Title: Video and audio decoding with torchcodec
State: closed
Labels: description: New feature or request
name: enhancement

### Feature request

Pytorch is migrating video processing to torchcodec and it's pretty cool. It would be nice to migrate both the audio and video features to use torchcodec instead of torchaudio/video.

### Motivation

My use case is I'm working on a multimodal AV model, and what's nice about torchcodec is I can extract the audio tensors directly from MP4 files. Also, I can easily resample video data to whatever fps I like on the fly. I haven't found an easy/efficient way to do this with torchvision.

### Your contribution

I’m modifying the Video dataclass to use torchcodec in place of the current backend, starting from a stable commit for a project I’m working on. If it ends up working well, I’m happy to open a PR on main.

Comments:
Good idea ! let me know if you have any question or if I can help

@lhoestq Almost finished, but I'm having trouble understanding this test case.
This is how it looks originally. The `map` function is called, and then `with_format` is called. According to the test case example["video"] is supposed to be a VideoReader. However, according to the [docs](https://huggingface.co/docs/datasets/package_reference/main_classes#datasets.Dataset.with_format) its supposed to be the type passed into  `with_format` (numpy in this case). My implementation with VideoDecoder currently does the latter, is that correct, or should it be a VideoDecoder object instead?
```
@require_torchvision
def test_dataset_with_video_map_and_formatted(shared_datadir):
    from torchvision.io import VideoReader

    video_path = str(shared_datadir / "test_video_66x50.mov")
    data = {"video": [video_path]}
    features = Features({"video": Video()})
    dset = Dataset.from_dict(data, features=features)
    dset = dset.map(lambda x: x).with_format("numpy")
    example = dset[0]
    assert isinstance(example["video"], VideoReader)
    # assert isinstance(example["video"][0], np.ndarray)

    # from bytes
    with open(video_path, "rb") as f:
        data = {"video": [f.read()]}
    dset = Dataset.from_dict(data, features=features)
    dset = dset.map(lambda x: x).with_format("numpy")
    example = dset[0]
    assert isinstance(example["video"], VideoReader)
    # assert isinstance(example["video"][0], np.ndarray)

```

Hi ! It's maybe more convenient for users to always have a VideoDecoder, since they might only access a few frames and not the full video. So IMO it's fine to always return a VideoDecoder (maybe later we can extend the VideoDecoder to return other types of tensors than numpy arrays though ? 👀 it's not crucial for now though)

@lhoestq ya that makes sense, looks like this functionality lives in `src/datasets/formatting`, where an exception is made for VideoReader objects to remain as themselves when being formatted. I'll make the necessary changes. 

@lhoestq I'm assuming this was also the case for torchaudio objects?

We're not using torchaudio but soundfile. But anyway we unfortunately decode full audio files instead of returning a Reader and it can be interesting to fix this. Currently it always returns a dict {"array": np.array(...), "sampling_rate": int(...)}, while it would be cool to return a reader with seek() and read() - like methods as for videos.

(there is a way to make the audio change backward compatible anyway by allowing `reader["array"]` to return the full array)

@lhoestq (sorry for the spam btw)
Looks like there's a # TODO to have these returned as np.arrays instead. I'm curious why the authors didn't do it initially. Maybe a performance thing?
This is from `/src/datasets/formatting/np_formatter.py` line 70
```
if config.TORCHVISION_AVAILABLE and "torchvision" in sys.modules:
            from torchvision.io import VideoReader

            if isinstance(value, VideoReader):
                return value  # TODO(QL): set output to np arrays ?
```

Oh cool ya this is something that I could implement with torchcodec. I can add that to the PR as well.

