***

description: Transcode clips with CPU or GPU encoders and tune for quality and performance
categories:

* video-curation
  tags:
* transcoding
* encoding
* ffmpeg
* nvenc
* h264
  personas:
* data-scientist-focused
* mle-focused
  difficulty: intermediate
  content\_type: howto
  modality: video-only

***

# Clip Encoding

Convert extracted clip buffers into compressed media files suitable for storage and training workflows using encoders. NeMo Curator provides both CPU and GPU-based encoders.

## Use Cases

* Convert raw clip buffers into a standard format (such as H.264 in MP4) for portability.
* Normalize heterogeneous inputs (encoding formats, bit rates, containers) into a consistent output.
* Reduce storage footprint with controlled quality settings.

## Before You Start

If you only need embeddings or analysis and do not require saved media files, you can skip encoding. When writing clips, NeMo Curator produces `.mp4` by default.

***

## Quickstart

Use the pipeline stage or the example script flags to encode clips with CPU or GPU encoders.

<Tabs>
  <Tab title="Pipeline Stage">
    ```python
    from nemo_curator.pipeline import Pipeline
    from nemo_curator.stages.video.clipping.clip_extraction_stages import FixedStrideExtractorStage, ClipTranscodingStage

    pipe = Pipeline(name="transcode_example")
    pipe.add_stage(FixedStrideExtractorStage(clip_len_s=10.0, clip_stride_s=10.0))
    pipe.add_stage(ClipTranscodingStage(encoder="libopenh264", encode_batch_size=16, encoder_threads=1, verbose=True))
    pipe.run()
    ```
  </Tab>

  <Tab title="Script Flags">
    ```bash
    python -m ray_curator.examples.video.video_split_clip_example \
      ...
      --transcode-encoder h264_nvenc \
      --transcode-use-hwaccel \
    ```
  </Tab>
</Tabs>

## Encoder Options

| Encoder       | Hardware           | Description                                                                          |
| ------------- | ------------------ | ------------------------------------------------------------------------------------ |
| `libx264`     | CPU                | Widely available, high quality, CPU-based.                                           |
| `libopenh264` | CPU                | Good quality and throughput balance. Often faster than `libx264` at similar presets. |
| `h264_nvenc`  | NVIDIA GPU (NVENC) | Uses NVENC for high-throughput H.264 encoding on NVIDIA GPU hardware.                |

<Tip>
  On systems with supported NVIDIA GPU hardware and an `ffmpeg` build with NVENC, `h264_nvenc` can significantly increase throughput. Refer to the verification steps below to confirm NVENC availability.
</Tip>

### Verify `ffmpeg`/NVENC Support

To use `h264_nvenc`, confirm that your `ffmpeg` build includes NVENC support and install the GPU drivers:

```bash
ffmpeg -hide_banner -encoders | grep nvenc
ffmpeg -hide_banner -hwaccels | grep -i nv
nvidia-smi
```

Expected output includes entries like `V..... h264_nvenc` and `cuda` in the hardware accelerators list. If not present, install an `ffmpeg` build with NVENC and ensure NVIDIA drivers and CUDA are available.

## Configure

Use `ClipTranscodingStage` to control encoder choice, batching, and acceleration:

```python
from nemo_curator.stages.video.clipping.clip_extraction_stages import ClipTranscodingStage

transcode = ClipTranscodingStage(
    encoder="h264_nvenc",        # or "libopenh264", "libx264"
    use_hwaccel=True,             # enable NVENC when using h264_nvenc
    encoder_threads=1,            # CPU thread count for CPU encoders
    encode_batch_size=16,         # number of clips per encode batch
    num_clips_per_chunk=32,       # chunking for downstream writing
    use_input_bit_rate=False,     # set True to preserve input bit rate when available
    num_cpus_per_worker=6.0,
    verbose=True,
)
```

### Parameters

| Parameter            | Description                                                                                          |
| -------------------- | ---------------------------------------------------------------------------------------------------- |
| `encoder`            | Selects the encoding backend. Recommended defaults: `libopenh264` (CPU) or `h264_nvenc` (GPU).       |
| `use_hwaccel`        | Enable when using GPU encoders like `h264_nvenc`.                                                    |
| `encoder_threads`    | CPU threads per worker for CPU encoders. Increase to use more CPU.                                   |
| `encode_batch_size`  | Batching size for clips; larger batches can improve throughput.                                      |
| `use_input_bit_rate` | If True, attempts to reuse the input bit rate; otherwise, the encoder uses its default rate control. |

<Note>
  Refer to the quickstart options in [Get Started with Video Curation](/get-started/video) for command-line flags `--transcode-encoder` and `--transcode-use-hwaccel`.
</Note>

## Troubleshooting

* "Encoder not found": Your `ffmpeg` build may lack the encoder; verify with `ffmpeg -encoders`.
* "No NVENC capable devices found": Install NVIDIA drivers/CUDA and ensure the GPU is visible in `nvidia-smi`.
* Output mismatch or low quality: Revisit encoder defaults; set explicit bit rate/quality settings as needed, or enable `use_input_bit_rate`.