Rework README example

README.md

@@ -6,7 +6,7 @@ TorchCodec is a Python library for decoding video and audio data into PyTorch
 tensors, on CPU and CUDA GPU. It also supports video and audio encoding on CPU!
 It aims to be fast, easy to use, and well integrated
 into the PyTorch ecosystem.  If you want to use PyTorch to train ML models on
-videos and audio, TorchCodec is how you turn these into data.
+videos and audio, or run inference, TorchCodec is how you turn these into data.
 
 We achieve these capabilities through:
 
@@ -19,13 +19,13 @@ We achieve these capabilities through:
 * Returning data as PyTorch tensors, ready to be fed into PyTorch transforms
   or used directly to train models.
 
-## Using TorchCodec
+## Usage Examples
 
-Here's a condensed summary of what you can do with TorchCodec. For more detailed
-examples, [check out our
+Below are some examples of what you can do with TorchCodec. For more detailed
+examples and more use-cases, [check out our
 documentation](https://meta-pytorch.org/torchcodec/stable/generated_examples/)!
 
-#### Decoding
+#### Video Decoding
 
 ```python
 from torchcodec.decoders import VideoDecoder
@@ -61,41 +61,31 @@ decoder.get_frames_played_at(seconds=[0.5, 10.4])
 #   duration_seconds: tensor([0.0334, 0.0334], dtype=torch.float64)
 ```
 
-#### Clip sampling
+You can use the following snippet to generate a video with FFmpeg and try out
+the `VideoDecoder`:
 
-```python
-
-from torchcodec.samplers import clips_at_regular_timestamps
-
-clips_at_regular_timestamps(
-    decoder,
-    seconds_between_clip_starts=1.5,
-    num_frames_per_clip=4,
-    seconds_between_frames=0.1
-)
-# FrameBatch:
-#   data (shape): torch.Size([9, 4, 3, 270, 480])
-#   pts_seconds: tensor([[ 0.0000,  0.0667,  0.1668,  0.2669],
-#         [ 1.4681,  1.5682,  1.6683,  1.7684],
-#         [ 2.9696,  3.0697,  3.1698,  3.2699],
-#         ... (truncated), dtype=torch.float64)
-#   duration_seconds: tensor([[0.0334, 0.0334, 0.0334, 0.0334],
-#         [0.0334, 0.0334, 0.0334, 0.0334],
-#         [0.0334, 0.0334, 0.0334, 0.0334],
-#         ... (truncated), dtype=torch.float64)
+```bash
+ffmpeg -f lavfi -i testsrc2=size=640x400:duration=10:rate=25 /tmp/output_video.mp4
 ```
 
-You can use the following snippet to generate a video with FFmpeg and tryout
-TorchCodec:
+#### Encoding
 
-```bash
-fontfile=/usr/share/fonts/dejavu-sans-mono-fonts/DejaVuSansMono-Bold.ttf
-output_video_file=/tmp/output_video.mp4
+```python
+from torchcodec.encoders import Encoder
 
-ffmpeg -f lavfi -i \
-    color=size=640x400:duration=10:rate=25:color=blue \
-    -vf "drawtext=fontfile=${fontfile}:fontsize=30:fontcolor=white:x=(w-text_w)/2:y=(h-text_h)/2:text='Frame %{frame_num}'" \
-    ${output_video_file}
+encoder = Encoder()
+video_stream = encoder.add_video(
+    height=height, width=width, frame_rate=frame_rate,
+)
+audio_stream = encoder.add_audio(
+    sample_rate=sample_rate, num_channels=num_channels,
+)
+with encoder.open_file("output.mp4"):
+    video_stream.add_frames(frames_batch_0)
+    audio_stream.add_samples(samples_batch_0)
+    video_stream.add_frames(frames_batch_1)
+    audio_stream.add_samples(samples_batch_1)
+    # ...
 ```
 
 ## Installing TorchCodec
@@ -220,22 +210,6 @@ format you want. Refer to Nvidia's GPU support matrix for more details
    conda install -c conda-forge "torchcodec=*=*cuda*"
    ```
 
-## Benchmark Results
-
-The following was generated by running [our benchmark script](./benchmarks/decoders/generate_readme_data.py) on a lightly loaded 22-core machine with an Nvidia A100 with
-5 [NVDEC decoders](https://docs.nvidia.com/video-technologies/video-codec-sdk/12.1/nvdec-application-note/index.html#).
-
-![benchmark_results](./benchmarks/decoders/benchmark_readme_chart.png)
-
-The top row is a [Mandelbrot](https://ffmpeg.org/ffmpeg-filters.html#mandelbrot) video
-generated from FFmpeg that has a resolution of 1280x720 at 60 fps and is 120 seconds long.
-The bottom row is [promotional video from NASA](https://download.pytorch.org/torchaudio/tutorial-assets/stream-api/NASAs_Most_Scientifically_Complex_Space_Observatory_Requires_Precision-MP4_small.mp4)
-that has a resolution of 960x540 at 29.7 fps and is 206 seconds long. Both videos were
-encoded with libx264 and yuv420p pixel format. All decoders, except for TorchVision, used FFmpeg 6.1.2. TorchVision used FFmpeg 4.2.2.
-
-For TorchCodec, the "approx" label means that it was using [approximate mode](https://meta-pytorch.org/torchcodec/stable/generated_examples/decoding/approximate_mode.html)
-for seeking.
-
 ## Contributing
 
 We welcome contributions to TorchCodec! Please see our [contributing