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import io
from typing import TYPE_CHECKING, Any, Tuple, Type, TypeVar
import numpy as np
from pydantic import parse_obj_as
from pydantic.validators import bytes_validator
from docarray.typing.abstract_type import AbstractType
from docarray.typing.proto_register import _register_proto
from docarray.typing.tensor.audio import AudioNdArray
from docarray.utils._internal.misc import import_library
if TYPE_CHECKING:
from pydantic.fields import BaseConfig, ModelField
from docarray.proto import NodeProto
T = TypeVar('T', bound='AudioBytes')
@_register_proto(proto_type_name='audio_bytes')
class AudioBytes(bytes, AbstractType):
"""
Bytes that store an audio and that can be load into an Audio tensor
"""
@classmethod
def validate(
cls: Type[T],
value: Any,
field: 'ModelField',
config: 'BaseConfig',
) -> T:
value = bytes_validator(value)
return cls(value)
@classmethod
def from_protobuf(cls: Type[T], pb_msg: T) -> T:
return parse_obj_as(cls, pb_msg)
def _to_node_protobuf(self: T) -> 'NodeProto':
from docarray.proto import NodeProto
return NodeProto(blob=self, type=self._proto_type_name)
def load(self) -> Tuple[AudioNdArray, int]:
"""
Load the Audio from the [`AudioBytes`][docarray.typing.AudioBytes] into an
[`AudioNdArray`][docarray.typing.AudioNdArray].
---
```python
from typing import Optional
from docarray import BaseDoc
from docarray.typing import AudioBytes, AudioNdArray, AudioUrl
class MyAudio(BaseDoc):
url: AudioUrl
tensor: Optional[AudioNdArray]
bytes_: Optional[AudioBytes]
frame_rate: Optional[float]
doc = MyAudio(url='https://www.kozco.com/tech/piano2.wav')
doc.bytes_ = doc.url.load_bytes()
doc.tensor, doc.frame_rate = doc.bytes_.load()
# Note this is equivalent to do
doc.tensor, doc.frame_rate = doc.url.load()
assert isinstance(doc.tensor, AudioNdArray)
```
---
:return: tuple of an [`AudioNdArray`][docarray.typing.AudioNdArray] representing the
audio bytes content, and an integer representing the frame rate.
"""
pydub = import_library('pydub', raise_error=True) # noqa: F841
from pydub import AudioSegment
segment = AudioSegment.from_file(io.BytesIO(self))
# Convert to float32 using NumPy
samples = np.array(segment.get_array_of_samples())
# Normalise float32 array so that values are between -1.0 and +1.0
samples_norm = samples / 2 ** (segment.sample_width * 8 - 1)
return parse_obj_as(AudioNdArray, samples_norm), segment.frame_rate