"""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
"""
import logging
import hashlib
import sys
import speechbrain
import torch
import torchaudio
import sentencepiece
from types import SimpleNamespace
from torch.nn import SyncBatchNorm
from torch.nn import DataParallel as DP
from hyperpyyaml import load_hyperpyyaml
from copy import copy
from speechbrain.pretrained.fetching import fetch
from speechbrain.dataio.preprocess import AudioNormalizer
import torch.nn.functional as F
from torch.nn.parallel import DistributedDataParallel as DDP
from speechbrain.utils.data_utils import split_path
from speechbrain.utils.distributed import run_on_main
from speechbrain.dataio.batch import PaddedBatch, PaddedData
from speechbrain.utils.data_pipeline import DataPipeline
from speechbrain.utils.callchains import lengths_arg_exists
from speechbrain.utils.superpowers import import_from_path
logger = logging.getLogger(__name__)
def foreign_class(
source,
hparams_file="hyperparams.yaml",
pymodule_file="custom.py",
classname="CustomInterface",
overrides={},
savedir=None,
use_auth_token=False,
download_only=False,
**kwargs,
):
"""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
Arguments
---------
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.
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.
Returns
-------
object
An instance of a class with the given classname from the given pymodule file.
"""
if savedir is None:
savedir = f"./pretrained_models/{classname}-{hashlib.md5(source.encode('UTF-8', errors='replace')).hexdigest()}"
hparams_local_path = fetch(
filename=hparams_file,
source=source,
savedir=savedir,
overwrite=False,
save_filename=None,
use_auth_token=use_auth_token,
revision=None,
)
pymodule_local_path = fetch(
filename=pymodule_file,
source=source,
savedir=savedir,
overwrite=False,
save_filename=None,
use_auth_token=use_auth_token,
revision=None,
)
sys.path.append(str(pymodule_local_path.parent))
with open(hparams_local_path) as fin:
hparams = load_hyperpyyaml(fin, overrides)
pretrainer = hparams["pretrainer"]
pretrainer.set_collect_in(savedir)
run_on_main(
pretrainer.collect_files, kwargs={"default_source": source},
)
if not download_only:
pretrainer.load_collected(device="cpu")
module = import_from_path(pymodule_local_path)
cls = getattr(module, classname)
return cls(modules=hparams["modules"], hparams=hparams, **kwargs)
class Pretrained(torch.nn.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).
Arguments
---------
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_module_keys
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 = []
def __init__(
self, modules=None, hparams=None, run_opts=None, freeze_params=True
):
super().__init__()
run_opt_defaults = {
"device": "cpu",
"data_parallel_count": -1,
"data_parallel_backend": False,
"distributed_launch": False,
"distributed_backend": "nccl",
"jit_module_keys": None,
}
for arg, default in run_opt_defaults.items():
if run_opts is not None and arg in run_opts:
setattr(self, arg, run_opts[arg])
else:
if hparams is not None and arg in hparams:
setattr(self, arg, hparams[arg])
else:
setattr(self, arg, default)
self.mods = torch.nn.ModuleDict(modules)
for module in self.mods.values():
if module is not None:
module.to(self.device)
if self.HPARAMS_NEEDED and hparams is None:
raise ValueError("Need to provide hparams dict.")
if hparams is not None:
for hp in self.HPARAMS_NEEDED:
if hp not in hparams:
raise ValueError(f"Need hparams['{hp}']")
self.hparams = SimpleNamespace(**hparams)
self._prepare_modules(freeze_params)
self.audio_normalizer = hparams.get(
"audio_normalizer", AudioNormalizer()
)
def _prepare_modules(self, freeze_params):
"""Prepare modules for computation, e.g. jit.
Arguments
---------
freeze_params : bool
Whether to freeze the parameters and call ``eval()``.
"""
self._compile_jit()
self._wrap_distributed()
if freeze_params:
self.mods.eval()
for p in self.mods.parameters():
p.requires_grad = False
def load_audio(self, path, savedir="audio_cache", **kwargs):
"""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.
"""
source, fl = split_path(path)
kwargs = copy(kwargs)
channels_first = kwargs.pop(
"channels_first", False
)
if kwargs:
fetch_kwargs = dict()
for key in [
"overwrite",
"save_filename",
"use_auth_token",
"revision",
"cache_dir",
"silent_local_fetch",
]:
if key in kwargs:
fetch_kwargs[key] = kwargs.pop(key)
path = fetch(fl, source=source, savedir=savedir, **fetch_kwargs)
else:
path = fetch(fl, source=source, savedir=savedir)
signal, sr = torchaudio.load(
str(path), channels_first=channels_first, **kwargs
)
return self.audio_normalizer(signal, sr)
def _compile_jit(self):
"""Compile requested modules with ``torch.jit.script``."""
if self.jit_module_keys is None:
return
for name in self.jit_module_keys:
if name not in self.mods:
raise ValueError(
"module " + name + " cannot be jit compiled because "
"it is not defined in your hparams file."
)
module = torch.jit.script(self.mods[name])
self.mods[name] = module.to(self.device)
def _wrap_distributed(self):
"""Wrap modules with distributed wrapper when requested."""
if not self.distributed_launch and not self.data_parallel_backend:
return
elif self.distributed_launch:
for name, module in self.mods.items():
if any(p.requires_grad for p in module.parameters()):
module = SyncBatchNorm.convert_sync_batchnorm(module)
module = DDP(module, device_ids=[self.device])
self.mods[name] = module
else:
for name, module in self.mods.items():
if any(p.requires_grad for p in module.parameters()):
if self.data_parallel_count == -1:
module = DP(module)
else:
module = DP(
module, [i for i in range(self.data_parallel_count)]
)
self.mods[name] = module
@classmethod
def from_hparams(
cls,
source,
hparams_file="hyperparams.yaml",
pymodule_file="custom.py",
overrides={},
savedir=None,
use_auth_token=False,
revision=None,
download_only=False,
**kwargs,
):
"""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
Arguments
---------
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
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.
"""
if savedir is None:
clsname = cls.__name__
savedir = f"./pretrained_models/{clsname}-{hashlib.md5(source.encode('UTF-8', errors='replace')).hexdigest()}"
hparams_local_path = fetch(
filename=hparams_file,
source=source,
savedir=savedir,
overwrite=False,
save_filename=None,
use_auth_token=use_auth_token,
revision=revision,
)
try:
pymodule_local_path = fetch(
filename=pymodule_file,
source=source,
savedir=savedir,
overwrite=False,
save_filename=None,
use_auth_token=use_auth_token,
revision=revision,
)
sys.path.append(str(pymodule_local_path.parent))
except ValueError:
if pymodule_file == "custom.py":
pass
else:
raise
with open(hparams_local_path) as fin:
hparams = load_hyperpyyaml(fin, overrides)
pretrainer = hparams["pretrainer"]
pretrainer.set_collect_in(savedir)
run_on_main(
pretrainer.collect_files, kwargs={"default_source": source},
)
if not download_only:
pretrainer.load_collected(device="cpu")
return cls(hparams["modules"], hparams, **kwargs)
class EndToEndSLU(Pretrained):
"""An end-to-end SLU model.
The class can be used either to run only the encoder (encode()) to extract
features or to run the entire model (decode()) to map the speech to its semantics.
Example
-------
>>> from speechbrain.pretrained import EndToEndSLU
>>> tmpdir = getfixture("tmpdir")
>>> slu_model = EndToEndSLU.from_hparams(
... source="speechbrain/slu-timers-and-such-direct-librispeech-asr",
... savedir=tmpdir,
... )
>>> slu_model.decode_file("tests/samples/single-mic/example6.wav")
"{'intent': 'SimpleMath', 'slots': {'number1': 37.67, 'number2': 75.7, 'op': ' minus '}}"
"""
HPARAMS_NEEDED = ["tokenizer", "asr_model_source"]
MODULES_NEEDED = ["slu_enc", "beam_searcher"]
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
self.tokenizer = self.hparams.tokenizer
self.asr_model = EncoderDecoderASR.from_hparams(
source=self.hparams.asr_model_source,
run_opts={"device": self.device},
)
def decode_file(self, path, **kwargs):
"""Maps the given audio file to a string representing the
semantic dictionary for the utterance.
Arguments
---------
path : str
Path to audio file to decode.
Returns
-------
str
The predicted semantics.
"""
waveform = self.load_audio(path, **kwargs)
waveform = waveform.to(self.device)
batch = waveform.unsqueeze(0)
rel_length = torch.tensor([1.0])
predicted_words, predicted_tokens = self.decode_batch(batch, rel_length)
return predicted_words[0]
def encode_batch(self, wavs, wav_lens):
"""Encodes the input audio into a sequence of hidden states
Arguments
---------
wavs : torch.Tensor
Batch of waveforms [batch, time, channels] or [batch, time]
depending on the model.
wav_lens : torch.Tensor
Lengths of the waveforms relative to the longest one in the
batch, tensor of shape [batch]. The longest one should have
relative length 1.0 and others len(waveform) / max_length.
Used for ignoring padding.
Returns
-------
torch.Tensor
The encoded batch
"""
wavs = wavs.float()
wavs, wav_lens = wavs.to(self.device), wav_lens.to(self.device)
ASR_encoder_out = self.asr_model.encode_batch(wavs.detach(), wav_lens)
encoder_out = self.mods.slu_enc(ASR_encoder_out)
return encoder_out
def decode_batch(self, wavs, wav_lens):
"""Maps the input audio to its semantics
Arguments
---------
wavs : torch.Tensor
Batch of waveforms [batch, time, channels] or [batch, time]
depending on the model.
wav_lens : torch.Tensor
Lengths of the waveforms relative to the longest one in the
batch, tensor of shape [batch]. The longest one should have
relative length 1.0 and others len(waveform) / max_length.
Used for ignoring padding.
Returns
-------
list
Each waveform in the batch decoded.
tensor
Each predicted token id.
"""
with torch.no_grad():
wavs, wav_lens = wavs.to(self.device), wav_lens.to(self.device)
encoder_out = self.encode_batch(wavs, wav_lens)
predicted_tokens, scores = self.mods.beam_searcher(
encoder_out, wav_lens
)
predicted_words = [
self.tokenizer.decode_ids(token_seq)
for token_seq in predicted_tokens
]
return predicted_words, predicted_tokens
def forward(self, wavs, wav_lens):
"""Runs full decoding - note: no gradients through decoding"""
return self.decode_batch(wavs, wav_lens)
class EncoderDecoderASR(Pretrained):
"""A ready-to-use Encoder-Decoder ASR model
The class can be used either to run only the encoder (encode()) to extract
features or to run the entire encoder-decoder model
(transcribe()) to transcribe speech. The given YAML must contain the fields
specified in the *_NEEDED[] lists.
