"""Defines interfaces for simple inference with pretrained models
Authors:
* Aku Rouhe 2021
* Peter Plantinga 2021
* Loren Lugosch 2020
* Mirco Ravanelli 2020
* Titouan Parcollet 2021
"""
import torch
import torchaudio
from types import SimpleNamespace
from torch.nn import SyncBatchNorm
from torch.nn import DataParallel as DP
from hyperpyyaml import load_hyperpyyaml
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
class Pretrained:
"""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).
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.)
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
):
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.modules = torch.nn.ModuleDict(modules)
for mod in self.modules:
self.modules[mod].to(self.device)
for mod in self.MODULES_NEEDED:
if mod not in modules:
raise ValueError(f"Need modules['{mod}']")
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.modules.eval()
for p in self.modules.parameters():
p.requires_grad = False
def load_audio(self, path, savedir="."):
"""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)
path = fetch(fl, source=source, savedir=savedir)
signal, sr = torchaudio.load(path, channels_first=False)
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.modules:
raise ValueError(
"module " + name + " cannot be jit compiled because "
"it is not defined in your hparams file."
)
module = torch.jit.script(self.modules[name])
self.modules[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.modules.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.modules[name] = module
else:
for name, module in self.modules.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.modules[name] = module
@classmethod
def from_hparams(
cls,
source,
hparams_file="hyperparams.yaml",
overrides={},
savedir=None,
use_auth_token=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
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
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.
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 majority of models are public.
"""
if savedir is None:
clsname = cls.__name__
savedir = f"./pretrained_models/{clsname}-{hash(source)}"
hparams_local_path = fetch(
hparams_file, source, savedir, use_auth_token
)
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})
pretrainer.load_collected(device="cpu")
return cls(hparams["modules"], hparams, **kwargs)
class EndToEndSLU(Pretrained):
"""A 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("samples/audio_samples/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):
"""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)
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)
with torch.no_grad():
ASR_encoder_out = self.asr_model.encode_batch(
wavs.detach(), wav_lens
)
encoder_out = self.modules.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.modules.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
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 contains 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("samples/audio_samples/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):
"""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[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.modules.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.modules.decoder(
encoder_out, wav_lens
)
predicted_words = [
self.tokenizer.decode_ids(token_seq)
for token_seq in predicted_tokens
]
return predicted_words, predicted_tokens
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,
... )
>>> # Compute embeddings
>>> signal, fs = torchaudio.load("samples/audio_samples/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 extract_feats(self, wavs, wav_lens=None):
wavs = wavs.to('cpu').float()
if wav_lens is None:
wav_lens = torch.ones(wavs.shape[0], device='cpu')
feats = self.modules.compute_features(wavs)
feats = self.modules.mean_var_norm(feats, wav_lens)
return feats
def feats_classify(self, feats, wav_lens=None):
emb = self.modules.embedding_model(feats, wav_lens)
out_prob = self.modules.classifier(emb).squeeze(1)
return out_prob
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.modules.compute_features(wavs)
feats = self.modules.mean_var_norm(feats, wav_lens)
embeddings = self.modules.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.modules.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):
"""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)
batch = waveform.unsqueeze(0)
rel_length = torch.tensor([1.0])
emb = self.encode_batch(batch, rel_length)
out_prob = self.modules.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
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("samples/audio_samples/example1.wav")
>>> signal2, fs = torchaudio.load("samples/audio_samples/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):
"""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)
waveform_y = self.load_audio(path_y)
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 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.modules.encoder(mix)
est_mask = self.modules.masknet(mix_w)
mix_w = torch.stack([mix_w] * self.hparams.num_spks)
sep_h = mix_w * est_mask
est_source = torch.cat(
[
self.modules.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="."):
"""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
)
batch = batch.mean(dim=0, keepdim=True)
batch = tf(batch)
est_sources = self.separate_batch(batch)
est_sources = est_sources / est_sources.max(dim=1, keepdim=True)[0]
return est_sources
class SpectralMaskEnhancement(Pretrained):
"""A ready-to-use model for speech enhancement.
Arguments
---------
See ``Pretrained``.
Example
-------
>>> import torchaudio
>>> from speechbrain.pretrained import SpectralMaskEnhancement
>>> # Model is downloaded from the speechbrain HuggingFace repo
>>> tmpdir = getfixture("tmpdir")
>>> enhancer = SpectralMaskEnhancement.from_hparams(
... source="speechbrain/mtl-mimic-voicebank",
... savedir=tmpdir,
... )
>>> noisy, fs = torchaudio.load("samples/audio_samples/example_noisy.wav")
>>> # Channel dimension is interpreted as batch dimension here
>>> enhanced = enhancer.enhance_batch(noisy)
"""
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.modules.enhance_model(noisy_features, lengths=lengths)
else:
mask = self.modules.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):
"""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)
noisy = noisy.to(self.device)
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)