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from typing import TYPE_CHECKING, Any, Dict, Optional, Type, TypeVar, Union
import numpy as np
from pydantic import Field
from docarray.base_doc import BaseDoc
from docarray.typing import AnyEmbedding, AudioUrl
from docarray.typing.bytes.audio_bytes import AudioBytes
from docarray.typing.tensor.abstract_tensor import AbstractTensor
from docarray.typing.tensor.audio.audio_tensor import AudioTensor
from docarray.utils._internal.misc import import_library
from docarray.utils._internal.pydantic import is_pydantic_v2
if is_pydantic_v2:
from pydantic import model_validator
if TYPE_CHECKING:
import tensorflow as tf # type: ignore
import torch
else:
torch = import_library('torch', raise_error=False)
tf = import_library('tensorflow', raise_error=False)
T = TypeVar('T', bound='AudioDoc')
class AudioDoc(BaseDoc):
"""
Document for handling audios.
The Audio Document can contain:
- an [`AudioUrl`][docarray.typing.url.AudioUrl] (`AudioDoc.url`)
- an [`AudioTensor`](../../../api_references/typing/tensor/audio) (`AudioDoc.tensor`)
- an [`AnyEmbedding`](../../../api_references/typing/tensor/embedding) (`AudioDoc.embedding`)
- an [`AudioBytes`][docarray.typing.bytes.AudioBytes] (`AudioDoc.bytes_`) object
- an integer representing the frame_rate (`AudioDoc.frame_rate`)
You can use this Document directly:
```python
from docarray.documents import AudioDoc
# use it directly
audio = AudioDoc(
url='https://github.com/docarray/docarray/blob/main/tests/toydata/hello.wav?raw=true'
)
audio.tensor, audio.frame_rate = audio.url.load()
# model = MyEmbeddingModel()
# audio.embedding = model(audio.tensor)
```
You can extend this Document:
```python
from docarray.documents import AudioDoc, TextDoc
from typing import Optional
# extend it
class MyAudio(AudioDoc):
name: Optional[TextDoc] = None
audio = MyAudio(
url='https://github.com/docarray/docarray/blob/main/tests/toydata/hello.wav?raw=true'
)
audio.name = TextDoc(text='my first audio')
audio.tensor, audio.frame_rate = audio.url.load()
# model = MyEmbeddingModel()
# audio.embedding = model(audio.tensor)
```
You can use this Document for composition:
```python
from docarray import BaseDoc
from docarray.documents import AudioDoc, TextDoc
# compose it
class MultiModalDoc(BaseDoc):
audio: AudioDoc
text: TextDoc
mmdoc = MultiModalDoc(
audio=AudioDoc(
url='https://github.com/docarray/docarray/blob/main/tests/toydata/hello.wav?raw=true'
),
text=TextDoc(text='hello world, how are you doing?'),
)
mmdoc.audio.tensor, mmdoc.audio.frame_rate = mmdoc.audio.url.load()
# equivalent to
mmdoc.audio.bytes_ = mmdoc.audio.url.load_bytes()
mmdoc.audio.tensor, mmdoc.audio.frame_rate = mmdoc.audio.bytes_.load()
```
"""
url: Optional[AudioUrl] = Field(
description='The url to a (potentially remote) audio file that can be loaded',
example='https://github.com/docarray/docarray/blob/main/tests/toydata/hello.mp3?raw=true',
default=None,
)
tensor: Optional[AudioTensor] = Field(
description='Tensor object of the audio which can be specified to one of `AudioNdArray`, `AudioTorchTensor`, `AudioTensorFlowTensor`',
default=None,
)
embedding: Optional[AnyEmbedding] = Field(
description='Store an embedding: a vector representation of the audio.',
example=[0, 1, 0],
default=None,
)
bytes_: Optional[AudioBytes] = Field(
description='Bytes representation pf the audio',
default=None,
)
frame_rate: Optional[int] = Field(
description='An integer representing the frame rate of the audio.',
example=24,
default=None,
)
@classmethod
def _validate(cls, value) -> Dict[str, Any]:
if isinstance(value, str):
value = dict(url=value)
elif isinstance(value, (AbstractTensor, np.ndarray)) or (
torch is not None
and isinstance(value, torch.Tensor)
or (tf is not None and isinstance(value, tf.Tensor))
):
value = dict(tensor=value)
return value
if is_pydantic_v2:
@model_validator(mode='before')
@classmethod
def validate_model_before(cls, value):
return cls._validate(value)
else:
@classmethod
def validate(
cls: Type[T],
value: Union[str, AbstractTensor, Any],
) -> T:
return super().validate(cls._validate(value))