真实接口预热
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yinsx
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@ -79,9 +79,10 @@ class TextToBlendShapesService:
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yield {'type': 'error', 'message': '文本为空'}
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return
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# 测试：只处理第一句
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sentences = sentences[:1]
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print(f"[测试模式] 只处理第一句: {sentences[0]}")
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# 确保 is_continuation 长度与 sentences 匹配
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if len(self.is_continuation) != len(sentences):
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print(f"[警告] is_continuation 长度 ({len(self.is_continuation)}) 与 sentences 长度 ({len(sentences)}) 不匹配，重置")
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self.is_continuation = [False] * len(sentences)
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yield {
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'type': 'status',
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@ -92,9 +93,10 @@ class TextToBlendShapesService:
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}
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# 打印句子列表用于调试
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print(f"[调试] 发送给前端的句子列表:")
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print(f"[调试] 发送给前端的句子列表 (共{len(sentences)}句, is_continuation长度={len(self.is_continuation)}):")
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for i, s in enumerate(sentences):
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print(f" [{i}] {s}")
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cont = self.is_continuation[i] if i < len(self.is_continuation) else False
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print(f" [{i}] {s} (连续={cont})")
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# 使用队列来收集处理完成的句子
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result_queue = queue.Queue()
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@ -113,70 +115,85 @@ class TextToBlendShapesService:
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result_queue.put((index, 'error', None, str(e)))
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# 提交所有句子到线程池并发处理（增加并发数以加速）
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with concurrent.futures.ThreadPoolExecutor(max_workers=5) as executor:
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for index, sentence in enumerate(sentences):
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executor.submit(process_and_queue, index, sentence)
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print(f"[调试] 准备提交 {len(sentences)} 个句子到线程池")
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try:
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with concurrent.futures.ThreadPoolExecutor(max_workers=5) as executor:
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futures = []
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for index, sentence in enumerate(sentences):
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future = executor.submit(process_and_queue, index, sentence)
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futures.append(future)
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print(f"[调试] 已提交句子 {index}: {sentence[:20]}...")
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# 按顺序从队列中取出结果并推送
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completed = {}
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next_index = 0
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total_frames = 0
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cumulative_time = 0.0 # 累计时间，用于连续句子
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# 按顺序从队列中取出结果并推送
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completed = {}
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next_index = 0
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total_frames = 0
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cumulative_time = 0.0 # 累计时间，用于连续句子
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while next_index < len(sentences):
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# 如果下一个句子还没完成，等待队列
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if next_index not in completed:
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yield {
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'type': 'status',
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'stage': 'processing',
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'sentence_index': next_index,
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'sentences': len(sentences),
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'message': f'正在处理 {next_index + 1}/{len(sentences)}'
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}
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print(f"[调试] 开始主循环，共 {len(sentences)} 个句子")
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while next_index < len(sentences):
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print(f"[调试] 主循环 next_index={next_index}, completed keys={list(completed.keys())}")
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# 如果下一个句子还没完成，等待队列
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if next_index not in completed:
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yield {
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'type': 'status',
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'stage': 'processing',
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'sentence_index': next_index,
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'sentences': len(sentences),
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'message': f'正在处理 {next_index + 1}/{len(sentences)}'
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}
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# 从队列中获取结果
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while next_index not in completed:
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try:
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index, status, frames, error = result_queue.get(timeout=1)
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completed[index] = (status, frames, error)
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print(f"[主线程] 收到句子 {index} 的处理结果")
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except queue.Empty:
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continue
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# 从队列中获取结果
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while next_index not in completed:
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try:
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index, status, frames, error = result_queue.get(timeout=1)
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completed[index] = (status, frames, error)
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print(f"[主线程] 收到句子 {index} 的处理结果, status={status}")
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except queue.Empty:
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print(f"[调试] 队列为空，继续等待 next_index={next_index}")
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continue
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# 推送下一个句子的帧
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status, frames, error = completed[next_index]
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if status == 'error':
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yield {'type': 'error', 'message': f'句子 {next_index} 处理失败: {error}'}
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return
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# 推送下一个句子的帧
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status, frames, error = completed[next_index]
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print(f"[主线程] 句子 {next_index} 状态: {status}, 帧数: {len(frames) if frames else 0}, 错误: {error}")
