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micromamba_root/envs/pytorch_env/Lib/site-packages/torchaudio/_backend/__init__.py

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+from typing import List, Optional
+
+from torchaudio._internal.module_utils import deprecated
+
+from . import utils
+from .common import AudioMetaData
+
+__all__ = [
+    "AudioMetaData",
+    "load",
+    "info",
+    "save",
+    "list_audio_backends",
+    "get_audio_backend",
+    "set_audio_backend",
+]
+
+
+info = utils.get_info_func()
+load = utils.get_load_func()
+save = utils.get_save_func()
+
+
+def list_audio_backends() -> List[str]:
+    """List available backends
+
+    Returns:
+        list of str: The list of available backends.
+
+        The possible values are; ``"ffmpeg"``, ``"sox"`` and ``"soundfile"``.
+    """
+
+    return list(utils.get_available_backends().keys())
+
+
+# Temporary until global backend is removed
+@deprecated("With dispatcher enabled, this function is no-op. You can remove the function call.")
+def get_audio_backend() -> Optional[str]:
+    """Get the name of the current global backend
+
+    Returns:
+        str or None:
+            If dispatcher mode is enabled, returns ``None`` otherwise,
+            the name of current backend or ``None`` (no backend is set).
+    """
+    return None
+
+
+# Temporary until global backend is removed
+@deprecated("With dispatcher enabled, this function is no-op. You can remove the function call.")
+def set_audio_backend(backend: Optional[str]):  # noqa
+    """Set the global backend.
+
+    This is a no-op when dispatcher mode is enabled.
+
+    Args:
+        backend (str or None): Name of the backend.
+            One of ``"sox_io"`` or ``"soundfile"`` based on availability
+            of the system. If ``None`` is provided the  current backend is unassigned.
+    """
+    pass

micromamba_root/envs/pytorch_env/Lib/site-packages/torchaudio/_backend/backend.py

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+import os
+from abc import ABC, abstractmethod
+from typing import BinaryIO, Optional, Tuple, Union
+
+from torch import Tensor
+from torchaudio.io import CodecConfig
+
+from .common import AudioMetaData
+
+
+class Backend(ABC):
+    @staticmethod
+    @abstractmethod
+    def info(uri: Union[BinaryIO, str, os.PathLike], format: Optional[str], buffer_size: int = 4096) -> AudioMetaData:
+        raise NotImplementedError
+
+    @staticmethod
+    @abstractmethod
+    def load(
+        uri: Union[BinaryIO, str, os.PathLike],
+        frame_offset: int = 0,
+        num_frames: int = -1,
+        normalize: bool = True,
+        channels_first: bool = True,
+        format: Optional[str] = None,
+        buffer_size: int = 4096,
+    ) -> Tuple[Tensor, int]:
+        raise NotImplementedError
+
+    @staticmethod
+    @abstractmethod
+    def save(
+        uri: Union[BinaryIO, str, os.PathLike],
+        src: Tensor,
+        sample_rate: int,
+        channels_first: bool = True,
+        format: Optional[str] = None,
+        encoding: Optional[str] = None,
+        bits_per_sample: Optional[int] = None,
+        buffer_size: int = 4096,
+        compression: Optional[Union[CodecConfig, float, int]] = None,
+    ) -> None:
+        raise NotImplementedError
+
+    @staticmethod
+    @abstractmethod
+    def can_decode(uri: Union[BinaryIO, str, os.PathLike], format: Optional[str]) -> bool:
+        raise NotImplementedError
+
+    @staticmethod
+    @abstractmethod
+    def can_encode(uri: Union[BinaryIO, str, os.PathLike], format: Optional[str]) -> bool:
+        raise NotImplementedError

micromamba_root/envs/pytorch_env/Lib/site-packages/torchaudio/_backend/common.py

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+class AudioMetaData:
+    """AudioMetaData()
+
+    Return type of ``torchaudio.info`` function.
+
+    :ivar int sample_rate: Sample rate
+    :ivar int num_frames: The number of frames
+    :ivar int num_channels: The number of channels
+    :ivar int bits_per_sample: The number of bits per sample. This is 0 for lossy formats,
+        or when it cannot be accurately inferred.
+    :ivar str encoding: Audio encoding
+        The values encoding can take are one of the following:
+
+            * ``PCM_S``: Signed integer linear PCM
+            * ``PCM_U``: Unsigned integer linear PCM
+            * ``PCM_F``: Floating point linear PCM
+            * ``FLAC``: Flac, Free Lossless Audio Codec
+            * ``ULAW``: Mu-law
+            * ``ALAW``: A-law
+            * ``MP3`` : MP3, MPEG-1 Audio Layer III
+            * ``VORBIS``: OGG Vorbis
+            * ``AMR_WB``: Adaptive Multi-Rate Wideband
+            * ``AMR_NB``: Adaptive Multi-Rate Narrowband
+            * ``OPUS``: Opus
+            * ``HTK``: Single channel 16-bit PCM
+            * ``UNKNOWN`` : None of above
+    """
+
+    def __init__(
+        self,
+        sample_rate: int,
+        num_frames: int,
+        num_channels: int,
+        bits_per_sample: int,
+        encoding: str,
+    ):
+        self.sample_rate = sample_rate
+        self.num_frames = num_frames
+        self.num_channels = num_channels
+        self.bits_per_sample = bits_per_sample
+        self.encoding = encoding
+
+    def __str__(self):
+        return (
+            f"AudioMetaData("
+            f"sample_rate={self.sample_rate}, "
+            f"num_frames={self.num_frames}, "
+            f"num_channels={self.num_channels}, "
+            f"bits_per_sample={self.bits_per_sample}, "
+            f"encoding={self.encoding}"
+            f")"
+        )

micromamba_root/envs/pytorch_env/Lib/site-packages/torchaudio/_backend/ffmpeg.py

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+import os
+import re
+import sys
+from typing import BinaryIO, Optional, Tuple, Union
+
+import torch
+import torchaudio
+
+from .backend import Backend
+from .common import AudioMetaData
+
+InputType = Union[BinaryIO, str, os.PathLike]
+
+
+def info_audio(
+    src: InputType,
+    format: Optional[str],
+    buffer_size: int = 4096,
+) -> AudioMetaData:
+    s = torchaudio.io.StreamReader(src, format, None, buffer_size)
+    sinfo = s.get_src_stream_info(s.default_audio_stream)
+    if sinfo.num_frames == 0:
+        waveform = _load_audio(s)
+        num_frames = waveform.size(1)
+    else:
+        num_frames = sinfo.num_frames
+    return AudioMetaData(
+        int(sinfo.sample_rate),
+        num_frames,
+        sinfo.num_channels,
+        sinfo.bits_per_sample,
+        sinfo.codec.upper(),
+    )
+
+
+def _get_load_filter(
+    frame_offset: int = 0,
+    num_frames: int = -1,
+    convert: bool = True,
+) -> Optional[str]:
+    if frame_offset < 0:
+        raise RuntimeError("Invalid argument: frame_offset must be non-negative. Found: {}".format(frame_offset))
+    if num_frames == 0 or num_frames < -1:
+        raise RuntimeError("Invalid argument: num_frames must be -1 or greater than 0. Found: {}".format(num_frames))
+
+    # All default values -> no filter
+    if frame_offset == 0 and num_frames == -1 and not convert:
+        return None
+    # Only convert
+    aformat = "aformat=sample_fmts=fltp"
+    if frame_offset == 0 and num_frames == -1 and convert:
+        return aformat
+    # At least one of frame_offset or num_frames has non-default value
+    if num_frames > 0:
+        atrim = "atrim=start_sample={}:end_sample={}".format(frame_offset, frame_offset + num_frames)
+    else:
+        atrim = "atrim=start_sample={}".format(frame_offset)
+    if not convert:
+        return atrim
+    return "{},{}".format(atrim, aformat)
+
+
+def _load_audio(
+    s: "torchaudio.io.StreamReader",
+    filter: Optional[str] = None,
+    channels_first: bool = True,
+) -> torch.Tensor:
+    s.add_audio_stream(-1, -1, filter_desc=filter)
+    s.process_all_packets()
+    chunk = s.pop_chunks()[0]
+    if chunk is None:
+        raise RuntimeError("Failed to decode audio.")
+    waveform = chunk._elem
+    return waveform.T if channels_first else waveform
+
+
+def load_audio(
+    src: InputType,
+    frame_offset: int = 0,
+    num_frames: int = -1,
+    convert: bool = True,
+    channels_first: bool = True,
+    format: Optional[str] = None,
+    buffer_size: int = 4096,
+) -> Tuple[torch.Tensor, int]:
+    if hasattr(src, "read") and format == "vorbis":
+        format = "ogg"
+    s = torchaudio.io.StreamReader(src, format, None, buffer_size)
+    sample_rate = int(s.get_src_stream_info(s.default_audio_stream).sample_rate)
+    filter = _get_load_filter(frame_offset, num_frames, convert)
+    waveform = _load_audio(s, filter, channels_first)
+    return waveform, sample_rate
+
+
+def _get_sample_format(dtype: torch.dtype) -> str:
+    dtype_to_format = {
+        torch.uint8: "u8",
+        torch.int16: "s16",
+        torch.int32: "s32",
+        torch.int64: "s64",
+        torch.float32: "flt",
+        torch.float64: "dbl",
+    }
+    format = dtype_to_format.get(dtype)
+    if format is None:
+        raise ValueError(f"No format found for dtype {dtype}; dtype must be one of {list(dtype_to_format.keys())}.")
+    return format
+
+
+def _native_endianness() -> str:
+    if sys.byteorder == "little":
+        return "le"
+    else:
+        return "be"
+
+
+def _get_encoder_for_wav(encoding: str, bits_per_sample: int) -> str:
+    if bits_per_sample not in {None, 8, 16, 24, 32, 64}:
+        raise ValueError(f"Invalid bits_per_sample {bits_per_sample} for WAV encoding.")
+    endianness = _native_endianness()
+    if not encoding:
+        if not bits_per_sample:
+            # default to PCM S16
+            return f"pcm_s16{endianness}"
+        if bits_per_sample == 8:
+            return "pcm_u8"
+        return f"pcm_s{bits_per_sample}{endianness}"
+    if encoding == "PCM_S":
+        if not bits_per_sample:
+            bits_per_sample = 16
+        if bits_per_sample == 8:
+            raise ValueError("For WAV signed PCM, 8-bit encoding is not supported.")
+        return f"pcm_s{bits_per_sample}{endianness}"
+    if encoding == "PCM_U":
+        if bits_per_sample in (None, 8):
+            return "pcm_u8"
+        raise ValueError("For WAV unsigned PCM, only 8-bit encoding is supported.")
+    if encoding == "PCM_F":
+        if not bits_per_sample:
+            bits_per_sample = 32
+        if bits_per_sample in (32, 64):
+            return f"pcm_f{bits_per_sample}{endianness}"
+        raise ValueError("For WAV float PCM, only 32- and 64-bit encodings are supported.")
+    if encoding == "ULAW":
+        if bits_per_sample in (None, 8):
+            return "pcm_mulaw"
+        raise ValueError("For WAV PCM mu-law, only 8-bit encoding is supported.")
+    if encoding == "ALAW":
+        if bits_per_sample in (None, 8):
+            return "pcm_alaw"
+        raise ValueError("For WAV PCM A-law, only 8-bit encoding is supported.")
+    raise ValueError(f"WAV encoding {encoding} is not supported.")
+
+
+def _get_flac_sample_fmt(bps):
+    if bps is None or bps == 16:
+        return "s16"
+    if bps == 24:
+        return "s32"
+    raise ValueError(f"FLAC only supports bits_per_sample values of 16 and 24 ({bps} specified).")
+
+
+def _parse_save_args(
+    ext: Optional[str],
+    format: Optional[str],
+    encoding: Optional[str],
+    bps: Optional[int],
+):
+    # torchaudio's save function accepts the followings, which do not 1to1 map
+    # to FFmpeg.
+    #
+    # - format: audio format
+    # - bits_per_sample: encoder sample format
+    # - encoding: such as PCM_U8.
+    #
+    # In FFmpeg, format is specified with the following three (and more)
+    #
+    # - muxer: could be audio format or container format.
+    # the one we passed to the constructor of StreamWriter
+    # - encoder: the audio encoder used to encode audio
+    # - encoder sample format: the format used by encoder to encode audio.
+    #
+    # If encoder sample format is different from source sample format, StreamWriter
+    # will insert a filter automatically.
+    #
+    def _type(spec):
+        # either format is exactly the specified one
+        # or extension matches to the spec AND there is no format override.
+        return format == spec or (format is None and ext == spec)
+
+    if _type("wav") or _type("amb"):
+        # wav is special because it supports different encoding through encoders
+        # each encoder only supports one encoder format
+        #
+        # amb format is a special case originated from libsox.
+        # It is basically a WAV format, with slight modification.
+        # https://github.com/chirlu/sox/commit/4a4ea33edbca5972a1ed8933cc3512c7302fa67a#diff-39171191a858add9df87f5f210a34a776ac2c026842ae6db6ce97f5e68836795
+        # It is a format so that decoders will recognize it as ambisonic.
+        # https://www.ambisonia.com/Members/mleese/file-format-for-b-format/
+        # FFmpeg does not recognize amb because it is basically a WAV format.
+        muxer = "wav"
+        encoder = _get_encoder_for_wav(encoding, bps)
+        sample_fmt = None
+    elif _type("vorbis"):
+        # FFpmeg does not recognize vorbis extension, while libsox used to do.
+        # For the sake of bakward compatibility, (and the simplicity),
+        # we support the case where users want to do save("foo.vorbis")
+        muxer = "ogg"
+        encoder = "vorbis"
+        sample_fmt = None
+    else:
+        muxer = format
+        encoder = None
+        sample_fmt = None
+        if _type("flac"):
+            sample_fmt = _get_flac_sample_fmt(bps)
+        if _type("ogg"):
+            sample_fmt = _get_flac_sample_fmt(bps)
+    return muxer, encoder, sample_fmt
+
+
+def save_audio(
+    uri: InputType,
+    src: torch.Tensor,
+    sample_rate: int,
+    channels_first: bool = True,
+    format: Optional[str] = None,
+    encoding: Optional[str] = None,
+    bits_per_sample: Optional[int] = None,
+    buffer_size: int = 4096,
+    compression: Optional[torchaudio.io.CodecConfig] = None,
+) -> None:
+    ext = None
+    if hasattr(uri, "write"):
+        if format is None:
+            raise RuntimeError("'format' is required when saving to file object.")
+    else:
+        uri = os.path.normpath(uri)
+        if tokens := str(uri).split(".")[1:]:
+            ext = tokens[-1].lower()
+
+    muxer, encoder, enc_fmt = _parse_save_args(ext, format, encoding, bits_per_sample)
+
+    if channels_first:
+        src = src.T
+
+    s = torchaudio.io.StreamWriter(uri, format=muxer, buffer_size=buffer_size)
+    s.add_audio_stream(
+        sample_rate,
+        num_channels=src.size(-1),
+        format=_get_sample_format(src.dtype),
+        encoder=encoder,
+        encoder_format=enc_fmt,
+        codec_config=compression,
+    )
+    with s.open():
+        s.write_audio_chunk(0, src)
+
+
+def _map_encoding(encoding: str) -> str:
+    for dst in ["PCM_S", "PCM_U", "PCM_F"]:
+        if dst in encoding:
+            return dst
+    if encoding == "PCM_MULAW":
+        return "ULAW"
+    elif encoding == "PCM_ALAW":
+        return "ALAW"
+    return encoding
+
+
+def _get_bits_per_sample(encoding: str, bits_per_sample: int) -> str:
+    if m := re.search(r"PCM_\w(\d+)\w*", encoding):
+        return int(m.group(1))
+    elif encoding in ["PCM_ALAW", "PCM_MULAW"]:
+        return 8
+    return bits_per_sample
+
+
+class FFmpegBackend(Backend):
+    @staticmethod
+    def info(uri: InputType, format: Optional[str], buffer_size: int = 4096) -> AudioMetaData:
+        metadata = info_audio(uri, format, buffer_size)
+        metadata.bits_per_sample = _get_bits_per_sample(metadata.encoding, metadata.bits_per_sample)
+        metadata.encoding = _map_encoding(metadata.encoding)
+        return metadata
+
+    @staticmethod
+    def load(
+        uri: InputType,
+        frame_offset: int = 0,
+        num_frames: int = -1,
+        normalize: bool = True,
+        channels_first: bool = True,
+        format: Optional[str] = None,
+        buffer_size: int = 4096,
+    ) -> Tuple[torch.Tensor, int]:
+        return load_audio(uri, frame_offset, num_frames, normalize, channels_first, format)
+
+    @staticmethod
+    def save(
+        uri: InputType,
+        src: torch.Tensor,
+        sample_rate: int,
+        channels_first: bool = True,
+        format: Optional[str] = None,
+        encoding: Optional[str] = None,
+        bits_per_sample: Optional[int] = None,
+        buffer_size: int = 4096,
+        compression: Optional[Union[torchaudio.io.CodecConfig, float, int]] = None,
+    ) -> None:
+        if not isinstance(compression, (torchaudio.io.CodecConfig, type(None))):
+            raise ValueError(
+                "FFmpeg backend expects non-`None` value for argument `compression` to be of ",
+                f"type `torchaudio.io.CodecConfig`, but received value of type {type(compression)}",
+            )
+        save_audio(
+            uri,
+            src,
+            sample_rate,
+            channels_first,
+            format,
+            encoding,
+            bits_per_sample,
+            buffer_size,
+            compression,
+        )
+
+    @staticmethod
+    def can_decode(uri: InputType, format: Optional[str]) -> bool:
+        return True
+
+    @staticmethod
+    def can_encode(uri: InputType, format: Optional[str]) -> bool:
+        return True

micromamba_root/envs/pytorch_env/Lib/site-packages/torchaudio/_backend/soundfile.py

@@ -0,0 +1,54 @@
+import os
+from typing import BinaryIO, Optional, Tuple, Union
+
+import torch
+from torchaudio.io import CodecConfig
+
+from . import soundfile_backend
+from .backend import Backend
+from .common import AudioMetaData
+
+
+class SoundfileBackend(Backend):
+    @staticmethod
+    def info(uri: Union[BinaryIO, str, os.PathLike], format: Optional[str], buffer_size: int = 4096) -> AudioMetaData:
+        return soundfile_backend.info(uri, format)
+
+    @staticmethod
+    def load(
+        uri: Union[BinaryIO, str, os.PathLike],
+        frame_offset: int = 0,
+        num_frames: int = -1,
+        normalize: bool = True,
+        channels_first: bool = True,
+        format: Optional[str] = None,
+        buffer_size: int = 4096,
+    ) -> Tuple[torch.Tensor, int]:
+        return soundfile_backend.load(uri, frame_offset, num_frames, normalize, channels_first, format)
+
+    @staticmethod
+    def save(
+        uri: Union[BinaryIO, str, os.PathLike],
+        src: torch.Tensor,
+        sample_rate: int,
+        channels_first: bool = True,
+        format: Optional[str] = None,
+        encoding: Optional[str] = None,
+        bits_per_sample: Optional[int] = None,
+        buffer_size: int = 4096,
+        compression: Optional[Union[CodecConfig, float, int]] = None,
+    ) -> None:
+        if compression:
+            raise ValueError("soundfile backend does not support argument `compression`.")
+
+        soundfile_backend.save(
+            uri, src, sample_rate, channels_first, format=format, encoding=encoding, bits_per_sample=bits_per_sample
+        )
+
+    @staticmethod
+    def can_decode(uri, format) -> bool:
+        return True
+
+    @staticmethod
+    def can_encode(uri, format) -> bool:
+        return True

micromamba_root/envs/pytorch_env/Lib/site-packages/torchaudio/_backend/soundfile_backend.py

