speechbrain.inference.interfaces module

Defines interfaces for simple inference with pretrained models

Authors:

  • Aku Rouhe 2021

  • Peter Plantinga 2021

  • Loren Lugosch 2020

  • Mirco Ravanelli 2020

  • Titouan Parcollet 2021

  • Abdel Heba 2021

  • Andreas Nautsch 2022, 2023

  • Pooneh Mousavi 2023

  • Sylvain de Langen 2023

  • Adel Moumen 2023

  • Pradnya Kandarkar 2023

Summary

Classes:

EncodeDecodePipelineMixin

A mixin for pretrained models that makes it possible to specify an encoding pipeline and a decoding pipeline

Pretrained

Takes a trained model and makes predictions on new data.

Functions:

foreign_class

Fetch and load an interface from an outside source

Reference

speechbrain.inference.interfaces.foreign_class(source, hparams_file='hyperparams.yaml', pymodule_file='custom.py', classname='CustomInterface', overrides={}, overrides_must_match=True, savedir=None, use_auth_token=False, download_only=False, huggingface_cache_dir=None, **kwargs)[source]

Fetch and load an interface from an outside source

The source can be a location on the filesystem or online/huggingface

The pymodule file should contain a class with the given classname. An instance of that class is returned. The idea is to have a custom Pretrained subclass in the file. The pymodule file is also added to the python path before the Hyperparams YAML file is loaded, so it can contain any custom implementations that are needed.

The hyperparams file should contain a “modules” key, which is a dictionary of torch modules used for computation.

The hyperparams file should contain a “pretrainer” key, which is a speechbrain.utils.parameter_transfer.Pretrainer

Parameters:

  • source (str or Path or FetchSource) – The location to use for finding the model. See speechbrain.pretrained.fetching.fetch for details.

  • hparams_file (str) – The name of the hyperparameters file to use for constructing the modules necessary for inference. Must contain two keys: “modules” and “pretrainer”, as described.

  • pymodule_file (str) – The name of the Python file that should be fetched.

  • classname (str) – The name of the Class, of which an instance is created and returned

  • overrides (dict) – Any changes to make to the hparams file when it is loaded.

  • overrides_must_match (bool) – Whether an error will be thrown when an override does not match a corresponding key in the yaml_stream.

  • savedir (str or Path) – Where to put the pretraining material. If not given, will use ./pretrained_models/<class-name>-hash(source).

  • use_auth_token (bool (default: False)) – If true Hugginface’s auth_token will be used to load private models from the HuggingFace Hub, default is False because the majority of models are public.

  • download_only (bool (default: False)) – If true, class and instance creation is skipped.

  • huggingface_cache_dir (str) – Path to HuggingFace cache; if None -> “~/.cache/huggingface” (default: None)

Returns:

An instance of a class with the given classname from the given pymodule file.

Return type:

object

class speechbrain.inference.interfaces.Pretrained(modules=None, hparams=None, run_opts=None, freeze_params=True)[source]

Bases: Module

Takes a trained model and makes predictions on new data.

This is a base class which handles some common boilerplate. It intentionally has an interface similar to Brain - these base classes handle similar things.

Subclasses of Pretrained should implement the actual logic of how the pretrained system runs, and add methods with descriptive names (e.g. transcribe_file() for ASR).

Pretrained is a torch.nn.Module so that methods like .to() or .eval() can work. Subclasses should provide a suitable forward() implementation: by convention, it should be a method that takes a batch of audio signals and runs the full model (as applicable).

Parameters:

  • modules (dict of str:torch.nn.Module pairs) – The Torch modules that make up the learned system. These can be treated in special ways (put on the right device, frozen, etc.). These are available as attributes under self.mods, like self.mods.model(x)

  • hparams (dict) – Each key:value pair should consist of a string key and a hyperparameter that is used within the overridden methods. These will be accessible via an hparams attribute, using “dot” notation: e.g., self.hparams.model(x).

  • run_opts (dict) –

    Options parsed from command line. See speechbrain.parse_arguments(). List that are supported here:

    • device

    • data_parallel_count

    • data_parallel_backend

    • distributed_launch

    • distributed_backend

    • jit

    • jit_module_keys

    • compule

    • compile_module_keys

    • compile_mode

    • compile_using_fullgraph

    • compile_using_dynamic_shape_tracing

  • freeze_params (bool) – To freeze (requires_grad=False) parameters or not. Normally in inference you want to freeze the params. Also calls .eval() on all modules.