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if status == 'error':
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print(f"[错误] 句子 {next_index} 处理失败: {error}")
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yield {'type': 'error', 'message': f'句子 {next_index} 处理失败: {error}'}
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return
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# 如果是连续句子，调整时间码使其无缝衔接
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is_continuation = self.is_continuation[next_index] if next_index < len(self.is_continuation) else False
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# 如果是连续句子，调整时间码使其无缝衔接
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is_continuation = self.is_continuation[next_index] if next_index < len(self.is_continuation) else False
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print(f"[主线程] 正在推送句子 {next_index} 的 {len(frames)} 帧 {'(连续)' if is_continuation else ''}")
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print(f"[调试] 句子 {next_index} 对应文本: {sentences[next_index] if next_index < len(sentences) else 'N/A'}")
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print(f"[主线程] 正在推送句子 {next_index}/{len(sentences)-1} 的 {len(frames)} 帧 {'(连续)' if is_continuation else ''}")
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print(f"[调试] 句子 {next_index} 对应文本: {sentences[next_index] if next_index < len(sentences) else 'N/A'}")
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# 如果不是连续句子，重置累计时间
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if not is_continuation and next_index > 0:
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cumulative_time = 0.0
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# 如果不是连续句子，重置累计时间
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if not is_continuation and next_index > 0:
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cumulative_time = 0.0
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for frame in frames:
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# 调整时间码：从累计时间开始
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frame['timeCode'] = cumulative_time + frame['timeCode']
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frame['sentenceIndex'] = next_index
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total_frames += 1
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yield {'type': 'frame', 'frame': frame}
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for frame in frames:
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# 调整时间码：从累计时间开始
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frame['timeCode'] = cumulative_time + frame['timeCode']
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frame['sentenceIndex'] = next_index
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total_frames += 1
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yield {'type': 'frame', 'frame': frame}
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# 更新累计时间为当前句子的最后一帧时间
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if frames:
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cumulative_time = frames[-1]['timeCode']
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# 更新累计时间为当前句子的最后一帧时间
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if frames:
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cumulative_time = frames[-1]['timeCode']
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next_index += 1
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next_index += 1
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print(f"[主线程] 流式传输完成，共 {total_frames} 帧")
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yield {
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'type': 'end',
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'frames': total_frames
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}
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print(f"[主线程] 流式传输完成，共 {total_frames} 帧，处理了 {next_index} 个句子")
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yield {
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'type': 'end',
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'frames': total_frames
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}
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except Exception as e:
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import traceback
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print(f"[错误] 流式处理异常: {e}")
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traceback.print_exc()
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yield {'type': 'error', 'message': f'流式处理异常: {str(e)}'}
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def _process_sentence(self, sentence, output_dir, index):
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"""处理单个句子: TTS -> A2F -> 解析"""
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@ -286,6 +303,9 @@ class TextToBlendShapesService:
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# 12字以上：前6字，再6字，剩下的
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parts = [first[:6], first[6:12], first[12:]]
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# 过滤空字符串
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parts = [p for p in parts if p.strip()]
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# 替换第一句为多个小句
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sentences = parts + sentences[1:]
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# 标记后续部分为连续播放
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@ -293,18 +313,31 @@ class TextToBlendShapesService:
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print(f"[拆分优化] 第一句({length}字)拆分为{len(parts)}部分: {[len(p) for p in parts]} - 连续播放")
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if not max_sentence_length or max_sentence_length <= 0:
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print(f"[拆分] 最终句子数: {len(sentences)}, is_continuation长度: {len(self.is_continuation)}")
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return sentences
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limited = []
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for sentence in sentences:
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new_is_continuation = []
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for i, sentence in enumerate(sentences):
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if len(sentence) <= max_sentence_length:
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limited.append(sentence)
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new_is_continuation.append(self.is_continuation[i] if i < len(self.is_continuation) else False)
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continue
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start = 0
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first_part = True
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while start < len(sentence):
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limited.append(sentence[start:start + max_sentence_length])
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# 第一部分继承原来的 is_continuation，后续部分标记为连续
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if first_part:
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new_is_continuation.append(self.is_continuation[i] if i < len(self.is_continuation) else False)
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first_part = False
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else:
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new_is_continuation.append(True)
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start += max_sentence_length
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self.is_continuation = new_is_continuation
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print(f"[拆分] 最终句子数: {len(limited)}, is_continuation长度: {len(self.is_continuation)}")
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return limited
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def _prepare_output_paths(self, output_dir: str = None, suffix: str = None):
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