Example
-------
>>> from speechbrain.pretrained import EncoderDecoderASR
>>> tmpdir = getfixture("tmpdir")
>>> asr_model = EncoderDecoderASR.from_hparams(
... source="speechbrain/asr-crdnn-rnnlm-librispeech",
... savedir=tmpdir,
... )
>>> asr_model.transcribe_file("tests/samples/single-mic/example2.flac")
"MY FATHER HAS REVEALED THE CULPRIT'S NAME"
"""
HPARAMS_NEEDED = ["tokenizer"]
MODULES_NEEDED = ["encoder", "decoder"]
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
self.tokenizer = self.hparams.tokenizer
def transcribe_file(self, path, **kwargs):
"""Transcribes the given audiofile into a sequence of words.
Arguments
---------
path : str
Path to audio file which to transcribe.
Returns
-------
str
The audiofile transcription produced by this ASR system.
"""
waveform = self.load_audio(path, **kwargs)
batch = waveform.unsqueeze(0)
rel_length = torch.tensor([1.0])
predicted_words, predicted_tokens = self.transcribe_batch(
batch, rel_length
)
return predicted_words[0]
def encode_batch(self, wavs, wav_lens):
"""Encodes the input audio into a sequence of hidden states
The waveforms should already be in the model's desired format.
You can call:
``normalized = EncoderDecoderASR.normalizer(signal, sample_rate)``
to get a correctly converted signal in most cases.
Arguments
---------
wavs : torch.Tensor
Batch of waveforms [batch, time, channels] or [batch, time]
depending on the model.
wav_lens : torch.Tensor
Lengths of the waveforms relative to the longest one in the
batch, tensor of shape [batch]. The longest one should have
relative length 1.0 and others len(waveform) / max_length.
Used for ignoring padding.
Returns
-------
torch.Tensor
The encoded batch
"""
wavs = wavs.float()
wavs, wav_lens = wavs.to(self.device), wav_lens.to(self.device)
encoder_out = self.mods.encoder(wavs, wav_lens)
return encoder_out
def transcribe_batch(self, wavs, wav_lens):
"""Transcribes the input audio into a sequence of words
The waveforms should already be in the model's desired format.
You can call:
``normalized = EncoderDecoderASR.normalizer(signal, sample_rate)``
to get a correctly converted signal in most cases.
Arguments
---------
wavs : torch.Tensor
Batch of waveforms [batch, time, channels] or [batch, time]
depending on the model.
wav_lens : torch.Tensor
Lengths of the waveforms relative to the longest one in the
batch, tensor of shape [batch]. The longest one should have
relative length 1.0 and others len(waveform) / max_length.
Used for ignoring padding.
Returns
-------
list
Each waveform in the batch transcribed.
tensor
Each predicted token id.
"""
with torch.no_grad():
wav_lens = wav_lens.to(self.device)
encoder_out = self.encode_batch(wavs, wav_lens)
predicted_tokens, scores = self.mods.decoder(encoder_out, wav_lens)
predicted_words = [
self.tokenizer.decode_ids(token_seq)
for token_seq in predicted_tokens
]
return predicted_words, predicted_tokens
def forward(self, wavs, wav_lens):
"""Runs full transcription - note: no gradients through decoding"""
return self.transcribe_batch(wavs, wav_lens)
class WaveformEncoder(Pretrained):
"""A ready-to-use waveformEncoder model
It can be used to wrap different embedding models such as SSL ones (wav2vec2)
or speaker ones (Xvector) etc. Two functions are available: encode_batch and
encode_file. They can be used to obtain the embeddings directly from an audio
file or from a batch of audio tensors respectively.
The given YAML must contain the fields specified in the *_NEEDED[] lists.
Example
-------
>>> from speechbrain.pretrained import WaveformEncoder
>>> tmpdir = getfixture("tmpdir")
>>> ssl_model = WaveformEncoder.from_hparams(
... source="speechbrain/ssl-wav2vec2-base-libri",
... savedir=tmpdir,
... ) # doctest: +SKIP
>>> ssl_model.encode_file("samples/audio_samples/example_fr.wav") # doctest: +SKIP
"""
MODULES_NEEDED = ["encoder"]
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
def encode_file(self, path, **kwargs):
"""Encode the given audiofile into a sequence of embeddings.
Arguments
---------
path : str
Path to audio file which to encode.
Returns
-------
torch.Tensor
The audiofile embeddings produced by this system.
"""
waveform = self.load_audio(path, **kwargs)
batch = waveform.unsqueeze(0)
rel_length = torch.tensor([1.0])
results = self.encode_batch(batch, rel_length)
return results["embeddings"]
def encode_batch(self, wavs, wav_lens):
"""Encodes the input audio into a sequence of hidden states
The waveforms should already be in the model's desired format.
Arguments
---------
wavs : torch.Tensor
Batch of waveforms [batch, time, channels] or [batch, time]
depending on the model.
wav_lens : torch.Tensor
Lengths of the waveforms relative to the longest one in the
batch, tensor of shape [batch]. The longest one should have
relative length 1.0 and others len(waveform) / max_length.
Used for ignoring padding.
Returns
-------
torch.Tensor
The encoded batch
"""
wavs = wavs.float()
wavs, wav_lens = wavs.to(self.device), wav_lens.to(self.device)
encoder_out = self.mods.encoder(wavs, wav_lens)
return encoder_out
def forward(self, wavs, wav_lens):
"""Runs the encoder"""
return self.encode_batch(wavs, wav_lens)
class EncoderASR(Pretrained):
"""A ready-to-use Encoder ASR model
The class can be used either to run only the encoder (encode()) to extract
features or to run the entire encoder + decoder function model
(transcribe()) to transcribe speech. The given YAML must contain the fields
specified in the *_NEEDED[] lists.
Example
-------
>>> from speechbrain.pretrained import EncoderASR
>>> tmpdir = getfixture("tmpdir")
>>> asr_model = EncoderASR.from_hparams(
... source="speechbrain/asr-wav2vec2-commonvoice-fr",
... savedir=tmpdir,
... ) # doctest: +SKIP
>>> asr_model.transcribe_file("samples/audio_samples/example_fr.wav") # doctest: +SKIP
"""
HPARAMS_NEEDED = ["tokenizer", "decoding_function"]
MODULES_NEEDED = ["encoder"]
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
self.tokenizer = self.hparams.tokenizer
self.decoding_function = self.hparams.decoding_function
def transcribe_file(self, path, **kwargs):
"""Transcribes the given audiofile into a sequence of words.
Arguments
---------
path : str
Path to audio file which to transcribe.
Returns
-------
str
The audiofile transcription produced by this ASR system.
"""
waveform = self.load_audio(path, **kwargs)
batch = waveform.unsqueeze(0)
rel_length = torch.tensor([1.0])
predicted_words, predicted_tokens = self.transcribe_batch(
batch, rel_length
)
return str(predicted_words[0])
def encode_batch(self, wavs, wav_lens):
"""Encodes the input audio into a sequence of hidden states
The waveforms should already be in the model's desired format.
You can call:
``normalized = EncoderASR.normalizer(signal, sample_rate)``
to get a correctly converted signal in most cases.
Arguments
---------
wavs : torch.Tensor
Batch of waveforms [batch, time, channels] or [batch, time]
depending on the model.
wav_lens : torch.Tensor
Lengths of the waveforms relative to the longest one in the
batch, tensor of shape [batch]. The longest one should have
relative length 1.0 and others len(waveform) / max_length.
Used for ignoring padding.
Returns
-------
torch.Tensor
The encoded batch
"""
wavs = wavs.float()
wavs, wav_lens = wavs.to(self.device), wav_lens.to(self.device)
encoder_out = self.mods.encoder(wavs, wav_lens)
return encoder_out
def transcribe_batch(self, wavs, wav_lens):
"""Transcribes the input audio into a sequence of words
The waveforms should already be in the model's desired format.
You can call:
``normalized = EncoderASR.normalizer(signal, sample_rate)``
to get a correctly converted signal in most cases.
Arguments
---------
wavs : torch.Tensor
Batch of waveforms [batch, time, channels] or [batch, time]
depending on the model.
wav_lens : torch.Tensor
Lengths of the waveforms relative to the longest one in the
batch, tensor of shape [batch]. The longest one should have
relative length 1.0 and others len(waveform) / max_length.
Used for ignoring padding.
Returns
-------
list
Each waveform in the batch transcribed.
tensor
Each predicted token id.
"""
with torch.no_grad():
wav_lens = wav_lens.to(self.device)
encoder_out = self.encode_batch(wavs, wav_lens)
predictions = self.decoding_function(encoder_out, wav_lens)
if isinstance(
self.tokenizer, speechbrain.dataio.encoder.CTCTextEncoder
):
predicted_words = [
"".join(self.tokenizer.decode_ndim(token_seq))
for token_seq in predictions
]
elif isinstance(
self.tokenizer, sentencepiece.SentencePieceProcessor
):
predicted_words = [
self.tokenizer.decode_ids(token_seq)
for token_seq in predictions
]
else:
sys.exit(
"The tokenizer must be sentencepiece or CTCTextEncoder"
)
return predicted_words, predictions
def forward(self, wavs, wav_lens):
"""Runs the encoder"""
return self.encode_batch(wavs, wav_lens)
class EncoderClassifier(Pretrained):
"""A ready-to-use class for utterance-level classification (e.g, speaker-id,
language-id, emotion recognition, keyword spotting, etc).
The class assumes that an encoder called "embedding_model" and a model
called "classifier" are defined in the yaml file. If you want to
convert the predicted index into a corresponding text label, please
provide the path of the label_encoder in a variable called 'lab_encoder_file'
within the yaml.
The class can be used either to run only the encoder (encode_batch()) to
extract embeddings or to run a classification step (classify_batch()).
```
Example
-------
>>> import torchaudio
>>> from speechbrain.pretrained import EncoderClassifier
>>> # Model is downloaded from the speechbrain HuggingFace repo
>>> tmpdir = getfixture("tmpdir")
>>> classifier = EncoderClassifier.from_hparams(
... source="speechbrain/spkrec-ecapa-voxceleb",
... savedir=tmpdir,
... )
>>> classifier.hparams.label_encoder.ignore_len()
>>> # Compute embeddings
>>> signal, fs = torchaudio.load("tests/samples/single-mic/example1.wav")
>>> embeddings = classifier.encode_batch(signal)
>>> # Classification
>>> prediction = classifier.classify_batch(signal)
"""
MODULES_NEEDED = [
"compute_features",
"mean_var_norm",
"embedding_model",
"classifier",
]
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
def encode_batch(self, wavs, wav_lens=None, normalize=False):
"""Encodes the input audio into a single vector embedding.