@@ -0,0 +1,457 @@
+"""The new soundfile backend which will become default in 0.8.0 onward"""
+import warnings
+from typing import Optional, Tuple
+
+import torch
+from torchaudio._internal import module_utils as _mod_utils
+
+from .common import AudioMetaData
+
+
+_IS_SOUNDFILE_AVAILABLE = False
+
+# TODO: import soundfile only when it is used.
+if _mod_utils.is_module_available("soundfile"):
+    try:
+        import soundfile
+
+        _requires_soundfile = _mod_utils.no_op
+        _IS_SOUNDFILE_AVAILABLE = True
+    except Exception:
+        _requires_soundfile = _mod_utils.fail_with_message(
+            "requires soundfile, but we failed to import it. Please check the installation of soundfile."
+        )
+else:
+    _requires_soundfile = _mod_utils.fail_with_message(
+        "requires soundfile, but it is not installed. Please install soundfile."
+    )
+
+
+# Mapping from soundfile subtype to number of bits per sample.
+# This is mostly heuristical and the value is set to 0 when it is irrelevant
+# (lossy formats) or when it can't be inferred.
+# For ADPCM (and G72X) subtypes, it's hard to infer the bit depth because it's not part of the standard:
+# According to https://en.wikipedia.org/wiki/Adaptive_differential_pulse-code_modulation#In_telephony,
+# the default seems to be 8 bits but it can be compressed further to 4 bits.
+# The dict is inspired from
+# https://github.com/bastibe/python-soundfile/blob/744efb4b01abc72498a96b09115b42a4cabd85e4/soundfile.py#L66-L94
+_SUBTYPE_TO_BITS_PER_SAMPLE = {
+    "PCM_S8": 8,  # Signed 8 bit data
+    "PCM_16": 16,  # Signed 16 bit data
+    "PCM_24": 24,  # Signed 24 bit data
+    "PCM_32": 32,  # Signed 32 bit data
+    "PCM_U8": 8,  # Unsigned 8 bit data (WAV and RAW only)
+    "FLOAT": 32,  # 32 bit float data
+    "DOUBLE": 64,  # 64 bit float data
+    "ULAW": 8,  # U-Law encoded. See https://en.wikipedia.org/wiki/G.711#Types
+    "ALAW": 8,  # A-Law encoded. See https://en.wikipedia.org/wiki/G.711#Types
+    "IMA_ADPCM": 0,  # IMA ADPCM.
+    "MS_ADPCM": 0,  # Microsoft ADPCM.
+    "GSM610": 0,  # GSM 6.10 encoding. (Wikipedia says 1.625 bit depth?? https://en.wikipedia.org/wiki/Full_Rate)
+    "VOX_ADPCM": 0,  # OKI / Dialogix ADPCM
+    "G721_32": 0,  # 32kbs G721 ADPCM encoding.
+    "G723_24": 0,  # 24kbs G723 ADPCM encoding.
+    "G723_40": 0,  # 40kbs G723 ADPCM encoding.
+    "DWVW_12": 12,  # 12 bit Delta Width Variable Word encoding.
+    "DWVW_16": 16,  # 16 bit Delta Width Variable Word encoding.
+    "DWVW_24": 24,  # 24 bit Delta Width Variable Word encoding.
+    "DWVW_N": 0,  # N bit Delta Width Variable Word encoding.
+    "DPCM_8": 8,  # 8 bit differential PCM (XI only)
+    "DPCM_16": 16,  # 16 bit differential PCM (XI only)
+    "VORBIS": 0,  # Xiph Vorbis encoding. (lossy)
+    "ALAC_16": 16,  # Apple Lossless Audio Codec (16 bit).
+    "ALAC_20": 20,  # Apple Lossless Audio Codec (20 bit).
+    "ALAC_24": 24,  # Apple Lossless Audio Codec (24 bit).
+    "ALAC_32": 32,  # Apple Lossless Audio Codec (32 bit).
+}
+
+
+def _get_bit_depth(subtype):
+    if subtype not in _SUBTYPE_TO_BITS_PER_SAMPLE:
+        warnings.warn(
+            f"The {subtype} subtype is unknown to TorchAudio. As a result, the bits_per_sample "
+            "attribute will be set to 0. If you are seeing this warning, please "
+            "report by opening an issue on github (after checking for existing/closed ones). "
+            "You may otherwise ignore this warning."
+        )
+    return _SUBTYPE_TO_BITS_PER_SAMPLE.get(subtype, 0)
+
+
+_SUBTYPE_TO_ENCODING = {
+    "PCM_S8": "PCM_S",
+    "PCM_16": "PCM_S",
+    "PCM_24": "PCM_S",
+    "PCM_32": "PCM_S",
+    "PCM_U8": "PCM_U",
+    "FLOAT": "PCM_F",
+    "DOUBLE": "PCM_F",
+    "ULAW": "ULAW",
+    "ALAW": "ALAW",
+    "VORBIS": "VORBIS",
+}
+
+
+def _get_encoding(format: str, subtype: str):
+    if format == "FLAC":
+        return "FLAC"
+    return _SUBTYPE_TO_ENCODING.get(subtype, "UNKNOWN")
+
+
+@_requires_soundfile
+def info(filepath: str, format: Optional[str] = None) -> AudioMetaData:
+    """Get signal information of an audio file.
+
+    Note:
+        ``filepath`` argument is intentionally annotated as ``str`` only, even though it accepts
+        ``pathlib.Path`` object as well. This is for the consistency with ``"sox_io"`` backend,
+        which has a restriction on type annotation due to TorchScript compiler compatiblity.
+
+    Args:
+        filepath (path-like object or file-like object):
+            Source of audio data.
+        format (str or None, optional):
+            Not used. PySoundFile does not accept format hint.
+
+    Returns:
+        AudioMetaData: meta data of the given audio.
+
+    """
+    sinfo = soundfile.info(filepath)
+    return AudioMetaData(
+        sinfo.samplerate,
+        sinfo.frames,
+        sinfo.channels,
+        bits_per_sample=_get_bit_depth(sinfo.subtype),
+        encoding=_get_encoding(sinfo.format, sinfo.subtype),
+    )
+
+
+_SUBTYPE2DTYPE = {
+    "PCM_S8": "int8",
+    "PCM_U8": "uint8",
+    "PCM_16": "int16",
+    "PCM_32": "int32",
+    "FLOAT": "float32",
+    "DOUBLE": "float64",
+}
+
+
+@_requires_soundfile
+def load(
+    filepath: str,
+    frame_offset: int = 0,
+    num_frames: int = -1,
+    normalize: bool = True,
+    channels_first: bool = True,
+    format: Optional[str] = None,
+) -> Tuple[torch.Tensor, int]:
+    """Load audio data from file.
+
+    Note:
+        The formats this function can handle depend on the soundfile installation.
+        This function is tested on the following formats;
+
+        * WAV
+
+            * 32-bit floating-point
+            * 32-bit signed integer
+            * 16-bit signed integer
+            * 8-bit unsigned integer
+
+        * FLAC
+        * OGG/VORBIS
+        * SPHERE
+
+    By default (``normalize=True``, ``channels_first=True``), this function returns Tensor with
+    ``float32`` dtype, and the shape of `[channel, time]`.
+
+    .. warning::
+
+       ``normalize`` argument does not perform volume normalization.
+       It only converts the sample type to `torch.float32` from the native sample
+       type.
+
+       When the input format is WAV with integer type, such as 32-bit signed integer, 16-bit
+       signed integer, 24-bit signed integer, and 8-bit unsigned integer, by providing ``normalize=False``,
+       this function can return integer Tensor, where the samples are expressed within the whole range
+       of the corresponding dtype, that is, ``int32`` tensor for 32-bit signed PCM,
+       ``int16`` for 16-bit signed PCM and ``uint8`` for 8-bit unsigned PCM. Since torch does not
+       support ``int24`` dtype, 24-bit signed PCM are converted to ``int32`` tensors.
+
+       ``normalize`` argument has no effect on 32-bit floating-point WAV and other formats, such as
+       ``flac`` and ``mp3``.
+
+       For these formats, this function always returns ``float32`` Tensor with values.
+
+    Note:
+        ``filepath`` argument is intentionally annotated as ``str`` only, even though it accepts
+        ``pathlib.Path`` object as well. This is for the consistency with ``"sox_io"`` backend,
+        which has a restriction on type annotation due to TorchScript compiler compatiblity.
+
+    Args:
+        filepath (path-like object or file-like object):
+            Source of audio data.
+        frame_offset (int, optional):
+            Number of frames to skip before start reading data.
+        num_frames (int, optional):
+            Maximum number of frames to read. ``-1`` reads all the remaining samples,
+            starting from ``frame_offset``.
+            This function may return the less number of frames if there is not enough
+            frames in the given file.
+        normalize (bool, optional):
+            When ``True``, this function converts the native sample type to ``float32``.
+            Default: ``True``.
+
+            If input file is integer WAV, giving ``False`` will change the resulting Tensor type to
+            integer type.
+            This argument has no effect for formats other than integer WAV type.
+
+        channels_first (bool, optional):
+            When True, the returned Tensor has dimension `[channel, time]`.
+            Otherwise, the returned Tensor's dimension is `[time, channel]`.
+        format (str or None, optional):
+            Not used. PySoundFile does not accept format hint.
+
+    Returns:
+        (torch.Tensor, int): Resulting Tensor and sample rate.
+            If the input file has integer wav format and normalization is off, then it has
+            integer type, else ``float32`` type. If ``channels_first=True``, it has
+            `[channel, time]` else `[time, channel]`.
+    """
+    with soundfile.SoundFile(filepath, "r") as file_:
+        if file_.format != "WAV" or normalize:
+            dtype = "float32"
+        elif file_.subtype not in _SUBTYPE2DTYPE:
+            raise ValueError(f"Unsupported subtype: {file_.subtype}")
+        else:
+            dtype = _SUBTYPE2DTYPE[file_.subtype]
+
+        frames = file_._prepare_read(frame_offset, None, num_frames)
+        waveform = file_.read(frames, dtype, always_2d=True)
+        sample_rate = file_.samplerate
+
+    waveform = torch.from_numpy(waveform)
+    if channels_first:
+        waveform = waveform.t()
+    return waveform, sample_rate
+
+
+def _get_subtype_for_wav(dtype: torch.dtype, encoding: str, bits_per_sample: int):
+    if not encoding:
+        if not bits_per_sample:
+            subtype = {
+                torch.uint8: "PCM_U8",
+                torch.int16: "PCM_16",
+                torch.int32: "PCM_32",
+                torch.float32: "FLOAT",
+                torch.float64: "DOUBLE",
+            }.get(dtype)
+            if not subtype:
+                raise ValueError(f"Unsupported dtype for wav: {dtype}")
+            return subtype
+        if bits_per_sample == 8:
+            return "PCM_U8"
+        return f"PCM_{bits_per_sample}"
+    if encoding == "PCM_S":
+        if not bits_per_sample:
+            return "PCM_32"
+        if bits_per_sample == 8:
+            raise ValueError("wav does not support 8-bit signed PCM encoding.")
+        return f"PCM_{bits_per_sample}"
+    if encoding == "PCM_U":
+        if bits_per_sample in (None, 8):
+            return "PCM_U8"
+        raise ValueError("wav only supports 8-bit unsigned PCM encoding.")
+    if encoding == "PCM_F":
+        if bits_per_sample in (None, 32):
+            return "FLOAT"
+        if bits_per_sample == 64:
+            return "DOUBLE"
+        raise ValueError("wav only supports 32/64-bit float PCM encoding.")
+    if encoding == "ULAW":
+        if bits_per_sample in (None, 8):
+            return "ULAW"
+        raise ValueError("wav only supports 8-bit mu-law encoding.")
+    if encoding == "ALAW":
+        if bits_per_sample in (None, 8):
+            return "ALAW"
+        raise ValueError("wav only supports 8-bit a-law encoding.")
+    raise ValueError(f"wav does not support {encoding}.")
+
+
+def _get_subtype_for_sphere(encoding: str, bits_per_sample: int):
+    if encoding in (None, "PCM_S"):
+        return f"PCM_{bits_per_sample}" if bits_per_sample else "PCM_32"
+    if encoding in ("PCM_U", "PCM_F"):
+        raise ValueError(f"sph does not support {encoding} encoding.")
+    if encoding == "ULAW":
+        if bits_per_sample in (None, 8):
+            return "ULAW"
+        raise ValueError("sph only supports 8-bit for mu-law encoding.")
+    if encoding == "ALAW":
+        return "ALAW"
+    raise ValueError(f"sph does not support {encoding}.")
+
+
+def _get_subtype(dtype: torch.dtype, format: str, encoding: str, bits_per_sample: int):
+    if format == "wav":
+        return _get_subtype_for_wav(dtype, encoding, bits_per_sample)
+    if format == "flac":
+        if encoding:
+            raise ValueError("flac does not support encoding.")
+        if not bits_per_sample:
+            return "PCM_16"
+        if bits_per_sample > 24:
+            raise ValueError("flac does not support bits_per_sample > 24.")
+        return "PCM_S8" if bits_per_sample == 8 else f"PCM_{bits_per_sample}"
+    if format in ("ogg", "vorbis"):
+        if bits_per_sample:
+            raise ValueError("ogg/vorbis does not support bits_per_sample.")
+        if encoding is None or encoding == "vorbis":
+            return "VORBIS"
+        if encoding == "opus":
+            return "OPUS"
+        raise ValueError(f"Unexpected encoding: {encoding}")
+    if format == "mp3":
+        return "MPEG_LAYER_III"
+    if format == "sph":
+        return _get_subtype_for_sphere(encoding, bits_per_sample)
+    if format in ("nis", "nist"):
+        return "PCM_16"
+    raise ValueError(f"Unsupported format: {format}")
+
+
+@_requires_soundfile
+def save(
+    filepath: str,
+    src: torch.Tensor,
+    sample_rate: int,
+    channels_first: bool = True,
+    compression: Optional[float] = None,
+    format: Optional[str] = None,
+    encoding: Optional[str] = None,
+    bits_per_sample: Optional[int] = None,
+):
+    """Save audio data to file.
+
+    Note:
+        The formats this function can handle depend on the soundfile installation.
+        This function is tested on the following formats;
+
+        * WAV
+
+            * 32-bit floating-point
+            * 32-bit signed integer
+            * 16-bit signed integer
+            * 8-bit unsigned integer
+
+        * FLAC
+        * OGG/VORBIS
+        * SPHERE
+
+    Note:
+        ``filepath`` argument is intentionally annotated as ``str`` only, even though it accepts
+        ``pathlib.Path`` object as well. This is for the consistency with ``"sox_io"`` backend,
+        which has a restriction on type annotation due to TorchScript compiler compatiblity.
+
+    Args:
+        filepath (str or pathlib.Path): Path to audio file.
+        src (torch.Tensor): Audio data to save. must be 2D tensor.
+        sample_rate (int): sampling rate
+        channels_first (bool, optional): If ``True``, the given tensor is interpreted as `[channel, time]`,
+            otherwise `[time, channel]`.
+        compression (float of None, optional): Not used.
+            It is here only for interface compatibility reson with "sox_io" backend.
+        format (str or None, optional): Override the audio format.
+            When ``filepath`` argument is path-like object, audio format is
+            inferred from file extension. If the file extension is missing or
+            different, you can specify the correct format with this argument.
+
+            When ``filepath`` argument is file-like object,
+            this argument is required.
+
+            Valid values are ``"wav"``, ``"ogg"``, ``"vorbis"``,
+            ``"flac"`` and ``"sph"``.
+        encoding (str or None, optional): Changes the encoding for supported formats.
+            This argument is effective only for supported formats, sush as
+            ``"wav"``, ``""flac"`` and ``"sph"``. Valid values are;
+
+                - ``"PCM_S"`` (signed integer Linear PCM)
+                - ``"PCM_U"`` (unsigned integer Linear PCM)
+                - ``"PCM_F"`` (floating point PCM)
+                - ``"ULAW"`` (mu-law)
+                - ``"ALAW"`` (a-law)
+
+        bits_per_sample (int or None, optional): Changes the bit depth for the
+            supported formats.
+            When ``format`` is one of ``"wav"``, ``"flac"`` or ``"sph"``,
+            you can change the bit depth.
+            Valid values are ``8``, ``16``, ``24``, ``32`` and ``64``.
+
+    Supported formats/encodings/bit depth/compression are:
+
+    ``"wav"``
+        - 32-bit floating-point PCM
+        - 32-bit signed integer PCM
+        - 24-bit signed integer PCM
+        - 16-bit signed integer PCM
+        - 8-bit unsigned integer PCM
+        - 8-bit mu-law
+        - 8-bit a-law
+
+        Note:
+            Default encoding/bit depth is determined by the dtype of
+            the input Tensor.
+
+    ``"flac"``
+        - 8-bit
+        - 16-bit (default)
+        - 24-bit
+
+    ``"ogg"``, ``"vorbis"``
+        - Doesn't accept changing configuration.
+
+    ``"sph"``
+        - 8-bit signed integer PCM
+        - 16-bit signed integer PCM
+        - 24-bit signed integer PCM
+        - 32-bit signed integer PCM (default)
+        - 8-bit mu-law
+        - 8-bit a-law
+        - 16-bit a-law
+        - 24-bit a-law
+        - 32-bit a-law
+
+    """
+    if src.ndim != 2:
+        raise ValueError(f"Expected 2D Tensor, got {src.ndim}D.")
+    if compression is not None:
+        warnings.warn(
+            '`save` function of "soundfile" backend does not support "compression" parameter. '
+            "The argument is silently ignored."
+        )
+    if hasattr(filepath, "write"):
+        if format is None:
+            raise RuntimeError("`format` is required when saving to file object.")
+        ext = format.lower()
+    else:
+        ext = str(filepath).split(".")[-1].lower()
+
+    if bits_per_sample not in (None, 8, 16, 24, 32, 64):
+        raise ValueError("Invalid bits_per_sample.")
+    if bits_per_sample == 24:
+        warnings.warn(
+            "Saving audio with 24 bits per sample might warp samples near -1. "
+            "Using 16 bits per sample might be able to avoid this."
+        )
+    subtype = _get_subtype(src.dtype, ext, encoding, bits_per_sample)
+
+    # sph is a extension used in TED-LIUM but soundfile does not recognize it as NIST format,
+    # so we extend the extensions manually here
+    if ext in ["nis", "nist", "sph"] and format is None:
+        format = "NIST"
+
+    if channels_first:
+        src = src.t()
+
+    soundfile.write(file=filepath, data=src, samplerate=sample_rate, subtype=subtype, format=format)

micromamba_root/envs/pytorch_env/Lib/site-packages/torchaudio/_backend/sox.py

@@ -0,0 +1,91 @@
+import os
+from typing import BinaryIO, Optional, Tuple, Union
+
+import torch
+import torchaudio
+
+from .backend import Backend
+from .common import AudioMetaData
+
+sox_ext = torchaudio._extension.lazy_import_sox_ext()
+
+
+class SoXBackend(Backend):
+    @staticmethod
+    def info(uri: Union[BinaryIO, str, os.PathLike], format: Optional[str], buffer_size: int = 4096) -> AudioMetaData:
+        if hasattr(uri, "read"):
+            raise ValueError(
+                "SoX backend does not support reading from file-like objects. ",
+                "Please use an alternative backend that does support reading from file-like objects, e.g. FFmpeg.",
+            )
+        else:
+            sinfo = sox_ext.get_info(uri, format)
+            if sinfo:
+                return AudioMetaData(*sinfo)
+            else:
+                raise RuntimeError(f"Failed to fetch metadata for {uri}.")
+
+    @staticmethod
+    def load(
+        uri: Union[BinaryIO, str, os.PathLike],
+        frame_offset: int = 0,
+        num_frames: int = -1,
+        normalize: bool = True,
+        channels_first: bool = True,
+        format: Optional[str] = None,
+        buffer_size: int = 4096,
+    ) -> Tuple[torch.Tensor, int]:
+        if hasattr(uri, "read"):
+            raise ValueError(
+                "SoX backend does not support loading from file-like objects. ",
+                "Please use an alternative backend that does support loading from file-like objects, e.g. FFmpeg.",
+            )
+        else:
+            ret = sox_ext.load_audio_file(uri, frame_offset, num_frames, normalize, channels_first, format)
+            if not ret:
+                raise RuntimeError(f"Failed to load audio from {uri}.")
+            return ret
+
+    @staticmethod
+    def save(
+        uri: Union[BinaryIO, str, os.PathLike],
+        src: torch.Tensor,
+        sample_rate: int,
+        channels_first: bool = True,
+        format: Optional[str] = None,
+        encoding: Optional[str] = None,
+        bits_per_sample: Optional[int] = None,
+        buffer_size: int = 4096,
+        compression: Optional[Union[torchaudio.io.CodecConfig, float, int]] = None,
+    ) -> None:
+        if not isinstance(compression, (float, int, type(None))):
+            raise ValueError(
+                "SoX backend expects non-`None` value for argument `compression` to be of ",
+                f"type `float` or `int`, but received value of type {type(compression)}",
+            )
+        if hasattr(uri, "write"):
+            raise ValueError(
+                "SoX backend does not support writing to file-like objects. ",
+                "Please use an alternative backend that does support writing to file-like objects, e.g. FFmpeg.",
+            )
+        else:
+            sox_ext.save_audio_file(
+                uri,
+                src,
+                sample_rate,
+                channels_first,
+                compression,
+                format,
+                encoding,
+                bits_per_sample,
+            )
+
+    @staticmethod
+    def can_decode(uri: Union[BinaryIO, str, os.PathLike], format: Optional[str]) -> bool:
+        # i.e. not a file-like object.
+        return not hasattr(uri, "read")
+
+    @staticmethod
+    def can_encode(uri: Union[BinaryIO, str, os.PathLike], format: Optional[str]) -> bool:
+        # i.e. not a file-like object.
+        return not hasattr(uri, "write")

micromamba_root/envs/pytorch_env/Lib/site-packages/torchaudio/_backend/utils.py

@@ -0,0 +1,317 @@
+import os
+from functools import lru_cache
+from typing import BinaryIO, Dict, Optional, Tuple, Type, Union
+
+import torch
+
+from torchaudio._extension import lazy_import_sox_ext
+from torchaudio.io import CodecConfig
+from torio._extension import lazy_import_ffmpeg_ext
+
+from . import soundfile_backend
+
+from .backend import Backend
+from .common import AudioMetaData
+from .ffmpeg import FFmpegBackend
+from .soundfile import SoundfileBackend
+from .sox import SoXBackend
+
+
+@lru_cache(None)
+def get_available_backends() -> Dict[str, Type[Backend]]:
+    backend_specs: Dict[str, Type[Backend]] = {}
+    if lazy_import_ffmpeg_ext().is_available():
+        backend_specs["ffmpeg"] = FFmpegBackend
+    if lazy_import_sox_ext().is_available():
+        backend_specs["sox"] = SoXBackend
+    if soundfile_backend._IS_SOUNDFILE_AVAILABLE:
+        backend_specs["soundfile"] = SoundfileBackend
+    return backend_specs
+
+
+def get_backend(backend_name, backends) -> Backend:
+    if backend := backends.get(backend_name):
+        return backend
+    else:
+        raise ValueError(
+            f"Unsupported backend '{backend_name}' specified; ",
+            f"please select one of {list(backends.keys())} instead.",
+        )
+
+
+def get_info_func():
+    backends = get_available_backends()
+
+    def dispatcher(
+        uri: Union[BinaryIO, str, os.PathLike], format: Optional[str], backend_name: Optional[str]
+    ) -> Backend:
+        if backend_name is not None:
+            return get_backend(backend_name, backends)
+
+        for backend in backends.values():
+            if backend.can_decode(uri, format):
+                return backend
+        raise RuntimeError(f"Couldn't find appropriate backend to handle uri {uri} and format {format}.")
+
+    def info(
+        uri: Union[BinaryIO, str, os.PathLike],
+        format: Optional[str] = None,
+        buffer_size: int = 4096,
+        backend: Optional[str] = None,
+    ) -> AudioMetaData:
+        """Get signal information of an audio file.
+
+        Note:
+            When the input type is file-like object, this function cannot
+            get the correct length (``num_samples``) for certain formats,
+            such as ``vorbis``.
+            In this case, the value of ``num_samples`` is ``0``.
+
+        Args:
+            uri (path-like object or file-like object):
+                Source of audio data. The following types are accepted:
+
+                * ``path-like``: File path or URL.
+                * ``file-like``: Object with ``read(size: int) -> bytes`` method,
+                  which returns byte string of at most ``size`` length.
+
+            format (str or None, optional):
+                If not ``None``, interpreted as hint that may allow backend to override the detected format.
+                (Default: ``None``)
+
+            buffer_size (int, optional):
+                Size of buffer to use when processing file-like objects, in bytes. (Default: ``4096``)
+
+            backend (str or None, optional):
+                I/O backend to use.
+                If ``None``, function selects backend given input and available backends.
+                Otherwise, must be one of [``"ffmpeg"``, ``"sox"``, ``"soundfile"``],
+                with the corresponding backend available.
+                (Default: ``None``)
+
+                .. seealso::
+                   :ref:`backend`
+
+        Returns:
+            AudioMetaData
+        """
+        backend = dispatcher(uri, format, backend)
+        return backend.info(uri, format, buffer_size)
+
+    return info
+
+
+def get_load_func():
+    backends = get_available_backends()
+
+    def dispatcher(
+        uri: Union[BinaryIO, str, os.PathLike], format: Optional[str], backend_name: Optional[str]
+    ) -> Backend:
+        if backend_name is not None:
+            return get_backend(backend_name, backends)
+
+        for backend in backends.values():
+            if backend.can_decode(uri, format):
+                return backend
+        raise RuntimeError(f"Couldn't find appropriate backend to handle uri {uri} and format {format}.")
+
+    def load(
+        uri: Union[BinaryIO, str, os.PathLike],
+        frame_offset: int = 0,
+        num_frames: int = -1,
+        normalize: bool = True,
+        channels_first: bool = True,
+        format: Optional[str] = None,
+        buffer_size: int = 4096,
+        backend: Optional[str] = None,
+    ) -> Tuple[torch.Tensor, int]:
+        """Load audio data from source.
+
+        By default (``normalize=True``, ``channels_first=True``), this function returns Tensor with
+        ``float32`` dtype, and the shape of `[channel, time]`.
+
+        Note:
+            The formats this function can handle depend on the availability of backends.
+            Please use the following functions to fetch the supported formats.
+
+            - FFmpeg: :py:func:`torchaudio.utils.ffmpeg_utils.get_audio_decoders`
+            - Sox: :py:func:`torchaudio.utils.sox_utils.list_read_formats`
+            - SoundFile: Refer to `the official document <https://pysoundfile.readthedocs.io/>`__.
+
+        .. warning::
+
+            ``normalize`` argument does not perform volume normalization.
+            It only converts the sample type to `torch.float32` from the native sample
+            type.
+
+            When the input format is WAV with integer type, such as 32-bit signed integer, 16-bit
+            signed integer, 24-bit signed integer, and 8-bit unsigned integer, by providing ``normalize=False``,
+            this function can return integer Tensor, where the samples are expressed within the whole range
+            of the corresponding dtype, that is, ``int32`` tensor for 32-bit signed PCM,
+            ``int16`` for 16-bit signed PCM and ``uint8`` for 8-bit unsigned PCM. Since torch does not
+            support ``int24`` dtype, 24-bit signed PCM are converted to ``int32`` tensors.
+
+            ``normalize`` argument has no effect on 32-bit floating-point WAV and other formats, such as
+            ``flac`` and ``mp3``.
+
+            For these formats, this function always returns ``float32`` Tensor with values.
+
+
+        Args:
+            uri (path-like object or file-like object):
+                Source of audio data.
+            frame_offset (int, optional):
+                Number of frames to skip before start reading data.
+            num_frames (int, optional):
+                Maximum number of frames to read. ``-1`` reads all the remaining samples,
+                starting from ``frame_offset``.
+                This function may return the less number of frames if there is not enough
+                frames in the given file.
+            normalize (bool, optional):
+                When ``True``, this function converts the native sample type to ``float32``.
+                Default: ``True``.
+
+                If input file is integer WAV, giving ``False`` will change the resulting Tensor type to
+                integer type.
+                This argument has no effect for formats other than integer WAV type.
+
+            channels_first (bool, optional):
+                When True, the returned Tensor has dimension `[channel, time]`.
+                Otherwise, the returned Tensor's dimension is `[time, channel]`.
+
+            format (str or None, optional):
+                If not ``None``, interpreted as hint that may allow backend to override the detected format.
+                (Default: ``None``)
+
+            buffer_size (int, optional):
+                Size of buffer to use when processing file-like objects, in bytes. (Default: ``4096``)
+
+            backend (str or None, optional):
+                I/O backend to use.
+                If ``None``, function selects backend given input and available backends.
+                Otherwise, must be one of [``"ffmpeg"``, ``"sox"``, ``"soundfile"``],
+                with the corresponding backend being available. (Default: ``None``)
+
+                .. seealso::
+                   :ref:`backend`
+
+        Returns:
+            (torch.Tensor, int): Resulting Tensor and sample rate.
+                If the input file has integer wav format and normalization is off, then it has
+                integer type, else ``float32`` type. If ``channels_first=True``, it has
+                `[channel, time]` else `[time, channel]`.
+        """
+        backend = dispatcher(uri, format, backend)
+        return backend.load(uri, frame_offset, num_frames, normalize, channels_first, format, buffer_size)
+
+    return load
+
+
+def get_save_func():
+    backends = get_available_backends()
+
+    def dispatcher(
+        uri: Union[BinaryIO, str, os.PathLike], format: Optional[str], backend_name: Optional[str]
+    ) -> Backend:
+        if backend_name is not None:
+            return get_backend(backend_name, backends)
+
+        for backend in backends.values():
+            if backend.can_encode(uri, format):
+                return backend
+        raise RuntimeError(f"Couldn't find appropriate backend to handle uri {uri} and format {format}.")
+
+    def save(
+        uri: Union[BinaryIO, str, os.PathLike],
+        src: torch.Tensor,
+        sample_rate: int,
+        channels_first: bool = True,
+        format: Optional[str] = None,
+        encoding: Optional[str] = None,
+        bits_per_sample: Optional[int] = None,
+        buffer_size: int = 4096,
+        backend: Optional[str] = None,
+        compression: Optional[Union[CodecConfig, float, int]] = None,
+    ):
+        """Save audio data to file.
+
+        Note:
+            The formats this function can handle depend on the availability of backends.
+            Please use the following functions to fetch the supported formats.
+
+            - FFmpeg: :py:func:`torchaudio.utils.ffmpeg_utils.get_audio_encoders`
+            - Sox: :py:func:`torchaudio.utils.sox_utils.list_write_formats`
+            - SoundFile: Refer to `the official document <https://pysoundfile.readthedocs.io/>`__.
+
+        Args:
+            uri (str or pathlib.Path): Path to audio file.
+            src (torch.Tensor): Audio data to save. must be 2D tensor.
+            sample_rate (int): sampling rate
+            channels_first (bool, optional): If ``True``, the given tensor is interpreted as `[channel, time]`,
+                otherwise `[time, channel]`.
+            format (str or None, optional): Override the audio format.
+                When ``uri`` argument is path-like object, audio format is
+                inferred from file extension. If the file extension is missing or
+                different, you can specify the correct format with this argument.
+
+                When ``uri`` argument is file-like object,
+                this argument is required.
+
+                Valid values are ``"wav"``, ``"ogg"``, and ``"flac"``.
+            encoding (str or None, optional): Changes the encoding for supported formats.
+                This argument is effective only for supported formats, i.e.
+                ``"wav"`` and ``""flac"```. Valid values are
+
+                - ``"PCM_S"`` (signed integer Linear PCM)
+                - ``"PCM_U"`` (unsigned integer Linear PCM)
+                - ``"PCM_F"`` (floating point PCM)
+                - ``"ULAW"`` (mu-law)
+                - ``"ALAW"`` (a-law)
+
+            bits_per_sample (int or None, optional): Changes the bit depth for the
+                supported formats.
+                When ``format`` is one of ``"wav"`` and ``"flac"``,
+                you can change the bit depth.
+                Valid values are ``8``, ``16``, ``24``, ``32`` and ``64``.
+
+            buffer_size (int, optional):
+                Size of buffer to use when processing file-like objects, in bytes. (Default: ``4096``)
+
+            backend (str or None, optional):
+                I/O backend to use.
+                If ``None``, function selects backend given input and available backends.
+                Otherwise, must be one of [``"ffmpeg"``, ``"sox"``, ``"soundfile"``],
+                with the corresponding backend being available.
+                (Default: ``None``)
+
+                .. seealso::
+                   :ref:`backend`
+
+            compression (CodecConfig, float, int, or None, optional):
+                Compression configuration to apply.
+
+                If the selected backend is FFmpeg, an instance of :py:class:`CodecConfig` must be provided.
+
+                Otherwise, if the selected backend is SoX, a float or int value corresponding to option ``-C`` of the
+                ``sox`` command line interface must be provided. For instance:
+
+                ``"mp3"``
+                    Either bitrate (in ``kbps``) with quality factor, such as ``128.2``, or
+                    VBR encoding with quality factor such as ``-4.2``. Default: ``-4.5``.
+
+                ``"flac"``
+                    Whole number from ``0`` to ``8``. ``8`` is default and highest compression.
+
+                ``"ogg"``, ``"vorbis"``
+                    Number from ``-1`` to ``10``; ``-1`` is the highest compression
+                    and lowest quality. Default: ``3``.
+
+                Refer to http://sox.sourceforge.net/soxformat.html for more details.
+
+        """
+        backend = dispatcher(uri, format, backend)
+        return backend.save(
+            uri, src, sample_rate, channels_first, format, encoding, bits_per_sample, buffer_size, compression
+        )
+
+    return save

micromamba_root/envs/pytorch_env/Lib/site-packages/torchaudio/_extension/__init__.py

