text stringlengths 1 93.6k |
|---|
cap.release()
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cv2.destroyAllWindows()
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# 检测 VAD 活动
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def check_vad_activity(audio_data):
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# 将音频数据分块检测
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num = 0
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step = int(AUDIO_RATE * 0.02) # 20ms 块大小
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flag_rate = round(0.8 * len(audio_data) // step)
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for i in range(0, len(audio_data), step):
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chunk = audio_data[i:i + step]
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if len(chunk) == step:
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if vad.is_speech(chunk, sample_rate=AUDIO_RATE):
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num += 1
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if num > flag_rate:
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return True
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return False
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# 保存音频和视频
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def save_audio_video():
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global segments_to_save, video_queue, last_vad_end_time, saved_intervals
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if not segments_to_save:
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return
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# 获取有效段的时间范围
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start_time = segments_to_save[0][1]
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end_time = segments_to_save[-1][1]
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# 检查是否与之前的片段重叠
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if saved_intervals and saved_intervals[-1][1] >= start_time:
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print("当前片段与之前片段重叠，跳过保存")
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segments_to_save.clear()
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return
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# 保存音频
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audio_frames = [seg[0] for seg in segments_to_save]
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audio_output_path = f"{OUTPUT_DIR}/audio_0.wav"
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wf = wave.open(audio_output_path, 'wb')
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wf.setnchannels(AUDIO_CHANNELS)
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wf.setsampwidth(2) # 16-bit PCM
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wf.setframerate(AUDIO_RATE)
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wf.writeframes(b''.join(audio_frames))
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wf.close()
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print(f"音频保存至 {audio_output_path}")
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# 保存视频
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video_frames = []
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while not video_queue.empty():
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frame, timestamp = video_queue.get()
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if start_time <= timestamp <= end_time:
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video_frames.append(frame)
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if video_frames:
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video_output_path = f"{OUTPUT_DIR}/video_0.avi"
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out = cv2.VideoWriter(video_output_path, cv2.VideoWriter_fourcc(*'XVID'), 20.0, (640, 480))
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for frame in video_frames:
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out.write(frame)
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out.release()
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print(f"视频保存至 {video_output_path}")
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# Inference()
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else:
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pass
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# print("无可保存的视频帧")
|
# 记录保存的区间
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saved_intervals.append((start_time, end_time))
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# 清空缓冲区
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segments_to_save.clear()
|
# --- 播放音频 -
|
def play_audio(file_path):
|
try:
|
pygame.mixer.init()
|
pygame.mixer.music.load(file_path)
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pygame.mixer.music.play()
|
while pygame.mixer.music.get_busy():
|
time.sleep(1) # 等待音频播放结束
|
print("播放完成！")
|
except Exception as e:
|
print(f"播放失败: {e}")
|
finally:
|
pygame.mixer.quit()
|
async def amain(TEXT, VOICE, OUTPUT_FILE) -> None:
|
"""Main function"""
|
communicate = edge_tts.Communicate(TEXT, VOICE)
|
await communicate.save(OUTPUT_FILE)
|
# # -------------- Load QWen2-VL Model ------------
|
# # default: Load the model on the available device(s)
|
# model = Qwen2VLForConditionalGeneration.from_pretrained(
|
# "Qwen/Qwen2-VL-2B-Instruct", torch_dtype="auto", device_map="auto"
|
# )
|
# # ------- 设置分辨率，降低现存占用 -------
|
# min_pixels = 256*28*28
|
# max_pixels = 512*28*28
|
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