The waveforms should already be in the model's desired format.
You can call:
``normalized = <this>.normalizer(signal, sample_rate)``
to get a correctly converted signal in most cases.
Arguments
---------
wavs : torch.Tensor
Batch of waveforms [batch, time, channels] or [batch, time]
depending on the model. Make sure the sample rate is fs=16000 Hz.
wav_lens : torch.Tensor
Lengths of the waveforms relative to the longest one in the
batch, tensor of shape [batch]. The longest one should have
relative length 1.0 and others len(waveform) / max_length.
Used for ignoring padding.
normalize : bool
If True, it normalizes the embeddings with the statistics
contained in mean_var_norm_emb.
Returns
-------
torch.Tensor
The encoded batch
"""
if len(wavs.shape) == 1:
wavs = wavs.unsqueeze(0)
if wav_lens is None:
wav_lens = torch.ones(wavs.shape[0], device=self.device)
wavs, wav_lens = wavs.to(self.device), wav_lens.to(self.device)
wavs = wavs.float()
feats = self.mods.compute_features(wavs)
feats = self.mods.mean_var_norm(feats, wav_lens)
embeddings = self.mods.embedding_model(feats, wav_lens)
if normalize:
embeddings = self.hparams.mean_var_norm_emb(
embeddings, torch.ones(embeddings.shape[0], device=self.device)
)
return embeddings
def classify_batch(self, wavs, wav_lens=None):
"""Performs classification on the top of the encoded features.
It returns the posterior probabilities, the index and, if the label
encoder is specified it also the text label.
Arguments
---------
wavs : torch.Tensor
Batch of waveforms [batch, time, channels] or [batch, time]
depending on the model. Make sure the sample rate is fs=16000 Hz.
wav_lens : torch.Tensor
Lengths of the waveforms relative to the longest one in the
batch, tensor of shape [batch]. The longest one should have
relative length 1.0 and others len(waveform) / max_length.
Used for ignoring padding.
Returns
-------
out_prob
The log posterior probabilities of each class ([batch, N_class])
score:
It is the value of the log-posterior for the best class ([batch,])
index
The indexes of the best class ([batch,])
text_lab:
List with the text labels corresponding to the indexes.
(label encoder should be provided).
"""
emb = self.encode_batch(wavs, wav_lens)
out_prob = self.mods.classifier(emb).squeeze(1)
score, index = torch.max(out_prob, dim=-1)
text_lab = self.hparams.label_encoder.decode_torch(index)
return out_prob, score, index, text_lab
def classify_file(self, path, **kwargs):
"""Classifies the given audiofile into the given set of labels.
Arguments
---------
path : str
Path to audio file to classify.
Returns
-------
out_prob
The log posterior probabilities of each class ([batch, N_class])
score:
It is the value of the log-posterior for the best class ([batch,])
index
The indexes of the best class ([batch,])
text_lab:
List with the text labels corresponding to the indexes.
(label encoder should be provided).
"""
waveform = self.load_audio(path, **kwargs)
batch = waveform.unsqueeze(0)
rel_length = torch.tensor([1.0])
emb = self.encode_batch(batch, rel_length)
out_prob = self.mods.classifier(emb).squeeze(1)
score, index = torch.max(out_prob, dim=-1)
text_lab = self.hparams.label_encoder.decode_torch(index)
return out_prob, score, index, text_lab
def forward(self, wavs, wav_lens=None):
"""Runs the classification"""
return self.classify_batch(wavs, wav_lens)
class SpeakerRecognition(EncoderClassifier):
"""A ready-to-use model for speaker recognition. It can be used to
perform speaker verification with verify_batch().
```
Example
-------
>>> import torchaudio
>>> from speechbrain.pretrained import SpeakerRecognition
>>> # Model is downloaded from the speechbrain HuggingFace repo
>>> tmpdir = getfixture("tmpdir")
>>> verification = SpeakerRecognition.from_hparams(
... source="speechbrain/spkrec-ecapa-voxceleb",
... savedir=tmpdir,
... )
>>> # Perform verification
>>> signal, fs = torchaudio.load("tests/samples/single-mic/example1.wav")
>>> signal2, fs = torchaudio.load("tests/samples/single-mic/example2.flac")
>>> score, prediction = verification.verify_batch(signal, signal2)
"""
MODULES_NEEDED = [
"compute_features",
"mean_var_norm",
"embedding_model",
"mean_var_norm_emb",
]
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
self.similarity = torch.nn.CosineSimilarity(dim=-1, eps=1e-6)
def verify_batch(
self, wavs1, wavs2, wav1_lens=None, wav2_lens=None, threshold=0.25
):
"""Performs speaker verification with cosine distance.
It returns the score and the decision (0 different speakers,
1 same speakers).
Arguments
---------
wavs1 : Torch.Tensor
Tensor containing the speech waveform1 (batch, time).
Make sure the sample rate is fs=16000 Hz.
wavs2 : Torch.Tensor
Tensor containing the speech waveform2 (batch, time).
Make sure the sample rate is fs=16000 Hz.
wav1_lens: Torch.Tensor
Tensor containing the relative length for each sentence
in the length (e.g., [0.8 0.6 1.0])
wav2_lens: Torch.Tensor
Tensor containing the relative length for each sentence
in the length (e.g., [0.8 0.6 1.0])
threshold: Float
Threshold applied to the cosine distance to decide if the
speaker is different (0) or the same (1).
Returns
-------
score
The score associated to the binary verification output
(cosine distance).
prediction
The prediction is 1 if the two signals in input are from the same
speaker and 0 otherwise.
"""
emb1 = self.encode_batch(wavs1, wav1_lens, normalize=True)
emb2 = self.encode_batch(wavs2, wav2_lens, normalize=True)
score = self.similarity(emb1, emb2)
return score, score > threshold
def verify_files(self, path_x, path_y, **kwargs):
"""Speaker verification with cosine distance
Returns the score and the decision (0 different speakers,
1 same speakers).
Returns
-------
score
The score associated to the binary verification output
(cosine distance).
prediction
The prediction is 1 if the two signals in input are from the same
speaker and 0 otherwise.
"""
waveform_x = self.load_audio(path_x, **kwargs)
waveform_y = self.load_audio(path_y, **kwargs)
batch_x = waveform_x.unsqueeze(0)
batch_y = waveform_y.unsqueeze(0)
score, decision = self.verify_batch(batch_x, batch_y)
return score[0], decision[0]
class VAD(Pretrained):
"""A ready-to-use class for Voice Activity Detection (VAD) using a
pre-trained model.
Example
-------
>>> import torchaudio
>>> from speechbrain.pretrained import VAD
>>> # Model is downloaded from the speechbrain HuggingFace repo
>>> tmpdir = getfixture("tmpdir")
>>> VAD = VAD.from_hparams(
... source="speechbrain/vad-crdnn-libriparty",
... savedir=tmpdir,
... )
>>> # Perform VAD
>>> boundaries = VAD.get_speech_segments("tests/samples/single-mic/example1.wav")
"""
HPARAMS_NEEDED = ["sample_rate", "time_resolution", "device"]
MODULES_NEEDED = ["compute_features", "mean_var_norm", "model"]
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
self.time_resolution = self.hparams.time_resolution
self.sample_rate = self.hparams.sample_rate
self.device = self.hparams.device
def get_speech_prob_file(
self,
audio_file,
large_chunk_size=30,
small_chunk_size=10,
overlap_small_chunk=False,
):
"""Outputs the frame-level speech probability of the input audio file
using the neural model specified in the hparam file. To make this code
both parallelizable and scalable to long sequences, it uses a
double-windowing approach. First, we sequentially read non-overlapping
large chunks of the input signal. We then split the large chunks into
smaller chunks and we process them in parallel.
Arguments
---------
audio_file: path
Path of the audio file containing the recording. The file is read
with torchaudio.
large_chunk_size: float
Size (in seconds) of the large chunks that are read sequentially
from the input audio file.
small_chunk_size:
Size (in seconds) of the small chunks extracted from the large ones.
The audio signal is processed in parallel within the small chunks.
Note that large_chunk_size/small_chunk_size must be an integer.
overlap_small_chunk: bool
True, creates overlapped small chunks. The probabilities of the
overlapped chunks are combined using hamming windows.
Returns
-------
prob_vad: torch.Tensor
Tensor containing the frame-level speech probabilities for the
input audio file.
"""
sample_rate, audio_len = self._get_audio_info(audio_file)
if sample_rate != self.sample_rate:
raise ValueError(
"The detected sample rate is different from that set in the hparam file"
)
long_chunk_len = int(sample_rate * large_chunk_size)
small_chunk_len = int(sample_rate * small_chunk_size)
small_chunk_step = small_chunk_size
if overlap_small_chunk:
small_chunk_step = small_chunk_size / 2
small_chunk_len_step = int(sample_rate * small_chunk_step)
prob_chunks = []
last_chunk = False
begin_sample = 0
while True:
large_chunk, fs = torchaudio.load(
audio_file, frame_offset=begin_sample, num_frames=long_chunk_len
)
large_chunk = large_chunk.to(self.device)
if last_chunk or large_chunk.shape[-1] < small_chunk_len:
padding = torch.zeros(
1, small_chunk_len, device=large_chunk.device
)
large_chunk = torch.cat([large_chunk, padding], dim=1)
small_chunks = torch.nn.functional.unfold(
large_chunk.unsqueeze(1).unsqueeze(2),
kernel_size=(1, small_chunk_len),
stride=(1, small_chunk_len_step),
)
small_chunks = small_chunks.squeeze(0).transpose(0, 1)
small_chunks_prob = self.get_speech_prob_chunk(small_chunks)
small_chunks_prob = small_chunks_prob[:, :-1, :]
if overlap_small_chunk:
small_chunks_prob = self._manage_overlapped_chunks(
small_chunks_prob
)
small_chunks_prob = small_chunks_prob.permute(2, 1, 0)
out_len = int(
large_chunk.shape[-1] / (sample_rate * self.time_resolution)
)
kernel_len = int(small_chunk_size / self.time_resolution)
step_len = int(small_chunk_step / self.time_resolution)
small_chunks_prob = torch.nn.functional.fold(
small_chunks_prob,
output_size=(1, out_len),
kernel_size=(1, kernel_len),
stride=(1, step_len),
)
small_chunks_prob = small_chunks_prob.squeeze(1).transpose(-1, -2)
prob_chunks.append(small_chunks_prob)
if last_chunk:
break
begin_sample = begin_sample + long_chunk_len
if begin_sample + long_chunk_len > audio_len:
last_chunk = True
prob_vad = torch.cat(prob_chunks, dim=1)
last_elem = int(audio_len / (self.time_resolution * sample_rate))
prob_vad = prob_vad[:, 0:last_elem, :]
return prob_vad
def _manage_overlapped_chunks(self, small_chunks_prob):
"""This support function manages overlapped the case in which the
small chunks have a 50% overlap."""
hamming_window = torch.hamming_window(
small_chunks_prob.shape[1], device=self.device
)
half_point = int(small_chunks_prob.shape[1] / 2)
small_chunks_prob[0, half_point:] = small_chunks_prob[
0, half_point:
] * hamming_window[half_point:].unsqueeze(1)
small_chunks_prob[-1, 0:half_point] = small_chunks_prob[
-1, 0:half_point
] * hamming_window[0:half_point].unsqueeze(1)
small_chunks_prob[1:-1] = small_chunks_prob[
1:-1
] * hamming_window.unsqueeze(0).unsqueeze(2)
return small_chunks_prob
def get_speech_prob_chunk(self, wavs, wav_lens=None):
"""Outputs the frame-level posterior probability for the input audio chunks
Outputs close to zero refers to time steps with a low probability of speech
activity, while outputs closer to one likely contain speech.
