prompt=x_vector,
max_new_tokens=params['max_new_tokens'],
temperature=params['temperature'],
top_k=params['top_k'],
top_p=params['top_p'],
repetition_penalty=params['repetition_penalty'],
)
)
import numpy as np
audio_data = wavs[0] if isinstance(wavs, list) else wavs
if isinstance(audio_data, list):