feat: add AVSynchronizer and examples (#324)

examples/video-stream/README.md

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+# Video and Audio Synchronization Examples
+
+This example demonstrates how to synchronize video and audio streams using the `AVSynchronizer` utility.
+
+## AVSynchronizer Usage
+
+The `AVSynchronizer` helps maintain synchronization between video and audio frames. The key principle is to push the initial synchronized video and audio frames together. After that, subsequent frames will be automatically synchronized according to the configured video FPS and audio sample rate.
+
+```python
+av_sync = AVSynchronizer(
+    audio_source=audio_source,
+    video_source=video_source,
+    video_fps=30.0,
+    video_queue_size_ms=100
+)
+
+# Push frames to synchronizer
+await av_sync.push(video_frame)
+await av_sync.push(audio_frame)
+```
+
+## Examples
+
+### 1. Video File Playback (`video_play.py`)
+Shows how to stream video and audio from separate sources while maintaining sync:
+
+- Reads video and audio streams separately from a media file
+- Uses separate tasks to push video and audio frames to the synchronizer
+- Since the streams are continuous, a larger `queue_size_ms` can be used, though this will increase memory usage
+
+### 2. Audio Visualization (`audio_wave.py`) 
+Demonstrates generating video based on audio input:
+
+- Generates audio frames with alternating sine waves and silence
+- Creates video frames visualizing the audio waveform
+- Shows how to handle cases with and without audio:
+  - When audio is present: Push synchronized video and audio frames
+  - During silence: Push only video frames
+- Since video and audio frames are pushed in the same loop, audio frames must be smaller than the audio source queue size to avoid blocking
+- Uses a small `queue_size_ms` (e.g. 50ms) to control frame generation speed during silence periods

examples/video-stream/audio_wave.py

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+import asyncio
+import logging
+import os
+import signal
+import time
+from collections import deque
+from dataclasses import dataclass
+from typing import AsyncIterable, Optional, Union
+
+import numpy as np
+from livekit import rtc, api
+
+try:
+    import cv2
+except ImportError:
+    raise RuntimeError(
+        "cv2 is required to run this example, "
+        "install with `pip install opencv-python`"
+    )
+
+# ensure LIVEKIT_URL, LIVEKIT_API_KEY, and LIVEKIT_API_SECRET are set
+
+logger = logging.getLogger(__name__)
+
+
+@dataclass
+class MediaInfo:
+    video_width: int
+    video_height: int
+    video_fps: float
+    audio_sample_rate: int
+    audio_channels: int
+
+
+class _AudioEndSentinel:
+    pass
+
+
+async def audio_generator(
+    media_info: MediaInfo,
+    output_audio: asyncio.Queue[Union[rtc.AudioFrame, _AudioEndSentinel]],
+):
+    """Generates audio frames with alternating sine wave and silence periods"""
+    frequency = 480  # Hz
+    amplitude = 0.5
+    period = 7.0
+    sine_duration = 5.0  # Duration of sine wave in each period
+    chunk_size = 1024
+
+    while True:
+        current_time = 0.0
+
+        # Generate audio for sine_duration seconds
+        while current_time < sine_duration:
+            t = np.linspace(
+                current_time,
+                current_time + chunk_size / media_info.audio_sample_rate,
+                num=chunk_size,
+                endpoint=False,
+            )
+            # Create volume envelope using sine wave
+            volume = np.abs(np.sin(2 * np.pi * current_time / sine_duration))
+            samples = amplitude * volume * np.sin(2 * np.pi * frequency * t)
+
+            # Convert to int16, (samples, channels)
+            samples = (samples[:, np.newaxis] * 32767).astype(np.int16)
+            if media_info.audio_channels > 1:
+                samples = np.repeat(samples, media_info.audio_channels, axis=1)
+
+            # Create audio frame
+            audio_frame = rtc.AudioFrame(
+                data=samples.tobytes(),
+                sample_rate=media_info.audio_sample_rate,
+                num_channels=samples.shape[1],
+                samples_per_channel=samples.shape[0],
+            )
+            await output_audio.put(audio_frame)
+            current_time += chunk_size / media_info.audio_sample_rate
+            await asyncio.sleep(0)
+        await output_audio.put(_AudioEndSentinel())
+
+        # Simulate silence