Arguments
---------
wavs : torch.Tensor
Batch of waveforms [batch, time, channels] or [batch, time]
depending on the model. Make sure the sample rate is fs=16000 Hz.
wav_lens : torch.Tensor
Lengths of the waveforms relative to the longest one in the
batch, tensor of shape [batch]. The longest one should have
relative length 1.0 and others len(waveform) / max_length.
Used for ignoring padding.
Returns
-------
torch.Tensor
The encoded batch
"""
if len(wavs.shape) == 1:
wavs = wavs.unsqueeze(0)
if wav_lens is None:
wav_lens = torch.ones(wavs.shape[0], device=self.device)
wavs, wav_lens = wavs.to(self.device), wav_lens.to(self.device)
wavs = wavs.float()
feats = self.mods.compute_features(wavs)
feats = self.mods.mean_var_norm(feats, wav_lens)
outputs = self.mods.cnn(feats)
outputs = outputs.reshape(
outputs.shape[0],
outputs.shape[1],
outputs.shape[2] * outputs.shape[3],
)
outputs, h = self.mods.rnn(outputs)
outputs = self.mods.dnn(outputs)
output_prob = torch.sigmoid(outputs)
return output_prob
def apply_threshold(
self, vad_prob, activation_th=0.5, deactivation_th=0.25
):
"""Scans the frame-level speech probabilities and applies a threshold
on them. Speech starts when a value larger than activation_th is
detected, while it ends when observing a value lower than
the deactivation_th.
Arguments
---------
vad_prob: torch.Tensor
Frame-level speech probabilities.
activation_th: float
Threshold for starting a speech segment.
deactivation_th: float
Threshold for ending a speech segment.
Returns
-------
vad_th: torch.Tensor
Tensor containing 1 for speech regions and 0 for non-speech regions.
"""
vad_activation = (vad_prob >= activation_th).int()
vad_deactivation = (vad_prob >= deactivation_th).int()
vad_th = vad_activation + vad_deactivation
for batch in range(vad_th.shape[0]):
for time_step in range(vad_th.shape[1] - 1):
if (
vad_th[batch, time_step] == 2
and vad_th[batch, time_step + 1] == 1
):
vad_th[batch, time_step + 1] = 2
vad_th[vad_th == 1] = 0
vad_th[vad_th == 2] = 1
return vad_th
def get_boundaries(self, prob_th, output_value="seconds"):
"""Computes the time boundaries where speech activity is detected.
It takes in input frame-level binary decisions
(1 for speech, 0 for non-speech) and outputs the begin/end second
(or sample) of each detected speech region.
Arguments
---------
prob_th: torch.Tensor
Frame-level binary decisions (1 for speech frame, 0 for a
non-speech one). The tensor can be obtained from apply_threshold.
output_value: 'seconds' or 'samples'
When the option 'seconds' is set, the returned boundaries are in
seconds, otherwise, it reports them in samples.
Returns
-------
boundaries: torch.Tensor
Tensor containing the start second (or sample) of speech segments
in even positions and their corresponding end in odd positions
(e.g, [1.0, 1.5, 5,.0 6.0] means that we have two speech segment;
one from 1.0 to 1.5 seconds and another from 5.0 to 6.0 seconds).
"""
prob_th_shifted = torch.roll(prob_th, dims=1, shifts=1)
prob_th_shifted[:, 0, :] = 0
prob_th = prob_th + prob_th_shifted
prob_th[:, 0, :] = (prob_th[:, 0, :] >= 1).int()
prob_th[:, -1, :] = (prob_th[:, -1, :] >= 1).int()
if (prob_th == 1).nonzero().shape[0] % 2 == 1:
prob_th = torch.cat(
(
prob_th,
torch.Tensor([1.0])
.unsqueeze(0)
.unsqueeze(2)
.to(self.device),
),
dim=1,
)
indexes = (prob_th == 1).nonzero()[:, 1].reshape(-1, 2)
indexes[:, -1] = indexes[:, -1] - 1
seconds = (indexes * self.time_resolution).float()
samples = (self.sample_rate * seconds).round().int()
if output_value == "seconds":
boundaries = seconds
else:
boundaries = samples
return boundaries
def merge_close_segments(self, boundaries, close_th=0.250):
"""Merges segments that are shorter than the given threshold.
Arguments
---------
boundaries : str
Tensor containing the speech boundaries. It can be derived using the
get_boundaries method.
close_th: float
If the distance between boundaries is smaller than close_th, the
segments will be merged.
Returns
-------
new_boundaries
The new boundaries with the merged segments.
"""
new_boundaries = []
if boundaries.shape[0] == 0:
return boundaries
prev_beg_seg = boundaries[0, 0].float()
prev_end_seg = boundaries[0, 1].float()
for i in range(1, boundaries.shape[0]):
beg_seg = boundaries[i, 0]
segment_distance = beg_seg - prev_end_seg
if segment_distance <= close_th:
prev_end_seg = boundaries[i, 1]
else:
new_boundaries.append([prev_beg_seg, prev_end_seg])
prev_beg_seg = beg_seg
prev_end_seg = boundaries[i, 1]
new_boundaries.append([prev_beg_seg, prev_end_seg])
new_boundaries = torch.FloatTensor(new_boundaries).to(boundaries.device)
return new_boundaries
def remove_short_segments(self, boundaries, len_th=0.250):
"""Removes segments that are too short.
Arguments
---------
boundaries : torch.Tensor
Tensor containing the speech boundaries. It can be derived using the
get_boundaries method.
len_th: float
If the length of the segment is smaller than close_th, the segments
will be merged.
Returns
-------
new_boundaries
The new boundaries without the short segments.
"""
new_boundaries = []
for i in range(boundaries.shape[0]):
seg_len = boundaries[i, 1] - boundaries[i, 0]
if seg_len > len_th:
new_boundaries.append([boundaries[i, 0], boundaries[i, 1]])
new_boundaries = torch.FloatTensor(new_boundaries).to(boundaries.device)
return new_boundaries
def save_boundaries(
self, boundaries, save_path=None, print_boundaries=True, audio_file=None
):
"""Saves the boundaries on a file (and/or prints them) in a readable format.
Arguments
---------
boundaries: torch.Tensor
Tensor containing the speech boundaries. It can be derived using the
get_boundaries method.
save_path: path
When to store the text file containing the speech/non-speech intervals.
print_boundaries: Bool
Prints the speech/non-speech intervals in the standard outputs.
audio_file: path
Path of the audio file containing the recording. The file is read
with torchaudio. It is used here to detect the length of the
signal.
"""
if save_path is not None:
f = open(save_path, mode="w", encoding="utf-8")
if audio_file is not None:
sample_rate, audio_len = self._get_audio_info(audio_file)
audio_len = audio_len / sample_rate
if boundaries.dtype == torch.int:
value_format = "% i"
else:
value_format = "% .2f "
last_end = 0
cnt_seg = 0
for i in range(boundaries.shape[0]):
begin_value = boundaries[i, 0]
end_value = boundaries[i, 1]
if last_end != begin_value:
cnt_seg = cnt_seg + 1
print_str = (
"segment_%03d " + value_format + value_format + "NON_SPEECH"
)
if print_boundaries:
print(print_str % (cnt_seg, last_end, begin_value))
if save_path is not None:
f.write(print_str % (cnt_seg, last_end, begin_value) + "\n")
cnt_seg = cnt_seg + 1
print_str = "segment_%03d " + value_format + value_format + "SPEECH"
if print_boundaries:
print(print_str % (cnt_seg, begin_value, end_value))
if save_path is not None:
f.write(print_str % (cnt_seg, begin_value, end_value) + "\n")
last_end = end_value
if audio_file is not None:
if last_end < audio_len:
cnt_seg = cnt_seg + 1
print_str = (
"segment_%03d " + value_format + value_format + "NON_SPEECH"
)
if print_boundaries:
print(print_str % (cnt_seg, end_value, audio_len))
if save_path is not None:
f.write(print_str % (cnt_seg, end_value, audio_len) + "\n")
if save_path is not None:
f.close()
def energy_VAD(
self,
audio_file,
boundaries,
activation_th=0.5,
deactivation_th=0.0,
eps=1e-6,
):
"""Applies energy-based VAD within the detected speech segments.The neural
network VAD often creates longer segments and tends to merge segments that
are close with each other.
The energy VAD post-processes can be useful for having a fine-grained voice
activity detection.
The energy VAD computes the energy within the small chunks. The energy is
normalized within the segment to have mean 0.5 and +-0.5 of std.
This helps to set the energy threshold.
Arguments
---------
audio_file: path
Path of the audio file containing the recording. The file is read
with torchaudio.
boundaries : torch.Tensor
Tensor containing the speech boundaries. It can be derived using the
get_boundaries method.
activation_th: float
A new speech segment is started it the energy is above activation_th.
deactivation_th: float
The segment is considered ended when the energy is <= deactivation_th.
eps: float
Small constant for numerical stability.
Returns
-------
new_boundaries
The new boundaries that are post-processed by the energy VAD.