@@ -0,0 +1,74 @@
+import logging
+import os
+import sys
+
+from torchaudio._internal.module_utils import fail_with_message, is_module_available, no_op
+
+from .utils import _check_cuda_version, _init_dll_path, _init_sox, _LazyImporter, _load_lib
+
+_LG = logging.getLogger(__name__)
+
+
+# Note:
+# `_check_cuda_version` is not meant to be used by regular users.
+# Builder uses it for debugging purpose, so we export it.
+# https://github.com/pytorch/builder/blob/e2e4542b8eb0bdf491214451a1a4128bd606cce2/test/smoke_test/smoke_test.py#L80
+__all__ = [
+    "_check_cuda_version",
+    "_IS_TORCHAUDIO_EXT_AVAILABLE",
+    "_IS_RIR_AVAILABLE",
+    "lazy_import_sox_ext",
+]
+
+
+if os.name == "nt" and (3, 8) <= sys.version_info < (3, 9):
+    _init_dll_path()
+
+
+# When the extension module is built, we initialize it.
+# In case of an error, we do not catch the failure as it suggests there is something
+# wrong with the installation.
+_IS_TORCHAUDIO_EXT_AVAILABLE = is_module_available("torchaudio.lib._torchaudio")
+# RIR features are implemented in _torchaudio extension, but they can be individually
+# turned on/off at build time. Available means that _torchaudio is loaded properly, and
+# RIR features are found there.
+_IS_RIR_AVAILABLE = False
+_IS_ALIGN_AVAILABLE = False
+if _IS_TORCHAUDIO_EXT_AVAILABLE:
+    _load_lib("libtorchaudio")
+
+    import torchaudio.lib._torchaudio  # noqa
+
+    _check_cuda_version()
+    _IS_RIR_AVAILABLE = torchaudio.lib._torchaudio.is_rir_available()
+    _IS_ALIGN_AVAILABLE = torchaudio.lib._torchaudio.is_align_available()
+
+
+_SOX_EXT = None
+
+
+def lazy_import_sox_ext():
+    """Load SoX integration based on availability in lazy manner"""
+
+    global _SOX_EXT
+    if _SOX_EXT is None:
+        _SOX_EXT = _LazyImporter("_torchaudio_sox", _init_sox)
+    return _SOX_EXT
+
+
+fail_if_no_rir = (
+    no_op
+    if _IS_RIR_AVAILABLE
+    else fail_with_message(
+        "requires RIR extension, but TorchAudio is not compiled with it. Please build TorchAudio with RIR support."
+    )
+)
+
+fail_if_no_align = (
+    no_op
+    if _IS_ALIGN_AVAILABLE
+    else fail_with_message(
+        "Requires alignment extension, but TorchAudio is not compiled with it. \
+        Please build TorchAudio with alignment support."
+    )
+)

micromamba_root/envs/pytorch_env/Lib/site-packages/torchaudio/_extension/utils.py

@@ -0,0 +1,180 @@
+"""Module to implement logics used for initializing extensions.
+
+The implementations here should be stateless.
+They should not depend on external state.
+Anything that depends on external state should happen in __init__.py
+"""
+import importlib
+import logging
+import os
+import types
+from pathlib import Path
+
+import torch
+from torchaudio._internal.module_utils import eval_env
+
+_LG = logging.getLogger(__name__)
+_LIB_DIR = Path(__file__).parent.parent / "lib"
+
+
+def _get_lib_path(lib: str):
+    suffix = "pyd" if os.name == "nt" else "so"
+    path = _LIB_DIR / f"{lib}.{suffix}"
+    return path
+
+
+def _load_lib(lib: str) -> bool:
+    """Load extension module
+
+    Note:
+        In case `torchaudio` is deployed with `pex` format, the library file
+        is not in a standard location.
+        In this case, we expect that `libtorchaudio` is available somewhere
+        in the search path of dynamic loading mechanism, so that importing
+        `_torchaudio` will have library loader find and load `libtorchaudio`.
+        This is the reason why the function should not raising an error when the library
+        file is not found.
+
+    Returns:
+        bool:
+            True if the library file is found AND the library loaded without failure.
+            False if the library file is not found (like in the case where torchaudio
+            is deployed with pex format, thus the shared library file is
+            in a non-standard location.).
+            If the library file is found but there is an issue loading the library,
+            (such as missing dependency) then this function raises the exception as-is.
+
+    Raises:
+        Exception:
+            If the library file is found, but there is an issue loading the library file,
+            (when underlying `ctype.DLL` throws an exception), this function will pass
+            the exception as-is, instead of catching it and returning bool.
+            The expected case is `OSError` thrown by `ctype.DLL` when a dynamic dependency
+            is not found.
+            This behavior was chosen because the expected failure case is not recoverable.
+            If a dependency is missing, then users have to install it.
+    """
+    path = _get_lib_path(lib)
+    if not path.exists():
+        return False
+    torch.ops.load_library(path)
+    return True
+
+
+def _import_sox_ext():
+    if os.name == "nt":
+        raise RuntimeError("sox extension is not supported on Windows")
+    if not eval_env("TORCHAUDIO_USE_SOX", True):
+        raise RuntimeError("sox extension is disabled. (TORCHAUDIO_USE_SOX=0)")
+
+    ext = "torchaudio.lib._torchaudio_sox"
+
+    if not importlib.util.find_spec(ext):
+        raise RuntimeError(
+            # fmt: off
+            "TorchAudio is not built with sox extension. "
+            "Please build TorchAudio with libsox support. (BUILD_SOX=1)"
+            # fmt: on
+        )
+
+    _load_lib("libtorchaudio_sox")
+    return importlib.import_module(ext)
+
+
+def _init_sox():
+    ext = _import_sox_ext()
+    ext.set_verbosity(0)
+
+    import atexit
+
+    torch.ops.torchaudio_sox.initialize_sox_effects()
+    atexit.register(torch.ops.torchaudio_sox.shutdown_sox_effects)
+
+    # Bundle functions registered with TORCH_LIBRARY into extension
+    # so that they can also be accessed in the same (lazy) manner
+    # from the extension.
+    keys = [
+        "get_info",
+        "load_audio_file",
+        "save_audio_file",
+        "apply_effects_tensor",
+        "apply_effects_file",
+    ]
+    for key in keys:
+        setattr(ext, key, getattr(torch.ops.torchaudio_sox, key))
+
+    return ext
+
+
+class _LazyImporter(types.ModuleType):
+    """Lazily import module/extension."""
+
+    def __init__(self, name, import_func):
+        super().__init__(name)
+        self.import_func = import_func
+        self.module = None
+
+    # Note:
+    # Python caches what was retrieved with `__getattr__`, so this method will not be
+    # called again for the same item.
+    def __getattr__(self, item):
+        self._import_once()
+        return getattr(self.module, item)
+
+    def __repr__(self):
+        if self.module is None:
+            return f"<module '{self.__module__}.{self.__class__.__name__}(\"{self.name}\")'>"
+        return repr(self.module)
+
+    def __dir__(self):
+        self._import_once()
+        return dir(self.module)
+
+    def _import_once(self):
+        if self.module is None:
+            self.module = self.import_func()
+            # Note:
+            # By attaching the module attributes to self,
+            # module attributes are directly accessible.
+            # This allows to avoid calling __getattr__ for every attribute access.
+            self.__dict__.update(self.module.__dict__)
+
+    def is_available(self):
+        try:
+            self._import_once()
+        except Exception:
+            return False
+        return True
+
+
+def _init_dll_path():
+    # On Windows Python-3.8+ has `os.add_dll_directory` call,
+    # which is called to configure dll search path.
+    # To find cuda related dlls we need to make sure the
+    # conda environment/bin path is configured Please take a look:
+    # https://stackoverflow.com/questions/59330863/cant-import-dll-module-in-python
+    # Please note: if some path can't be added using add_dll_directory we simply ignore this path
+    for path in os.environ.get("PATH", "").split(";"):
+        if os.path.exists(path):
+            try:
+                os.add_dll_directory(path)
+            except Exception:
+                pass
+
+
+def _check_cuda_version():
+    import torchaudio.lib._torchaudio
+
+    version = torchaudio.lib._torchaudio.cuda_version()
+    if version is not None and torch.version.cuda is not None:
+        version_str = str(version)
+        ta_version = f"{version_str[:-3]}.{version_str[-2]}"
+        t_version = torch.version.cuda.split(".")
+        t_version = f"{t_version[0]}.{t_version[1]}"
+        if ta_version != t_version:
+            raise RuntimeError(
+                "Detected that PyTorch and TorchAudio were compiled with different CUDA versions. "
+                f"PyTorch has CUDA version {t_version} whereas TorchAudio has CUDA version {ta_version}. "
+                "Please install the TorchAudio version that matches your PyTorch version."
+            )
+    return version

micromamba_root/envs/pytorch_env/Lib/site-packages/torchaudio/_internal/__init__.py

@@ -0,0 +1,10 @@
+try:
+    from .fb import download_url_to_file, load_state_dict_from_url
+except ImportError:
+    from torch.hub import download_url_to_file, load_state_dict_from_url
+
+
+__all__ = [
+    "load_state_dict_from_url",
+    "download_url_to_file",
+]

micromamba_root/envs/pytorch_env/Lib/site-packages/torchaudio/_internal/module_utils.py

@@ -0,0 +1,113 @@
+import importlib.util
+import os
+import warnings
+from functools import wraps
+from typing import Optional
+
+
+def eval_env(var, default):
+    """Check if environment varable has True-y value"""
+    if var not in os.environ:
+        return default
+
+    val = os.environ.get(var, "0")
+    trues = ["1", "true", "TRUE", "on", "ON", "yes", "YES"]
+    falses = ["0", "false", "FALSE", "off", "OFF", "no", "NO"]
+    if val in trues:
+        return True
+    if val not in falses:
+        # fmt: off
+        raise RuntimeError(
+            f"Unexpected environment variable value `{var}={val}`. "
+            f"Expected one of {trues + falses}")
+        # fmt: on
+    return False
+
+
+def is_module_available(*modules: str) -> bool:
+    r"""Returns if a top-level module with :attr:`name` exists *without**
+    importing it. This is generally safer than try-catch block around a
+    `import X`. It avoids third party libraries breaking assumptions of some of
+    our tests, e.g., setting multiprocessing start method when imported
+    (see librosa/#747, torchvision/#544).
+    """
+    return all(importlib.util.find_spec(m) is not None for m in modules)
+
+
+def requires_module(*modules: str):
+    """Decorate function to give error message if invoked without required optional modules.
+
+    This decorator is to give better error message to users rather
+    than raising ``NameError:  name 'module' is not defined`` at random places.
+    """
+    missing = [m for m in modules if not is_module_available(m)]
+
+    if not missing:
+        # fall through. If all the modules are available, no need to decorate
+        def decorator(func):
+            return func
+
+    else:
+        req = f"module: {missing[0]}" if len(missing) == 1 else f"modules: {missing}"
+
+        def decorator(func):
+            @wraps(func)
+            def wrapped(*args, **kwargs):
+                raise RuntimeError(f"{func.__module__}.{func.__name__} requires {req}")
+
+            return wrapped
+
+    return decorator
+
+
+def deprecated(direction: str, version: Optional[str] = None, remove: bool = False):
+    """Decorator to add deprecation message
+
+    Args:
+        direction (str): Migration steps to be given to users.
+        version (str or int): The version when the object will be removed
+        remove (bool): If enabled, append future removal message.
+    """
+
+    def decorator(func):
+        @wraps(func)
+        def wrapped(*args, **kwargs):
+            message = f"{func.__module__}.{func.__name__} has been deprecated. {direction}"
+            if remove:
+                message += f' It will be removed from {"future" if version is None else version} release. '
+            warnings.warn(message, stacklevel=2)
+            return func(*args, **kwargs)
+
+        message = "This function has been deprecated. "
+        if remove:
+            message += f'It will be removed from {"future" if version is None else version} release. '
+
+        wrapped.__doc__ = f"""DEPRECATED: {func.__doc__}
+
+    .. warning::
+
+       {message}
+       {direction}
+        """
+
+        return wrapped
+
+    return decorator
+
+
+def fail_with_message(message):
+    """Generate decorator to give users message about missing TorchAudio extension."""
+
+    def decorator(func):
+        @wraps(func)
+        def wrapped(*args, **kwargs):
+            raise RuntimeError(f"{func.__module__}.{func.__name__} {message}")
+
+        return wrapped
+
+    return decorator
+
+
+def no_op(func):
+    """Op-op decorator. Used in place of fail_with_message when a functionality that requires extension works fine."""
+    return func

micromamba_root/envs/pytorch_env/Lib/site-packages/torchaudio/backend/__init__.py

@@ -0,0 +1,8 @@
+# NOTE:
+# The entire `torchaudio.backend` module is deprecated.
+# New things should be added to `torchaudio._backend`.
+# Only things related to backward compatibility should be placed here.
+
+from . import common, no_backend, soundfile_backend, sox_io_backend  # noqa
+
+__all__ = []

micromamba_root/envs/pytorch_env/Lib/site-packages/torchaudio/backend/_no_backend.py

@@ -0,0 +1,25 @@
+from pathlib import Path
+from typing import Callable, Optional, Tuple, Union
+
+from torch import Tensor
+from torchaudio import AudioMetaData
+
+
+def load(
+    filepath: Union[str, Path],
+    out: Optional[Tensor] = None,
+    normalization: Union[bool, float, Callable] = True,
+    channels_first: bool = True,
+    num_frames: int = 0,
+    offset: int = 0,
+    filetype: Optional[str] = None,
+) -> Tuple[Tensor, int]:
+    raise RuntimeError("No audio I/O backend is available.")
+
+
+def save(filepath: str, src: Tensor, sample_rate: int, precision: int = 16, channels_first: bool = True) -> None:
+    raise RuntimeError("No audio I/O backend is available.")
+
+
+def info(filepath: str) -> AudioMetaData:
+    raise RuntimeError("No audio I/O backend is available.")

micromamba_root/envs/pytorch_env/Lib/site-packages/torchaudio/backend/_sox_io_backend.py

@@ -0,0 +1,294 @@
+import os
+from typing import Optional, Tuple
+
+import torch
+import torchaudio
+from torchaudio import AudioMetaData
+
+sox_ext = torchaudio._extension.lazy_import_sox_ext()
+
+
+def info(
+    filepath: str,
+    format: Optional[str] = None,
+) -> AudioMetaData:
+    """Get signal information of an audio file.
+
+    Args:
+        filepath (str):
+            Source of audio data.
+
+        format (str or None, optional):
+            Override the format detection with the given format.
+            Providing the argument might help when libsox can not infer the format
+            from header or extension.
+
+    Returns:
+        AudioMetaData: Metadata of the given audio.
+    """
+    if not torch.jit.is_scripting():
+        if hasattr(filepath, "read"):
+            raise RuntimeError("sox_io backend does not support file-like object.")
+        filepath = os.fspath(filepath)
+    sinfo = sox_ext.get_info(filepath, format)
+    return AudioMetaData(*sinfo)
+
+
+def load(
+    filepath: str,
+    frame_offset: int = 0,
+    num_frames: int = -1,
+    normalize: bool = True,
+    channels_first: bool = True,
+    format: Optional[str] = None,
+) -> Tuple[torch.Tensor, int]:
+    """Load audio data from file.
+
+    Note:
+        This function can handle all the codecs that underlying libsox can handle,
+        however it is tested on the following formats;
+
+        * WAV, AMB
+
+            * 32-bit floating-point
+            * 32-bit signed integer
+            * 24-bit signed integer
+            * 16-bit signed integer
+            * 8-bit unsigned integer (WAV only)
+
+        * MP3
+        * FLAC
+        * OGG/VORBIS
+        * OPUS
+        * SPHERE
+        * AMR-NB
+
+        To load ``MP3``, ``FLAC``, ``OGG/VORBIS``, ``OPUS`` and other codecs ``libsox`` does not
+        handle natively, your installation of ``torchaudio`` has to be linked to ``libsox``
+        and corresponding codec libraries such as ``libmad`` or ``libmp3lame`` etc.
+
+    By default (``normalize=True``, ``channels_first=True``), this function returns Tensor with
+    ``float32`` dtype, and the shape of `[channel, time]`.
+
+    .. warning::
+
+       ``normalize`` argument does not perform volume normalization.
+       It only converts the sample type to `torch.float32` from the native sample
+       type.
+
+       When the input format is WAV with integer type, such as 32-bit signed integer, 16-bit
+       signed integer, 24-bit signed integer, and 8-bit unsigned integer, by providing ``normalize=False``,
+       this function can return integer Tensor, where the samples are expressed within the whole range
+       of the corresponding dtype, that is, ``int32`` tensor for 32-bit signed PCM,
+       ``int16`` for 16-bit signed PCM and ``uint8`` for 8-bit unsigned PCM. Since torch does not
+       support ``int24`` dtype, 24-bit signed PCM are converted to ``int32`` tensors.
+
+       ``normalize`` argument has no effect on 32-bit floating-point WAV and other formats, such as
+       ``flac`` and ``mp3``.
+
+       For these formats, this function always returns ``float32`` Tensor with values.
+
+    Args:
+        filepath (path-like object): Source of audio data.
+        frame_offset (int):
+            Number of frames to skip before start reading data.
+        num_frames (int, optional):
+            Maximum number of frames to read. ``-1`` reads all the remaining samples,
+            starting from ``frame_offset``.
+            This function may return the less number of frames if there is not enough
+            frames in the given file.
+        normalize (bool, optional):
+            When ``True``, this function converts the native sample type to ``float32``.
+            Default: ``True``.
+
+            If input file is integer WAV, giving ``False`` will change the resulting Tensor type to
+            integer type.
+            This argument has no effect for formats other than integer WAV type.
+
+        channels_first (bool, optional):
+            When True, the returned Tensor has dimension `[channel, time]`.
+            Otherwise, the returned Tensor's dimension is `[time, channel]`.
+        format (str or None, optional):
+            Override the format detection with the given format.
+            Providing the argument might help when libsox can not infer the format
+            from header or extension.
+
+    Returns:
+        (torch.Tensor, int): Resulting Tensor and sample rate.
+            If the input file has integer wav format and ``normalize=False``, then it has
+            integer type, else ``float32`` type. If ``channels_first=True``, it has
+            `[channel, time]` else `[time, channel]`.
+    """
+    if not torch.jit.is_scripting():
+        if hasattr(filepath, "read"):
+            raise RuntimeError("sox_io backend does not support file-like object.")
+        filepath = os.fspath(filepath)
+    return sox_ext.load_audio_file(filepath, frame_offset, num_frames, normalize, channels_first, format)
+
+
+def save(
+    filepath: str,
+    src: torch.Tensor,
+    sample_rate: int,
+    channels_first: bool = True,
+    compression: Optional[float] = None,
+    format: Optional[str] = None,
+    encoding: Optional[str] = None,
+    bits_per_sample: Optional[int] = None,
+):
+    """Save audio data to file.
+
+    Args:
+        filepath (path-like object): Path to save file.
+        src (torch.Tensor): Audio data to save. must be 2D tensor.
+        sample_rate (int): sampling rate
+        channels_first (bool, optional): If ``True``, the given tensor is interpreted as `[channel, time]`,
+            otherwise `[time, channel]`.
+        compression (float or None, optional): Used for formats other than WAV.
+            This corresponds to ``-C`` option of ``sox`` command.
+
+            ``"mp3"``
+                Either bitrate (in ``kbps``) with quality factor, such as ``128.2``, or
+                VBR encoding with quality factor such as ``-4.2``. Default: ``-4.5``.
+
+            ``"flac"``
+                Whole number from ``0`` to ``8``. ``8`` is default and highest compression.
+
+            ``"ogg"``, ``"vorbis"``
+                Number from ``-1`` to ``10``; ``-1`` is the highest compression
+                and lowest quality. Default: ``3``.
+
+            See the detail at http://sox.sourceforge.net/soxformat.html.
+        format (str or None, optional): Override the audio format.
+            When ``filepath`` argument is path-like object, audio format is infered from
+            file extension. If file extension is missing or different, you can specify the
+            correct format with this argument.
+
+            When ``filepath`` argument is file-like object, this argument is required.
+
+            Valid values are ``"wav"``, ``"mp3"``, ``"ogg"``, ``"vorbis"``, ``"amr-nb"``,
+            ``"amb"``, ``"flac"``, ``"sph"``, ``"gsm"``, and ``"htk"``.
+
+        encoding (str or None, optional): Changes the encoding for the supported formats.
+            This argument is effective only for supported formats, such as ``"wav"``, ``""amb"``
+            and ``"sph"``. Valid values are;
+
+                - ``"PCM_S"`` (signed integer Linear PCM)
+                - ``"PCM_U"`` (unsigned integer Linear PCM)
+                - ``"PCM_F"`` (floating point PCM)
+                - ``"ULAW"`` (mu-law)
+                - ``"ALAW"`` (a-law)
+
+            Default values
+                If not provided, the default value is picked based on ``format`` and ``bits_per_sample``.
+
+                ``"wav"``, ``"amb"``
+                    - | If both ``encoding`` and ``bits_per_sample`` are not provided, the ``dtype`` of the
+                      | Tensor is used to determine the default value.
+
+                        - ``"PCM_U"`` if dtype is ``uint8``
+                        - ``"PCM_S"`` if dtype is ``int16`` or ``int32``
+                        - ``"PCM_F"`` if dtype is ``float32``
+
+                    - ``"PCM_U"`` if ``bits_per_sample=8``
+                    - ``"PCM_S"`` otherwise
+
+                ``"sph"`` format;
+                    - the default value is ``"PCM_S"``
+
+        bits_per_sample (int or None, optional): Changes the bit depth for the supported formats.
+            When ``format`` is one of ``"wav"``, ``"flac"``, ``"sph"``, or ``"amb"``, you can change the
+            bit depth. Valid values are ``8``, ``16``, ``32`` and ``64``.
+
+            Default Value;
+                If not provided, the default values are picked based on ``format`` and ``"encoding"``;
+
+                ``"wav"``, ``"amb"``;
+                    - | If both ``encoding`` and ``bits_per_sample`` are not provided, the ``dtype`` of the
+                      | Tensor is used.
+
+                        - ``8`` if dtype is ``uint8``
+                        - ``16`` if dtype is ``int16``
+                        - ``32`` if dtype is  ``int32`` or ``float32``
+
+                    - ``8`` if ``encoding`` is ``"PCM_U"``, ``"ULAW"`` or ``"ALAW"``
+                    - ``16`` if ``encoding`` is ``"PCM_S"``
+                    - ``32`` if ``encoding`` is ``"PCM_F"``
+
+                ``"flac"`` format;
+                    - the default value is ``24``
+
+                ``"sph"`` format;
+                    - ``16`` if ``encoding`` is ``"PCM_U"``, ``"PCM_S"``, ``"PCM_F"`` or not provided.
+                    - ``8`` if ``encoding`` is ``"ULAW"`` or ``"ALAW"``
+
+                ``"amb"`` format;
+                    - ``8`` if ``encoding`` is ``"PCM_U"``, ``"ULAW"`` or ``"ALAW"``
+                    - ``16`` if ``encoding`` is ``"PCM_S"`` or not provided.
+                    - ``32`` if ``encoding`` is ``"PCM_F"``
+
+    Supported formats/encodings/bit depth/compression are;
+
+    ``"wav"``, ``"amb"``
+        - 32-bit floating-point PCM
+        - 32-bit signed integer PCM
+        - 24-bit signed integer PCM
+        - 16-bit signed integer PCM
+        - 8-bit unsigned integer PCM
+        - 8-bit mu-law
+        - 8-bit a-law
+
+        Note: Default encoding/bit depth is determined by the dtype of the input Tensor.
+
+    ``"mp3"``
+        Fixed bit rate (such as 128kHz) and variable bit rate compression.
+        Default: VBR with high quality.
+
+    ``"flac"``
+        - 8-bit
+        - 16-bit
+        - 24-bit (default)
+
+    ``"ogg"``, ``"vorbis"``
+        - Different quality level. Default: approx. 112kbps
+
+    ``"sph"``
+        - 8-bit signed integer PCM
+        - 16-bit signed integer PCM
+        - 24-bit signed integer PCM
+        - 32-bit signed integer PCM (default)
+        - 8-bit mu-law
+        - 8-bit a-law
+        - 16-bit a-law
+        - 24-bit a-law
+        - 32-bit a-law
+
+    ``"amr-nb"``
+        Bitrate ranging from 4.75 kbit/s to 12.2 kbit/s. Default: 4.75 kbit/s
+
+    ``"gsm"``
+        Lossy Speech Compression, CPU intensive.
+
+    ``"htk"``
+        Uses a default single-channel 16-bit PCM format.
+
+    Note:
+        To save into formats that ``libsox`` does not handle natively, (such as ``"mp3"``,
+        ``"flac"``, ``"ogg"`` and ``"vorbis"``), your installation of ``torchaudio`` has
+        to be linked to ``libsox`` and corresponding codec libraries such as ``libmad``
+        or ``libmp3lame`` etc.
+    """
+    if not torch.jit.is_scripting():
+        if hasattr(filepath, "write"):
+            raise RuntimeError("sox_io backend does not handle file-like object.")
+        filepath = os.fspath(filepath)
+    sox_ext.save_audio_file(
+        filepath,
+        src,
+        sample_rate,
+        channels_first,
+        compression,
+        format,
+        encoding,
+        bits_per_sample,
+    )

micromamba_root/envs/pytorch_env/Lib/site-packages/torchaudio/backend/common.py

@@ -0,0 +1,13 @@
+def __getattr__(name: str):
+    if name == "AudioMetaData":
+        import warnings
+
+        warnings.warn(
+            "`torchaudio.backend.common.AudioMetaData` has been moved to "
+            "`torchaudio.AudioMetaData`. Please update the import path.",
+            stacklevel=2,
+        )
+        from torchaudio import AudioMetaData
+
+        return AudioMetaData
+    raise AttributeError(f"module {__name__!r} has no attribute {name!r}")

micromamba_root/envs/pytorch_env/Lib/site-packages/torchaudio/backend/no_backend.py

@@ -0,0 +1,14 @@
+def __getattr__(name: str):
+    import warnings
+
+    warnings.warn(
+        "Torchaudio's I/O functions now support par-call bakcend dispatch. "
+        "Importing backend implementation directly is no longer guaranteed to work. "
+        "Please use `backend` keyword with load/save/info function, instead of "
+        "calling the udnerlying implementation directly.",
+        stacklevel=2,
+    )
+
+    from . import _no_backend
+
+    return getattr(_no_backend, name)

micromamba_root/envs/pytorch_env/Lib/site-packages/torchaudio/backend/soundfile_backend.py

@@ -0,0 +1,14 @@
+def __getattr__(name: str):
+    import warnings
+
+    warnings.warn(
+        "Torchaudio's I/O functions now support par-call bakcend dispatch. "
+        "Importing backend implementation directly is no longer guaranteed to work. "
+        "Please use `backend` keyword with load/save/info function, instead of "
+        "calling the udnerlying implementation directly.",
+        stacklevel=2,
+    )
+
+    from torchaudio._backend import soundfile_backend
+
+    return getattr(soundfile_backend, name)

micromamba_root/envs/pytorch_env/Lib/site-packages/torchaudio/backend/sox_io_backend.py

@@ -0,0 +1,14 @@
+def __getattr__(name: str):
+    import warnings
+
+    warnings.warn(
+        "Torchaudio's I/O functions now support par-call bakcend dispatch. "
+        "Importing backend implementation directly is no longer guaranteed to work. "
+        "Please use `backend` keyword with load/save/info function, instead of "
+        "calling the udnerlying implementation directly.",
+        stacklevel=2,
+    )
+
+    from . import _sox_io_backend
+
+    return getattr(_sox_io_backend, name)

micromamba_root/envs/pytorch_env/Lib/site-packages/torchaudio/compliance/__init__.py