+        silence_duration = period - sine_duration
+        await asyncio.sleep(silence_duration)
+
+
+class WaveformVisualizer:
+    def __init__(self, history_length: int = 1000):
+        self.history_length = history_length
+        self.volume_history: deque[float] = deque(maxlen=history_length)
+        self.start_time = time.time()
+
+    def draw_timestamp(self, canvas: np.ndarray, fps: float):
+        height, width = canvas.shape[:2]
+        text = f"{time.time() - self.start_time:.1f}s @ {fps:.1f}fps"
+        font_face = cv2.FONT_HERSHEY_SIMPLEX
+        font_scale = 2.0
+        thickness = 2
+
+        (text_width, text_height), baseline = cv2.getTextSize(
+            text, font_face, font_scale, thickness
+        )
+        x = (width - text_width) // 2
+        y = int((height - text_height) * 0.4 + baseline)
+        cv2.putText(canvas, text, (x, y), font_face, font_scale, (0, 0, 0), thickness)
+
+    def draw_current_wave(
+        self, canvas: np.ndarray, audio_samples: np.ndarray
+    ) -> np.ndarray:
+        """Draw the current waveform and return the current values"""
+        height, width = canvas.shape[:2]
+        center_y = height // 2 + 100
+
+        normalized_samples = audio_samples.astype(np.float32) / 32767.0
+        normalized_samples = normalized_samples.mean(axis=1)  # (samples,)
+        num_points = min(width, len(normalized_samples))
+
+        if len(normalized_samples) > num_points:
+            indices = np.linspace(0, len(normalized_samples) - 1, num_points, dtype=int)
+            plot_data = normalized_samples[indices]
+        else:
+            plot_data = normalized_samples
+
+        x_coords = np.linspace(0, width, num_points, dtype=int)
+        y_coords = (plot_data * 200) + center_y
+
+        cv2.line(canvas, (0, center_y), (width, center_y), (200, 200, 200), 1)
+        points = np.column_stack((x_coords, y_coords.astype(int)))
+        for i in range(len(points) - 1):
+            cv2.line(canvas, tuple(points[i]), tuple(points[i + 1]), (0, 255, 0), 2)
+
+        return plot_data
+
+    def draw_volume_history(self, canvas: np.ndarray, current_volume: float):
+        height, width = canvas.shape[:2]
+        center_y = height // 2
+
+        self.volume_history.append(current_volume)
+        cv2.line(
+            canvas, (0, center_y - 250), (width, center_y - 250), (200, 200, 200), 1
+        )
+
+        volume_x = np.linspace(0, width, len(self.volume_history), dtype=int)
+        volume_y = center_y - 250 + (np.array(self.volume_history) * 200)
+        points = np.column_stack((volume_x, volume_y.astype(int)))
+        for i in range(len(points) - 1):
+            cv2.line(canvas, tuple(points[i]), tuple(points[i + 1]), (255, 0, 0), 2)
+
+    def draw(self, canvas: np.ndarray, audio_samples: np.ndarray, fps: float):
+        self.draw_timestamp(canvas, fps)
+        plot_data = self.draw_current_wave(canvas, audio_samples)
+        current_volume = np.abs(plot_data).mean()
+        self.draw_volume_history(canvas, current_volume)
+
+
+async def video_generator(
+    media_info: MediaInfo,
+    input_audio: asyncio.Queue[Union[rtc.AudioFrame, _AudioEndSentinel]],
+    av_sync: rtc.AVSynchronizer,  # only used for drawing the actual fps on the video
+) -> AsyncIterable[tuple[rtc.VideoFrame, Optional[rtc.AudioFrame]]]:
+    canvas = np.zeros(
+        (media_info.video_height, media_info.video_width, 4), dtype=np.uint8
+    )
+    canvas.fill(255)
+
+    def _np_to_video_frame(image: np.ndarray) -> rtc.VideoFrame:
+        return rtc.VideoFrame(
+            width=image.shape[1],
+            height=image.shape[0],
+            type=rtc.VideoBufferType.RGBA,
+            data=image.tobytes(),
+        )
+
+    audio_samples_per_frame = int(media_info.audio_sample_rate / media_info.video_fps)
+    audio_buffer = np.zeros((0, media_info.audio_channels), dtype=np.int16)
+    wave_visualizer = WaveformVisualizer()
+    while True:
+        try:
+            # timeout has to be shorter than the frame interval to avoid starvation
+            audio_frame = await asyncio.wait_for(
+                input_audio.get(), timeout=0.5 / media_info.video_fps
+            )
+        except asyncio.TimeoutError:
+            # generate frame without audio (e.g. silence state)
+            new_frame = canvas.copy()
+            wave_visualizer.draw(new_frame, np.zeros((1, 2)), av_sync.actual_fps)
+            video_frame = _np_to_video_frame(new_frame)