"""
sample_rate, audio_len = self._get_audio_info(audio_file)
if sample_rate != self.sample_rate:
raise ValueError(
"The detected sample rate is different from that set in the hparam file"
)
chunk_len = int(self.time_resolution * sample_rate)
new_boundaries = []
for i in range(boundaries.shape[0]):
begin_sample = int(boundaries[i, 0] * sample_rate)
end_sample = int(boundaries[i, 1] * sample_rate)
seg_len = end_sample - begin_sample
segment, _ = torchaudio.load(
audio_file, frame_offset=begin_sample, num_frames=seg_len
)
segment_chunks = self.create_chunks(
segment, chunk_size=chunk_len, chunk_stride=chunk_len
)
energy_chunks = segment_chunks.abs().sum(-1) + eps
energy_chunks = energy_chunks.log()
energy_chunks = (
(energy_chunks - energy_chunks.mean())
/ (2 * energy_chunks.std())
) + 0.5
energy_chunks = energy_chunks.unsqueeze(0).unsqueeze(2)
energy_vad = self.apply_threshold(
energy_chunks,
activation_th=activation_th,
deactivation_th=deactivation_th,
)
energy_boundaries = self.get_boundaries(
energy_vad, output_value="seconds"
)
for j in range(energy_boundaries.shape[0]):
start_en = boundaries[i, 0] + energy_boundaries[j, 0]
end_end = boundaries[i, 0] + energy_boundaries[j, 1]
new_boundaries.append([start_en, end_end])
new_boundaries = torch.FloatTensor(new_boundaries).to(boundaries.device)
return new_boundaries
def create_chunks(self, x, chunk_size=16384, chunk_stride=16384):
"""Splits the input into smaller chunks of size chunk_size with
an overlap chunk_stride. The chunks are concatenated over
the batch axis.
Arguments
---------
x: torch.Tensor
Signal to split into chunks.
chunk_size : str
The size of each chunk.
chunk_stride:
The stride (hop) of each chunk.
Returns
-------
x: torch.Tensor
A new tensors with the chunks derived from the input signal.
"""
x = x.unfold(1, chunk_size, chunk_stride)
x = x.reshape(x.shape[0] * x.shape[1], -1)
return x
def _get_audio_info(self, audio_file):
"""Returns the sample rate and the length of the input audio file"""
metadata = torchaudio.info(audio_file)
sample_rate = metadata.sample_rate
audio_len = metadata.num_frames
return sample_rate, audio_len
def upsample_VAD(self, vad_out, audio_file, time_resolution=0.01):
"""Upsamples the output of the vad to help visualization. It creates a
signal that is 1 when there is speech and 0 when there is no speech.
The vad signal has the same resolution as the input one and can be
opened with it (e.g, using audacity) to visually figure out VAD regions.
Arguments
---------
vad_out: torch.Tensor
Tensor containing 1 for each frame of speech and 0 for each non-speech
frame.
audio_file: path
The original audio file used to compute vad_out
time_resolution : float
Time resolution of the vad_out signal.
Returns
-------
vad_signal
The upsampled version of the vad_out tensor.
"""
sample_rate, sig_len = self._get_audio_info(audio_file)
if sample_rate != self.sample_rate:
raise ValueError(
"The detected sample rate is different from that set in the hparam file"
)
beg_samp = 0
step_size = int(time_resolution * sample_rate)
end_samp = step_size
index = 0
vad_signal = torch.zeros(1, sig_len, device=vad_out.device)
while end_samp < sig_len:
vad_signal[0, beg_samp:end_samp] = vad_out[0, index, 0]
index = index + 1
beg_samp = beg_samp + step_size
end_samp = beg_samp + step_size
return vad_signal
def upsample_boundaries(self, boundaries, audio_file):
"""Based on the input boundaries, this method creates a signal that is 1
when there is speech and 0 when there is no speech.
The vad signal has the same resolution as the input one and can be
opened with it (e.g, using audacity) to visually figure out VAD regions.
Arguments
---------
boundaries: torch.Tensor
Tensor containing the boundaries of the speech segments.
audio_file: path
The original audio file used to compute vad_out
Returns
-------
vad_signal
The output vad signal with the same resolution of the input one.
"""
sample_rate, sig_len = self._get_audio_info(audio_file)
if sample_rate != self.sample_rate:
raise ValueError(
"The detected sample rate is different from that set in the hparam file"
)
vad_signal = torch.zeros(1, sig_len, device=boundaries.device)
for i in range(boundaries.shape[0]):
beg_sample = int(boundaries[i, 0] * sample_rate)
end_sample = int(boundaries[i, 1] * sample_rate)
vad_signal[0, beg_sample:end_sample] = 1.0
return vad_signal
def double_check_speech_segments(
self, boundaries, audio_file, speech_th=0.5
):
"""Takes in input the boundaries of the detected speech segments and
double checks (using the neural VAD) that they actually contain speech.
Arguments
---------
boundaries: torch.Tensor
Tensor containing the boundaries of the speech segments.
audio_file: path
The original audio file used to compute vad_out.
speech_th: float
Threshold on the mean posterior probability over which speech is
confirmed. Below that threshold, the segment is re-assigned to a
non-speech region.
Returns
-------
new_boundaries
The boundaries of the segments where speech activity is confirmed.
"""
sample_rate, sig_len = self._get_audio_info(audio_file)
new_boundaries = []
for i in range(boundaries.shape[0]):
beg_sample = int(boundaries[i, 0] * sample_rate)
end_sample = int(boundaries[i, 1] * sample_rate)
len_seg = end_sample - beg_sample
segment, fs = torchaudio.load(
audio_file, frame_offset=beg_sample, num_frames=len_seg
)
speech_prob = self.get_speech_prob_chunk(segment)
if speech_prob.mean() > speech_th:
new_boundaries.append([boundaries[i, 0], boundaries[i, 1]])
new_boundaries = torch.FloatTensor(new_boundaries).to(boundaries.device)
return new_boundaries
def get_segments(
self, boundaries, audio_file, before_margin=0.1, after_margin=0.1
):
"""Returns a list containing all the detected speech segments.
Arguments
---------
boundaries: torch.Tensor
Tensor containing the boundaries of the speech segments.
audio_file: path
The original audio file used to compute vad_out.
before_margin: float
Used to cut the segments samples a bit before the detected margin.
after_margin: float
Use to cut the segments samples a bit after the detected margin.
Returns
-------
segments: list
List containing the detected speech segments
"""
sample_rate, sig_len = self._get_audio_info(audio_file)
if sample_rate != self.sample_rate:
raise ValueError(
"The detected sample rate is different from that set in the hparam file"
)
segments = []
for i in range(boundaries.shape[0]):
beg_sample = boundaries[i, 0] * sample_rate
end_sample = boundaries[i, 1] * sample_rate
beg_sample = int(max(0, beg_sample - before_margin * sample_rate))
end_sample = int(
min(sig_len, end_sample + after_margin * sample_rate)
)
len_seg = end_sample - beg_sample
vad_segment, fs = torchaudio.load(
audio_file, frame_offset=beg_sample, num_frames=len_seg
)
segments.append(vad_segment)
return segments
def get_speech_segments(
self,
audio_file,
large_chunk_size=30,
small_chunk_size=10,
overlap_small_chunk=False,
apply_energy_VAD=False,
double_check=True,
close_th=0.250,
len_th=0.250,
activation_th=0.5,
deactivation_th=0.25,
en_activation_th=0.5,
en_deactivation_th=0.0,
speech_th=0.50,
):
"""Detects speech segments within the input file. The input signal can
be both a short or a long recording. The function computes the
posterior probabilities on large chunks (e.g, 30 sec), that are read
sequentially (to avoid storing big signals in memory).
Each large chunk is, in turn, split into smaller chunks (e.g, 10 seconds)
that are processed in parallel. The pipeline for detecting the speech
segments is the following:
1- Compute posteriors probabilities at the frame level.
2- Apply a threshold on the posterior probability.
3- Derive candidate speech segments on top of that.
4- Apply energy VAD within each candidate segment (optional).
5- Merge segments that are too close.
6- Remove segments that are too short.
7- Double check speech segments (optional).
Arguments
---------
audio_file : str
Path to audio file.
large_chunk_size: float
Size (in seconds) of the large chunks that are read sequentially
from the input audio file.
small_chunk_size: float
Size (in seconds) of the small chunks extracted from the large ones.
The audio signal is processed in parallel within the small chunks.
Note that large_chunk_size/small_chunk_size must be an integer.
overlap_small_chunk: bool
If True, it creates overlapped small chunks (with 50% overlap).
The probabilities of the overlapped chunks are combined using
hamming windows.
apply_energy_VAD: bool
If True, a energy-based VAD is used on the detected speech segments.
The neural network VAD often creates longer segments and tends to
merge close segments together. The energy VAD post-processes can be
useful for having a fine-grained voice activity detection.
The energy thresholds is managed by activation_th and
deactivation_th (see below).
double_check: bool
If True, double checks (using the neural VAD) that the candidate
speech segments actually contain speech. A threshold on the mean
posterior probabilities provided by the neural network is applied
based on the speech_th parameter (see below).
activation_th: float
Threshold of the neural posteriors above which starting a speech segment.
deactivation_th: float
Threshold of the neural posteriors below which ending a speech segment.
en_activation_th: float
A new speech segment is started it the energy is above activation_th.
This is active only if apply_energy_VAD is True.
en_deactivation_th: float
The segment is considered ended when the energy is <= deactivation_th.
This is active only if apply_energy_VAD is True.
speech_th: float
Threshold on the mean posterior probability within the candidate
speech segment. Below that threshold, the segment is re-assigned to
a non-speech region. This is active only if double_check is True.
close_th: float
If the distance between boundaries is smaller than close_th, the
segments will be merged.
len_th: float
If the length of the segment is smaller than close_th, the segments
will be merged.
Returns
-------
boundaries: torch.Tensor
Tensor containing the start second of speech segments in even
positions and their corresponding end in odd positions
(e.g, [1.0, 1.5, 5,.0 6.0] means that we have two speech segment;
one from 1.0 to 1.5 seconds and another from 5.0 to 6.0 seconds).
"""
source, fl = split_path(audio_file)
audio_file = fetch(fl, source=source)
prob_chunks = self.get_speech_prob_file(
audio_file,
large_chunk_size=large_chunk_size,
small_chunk_size=small_chunk_size,
overlap_small_chunk=overlap_small_chunk,
)
prob_th = self.apply_threshold(
prob_chunks,
activation_th=activation_th,
deactivation_th=deactivation_th,
).float()
boundaries = self.get_boundaries(prob_th, output_value="seconds")
if apply_energy_VAD:
boundaries = self.energy_VAD(
audio_file,
boundaries,
activation_th=en_activation_th,
deactivation_th=en_deactivation_th,
)
boundaries = self.merge_close_segments(boundaries, close_th=close_th)
boundaries = self.remove_short_segments(boundaries, len_th=len_th)
if double_check:
boundaries = self.double_check_speech_segments(
boundaries, audio_file, speech_th=speech_th
)
return boundaries
def forward(self, wavs, wav_lens=None):
"""Gets frame-level speech-activity predictions"""
return self.get_speech_prob_chunk(wavs, wav_lens)
class SepformerSeparation(Pretrained):
"""A "ready-to-use" speech separation model.