@@ -0,0 +1,5 @@
+from . import kaldi
+
+__all__ = [
+    "kaldi",
+]

micromamba_root/envs/pytorch_env/Lib/site-packages/torchaudio/compliance/kaldi.py

@@ -0,0 +1,813 @@
+import math
+from typing import Tuple
+
+import torch
+import torchaudio
+from torch import Tensor
+
+__all__ = [
+    "get_mel_banks",
+    "inverse_mel_scale",
+    "inverse_mel_scale_scalar",
+    "mel_scale",
+    "mel_scale_scalar",
+    "spectrogram",
+    "fbank",
+    "mfcc",
+    "vtln_warp_freq",
+    "vtln_warp_mel_freq",
+]
+
+# numeric_limits<float>::epsilon() 1.1920928955078125e-07
+EPSILON = torch.tensor(torch.finfo(torch.float).eps)
+# 1 milliseconds = 0.001 seconds
+MILLISECONDS_TO_SECONDS = 0.001
+
+# window types
+HAMMING = "hamming"
+HANNING = "hanning"
+POVEY = "povey"
+RECTANGULAR = "rectangular"
+BLACKMAN = "blackman"
+WINDOWS = [HAMMING, HANNING, POVEY, RECTANGULAR, BLACKMAN]
+
+
+def _get_epsilon(device, dtype):
+    return EPSILON.to(device=device, dtype=dtype)
+
+
+def _next_power_of_2(x: int) -> int:
+    r"""Returns the smallest power of 2 that is greater than x"""
+    return 1 if x == 0 else 2 ** (x - 1).bit_length()
+
+
+def _get_strided(waveform: Tensor, window_size: int, window_shift: int, snip_edges: bool) -> Tensor:
+    r"""Given a waveform (1D tensor of size ``num_samples``), it returns a 2D tensor (m, ``window_size``)
+    representing how the window is shifted along the waveform. Each row is a frame.
+
+    Args:
+        waveform (Tensor): Tensor of size ``num_samples``
+        window_size (int): Frame length
+        window_shift (int): Frame shift
+        snip_edges (bool): If True, end effects will be handled by outputting only frames that completely fit
+            in the file, and the number of frames depends on the frame_length.  If False, the number of frames
+            depends only on the frame_shift, and we reflect the data at the ends.
+
+    Returns:
+        Tensor: 2D tensor of size (m, ``window_size``) where each row is a frame
+    """
+    assert waveform.dim() == 1
+    num_samples = waveform.size(0)
+    strides = (window_shift * waveform.stride(0), waveform.stride(0))
+
+    if snip_edges:
+        if num_samples < window_size:
+            return torch.empty((0, 0), dtype=waveform.dtype, device=waveform.device)
+        else:
+            m = 1 + (num_samples - window_size) // window_shift
+    else:
+        reversed_waveform = torch.flip(waveform, [0])
+        m = (num_samples + (window_shift // 2)) // window_shift
+        pad = window_size // 2 - window_shift // 2
+        pad_right = reversed_waveform
+        if pad > 0:
+            # torch.nn.functional.pad returns [2,1,0,1,2] for 'reflect'
+            # but we want [2, 1, 0, 0, 1, 2]
+            pad_left = reversed_waveform[-pad:]
+            waveform = torch.cat((pad_left, waveform, pad_right), dim=0)
+        else:
+            # pad is negative so we want to trim the waveform at the front
+            waveform = torch.cat((waveform[-pad:], pad_right), dim=0)
+
+    sizes = (m, window_size)
+    return waveform.as_strided(sizes, strides)
+
+
+def _feature_window_function(
+    window_type: str,
+    window_size: int,
+    blackman_coeff: float,
+    device: torch.device,
+    dtype: int,
+) -> Tensor:
+    r"""Returns a window function with the given type and size"""
+    if window_type == HANNING:
+        return torch.hann_window(window_size, periodic=False, device=device, dtype=dtype)
+    elif window_type == HAMMING:
+        return torch.hamming_window(window_size, periodic=False, alpha=0.54, beta=0.46, device=device, dtype=dtype)
+    elif window_type == POVEY:
+        # like hanning but goes to zero at edges
+        return torch.hann_window(window_size, periodic=False, device=device, dtype=dtype).pow(0.85)
+    elif window_type == RECTANGULAR:
+        return torch.ones(window_size, device=device, dtype=dtype)
+    elif window_type == BLACKMAN:
+        a = 2 * math.pi / (window_size - 1)
+        window_function = torch.arange(window_size, device=device, dtype=dtype)
+        # can't use torch.blackman_window as they use different coefficients
+        return (
+            blackman_coeff
+            - 0.5 * torch.cos(a * window_function)
+            + (0.5 - blackman_coeff) * torch.cos(2 * a * window_function)
+        ).to(device=device, dtype=dtype)
+    else:
+        raise Exception("Invalid window type " + window_type)
+
+
+def _get_log_energy(strided_input: Tensor, epsilon: Tensor, energy_floor: float) -> Tensor:
+    r"""Returns the log energy of size (m) for a strided_input (m,*)"""
+    device, dtype = strided_input.device, strided_input.dtype
+    log_energy = torch.max(strided_input.pow(2).sum(1), epsilon).log()  # size (m)
+    if energy_floor == 0.0:
+        return log_energy
+    return torch.max(log_energy, torch.tensor(math.log(energy_floor), device=device, dtype=dtype))
+
+
+def _get_waveform_and_window_properties(
+    waveform: Tensor,
+    channel: int,
+    sample_frequency: float,
+    frame_shift: float,
+    frame_length: float,
+    round_to_power_of_two: bool,
+    preemphasis_coefficient: float,
+) -> Tuple[Tensor, int, int, int]:
+    r"""Gets the waveform and window properties"""
+    channel = max(channel, 0)
+    assert channel < waveform.size(0), "Invalid channel {} for size {}".format(channel, waveform.size(0))
+    waveform = waveform[channel, :]  # size (n)
+    window_shift = int(sample_frequency * frame_shift * MILLISECONDS_TO_SECONDS)
+    window_size = int(sample_frequency * frame_length * MILLISECONDS_TO_SECONDS)
+    padded_window_size = _next_power_of_2(window_size) if round_to_power_of_two else window_size
+
+    assert 2 <= window_size <= len(waveform), "choose a window size {} that is [2, {}]".format(
+        window_size, len(waveform)
+    )
+    assert 0 < window_shift, "`window_shift` must be greater than 0"
+    assert padded_window_size % 2 == 0, (
+        "the padded `window_size` must be divisible by two." " use `round_to_power_of_two` or change `frame_length`"
+    )
+    assert 0.0 <= preemphasis_coefficient <= 1.0, "`preemphasis_coefficient` must be between [0,1]"
+    assert sample_frequency > 0, "`sample_frequency` must be greater than zero"
+    return waveform, window_shift, window_size, padded_window_size
+
+
+def _get_window(
+    waveform: Tensor,
+    padded_window_size: int,
+    window_size: int,
+    window_shift: int,
+    window_type: str,
+    blackman_coeff: float,
+    snip_edges: bool,
+    raw_energy: bool,
+    energy_floor: float,
+    dither: float,
+    remove_dc_offset: bool,
+    preemphasis_coefficient: float,
+) -> Tuple[Tensor, Tensor]:
+    r"""Gets a window and its log energy
+
+    Returns:
+        (Tensor, Tensor): strided_input of size (m, ``padded_window_size``) and signal_log_energy of size (m)
+    """
+    device, dtype = waveform.device, waveform.dtype
+    epsilon = _get_epsilon(device, dtype)
+
+    # size (m, window_size)
+    strided_input = _get_strided(waveform, window_size, window_shift, snip_edges)
+
+    if dither != 0.0:
+        rand_gauss = torch.randn(strided_input.shape, device=device, dtype=dtype)
+        strided_input = strided_input + rand_gauss * dither
+
+    if remove_dc_offset:
+        # Subtract each row/frame by its mean
+        row_means = torch.mean(strided_input, dim=1).unsqueeze(1)  # size (m, 1)
+        strided_input = strided_input - row_means
+
+    if raw_energy:
+        # Compute the log energy of each row/frame before applying preemphasis and
+        # window function
+        signal_log_energy = _get_log_energy(strided_input, epsilon, energy_floor)  # size (m)
+
+    if preemphasis_coefficient != 0.0:
+        # strided_input[i,j] -= preemphasis_coefficient * strided_input[i, max(0, j-1)] for all i,j
+        offset_strided_input = torch.nn.functional.pad(strided_input.unsqueeze(0), (1, 0), mode="replicate").squeeze(
+            0
+        )  # size (m, window_size + 1)
+        strided_input = strided_input - preemphasis_coefficient * offset_strided_input[:, :-1]
+
+    # Apply window_function to each row/frame
+    window_function = _feature_window_function(window_type, window_size, blackman_coeff, device, dtype).unsqueeze(
+        0
+    )  # size (1, window_size)
+    strided_input = strided_input * window_function  # size (m, window_size)
+
+    # Pad columns with zero until we reach size (m, padded_window_size)
+    if padded_window_size != window_size:
+        padding_right = padded_window_size - window_size
+        strided_input = torch.nn.functional.pad(
+            strided_input.unsqueeze(0), (0, padding_right), mode="constant", value=0
+        ).squeeze(0)
+
+    # Compute energy after window function (not the raw one)
+    if not raw_energy:
+        signal_log_energy = _get_log_energy(strided_input, epsilon, energy_floor)  # size (m)
+
+    return strided_input, signal_log_energy
+
+
+def _subtract_column_mean(tensor: Tensor, subtract_mean: bool) -> Tensor:
+    # subtracts the column mean of the tensor size (m, n) if subtract_mean=True
+    # it returns size (m, n)
+    if subtract_mean:
+        col_means = torch.mean(tensor, dim=0).unsqueeze(0)
+        tensor = tensor - col_means
+    return tensor
+
+
+def spectrogram(
+    waveform: Tensor,
+    blackman_coeff: float = 0.42,
+    channel: int = -1,
+    dither: float = 0.0,
+    energy_floor: float = 1.0,
+    frame_length: float = 25.0,
+    frame_shift: float = 10.0,
+    min_duration: float = 0.0,
+    preemphasis_coefficient: float = 0.97,
+    raw_energy: bool = True,
+    remove_dc_offset: bool = True,
+    round_to_power_of_two: bool = True,
+    sample_frequency: float = 16000.0,
+    snip_edges: bool = True,
+    subtract_mean: bool = False,
+    window_type: str = POVEY,
+) -> Tensor:
+    r"""Create a spectrogram from a raw audio signal. This matches the input/output of Kaldi's
+    compute-spectrogram-feats.
+
+    Args:
+        waveform (Tensor): Tensor of audio of size (c, n) where c is in the range [0,2)
+        blackman_coeff (float, optional): Constant coefficient for generalized Blackman window. (Default: ``0.42``)
+        channel (int, optional): Channel to extract (-1 -> expect mono, 0 -> left, 1 -> right) (Default: ``-1``)
+        dither (float, optional): Dithering constant (0.0 means no dither). If you turn this off, you should set
+            the energy_floor option, e.g. to 1.0 or 0.1 (Default: ``0.0``)
+        energy_floor (float, optional): Floor on energy (absolute, not relative) in Spectrogram computation.  Caution:
+            this floor is applied to the zeroth component, representing the total signal energy.  The floor on the
+            individual spectrogram elements is fixed at std::numeric_limits<float>::epsilon(). (Default: ``1.0``)
+        frame_length (float, optional): Frame length in milliseconds (Default: ``25.0``)
+        frame_shift (float, optional): Frame shift in milliseconds (Default: ``10.0``)
+        min_duration (float, optional): Minimum duration of segments to process (in seconds). (Default: ``0.0``)
+        preemphasis_coefficient (float, optional): Coefficient for use in signal preemphasis (Default: ``0.97``)
+        raw_energy (bool, optional): If True, compute energy before preemphasis and windowing (Default: ``True``)
+        remove_dc_offset (bool, optional): Subtract mean from waveform on each frame (Default: ``True``)
+        round_to_power_of_two (bool, optional): If True, round window size to power of two by zero-padding input
+            to FFT. (Default: ``True``)
+        sample_frequency (float, optional): Waveform data sample frequency (must match the waveform file, if
+            specified there) (Default: ``16000.0``)
+        snip_edges (bool, optional): If True, end effects will be handled by outputting only frames that completely fit
+            in the file, and the number of frames depends on the frame_length.  If False, the number of frames
+            depends only on the frame_shift, and we reflect the data at the ends. (Default: ``True``)
+        subtract_mean (bool, optional): Subtract mean of each feature file [CMS]; not recommended to do
+            it this way.  (Default: ``False``)
+        window_type (str, optional): Type of window ('hamming'|'hanning'|'povey'|'rectangular'|'blackman')
+         (Default: ``'povey'``)
+
+    Returns:
+        Tensor: A spectrogram identical to what Kaldi would output. The shape is
+        (m, ``padded_window_size // 2 + 1``) where m is calculated in _get_strided
+    """
+    device, dtype = waveform.device, waveform.dtype
+    epsilon = _get_epsilon(device, dtype)
+
+    waveform, window_shift, window_size, padded_window_size = _get_waveform_and_window_properties(
+        waveform, channel, sample_frequency, frame_shift, frame_length, round_to_power_of_two, preemphasis_coefficient
+    )
+
+    if len(waveform) < min_duration * sample_frequency:
+        # signal is too short
+        return torch.empty(0)
+
+    strided_input, signal_log_energy = _get_window(
+        waveform,
+        padded_window_size,
+        window_size,
+        window_shift,
+        window_type,
+        blackman_coeff,
+        snip_edges,
+        raw_energy,
+        energy_floor,
+        dither,
+        remove_dc_offset,
+        preemphasis_coefficient,
+    )
+
+    # size (m, padded_window_size // 2 + 1, 2)
+    fft = torch.fft.rfft(strided_input)
+
+    # Convert the FFT into a power spectrum
+    power_spectrum = torch.max(fft.abs().pow(2.0), epsilon).log()  # size (m, padded_window_size // 2 + 1)
+    power_spectrum[:, 0] = signal_log_energy
+
+    power_spectrum = _subtract_column_mean(power_spectrum, subtract_mean)
+    return power_spectrum
+
+
+def inverse_mel_scale_scalar(mel_freq: float) -> float:
+    return 700.0 * (math.exp(mel_freq / 1127.0) - 1.0)
+
+
+def inverse_mel_scale(mel_freq: Tensor) -> Tensor:
+    return 700.0 * ((mel_freq / 1127.0).exp() - 1.0)
+
+
+def mel_scale_scalar(freq: float) -> float:
+    return 1127.0 * math.log(1.0 + freq / 700.0)
+
+
+def mel_scale(freq: Tensor) -> Tensor:
+    return 1127.0 * (1.0 + freq / 700.0).log()
+
+
+def vtln_warp_freq(
+    vtln_low_cutoff: float,
+    vtln_high_cutoff: float,
+    low_freq: float,
+    high_freq: float,
+    vtln_warp_factor: float,
+    freq: Tensor,
+) -> Tensor:
+    r"""This computes a VTLN warping function that is not the same as HTK's one,
+    but has similar inputs (this function has the advantage of never producing
+    empty bins).
+
+    This function computes a warp function F(freq), defined between low_freq
+    and high_freq inclusive, with the following properties:
+        F(low_freq) == low_freq
+        F(high_freq) == high_freq
+    The function is continuous and piecewise linear with two inflection
+        points.
+    The lower inflection point (measured in terms of the unwarped
+        frequency) is at frequency l, determined as described below.
+    The higher inflection point is at a frequency h, determined as
+        described below.
+    If l <= f <= h, then F(f) = f/vtln_warp_factor.
+    If the higher inflection point (measured in terms of the unwarped
+        frequency) is at h, then max(h, F(h)) == vtln_high_cutoff.
+        Since (by the last point) F(h) == h/vtln_warp_factor, then
+        max(h, h/vtln_warp_factor) == vtln_high_cutoff, so
+        h = vtln_high_cutoff / max(1, 1/vtln_warp_factor).
+          = vtln_high_cutoff * min(1, vtln_warp_factor).
+    If the lower inflection point (measured in terms of the unwarped
+        frequency) is at l, then min(l, F(l)) == vtln_low_cutoff
+        This implies that l = vtln_low_cutoff / min(1, 1/vtln_warp_factor)
+                            = vtln_low_cutoff * max(1, vtln_warp_factor)
+    Args:
+        vtln_low_cutoff (float): Lower frequency cutoffs for VTLN
+        vtln_high_cutoff (float): Upper frequency cutoffs for VTLN
+        low_freq (float): Lower frequency cutoffs in mel computation
+        high_freq (float): Upper frequency cutoffs in mel computation
+        vtln_warp_factor (float): Vtln warp factor
+        freq (Tensor): given frequency in Hz
+
+    Returns:
+        Tensor: Freq after vtln warp
+    """
+    assert vtln_low_cutoff > low_freq, "be sure to set the vtln_low option higher than low_freq"
+    assert vtln_high_cutoff < high_freq, "be sure to set the vtln_high option lower than high_freq [or negative]"
+    l = vtln_low_cutoff * max(1.0, vtln_warp_factor)
+    h = vtln_high_cutoff * min(1.0, vtln_warp_factor)
+    scale = 1.0 / vtln_warp_factor
+    Fl = scale * l  # F(l)
+    Fh = scale * h  # F(h)
+    assert l > low_freq and h < high_freq
+    # slope of left part of the 3-piece linear function
+    scale_left = (Fl - low_freq) / (l - low_freq)
+    # [slope of center part is just "scale"]
+
+    # slope of right part of the 3-piece linear function
+    scale_right = (high_freq - Fh) / (high_freq - h)
+
+    res = torch.empty_like(freq)
+
+    outside_low_high_freq = torch.lt(freq, low_freq) | torch.gt(freq, high_freq)  # freq < low_freq || freq > high_freq
+    before_l = torch.lt(freq, l)  # freq < l
+    before_h = torch.lt(freq, h)  # freq < h
+    after_h = torch.ge(freq, h)  # freq >= h
+
+    # order of operations matter here (since there is overlapping frequency regions)
+    res[after_h] = high_freq + scale_right * (freq[after_h] - high_freq)
+    res[before_h] = scale * freq[before_h]
+    res[before_l] = low_freq + scale_left * (freq[before_l] - low_freq)
+    res[outside_low_high_freq] = freq[outside_low_high_freq]
+
+    return res
+
+
+def vtln_warp_mel_freq(
+    vtln_low_cutoff: float,
+    vtln_high_cutoff: float,
+    low_freq,
+    high_freq: float,
+    vtln_warp_factor: float,
+    mel_freq: Tensor,
+) -> Tensor:
+    r"""
+    Args:
+        vtln_low_cutoff (float): Lower frequency cutoffs for VTLN
+        vtln_high_cutoff (float): Upper frequency cutoffs for VTLN
+        low_freq (float): Lower frequency cutoffs in mel computation
+        high_freq (float): Upper frequency cutoffs in mel computation
+        vtln_warp_factor (float): Vtln warp factor
+        mel_freq (Tensor): Given frequency in Mel
+
+    Returns:
+        Tensor: ``mel_freq`` after vtln warp
+    """
+    return mel_scale(
+        vtln_warp_freq(
+            vtln_low_cutoff, vtln_high_cutoff, low_freq, high_freq, vtln_warp_factor, inverse_mel_scale(mel_freq)
+        )
+    )
+
+
+def get_mel_banks(
+    num_bins: int,
+    window_length_padded: int,
+    sample_freq: float,
+    low_freq: float,
+    high_freq: float,
+    vtln_low: float,
+    vtln_high: float,
+    vtln_warp_factor: float,
+) -> Tuple[Tensor, Tensor]:
+    """
+    Returns:
+        (Tensor, Tensor): The tuple consists of ``bins`` (which is
+        melbank of size (``num_bins``, ``num_fft_bins``)) and ``center_freqs`` (which is
+        center frequencies of bins of size (``num_bins``)).
+    """
+    assert num_bins > 3, "Must have at least 3 mel bins"
+    assert window_length_padded % 2 == 0
+    num_fft_bins = window_length_padded / 2
+    nyquist = 0.5 * sample_freq
+
+    if high_freq <= 0.0:
+        high_freq += nyquist
+
+    assert (
+        (0.0 <= low_freq < nyquist) and (0.0 < high_freq <= nyquist) and (low_freq < high_freq)
+    ), "Bad values in options: low-freq {} and high-freq {} vs. nyquist {}".format(low_freq, high_freq, nyquist)
+
+    # fft-bin width [think of it as Nyquist-freq / half-window-length]
+    fft_bin_width = sample_freq / window_length_padded
+    mel_low_freq = mel_scale_scalar(low_freq)
+    mel_high_freq = mel_scale_scalar(high_freq)
+
+    # divide by num_bins+1 in next line because of end-effects where the bins
+    # spread out to the sides.
+    mel_freq_delta = (mel_high_freq - mel_low_freq) / (num_bins + 1)
+
+    if vtln_high < 0.0:
+        vtln_high += nyquist
+
+    assert vtln_warp_factor == 1.0 or (
+        (low_freq < vtln_low < high_freq) and (0.0 < vtln_high < high_freq) and (vtln_low < vtln_high)
+    ), "Bad values in options: vtln-low {} and vtln-high {}, versus " "low-freq {} and high-freq {}".format(
+        vtln_low, vtln_high, low_freq, high_freq
+    )
+
+    bin = torch.arange(num_bins).unsqueeze(1)
+    left_mel = mel_low_freq + bin * mel_freq_delta  # size(num_bins, 1)
+    center_mel = mel_low_freq + (bin + 1.0) * mel_freq_delta  # size(num_bins, 1)
+    right_mel = mel_low_freq + (bin + 2.0) * mel_freq_delta  # size(num_bins, 1)
+
+    if vtln_warp_factor != 1.0:
+        left_mel = vtln_warp_mel_freq(vtln_low, vtln_high, low_freq, high_freq, vtln_warp_factor, left_mel)
+        center_mel = vtln_warp_mel_freq(vtln_low, vtln_high, low_freq, high_freq, vtln_warp_factor, center_mel)
+        right_mel = vtln_warp_mel_freq(vtln_low, vtln_high, low_freq, high_freq, vtln_warp_factor, right_mel)
+
+    center_freqs = inverse_mel_scale(center_mel)  # size (num_bins)
+    # size(1, num_fft_bins)
+    mel = mel_scale(fft_bin_width * torch.arange(num_fft_bins)).unsqueeze(0)
+
+    # size (num_bins, num_fft_bins)
+    up_slope = (mel - left_mel) / (center_mel - left_mel)
+    down_slope = (right_mel - mel) / (right_mel - center_mel)
+
+    if vtln_warp_factor == 1.0:
+        # left_mel < center_mel < right_mel so we can min the two slopes and clamp negative values
+        bins = torch.max(torch.zeros(1), torch.min(up_slope, down_slope))
+    else:
+        # warping can move the order of left_mel, center_mel, right_mel anywhere
+        bins = torch.zeros_like(up_slope)
+        up_idx = torch.gt(mel, left_mel) & torch.le(mel, center_mel)  # left_mel < mel <= center_mel
+        down_idx = torch.gt(mel, center_mel) & torch.lt(mel, right_mel)  # center_mel < mel < right_mel
+        bins[up_idx] = up_slope[up_idx]
+        bins[down_idx] = down_slope[down_idx]
+
+    return bins, center_freqs
+
+
+def fbank(
+    waveform: Tensor,
+    blackman_coeff: float = 0.42,
+    channel: int = -1,
+    dither: float = 0.0,
+    energy_floor: float = 1.0,
+    frame_length: float = 25.0,
+    frame_shift: float = 10.0,
+    high_freq: float = 0.0,
+    htk_compat: bool = False,
+    low_freq: float = 20.0,
+    min_duration: float = 0.0,
+    num_mel_bins: int = 23,
+    preemphasis_coefficient: float = 0.97,
+    raw_energy: bool = True,
+    remove_dc_offset: bool = True,
+    round_to_power_of_two: bool = True,
+    sample_frequency: float = 16000.0,
+    snip_edges: bool = True,
+    subtract_mean: bool = False,
+    use_energy: bool = False,
+    use_log_fbank: bool = True,
+    use_power: bool = True,
+    vtln_high: float = -500.0,
+    vtln_low: float = 100.0,
+    vtln_warp: float = 1.0,
+    window_type: str = POVEY,
+) -> Tensor:
+    r"""Create a fbank from a raw audio signal. This matches the input/output of Kaldi's
+    compute-fbank-feats.
+
+    Args:
+        waveform (Tensor): Tensor of audio of size (c, n) where c is in the range [0,2)
+        blackman_coeff (float, optional): Constant coefficient for generalized Blackman window. (Default: ``0.42``)
+        channel (int, optional): Channel to extract (-1 -> expect mono, 0 -> left, 1 -> right) (Default: ``-1``)
+        dither (float, optional): Dithering constant (0.0 means no dither). If you turn this off, you should set
+            the energy_floor option, e.g. to 1.0 or 0.1 (Default: ``0.0``)
+        energy_floor (float, optional): Floor on energy (absolute, not relative) in Spectrogram computation.  Caution:
+            this floor is applied to the zeroth component, representing the total signal energy.  The floor on the
+            individual spectrogram elements is fixed at std::numeric_limits<float>::epsilon(). (Default: ``1.0``)
+        frame_length (float, optional): Frame length in milliseconds (Default: ``25.0``)
+        frame_shift (float, optional): Frame shift in milliseconds (Default: ``10.0``)
+        high_freq (float, optional): High cutoff frequency for mel bins (if <= 0, offset from Nyquist)
+         (Default: ``0.0``)
+        htk_compat (bool, optional): If true, put energy last.  Warning: not sufficient to get HTK compatible features
+         (need to change other parameters). (Default: ``False``)
+        low_freq (float, optional): Low cutoff frequency for mel bins (Default: ``20.0``)
+        min_duration (float, optional): Minimum duration of segments to process (in seconds). (Default: ``0.0``)
+        num_mel_bins (int, optional): Number of triangular mel-frequency bins (Default: ``23``)
+        preemphasis_coefficient (float, optional): Coefficient for use in signal preemphasis (Default: ``0.97``)
+        raw_energy (bool, optional): If True, compute energy before preemphasis and windowing (Default: ``True``)
+        remove_dc_offset (bool, optional): Subtract mean from waveform on each frame (Default: ``True``)
+        round_to_power_of_two (bool, optional): If True, round window size to power of two by zero-padding input
+            to FFT. (Default: ``True``)
+        sample_frequency (float, optional): Waveform data sample frequency (must match the waveform file, if
+            specified there) (Default: ``16000.0``)
+        snip_edges (bool, optional): If True, end effects will be handled by outputting only frames that completely fit
+            in the file, and the number of frames depends on the frame_length.  If False, the number of frames
+            depends only on the frame_shift, and we reflect the data at the ends. (Default: ``True``)
+        subtract_mean (bool, optional): Subtract mean of each feature file [CMS]; not recommended to do
+            it this way.  (Default: ``False``)
+        use_energy (bool, optional): Add an extra dimension with energy to the FBANK output. (Default: ``False``)
+        use_log_fbank (bool, optional):If true, produce log-filterbank, else produce linear. (Default: ``True``)
+        use_power (bool, optional): If true, use power, else use magnitude. (Default: ``True``)
+        vtln_high (float, optional): High inflection point in piecewise linear VTLN warping function (if
+            negative, offset from high-mel-freq (Default: ``-500.0``)
+        vtln_low (float, optional): Low inflection point in piecewise linear VTLN warping function (Default: ``100.0``)
+        vtln_warp (float, optional): Vtln warp factor (only applicable if vtln_map not specified) (Default: ``1.0``)
+        window_type (str, optional): Type of window ('hamming'|'hanning'|'povey'|'rectangular'|'blackman')
+         (Default: ``'povey'``)
+
+    Returns:
+        Tensor: A fbank identical to what Kaldi would output. The shape is (m, ``num_mel_bins + use_energy``)
+        where m is calculated in _get_strided
+    """
+    device, dtype = waveform.device, waveform.dtype
+
+    waveform, window_shift, window_size, padded_window_size = _get_waveform_and_window_properties(
+        waveform, channel, sample_frequency, frame_shift, frame_length, round_to_power_of_two, preemphasis_coefficient
+    )
+
+    if len(waveform) < min_duration * sample_frequency:
+        # signal is too short
+        return torch.empty(0, device=device, dtype=dtype)
+
+    # strided_input, size (m, padded_window_size) and signal_log_energy, size (m)
+    strided_input, signal_log_energy = _get_window(
+        waveform,
+        padded_window_size,
+        window_size,
+        window_shift,
+        window_type,
+        blackman_coeff,
+        snip_edges,
+        raw_energy,
+        energy_floor,
+        dither,
+        remove_dc_offset,
+        preemphasis_coefficient,
+    )
+
+    # size (m, padded_window_size // 2 + 1)
+    spectrum = torch.fft.rfft(strided_input).abs()
+    if use_power:
+        spectrum = spectrum.pow(2.0)
+
+    # size (num_mel_bins, padded_window_size // 2)
+    mel_energies, _ = get_mel_banks(
+        num_mel_bins, padded_window_size, sample_frequency, low_freq, high_freq, vtln_low, vtln_high, vtln_warp
+    )
+    mel_energies = mel_energies.to(device=device, dtype=dtype)
+
+    # pad right column with zeros and add dimension, size (num_mel_bins, padded_window_size // 2 + 1)
+    mel_energies = torch.nn.functional.pad(mel_energies, (0, 1), mode="constant", value=0)
+
+    # sum with mel fiterbanks over the power spectrum, size (m, num_mel_bins)
+    mel_energies = torch.mm(spectrum, mel_energies.T)
+    if use_log_fbank:
+        # avoid log of zero (which should be prevented anyway by dithering)
+        mel_energies = torch.max(mel_energies, _get_epsilon(device, dtype)).log()
+
+    # if use_energy then add it as the last column for htk_compat == true else first column
+    if use_energy:
+        signal_log_energy = signal_log_energy.unsqueeze(1)  # size (m, 1)
+        # returns size (m, num_mel_bins + 1)
+        if htk_compat:
+            mel_energies = torch.cat((mel_energies, signal_log_energy), dim=1)
+        else:
+            mel_energies = torch.cat((signal_log_energy, mel_energies), dim=1)
+
+    mel_energies = _subtract_column_mean(mel_energies, subtract_mean)
+    return mel_energies
+
+
+def _get_dct_matrix(num_ceps: int, num_mel_bins: int) -> Tensor:
+    # returns a dct matrix of size (num_mel_bins, num_ceps)
+    # size (num_mel_bins, num_mel_bins)
+    dct_matrix = torchaudio.functional.create_dct(num_mel_bins, num_mel_bins, "ortho")
+    # kaldi expects the first cepstral to be weighted sum of factor sqrt(1/num_mel_bins)
+    # this would be the first column in the dct_matrix for torchaudio as it expects a
+    # right multiply (which would be the first column of the kaldi's dct_matrix as kaldi
+    # expects a left multiply e.g. dct_matrix * vector).
+    dct_matrix[:, 0] = math.sqrt(1 / float(num_mel_bins))
+    dct_matrix = dct_matrix[:, :num_ceps]
+    return dct_matrix
+
+
+def _get_lifter_coeffs(num_ceps: int, cepstral_lifter: float) -> Tensor:
+    # returns size (num_ceps)
+    # Compute liftering coefficients (scaling on cepstral coeffs)
+    # coeffs are numbered slightly differently from HTK: the zeroth index is C0, which is not affected.
+    i = torch.arange(num_ceps)
+    return 1.0 + 0.5 * cepstral_lifter * torch.sin(math.pi * i / cepstral_lifter)
+
+
+def mfcc(
+    waveform: Tensor,
+    blackman_coeff: float = 0.42,
+    cepstral_lifter: float = 22.0,
+    channel: int = -1,
+    dither: float = 0.0,
+    energy_floor: float = 1.0,
+    frame_length: float = 25.0,
+    frame_shift: float = 10.0,
+    high_freq: float = 0.0,
+    htk_compat: bool = False,
+    low_freq: float = 20.0,
+    num_ceps: int = 13,
+    min_duration: float = 0.0,
+    num_mel_bins: int = 23,
+    preemphasis_coefficient: float = 0.97,
+    raw_energy: bool = True,
+    remove_dc_offset: bool = True,
+    round_to_power_of_two: bool = True,
+    sample_frequency: float = 16000.0,
+    snip_edges: bool = True,
+    subtract_mean: bool = False,
+    use_energy: bool = False,
+    vtln_high: float = -500.0,
+    vtln_low: float = 100.0,
+    vtln_warp: float = 1.0,
+    window_type: str = POVEY,
+) -> Tensor:
+    r"""Create a mfcc from a raw audio signal. This matches the input/output of Kaldi's
+    compute-mfcc-feats.
+
+    Args:
+        waveform (Tensor): Tensor of audio of size (c, n) where c is in the range [0,2)
+        blackman_coeff (float, optional): Constant coefficient for generalized Blackman window. (Default: ``0.42``)
+        cepstral_lifter (float, optional): Constant that controls scaling of MFCCs (Default: ``22.0``)
+        channel (int, optional): Channel to extract (-1 -> expect mono, 0 -> left, 1 -> right) (Default: ``-1``)
+        dither (float, optional): Dithering constant (0.0 means no dither). If you turn this off, you should set
+            the energy_floor option, e.g. to 1.0 or 0.1 (Default: ``0.0``)
+        energy_floor (float, optional): Floor on energy (absolute, not relative) in Spectrogram computation.  Caution:
+            this floor is applied to the zeroth component, representing the total signal energy.  The floor on the
+            individual spectrogram elements is fixed at std::numeric_limits<float>::epsilon(). (Default: ``1.0``)
+        frame_length (float, optional): Frame length in milliseconds (Default: ``25.0``)
+        frame_shift (float, optional): Frame shift in milliseconds (Default: ``10.0``)
+        high_freq (float, optional): High cutoff frequency for mel bins (if <= 0, offset from Nyquist)
+         (Default: ``0.0``)
+        htk_compat (bool, optional): If true, put energy last.  Warning: not sufficient to get HTK compatible
+         features (need to change other parameters). (Default: ``False``)
+        low_freq (float, optional): Low cutoff frequency for mel bins (Default: ``20.0``)
+        num_ceps (int, optional): Number of cepstra in MFCC computation (including C0) (Default: ``13``)
+        min_duration (float, optional): Minimum duration of segments to process (in seconds). (Default: ``0.0``)
+        num_mel_bins (int, optional): Number of triangular mel-frequency bins (Default: ``23``)
+        preemphasis_coefficient (float, optional): Coefficient for use in signal preemphasis (Default: ``0.97``)
+        raw_energy (bool, optional): If True, compute energy before preemphasis and windowing (Default: ``True``)
+        remove_dc_offset (bool, optional): Subtract mean from waveform on each frame (Default: ``True``)
+        round_to_power_of_two (bool, optional): If True, round window size to power of two by zero-padding input
+            to FFT. (Default: ``True``)
+        sample_frequency (float, optional): Waveform data sample frequency (must match the waveform file, if
+            specified there) (Default: ``16000.0``)
+        snip_edges (bool, optional): If True, end effects will be handled by outputting only frames that completely fit
+            in the file, and the number of frames depends on the frame_length.  If False, the number of frames
+            depends only on the frame_shift, and we reflect the data at the ends. (Default: ``True``)
+        subtract_mean (bool, optional): Subtract mean of each feature file [CMS]; not recommended to do
+            it this way.  (Default: ``False``)
+        use_energy (bool, optional): Add an extra dimension with energy to the FBANK output. (Default: ``False``)
+        vtln_high (float, optional): High inflection point in piecewise linear VTLN warping function (if
+            negative, offset from high-mel-freq (Default: ``-500.0``)
+        vtln_low (float, optional): Low inflection point in piecewise linear VTLN warping function (Default: ``100.0``)
+        vtln_warp (float, optional): Vtln warp factor (only applicable if vtln_map not specified) (Default: ``1.0``)
+        window_type (str, optional): Type of window ('hamming'|'hanning'|'povey'|'rectangular'|'blackman')
+         (Default: ``"povey"``)
+
+    Returns:
+        Tensor: A mfcc identical to what Kaldi would output. The shape is (m, ``num_ceps``)
+        where m is calculated in _get_strided
+    """
+    assert num_ceps <= num_mel_bins, "num_ceps cannot be larger than num_mel_bins: %d vs %d" % (num_ceps, num_mel_bins)
+
+    device, dtype = waveform.device, waveform.dtype
+
+    # The mel_energies should not be squared (use_power=True), not have mean subtracted
+    # (subtract_mean=False), and use log (use_log_fbank=True).
+    # size (m, num_mel_bins + use_energy)
+    feature = fbank(
+        waveform=waveform,
+        blackman_coeff=blackman_coeff,
+        channel=channel,
+        dither=dither,
+        energy_floor=energy_floor,
+        frame_length=frame_length,
+        frame_shift=frame_shift,
+        high_freq=high_freq,
+        htk_compat=htk_compat,
+        low_freq=low_freq,
+        min_duration=min_duration,
+        num_mel_bins=num_mel_bins,
+        preemphasis_coefficient=preemphasis_coefficient,
+        raw_energy=raw_energy,
+        remove_dc_offset=remove_dc_offset,
+        round_to_power_of_two=round_to_power_of_two,
+        sample_frequency=sample_frequency,
+        snip_edges=snip_edges,
+        subtract_mean=False,
+        use_energy=use_energy,
+        use_log_fbank=True,
+        use_power=True,
+        vtln_high=vtln_high,
+        vtln_low=vtln_low,
+        vtln_warp=vtln_warp,
+        window_type=window_type,
+    )
+
+    if use_energy:
+        # size (m)
+        signal_log_energy = feature[:, num_mel_bins if htk_compat else 0]
+        # offset is 0 if htk_compat==True else 1
+        mel_offset = int(not htk_compat)
+        feature = feature[:, mel_offset : (num_mel_bins + mel_offset)]
+
+    # size (num_mel_bins, num_ceps)
+    dct_matrix = _get_dct_matrix(num_ceps, num_mel_bins).to(dtype=dtype, device=device)
+
+    # size (m, num_ceps)
+    feature = feature.matmul(dct_matrix)
+
+    if cepstral_lifter != 0.0:
+        # size (1, num_ceps)
+        lifter_coeffs = _get_lifter_coeffs(num_ceps, cepstral_lifter).unsqueeze(0)
+        feature *= lifter_coeffs.to(device=device, dtype=dtype)
+
+    # if use_energy then replace the last column for htk_compat == true else first column
+    if use_energy:
+        feature[:, 0] = signal_log_energy
+
+    if htk_compat:
+        energy = feature[:, 0].unsqueeze(1)  # size (m, 1)
+        feature = feature[:, 1:]  # size (m, num_ceps - 1)
+        if not use_energy:
+            # scale on C0 (actually removing a scale we previously added that's
+            # part of one common definition of the cosine transform.)
+            energy *= math.sqrt(2)
+
+        feature = torch.cat((feature, energy), dim=1)
+
+    feature = _subtract_column_mean(feature, subtract_mean)
+    return feature

