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import os
from dataclasses import dataclass, field
from io import BytesIO
from pathlib import Path
from typing import TYPE_CHECKING, Any, ClassVar, Optional, Union
import numpy as np
import pyarrow as pa
from .. import config
from ..download.download_config import DownloadConfig
from ..table import array_cast
from ..utils.file_utils import is_local_path, xopen
from ..utils.py_utils import no_op_if_value_is_null, string_to_dict
if TYPE_CHECKING:
from torchcodec.decoders import AudioDecoder
from .features import FeatureType
@dataclass
class Audio:
"""Audio [`Feature`] to extract audio data from an audio file.
Input: The Audio feature accepts as input:
- A `str`: Absolute path to the audio file (i.e. random access is allowed).
- A `pathlib.Path`: path to the audio file (i.e. random access is allowed).
- A `dict` with the keys:
- `path`: String with relative path of the audio file to the archive file.
- `bytes`: Bytes content of the audio file.
This is useful for parquet or webdataset files which embed audio files.
- A `dict` with the keys:
- `array`: Array containing the audio sample
- `sampling_rate`: Integer corresponding to the sampling rate of the audio sample.
- A `torchcodec.decoders.AudioDecoder`: torchcodec audio decoder object.
Output: The Audio features output data as `torchcodec.decoders.AudioDecoder` objects, with additional keys:
- `array`: Array containing the audio sample
- `sampling_rate`: Integer corresponding to the sampling rate of the audio sample.
Args:
sampling_rate (`int`, *optional*):
Target sampling rate. If `None`, the native sampling rate is used.
mono (`bool`, defaults to `True`):
Whether to convert the audio signal to mono by averaging samples across
channels.
decode (`bool`, defaults to `True`):
Whether to decode the audio data. If `False`,
returns the underlying dictionary in the format `{"path": audio_path, "bytes": audio_bytes}`.
stream_index (`int`, *optional*):
The streaming index to use from the file. If `None` defaults to the "best" index.
Example:
```py
>>> from datasets import load_dataset, Audio
>>> ds = load_dataset("PolyAI/minds14", name="en-US", split="train")
>>> ds = ds.cast_column("audio", Audio(sampling_rate=44100))
>>> ds[0]["audio"]
<datasets.features._torchcodec.AudioDecoder object at 0x11642b6a0>
>>> audio = ds[0]["audio"]
>>> audio.get_samples_played_in_range(0, 10)
AudioSamples:
data (shape): torch.Size([2, 110592])
pts_seconds: 0.0
duration_seconds: 2.507755102040816
sample_rate: 44100
```
"""
sampling_rate: Optional[int] = None
decode: bool = True
stream_index: Optional[int] = None
id: Optional[str] = field(default=None, repr=False)
# Automatically constructed
dtype: ClassVar[str] = "dict"
pa_type: ClassVar[Any] = pa.struct({"bytes": pa.binary(), "path": pa.string()})
_type: str = field(default="Audio", init=False, repr=False)
def __call__(self):
return self.pa_type
def encode_example(self, value: Union[str, bytes, bytearray, dict, "AudioDecoder"]) -> dict:
"""Encode example into a format for Arrow.
Args:
value (`str`, `bytes`,`bytearray`,`dict`, `AudioDecoder`):
Data passed as input to Audio feature.