Uses Sepformer architecture.
Example
-------
>>> tmpdir = getfixture("tmpdir")
>>> model = SepformerSeparation.from_hparams(
... source="speechbrain/sepformer-wsj02mix",
... savedir=tmpdir)
>>> mix = torch.randn(1, 400)
>>> est_sources = model.separate_batch(mix)
>>> print(est_sources.shape)
torch.Size([1, 400, 2])
"""
MODULES_NEEDED = ["encoder", "masknet", "decoder"]
def separate_batch(self, mix):
"""Run source separation on batch of audio.
Arguments
---------
mix : torch.Tensor
The mixture of sources.
Returns
-------
tensor
Separated sources
"""
mix = mix.to(self.device)
mix_w = self.mods.encoder(mix)
est_mask = self.mods.masknet(mix_w)
mix_w = torch.stack([mix_w] * self.hparams.num_spks)
sep_h = mix_w * est_mask
est_source = torch.cat(
[
self.mods.decoder(sep_h[i]).unsqueeze(-1)
for i in range(self.hparams.num_spks)
],
dim=-1,
)
T_origin = mix.size(1)
T_est = est_source.size(1)
if T_origin > T_est:
est_source = F.pad(est_source, (0, 0, 0, T_origin - T_est))
else:
est_source = est_source[:, :T_origin, :]
return est_source
def separate_file(self, path, savedir="audio_cache"):
"""Separate sources from file.
Arguments
---------
path : str
Path to file which has a mixture of sources. It can be a local
path, a web url, or a huggingface repo.
savedir : path
Path where to store the wav signals (when downloaded from the web).
Returns
-------
tensor
Separated sources
"""
source, fl = split_path(path)
path = fetch(fl, source=source, savedir=savedir)
batch, fs_file = torchaudio.load(path)
batch = batch.to(self.device)
fs_model = self.hparams.sample_rate
if fs_file != fs_model:
print(
"Resampling the audio from {} Hz to {} Hz".format(
fs_file, fs_model
)
)
tf = torchaudio.transforms.Resample(
orig_freq=fs_file, new_freq=fs_model
).to(self.device)
batch = batch.mean(dim=0, keepdim=True)
batch = tf(batch)
est_sources = self.separate_batch(batch)
est_sources = (
est_sources / est_sources.abs().max(dim=1, keepdim=True)[0]
)
return est_sources
def forward(self, mix):
"""Runs separation on the input mix"""
return self.separate_batch(mix)
class SpectralMaskEnhancement(Pretrained):
"""A ready-to-use model for speech enhancement.
Arguments
---------
See ``Pretrained``.
Example
-------
>>> import torch
>>> from speechbrain.pretrained import SpectralMaskEnhancement
>>> # Model is downloaded from the speechbrain HuggingFace repo
>>> tmpdir = getfixture("tmpdir")
>>> enhancer = SpectralMaskEnhancement.from_hparams(
... source="speechbrain/metricgan-plus-voicebank",
... savedir=tmpdir,
... )
>>> enhanced = enhancer.enhance_file(
... "speechbrain/metricgan-plus-voicebank/example.wav"
... )
"""
HPARAMS_NEEDED = ["compute_stft", "spectral_magnitude", "resynth"]
MODULES_NEEDED = ["enhance_model"]
def compute_features(self, wavs):
"""Compute the log spectral magnitude features for masking.
Arguments
---------
wavs : torch.Tensor
A batch of waveforms to convert to log spectral mags.
"""
feats = self.hparams.compute_stft(wavs)
feats = self.hparams.spectral_magnitude(feats)
return torch.log1p(feats)
def enhance_batch(self, noisy, lengths=None):
"""Enhance a batch of noisy waveforms.
Arguments
---------
noisy : torch.Tensor
A batch of waveforms to perform enhancement on.
lengths : torch.Tensor
The lengths of the waveforms if the enhancement model handles them.
Returns
-------
torch.Tensor
A batch of enhanced waveforms of the same shape as input.
"""
noisy = noisy.to(self.device)
noisy_features = self.compute_features(noisy)
if lengths is not None:
mask = self.mods.enhance_model(noisy_features, lengths=lengths)
else:
mask = self.mods.enhance_model(noisy_features)
enhanced = torch.mul(mask, noisy_features)
return self.hparams.resynth(torch.expm1(enhanced), noisy)
def enhance_file(self, filename, output_filename=None, **kwargs):
"""Enhance a wav file.
Arguments
---------
filename : str
Location on disk to load file for enhancement.
output_filename : str
If provided, writes enhanced data to this file.
"""
noisy = self.load_audio(filename, **kwargs)
noisy = noisy.to(self.device)
batch = noisy.unsqueeze(0)
if lengths_arg_exists(self.enhance_batch):
enhanced = self.enhance_batch(batch, lengths=torch.tensor([1.0]))
else:
enhanced = self.enhance_batch(batch)
if output_filename is not None:
torchaudio.save(output_filename, enhanced, channels_first=False)
return enhanced.squeeze(0)
class EncodeDecodePipelineMixin:
"""
A mixin for pretrained models that makes it possible to specify an encoding pipeline and a decoding pipeline
"""
def create_pipelines(self):
"""
Initializes the encode and decode pipeline
"""
self._run_init_steps(self.hparams.encode_pipeline)
self._run_init_steps(self.hparams.decode_pipeline)
self.encode_pipeline = DataPipeline(
static_data_keys=self.INPUT_STATIC_KEYS,
dynamic_items=self.hparams.encode_pipeline["steps"],
output_keys=self.hparams.encode_pipeline["output_keys"],
)
self.decode_pipeline = DataPipeline(
static_data_keys=self.hparams.model_output_keys,
dynamic_items=self.hparams.decode_pipeline["steps"],
output_keys=self.OUTPUT_KEYS,
)
def _run_init_steps(self, pipeline_definition):
"""Encode/decode pipelines may include initialization
steps, such as filling text encoders with tokens. Calling
this method will run them, if defined"""
steps = pipeline_definition.get("init", [])
for step in steps:
step_func = step.get("func")
if not step_func or not callable(step_func):
raise ValueError("Invalid pipeline init definition")
step_func()
def _run_pipeline(self, pipeline, input, batch):
if batch:
output = pipeline(input)
else:
output = [pipeline(item) for item in input]
return output
def _get_encode_pipeline_input(self, input):
return input if self.batch_inputs else self._itemize(input)
def _get_decode_pipeline_input(self, model_output):
model_output_keys = getattr(self.hparams, "model_output_keys", None)
pipeline_input = model_output
if len(model_output_keys) == 1:
pipeline_input = (pipeline_input,)
if model_output_keys:
pipeline_input = dict(zip(model_output_keys, pipeline_input))
if not self.batch_outputs:
pipeline_input = self._itemize(pipeline_input)
return pipeline_input
def _itemize(self, pipeline_input):
first_item = next(iter(pipeline_input.values()))
keys, values = pipeline_input.keys(), pipeline_input.values()
batch_length = len(first_item)
return [
dict(zip(keys, [value[idx] for value in values]))
for idx in range(batch_length)
]
def to_dict(self, data):
"""
Converts padded batches to dictionaries, leaves
other data types as is
Arguments
---------
data: object
a dictionary or a padded batch
Returns
-------
results: dict
the dictionary
"""
if isinstance(data, PaddedBatch):
data = {
key: self._get_value(data, key)
for key in self.hparams.encode_pipeline["output_keys"]
}
return data
def _get_value(self, data, key):
"""
Retrieves the value associated with the specified key, dereferencing
.data where applicable
Arguments
---------
data: PaddedBatch
a padded batch
key: str
the key
Returns
-------
result: object
the result
"""
value = getattr(data, key)
if not self.input_use_padded_data and isinstance(value, PaddedData):
value = value.data
return value
@property
def batch_inputs(self):
"""
Determines whether the input pipeline
operates on batches or individual examples
(true means batched)
Returns
-------
batch_inputs: bool
"""
return self.hparams.encode_pipeline.get("batch", True)
@property
def input_use_padded_data(self):
"""
If turned on, raw PaddedData instances will be passed to
the model. If turned off, only .data will be used
Returns
-------
result: bool
whether padded data is used as is
"""
return self.hparams.encode_pipeline.get("use_padded_data", False)
@property
def batch_outputs(self):
"""
Determines whether the output pipeline
operates on batches or individual examples
(true means batched)
Returns
-------
batch_outputs: bool
"""
return self.hparams.decode_pipeline.get("batch", True)
def _collate(self, data):
if not self.batch_inputs:
collate_fn = getattr(self.hparams, "collate_fn", PaddedBatch)
data = collate_fn(data)
return data
def encode_input(self, input):
"""
Encodes the inputs using the pipeline
Arguments
---------
input: dict
the raw inputs
Returns
-------
results: object
"""
pipeline_input = self._get_encode_pipeline_input(input)
model_input = self._run_pipeline(
pipeline=self.encode_pipeline,
input=pipeline_input,
batch=self.batch_inputs,
)
model_input = self._collate(model_input)
if hasattr(model_input, "to"):
model_input = model_input.to(self.device)
return self.to_dict(model_input)
def decode_output(self, output):
"""
Decodes the raw model outputs
Arguments
---------
output: tuple
raw model outputs
Returns
-------
result: dict or list
the output of the pipeline
"""
pipeline_input = self._get_decode_pipeline_input(output)
return self._run_pipeline(
pipeline=self.decode_pipeline,
input=pipeline_input,
batch=self.batch_outputs,
)
class GraphemeToPhoneme(Pretrained, EncodeDecodePipelineMixin):
"""
A pretrained model implementation for Grapheme-to-Phoneme (G2P) models
that take raw natural language text as an input and
Example
-------
>>> text = ("English is tough. It can be understood "
... "through thorough thought though")
>>> from speechbrain.pretrained import GraphemeToPhoneme
>>> tmpdir = getfixture('tmpdir')
>>> g2p = GraphemeToPhoneme.from_hparams('path/to/model', savedir=tmpdir) # doctest: +SKIP
>>> phonemes = g2p.g2p(text) # doctest: +SKIP
"""
INPUT_STATIC_KEYS = ["txt"]
OUTPUT_KEYS = ["phonemes"]
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
self.create_pipelines()
self.load_dependencies()
@property
def phonemes(self):
"""Returns the available phonemes"""
return self.hparams.phonemes
@property
def language(self):
"""Returns the language for which this model is available"""
return self.hparams.language
def g2p(self, text):
"""Performs the Grapheme-to-Phoneme conversion
Arguments
---------
text: str or list[str]
a single string to be encoded to phonemes - or a
sequence of strings
Returns
-------
result: list
if a single example was provided, the return value is a
single list of phonemes
"""
single = isinstance(text, str)
if single:
text = [text]
model_inputs = self.encode_input({"txt": text})
self._update_graphemes(model_inputs)
model_outputs = self.mods.model(**model_inputs)
decoded_output = self.decode_output(model_outputs)
phonemes = decoded_output["phonemes"]
if single:
phonemes = phonemes[0]
return phonemes
def _update_graphemes(self, model_inputs):
grapheme_sequence_mode = getattr(self.hparams, "grapheme_sequence_mode")
if grapheme_sequence_mode and grapheme_sequence_mode != "raw":
grapheme_encoded_key = f"grapheme_encoded_{grapheme_sequence_mode}"
if grapheme_encoded_key in model_inputs:
model_inputs["grapheme_encoded"] = model_inputs[
grapheme_encoded_key
]
def load_dependencies(self):
"""Loads any relevant model dependencies"""
deps_pretrainer = getattr(self.hparams, "deps_pretrainer", None)
if deps_pretrainer:
deps_pretrainer.collect_files()
deps_pretrainer.load_collected(device=self.device)
def __call__(self, text):
"""A convenience callable wrapper - same as G2P
Arguments
---------
text: str or list[str]
a single string to be encoded to phonemes - or a
sequence of strings
Returns
-------
result: list
if a single example was provided, the return value is a
single list of phonemes
"""
return self.g2p(text)
def forward(self, noisy, lengths=None):
"""Runs enhancement on the noisy input"""
return self.enhance_batch(noisy, lengths)
class WaveformEnhancement(Pretrained):
"""A ready-to-use model for speech enhancement.