micromamba_root/envs/pytorch_env/Lib/site-packages/torchaudio/datasets/__init__.py

@@ -0,0 +1,47 @@
+from .cmuarctic import CMUARCTIC
+from .cmudict import CMUDict
+from .commonvoice import COMMONVOICE
+from .dr_vctk import DR_VCTK
+from .fluentcommands import FluentSpeechCommands
+from .gtzan import GTZAN
+from .iemocap import IEMOCAP
+from .librilight_limited import LibriLightLimited
+from .librimix import LibriMix
+from .librispeech import LIBRISPEECH
+from .librispeech_biasing import LibriSpeechBiasing
+from .libritts import LIBRITTS
+from .ljspeech import LJSPEECH
+from .musdb_hq import MUSDB_HQ
+from .quesst14 import QUESST14
+from .snips import Snips
+from .speechcommands import SPEECHCOMMANDS
+from .tedlium import TEDLIUM
+from .vctk import VCTK_092
+from .voxceleb1 import VoxCeleb1Identification, VoxCeleb1Verification
+from .yesno import YESNO
+
+
+__all__ = [
+    "COMMONVOICE",
+    "LIBRISPEECH",
+    "LibriSpeechBiasing",
+    "LibriLightLimited",
+    "SPEECHCOMMANDS",
+    "VCTK_092",
+    "DR_VCTK",
+    "YESNO",
+    "LJSPEECH",
+    "GTZAN",
+    "CMUARCTIC",
+    "CMUDict",
+    "LibriMix",
+    "LIBRITTS",
+    "TEDLIUM",
+    "QUESST14",
+    "MUSDB_HQ",
+    "FluentSpeechCommands",
+    "VoxCeleb1Identification",
+    "VoxCeleb1Verification",
+    "IEMOCAP",
+    "Snips",
+]

micromamba_root/envs/pytorch_env/Lib/site-packages/torchaudio/datasets/cmuarctic.py

@@ -0,0 +1,157 @@
+import csv
+import os
+from pathlib import Path
+from typing import Tuple, Union
+
+import torchaudio
+from torch import Tensor
+from torch.utils.data import Dataset
+from torchaudio._internal import download_url_to_file
+from torchaudio.datasets.utils import _extract_tar
+
+URL = "aew"
+FOLDER_IN_ARCHIVE = "ARCTIC"
+_CHECKSUMS = {
+    "http://festvox.org/cmu_arctic/packed/cmu_us_aew_arctic.tar.bz2": "645cb33c0f0b2ce41384fdd8d3db2c3f5fc15c1e688baeb74d2e08cab18ab406",  # noqa: E501
+    "http://festvox.org/cmu_arctic/packed/cmu_us_ahw_arctic.tar.bz2": "024664adeb892809d646a3efd043625b46b5bfa3e6189b3500b2d0d59dfab06c",  # noqa: E501
+    "http://festvox.org/cmu_arctic/packed/cmu_us_aup_arctic.tar.bz2": "2c55bc3050caa996758869126ad10cf42e1441212111db034b3a45189c18b6fc",  # noqa: E501
+    "http://festvox.org/cmu_arctic/packed/cmu_us_awb_arctic.tar.bz2": "d74a950c9739a65f7bfc4dfa6187f2730fa03de5b8eb3f2da97a51b74df64d3c",  # noqa: E501
+    "http://festvox.org/cmu_arctic/packed/cmu_us_axb_arctic.tar.bz2": "dd65c3d2907d1ee52f86e44f578319159e60f4bf722a9142be01161d84e330ff",  # noqa: E501
+    "http://festvox.org/cmu_arctic/packed/cmu_us_bdl_arctic.tar.bz2": "26b91aaf48b2799b2956792b4632c2f926cd0542f402b5452d5adecb60942904",  # noqa: E501
+    "http://festvox.org/cmu_arctic/packed/cmu_us_clb_arctic.tar.bz2": "3f16dc3f3b97955ea22623efb33b444341013fc660677b2e170efdcc959fa7c6",  # noqa: E501
+    "http://festvox.org/cmu_arctic/packed/cmu_us_eey_arctic.tar.bz2": "8a0ee4e5acbd4b2f61a4fb947c1730ab3adcc9dc50b195981d99391d29928e8a",  # noqa: E501
+    "http://festvox.org/cmu_arctic/packed/cmu_us_fem_arctic.tar.bz2": "3fcff629412b57233589cdb058f730594a62c4f3a75c20de14afe06621ef45e2",  # noqa: E501
+    "http://festvox.org/cmu_arctic/packed/cmu_us_gka_arctic.tar.bz2": "dc82e7967cbd5eddbed33074b0699128dbd4482b41711916d58103707e38c67f",  # noqa: E501
+    "http://festvox.org/cmu_arctic/packed/cmu_us_jmk_arctic.tar.bz2": "3a37c0e1dfc91e734fdbc88b562d9e2ebca621772402cdc693bbc9b09b211d73",  # noqa: E501
+    "http://festvox.org/cmu_arctic/packed/cmu_us_ksp_arctic.tar.bz2": "8029cafce8296f9bed3022c44ef1e7953332b6bf6943c14b929f468122532717",  # noqa: E501
+    "http://festvox.org/cmu_arctic/packed/cmu_us_ljm_arctic.tar.bz2": "b23993765cbf2b9e7bbc3c85b6c56eaf292ac81ee4bb887b638a24d104f921a0",  # noqa: E501
+    "http://festvox.org/cmu_arctic/packed/cmu_us_lnh_arctic.tar.bz2": "4faf34d71aa7112813252fb20c5433e2fdd9a9de55a00701ffcbf05f24a5991a",  # noqa: E501
+    "http://festvox.org/cmu_arctic/packed/cmu_us_rms_arctic.tar.bz2": "c6dc11235629c58441c071a7ba8a2d067903dfefbaabc4056d87da35b72ecda4",  # noqa: E501
+    "http://festvox.org/cmu_arctic/packed/cmu_us_rxr_arctic.tar.bz2": "1fa4271c393e5998d200e56c102ff46fcfea169aaa2148ad9e9469616fbfdd9b",  # noqa: E501
+    "http://festvox.org/cmu_arctic/packed/cmu_us_slp_arctic.tar.bz2": "54345ed55e45c23d419e9a823eef427f1cc93c83a710735ec667d068c916abf1",  # noqa: E501
+    "http://festvox.org/cmu_arctic/packed/cmu_us_slt_arctic.tar.bz2": "7c173297916acf3cc7fcab2713be4c60b27312316765a90934651d367226b4ea",  # noqa: E501
+}
+
+
+def load_cmuarctic_item(line: str, path: str, folder_audio: str, ext_audio: str) -> Tuple[Tensor, int, str, str]:
+
+    utterance_id, transcript = line[0].strip().split(" ", 2)[1:]
+
+    # Remove space, double quote, and single parenthesis from transcript
+    transcript = transcript[1:-3]
+
+    file_audio = os.path.join(path, folder_audio, utterance_id + ext_audio)
+
+    # Load audio
+    waveform, sample_rate = torchaudio.load(file_audio)
+
+    return (waveform, sample_rate, transcript, utterance_id.split("_")[1])
+
+
+class CMUARCTIC(Dataset):
+    """*CMU ARCTIC* :cite:`Kominek03cmuarctic` dataset.
+
+    Args:
+        root (str or Path): Path to the directory where the dataset is found or downloaded.
+        url (str, optional):
+            The URL to download the dataset from or the type of the dataset to download.
+            (default: ``"aew"``)
+            Allowed type values are ``"aew"``, ``"ahw"``, ``"aup"``, ``"awb"``, ``"axb"``, ``"bdl"``,
+            ``"clb"``, ``"eey"``, ``"fem"``, ``"gka"``, ``"jmk"``, ``"ksp"``, ``"ljm"``, ``"lnh"``,
+            ``"rms"``, ``"rxr"``, ``"slp"`` or ``"slt"``.
+        folder_in_archive (str, optional):
+            The top-level directory of the dataset. (default: ``"ARCTIC"``)
+        download (bool, optional):
+            Whether to download the dataset if it is not found at root path. (default: ``False``).
+    """
+
+    _file_text = "txt.done.data"
+    _folder_text = "etc"
+    _ext_audio = ".wav"
+    _folder_audio = "wav"
+
+    def __init__(
+        self, root: Union[str, Path], url: str = URL, folder_in_archive: str = FOLDER_IN_ARCHIVE, download: bool = False
+    ) -> None:
+
+        if url in [
+            "aew",
+            "ahw",
+            "aup",
+            "awb",
+            "axb",
+            "bdl",
+            "clb",
+            "eey",
+            "fem",
+            "gka",
+            "jmk",
+            "ksp",
+            "ljm",
+            "lnh",
+            "rms",
+            "rxr",
+            "slp",
+            "slt",
+        ]:
+
+            url = "cmu_us_" + url + "_arctic"
+            ext_archive = ".tar.bz2"
+            base_url = "http://www.festvox.org/cmu_arctic/packed/"
+
+            url = os.path.join(base_url, url + ext_archive)
+
+        # Get string representation of 'root' in case Path object is passed
+        root = os.fspath(root)
+
+        basename = os.path.basename(url)
+        root = os.path.join(root, folder_in_archive)
+        if not os.path.isdir(root):
+            os.mkdir(root)
+        archive = os.path.join(root, basename)
+
+        basename = basename.split(".")[0]
+
+        self._path = os.path.join(root, basename)
+
+        if download:
+            if not os.path.isdir(self._path):
+                if not os.path.isfile(archive):
+                    checksum = _CHECKSUMS.get(url, None)
+                    download_url_to_file(url, archive, hash_prefix=checksum)
+                _extract_tar(archive)
+        else:
+            if not os.path.exists(self._path):
+                raise RuntimeError(
+                    f"The path {self._path} doesn't exist. "
+                    "Please check the ``root`` path or set `download=True` to download it"
+                )
+        self._text = os.path.join(self._path, self._folder_text, self._file_text)
+
+        with open(self._text, "r") as text:
+            walker = csv.reader(text, delimiter="\n")
+            self._walker = list(walker)
+
+    def __getitem__(self, n: int) -> Tuple[Tensor, int, str, str]:
+        """Load the n-th sample from the dataset.
+
+        Args:
+            n (int): The index of the sample to be loaded
+
+        Returns:
+            Tuple of the following items;
+
+            Tensor:
+                Waveform
+            int:
+                Sample rate
+            str:
+                Transcript
+            str:
+                Utterance ID
+        """
+        line = self._walker[n]
+        return load_cmuarctic_item(line, self._path, self._folder_audio, self._ext_audio)
+
+    def __len__(self) -> int:
+        return len(self._walker)

micromamba_root/envs/pytorch_env/Lib/site-packages/torchaudio/datasets/cmudict.py

@@ -0,0 +1,186 @@
+import os
+import re
+from pathlib import Path
+from typing import Iterable, List, Tuple, Union
+
+from torch.utils.data import Dataset
+from torchaudio._internal import download_url_to_file
+
+
+_CHECKSUMS = {
+    "http://svn.code.sf.net/p/cmusphinx/code/trunk/cmudict/cmudict-0.7b": "209a8b4cd265013e96f4658632a9878103b0c5abf62b50d4ef3ae1be226b29e4",  # noqa: E501
+    "http://svn.code.sf.net/p/cmusphinx/code/trunk/cmudict/cmudict-0.7b.symbols": "408ccaae803641c6d7b626b6299949320c2dbca96b2220fd3fb17887b023b027",  # noqa: E501
+}
+_PUNCTUATIONS = {
+    "!EXCLAMATION-POINT",
+    '"CLOSE-QUOTE',
+    '"DOUBLE-QUOTE',
+    '"END-OF-QUOTE',
+    '"END-QUOTE',
+    '"IN-QUOTES',
+    '"QUOTE',
+    '"UNQUOTE',
+    "#HASH-MARK",
+    "#POUND-SIGN",
+    "#SHARP-SIGN",
+    "%PERCENT",
+    "&AMPERSAND",
+    "'END-INNER-QUOTE",
+    "'END-QUOTE",
+    "'INNER-QUOTE",
+    "'QUOTE",
+    "'SINGLE-QUOTE",
+    "(BEGIN-PARENS",
+    "(IN-PARENTHESES",
+    "(LEFT-PAREN",
+    "(OPEN-PARENTHESES",
+    "(PAREN",
+    "(PARENS",
+    "(PARENTHESES",
+    ")CLOSE-PAREN",
+    ")CLOSE-PARENTHESES",
+    ")END-PAREN",
+    ")END-PARENS",
+    ")END-PARENTHESES",
+    ")END-THE-PAREN",
+    ")PAREN",
+    ")PARENS",
+    ")RIGHT-PAREN",
+    ")UN-PARENTHESES",
+    "+PLUS",
+    ",COMMA",
+    "--DASH",
+    "-DASH",
+    "-HYPHEN",
+    "...ELLIPSIS",
+    ".DECIMAL",
+    ".DOT",
+    ".FULL-STOP",
+    ".PERIOD",
+    ".POINT",
+    "/SLASH",
+    ":COLON",
+    ";SEMI-COLON",
+    ";SEMI-COLON(1)",
+    "?QUESTION-MARK",
+    "{BRACE",
+    "{LEFT-BRACE",
+    "{OPEN-BRACE",
+    "}CLOSE-BRACE",
+    "}RIGHT-BRACE",
+}
+
+
+def _parse_dictionary(lines: Iterable[str], exclude_punctuations: bool) -> List[str]:
+    _alt_re = re.compile(r"\([0-9]+\)")
+    cmudict: List[Tuple[str, List[str]]] = []
+    for line in lines:
+        if not line or line.startswith(";;;"):  # ignore comments
+            continue
+
+        word, phones = line.strip().split("  ")
+        if word in _PUNCTUATIONS:
+            if exclude_punctuations:
+                continue
+            # !EXCLAMATION-POINT -> !
+            # --DASH -> --
+            # ...ELLIPSIS -> ...
+            if word.startswith("..."):
+                word = "..."
+            elif word.startswith("--"):
+                word = "--"
+            else:
+                word = word[0]
+
+        # if a word have multiple pronunciations, there will be (number) appended to it
+        # for example, DATAPOINTS and DATAPOINTS(1),
+        # the regular expression `_alt_re` removes the '(1)' and change the word DATAPOINTS(1) to DATAPOINTS
+        word = re.sub(_alt_re, "", word)
+        phones = phones.split(" ")
+        cmudict.append((word, phones))
+
+    return cmudict
+
+
+class CMUDict(Dataset):
+    """*CMU Pronouncing Dictionary* :cite:`cmudict` (CMUDict) dataset.
+
+    Args:
+        root (str or Path): Path to the directory where the dataset is found or downloaded.
+        exclude_punctuations (bool, optional):
+            When enabled, exclude the pronounciation of punctuations, such as
+            `!EXCLAMATION-POINT` and `#HASH-MARK`.
+        download (bool, optional):
+            Whether to download the dataset if it is not found at root path. (default: ``False``).
+        url (str, optional):
+            The URL to download the dictionary from.
+            (default: ``"http://svn.code.sf.net/p/cmusphinx/code/trunk/cmudict/cmudict-0.7b"``)
+        url_symbols (str, optional):
+            The URL to download the list of symbols from.
+            (default: ``"http://svn.code.sf.net/p/cmusphinx/code/trunk/cmudict/cmudict-0.7b.symbols"``)
+    """
+
+    def __init__(
+        self,
+        root: Union[str, Path],
+        exclude_punctuations: bool = True,
+        *,
+        download: bool = False,
+        url: str = "http://svn.code.sf.net/p/cmusphinx/code/trunk/cmudict/cmudict-0.7b",
+        url_symbols: str = "http://svn.code.sf.net/p/cmusphinx/code/trunk/cmudict/cmudict-0.7b.symbols",
+    ) -> None:
+
+        self.exclude_punctuations = exclude_punctuations
+
+        self._root_path = Path(root)
+        if not os.path.isdir(self._root_path):
+            raise RuntimeError(f"The root directory does not exist; {root}")
+
+        dict_file = self._root_path / os.path.basename(url)
+        symbol_file = self._root_path / os.path.basename(url_symbols)
+        if not os.path.exists(dict_file):
+            if not download:
+                raise RuntimeError(
+                    "The dictionary file is not found in the following location. "
+                    f"Set `download=True` to download it. {dict_file}"
+                )
+            checksum = _CHECKSUMS.get(url, None)
+            download_url_to_file(url, dict_file, checksum)
+        if not os.path.exists(symbol_file):
+            if not download:
+                raise RuntimeError(
+                    "The symbol file is not found in the following location. "
+                    f"Set `download=True` to download it. {symbol_file}"
+                )
+            checksum = _CHECKSUMS.get(url_symbols, None)
+            download_url_to_file(url_symbols, symbol_file, checksum)
+
+        with open(symbol_file, "r") as text:
+            self._symbols = [line.strip() for line in text.readlines()]
+
+        with open(dict_file, "r", encoding="latin-1") as text:
+            self._dictionary = _parse_dictionary(text.readlines(), exclude_punctuations=self.exclude_punctuations)
+
+    def __getitem__(self, n: int) -> Tuple[str, List[str]]:
+        """Load the n-th sample from the dataset.
+
+        Args:
+            n (int): The index of the sample to be loaded.
+
+        Returns:
+            Tuple of a word and its phonemes
+
+            str:
+                Word
+            List[str]:
+                Phonemes
+        """
+        return self._dictionary[n]
+
+    def __len__(self) -> int:
+        return len(self._dictionary)
+
+    @property
+    def symbols(self) -> List[str]:
+        """list[str]: A list of phonemes symbols, such as ``"AA"``, ``"AE"``, ``"AH"``."""
+        return self._symbols.copy()

micromamba_root/envs/pytorch_env/Lib/site-packages/torchaudio/datasets/commonvoice.py