+            yield video_frame, None
+
+            # speed is controlled by the video fps in av_sync
+            await asyncio.sleep(0)
+            continue
+
+        if isinstance(audio_frame, _AudioEndSentinel):
+            # drop the audio buffer when the audio finished
+            audio_buffer = np.zeros((0, media_info.audio_channels), dtype=np.int16)
+            continue
+
+        audio_samples = np.frombuffer(audio_frame.data, dtype=np.int16).reshape(
+            -1, audio_frame.num_channels
+        )  # (samples, channels)
+        # accumulate audio samples to the buffer
+        audio_buffer = np.concatenate([audio_buffer, audio_samples], axis=0)
+
+        while audio_buffer.shape[0] >= audio_samples_per_frame:
+            sub_samples = audio_buffer[:audio_samples_per_frame, :]
+            audio_buffer = audio_buffer[audio_samples_per_frame:, :]
+
+            new_frame = canvas.copy()
+            wave_visualizer.draw(new_frame, sub_samples, av_sync.actual_fps)
+            video_frame = _np_to_video_frame(new_frame)
+            sub_audio_frame = rtc.AudioFrame(
+                data=sub_samples.tobytes(),
+                sample_rate=audio_frame.sample_rate,
+                num_channels=sub_samples.shape[1],
+                samples_per_channel=sub_samples.shape[0],
+            )
+            yield video_frame, sub_audio_frame
+
+
+async def main(room: rtc.Room):
+    token = (
+        api.AccessToken()
+        .with_identity("python-publisher")
+        .with_name("Python Publisher")
+        .with_grants(
+            api.VideoGrants(
+                room_join=True,
+                room="room-ysBA-Q0hM",
+                agent=True,
+            )
+        )
+        .to_jwt()
+    )
+    url = os.getenv("LIVEKIT_URL")
+    logging.info("connecting to %s", url)
+
+    try:
+        await room.connect(url, token)
+        logging.info("connected to room %s", room.name)
+    except rtc.ConnectError as e:
+        logging.error("failed to connect to the room: %s", e)
+        return
+
+    # Create media info
+    media_info = MediaInfo(
+        video_width=1280,
+        video_height=720,
+        video_fps=30.0,
+        audio_sample_rate=48000,
+        audio_channels=2,
+    )
+
+    # Create video and audio sources/tracks
+    queue_size_ms = 50
+    video_source = rtc.VideoSource(
+        width=media_info.video_width,
+        height=media_info.video_height,
+    )
+    audio_source = rtc.AudioSource(
+        sample_rate=media_info.audio_sample_rate,
+        num_channels=media_info.audio_channels,
+        queue_size_ms=queue_size_ms,
+    )
+
+    video_track = rtc.LocalVideoTrack.create_video_track("video", video_source)
+    audio_track = rtc.LocalAudioTrack.create_audio_track("audio", audio_source)
+
+    # Publish tracks
+    video_options = rtc.TrackPublishOptions(source=rtc.TrackSource.SOURCE_CAMERA)
+    audio_options = rtc.TrackPublishOptions(source=rtc.TrackSource.SOURCE_MICROPHONE)
+
+    await room.local_participant.publish_track(video_track, video_options)
+    await room.local_participant.publish_track(audio_track, audio_options)
+
+    # Create AV synchronizer
+    av_sync = rtc.AVSynchronizer(
+        audio_source=audio_source,
+        video_source=video_source,
+        video_fps=media_info.video_fps,
+        video_queue_size_ms=queue_size_ms,
+    )
+
+    # Start audio generator
+    audio_queue = asyncio.Queue[Union[rtc.AudioFrame, _AudioEndSentinel]](maxsize=1)
+    audio_task = asyncio.create_task(audio_generator(media_info, audio_queue))
+
+    try:
+        async for video_frame, audio_frame in video_generator(
+            media_info, audio_queue, av_sync=av_sync
+        ):
+            await av_sync.push(video_frame)
+            if audio_frame:
+                await av_sync.push(audio_frame)
+    finally:
+        audio_task.cancel()
+        await av_sync.aclose()
+
+
+if __name__ == "__main__":
+    logging.basicConfig(
+        level=logging.INFO,
+        handlers=[logging.FileHandler("audio_wave.log"), logging.StreamHandler()],
+    )
+
+    loop = asyncio.get_event_loop()
+    room = rtc.Room(loop=loop)
+
+    async def cleanup():
+        await room.disconnect()
+        loop.stop()
+
+    asyncio.ensure_future(main(room))
+    for signal in [signal.SIGINT, signal.SIGTERM]:
+        loop.add_signal_handler(signal, lambda: asyncio.ensure_future(cleanup()))
+
+    try:
+        loop.run_forever()
+    finally:
+        loop.close()