Arguments
---------
See ``Pretrained``.
Example
-------
>>> from speechbrain.pretrained import WaveformEnhancement
>>> # Model is downloaded from the speechbrain HuggingFace repo
>>> tmpdir = getfixture("tmpdir")
>>> enhancer = WaveformEnhancement.from_hparams(
... source="speechbrain/mtl-mimic-voicebank",
... savedir=tmpdir,
... )
>>> enhanced = enhancer.enhance_file(
... "speechbrain/mtl-mimic-voicebank/example.wav"
... )
"""
MODULES_NEEDED = ["enhance_model"]
def enhance_batch(self, noisy, lengths=None):
"""Enhance a batch of noisy waveforms.
Arguments
---------
noisy : torch.Tensor
A batch of waveforms to perform enhancement on.
lengths : torch.Tensor
The lengths of the waveforms if the enhancement model handles them.
Returns
-------
torch.Tensor
A batch of enhanced waveforms of the same shape as input.
"""
noisy = noisy.to(self.device)
enhanced_wav, _ = self.mods.enhance_model(noisy)
return enhanced_wav
def enhance_file(self, filename, output_filename=None, **kwargs):
"""Enhance a wav file.
Arguments
---------
filename : str
Location on disk to load file for enhancement.
output_filename : str
If provided, writes enhanced data to this file.
"""
noisy = self.load_audio(filename, **kwargs)
batch = noisy.unsqueeze(0)
enhanced = self.enhance_batch(batch)
if output_filename is not None:
torchaudio.save(output_filename, enhanced, channels_first=False)
return enhanced.squeeze(0)
def forward(self, noisy, lengths=None):
"""Runs enhancement on the noisy input"""
return self.enhance_batch(noisy, lengths)
class SNREstimator(Pretrained):
"""A "ready-to-use" SNR estimator.
"""
MODULES_NEEDED = ["encoder", "encoder_out"]
HPARAMS_NEEDED = ["stat_pooling", "snrmax", "snrmin"]
def estimate_batch(self, mix, predictions):
"""Run SI-SNR estimation on the estimated sources, and mixture.
Arguments
---------
mix : torch.Tensor
The mixture of sources of shape B X T
predictions : torch.Tensor
of size (B x T x C),
where B is batch size
T is number of time points
C is number of sources
Returns
-------
tensor
Estimate of SNR
"""
predictions = predictions.permute(0, 2, 1)
predictions = predictions.reshape(-1, predictions.size(-1))
if hasattr(self.hparams, "separation_norm_type"):
if self.hparams.separation_norm_type == "max":
predictions = (
predictions / predictions.max(dim=1, keepdim=True)[0]
)
mix = mix / mix.max(dim=1, keepdim=True)[0]
elif self.hparams.separation_norm_type == "stnorm":
predictions = (
predictions - predictions.mean(dim=1, keepdim=True)
) / predictions.std(dim=1, keepdim=True)
mix = (mix - mix.mean(dim=1, keepdim=True)) / mix.std(
dim=1, keepdim=True
)
min_T = min(predictions.shape[1], mix.shape[1])
assert predictions.shape[1] == mix.shape[1], "lengths change"
mix_repeat = mix.repeat(2, 1)
inp_cat = torch.cat(
[
predictions[:, :min_T].unsqueeze(1),
mix_repeat[:, :min_T].unsqueeze(1),
],
dim=1,
)
enc = self.mods.encoder(inp_cat)
enc = enc.permute(0, 2, 1)
enc_stats = self.hparams.stat_pooling(enc)
snrhat = self.mods.encoder_out(enc_stats).squeeze()
snrhat = self.gettrue_snrrange(snrhat)
return snrhat
def forward(self, mix, predictions):
"""Just run the batch estimate"""
return self.estimate_batch(mix, predictions)
def gettrue_snrrange(self, inp):
"""Convert from 0-1 range to true snr range"""
rnge = self.hparams.snrmax - self.hparams.snrmin
inp = inp * rnge
inp = inp + self.hparams.snrmin
return inp
class Tacotron2(Pretrained):
"""
A ready-to-use wrapper for Tacotron2 (text -> mel_spec).
Arguments
---------
hparams
Hyperparameters (from HyperPyYAML)
Example
-------
>>> tmpdir_vocoder = getfixture('tmpdir') / "vocoder"
>>> tacotron2 = Tacotron2.from_hparams(source="speechbrain/tts-tacotron2-ljspeech", savedir=tmpdir_vocoder)
>>> mel_output, mel_length, alignment = tacotron2.encode_text("Mary had a little lamb")
>>> items = [
... "A quick brown fox jumped over the lazy dog",
... "How much wood would a woodchuck chuck?",
... "Never odd or even"
... ]
>>> mel_outputs, mel_lengths, alignments = tacotron2.encode_batch(items)
>>> # One can combine the TTS model with a vocoder (that generates the final waveform)
>>> # Initialize the Vocoder (HiFIGAN)
>>> tmpdir_tts = getfixture('tmpdir') / "tts"
>>> hifi_gan = HIFIGAN.from_hparams(source="speechbrain/tts-hifigan-ljspeech", savedir=tmpdir_tts)
>>> # Running the TTS
>>> mel_output, mel_length, alignment = tacotron2.encode_text("Mary had a little lamb")
>>> # Running Vocoder (spectrogram-to-waveform)
>>> waveforms = hifi_gan.decode_batch(mel_output)
"""
HPARAMS_NEEDED = ["model", "text_to_sequence"]
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
self.text_cleaners = getattr(
self.hparams, "text_cleaners", ["english_cleaners"]
)
self.infer = self.hparams.model.infer
def text_to_seq(self, txt):
"""Encodes raw text into a tensor with a customer text-to-sequence function
"""
sequence = self.hparams.text_to_sequence(txt, self.text_cleaners)
return sequence, len(sequence)
def encode_batch(self, texts):
"""Computes mel-spectrogram for a list of texts
Texts must be sorted in decreasing order on their lengths
Arguments
---------
texts: List[str]
texts to be encoded into spectrogram
Returns
-------
tensors of output spectrograms, output lengths and alignments
"""
with torch.no_grad():
inputs = [
{
"text_sequences": torch.tensor(
self.text_to_seq(item)[0], device=self.device
)
}
for item in texts
]
inputs = speechbrain.dataio.batch.PaddedBatch(inputs)
lens = [self.text_to_seq(item)[1] for item in texts]
assert lens == sorted(
lens, reverse=True
), "input lengths must be sorted in decreasing order"
input_lengths = torch.tensor(lens, device=self.device)
mel_outputs_postnet, mel_lengths, alignments = self.infer(
inputs.text_sequences.data, input_lengths
)
return mel_outputs_postnet, mel_lengths, alignments
def encode_text(self, text):
"""Runs inference for a single text str"""
return self.encode_batch([text])
def forward(self, texts):
"Encodes the input texts."
return self.encode_batch(texts)
class HIFIGAN(Pretrained):
"""
A ready-to-use wrapper for HiFiGAN (mel_spec -> waveform).
Arguments
---------
hparams
Hyperparameters (from HyperPyYAML)
Example
-------
>>> tmpdir_vocoder = getfixture('tmpdir') / "vocoder"
>>> hifi_gan = HIFIGAN.from_hparams(source="speechbrain/tts-hifigan-ljspeech", savedir=tmpdir_vocoder)
>>> mel_specs = torch.rand(2, 80,298)
>>> waveforms = hifi_gan.decode_batch(mel_specs)
>>> # You can use the vocoder coupled with a TTS system
>>> # Initialize TTS (tacotron2)
>>> tmpdir_tts = getfixture('tmpdir') / "tts"
>>> tacotron2 = Tacotron2.from_hparams(source="speechbrain/tts-tacotron2-ljspeech", savedir=tmpdir_tts)
>>> # Running the TTS
>>> mel_output, mel_length, alignment = tacotron2.encode_text("Mary had a little lamb")
>>> # Running Vocoder (spectrogram-to-waveform)
>>> waveforms = hifi_gan.decode_batch(mel_output)
"""
HPARAMS_NEEDED = ["generator"]
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
self.infer = self.hparams.generator.inference
self.first_call = True
def decode_batch(self, spectrogram):
"""Computes waveforms from a batch of mel-spectrograms
Arguments
---------
spectrogram: torch.Tensor
Batch of mel-spectrograms [batch, mels, time]
Returns
-------
waveforms: torch.Tensor
Batch of mel-waveforms [batch, 1, time]
"""
if self.first_call:
self.hparams.generator.remove_weight_norm()
self.first_call = False
with torch.no_grad():
waveform = self.infer(spectrogram.to(self.device))
return waveform
def decode_spectrogram(self, spectrogram):
"""Computes waveforms from a single mel-spectrogram
Arguments
---------
spectrogram: torch.Tensor
mel-spectrogram [mels, time]
Returns
-------
waveform: torch.Tensor
waveform [1, time]
audio can be saved by:
>>> waveform = torch.rand(1, 666666)
>>> sample_rate = 22050
>>> torchaudio.save(str(getfixture('tmpdir') / "test.wav"), waveform, sample_rate)
"""
if self.first_call:
self.hparams.generator.remove_weight_norm()
self.first_call = False
with torch.no_grad():
waveform = self.infer(spectrogram.unsqueeze(0).to(self.device))
return waveform.squeeze(0)
def forward(self, spectrogram):
"Decodes the input spectrograms"
return self.decode_batch(spectrogram)
class WhisperASR(Pretrained):
"""A ready-to-use Whisper ASR model
The class can be used to run the entire encoder-decoder whisper model
(transcribe()) to transcribe speech. The given YAML must contains the fields
specified in the *_NEEDED[] lists.