HPARAMS_NEEDED = []
MODULES_NEEDED = []
load_audio(path, savedir='.')[source]

Load an audio file with this model’s input spec

When using a speech model, it is important to use the same type of data, as was used to train the model. This means for example using the same sampling rate and number of channels. It is, however, possible to convert a file from a higher sampling rate to a lower one (downsampling). Similarly, it is simple to downmix a stereo file to mono. The path can be a local path, a web url, or a link to a huggingface repo.

classmethod from_hparams(source, hparams_file='hyperparams.yaml', pymodule_file='custom.py', overrides={}, savedir=None, use_auth_token=False, revision=None, download_only=False, huggingface_cache_dir=None, **kwargs)[source]

Fetch and load based from outside source based on HyperPyYAML file

The source can be a location on the filesystem or online/huggingface

You can use the pymodule_file to include any custom implementations that are needed: if that file exists, then its location is added to sys.path before Hyperparams YAML is loaded, so it can be referenced in the YAML.

The hyperparams file should contain a “modules” key, which is a dictionary of torch modules used for computation.

The hyperparams file should contain a “pretrainer” key, which is a speechbrain.utils.parameter_transfer.Pretrainer

Parameters:

  • source (str) – The location to use for finding the model. See speechbrain.pretrained.fetching.fetch for details.

  • hparams_file (str) – The name of the hyperparameters file to use for constructing the modules necessary for inference. Must contain two keys: “modules” and “pretrainer”, as described.

  • pymodule_file (str) – A Python file can be fetched. This allows any custom implementations to be included. The file’s location is added to sys.path before the hyperparams YAML file is loaded, so it can be referenced in YAML. This is optional, but has a default: “custom.py”. If the default file is not found, this is simply ignored, but if you give a different filename, then this will raise in case the file is not found.

  • overrides (dict) – Any changes to make to the hparams file when it is loaded.

  • savedir (str or Path) – Where to put the pretraining material. If not given, will use ./pretrained_models/<class-name>-hash(source).

  • use_auth_token (bool (default: False)) – If true Hugginface’s auth_token will be used to load private models from the HuggingFace Hub, default is False because the majority of models are public.

  • revision (str) – The model revision corresponding to the HuggingFace Hub model revision. This is particularly useful if you wish to pin your code to a particular version of a model hosted at HuggingFace.

  • download_only (bool (default: False)) – If true, class and instance creation is skipped.

  • revision – The model revision corresponding to the HuggingFace Hub model revision. This is particularly useful if you wish to pin your code to a particular version of a model hosted at HuggingFace.

  • huggingface_cache_dir (str) – Path to HuggingFace cache; if None -> “~/.cache/huggingface” (default: None)

training: bool
class speechbrain.inference.interfaces.EncodeDecodePipelineMixin[source]

Bases: object

A mixin for pretrained models that makes it possible to specify an encoding pipeline and a decoding pipeline

create_pipelines()[source]

Initializes the encode and decode pipeline

to_dict(data)[source]

Converts padded batches to dictionaries, leaves other data types as is

Parameters:

data (object) – a dictionary or a padded batch

Returns:

results – the dictionary

Return type:

dict

property batch_inputs

Determines whether the input pipeline operates on batches or individual examples (true means batched)

Returns:

batch_inputs

Return type:

bool

property input_use_padded_data

If turned on, raw PaddedData instances will be passed to the model. If turned off, only .data will be used

Returns:

result – whether padded data is used as is

Return type:

bool

property batch_outputs

Determines whether the output pipeline operates on batches or individual examples (true means batched)

Returns:

batch_outputs

Return type:

bool

encode_input(input)[source]

Encodes the inputs using the pipeline

Parameters:

input (dict) – the raw inputs

Returns:

results

Return type:

object

decode_output(output)[source]

Decodes the raw model outputs

Parameters:

output (tuple) – raw model outputs

Returns:

result – the output of the pipeline

Return type:

dict or list