@@ -0,0 +1,86 @@
+import csv
+import os
+from pathlib import Path
+from typing import Dict, List, Tuple, Union
+
+import torchaudio
+from torch import Tensor
+from torch.utils.data import Dataset
+
+
+def load_commonvoice_item(
+    line: List[str], header: List[str], path: str, folder_audio: str, ext_audio: str
+) -> Tuple[Tensor, int, Dict[str, str]]:
+    # Each line as the following data:
+    # client_id, path, sentence, up_votes, down_votes, age, gender, accent
+
+    if header[1] != "path":
+        raise ValueError(f"expect `header[1]` to be 'path', but got {header[1]}")
+    fileid = line[1]
+    filename = os.path.join(path, folder_audio, fileid)
+    if not filename.endswith(ext_audio):
+        filename += ext_audio
+    waveform, sample_rate = torchaudio.load(filename)
+
+    dic = dict(zip(header, line))
+
+    return waveform, sample_rate, dic
+
+
+class COMMONVOICE(Dataset):
+    """*CommonVoice* :cite:`ardila2020common` dataset.
+
+    Args:
+        root (str or Path): Path to the directory where the dataset is located.
+             (Where the ``tsv`` file is present.)
+        tsv (str, optional):
+            The name of the tsv file used to construct the metadata, such as
+            ``"train.tsv"``, ``"test.tsv"``, ``"dev.tsv"``, ``"invalidated.tsv"``,
+            ``"validated.tsv"`` and ``"other.tsv"``. (default: ``"train.tsv"``)
+    """
+
+    _ext_txt = ".txt"
+    _ext_audio = ".mp3"
+    _folder_audio = "clips"
+
+    def __init__(self, root: Union[str, Path], tsv: str = "train.tsv") -> None:
+
+        # Get string representation of 'root' in case Path object is passed
+        self._path = os.fspath(root)
+        self._tsv = os.path.join(self._path, tsv)
+
+        with open(self._tsv, "r") as tsv_:
+            walker = csv.reader(tsv_, delimiter="\t")
+            self._header = next(walker)
+            self._walker = list(walker)
+
+    def __getitem__(self, n: int) -> Tuple[Tensor, int, Dict[str, str]]:
+        """Load the n-th sample from the dataset.
+
+        Args:
+            n (int): The index of the sample to be loaded
+
+        Returns:
+            Tuple of the following items;
+
+            Tensor:
+                Waveform
+            int:
+                Sample rate
+            Dict[str, str]:
+                Dictionary containing the following items from the corresponding TSV file;
+
+                * ``"client_id"``
+                * ``"path"``
+                * ``"sentence"``
+                * ``"up_votes"``
+                * ``"down_votes"``
+                * ``"age"``
+                * ``"gender"``
+                * ``"accent"``
+        """
+        line = self._walker[n]
+        return load_commonvoice_item(line, self._header, self._path, self._folder_audio, self._ext_audio)
+
+    def __len__(self) -> int:
+        return len(self._walker)

micromamba_root/envs/pytorch_env/Lib/site-packages/torchaudio/datasets/dr_vctk.py

@@ -0,0 +1,121 @@
+from pathlib import Path
+from typing import Dict, Tuple, Union
+
+import torchaudio
+from torch import Tensor
+from torch.utils.data import Dataset
+from torchaudio._internal import download_url_to_file
+from torchaudio.datasets.utils import _extract_zip
+
+
+_URL = "https://datashare.ed.ac.uk/bitstream/handle/10283/3038/DR-VCTK.zip"
+_CHECKSUM = "781f12f4406ed36ed27ae3bce55da47ba176e2d8bae67319e389e07b2c9bd769"
+_SUPPORTED_SUBSETS = {"train", "test"}
+
+
+class DR_VCTK(Dataset):
+    """*Device Recorded VCTK (Small subset version)* :cite:`Sarfjoo2018DeviceRV` dataset.
+
+    Args:
+        root (str or Path): Root directory where the dataset's top level directory is found.
+        subset (str): The subset to use. Can be one of ``"train"`` and ``"test"``. (default: ``"train"``).
+        download (bool):
+            Whether to download the dataset if it is not found at root path. (default: ``False``).
+        url (str): The URL to download the dataset from.
+            (default: ``"https://datashare.ed.ac.uk/bitstream/handle/10283/3038/DR-VCTK.zip"``)
+    """
+
+    def __init__(
+        self,
+        root: Union[str, Path],
+        subset: str = "train",
+        *,
+        download: bool = False,
+        url: str = _URL,
+    ) -> None:
+        if subset not in _SUPPORTED_SUBSETS:
+            raise RuntimeError(
+                f"The subset '{subset}' does not match any of the supported subsets: {_SUPPORTED_SUBSETS}"
+            )
+
+        root = Path(root).expanduser()
+        archive = root / "DR-VCTK.zip"
+
+        self._subset = subset
+        self._path = root / "DR-VCTK" / "DR-VCTK"
+        self._clean_audio_dir = self._path / f"clean_{self._subset}set_wav_16k"
+        self._noisy_audio_dir = self._path / f"device-recorded_{self._subset}set_wav_16k"
+        self._config_filepath = self._path / "configurations" / f"{self._subset}_ch_log.txt"
+
+        if not self._path.is_dir():
+            if not archive.is_file():
+                if not download:
+                    raise RuntimeError("Dataset not found. Please use `download=True` to download it.")
+                download_url_to_file(url, archive, hash_prefix=_CHECKSUM)
+            _extract_zip(archive, root)
+
+        self._config = self._load_config(self._config_filepath)
+        self._filename_list = sorted(self._config)
+
+    def _load_config(self, filepath: str) -> Dict[str, Tuple[str, int]]:
+        # Skip header
+        skip_rows = 2 if self._subset == "train" else 1
+
+        config = {}
+        with open(filepath) as f:
+            for i, line in enumerate(f):
+                if i < skip_rows or not line:
+                    continue
+                filename, source, channel_id = line.strip().split("\t")
+                config[filename] = (source, int(channel_id))
+        return config
+
+    def _load_dr_vctk_item(self, filename: str) -> Tuple[Tensor, int, Tensor, int, str, str, str, int]:
+        speaker_id, utterance_id = filename.split(".")[0].split("_")
+        source, channel_id = self._config[filename]
+        file_clean_audio = self._clean_audio_dir / filename
+        file_noisy_audio = self._noisy_audio_dir / filename
+        waveform_clean, sample_rate_clean = torchaudio.load(file_clean_audio)
+        waveform_noisy, sample_rate_noisy = torchaudio.load(file_noisy_audio)
+        return (
+            waveform_clean,
+            sample_rate_clean,
+            waveform_noisy,
+            sample_rate_noisy,
+            speaker_id,
+            utterance_id,
+            source,
+            channel_id,
+        )
+
+    def __getitem__(self, n: int) -> Tuple[Tensor, int, Tensor, int, str, str, str, int]:
+        """Load the n-th sample from the dataset.
+
+        Args:
+            n (int): The index of the sample to be loaded
+
+        Returns:
+            Tuple of the following items;
+
+            Tensor:
+                Clean waveform
+            int:
+                Sample rate of the clean waveform
+            Tensor:
+                Noisy waveform
+            int:
+                Sample rate of the noisy waveform
+            str:
+                Speaker ID
+            str:
+                Utterance ID
+            str:
+                Source
+            int:
+                Channel ID
+        """
+        filename = self._filename_list[n]
+        return self._load_dr_vctk_item(filename)
+
+    def __len__(self) -> int:
+        return len(self._filename_list)

micromamba_root/envs/pytorch_env/Lib/site-packages/torchaudio/datasets/fluentcommands.py

@@ -0,0 +1,108 @@
+import csv
+import os
+from pathlib import Path
+from typing import Tuple, Union
+
+from torch import Tensor
+from torch.utils.data import Dataset
+from torchaudio.datasets.utils import _load_waveform
+
+SAMPLE_RATE = 16000
+
+
+class FluentSpeechCommands(Dataset):
+    """*Fluent Speech Commands* :cite:`fluent` dataset
+
+    Args:
+        root (str of Path): Path to the directory where the dataset is found.
+        subset (str, optional): subset of the dataset to use.
+            Options: [``"train"``, ``"valid"``, ``"test"``].
+            (Default: ``"train"``)
+    """
+
+    def __init__(self, root: Union[str, Path], subset: str = "train"):
+        if subset not in ["train", "valid", "test"]:
+            raise ValueError("`subset` must be one of ['train', 'valid', 'test']")
+
+        root = os.fspath(root)
+        self._path = os.path.join(root, "fluent_speech_commands_dataset")
+
+        if not os.path.isdir(self._path):
+            raise RuntimeError("Dataset not found.")
+
+        subset_path = os.path.join(self._path, "data", f"{subset}_data.csv")
+        with open(subset_path) as subset_csv:
+            subset_reader = csv.reader(subset_csv)
+            data = list(subset_reader)
+
+        self.header = data[0]
+        self.data = data[1:]
+
+    def get_metadata(self, n: int) -> Tuple[str, int, str, int, str, str, str, str]:
+        """Get metadata for the n-th sample from the dataset. Returns filepath instead of waveform,
+        but otherwise returns the same fields as :py:func:`__getitem__`.
+
+        Args:
+            n (int): The index of the sample to be loaded
+
+        Returns:
+            Tuple of the following items;
+
+            str:
+                Path to audio
+            int:
+                Sample rate
+            str:
+                File name
+            int:
+                Speaker ID
+            str:
+                Transcription
+            str:
+                Action
+            str:
+                Object
+            str:
+                Location
+        """
+        sample = self.data[n]
+
+        file_name = sample[self.header.index("path")].split("/")[-1]
+        file_name = file_name.split(".")[0]
+        speaker_id, transcription, action, obj, location = sample[2:]
+        file_path = os.path.join("wavs", "speakers", speaker_id, f"{file_name}.wav")
+
+        return file_path, SAMPLE_RATE, file_name, speaker_id, transcription, action, obj, location
+
+    def __len__(self) -> int:
+        return len(self.data)
+
+    def __getitem__(self, n: int) -> Tuple[Tensor, int, str, int, str, str, str, str]:
+        """Load the n-th sample from the dataset.
+
+        Args:
+            n (int): The index of the sample to be loaded
+
+        Returns:
+            Tuple of the following items;
+
+            Tensor:
+                Waveform
+            int:
+                Sample rate
+            str:
+                File name
+            int:
+                Speaker ID
+            str:
+                Transcription
+            str:
+                Action
+            str:
+                Object
+            str:
+                Location
+        """
+        metadata = self.get_metadata(n)
+        waveform = _load_waveform(self._path, metadata[0], metadata[1])
+        return (waveform,) + metadata[1:]