Example
-------
>>> from speechbrain.pretrained import WhisperASR
>>> tmpdir = getfixture("tmpdir")
>>> asr_model = WhisperASR.from_hparams(source="speechbrain/asr-whisper-large-v2-commonvoice-fr", savedir=tmpdir,) # doctest: +SKIP
>>> asr_model.transcribe_file("speechbrain/asr-whisper-large-v2-commonvoice-fr/example-fr.mp3") # doctest: +SKIP
"""
HPARAMS_NEEDED = ["language"]
MODULES_NEEDED = ["whisper", "decoder"]
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
self.tokenizer = self.hparams.whisper.tokenizer
self.tokenizer.set_prefix_tokens(
self.hparams.language, "transcribe", False
)
self.hparams.decoder.set_decoder_input_tokens(
self.tokenizer.prefix_tokens
)
def transcribe_file(self, path):
"""Transcribes the given audiofile into a sequence of words.
Arguments
---------
path : str
Path to audio file which to transcribe.
Returns
-------
str
The audiofile transcription produced by this ASR system.
"""
waveform = self.load_audio(path)
batch = waveform.unsqueeze(0)
rel_length = torch.tensor([1.0])
predicted_words, predicted_tokens = self.transcribe_batch(
batch, rel_length
)
return predicted_words
def encode_batch(self, wavs, wav_lens):
"""Encodes the input audio into a sequence of hidden states
The waveforms should already be in the model's desired format.
You can call:
``normalized = EncoderDecoderASR.normalizer(signal, sample_rate)``
to get a correctly converted signal in most cases.
Arguments
---------
wavs : torch.tensor
Batch of waveforms [batch, time, channels].
wav_lens : torch.tensor
Lengths of the waveforms relative to the longest one in the
batch, tensor of shape [batch]. The longest one should have
relative length 1.0 and others len(waveform) / max_length.
Used for ignoring padding.
Returns
-------
torch.tensor
The encoded batch
"""
wavs = wavs.float()
wavs, wav_lens = wavs.to(self.device), wav_lens.to(self.device)
encoder_out = self.mods.whisper.forward_encoder(wavs)
return encoder_out
def transcribe_batch(self, wavs, wav_lens):
"""Transcribes the input audio into a sequence of words
The waveforms should already be in the model's desired format.
You can call:
``normalized = EncoderDecoderASR.normalizer(signal, sample_rate)``
to get a correctly converted signal in most cases.
Arguments
---------
wavs : torch.tensor
Batch of waveforms [batch, time, channels].
wav_lens : torch.tensor
Lengths of the waveforms relative to the longest one in the
batch, tensor of shape [batch]. The longest one should have
relative length 1.0 and others len(waveform) / max_length.
Used for ignoring padding.
Returns
-------
list
Each waveform in the batch transcribed.
tensor
Each predicted token id.
"""
with torch.no_grad():
wav_lens = wav_lens.to(self.device)
encoder_out = self.encode_batch(wavs, wav_lens)
predicted_tokens, scores = self.mods.decoder(encoder_out, wav_lens)
predicted_words = self.tokenizer.batch_decode(
predicted_tokens, skip_special_tokens=True
)
if self.hparams.normalized_transcripts:
predicted_words = [
self.tokenizer._normalize(text).split(" ")
for text in predicted_words
]
return predicted_words, predicted_tokens
def forward(self, wavs, wav_lens):
"""Runs full transcription - note: no gradients through decoding"""
return self.transcribe_batch(wavs, wav_lens)
class Speech_Emotion_Diarization(Pretrained):
"""A ready-to-use SED interface (audio -> emotions and their durations)
Arguments
---------
hparams
Hyperparameters (from HyperPyYAML)
Example
-------
>>> from speechbrain.pretrained import Speech_Emotion_Diarization
>>> tmpdir = getfixture("tmpdir")
>>> sed_model = Speech_Emotion_Diarization.from_hparams(source="speechbrain/emotion-diarization-wavlm-large", savedir=tmpdir,) # doctest: +SKIP
>>> sed_model.diarize_file("speechbrain/emotion-diarization-wavlm-large/example.wav") # doctest: +SKIP
"""
MODULES_NEEDED = ["input_norm", "wav2vec", "output_mlp"]
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
def diarize_file(self, path):
"""Get emotion diarization of a spoken utterance.
Arguments
---------
path : str
Path to audio file which to diarize.
Returns
-------
dict
The emotions and their boundaries.
"""
waveform = self.load_audio(path)
batch = waveform.unsqueeze(0)
rel_length = torch.tensor([1.0])
frame_class = self.diarize_batch(batch, rel_length, [path])
return frame_class
def encode_batch(self, wavs, wav_lens):
"""Encodes audios into fine-grained emotional embeddings
Arguments
---------
wavs : torch.tensor
Batch of waveforms [batch, time, channels].
wav_lens : torch.tensor
Lengths of the waveforms relative to the longest one in the
batch, tensor of shape [batch]. The longest one should have
relative length 1.0 and others len(waveform) / max_length.
Used for ignoring padding.
Returns
-------
torch.tensor
The encoded batch
"""
if len(wavs.shape) == 1:
wavs = wavs.unsqueeze(0)
if wav_lens is None:
wav_lens = torch.ones(wavs.shape[0], device=self.device)
wavs, wav_lens = wavs.to(self.device), wav_lens.to(self.device)
wavs = self.mods.input_norm(wavs, wav_lens)
outputs = self.mods.wav2vec2(wavs)
return outputs
def diarize_batch(self, wavs, wav_lens, batch_id):
"""Get emotion diarization of a batch of waveforms.
The waveforms should already be in the model's desired format.
You can call:
``normalized = EncoderDecoderASR.normalizer(signal, sample_rate)``
to get a correctly converted signal in most cases.
Arguments
---------
wavs : torch.tensor
Batch of waveforms [batch, time, channels].
wav_lens : torch.tensor
Lengths of the waveforms relative to the longest one in the
batch, tensor of shape [batch]. The longest one should have
relative length 1.0 and others len(waveform) / max_length.
Used for ignoring padding.
batch_id : torch.tensor
id of each batch (file names etc.)
Returns
-------
torch.tensor
The frame-wise predictions
"""
outputs = self.encode_batch(wavs, wav_lens)
averaged_out = self.hparams.avg_pool(outputs)
outputs = self.mods.output_mlp(averaged_out)
outputs = self.hparams.log_softmax(outputs)
score, index = torch.max(outputs, dim=-1)
preds = self.hparams.label_encoder.decode_torch(index)
results = self.preds_to_diarization(preds, batch_id)
return results
def preds_to_diarization(self, prediction, batch_id):
"""Convert frame-wise predictions into a dictionary of
diarization results.
Returns
-------
dictionary
A dictionary with the start/end of each emotion
"""
results = {}
for i in range(len(prediction)):
pred = prediction[i]
lol = []
for j in range(len(pred)):
start = round(self.hparams.stride * 0.02 * j, 2)
end = round(start + self.hparams.window_length * 0.02, 2)
lol.append([batch_id[i], start, end, pred[j]])
lol = self.merge_ssegs_same_emotion_adjacent(lol)
results[batch_id[i]] = [
{"start": k[1], "end": k[2], "emotion": k[3]} for k in lol
]
return results
def forward(self, wavs, wav_lens):
"""Runs full transcription - note: no gradients through decoding"""
return self.transcribe_batch(wavs, wav_lens)
def is_overlapped(self, end1, start2):
"""Returns True if segments are overlapping.
Arguments
---------
end1 : float
End time of the first segment.
start2 : float
Start time of the second segment.
Returns
-------
overlapped : bool
True of segments overlapped else False.
Example
-------
>>> from speechbrain.processing import diarization as diar
>>> diar.is_overlapped(5.5, 3.4)
True
>>> diar.is_overlapped(5.5, 6.4)
False
"""
if start2 > end1:
return False
else:
return True
def merge_ssegs_same_emotion_adjacent(self, lol):
"""Merge adjacent sub-segs if they are the same emotion.
Arguments
---------
lol : list of list
Each list contains [utt_id, sseg_start, sseg_end, emo_label].
Returns
-------
new_lol : list of list
new_lol contains adjacent segments merged from the same emotion ID.
Example
-------
>>> from speechbrain.utils.EDER import merge_ssegs_same_emotion_adjacent
>>> lol=[['u1', 0.0, 7.0, 'a'],
... ['u1', 7.0, 9.0, 'a'],
... ['u1', 9.0, 11.0, 'n'],
... ['u1', 11.0, 13.0, 'n'],
... ['u1', 13.0, 15.0, 'n'],
... ['u1', 15.0, 16.0, 'a']]
>>> merge_ssegs_same_emotion_adjacent(lol)
[['u1', 0.0, 9.0, 'a'], ['u1', 9.0, 15.0, 'n'], ['u1', 15.0, 16.0, 'a']]
"""
new_lol = []
sseg = lol[0]
flag = False
for i in range(1, len(lol)):
next_sseg = lol[i]
if (
self.is_overlapped(sseg[2], next_sseg[1])
and sseg[3] == next_sseg[3]
):
sseg[2] = next_sseg[2]
if i == len(lol) - 1:
flag = True
new_lol.append(sseg)
else:
new_lol.append(sseg)
sseg = next_sseg
if flag is False:
new_lol.append(lol[-1])
return new_lol