> Looks like there's a # TODO to have these returned as np.arrays instead. I'm curious why the authors didn't do it initially. Maybe a performance thing?

yea that was me, I focused on a simple logic to start with, since I knew there was torchcodec coming and maybe wasn't worth it at the time ^^

but anyway it's fine to start with a logic without formatting to start with and then iterate

Hey @lhoestq I ran into an error with this test case for the Audio feature

```
@require_sndfile
@require_torchcodec
def test_dataset_with_audio_feature_map_is_decoded(shared_datadir):
    audio_path = str(shared_datadir / "test_audio_44100.wav")
    data = {"audio": [audio_path], "text": ["Hello"]}
    features = Features({"audio": Audio(), "text": Value("string")})
    dset = Dataset.from_dict(data, features=features)

    def process_audio_sampling_rate_by_example(example):
        sample_rate = example["audio"].get_all_samples().sample_rate
        example["double_sampling_rate"] = 2 * sample_rate
        return example

    decoded_dset = dset.map(process_audio_sampling_rate_by_example)
    for item in decoded_dset.cast_column("audio", Audio(decode=False)):
        assert item.keys() == {"audio", "text", "double_sampling_rate"}
        assert item["double_sampling_rate"] == 88200

    def process_audio_sampling_rate_by_batch(batch):
        double_sampling_rates = []
        for audio in batch["audio"]:
            double_sampling_rates.append(2 * audio.get_all_samples().sample_rate)
        batch["double_sampling_rate"] = double_sampling_rates
        return batch

    decoded_dset = dset.map(process_audio_sampling_rate_by_batch, batched=True)
    for item in decoded_dset.cast_column("audio", Audio(decode=False)):
        assert item.keys() == {"audio", "text", "double_sampling_rate"}
        assert item["double_sampling_rate"] == 88200
```

this is the error below
```
src/datasets/arrow_writer.py:626: in write_batch
    arrays.append(pa.array(typed_sequence))
.....
FAILED tests/features/test_audio.py::test_dataset_with_audio_feature_map_is_decoded - pyarrow.lib.ArrowInvalid: Could not convert <torchcodec.decoders._audio_decoder.AudioDecoder object at 0x138cdd810> with type AudioDecoder: did not recognize Python value type when inferring an Arrow data type
```

By the way I copied the test case and ran it on the original implementation of the Video feature, which uses the torchvision backend and I got a similar error.
```
def test_dataset_with_video_feature_map_is_decoded(shared_datadir):
    video_path = str(shared_datadir / "test_video_66x50.mov")
    data = {"video": [video_path], "text": ["Hello"]}
    features = Features({"video": Video(), "text": Value("string")})
    dset = Dataset.from_dict(data, features=features)

    def process_audio_sampling_rate_by_example(example):
        metadata = example["video"].get_metadata()
        example["double_fps"] = 2 * metadata["video"]["fps"][0]
        return example

    decoded_dset = dset.map(process_audio_sampling_rate_by_example)
    for item in decoded_dset.cast_column("video", Video(decode=False)):
        assert item.keys() == {"video", "text", "double_fps"}
        assert item["double_fps"] == 2 * 10 # prollly wont work past 2*10 is made up!! shouldn't pass

    def process_audio_sampling_rate_by_batch(batch):
        double_fps = []
        for video in batch["video"]:
            double_fps.append(2 * video.metadata.begin_stream_seconds)
        batch["double_fps"] = double_fps
        return batch

    decoded_dset = dset.map(process_audio_sampling_rate_by_batch, batched=True)
    for item in decoded_dset.cast_column("video", Video(decode=False)):
        assert item.keys() == {"video", "text", "double_fps"}
        assert item["double_fps"] == 2 * 10 # prollly wont work past this no reason it should
```

I was wondering if these error's are expected. They seem to be coming from the fact that the function `_cast_to_python_objects` in `src/datasets/features/features.py` doesn't handle VideoDecoders or AudioDecoders. I was able to fix it and get rid of the error by adding this to the bottom of the function
```
    elif config.TORCHCODEC_AVAILABLE and "torchcodec" in sys.modules and isinstance(obj, VideoDecoder):
        v = Video()
        return v.encode_example(obj), True
    elif config.TORCHCODEC_AVAILABLE and "torchcodec" in sys.modules and isinstance(obj, AudioDecoder):
        a = Audio()
        return a.encode_example(obj), True
```
This fixed it, but I just want to make sure I'm not adding things that are messing up the intended functionality.