micromamba_root/envs/pytorch_env/Lib/site-packages/torchaudio/datasets/gtzan.py

@@ -0,0 +1,1118 @@
+import os
+from pathlib import Path
+from typing import Optional, Tuple, Union
+
+import torchaudio
+from torch import Tensor
+from torch.utils.data import Dataset
+from torchaudio._internal import download_url_to_file
+from torchaudio.datasets.utils import _extract_tar
+
+# The following lists prefixed with `filtered_` provide a filtered split
+# that:
+#
+# a. Mitigate a known issue with GTZAN (duplication)
+#
+# b. Provide a standard split for testing it against other
+#    methods (e.g. the one in jordipons/sklearn-audio-transfer-learning).
+#
+# Those are used when GTZAN is initialised with the `filtered` keyword.
+# The split was taken from (github) jordipons/sklearn-audio-transfer-learning.
+
+gtzan_genres = [
+    "blues",
+    "classical",
+    "country",
+    "disco",
+    "hiphop",
+    "jazz",
+    "metal",
+    "pop",
+    "reggae",
+    "rock",
+]
+
+filtered_test = [
+    "blues.00012",
+    "blues.00013",
+    "blues.00014",
+    "blues.00015",
+    "blues.00016",
+    "blues.00017",
+    "blues.00018",
+    "blues.00019",
+    "blues.00020",
+    "blues.00021",
+    "blues.00022",
+    "blues.00023",
+    "blues.00024",
+    "blues.00025",
+    "blues.00026",
+    "blues.00027",
+    "blues.00028",
+    "blues.00061",
+    "blues.00062",
+    "blues.00063",
+    "blues.00064",
+    "blues.00065",
+    "blues.00066",
+    "blues.00067",
+    "blues.00068",
+    "blues.00069",
+    "blues.00070",
+    "blues.00071",
+    "blues.00072",
+    "blues.00098",
+    "blues.00099",
+    "classical.00011",
+    "classical.00012",
+    "classical.00013",
+    "classical.00014",
+    "classical.00015",
+    "classical.00016",
+    "classical.00017",
+    "classical.00018",
+    "classical.00019",
+    "classical.00020",
+    "classical.00021",
+    "classical.00022",
+    "classical.00023",
+    "classical.00024",
+    "classical.00025",
+    "classical.00026",
+    "classical.00027",
+    "classical.00028",
+    "classical.00029",
+    "classical.00034",
+    "classical.00035",
+    "classical.00036",
+    "classical.00037",
+    "classical.00038",
+    "classical.00039",
+    "classical.00040",
+    "classical.00041",
+    "classical.00049",
+    "classical.00077",
+    "classical.00078",
+    "classical.00079",
+    "country.00030",
+    "country.00031",
+    "country.00032",
+    "country.00033",
+    "country.00034",
+    "country.00035",
+    "country.00036",
+    "country.00037",
+    "country.00038",
+    "country.00039",
+    "country.00040",
+    "country.00043",
+    "country.00044",
+    "country.00046",
+    "country.00047",
+    "country.00048",
+    "country.00050",
+    "country.00051",
+    "country.00053",
+    "country.00054",
+    "country.00055",
+    "country.00056",
+    "country.00057",
+    "country.00058",
+    "country.00059",
+    "country.00060",
+    "country.00061",
+    "country.00062",
+    "country.00063",
+    "country.00064",
+    "disco.00001",
+    "disco.00021",
+    "disco.00058",
+    "disco.00062",
+    "disco.00063",
+    "disco.00064",
+    "disco.00065",
+    "disco.00066",
+    "disco.00069",
+    "disco.00076",
+    "disco.00077",
+    "disco.00078",
+    "disco.00079",
+    "disco.00080",
+    "disco.00081",
+    "disco.00082",
+    "disco.00083",
+    "disco.00084",
+    "disco.00085",
+    "disco.00086",
+    "disco.00087",
+    "disco.00088",
+    "disco.00091",
+    "disco.00092",
+    "disco.00093",
+    "disco.00094",
+    "disco.00096",
+    "disco.00097",
+    "disco.00099",
+    "hiphop.00000",
+    "hiphop.00026",
+    "hiphop.00027",
+    "hiphop.00030",
+    "hiphop.00040",
+    "hiphop.00043",
+    "hiphop.00044",
+    "hiphop.00045",
+    "hiphop.00051",
+    "hiphop.00052",
+    "hiphop.00053",
+    "hiphop.00054",
+    "hiphop.00062",
+    "hiphop.00063",
+    "hiphop.00064",
+    "hiphop.00065",
+    "hiphop.00066",
+    "hiphop.00067",
+    "hiphop.00068",
+    "hiphop.00069",
+    "hiphop.00070",
+    "hiphop.00071",
+    "hiphop.00072",
+    "hiphop.00073",
+    "hiphop.00074",
+    "hiphop.00075",
+    "hiphop.00099",
+    "jazz.00073",
+    "jazz.00074",
+    "jazz.00075",
+    "jazz.00076",
+    "jazz.00077",
+    "jazz.00078",
+    "jazz.00079",
+    "jazz.00080",
+    "jazz.00081",
+    "jazz.00082",
+    "jazz.00083",
+    "jazz.00084",
+    "jazz.00085",
+    "jazz.00086",
+    "jazz.00087",
+    "jazz.00088",
+    "jazz.00089",
+    "jazz.00090",
+    "jazz.00091",
+    "jazz.00092",
+    "jazz.00093",
+    "jazz.00094",
+    "jazz.00095",
+    "jazz.00096",
+    "jazz.00097",
+    "jazz.00098",
+    "jazz.00099",
+    "metal.00012",
+    "metal.00013",
+    "metal.00014",
+    "metal.00015",
+    "metal.00022",
+    "metal.00023",
+    "metal.00025",
+    "metal.00026",
+    "metal.00027",
+    "metal.00028",
+    "metal.00029",
+    "metal.00030",
+    "metal.00031",
+    "metal.00032",
+    "metal.00033",
+    "metal.00038",
+    "metal.00039",
+    "metal.00067",
+    "metal.00070",
+    "metal.00073",
+    "metal.00074",
+    "metal.00075",
+    "metal.00078",
+    "metal.00083",
+    "metal.00085",
+    "metal.00087",
+    "metal.00088",
+    "pop.00000",
+    "pop.00001",
+    "pop.00013",
+    "pop.00014",
+    "pop.00043",
+    "pop.00063",
+    "pop.00064",
+    "pop.00065",
+    "pop.00066",
+    "pop.00069",
+    "pop.00070",
+    "pop.00071",
+    "pop.00072",
+    "pop.00073",
+    "pop.00074",
+    "pop.00075",
+    "pop.00076",
+    "pop.00077",
+    "pop.00078",
+    "pop.00079",
+    "pop.00082",
+    "pop.00088",
+    "pop.00089",
+    "pop.00090",
+    "pop.00091",
+    "pop.00092",
+    "pop.00093",
+    "pop.00094",
+    "pop.00095",
+    "pop.00096",
+    "reggae.00034",
+    "reggae.00035",
+    "reggae.00036",
+    "reggae.00037",
+    "reggae.00038",
+    "reggae.00039",
+    "reggae.00040",
+    "reggae.00046",
+    "reggae.00047",
+    "reggae.00048",
+    "reggae.00052",
+    "reggae.00053",
+    "reggae.00064",
+    "reggae.00065",
+    "reggae.00066",
+    "reggae.00067",
+    "reggae.00068",
+    "reggae.00071",
+    "reggae.00079",
+    "reggae.00082",
+    "reggae.00083",
+    "reggae.00084",
+    "reggae.00087",
+    "reggae.00088",
+    "reggae.00089",
+    "reggae.00090",
+    "rock.00010",
+    "rock.00011",
+    "rock.00012",
+    "rock.00013",
+    "rock.00014",
+    "rock.00015",
+    "rock.00027",
+    "rock.00028",
+    "rock.00029",
+    "rock.00030",
+    "rock.00031",
+    "rock.00032",
+    "rock.00033",
+    "rock.00034",
+    "rock.00035",
+    "rock.00036",
+    "rock.00037",
+    "rock.00039",
+    "rock.00040",
+    "rock.00041",
+    "rock.00042",
+    "rock.00043",
+    "rock.00044",
+    "rock.00045",
+    "rock.00046",
+    "rock.00047",
+    "rock.00048",
+    "rock.00086",
+    "rock.00087",
+    "rock.00088",
+    "rock.00089",
+    "rock.00090",
+]
+
+filtered_train = [
+    "blues.00029",
+    "blues.00030",
+    "blues.00031",
+    "blues.00032",
+    "blues.00033",
+    "blues.00034",
+    "blues.00035",
+    "blues.00036",
+    "blues.00037",
+    "blues.00038",
+    "blues.00039",
+    "blues.00040",
+    "blues.00041",
+    "blues.00042",
+    "blues.00043",
+    "blues.00044",
+    "blues.00045",
+    "blues.00046",
+    "blues.00047",
+    "blues.00048",
+    "blues.00049",
+    "blues.00073",
+    "blues.00074",
+    "blues.00075",
+    "blues.00076",
+    "blues.00077",
+    "blues.00078",
+    "blues.00079",
+    "blues.00080",
+    "blues.00081",
+    "blues.00082",
+    "blues.00083",
+    "blues.00084",
+    "blues.00085",
+    "blues.00086",
+    "blues.00087",
+    "blues.00088",
+    "blues.00089",
+    "blues.00090",
+    "blues.00091",
+    "blues.00092",
+    "blues.00093",
+    "blues.00094",
+    "blues.00095",
+    "blues.00096",
+    "blues.00097",
+    "classical.00030",
+    "classical.00031",
+    "classical.00032",
+    "classical.00033",
+    "classical.00043",
+    "classical.00044",
+    "classical.00045",
+    "classical.00046",
+    "classical.00047",
+    "classical.00048",
+    "classical.00050",
+    "classical.00051",
+    "classical.00052",
+    "classical.00053",
+    "classical.00054",
+    "classical.00055",
+    "classical.00056",
+    "classical.00057",
+    "classical.00058",
+    "classical.00059",
+    "classical.00060",
+    "classical.00061",
+    "classical.00062",
+    "classical.00063",
+    "classical.00064",
+    "classical.00065",
+    "classical.00066",
+    "classical.00067",
+    "classical.00080",
+    "classical.00081",
+    "classical.00082",
+    "classical.00083",
+    "classical.00084",
+    "classical.00085",
+    "classical.00086",
+    "classical.00087",
+    "classical.00088",
+    "classical.00089",
+    "classical.00090",
+    "classical.00091",
+    "classical.00092",
+    "classical.00093",
+    "classical.00094",
+    "classical.00095",
+    "classical.00096",
+    "classical.00097",
+    "classical.00098",
+    "classical.00099",
+    "country.00019",
+    "country.00020",
+    "country.00021",
+    "country.00022",
+    "country.00023",
+    "country.00024",
+    "country.00025",
+    "country.00026",
+    "country.00028",
+    "country.00029",
+    "country.00065",
+    "country.00066",
+    "country.00067",
+    "country.00068",
+    "country.00069",
+    "country.00070",
+    "country.00071",
+    "country.00072",
+    "country.00073",
+    "country.00074",
+    "country.00075",
+    "country.00076",
+    "country.00077",
+    "country.00078",
+    "country.00079",
+    "country.00080",
+    "country.00081",
+    "country.00082",
+    "country.00083",
+    "country.00084",
+    "country.00085",
+    "country.00086",
+    "country.00087",
+    "country.00088",
+    "country.00089",
+    "country.00090",
+    "country.00091",
+    "country.00092",
+    "country.00093",
+    "country.00094",
+    "country.00095",
+    "country.00096",
+    "country.00097",
+    "country.00098",
+    "country.00099",
+    "disco.00005",
+    "disco.00015",
+    "disco.00016",
+    "disco.00017",
+    "disco.00018",
+    "disco.00019",
+    "disco.00020",
+    "disco.00022",
+    "disco.00023",
+    "disco.00024",
+    "disco.00025",
+    "disco.00026",
+    "disco.00027",
+    "disco.00028",
+    "disco.00029",
+    "disco.00030",
+    "disco.00031",
+    "disco.00032",
+    "disco.00033",
+    "disco.00034",
+    "disco.00035",
+    "disco.00036",
+    "disco.00037",
+    "disco.00039",
+    "disco.00040",
+    "disco.00041",
+    "disco.00042",
+    "disco.00043",
+    "disco.00044",
+    "disco.00045",
+    "disco.00047",
+    "disco.00049",
+    "disco.00053",
+    "disco.00054",
+    "disco.00056",
+    "disco.00057",
+    "disco.00059",
+    "disco.00061",
+    "disco.00070",
+    "disco.00073",
+    "disco.00074",
+    "disco.00089",
+    "hiphop.00002",
+    "hiphop.00003",
+    "hiphop.00004",
+    "hiphop.00005",
+    "hiphop.00006",
+    "hiphop.00007",
+    "hiphop.00008",
+    "hiphop.00009",
+    "hiphop.00010",
+    "hiphop.00011",
+    "hiphop.00012",
+    "hiphop.00013",
+    "hiphop.00014",
+    "hiphop.00015",
+    "hiphop.00016",
+    "hiphop.00017",
+    "hiphop.00018",
+    "hiphop.00019",
+    "hiphop.00020",
+    "hiphop.00021",
+    "hiphop.00022",
+    "hiphop.00023",
+    "hiphop.00024",
+    "hiphop.00025",
+    "hiphop.00028",
+    "hiphop.00029",
+    "hiphop.00031",
+    "hiphop.00032",
+    "hiphop.00033",
+    "hiphop.00034",
+    "hiphop.00035",
+    "hiphop.00036",
+    "hiphop.00037",
+    "hiphop.00038",
+    "hiphop.00041",
+    "hiphop.00042",
+    "hiphop.00055",
+    "hiphop.00056",
+    "hiphop.00057",
+    "hiphop.00058",
+    "hiphop.00059",
+    "hiphop.00060",
+    "hiphop.00061",
+    "hiphop.00077",
+    "hiphop.00078",
+    "hiphop.00079",
+    "hiphop.00080",
+    "jazz.00000",
+    "jazz.00001",
+    "jazz.00011",
+    "jazz.00012",
+    "jazz.00013",
+    "jazz.00014",
+    "jazz.00015",
+    "jazz.00016",
+    "jazz.00017",
+    "jazz.00018",
+    "jazz.00019",
+    "jazz.00020",
+    "jazz.00021",
+    "jazz.00022",
+    "jazz.00023",
+    "jazz.00024",
+    "jazz.00041",
+    "jazz.00047",
+    "jazz.00048",
+    "jazz.00049",
+    "jazz.00050",
+    "jazz.00051",
+    "jazz.00052",
+    "jazz.00053",
+    "jazz.00054",
+    "jazz.00055",
+    "jazz.00056",
+    "jazz.00057",
+    "jazz.00058",
+    "jazz.00059",
+    "jazz.00060",
+    "jazz.00061",
+    "jazz.00062",
+    "jazz.00063",
+    "jazz.00064",
+    "jazz.00065",
+    "jazz.00066",
+    "jazz.00067",
+    "jazz.00068",
+    "jazz.00069",
+    "jazz.00070",
+    "jazz.00071",
+    "jazz.00072",
+    "metal.00002",
+    "metal.00003",
+    "metal.00005",
+    "metal.00021",
+    "metal.00024",
+    "metal.00035",
+    "metal.00046",
+    "metal.00047",
+    "metal.00048",
+    "metal.00049",
+    "metal.00050",
+    "metal.00051",
+    "metal.00052",
+    "metal.00053",
+    "metal.00054",
+    "metal.00055",
+    "metal.00056",
+    "metal.00057",
+    "metal.00059",
+    "metal.00060",
+    "metal.00061",
+    "metal.00062",
+    "metal.00063",
+    "metal.00064",
+    "metal.00065",
+    "metal.00066",
+    "metal.00069",
+    "metal.00071",
+    "metal.00072",
+    "metal.00079",
+    "metal.00080",
+    "metal.00084",
+    "metal.00086",
+    "metal.00089",
+    "metal.00090",
+    "metal.00091",
+    "metal.00092",
+    "metal.00093",
+    "metal.00094",
+    "metal.00095",
+    "metal.00096",
+    "metal.00097",
+    "metal.00098",
+    "metal.00099",
+    "pop.00002",
+    "pop.00003",
+    "pop.00004",
+    "pop.00005",
+    "pop.00006",
+    "pop.00007",
+    "pop.00008",
+    "pop.00009",
+    "pop.00011",
+    "pop.00012",
+    "pop.00016",
+    "pop.00017",
+    "pop.00018",
+    "pop.00019",
+    "pop.00020",
+    "pop.00023",
+    "pop.00024",
+    "pop.00025",
+    "pop.00026",
+    "pop.00027",
+    "pop.00028",
+    "pop.00029",
+    "pop.00031",
+    "pop.00032",
+    "pop.00033",
+    "pop.00034",
+    "pop.00035",
+    "pop.00036",
+    "pop.00038",
+    "pop.00039",
+    "pop.00040",
+    "pop.00041",
+    "pop.00042",
+    "pop.00044",
+    "pop.00046",
+    "pop.00049",
+    "pop.00050",
+    "pop.00080",
+    "pop.00097",
+    "pop.00098",
+    "pop.00099",
+    "reggae.00000",
+    "reggae.00001",
+    "reggae.00002",
+    "reggae.00004",
+    "reggae.00006",
+    "reggae.00009",
+    "reggae.00011",
+    "reggae.00012",
+    "reggae.00014",
+    "reggae.00015",
+    "reggae.00016",
+    "reggae.00017",
+    "reggae.00018",
+    "reggae.00019",
+    "reggae.00020",
+    "reggae.00021",
+    "reggae.00022",
+    "reggae.00023",
+    "reggae.00024",
+    "reggae.00025",
+    "reggae.00026",
+    "reggae.00027",
+    "reggae.00028",
+    "reggae.00029",
+    "reggae.00030",
+    "reggae.00031",
+    "reggae.00032",
+    "reggae.00042",
+    "reggae.00043",
+    "reggae.00044",
+    "reggae.00045",
+    "reggae.00049",
+    "reggae.00050",
+    "reggae.00051",
+    "reggae.00054",
+    "reggae.00055",
+    "reggae.00056",
+    "reggae.00057",
+    "reggae.00058",
+    "reggae.00059",
+    "reggae.00060",
+    "reggae.00063",
+    "reggae.00069",
+    "rock.00000",
+    "rock.00001",
+    "rock.00002",
+    "rock.00003",
+    "rock.00004",
+    "rock.00005",
+    "rock.00006",
+    "rock.00007",
+    "rock.00008",
+    "rock.00009",
+    "rock.00016",
+    "rock.00017",
+    "rock.00018",
+    "rock.00019",
+    "rock.00020",
+    "rock.00021",
+    "rock.00022",
+    "rock.00023",
+    "rock.00024",
+    "rock.00025",
+    "rock.00026",
+    "rock.00057",
+    "rock.00058",
+    "rock.00059",
+    "rock.00060",
+    "rock.00061",
+    "rock.00062",
+    "rock.00063",
+    "rock.00064",
+    "rock.00065",
+    "rock.00066",
+    "rock.00067",
+    "rock.00068",
+    "rock.00069",
+    "rock.00070",
+    "rock.00091",
+    "rock.00092",
+    "rock.00093",
+    "rock.00094",
+    "rock.00095",
+    "rock.00096",
+    "rock.00097",
+    "rock.00098",
+    "rock.00099",
+]
+
+filtered_valid = [
+    "blues.00000",
+    "blues.00001",
+    "blues.00002",
+    "blues.00003",
+    "blues.00004",
+    "blues.00005",
+    "blues.00006",
+    "blues.00007",
+    "blues.00008",
+    "blues.00009",
+    "blues.00010",
+    "blues.00011",
+    "blues.00050",
+    "blues.00051",
+    "blues.00052",
+    "blues.00053",
+    "blues.00054",
+    "blues.00055",
+    "blues.00056",
+    "blues.00057",
+    "blues.00058",
+    "blues.00059",
+    "blues.00060",
+    "classical.00000",
+    "classical.00001",
+    "classical.00002",
+    "classical.00003",
+    "classical.00004",
+    "classical.00005",
+    "classical.00006",
+    "classical.00007",
+    "classical.00008",
+    "classical.00009",
+    "classical.00010",
+    "classical.00068",
+    "classical.00069",
+    "classical.00070",
+    "classical.00071",
+    "classical.00072",
+    "classical.00073",
+    "classical.00074",
+    "classical.00075",
+    "classical.00076",
+    "country.00000",
+    "country.00001",
+    "country.00002",
+    "country.00003",
+    "country.00004",
+    "country.00005",
+    "country.00006",
+    "country.00007",
+    "country.00009",
+    "country.00010",
+    "country.00011",
+    "country.00012",
+    "country.00013",
+    "country.00014",
+    "country.00015",
+    "country.00016",
+    "country.00017",
+    "country.00018",
+    "country.00027",
+    "country.00041",
+    "country.00042",
+    "country.00045",
+    "country.00049",
+    "disco.00000",
+    "disco.00002",
+    "disco.00003",
+    "disco.00004",
+    "disco.00006",
+    "disco.00007",
+    "disco.00008",
+    "disco.00009",
+    "disco.00010",
+    "disco.00011",
+    "disco.00012",
+    "disco.00013",
+    "disco.00014",
+    "disco.00046",
+    "disco.00048",
+    "disco.00052",
+    "disco.00067",
+    "disco.00068",
+    "disco.00072",
+    "disco.00075",
+    "disco.00090",
+    "disco.00095",
+    "hiphop.00081",
+    "hiphop.00082",
+    "hiphop.00083",
+    "hiphop.00084",
+    "hiphop.00085",
+    "hiphop.00086",
+    "hiphop.00087",
+    "hiphop.00088",
+    "hiphop.00089",
+    "hiphop.00090",
+    "hiphop.00091",
+    "hiphop.00092",
+    "hiphop.00093",
+    "hiphop.00094",
+    "hiphop.00095",
+    "hiphop.00096",
+    "hiphop.00097",
+    "hiphop.00098",
+    "jazz.00002",
+    "jazz.00003",
+    "jazz.00004",
+    "jazz.00005",
+    "jazz.00006",
+    "jazz.00007",
+    "jazz.00008",
+    "jazz.00009",
+    "jazz.00010",
+    "jazz.00025",
+    "jazz.00026",
+    "jazz.00027",
+    "jazz.00028",
+    "jazz.00029",
+    "jazz.00030",
+    "jazz.00031",
+    "jazz.00032",
+    "metal.00000",
+    "metal.00001",
+    "metal.00006",
+    "metal.00007",
+    "metal.00008",
+    "metal.00009",
+    "metal.00010",
+    "metal.00011",
+    "metal.00016",
+    "metal.00017",
+    "metal.00018",
+    "metal.00019",
+    "metal.00020",
+    "metal.00036",
+    "metal.00037",
+    "metal.00068",
+    "metal.00076",
+    "metal.00077",
+    "metal.00081",
+    "metal.00082",
+    "pop.00010",
+    "pop.00053",
+    "pop.00055",
+    "pop.00058",
+    "pop.00059",
+    "pop.00060",
+    "pop.00061",
+    "pop.00062",
+    "pop.00081",
+    "pop.00083",
+    "pop.00084",
+    "pop.00085",
+    "pop.00086",
+    "reggae.00061",
+    "reggae.00062",
+    "reggae.00070",
+    "reggae.00072",
+    "reggae.00074",
+    "reggae.00076",
+    "reggae.00077",
+    "reggae.00078",
+    "reggae.00085",
+    "reggae.00092",
+    "reggae.00093",
+    "reggae.00094",
+    "reggae.00095",
+    "reggae.00096",
+    "reggae.00097",
+    "reggae.00098",
+    "reggae.00099",
+    "rock.00038",
+    "rock.00049",
+    "rock.00050",
+    "rock.00051",
+    "rock.00052",
+    "rock.00053",
+    "rock.00054",
+    "rock.00055",
+    "rock.00056",
+    "rock.00071",
+    "rock.00072",
+    "rock.00073",
+    "rock.00074",
+    "rock.00075",
+    "rock.00076",
+    "rock.00077",
+    "rock.00078",
+    "rock.00079",
+    "rock.00080",
+    "rock.00081",
+    "rock.00082",
+    "rock.00083",
+    "rock.00084",
+    "rock.00085",
+]
+
+
+URL = "http://opihi.cs.uvic.ca/sound/genres.tar.gz"
+FOLDER_IN_ARCHIVE = "genres"
+_CHECKSUMS = {
+    "http://opihi.cs.uvic.ca/sound/genres.tar.gz": "24347e0223d2ba798e0a558c4c172d9d4a19c00bb7963fe055d183dadb4ef2c6"
+}
+
+
+def load_gtzan_item(fileid: str, path: str, ext_audio: str) -> Tuple[Tensor, str]:
+    """
+    Loads a file from the dataset and returns the raw waveform
+    as a Torch Tensor, its sample rate as an integer, and its
+    genre as a string.
+    """
+    # Filenames are of the form label.id, e.g. blues.00078
+    label, _ = fileid.split(".")
+
+    # Read wav
+    file_audio = os.path.join(path, label, fileid + ext_audio)
+    waveform, sample_rate = torchaudio.load(file_audio)
+
+    return waveform, sample_rate, label
+
+
+class GTZAN(Dataset):
+    """*GTZAN* :cite:`tzanetakis_essl_cook_2001` dataset.
+
+    Note:
+        Please see http://marsyas.info/downloads/datasets.html if you are planning to use
+        this dataset to publish results.
+
+    Note:
+        As of October 2022, the download link is not currently working. Setting ``download=True``
+        in GTZAN dataset will result in a URL connection error.
+
+    Args:
+        root (str or Path): Path to the directory where the dataset is found or downloaded.
+        url (str, optional): The URL to download the dataset from.
+            (default: ``"http://opihi.cs.uvic.ca/sound/genres.tar.gz"``)
+        folder_in_archive (str, optional): The top-level directory of the dataset.
+        download (bool, optional):
+            Whether to download the dataset if it is not found at root path. (default: ``False``).
+        subset (str or None, optional): Which subset of the dataset to use.
+            One of ``"training"``, ``"validation"``, ``"testing"`` or ``None``.
+            If ``None``, the entire dataset is used. (default: ``None``).
+    """
+
+    _ext_audio = ".wav"
+
+    def __init__(
+        self,
+        root: Union[str, Path],
+        url: str = URL,
+        folder_in_archive: str = FOLDER_IN_ARCHIVE,
+        download: bool = False,
+        subset: Optional[str] = None,
+    ) -> None:
+
+        # super(GTZAN, self).__init__()
+
+        # Get string representation of 'root' in case Path object is passed
+        root = os.fspath(root)
+
+        self.root = root
+        self.url = url
+        self.folder_in_archive = folder_in_archive
+        self.download = download
+        self.subset = subset
+
+        if subset is not None and subset not in ["training", "validation", "testing"]:
+            raise ValueError("When `subset` is not None, it must be one of ['training', 'validation', 'testing'].")
+
+        archive = os.path.basename(url)
+        archive = os.path.join(root, archive)
+        self._path = os.path.join(root, folder_in_archive)
+
+        if download:
+            if not os.path.isdir(self._path):
+                if not os.path.isfile(archive):
+                    checksum = _CHECKSUMS.get(url, None)
+                    download_url_to_file(url, archive, hash_prefix=checksum)
+                _extract_tar(archive)
+
+        if not os.path.isdir(self._path):
+            raise RuntimeError("Dataset not found. Please use `download=True` to download it.")
+
+        if self.subset is None:
+            # Check every subdirectory under dataset root
+            # which has the same name as the genres in
+            # GTZAN (e.g. `root_dir'/blues/, `root_dir'/rock, etc.)
+            # This lets users remove or move around song files,
+            # useful when e.g. they want to use only some of the files
+            # in a genre or want to label other files with a different
+            # genre.
+            self._walker = []
+
+            root = os.path.expanduser(self._path)
+
+            for directory in gtzan_genres:
+                fulldir = os.path.join(root, directory)
+
+                if not os.path.exists(fulldir):
+                    continue
+
+                songs_in_genre = os.listdir(fulldir)
+                songs_in_genre.sort()
+                for fname in songs_in_genre:
+                    name, ext = os.path.splitext(fname)
+                    if ext.lower() == ".wav" and "." in name:
+                        # Check whether the file is of the form
+                        # `gtzan_genre`.`5 digit number`.wav
+                        genre, num = name.split(".")
+                        if genre in gtzan_genres and len(num) == 5 and num.isdigit():
+                            self._walker.append(name)
+        else:
+            if self.subset == "training":
+                self._walker = filtered_train
+            elif self.subset == "validation":
+                self._walker = filtered_valid
+            elif self.subset == "testing":
+                self._walker = filtered_test
+
+    def __getitem__(self, n: int) -> Tuple[Tensor, int, str]:
+        """Load the n-th sample from the dataset.
+
+        Args:
+            n (int): The index of the sample to be loaded
+
+        Returns:
+            Tuple of the following items;
+
+            Tensor:
+                Waveform
+            int:
+                Sample rate
+            str:
+                Label
+        """
+        fileid = self._walker[n]
+        item = load_gtzan_item(fileid, self._path, self._ext_audio)
+        waveform, sample_rate, label = item
+        return waveform, sample_rate, label
+
+    def __len__(self) -> int:
+        return len(self._walker)

micromamba_root/envs/pytorch_env/Lib/site-packages/torchaudio/datasets/iemocap.py

@@ -0,0 +1,147 @@
+import os
+import re
+from pathlib import Path
+from typing import Optional, Tuple, Union
+
+from torch import Tensor
+from torch.utils.data import Dataset
+from torchaudio.datasets.utils import _load_waveform
+
+
+_SAMPLE_RATE = 16000
+
+
+def _get_wavs_paths(data_dir):
+    wav_dir = data_dir / "sentences" / "wav"
+    wav_paths = sorted(str(p) for p in wav_dir.glob("*/*.wav"))
+    relative_paths = []
+    for wav_path in wav_paths:
+        start = wav_path.find("Session")
+        wav_path = wav_path[start:]
+        relative_paths.append(wav_path)
+    return relative_paths
+
+
+class IEMOCAP(Dataset):
+    """*IEMOCAP* :cite:`iemocap` dataset.
+
+    Args:
+        root (str or Path): Root directory where the dataset's top level directory is found
+        sessions (Tuple[int]): Tuple of sessions (1-5) to use. (Default: ``(1, 2, 3, 4, 5)``)
+        utterance_type (str or None, optional): Which type(s) of utterances to include in the dataset.
+            Options: ("scripted", "improvised", ``None``). If ``None``, both scripted and improvised
+            data are used.
+    """
+
+    def __init__(
+        self,
+        root: Union[str, Path],
+        sessions: Tuple[str] = (1, 2, 3, 4, 5),
+        utterance_type: Optional[str] = None,
+    ):
+        root = Path(root)
+        self._path = root / "IEMOCAP"
+
+        if not os.path.isdir(self._path):
+            raise RuntimeError("Dataset not found.")
+
+        if utterance_type not in ["scripted", "improvised", None]:
+            raise ValueError("utterance_type must be one of ['scripted', 'improvised', or None]")
+
+        all_data = []
+        self.data = []
+        self.mapping = {}
+
+        for session in sessions:
+            session_name = f"Session{session}"
+            session_dir = self._path / session_name
+
+            # get wav paths
+            wav_paths = _get_wavs_paths(session_dir)
+            for wav_path in wav_paths:
+                wav_stem = str(Path(wav_path).stem)
+                all_data.append(wav_stem)
+
+            # add labels
+            label_dir = session_dir / "dialog" / "EmoEvaluation"
+            query = "*.txt"
+            if utterance_type == "scripted":
+                query = "*script*.txt"
+            elif utterance_type == "improvised":
+                query = "*impro*.txt"
+            label_paths = label_dir.glob(query)
+
+            for label_path in label_paths:
+                with open(label_path, "r") as f:
+                    for line in f:
+                        if not line.startswith("["):
+                            continue
+                        line = re.split("[\t\n]", line)
+                        wav_stem = line[1]
+                        label = line[2]
+                        if wav_stem not in all_data:
+                            continue
+                        if label not in ["neu", "hap", "ang", "sad", "exc", "fru"]:
+                            continue
+                        self.mapping[wav_stem] = {}
+                        self.mapping[wav_stem]["label"] = label
+
+            for wav_path in wav_paths:
+                wav_stem = str(Path(wav_path).stem)
+                if wav_stem in self.mapping:
+                    self.data.append(wav_stem)
+                    self.mapping[wav_stem]["path"] = wav_path
+
+    def get_metadata(self, n: int) -> Tuple[str, int, str, str, str]:
+        """Get metadata for the n-th sample from the dataset. Returns filepath instead of waveform,
+        but otherwise returns the same fields as :py:meth:`__getitem__`.
+
+        Args:
+            n (int): The index of the sample to be loaded
+
+        Returns:
+            Tuple of the following items;
+
+            str:
+                Path to audio
+            int:
+                Sample rate
+            str:
+                File name
+            str:
+                Label (one of ``"neu"``, ``"hap"``, ``"ang"``, ``"sad"``, ``"exc"``, ``"fru"``)
+            str:
+                Speaker
+        """
+        wav_stem = self.data[n]
+        wav_path = self.mapping[wav_stem]["path"]
+        label = self.mapping[wav_stem]["label"]
+        speaker = wav_stem.split("_")[0]
+        return (wav_path, _SAMPLE_RATE, wav_stem, label, speaker)
+
+    def __getitem__(self, n: int) -> Tuple[Tensor, int, str, str, str]:
+        """Load the n-th sample from the dataset.
+
+        Args:
+            n (int): The index of the sample to be loaded
+
+        Returns:
+            Tuple of the following items;
+
+            Tensor:
+                Waveform
+            int:
+                Sample rate
+            str:
+                File name
+            str:
+                Label (one of ``"neu"``, ``"hap"``, ``"ang"``, ``"sad"``, ``"exc"``, ``"fru"``)
+            str:
+                Speaker
+        """
+        metadata = self.get_metadata(n)
+        waveform = _load_waveform(self._path, metadata[0], metadata[1])
+        return (waveform,) + metadata[1:]
+
+    def __len__(self):
+        return len(self.data)

micromamba_root/envs/pytorch_env/Lib/site-packages/torchaudio/datasets/librilight_limited.py

@@ -0,0 +1,111 @@
+import os
+from pathlib import Path
+from typing import List, Tuple, Union
+
+import torchaudio
+from torch import Tensor
+from torch.utils.data import Dataset
+from torchaudio._internal import download_url_to_file
+from torchaudio.datasets.librispeech import _get_librispeech_metadata
+from torchaudio.datasets.utils import _extract_tar
+
+
+_ARCHIVE_NAME = "librispeech_finetuning"
+_URL = "https://dl.fbaipublicfiles.com/librilight/data/librispeech_finetuning.tgz"
+_CHECKSUM = "5d1efdc777b548194d7e09ba89126e2188026df9fd57aa57eb14408d2b2342af"
+_SUBSET_MAP = {"10min": ["1h/0"], "1h": ["1h/*"], "10h": ["1h/*", "9h"]}
+
+
+def _get_fileids_paths(path: Path, folders: List[str], _ext_audio: str) -> List[Tuple[str, str]]:
+    """Get the file names and the corresponding file paths without `speaker_id`
+    and `chapter_id` directories.
+    The format of path is like:
+        {root}/{_ARCHIVE_NAME}/1h/[0-5]/[clean, other] or
+        {root}/{_ARCHIVE_NAME}/9h/[clean, other]
+
+    Args:
+        path (Path): Root path to the dataset.
+        folders (List[str]): Folders that contain the desired audio files.
+        _ext_audio (str): Extension of audio files.
+
+    Returns:
+        List[Tuple[str, str]]:
+            List of tuples where the first element is the relative path to the audio file.
+            The format of relative path is like:
+            1h/[0-5]/[clean, other] or 9h/[clean, other]
+            The second element is the file name without audio extension.
+    """
+
+    path = Path(path)
+    files_paths = []
+    for folder in folders:
+        paths = [p.relative_to(path) for p in path.glob(f"{folder}/*/*/*/*{_ext_audio}")]
+        files_paths += [(str(p.parent.parent.parent), str(p.stem)) for p in paths]  # get subset folder and file name
+    files_paths.sort(key=lambda x: x[0] + x[1])
+    return files_paths
+
+
+class LibriLightLimited(Dataset):
+    """Subset of Libri-light :cite:`librilight` dataset,
+    which was used in HuBERT :cite:`hsu2021hubert` for supervised fine-tuning.
+
+    Args:
+        root (str or Path): Path to the directory where the dataset is found or downloaded.
+        subset (str, optional): The subset to use. Options: [``"10min"``, ``"1h"``, ``"10h"``]
+            (Default: ``"10min"``).
+        download (bool, optional):
+            Whether to download the dataset if it is not found at root path. (default: ``False``).
+    """
+
+    _ext_txt = ".trans.txt"
+    _ext_audio = ".flac"
+
+    def __init__(
+        self,
+        root: Union[str, Path],
+        subset: str = "10min",
+        download: bool = False,
+    ) -> None:
+        if subset not in _SUBSET_MAP:
+            raise ValueError(f"`subset` must be one of {_SUBSET_MAP.keys()}. Found: {subset}")
+        folders = _SUBSET_MAP[subset]
+
+        root = os.fspath(root)
+        self._path = os.path.join(root, _ARCHIVE_NAME)
+        archive = os.path.join(root, f"{_ARCHIVE_NAME}.tgz")
+        if not os.path.isdir(self._path):
+            if not download:
+                raise RuntimeError("Dataset not found. Please use `download=True` to download")
+            if not os.path.isfile(archive):
+                download_url_to_file(_URL, archive, hash_prefix=_CHECKSUM)
+            _extract_tar(archive)
+        self._fileids_paths = _get_fileids_paths(self._path, folders, self._ext_audio)
+
+    def __getitem__(self, n: int) -> Tuple[Tensor, int, str, int, int, int]:
+        """Load the n-th sample from the dataset.
+
+        Args:
+            n (int): The index of the sample to be loaded
+        Returns:
+            Tuple of the following items;
+
+            Tensor:
+                Waveform
+            int:
+                Sample rate
+            str:
+                Transcript
+            int:
+                Speaker ID
+            int:
+                Chapter ID
+            int:
+                Utterance ID
+        """
+        file_path, fileid = self._fileids_paths[n]
+        metadata = _get_librispeech_metadata(fileid, self._path, file_path, self._ext_audio, self._ext_txt)
+        waveform, _ = torchaudio.load(os.path.join(self._path, metadata[0]))
+        return (waveform,) + metadata[1:]
+
+    def __len__(self) -> int:
+        return len(self._fileids_paths)

micromamba_root/envs/pytorch_env/Lib/site-packages/torchaudio/datasets/librimix.py

