set things up for eventual text conditioned audio synthesis

from shutil import rmtree

from typing import Union, List, Optional

from typeguard import typechecked

from typing_extensions import Annotated

from beartype import beartype

from beartype.door import is_bearable

from beartype.vale import Is

import torch

import torchaudio

DEFAULT_SAMPLE_RATE = 16000

# for automatically routing data emitted from a dataset to keywords of the transformer wrappers

DATASET_FIELD_TYPE_CONFIG = dict(

    raw_wave = Annotated[

        torch.Tensor,

        Is[lambda t: t.dtype == torch.float and t.ndim in {2, 3}]

    ],

    text = List[str],

    text_embeds = Annotated[

        torch.Tensor,

        Is[lambda t: t.dtype == torch.float and t.ndim == 3]

    ],

)

# helpers

def exists(val):

        log[key] = old_value + new_value

    return log

# auto data to module keyword argument routing functions

def has_duplicates(tup):

    counts = dict()

    for el in tup:

        if el not in counts:

            counts[el] = 0

        counts[el] += 1

    return any(filter(lambda count: count > 1, counts.values()))

def determine_types(data, config):

    output = []

    for el in data:

        for name, data_type in config.items():

            if is_bearable(el, data_type):

                output.append(name)

                break

        else:

            raise TypeError(f'unable to determine type of {data}')

    return tuple(output)

# main trainer class

class SoundStreamTrainer(nn.Module):

# semantic transformer trainer

@typechecked

@beartype

class SemanticTransformerTrainer(nn.Module):

    def __init__(

        self,

# fine transformer trainer

@typechecked

@beartype

class CoarseTransformerTrainer(nn.Module):

    def __init__(

        self,

# fine transformer trainer

@typechecked

@beartype

class FineTransformerTrainer(nn.Module):

    def __init__(

        self,

setup(

  name = 'audiolm-pytorch',

  packages = find_packages(exclude=[]),

  version = '0.1.12',

  version = '0.1.14',

  license='MIT',

  description = 'AudioLM - Language Modeling Approach to Audio Generation from Google Research - Pytorch',

  author = 'Phil Wang',