Returns:
`dict`
"""
try:
import torch
from torchcodec.encoders import AudioEncoder # needed to write audio files
except ImportError as err:
raise ImportError("To support encoding audio data, please install 'torchcodec'.") from err
if value is None:
raise ValueError("value must be provided")
if config.TORCHCODEC_AVAILABLE:
from torchcodec.decoders import AudioDecoder
else:
AudioDecoder = None
if isinstance(value, str):
return {"bytes": None, "path": value}
elif isinstance(value, Path):
return {"bytes": None, "path": str(value.absolute())}
elif isinstance(value, (bytes, bytearray)):
return {"bytes": value, "path": None}
elif AudioDecoder is not None and isinstance(value, AudioDecoder):
return encode_torchcodec_audio(value)
elif "array" in value:
# convert the audio array to wav bytes
buffer = BytesIO()
AudioEncoder(
torch.from_numpy(value["array"].astype(np.float32)), sample_rate=value["sampling_rate"]
).to_file_like(buffer, format="wav")
return {"bytes": buffer.getvalue(), "path": None}
elif value.get("path") is not None and os.path.isfile(value["path"]):
# we set "bytes": None to not duplicate the data if they're already available locally
if value["path"].endswith("pcm"):
# "PCM" only has raw audio bytes
if value.get("sampling_rate") is None:
# At least, If you want to convert "PCM-byte" to "WAV-byte", you have to know sampling rate
raise KeyError("To use PCM files, please specify a 'sampling_rate' in Audio object")
if value.get("bytes"):
# If we already had PCM-byte, we don`t have to make "read file, make bytes" (just use it!)
bytes_value = np.frombuffer(value["bytes"], dtype=np.int16).astype(np.float32) / 32767
else:
bytes_value = np.memmap(value["path"], dtype="h", mode="r").astype(np.float32) / 32767
buffer = BytesIO()
AudioEncoder(torch.from_numpy(bytes_value), sample_rate=value["sampling_rate"]).to_file_like(
buffer, format="wav"
)
return {"bytes": buffer.getvalue(), "path": None}
else:
return {"bytes": None, "path": value.get("path")}
elif value.get("bytes") is not None or value.get("path") is not None:
# store the audio bytes, and path is used to infer the audio format using the file extension
return {"bytes": value.get("bytes"), "path": value.get("path")}
else:
raise ValueError(
f"An audio sample should have one of 'path' or 'bytes' but they are missing or None in {value}."
)
def decode_example(
self, value: dict, token_per_repo_id: Optional[dict[str, Union[str, bool, None]]] = None
) -> "AudioDecoder":
"""Decode example audio file into audio data.
Args:
value (`dict`):
A dictionary with keys:
- `path`: String with relative audio file path.
- `bytes`: Bytes of the audio file.
token_per_repo_id (`dict`, *optional*):
To access and decode
audio files from private repositories on the Hub, you can pass
a dictionary repo_id (`str`) -> token (`bool` or `str`)
Returns:
`torchcodec.decoders.AudioDecoder`
"""
if config.TORCHCODEC_AVAILABLE:
from ._torchcodec import AudioDecoder
else:
raise ImportError("To support decoding audio data, please install 'torchcodec'.")
if not self.decode:
raise RuntimeError("Decoding is disabled for this feature. Please use Audio(decode=True) instead.")
path, bytes = (value["path"], value["bytes"]) if value["bytes"] is not None else (value["path"], None)
if path is None and bytes is None:
raise ValueError(f"An audio sample should have one of 'path' or 'bytes' but both are None in {value}.")
if bytes is None and is_local_path(path):
audio = AudioDecoder(path, stream_index=self.stream_index, sample_rate=self.sampling_rate)
elif bytes is None:
token_per_repo_id = token_per_repo_id or {}
source_url = path.split("::")[-1]
pattern = (
config.HUB_DATASETS_URL if source_url.startswith(config.HF_ENDPOINT) else config.HUB_DATASETS_HFFS_URL
)
source_url_fields = string_to_dict(source_url, pattern)
token = token_per_repo_id.get(source_url_fields["repo_id"]) if source_url_fields is not None else None
download_config = DownloadConfig(token=token)
f = xopen(path, "rb", download_config=download_config)
audio = AudioDecoder(f, stream_index=self.stream_index, sample_rate=self.sampling_rate)
else:
audio = AudioDecoder(bytes, stream_index=self.stream_index, sample_rate=self.sampling_rate)
audio._hf_encoded = {"path": path, "bytes": bytes}
audio.metadata.path = path
return audio
def flatten(self) -> Union["FeatureType", dict[str, "FeatureType"]]:
"""If in the decodable state, raise an error, otherwise flatten the feature into a dictionary."""
from .features import Value
if self.decode:
raise ValueError("Cannot flatten a decoded Audio feature.")
return {
"bytes": Value("binary"),
"path": Value("string"),
}
def cast_storage(self, storage: Union[pa.StringArray, pa.StructArray]) -> pa.StructArray:
"""Cast an Arrow array to the Audio arrow storage type.