@@ -0,0 +1,133 @@
+import os
+from pathlib import Path
+from typing import List, Tuple, Union
+
+import torch
+from torch.utils.data import Dataset
+from torchaudio.datasets.utils import _load_waveform
+
+_TASKS_TO_MIXTURE = {
+    "sep_clean": "mix_clean",
+    "enh_single": "mix_single",
+    "enh_both": "mix_both",
+    "sep_noisy": "mix_both",
+}
+
+
+class LibriMix(Dataset):
+    r"""*LibriMix* :cite:`cosentino2020librimix` dataset.
+
+    Args:
+        root (str or Path): The path where the directory ``Libri2Mix`` or
+            ``Libri3Mix`` is stored. Not the path of those directories.
+        subset (str, optional): The subset to use. Options: [``"train-360"``, ``"train-100"``,
+            ``"dev"``, and ``"test"``] (Default: ``"train-360"``).
+        num_speakers (int, optional): The number of speakers, which determines the directories
+            to traverse. The Dataset will traverse ``s1`` to ``sN`` directories to collect
+            N source audios. (Default: 2)
+        sample_rate (int, optional): Sample rate of audio files. The ``sample_rate`` determines
+            which subdirectory the audio are fetched. If any of the audio has a different sample
+            rate, raises ``ValueError``. Options: [8000, 16000] (Default: 8000)
+        task (str, optional): The task of LibriMix.
+            Options: [``"enh_single"``, ``"enh_both"``, ``"sep_clean"``, ``"sep_noisy"``]
+            (Default: ``"sep_clean"``)
+        mode (str, optional): The mode when creating the mixture. If set to ``"min"``, the lengths of mixture
+            and sources are the minimum length of all sources. If set to ``"max"``, the lengths of mixture and
+            sources are zero padded to the maximum length of all sources.
+            Options: [``"min"``, ``"max"``]
+            (Default: ``"min"``)
+
+    Note:
+        The LibriMix dataset needs to be manually generated. Please check https://github.com/JorisCos/LibriMix
+    """
+
+    def __init__(
+        self,
+        root: Union[str, Path],
+        subset: str = "train-360",
+        num_speakers: int = 2,
+        sample_rate: int = 8000,
+        task: str = "sep_clean",
+        mode: str = "min",
+    ):
+        self.root = Path(root) / f"Libri{num_speakers}Mix"
+        if not os.path.exists(self.root):
+            raise RuntimeError(
+                f"The path {self.root} doesn't exist. "
+                "Please check the ``root`` path and ``num_speakers`` or download the dataset manually."
+            )
+        if mode not in ["max", "min"]:
+            raise ValueError(f'Expect ``mode`` to be one in ["min", "max"]. Found {mode}.')
+        if sample_rate == 8000:
+            mix_dir = self.root / "wav8k" / mode / subset
+        elif sample_rate == 16000:
+            mix_dir = self.root / "wav16k" / mode / subset
+        else:
+            raise ValueError(f"Unsupported sample rate. Found {sample_rate}.")
+        self.sample_rate = sample_rate
+        self.task = task
+
+        self.mix_dir = mix_dir / _TASKS_TO_MIXTURE[task]
+        if task == "enh_both":
+            self.src_dirs = [(mix_dir / "mix_clean")]
+        else:
+            self.src_dirs = [(mix_dir / f"s{i+1}") for i in range(num_speakers)]
+
+        self.files = [p.name for p in self.mix_dir.glob("*.wav")]
+        self.files.sort()
+
+    def _load_sample(self, key) -> Tuple[int, torch.Tensor, List[torch.Tensor]]:
+        metadata = self.get_metadata(key)
+        mixed = _load_waveform(self.root, metadata[1], metadata[0])
+        srcs = []
+        for i, path_ in enumerate(metadata[2]):
+            src = _load_waveform(self.root, path_, metadata[0])
+            if mixed.shape != src.shape:
+                raise ValueError(f"Different waveform shapes. mixed: {mixed.shape}, src[{i}]: {src.shape}")
+            srcs.append(src)
+        return self.sample_rate, mixed, srcs
+
+    def get_metadata(self, key: int) -> Tuple[int, str, List[str]]:
+        """Get metadata for the n-th sample from the dataset.
+
+        Args:
+            key (int): The index of the sample to be loaded
+
+        Returns:
+            Tuple of the following items;
+
+            int:
+                Sample rate
+            str:
+                Path to mixed audio
+            List of str:
+                List of paths to source audios
+        """
+        filename = self.files[key]
+        mixed_path = os.path.relpath(self.mix_dir / filename, self.root)
+        srcs_paths = []
+        for dir_ in self.src_dirs:
+            src = os.path.relpath(dir_ / filename, self.root)
+            srcs_paths.append(src)
+        return self.sample_rate, mixed_path, srcs_paths
+
+    def __len__(self) -> int:
+        return len(self.files)
+
+    def __getitem__(self, key: int) -> Tuple[int, torch.Tensor, List[torch.Tensor]]:
+        """Load the n-th sample from the dataset.
+
+        Args:
+            key (int): The index of the sample to be loaded
+
+        Returns:
+            Tuple of the following items;
+
+            int:
+                Sample rate
+            Tensor:
+                Mixture waveform
+            List of Tensors:
+                List of source waveforms
+        """
+        return self._load_sample(key)

micromamba_root/envs/pytorch_env/Lib/site-packages/torchaudio/datasets/librispeech.py

@@ -0,0 +1,174 @@
+import os
+from pathlib import Path
+from typing import Tuple, Union
+
+from torch import Tensor
+from torch.utils.data import Dataset
+from torchaudio._internal import download_url_to_file
+from torchaudio.datasets.utils import _extract_tar, _load_waveform
+
+URL = "train-clean-100"
+FOLDER_IN_ARCHIVE = "LibriSpeech"
+SAMPLE_RATE = 16000
+_DATA_SUBSETS = [
+    "dev-clean",
+    "dev-other",
+    "test-clean",
+    "test-other",
+    "train-clean-100",
+    "train-clean-360",
+    "train-other-500",
+]
+_CHECKSUMS = {
+    "http://www.openslr.org/resources/12/dev-clean.tar.gz": "76f87d090650617fca0cac8f88b9416e0ebf80350acb97b343a85fa903728ab3",  # noqa: E501
+    "http://www.openslr.org/resources/12/dev-other.tar.gz": "12661c48e8c3fe1de2c1caa4c3e135193bfb1811584f11f569dd12645aa84365",  # noqa: E501
+    "http://www.openslr.org/resources/12/test-clean.tar.gz": "39fde525e59672dc6d1551919b1478f724438a95aa55f874b576be21967e6c23",  # noqa: E501
+    "http://www.openslr.org/resources/12/test-other.tar.gz": "d09c181bba5cf717b3dee7d4d592af11a3ee3a09e08ae025c5506f6ebe961c29",  # noqa: E501
+    "http://www.openslr.org/resources/12/train-clean-100.tar.gz": "d4ddd1d5a6ab303066f14971d768ee43278a5f2a0aa43dc716b0e64ecbbbf6e2",  # noqa: E501
+    "http://www.openslr.org/resources/12/train-clean-360.tar.gz": "146a56496217e96c14334a160df97fffedd6e0a04e66b9c5af0d40be3c792ecf",  # noqa: E501
+    "http://www.openslr.org/resources/12/train-other-500.tar.gz": "ddb22f27f96ec163645d53215559df6aa36515f26e01dd70798188350adcb6d2",  # noqa: E501
+}
+
+
+def _download_librispeech(root, url):
+    base_url = "http://www.openslr.org/resources/12/"
+    ext_archive = ".tar.gz"
+
+    filename = url + ext_archive
+    archive = os.path.join(root, filename)
+    download_url = os.path.join(base_url, filename)
+    if not os.path.isfile(archive):
+        checksum = _CHECKSUMS.get(download_url, None)
+        download_url_to_file(download_url, archive, hash_prefix=checksum)
+    _extract_tar(archive)
+
+
+def _get_librispeech_metadata(
+    fileid: str, root: str, folder: str, ext_audio: str, ext_txt: str
+) -> Tuple[str, int, str, int, int, int]:
+    speaker_id, chapter_id, utterance_id = fileid.split("-")
+
+    # Get audio path and sample rate
+    fileid_audio = f"{speaker_id}-{chapter_id}-{utterance_id}"
+    filepath = os.path.join(folder, speaker_id, chapter_id, f"{fileid_audio}{ext_audio}")
+
+    # Load text
+    file_text = f"{speaker_id}-{chapter_id}{ext_txt}"
+    file_text = os.path.join(root, folder, speaker_id, chapter_id, file_text)
+    with open(file_text) as ft:
+        for line in ft:
+            fileid_text, transcript = line.strip().split(" ", 1)
+            if fileid_audio == fileid_text:
+                break
+        else:
+            # Translation not found
+            raise FileNotFoundError(f"Translation not found for {fileid_audio}")
+
+    return (
+        filepath,
+        SAMPLE_RATE,
+        transcript,
+        int(speaker_id),
+        int(chapter_id),
+        int(utterance_id),
+    )
+
+
+class LIBRISPEECH(Dataset):
+    """*LibriSpeech* :cite:`7178964` dataset.
+
+    Args:
+        root (str or Path): Path to the directory where the dataset is found or downloaded.
+        url (str, optional): The URL to download the dataset from,
+            or the type of the dataset to dowload.
+            Allowed type values are ``"dev-clean"``, ``"dev-other"``, ``"test-clean"``,
+            ``"test-other"``, ``"train-clean-100"``, ``"train-clean-360"`` and
+            ``"train-other-500"``. (default: ``"train-clean-100"``)
+        folder_in_archive (str, optional):
+            The top-level directory of the dataset. (default: ``"LibriSpeech"``)
+        download (bool, optional):
+            Whether to download the dataset if it is not found at root path. (default: ``False``).
+    """
+
+    _ext_txt = ".trans.txt"
+    _ext_audio = ".flac"
+
+    def __init__(
+        self,
+        root: Union[str, Path],
+        url: str = URL,
+        folder_in_archive: str = FOLDER_IN_ARCHIVE,
+        download: bool = False,
+    ) -> None:
+        self._url = url
+        if url not in _DATA_SUBSETS:
+            raise ValueError(f"Invalid url '{url}' given; please provide one of {_DATA_SUBSETS}.")
+
+        root = os.fspath(root)
+        self._archive = os.path.join(root, folder_in_archive)
+        self._path = os.path.join(root, folder_in_archive, url)
+
+        if not os.path.isdir(self._path):
+            if download:
+                _download_librispeech(root, url)
+            else:
+                raise RuntimeError(
+                    f"Dataset not found at {self._path}. Please set `download=True` to download the dataset."
+                )
+
+        self._walker = sorted(str(p.stem) for p in Path(self._path).glob("*/*/*" + self._ext_audio))
+
+    def get_metadata(self, n: int) -> Tuple[str, int, str, int, int, int]:
+        """Get metadata for the n-th sample from the dataset. Returns filepath instead of waveform,
+        but otherwise returns the same fields as :py:func:`__getitem__`.
+
+        Args:
+            n (int): The index of the sample to be loaded
+
+        Returns:
+            Tuple of the following items;
+
+            str:
+                Path to audio
+            int:
+                Sample rate
+            str:
+                Transcript
+            int:
+                Speaker ID
+            int:
+                Chapter ID
+            int:
+                Utterance ID
+        """
+        fileid = self._walker[n]
+        return _get_librispeech_metadata(fileid, self._archive, self._url, self._ext_audio, self._ext_txt)
+
+    def __getitem__(self, n: int) -> Tuple[Tensor, int, str, int, int, int]:
+        """Load the n-th sample from the dataset.
+
+        Args:
+            n (int): The index of the sample to be loaded
+
+        Returns:
+            Tuple of the following items;
+
+            Tensor:
+                Waveform
+            int:
+                Sample rate
+            str:
+                Transcript
+            int:
+                Speaker ID
+            int:
+                Chapter ID
+            int:
+                Utterance ID
+        """
+        metadata = self.get_metadata(n)
+        waveform = _load_waveform(self._archive, metadata[0], metadata[1])
+        return (waveform,) + metadata[1:]
+
+    def __len__(self) -> int:
+        return len(self._walker)

micromamba_root/envs/pytorch_env/Lib/site-packages/torchaudio/datasets/librispeech_biasing.py

@@ -0,0 +1,189 @@
+import os
+from pathlib import Path
+from typing import List, Tuple, Union
+
+from torch import Tensor
+from torch.utils.data import Dataset
+from torchaudio._internal import download_url_to_file
+from torchaudio.datasets.utils import _extract_tar, _load_waveform
+
+URL = "train-clean-100"
+FOLDER_IN_ARCHIVE = "LibriSpeech"
+SAMPLE_RATE = 16000
+_DATA_SUBSETS = [
+    "dev-clean",
+    "dev-other",
+    "test-clean",
+    "test-other",
+    "train-clean-100",
+    "train-clean-360",
+    "train-other-500",
+]
+_CHECKSUMS = {
+    "http://www.openslr.org/resources/12/dev-clean.tar.gz": "76f87d090650617fca0cac8f88b9416e0ebf80350acb97b343a85fa903728ab3",  # noqa: E501
+    "http://www.openslr.org/resources/12/dev-other.tar.gz": "12661c48e8c3fe1de2c1caa4c3e135193bfb1811584f11f569dd12645aa84365",  # noqa: E501
+    "http://www.openslr.org/resources/12/test-clean.tar.gz": "39fde525e59672dc6d1551919b1478f724438a95aa55f874b576be21967e6c23",  # noqa: E501
+    "http://www.openslr.org/resources/12/test-other.tar.gz": "d09c181bba5cf717b3dee7d4d592af11a3ee3a09e08ae025c5506f6ebe961c29",  # noqa: E501
+    "http://www.openslr.org/resources/12/train-clean-100.tar.gz": "d4ddd1d5a6ab303066f14971d768ee43278a5f2a0aa43dc716b0e64ecbbbf6e2",  # noqa: E501
+    "http://www.openslr.org/resources/12/train-clean-360.tar.gz": "146a56496217e96c14334a160df97fffedd6e0a04e66b9c5af0d40be3c792ecf",  # noqa: E501
+    "http://www.openslr.org/resources/12/train-other-500.tar.gz": "ddb22f27f96ec163645d53215559df6aa36515f26e01dd70798188350adcb6d2",  # noqa: E501
+}
+
+
+def _download_librispeech(root, url):
+    base_url = "http://www.openslr.org/resources/12/"
+    ext_archive = ".tar.gz"
+
+    filename = url + ext_archive
+    archive = os.path.join(root, filename)
+    download_url = os.path.join(base_url, filename)
+    if not os.path.isfile(archive):
+        checksum = _CHECKSUMS.get(download_url, None)
+        download_url_to_file(download_url, archive, hash_prefix=checksum)
+    _extract_tar(archive)
+
+
+def _get_librispeech_metadata(
+    fileid: str, root: str, folder: str, ext_audio: str, ext_txt: str, blist: List[str]
+) -> Tuple[str, int, str, int, int, int]:
+    blist = blist or []
+    speaker_id, chapter_id, utterance_id = fileid.split("-")
+
+    # Get audio path and sample rate
+    fileid_audio = f"{speaker_id}-{chapter_id}-{utterance_id}"
+    filepath = os.path.join(folder, speaker_id, chapter_id, f"{fileid_audio}{ext_audio}")
+
+    # Load text
+    file_text = f"{speaker_id}-{chapter_id}{ext_txt}"
+    file_text = os.path.join(root, folder, speaker_id, chapter_id, file_text)
+    uttblist = []
+    with open(file_text) as ft:
+        for line in ft:
+            fileid_text, transcript = line.strip().split(" ", 1)
+            if fileid_audio == fileid_text:
+                # get utterance biasing list
+                for word in transcript.split():
+                    if word in blist and word not in uttblist:
+                        uttblist.append(word)
+                break
+        else:
+            # Translation not found
+            raise FileNotFoundError(f"Translation not found for {fileid_audio}")
+
+    return (
+        filepath,
+        SAMPLE_RATE,
+        transcript,
+        int(speaker_id),
+        int(chapter_id),
+        int(utterance_id),
+        uttblist,
+    )
+
+
+class LibriSpeechBiasing(Dataset):
+    """*LibriSpeech* :cite:`7178964` dataset with prefix-tree construction and biasing support.
+
+    Args:
+        root (str or Path): Path to the directory where the dataset is found or downloaded.
+        url (str, optional): The URL to download the dataset from,
+            or the type of the dataset to dowload.
+            Allowed type values are ``"dev-clean"``, ``"dev-other"``, ``"test-clean"``,
+            ``"test-other"``, ``"train-clean-100"``, ``"train-clean-360"`` and
+            ``"train-other-500"``. (default: ``"train-clean-100"``)
+        folder_in_archive (str, optional):
+            The top-level directory of the dataset. (default: ``"LibriSpeech"``)
+        download (bool, optional):
+            Whether to download the dataset if it is not found at root path. (default: ``False``).
+        blist (list, optional):
+            The list of biasing words (default: ``[]``).
+    """
+
+    _ext_txt = ".trans.txt"
+    _ext_audio = ".flac"
+
+    def __init__(
+        self,
+        root: Union[str, Path],
+        url: str = URL,
+        folder_in_archive: str = FOLDER_IN_ARCHIVE,
+        download: bool = False,
+        blist: List[str] = None,
+    ) -> None:
+        self._url = url
+        if url not in _DATA_SUBSETS:
+            raise ValueError(f"Invalid url '{url}' given; please provide one of {_DATA_SUBSETS}.")
+
+        root = os.fspath(root)
+        self._archive = os.path.join(root, folder_in_archive)
+        self._path = os.path.join(root, folder_in_archive, url)
+
+        if not os.path.isdir(self._path):
+            if download:
+                _download_librispeech(root, url)
+            else:
+                raise RuntimeError(
+                    f"Dataset not found at {self._path}. Please set `download=True` to download the dataset."
+                )
+
+        self._walker = sorted(str(p.stem) for p in Path(self._path).glob("*/*/*" + self._ext_audio))
+        self.blist = blist
+
+    def get_metadata(self, n: int) -> Tuple[str, int, str, int, int, int]:
+        """Get metadata for the n-th sample from the dataset. Returns filepath instead of waveform,
+        but otherwise returns the same fields as :py:func:`__getitem__`.
+
+        Args:
+            n (int): The index of the sample to be loaded
+
+        Returns:
+            Tuple of the following items;
+
+            str:
+                Path to audio
+            int:
+                Sample rate
+            str:
+                Transcript
+            int:
+                Speaker ID
+            int:
+                Chapter ID
+            int:
+                Utterance ID
+            list:
+                List of biasing words in the utterance
+        """
+        fileid = self._walker[n]
+        return _get_librispeech_metadata(fileid, self._archive, self._url, self._ext_audio, self._ext_txt, self.blist)
+
+    def __getitem__(self, n: int) -> Tuple[Tensor, int, str, int, int, int]:
+        """Load the n-th sample from the dataset.
+
+        Args:
+            n (int): The index of the sample to be loaded
+
+        Returns:
+            Tuple of the following items;
+
+            Tensor:
+                Waveform
+            int:
+                Sample rate
+            str:
+                Transcript
+            int:
+                Speaker ID
+            int:
+                Chapter ID
+            int:
+                Utterance ID
+            list:
+                List of biasing words in the utterance
+        """
+        metadata = self.get_metadata(n)
+        waveform = _load_waveform(self._archive, metadata[0], metadata[1])
+        return (waveform,) + metadata[1:]
+
+    def __len__(self) -> int:
+        return len(self._walker)

micromamba_root/envs/pytorch_env/Lib/site-packages/torchaudio/datasets/libritts.py

@@ -0,0 +1,168 @@
+import os
+from pathlib import Path
+from typing import Tuple, Union
+
+import torchaudio
+from torch import Tensor
+from torch.utils.data import Dataset
+from torchaudio._internal import download_url_to_file
+from torchaudio.datasets.utils import _extract_tar
+
+URL = "train-clean-100"
+FOLDER_IN_ARCHIVE = "LibriTTS"
+_CHECKSUMS = {
+    "http://www.openslr.org/resources/60/dev-clean.tar.gz": "da0864e1bd26debed35da8a869dd5c04dfc27682921936de7cff9c8a254dbe1a",  # noqa: E501
+    "http://www.openslr.org/resources/60/dev-other.tar.gz": "d413eda26f3a152ac7c9cf3658ef85504dfb1b625296e5fa83727f5186cca79c",  # noqa: E501
+    "http://www.openslr.org/resources/60/test-clean.tar.gz": "234ea5b25859102a87024a4b9b86641f5b5aaaf1197335c95090cde04fe9a4f5",  # noqa: E501
+    "http://www.openslr.org/resources/60/test-other.tar.gz": "33a5342094f3bba7ccc2e0500b9e72d558f72eb99328ac8debe1d9080402f10d",  # noqa: E501
+    "http://www.openslr.org/resources/60/train-clean-100.tar.gz": "c5608bf1ef74bb621935382b8399c5cdd51cd3ee47cec51f00f885a64c6c7f6b",  # noqa: E501
+    "http://www.openslr.org/resources/60/train-clean-360.tar.gz": "ce7cff44dcac46009d18379f37ef36551123a1dc4e5c8e4eb73ae57260de4886",  # noqa: E501
+    "http://www.openslr.org/resources/60/train-other-500.tar.gz": "e35f7e34deeb2e2bdfe4403d88c8fdd5fbf64865cae41f027a185a6965f0a5df",  # noqa: E501
+}
+
+
+def load_libritts_item(
+    fileid: str,
+    path: str,
+    ext_audio: str,
+    ext_original_txt: str,
+    ext_normalized_txt: str,
+) -> Tuple[Tensor, int, str, str, int, int, str]:
+    speaker_id, chapter_id, segment_id, utterance_id = fileid.split("_")
+    utterance_id = fileid
+
+    normalized_text = utterance_id + ext_normalized_txt
+    normalized_text = os.path.join(path, speaker_id, chapter_id, normalized_text)
+
+    original_text = utterance_id + ext_original_txt
+    original_text = os.path.join(path, speaker_id, chapter_id, original_text)
+
+    file_audio = utterance_id + ext_audio
+    file_audio = os.path.join(path, speaker_id, chapter_id, file_audio)
+
+    # Load audio
+    waveform, sample_rate = torchaudio.load(file_audio)
+
+    # Load original text
+    with open(original_text) as ft:
+        original_text = ft.readline()
+
+    # Load normalized text
+    with open(normalized_text, "r") as ft:
+        normalized_text = ft.readline()
+
+    return (
+        waveform,
+        sample_rate,
+        original_text,
+        normalized_text,
+        int(speaker_id),
+        int(chapter_id),
+        utterance_id,
+    )
+
+
+class LIBRITTS(Dataset):
+    """*LibriTTS* :cite:`Zen2019LibriTTSAC` dataset.
+
+    Args:
+        root (str or Path): Path to the directory where the dataset is found or downloaded.
+        url (str, optional): The URL to download the dataset from,
+            or the type of the dataset to dowload.
+            Allowed type values are ``"dev-clean"``, ``"dev-other"``, ``"test-clean"``,
+            ``"test-other"``, ``"train-clean-100"``, ``"train-clean-360"`` and
+            ``"train-other-500"``. (default: ``"train-clean-100"``)
+        folder_in_archive (str, optional):
+            The top-level directory of the dataset. (default: ``"LibriTTS"``)
+        download (bool, optional):
+            Whether to download the dataset if it is not found at root path. (default: ``False``).
+    """
+
+    _ext_original_txt = ".original.txt"
+    _ext_normalized_txt = ".normalized.txt"
+    _ext_audio = ".wav"
+
+    def __init__(
+        self,
+        root: Union[str, Path],
+        url: str = URL,
+        folder_in_archive: str = FOLDER_IN_ARCHIVE,
+        download: bool = False,
+    ) -> None:
+
+        if url in [
+            "dev-clean",
+            "dev-other",
+            "test-clean",
+            "test-other",
+            "train-clean-100",
+            "train-clean-360",
+            "train-other-500",
+        ]:
+
+            ext_archive = ".tar.gz"
+            base_url = "http://www.openslr.org/resources/60/"
+
+            url = os.path.join(base_url, url + ext_archive)
+
+        # Get string representation of 'root' in case Path object is passed
+        root = os.fspath(root)
+
+        basename = os.path.basename(url)
+        archive = os.path.join(root, basename)
+
+        basename = basename.split(".")[0]
+        folder_in_archive = os.path.join(folder_in_archive, basename)
+
+        self._path = os.path.join(root, folder_in_archive)
+
+        if download:
+            if not os.path.isdir(self._path):
+                if not os.path.isfile(archive):
+                    checksum = _CHECKSUMS.get(url, None)
+                    download_url_to_file(url, archive, hash_prefix=checksum)
+                _extract_tar(archive)
+        else:
+            if not os.path.exists(self._path):
+                raise RuntimeError(
+                    f"The path {self._path} doesn't exist. "
+                    "Please check the ``root`` path or set `download=True` to download it"
+                )
+
+        self._walker = sorted(str(p.stem) for p in Path(self._path).glob("*/*/*" + self._ext_audio))
+
+    def __getitem__(self, n: int) -> Tuple[Tensor, int, str, str, int, int, str]:
+        """Load the n-th sample from the dataset.
+
+        Args:
+            n (int): The index of the sample to be loaded
+
+        Returns:
+            Tuple of the following items;
+
+            Tensor:
+                Waveform
+            int:
+                Sample rate
+            str:
+                Original text
+            str:
+                Normalized text
+            int:
+                Speaker ID
+            int:
+                Chapter ID
+            str:
+                Utterance ID
+        """
+        fileid = self._walker[n]
+        return load_libritts_item(
+            fileid,
+            self._path,
+            self._ext_audio,
+            self._ext_original_txt,
+            self._ext_normalized_txt,
+        )
+
+    def __len__(self) -> int:
+        return len(self._walker)

micromamba_root/envs/pytorch_env/Lib/site-packages/torchaudio/datasets/ljspeech.py

@@ -0,0 +1,107 @@
+import csv
+import os
+from pathlib import Path
+from typing import Tuple, Union
+
+import torchaudio
+from torch import Tensor
+from torch.utils.data import Dataset
+from torchaudio._internal import download_url_to_file
+from torchaudio.datasets.utils import _extract_tar
+
+
+_RELEASE_CONFIGS = {
+    "release1": {
+        "folder_in_archive": "wavs",
+        "url": "https://data.keithito.com/data/speech/LJSpeech-1.1.tar.bz2",
+        "checksum": "be1a30453f28eb8dd26af4101ae40cbf2c50413b1bb21936cbcdc6fae3de8aa5",
+    }
+}
+
+
+class LJSPEECH(Dataset):
+    """*LJSpeech-1.1* :cite:`ljspeech17` dataset.
+
+    Args:
+        root (str or Path): Path to the directory where the dataset is found or downloaded.
+        url (str, optional): The URL to download the dataset from.
+            (default: ``"https://data.keithito.com/data/speech/LJSpeech-1.1.tar.bz2"``)
+        folder_in_archive (str, optional):
+            The top-level directory of the dataset. (default: ``"wavs"``)
+        download (bool, optional):
+            Whether to download the dataset if it is not found at root path. (default: ``False``).
+    """
+
+    def __init__(
+        self,
+        root: Union[str, Path],
+        url: str = _RELEASE_CONFIGS["release1"]["url"],
+        folder_in_archive: str = _RELEASE_CONFIGS["release1"]["folder_in_archive"],
+        download: bool = False,
+    ) -> None:
+
+        self._parse_filesystem(root, url, folder_in_archive, download)
+
+    def _parse_filesystem(self, root: str, url: str, folder_in_archive: str, download: bool) -> None:
+        root = Path(root)
+
+        basename = os.path.basename(url)
+        archive = root / basename
+
+        basename = Path(basename.split(".tar.bz2")[0])
+        folder_in_archive = basename / folder_in_archive
+
+        self._path = root / folder_in_archive
+        self._metadata_path = root / basename / "metadata.csv"
+
+        if download:
+            if not os.path.isdir(self._path):
+                if not os.path.isfile(archive):
+                    checksum = _RELEASE_CONFIGS["release1"]["checksum"]
+                    download_url_to_file(url, archive, hash_prefix=checksum)
+                _extract_tar(archive)
+        else:
+            if not os.path.exists(self._path):
+                raise RuntimeError(
+                    f"The path {self._path} doesn't exist. "
+                    "Please check the ``root`` path or set `download=True` to download it"
+                )
+
+        with open(self._metadata_path, "r", newline="") as metadata:
+            flist = csv.reader(metadata, delimiter="|", quoting=csv.QUOTE_NONE)
+            self._flist = list(flist)
+
+    def __getitem__(self, n: int) -> Tuple[Tensor, int, str, str]:
+        """Load the n-th sample from the dataset.
+
+        Args:
+            n (int): The index of the sample to be loaded
+
+        Returns:
+            Tuple of the following items;
+
+            Tensor:
+                Waveform
+            int:
+                Sample rate
+            str:
+                Transcript
+            str:
+                Normalized Transcript
+        """
+        line = self._flist[n]
+        fileid, transcript, normalized_transcript = line
+        fileid_audio = self._path / (fileid + ".wav")
+
+        # Load audio
+        waveform, sample_rate = torchaudio.load(fileid_audio)
+
+        return (
+            waveform,
+            sample_rate,
+            transcript,
+            normalized_transcript,
+        )
+
+    def __len__(self) -> int:
+        return len(self._flist)