This is the right fix ! :)

Btw I just remembered that we were using soundfile because it can support a wide range of audio formats, is it also the case for torchcodec ? including ogg, opus for example

Yes from what I understand torchcodec supports everything ffmpeg supports.

Okay just finished. However, I wasn't able to pass this test case:
```python
@require_torchcodec
@require_sndfile
@pytest.mark.parametrize("streaming", [False, True])
def test_load_dataset_with_audio_feature(streaming, jsonl_audio_dataset_path, shared_datadir):
    from torchcodec.decoders import AudioDecoder
    audio_path = str(shared_datadir / "test_audio_44100.wav")
    data_files = jsonl_audio_dataset_path
    features = Features({"audio": Audio(), "text": Value("string")})
    dset = load_dataset("json", split="train", data_files=data_files, features=features, streaming=streaming)
    item = dset[0] if not streaming else next(iter(dset))
    assert item.keys() == {"audio", "text"}
    assert isinstance(item["audio"], AudioDecoder)
    samples = item["audio"].get_all_samples()
    assert samples.sample_rate == 44100
    assert samples.data.shape == (1, 202311)
```

It returned this error
```
streaming = False, jsonl_audio_dataset_path = '/private/var/folders/47/c7dlgs_n6lx8rtr8f5w5m1m00000gn/T/pytest-of-tytodd/pytest-103/data2/audio_dataset.jsonl'
shared_datadir = PosixPath('/private/var/folders/47/c7dlgs_n6lx8rtr8f5w5m1m00000gn/T/pytest-of-tytodd/pytest-103/test_load_dataset_with_audio_f0/data')

    @require_torchcodec
    @require_sndfile
    @pytest.mark.parametrize("streaming", [False, True])
    def test_load_dataset_with_audio_feature(streaming, jsonl_audio_dataset_path, shared_datadir):
        from torchcodec.decoders import AudioDecoder
        audio_path = str(shared_datadir / "test_audio_44100.wav")
        data_files = jsonl_audio_dataset_path
        features = Features({"audio": Audio(), "text": Value("string")})
>       dset = load_dataset("json", split="train", data_files=data_files, features=features, streaming=streaming)

tests/features/test_audio.py:686: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
src/datasets/load.py:1418: in load_dataset
    builder_instance.download_and_prepare(
src/datasets/builder.py:925: in download_and_prepare
    self._download_and_prepare(
src/datasets/builder.py:1019: in _download_and_prepare
    verify_splits(self.info.splits, split_dict)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _

expected_splits = {'train': SplitInfo(name='train', num_bytes=2351563, num_examples=10000, shard_lengths=None, dataset_name=None), 'validation': SplitInfo(name='validation', num_bytes=238418, num_examples=1000, shard_lengths=None, dataset_name=None)}
recorded_splits = {'train': SplitInfo(name='train', num_bytes=167, num_examples=1, shard_lengths=None, dataset_name='json')}

    def verify_splits(expected_splits: Optional[dict], recorded_splits: dict):
        if expected_splits is None:
            logger.info("Unable to verify splits sizes.")
            return
        if len(set(expected_splits) - set(recorded_splits)) > 0:
>           raise ExpectedMoreSplitsError(str(set(expected_splits) - set(recorded_splits)))
E           datasets.exceptions.ExpectedMoreSplitsError: {'validation'}

src/datasets/utils/info_utils.py:68: ExpectedMoreSplitsError
```

It looks like this test case wasn't passing when I forked the repo, so I assume I didn't do anything to break it. I also added this case to `test_video.py`, and it fails there as well. If this looks good, I'll go ahead and submit the PR.

Awesome ! yes feel free to submit the PR, I can see what I can do for the remaining tests

@lhoestq just submitted it #7616