The Arrow types that can be converted to the Audio pyarrow storage type are:
- `pa.string()` - it must contain the "path" data
- `pa.binary()` - it must contain the audio bytes
- `pa.struct({"bytes": pa.binary()})`
- `pa.struct({"path": pa.string()})`
- `pa.struct({"bytes": pa.binary(), "path": pa.string()})` - order doesn't matter
Args:
storage (`Union[pa.StringArray, pa.StructArray]`):
PyArrow array to cast.
Returns:
`pa.StructArray`: Array in the Audio arrow storage type, that is
`pa.struct({"bytes": pa.binary(), "path": pa.string()})`
"""
if pa.types.is_string(storage.type):
bytes_array = pa.array([None] * len(storage), type=pa.binary())
storage = pa.StructArray.from_arrays([bytes_array, storage], ["bytes", "path"], mask=storage.is_null())
elif pa.types.is_binary(storage.type):
path_array = pa.array([None] * len(storage), type=pa.string())
storage = pa.StructArray.from_arrays([storage, path_array], ["bytes", "path"], mask=storage.is_null())
elif pa.types.is_struct(storage.type) and storage.type.get_all_field_indices("array"):
storage = pa.array(
[Audio().encode_example(x) if x is not None else None for x in storage.to_numpy(zero_copy_only=False)]
)
elif pa.types.is_struct(storage.type):
if storage.type.get_field_index("bytes") >= 0:
bytes_array = storage.field("bytes")
else:
bytes_array = pa.array([None] * len(storage), type=pa.binary())
if storage.type.get_field_index("path") >= 0:
path_array = storage.field("path")
else:
path_array = pa.array([None] * len(storage), type=pa.string())
storage = pa.StructArray.from_arrays([bytes_array, path_array], ["bytes", "path"], mask=storage.is_null())
return array_cast(storage, self.pa_type)
def embed_storage(self, storage: pa.StructArray, token_per_repo_id=None) -> pa.StructArray:
"""Embed audio files into the Arrow array.
Args:
storage (`pa.StructArray`):
PyArrow array to embed.
Returns:
`pa.StructArray`: Array in the Audio arrow storage type, that is
`pa.struct({"bytes": pa.binary(), "path": pa.string()})`.
"""
if token_per_repo_id is None:
token_per_repo_id = {}
@no_op_if_value_is_null
def path_to_bytes(path):
source_url = path.split("::")[-1]
pattern = (
config.HUB_DATASETS_URL if source_url.startswith(config.HF_ENDPOINT) else config.HUB_DATASETS_HFFS_URL
)
source_url_fields = string_to_dict(source_url, pattern)
token = token_per_repo_id.get(source_url_fields["repo_id"]) if source_url_fields is not None else None
download_config = DownloadConfig(token=token)
with xopen(path, "rb", download_config=download_config) as f:
return f.read()
bytes_array = pa.array(
[
(path_to_bytes(x["path"]) if x["bytes"] is None else x["bytes"]) if x is not None else None
for x in storage.to_pylist()
],
type=pa.binary(),
)
path_array = pa.array(
[os.path.basename(path) if path is not None else None for path in storage.field("path").to_pylist()],
type=pa.string(),
)
storage = pa.StructArray.from_arrays([bytes_array, path_array], ["bytes", "path"], mask=bytes_array.is_null())
return array_cast(storage, self.pa_type)
def encode_torchcodec_audio(audio: "AudioDecoder") -> dict:
if hasattr(audio, "_hf_encoded"):
return audio._hf_encoded
else:
try:
from torchcodec.encoders import AudioEncoder # needed to write audio files
except ImportError as err:
raise ImportError("To support encoding audio data, please install 'torchcodec'.") from err
samples = audio.get_all_samples()
buffer = BytesIO()
AudioEncoder(samples.data.cpu(), sample_rate=samples.sample_rate).to_file_like(buffer, format="wav")
return {"bytes": buffer.getvalue(), "path": None}
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