""" Specifies the inference interfaces for speaker recognition modules.
Authors:
* Aku Rouhe 2021
* Peter Plantinga 2021
* Loren Lugosch 2020
* Mirco Ravanelli 2020
* Titouan Parcollet 2021
* Abdel Heba 2021
* Andreas Nautsch 2022, 2023
* Pooneh Mousavi 2023
* Sylvain de Langen 2023
* Adel Moumen 2023
* Pradnya Kandarkar 2023
"""
import torch
from speechbrain.inference.classifiers import EncoderClassifier
[docs]
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.inference.speaker 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)
[docs]
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=False)
emb2 = self.encode_batch(wavs2, wav2_lens, normalize=False)
score = self.similarity(emb1, emb2)
return score, score > threshold
[docs]
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)
# Fake batches:
batch_x = waveform_x.unsqueeze(0)
batch_y = waveform_y.unsqueeze(0)
# Verify:
score, decision = self.verify_batch(batch_x, batch_y)
# Squeeze:
return score[0], decision[0]